Find adoption answers, support, training, or professional resources

  • Mission and Vision
  • Staff and Board
  • Internships
  • Media & Press Room
  • Job Openings for Adoption Professionals
  • Social Work CE
  • Training for Professionals
  • Pre-Adoptive Parents
  • Adoptive Parents
  • Adopted Persons
  • Birth Parents
  • Expectant Parents
  • Adoption Advocate Publication

Adoption by the Numbers

Profiles in adoption.

  • Take Action
  • Upcoming Events
  • Adoption Hall of Fame
  • National Adoption Conference
  • Legacy Society
  • Families For All Development Fund
  • Family Formation Partners
  • Annual Report

Adoption Research

We provide accurate, reliable, and up-to-date reports that inform and equip professionals, policymakers, and the public at large to improve and strengthen adoption.

In 2021, we conducted the largest survey ever of adoptive parents. NCFA explored the profile of adoptive parents, their experiences, and what has changed in adoption over time.

In 2022, we surveyed birth parents across the U.S. and conducted focus groups with birth moms to better understand this diverse population, their decision-making and levels of satisfaction, their relationship with their birth children, experiences with stigma and support, and much more.

In 2023, we researched the experiences of adult adoptees through a nationwide survey.

The Importance of Adoption Research

Policymakers and legislators look to research-based facts and statistics to inform their decision-making. Professionals draw from the most recent studies and reports to better understand the needs of the populations they are serving and identify areas for growth in their work. Members of the media, authors, and other content creators look to NCFA's expertise to help them identify the most relevant and accurate information about adoption as they cover current events and raise awareness about stories and issues in adoption.

decorative image with a teal background and the shape of the United States with text that says "Adoption by the Numbers"

A comprehensive study of adoption data from 2019 and 2020 across all 50 states.

decorative image of colorful profile silhouettes of birth parents

An ongoing research project to explore the demographic characteristics and personal experiences of adoptive parents, birth parents, and adoptees.

blue decorative image with white graph and chart icons

General Adoption Statistics & Data

Quick links to the most recent national statistics

  • Trends by type of adoption
  • Adoptions from Foster Care
  • Intercountry Adoptions
  • Nationally Representative Data on Adoption Children in the United States

Start typing & press 'enter'

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Clinical Research Article
  • Open access
  • Published: 26 May 2021

Family environment and development in children adopted from institutionalized care

  • Margaret F. Keil 1 ,
  • Adela Leahu 1 ,
  • Megan Rescigno 2 ,
  • Jennifer Myles 3 &
  • Constantine A. Stratakis 1  

Pediatric Research volume  91 ,  pages 1562–1570 ( 2022 ) Cite this article

7227 Accesses

1 Citations

132 Altmetric

Metrics details

After adoption, children exposed to institutionalized care show significant improvement, but incomplete recovery of growth and developmental milestones. There is a paucity of data regarding risk and protective factors in children adopted from institutionalized care. This prospective study followed children recently adopted from institutionalized care to investigate the relationship between family environment, executive function, and behavioral outcomes.

Anthropometric measurements, physical examination, endocrine and bone age evaluations, neurocognitive testing, and behavioral questionnaires were evaluated over a 2-year period with children adopted from institutionalized care and non-adopted controls.

Adopted children had significant deficits in growth, cognitive, and developmental measurements compared to controls that improved; however, residual deficits remained. Family cohesiveness and expressiveness were protective influences, associated with less behavioral problems, while family conflict and greater emphasis on rules were associated with greater risk for executive dysfunction.


Our data suggest that a cohesive and expressive family environment moderated the effect of pre-adoption adversity on cognitive and behavioral development in toddlers, while family conflict and greater emphasis on rules were associated with greater risk for executive dysfunction. Early assessment of child temperament and parenting context may serve to optimize the fit between parenting style, family environment, and the child’s development.

Children who experience institutionalized care are at increased risk for significant deficits in developmental, cognitive, and social functioning associated with a disruption in the development of the prefrontal cortex. Aspects of the family caregiving environment moderate the effect of early life social deprivation in children.

Family cohesiveness and expressiveness were protective influences, while family conflict and greater emphasis on rules were associated with a greater risk for executive dysfunction problems.

This study should be viewed as preliminary data to be referenced by larger studies investigating developmental and behavioral outcomes of children adopted from institutional care.

Similar content being viewed by others

research articles about adoption

Parental and social factors in relation to child psychopathology, behavior, and cognitive function

research articles about adoption

Exceeding expectations after perinatal risks for poor development: associations in term- and preterm-born preschoolers

research articles about adoption

Pathways link environmental and genetic factors with structural brain networks and psychopathology in youth


The science of early childhood development is clear about the importance of early experiences, caregiving environment, and environmental threats on biological, cognitive, and behavioral development. Young children exposed to institutionalized care, which often corresponds with social deprivation and low caregiving quality, have an increased risk for behavioral problems and psychopathology. 1 , 2 , 3 , 4 , 5 , 6 Intervention studies of children who experienced institutionalized care and are later adopted or placed into foster care provide evidence that a more favorable caregiving environment may lead to improved outcomes in growth, health, and development, and an overall reduced risk for psychopathology 7 , 8 , 9 , 10 , 11 and may reverse the negative effects of early deprivation on hypothalamic pituitary axis functioning and neurobehavioral development. 8 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20

Prior studies have addressed the effects of institutionalized care on neurodevelopment and identified significant deficits in cognitive and social functioning, and developmental delay in children adopted post institutionalization. 3 , 5 , 6 , 8 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 Age at adoption and time spent in institutionalization are associated with significant and often detrimental effects on overall outcomes. 21 , 22 Institutionalized care and accompanying stimulus deprivation affect the development of the prefrontal cortex. 23 , 29 , 30 , 31 , 32 , 33 , 34 , 35 The prefrontal cortex has a key role in the development and regulation of executive functions as well as the control of the autonomic system balance. Executive functions refer to a group of higher-order cognitive processes that coordinate the planning and execution of thoughts, emotions, and behaviors, as well as the storage of information in working memory. 36 , 37 , 38 , 39 Executive skills are critical building blocks for the early development of cognitive and social capabilities; the gradual acquisition of these skills correspond to the development of the prefrontal cortex and other brain areas from infancy to adulthood. 36 , 37 , 38 , 39

There is a paucity of research about post-adoption parenting styles that may promote recovery in children after institutionalized care. Ample evidence supports that the early caregiving environment is a consistent predictor of developmental outcomes and executive skills. 40 , 41 , 42 , 43 , 44 The developing executive function system is influenced by a child’s experiences, response to stress, and structural and molecular changes associated with changes in the hormonal milieu in the brain during sensitive periods of development. Dehydroepiandrosterone (DHEA) has a critical role in human brain development and cognition likely due to the effects of this steroid in enhancing brain plasticity. 45 , 46 Results of recent studies suggest that DHEA affects the development of cortico-amygdala 46 and cortico-hippocampal functions 47 that are important to encoding and processing of emotional, spatial, and social cues, as well as attention and working memory processes. In addition, steroids that are DHEA precursors, such as progesterone and allopregnanolone, have critical roles in neuroprotection. 36 , 37 , 38 , 39

In this prospective study, we followed the development of children who experienced institutionalized care 2 years post adoption by a family in the United States. We examined the relationship between family environment, growth, endocrine and levels of neurosteroids, executive functioning, and cognitive development in children adopted from institutionalized care and non-adopted controls to identify factors related to developmental recovery and behavioral outcomes.


We recruited children adopted from institutionalized care in Eastern Europe within 2 months of adoption by a US family. Eligible participants had no history of significant medical, developmental, or behavioral problems. Participants were screened to determine that they spent at least 8 months in the institution/orphanage setting and were placed in the institution/orphanage at 6 months of age or less. Participants were recruited from local adoption referral centers. Child participants were  recruited for a control group and were cohort age–sex-matched with the adopted subjects. The controls were healthy children with no history of significant medical, psychological, or behavioral disorders. Exclusion criteria for the study included documented history of growth hormone deficiency, history of chronic illness (i.e., renal failure, chronic lung disease, diabetes, hypothyroidism, chromosomal abnormalities, medical conditions known to be associated with developmental delay (i.e., fetal alcohol syndrome (subjects were screened using criteria developed by Hoyme et al. 48 )) chronic infectious disease (e.g., AIDS, hepatitis), or precocious puberty. Socio-economic scores were similar between groups.

Participants were seen at baseline (within 2 months of arrival in the United States for adopted subjects) at 1- and 2-year follow-up. All studies were conducted under protocol 06-CH-0223 that was approved by the Eunice Kennedy Shriver National Institute of Child Health and Human Development Institutional Review Board. Informed consent was obtained from the parent/legal guardian. A total of 11 adopted children and 27 controls were recruited. Ten adopted children and 19 controls completed at least two follow-up visits and were included in the analysis. The study was closed to recruitment earlier than anticipated due to the suspension of adoptions from Eastern Europe to the United States.

Anthropometric measurements, physical examination, neurocognitive testing, behavioral questionnaires, and endocrine labs and bone age (adopted children only) were evaluated over a 2-year period. Anthropometric measures included height, weight, body mass index (BMI), mid-arm circumference (MAC), triceps skinfold (TSF), subscapular skinfold (SSF), waist circumference (WC), and occipitofrontal circumference (OFC) by a registered dietitian.

Due to the participants’ age and ethical issues related to procedures that expose healthy child participants to risk, blood and bone age x-rays to assess nutritional and endocrine status were obtained for adopted children only (along with clinically indicated laboratory tests). Serum cortisol, DHEA, testosterone, estradiol, and serum neurosteroid profile were also collected (convenience sample: between 11 a.m. and 1 p.m.).

Neurocognitive testing was performed by a pediatric neuropsychologist and included either the Bayley III or Differential Abilities Scale II (DAS) based on age-appropriate guidelines. Behavioral questionnaires included Child Behavior Checklist (CBCL), Behavior Rating Inventory of Executive Function- Preschool (BRIEF-P), Infant Toddler Social Emotional Assessment (ITSEA), Colorado Child Temperament Inventory (CCTI), and Family Environment Scale (FES). Waters Attachment Behavior Q-sort (AQS) assessment of child attachment (Waters, SUNY) was performed by two trained observers at the initial visit.

The Bayley III is a clinical evaluation by a trained clinician to identify developmental issues in infants and toddlers and consists of the following domains, adaptive behavior, cognition, language, motor skills, and social–emotional capacities. Mean scores for scales are 10, with an SD of three. 49 The DAS is a nationally normed (US) battery of cognitive and achievement tests for children aged 2 years 6 months to 17 years 11 months across a range of developmental levels; mean is 100, SD of 15. 50 The CBCL questionnaire is a validated parent-report measure to assess emotional (internalizing and externalizing symptoms) and maladaptive behavior in children. 27 The BRIEF-P is a reliable, valid parent-report inventory to assess executive function in preschool children; our analysis focused on the clinical scales of: inhibit (control behavioral response), shift (ability to alternate attention), emotional control (regulate emotional responses), working memory (ability to hold information when completing a task), plan/organization (to plan, organize), and Global Executive Composite (GEC). Scores on the CBCL and BRIEF-P are normalized to a mean of 50 (SD 10), with higher scores indicative of greater degrees of dysfunction and scores >65 considered to be clinically significant. 51 ITSEA is a validated measure completed by the parent to assess social–emotional problems and competence in children (1–3 years of age) and is comprised of four domains, externalizing (impulsive, aggression), internalizing (depression, anxiety, separation distress, inhibition to novelty), dysregulation (sleep problems, negative emotions, sensory sensitivity), and competence (attention, compliance, play, mastery, empathy, prosocial peer relations). 52

The CCTI is a validated inventory designed to assess the temperament of children by parental report. 53 The FES is a self-reported questionnaire to assess social climate and environmental family characteristics and family functioning and emotions. The FES is categorized into three domains with ten subscales—relationship dimensions (cohesion, expressiveness, and conflict), personal growth dimensions (independence, achievement orientation, intellectual–cultural orientation, active–recreational orientation, and moral–religious aspect), and system maintenance dimensions (organization and control). 54 The AQS is widely used to assess child attachment behavior and is based on Ainsworth’s study of secure attachment behavior in infants. The AQS assesses the correlation between secure attachment type and child–parent boundaries and has high validity. The AQS security score is the correlation of a specific child’s Q-sort to prototypical secure child and the score range is from −1.0 to +1.0. 55 , 56

We hypothesized that aspects of the family environment, as measured by FES, would be associated with outcome measures of cognitive, executive function, and behavioral problems.

Statistical analysis

To compare children of different ages, anthropometric measurements, and cognitive function scores were converted to z -scores (the difference between the child’s measurement/score and the age mean or the mean provided by standardized cognitive test, divided by the standard deviation (SD)). For length, height, weight, BMI, OFC, MAC, TSF, SSF, and WC z -scores were calculated using the program PediTools, 57 based on means for age and SDs obtained by the National Health and Nutrition Examination Survey (Center for Disease Control and Prevention (CDC)). The CDC provides a set of growth measurements that are standardized among an ethnically diverse population.

Descriptive statistics were examined, and analysis of variance (ANOVA) was conducted to evaluate group differences in growth, cognitive, and behavior problems. Statistical comparisons included paired t tests, ANOVAs, correlation, and regression analysis. Regression analyses were conducted to examine which aspects of the family environment predicted cognitive or behavioral outcome measures. Analyses were conducted using the SPSS software. A p value <0.05 was considered for statistical significance.

There was no significant age or sex difference between adopted and control groups at the initial visit (adopted: 27.5 ± 9.3 months (range 14–40 months), 6 females, 4 males; control: 30.7 ± 14 months (range 10–58 months), 9 females, 10 males). For adopted subjects, the average time spent in institutionalized care was 23.6 ± 9 months. All the adopted children in our study were engaged with early intervention educational services.

At baseline, adopted subjects had significantly lower z -scores for height/length, weight, OFC, and MAC compared to controls ( p  < 0.5). At baseline, one adopted subject had height and weight z -score <2 SD, compared to one subject in the control group with weight <2 SD; six adopted subjects had OFC <2 SD compared to one control subject with OFC <2 SD. No significant differences were found for z -scores for TSF or SSF or WC. At 2-year follow-up, adopted subjects showed significant improvement in z -scores of height and weight; there were no differences between the two groups for anthropometric measures. For adopted subjects at follow-up, one child had weight SD < 2 SD and four children had OFC < 2 SD. OFC was not obtained in most control subjects at 2-year follow-up. (Table  1 ).

Endocrine and metabolic measures (adopted children)

Serum cortisol was obtained between 11 a.m. and 1 p.m. The range of cortisol levels was 4.2 to 16.3 μg/dL. Time in orphanage care was positively associated with serum cortisol at baseline ( R 2  = 0.61, p  < 0.06) (Fig.  1 ). Due to the small sample size, the two outliers with longer time in orphanage care may have skewed the results; however, serum cortisol levels at follow-up were not statistically different from baseline values. We planned to collect salivary cortisol levels (diurnal) for both adopted and control subjects; however, due to poor compliance or lack of ample quantity of sample collected, there was insufficient data for analysis. At baseline, thyroid function results were within normal limits, except for one child who had mildly elevated thyroid-stimulating hormone with normal free T4, which normalized at follow-up visit. Other endocrine hormone levels were within normal limits for age/sex. Insulin-like growth factor-1 (IGF-1) and insulin-like growth factor-binding protein 3 (IGFBP3) z -scores at baseline (0.62 ± 0.2, 1.2 ± 0.3, respectively) and follow-up (0.43 ± 0.3, 1.58 ± 0.3, respectively) were within normal range. Growth factors were not a predictor of cognitive outcome. At the initial visit, bone age was consistent with chronological age in five children, advanced in three children, and delayed in two children. At follow-up, bone age was consistent with chronological age in six, advanced in two, and delayed in two children.

figure 1

Cortisol levels in adopted children: time in orphanage care is positively correlated with serum cortisol at baseline ( r 2  = 0.608, p  < 0.06). Serum cortisol was obtained between 11 am and 1 pm. (convenience sample). Cortisol levels ranged from 4.2 to 16.3 μg/dL.

A serum lipid panel was obtained (convenience sample, non-fasting). At baseline, serum cholesterol and low-density lipoprotein levels were within normal limits for age. Serum high-density lipoprotein levels were <40 mg/dL in six of the ten subjects, and at follow-up remained <40 mg/dL in two of the nine subjects.

Serum neurosteroids were measured at baseline ( n  = 6) and follow-up ( n  = 9) by isotope dilution high-performance liquid chromatography-tandem mass spectrometry. 58 Allopregnanolone levels were within the expected range for the assay and levels were similar to a recent report in a healthy population of toddlers that found no significant diurnal variation, as well as no differences between males and females, in the first 3 years of life. 59 Serum tetrahydro-11 deoxycortisol, tetrahydrodeoxycorticosterone, and DHEA levels were at the lower limit of detection for the assay and did not change in the six subjects who had both baseline and follow-up measured (Table  2 ).

Cognitive data

At baseline, adopted subjects had significantly lower scores compared to controls on all cognitive measures (Bayley III): cognitive, language receptive, language expressive, fine motor, and gross motor ( n  = 9 of adopted and 10 of controls were age appropriate for testing with Bayley III). To compare changes in scores from baseline to follow-up, overall cognitive z -scores were calculated ( z -score of Bayley III or DAS General Cognitive Ability) and ANOVA analysis was performed. At baseline, general cognitive z -scores were significantly lower for adopted vs. controls; at 2-year follow-up, there was a trend for improvement in scores for adopted; however, residual differences remained compared to controls. For adopted subjects, lower OFC z -scores (baseline) were associated with lower cognitive scores at follow-up (Table  3 and Fig.  2 ).

figure 2

a Comparison of mean scores on Bayley III at baseline. Adopted subjects had significantly lower scores in all subscales compared to controls. b Comparison of baseline and follow-up cognitive z -scores. Adopted subjects had significantly lower z -scores at baseline and although a trend was noted for improvement in adopted subjects’ scores from baseline to follow-up, residual differences remained. Error bars indicate standard error. * P  < 0.05.

Behavioral data

At baseline, adopted children had significantly lower scores than controls for the ITSEA competence subscale ( p  < 0.001; F  = 19.017); lower scores are associated with lower social–emotional competence. Since most subjects were above the age limit for use of ITSEA at follow-up, these data were not included in the analysis. At baseline, adopted children had significantly higher scores on the emotional subscale of the CCTI compared to controls ( p  < 0.03; F  = 5.516). Baseline CBCL results showed no difference between the adopted and control group for any subscale scores. At 2-year follow-up, adopted children had significantly higher scores on externalizing symptom subscales compared to controls ( p  < 0.03; F  = 5.251).

For adopted subjects at baseline, parent responses for the BRIEF endorsed clinically significant inhibitory control in half the children ( p  < 0.05; F  = 4.424); no significant difference was found between the adopted and control groups for other subscales. At follow-up the adopted group had significantly higher scores (higher scores associated with more problems) compared to controls for the following subscales: inhibition ( p  < 0.04; F  = 5.027), inhibitory self-control ( p  < 0.03; F  = 5.328), with a trend noted for working memory and GEC (Fig. 3 ).

figure 3

Comparison of mean scores on a ITSEA-Emotional Assessment (baseline); b CCTI-Temperament Assessment (baseline); c CBCL-Behavioral Assessment (baseline and follow-up); and d BRIEF-P-Executive Function (baseline and follow-up) of adopted vs. controls. Error bars indicate standard error. * P  < 0.05.

Waters Q attachment scores showed no difference in attachment between adopted children and controls; AQS scores strongly correlated with norms for a sensitive response. Based on that, we concluded that there were no differences between parents’ sensitivity and child attachment in either group and their secure–insecure attachment distribution was comparable with that of normative groups (data not shown). FES scores at baseline showed a significant difference for only the independence subscale score between adopted vs. control groups ( p  < 0.05; F  = 4.418).

To identify sociodemographic and family environment factors associated with increased risk for executive dysfunction or behavioral problems, a correlational analysis was performed between demographic variables of child gender and age and executive function variables to determine possible covariate variables. Sex was not significantly correlated with any executive function variables and therefore not included in any future analysis. However, age at baseline was significantly correlated with BRIEF subscales; correlation and linear regression analyses were used for these executive function variables.

For adopted subjects, the baseline FES subscales control and conflict were predictors of higher GEC scores at follow-up (BRIEF measure; higher scores associated with dysfunction) ( R 2  = 0.91; F  = 14.48, p  = 0.03). FES subscale achievement positively correlated with change in cognitive z -scores ( R 2  = 0.433; F  = 6.106, p  = 0.04). FES subscales cohesion and expressiveness were negatively associated with a change in internalizing scores of CBCL ( R 2  = −0.9; p  = 0.04), that is, greater cohesion and expressiveness were associated with lower scores on internalizing symptoms of CBCL. FES subscale control was a predictor of a higher internalizing score (CBCL) at follow-up ( R 2  = 0.74; F  = 10.893, p  = 0.03); greater emphasis on rules and procedures were associated with more internalizing symptoms, which is a reflection of mood disturbance (i.e., anxiety, depression, social withdrawal). CCTI emotionality was associated with an increase in externalizing scores of CBCL for adopted subjects ( R 2  = 0.97; p  < 0.005) (Tables  4 and 5 ).

This prospective study followed the development of children adopted from institutionalized care for 2 years post adoption compared to controls. Broadly, our findings are consistent with the literature, showing significant but not complete growth and developmental recovery post adoption for children exposed to institutionalized care. Kroupina et al. 28 reported that growth factors (IGFBP3) at baseline were a negative predictor and change of head circumference and cognitive scores at 6 months were positive predictors, of cognitive outcomes at 30 months post adoption. Our data did not show a correlation between baseline growth factor z -scores and cognitive outcome at follow-up, perhaps due to the constraints of our small sample size. However, OFC z -scores at baseline were a predictor of cognitive scores at 2-year follow-up. Also, Kroupina et al. 28 reported that smaller stature at baseline and weight gain were associated with improved height outcome at 30- month follow-up, and younger age and lower weight at baseline were a predictor of better catch-up growth. Our data did not replicate the findings of Kroupina et al. 28 regarding predictors of catch-up growth, likely due to the constraints of our sample size. Baseline z -scores for height, weight, and OFC were similar between our study and Kroupina et al., 28 which had a larger sample size. As expected, there was a negative correlation between time in orphanage care and baseline height and weight z -scores. Consistent with previous studies, 8 , 21 , 24 , 26 , 34 , 60 , 61 , 62 , 63 , 64 our results support specific aspects of the family environment that are associated with executive function and behavioral symptomology 2 years after adoption. 65 , 66 Specifically, greater conflict and less flexible rules in a family were predictors of higher scores of global executive dysfunction. BRIEF scores reflect the parent’s observations of the child’s everyday executive functioning relative to the parent’s expectations (not an absolute level of functioning) and thus serve as a screening tool for executive dysfunction. Also, in this study, adopted children were found to have higher scores for behavioral inhibition, an aspect of temperament characterized as social reticence that is reported to be stable across childhood and is associated with greater risk for developing social withdrawal, anxiety disorders, and internalizing problems. Prior studies report that developmental outcomes associated with behavioral inhibition can be influenced by the caregiving context; authoritarian style (i.e., lack of emotional warmth, non-transparent declaration of rules, and high levels of control) is detrimental for social developmental outcomes. 67

Family cohesion and expressiveness were a protective influence; at 2-year follow-up, stronger family cohesion and expressiveness were associated with lower internalizing scores (i.e., less problems with mood disturbance, including anxiety, depression, and social withdrawal). Prior studies of internationally adopted children reported either higher mean internalizing symptoms or no differences in internalizing scores between adopted vs. non-adopted children. 66 , 68 , 69 Consistent with prior studies, we found higher externalizing scores (i.e., greater problems with aggression, conflict, and violation of social norms) on the CBCL at 2-year follow-up for adopted children that were associated with higher emotionality scores on CCTI. 70 Scores on the FES at baseline did not differ significantly between groups, suggesting that there were no differences in perceived family characteristics between adopted and controls. 54

As expected, at baseline visit there were significant differences in measures of cognitive function between adopted children and controls; overall mean scores improved but remained lower than controls at 2-year follow-up. Cognitive scores were negatively associated with OFC z -scores (baseline visit). At baseline, compared to controls, adopted children scored lower on measures of competence (as measured by ITSEA) and scored higher (associated with more problems) on measures of emotionality (as measured by CCTI) and inhibitory control (as measured by BRIEF). At follow-up, adopted children scored higher (associated with more problems) on measures of externalizing symptoms, inhibition, inhibitory self-control, behavioral flexibility, working memory, and GEC (BRIEF). The developing executive function system is influenced by a child’s experiences and response to stress, which impacts the developing prefrontal cortex. In this study, although the measurement of neurosteroids did not reveal any relationship to measures of cognitive or behavioral symptomology; the small sample size and lack of data in the control group limit interpretation and future research is warranted.

We did not identify differences in attachment measures in adopted vs. controls. We observed “indiscriminate friendliness” in many of the adopted subjects, as has been described in the literature. 5 , 63 Our observations are consistent with prior studies that note indiscriminate sociability in children with secure attachment. 71 , 72

The strengths of this study are the prospective design and the differentiation of behavioral issues noted at adoption placement versus those that manifest later. Limitations of the study include the small number of participants (the study was terminated prematurely due to the cessation of adoptions from East Europe). Another limitation was that measures of internalizing, externalizing behaviors, and executive function included only parental assessments of behavior. Also, the lack of salivary cortisol data (due to either inadequate quantity of samples collected or poor compliance with collection in this infant/toddler population) is regrettable since salivary cortisol levels are widely used and are an invaluable tool for pediatric studies and would have provided useful information for comparison of adopted and control subjects.

This study, in the context of a small sample size, should be viewed as a pilot study in the field of developmental pediatrics. Here we find that specific aspects of the family caregiving environment moderate the effects of social deprivation during early childhood on executive function and behavioral problems. These findings provide preliminary data for larger studies that will further investigate the developmental effects that manifest in institutionalized children.

In summary, findings from this study support a cohesive and expressive family environment moderated the effect of prior pre-adoption adversity on cognitive and behavioral development in toddlers. Family conflict and greater emphasis on rules/procedures were associated with a greater risk for behavioral problems at 2-year follow-up. Early assessment of child temperament child and parenting context may provide useful information to optimize the fit between parenting style, family environment structure, and the child’s development.

Harlow, H. F. Total social isolation: effects on macaque monkey behavior. Science 148 , 666 (1965).

Article   CAS   PubMed   Google Scholar  

Heim, C. & Nemeroff, C. B. The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies. Biol. Psychiatry 49 , 1023–1039 (2001).

Gunnar, M. R. & van Dulmen, M. H., International Adoption Project T. Behavior problems in postinstitutionalized internationally adopted children. Dev. Psychopathol. 19 , 129–148 (2007).

Smyke, A. T., Dumitrescu, A. & Zeanah, C. H. Attachment disturbances in young children. I: the continuum of caretaking casualty. J. Am. Acad. Child Adolesc. Psychiatry 41 , 972–982 (2002).

Article   PubMed   Google Scholar  

Tizard, B. & Rees, J. The effect of early institutional rearing on the behaviour problems and affectional relationships of four-year-old children. J. Child Psychol. Psychiatry 16 , 61–73 (1975).

Rutter, M. et al. Early adolescent outcomes of institutionally deprived and non-deprived adoptees. III. Quasi-autism. J. Child Psychol. Psychiatry 48 , 1200–1207 (2007).

Hellerstedt, W. L. et al. The International Adoption Project: population-based surveillance of Minnesota parents who adopted children internationally. Matern. Child Health J. 12 , 162–171 (2008).

Kroupina, M. G. et al. Adoption as an intervention for institutionally reared children: HPA functioning and developmental status. Infant Behav. Dev. 35 , 829–837 (2012).

O’Connor, T. G. Early experiences and psychological development: conceptual questions, empirical illustrations, and implications for intervention. Dev. Psychopathol. 15 , 671–690 (2003).

Rutter, M. et al. Early adolescent outcomes for institutionally-deprived and non-deprived adoptees. I: disinhibited attachment. J. Child Psychol. Psychiatry 48 , 17–30 (2007).

Weitzman, C. & Albers, L. Long-term developmental, behavioral, and attachment outcomes after international adoption. Pediatr. Clin. N. Am. 52 , 1395–1419 (2005).

Article   Google Scholar  

De Kloet, E. R., Vreugdenhil, E., Oitzl, M. S. & Joels, M. Brain corticosteroid receptor balance in health and disease. Endocr. Rev. 19 , 269–301 (1998).

PubMed   Google Scholar  

Heim, C., Plotsky, P. M. & Nemeroff, C. B. Importance of studying the contributions of early adverse experience to neurobiological findings in depression. Neuropsychopharmacology 29 , 641–648 (2004).

Suomi, S. J., Delizio, R. & Harlow, H. F. Social rehabilitation of separation-induced depressive disorders in monkeys. Am. J. Psychiatry 133 , 1279–1285 (1976).

Meaney, M. J. Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annu. Rev. Neurosci. 24 , 1161–1192 (2001).

Imanaka, A. et al. Neonatal tactile stimulation reverses the effect of neonatal isolation on open-field and anxiety-like behavior, and pain sensitivity in male and female adult Sprague-Dawley rats. Behav. Brain Res. 186 , 91–97 (2008).

Holmes, A. et al. Early life genetic, epigenetic and environmental factors shaping emotionality in rodents. Neurosci. Biobehav Rev. 29 , 1335–1346 (2005).

Brand, A. E. & Brinich, P. M. Behavior problems and mental health contacts in adopted, foster, and nonadopted children. J. Child Psychol. Psychiatry 40 , 1221–1229 (1999).

Leve, L. D., Fisher, P. A. & Chamberlain, P. Multidimensional treatment foster care as a preventive intervention to promote resiliency among youth in the child welfare system. J. Pers. 77 , 1869–1902 (2009).

Article   PubMed   PubMed Central   Google Scholar  

Fox, N. A. et al. The effects of severe psychosocial deprivation and foster care intervention on cognitive development at 8 years of age: findings from the Bucharest Early Intervention Project. J. Child Psychol. Psychiatry 52 , 919–928 (2011).

Johnson, D. E. Adoption and the effect on children’s development. Early Hum. Dev. 68 , 39–54 (2002).

Beckett, C. et al. Behavior patterns associated with institutional deprivation: a study of children adopted from Romania. J. Dev. Behav. Pediatr. 23 , 297–303 (2002).

Danese, A. & McEwen, B. S. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol. Behav. 106 , 29–39 (2012).

Judge, S. Developmental recovery and deficit in children adopted from Eastern European orphanages. Child Psychiatry Hum. Dev. 34 , 49–62 (2003).

Kaler, S. R. & Freeman, B. J. Analysis of environmental deprivation: cognitive and social development in Romanian orphans. J. Child Psychol. Psychiatry 35 , 769–781 (1994).

Miller, L. C., Kiernan, M. T., Mathers, M. I. & Klein-Gitelman, M. Developmental and nutritional status of internationally adopted children. Arch. Pediatr. Adolesc. Med. 149 , 40–44 (1995).

Achenbach, T. M. & Rescorla, L. A. Manual for the AESBA School-Age Forms and Profiles (Research Center for Children, Youth, and Families, University of Vermont, Burlington, 2001).

Kroupina, M. G. et al. Associations between physical growth and general cognitive functioning in international adoptees from Eastern Europe at 30 months post-arrival. J. Neurodev. Disord. 7 , 36 (2015).

Fishbein, D. H. et al. Mediators of the stress-substance-use relationship in urban male adolescents. Prev. Sci. 7 , 113–126 (2006).

Arnsten, A. F. Stress signalling pathways that impair prefrontal cortex structure and function. Nat. Rev. Neurosci. 10 , 410–422 (2009).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ohta, K. I. et al. The effects of early life stress on the excitatory/inhibitory balance of the medial prefrontal cortex. Behav. Brain Res. 379 , 112306 (2020).

Pena, C. J., Nestler, E. J. & Bagot, R. C. Environmental programming of susceptibility and resilience to stress in adulthood in male mice. Front. Behav. Neurosci. 13 , 40 (2019).

Callaghan, B. L., Sullivan, R. M., Howell, B. & Tottenham, N. The international society for developmental psychobiology Sackler symposium: early adversity and the maturation of emotion circuits–a cross-species analysis. Dev. Psychobiol. 56 , 1635–1650 (2014).

Bos, K. J., Fox, N., Zeanah, C. H. & Nelson Iii, C. A. Effects of early psychosocial deprivation on the development of memory and executive function. Front. Behav. Neurosci. 3 , 16 (2009).

Liston, C., McEwen, B. S. & Casey, B. J. Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proc. Natl Acad. Sci. USA 106 , 912–917 (2009).

Shaw, P. et al. Intellectual ability and cortical development in children and adolescents. Nature 440 , 676–679 (2006).

Lenroot, R. K. et al. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum. Brain Mapp. 30 , 163–174 (2009).

Tsujimoto, S. The prefrontal cortex: functional neural development during early childhood. Neuroscientist 14 , 345–358 (2008).

IoMaNR Council. From Neurons to Neighborhoods: The Science of Early Childhood Development (The National Academies Press, Washington, 2000).

Dowsett, S. M. & Livesey, D. J. The development of inhibitory control in preschool children: effects of “executive skills” training. Dev. Psychobiol. 36 , 161–174 (2000).

Bell, M. A. & Wolfe, C. D. Emotion and cognition: an intricately bound developmental process. Child Dev. 75 , 366–370 (2004).

Wolfe, C. D. & Bell, M. A. Working memory and inhibitory control in early childhood: Contributions from physiology, temperament, and language. Dev. Psychobiol. 44 , 68–83 (2004).

EKSNIoCHaH Development. The NICHD Study of Early Child Care and Youth Development (SECCYD): Findings for Children up to Age 4 1/2 Years (Government Printing Office, Washington, 2006).

Rhoades, B. L., Greenberg, M. T., Lanza, S. T. & Blair, C. Demographic and familial predictors of early executive function development: contribution of a person-centered perspective. J. Exp. Child Psychol. 108 , 638–662 (2011).

Nguyen, T. V. et al. Interactive effects of dehydroepiandrosterone and testosterone on cortical thickness during early brain development. J. Neurosci. 33 , 10840–10848 (2013).

Nguyen, T. V. et al. A testosterone-related structural brain phenotype predicts aggressive behavior from childhood to adulthood. Psychoneuroendocrinology 72 , 219 (2016); erratum 63 , 109–118 (2016).

Nguyen, T. V. et al. Dehydroepiandrosterone impacts working memory by shaping cortico-hippocampal structural covariance during development. Psychoneuroendocrinology 86 , 110–121 (2017).

Hoyme, H. E. et al. A practical clinical approach to diagnosis of fetal alcohol spectrum disorders: clarification of the 1996 institute of medicine criteria. Pediatrics 115 , 39–47 (2005).

Bayley, N. Bayley Scales of Infant and Toddler Development: Administration Manual 3rd edn (Pearson Psychcorp, San Antonio, 2006).

Elliott, C. D., Salerno, J. D., Dumont, R. & Willis, J. O. The Differential Ability Scales—Second Edition: Contempory Intellectual Assessment: Theories, Tests, and Issues (Guilford Press, 2018).

Gioia, G. A., Isquith, P. K., Guy, S. C. & Kenworthy, L. Behavior Rating Inventory of Executive Function. Preschool Edition (PAR Inc., Lutz, 2000).

Carter, A. S., Briggs-Gowan, M. J., Jones, S. M. & Little, T. D. The Infant-Toddler Social and Emotional Assessment (ITSEA): factor structure, reliability, and validity. J. Abnorm. Child Psychol. 31 , 495–514 (2003).

Rowe, D. C. & Plomin, R. Temperament in early childhood. J. Pers. Assess. 41 , 150–156 (1977).

Moos, R. H. & Moos, B. S. Family Environment Scale Manual (Consulting Psychologist Press, Palo Alto, 1981).

Ainsworth, M. D. S. Patterns of Attachment: A Psychological Study of the Strange Situation (Erlbaum, Hillsdale, 1978).

Waters, E. D. K. Defining and assessing individual differences in attachment relationships: Q-methodology and the organization of behavior in infancy and early childhood. Monogr. Soc. Res. Child Dev. 50 , 41–65 (1985).

Chou, J. H., Roumiantsev, S. & Singh, R. PediTools Electronic Growth Chart Calculators: applications in clinical care, research, and quality improvement. J. Med. Internet Res. 22 , e16204 (2020).

Parikh, T. P. et al. Diurnal variation of steroid hormones and their reference intervals using mass spectrometric analysis. Endocr. Connect. 7 , 1354–1361 (2018).

Fadalti, M. et al. Changes of serum allopregnanolone levels in the first 2 years of life and during pubertal development. Pediatr. Res. 46 , 323–327 (1999).

McGuinness, T. M. & Dyer, J. G. International adoption as a natural experiment. J. Pediatr. Nurs. 21 , 276–288 (2006).

Albers, L. H. et al. Health of children adopted from the former Soviet Union and Eastern Europe. Comparison with preadoptive medical records. JAMA 278 , 922–924 (1997).

Benoit, T. C., Jocelyn, L. J., Moddemann, D. M. & Embree, J. E. Romanian adoption. The Manitoba experience. Arch. Pediatr. Adolesc. Med. 150 , 1278–1282 (1996).

Chisholm, K. A three year follow-up of attachment and indiscriminate friendliness in children adopted from Romanian orphanages. Child Dev. 69 , 1092–1096 (1998).

Rutter, M., English and Romanian Adoptees (ERA) Study Team. Developmental catch-up, and deficit, following adoption after severe global early privation. J. Child Psychol. Psychiatry 39 , 465–476 (1998).

Juffer, F., Bakermans-Kranenburg, M. J. & van, I. M. H. The importance of parenting in the development of disorganized attachment: evidence from a preventive intervention study in adoptive families. J. Child Psychol. Psychiatry 46 , 263–274 (2005).

Schoemaker, N. K. et al. A meta-analytic review of parenting interventions in foster care and adoption. Dev Psychopathol. 46 , 1–24 (2019).

Guyer, A. E. et al. Temperament and parenting styles in early childhood differentially influence neural response to peer evaluation in adolescence. J. Abnorm. Child Psychol. 43 , 863–874 (2015).

Hawk, B. N. et al. Caregiver sensitivity and consistency and children’s prior family experience as contexts for early development within institutions. Infant Ment. Health J. 39 , 432–448 (2018).

McCall, R. B. et al. Early caregiver-child interaction and children’s development: lessons from the St. Petersburg-USA orphanage intervention research project. Clin. Child Fam. Psychol. Rev. 22 , 208–224 (2019).

Juffer, F. & van Ijzendoorn, M. H. Behavior problems and mental health referrals of international adoptees: a meta-analysis. JAMA 293 , 2501–2515 (2005).

O’Connor, T. G. et al. Child-parent attachment following early institutional deprivation. Dev. Psychopathol. 15 , 19–38 (2003).

O’Connor, T. G. & Rutter, M., English and Romanian Adoptees Study Team. Attachment disorder behavior following early severe deprivation: extension and longitudinal follow-up. J. Am. Acad. Child Adolesc. Psychiatry 39 , 703–712 (2000).

Download references


We thank the children and their families for their participation in this study. We thank Dr. Patrick Mason (International Adoption Center, Fairfax, VA), Dr. Penny Glass (CNMC), Dr. Sharon Singh (CNMC), Dr. Pedro Martinez (NIMH), Dr. Steven Soldin (NIH CC DLM), and Dr. Moommal Shaihh (NICHD) for their assistance. We acknowledge the University of Nevada School of Medicine for support of Dr. Rescigno’s elective rotation with NICHD/NIH. This study was supported by NIH grant Z01-HD008920.

Author information

Authors and affiliations.

Section on Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

Margaret F. Keil, Adela Leahu & Constantine A. Stratakis

University of Nevada School of Medicine, Reno, NV, USA

Megan Rescigno

Nutrition Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA

Jennifer Myles

You can also search for this author in PubMed   Google Scholar


M.F.K.: conceptualized and designed the study, coordinated and supervised data collection, drafted the initial manuscript, and reviewed and revised the manuscript. A.L. and J.M.: collected data and carried out the initial analysis and reviewed and revised the manuscript. M.R.: assisted with the analyses and reviewed and revised the manuscript. C.A.S.: conceptualized and designed the study, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Margaret F. Keil .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Consent for participation in this study was obtained from the legal guardians of the participating children.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit .

Reprints and permissions

About this article

Cite this article.

Keil, M.F., Leahu, A., Rescigno, M. et al. Family environment and development in children adopted from institutionalized care. Pediatr Res 91 , 1562–1570 (2022).

Download citation

Received : 02 September 2020

Revised : 13 November 2020

Accepted : 02 December 2020

Published : 26 May 2021

Issue Date : May 2022


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

research articles about adoption

Adoption research, practice, and societal trends: Ten years of progress


  • 1 Independent Practice.
  • PMID: 29283657
  • DOI: 10.1037/amp0000218

Adoption involves the legal transfer of parental rights and responsibilities from a child's birth parents to adults who will raise the child (Reitz & Watson, 1992). Research related to adoption has expanded over the past 10 years and has incorporated more focus on implications for practice and public policy. This expansion has reflected increased awareness of the lived experience of adopted individuals, in addition to that of adoptive families and birth or first parents and families, collectively known as the adoption kinship network (Grotevant & McRoy, 1998). Trends discussed included research and social trends or movements (2007-2017) since the publication of the final article in a series of articles in the psychological literature related to adoption in The Counseling Psychologist (Baden & Wiley, 2007; Lee, 2003; O'Brien & Zamostny, 2003; Wiley & Baden, 2005; Zamostny, O'Brien, Baden, & Wiley, 2003; Zamostny, Wiley, O'Brien, Lee, & Baden, 2003). This article summarizes the social trends and research related to adoption over the last 10 years, including longitudinal and meta-analytic studies, increased research and conceptualization of ethnic and racial identity development, research on microaggressions, and research on diverse adoptive families, including those with gay and lesbian parents. Social trends included increased knowledge related to Internet accessibility, genetic information, continued focus on openness, and viewing adoption through a more critical lens. Implications are discussed for the development of programs that enhance competence of mental health professionals and adoption professionals in adoption-competent practice. (PsycINFO Database Record

(c) 2017 APA, all rights reserved).

  • Adoption / psychology*
  • Psychology / education
  • Social Adjustment
  • Social Perception

Search this site

Around the o menu.

Around the O logo

Around the O

Adoption study links child behavior issues with mother’s trauma.

Sad child holding parent's hand

Mothers’ childhood experiences of trauma can predict their children’s behavior problems, even when the mothers did not raise their children, who were placed for adoption as newborns, a new University of Oregon study shows.

The research team, led by Leslie Leve , a professor in the UO College of Education and scientist with the Prevention Science Institute , found a link between birth mothers who had experienced stressful childhood events, such as abuse, neglect, violence or poverty, and their children’s behavior problems. This was true even though the children were raised by their adoptive parents and were never directly exposed to the stresses their birth mothers had experienced.

If a child’s adoptive mother also experienced stressful events as a child, then the child’s behavior issues were even more pronounced, the researchers found.      

The paper in the journal Development and Psychopathology was recently published online.

This research underscores the importance of efforts to prevent child neglect, poverty, and sexual and physical abuse, and to intervene with help and support when children experience them.

“We can’t always prevent bad things from happening to young children,” Leve said. “But we can provide behavioral health supports to individuals who have been exposed to childhood trauma or neglect to help them develop coping skills and support networks, so that difficult childhood experiences are less likely to negatively impact them — or the next generation.”

Leve is the Lorry Lokey Chair in Education and head of the counseling psychology and human services department. 

In the only study of its kind, Leve and other researchers have followed 561 adopted children, their birth parents and adoptive parents for more than a decade. Participants were recruited through 45 adoption agencies in 15 states nationwide. The researchers collected data from the birth parents when children in the study were infants and from the adoptive parents when the children were age 6-7 and again at age 11.

The researchers found when birth mothers reported more adverse childhood experiences and other life stress when they were young, their children showed less “effortful control” at age 7. Examples of “effortful control” include the child being able to wait before initiating new activities when asked and being able to easily stop an activity when told “No.”

At age 11, the children of these same mothers showed more “externalizing behavior,” such as rule-breaking and aggressive behavior.

The study also points the way for additional inquiry. For example, exactly how does stress in one generation become associated with behavior in the next generation? 

“We know from nonhuman animal studies that stress can change the expression of genes by essentially changing which genes are turned “on” or “off” when passed on to the next generation,” Leve said. “That could be a plausible pathway.”

Further, what is the effect of the environment in which the child was raised?

“Can we find something positive in the rearing environment, perhaps parents’ warmth or sensitivity, that can help offset the child’s genetic or biologic risk for impulsive or externalizing behavior?” Leve asked. That is the next question the research team is asking.

Along with Leve, the study’s authors include Veronica Oro and David DeGarmo with the UO’s Prevention Science Institute; Misaki Natsuaki with University of California, Riverside; Gordon Harold, University of Cambridge; Jenae Neiderhiser, The Pennsylvania State University; Jody Ganiban, George Washington University; and Daniel Shaw, University of Pittsburgh.

— By Sherri Buri McDonald, University Communications

This research was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health & Human Development; the National Institute on Drug Abuse; National Institutes of Health Office of Behavioral and Social Sciences Research; National Institute of Mental Health; National Institute of Diabetes and Digestive and Kidney Disease; National Institute of Health’s Office of the Director; and the Andrew and Virginia Rudd Family Foundation.

Submit Your Story Idea

Subscribe to Around the O

  • Research article
  • Open access
  • Published: 05 February 2021

Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation

  • Janae Bradley 1 &
  • Suchithra Rajendran   ORCID: 2 , 3  

BMC Veterinary Research volume  17 , Article number:  70 ( 2021 ) Cite this article

40k Accesses

18 Citations

74 Altmetric

Metrics details

Among the 6–8 million animals that enter the rescue shelters every year, nearly 3–4 million (i.e., 50% of the incoming animals) are euthanized, and 10–25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal at shelters considering key features such as animal type (dog, cat, etc.), age, gender, breed, animal size, and shelter location.

Logistic regression, artificial neural network, gradient boosting, and the random forest algorithms were used to develop models to predict the length of stay. The performance of these models was determined using three performance metrics: precision, recall, and F1 score. The results demonstrated that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Upon further observation of the results, it was found that age for dogs (puppy, super senior), multicolor, and large and small size were important predictor variables.

The findings from this study can be utilized to predict and minimize the animal length of stay in a shelter and euthanization. Future studies involve determining which shelter location will most likely lead to the adoption of that animal. The proposed two-phased tool can be used by rescue shelters to achieve the best compromise solution by making a tradeoff between the adoption speed and relocation cost.

As the problem of overpopulation of domestic animals continues to rise, animal shelters across the nation are faced with the challenge of finding solutions to increase the adoption rates. In the United States, about 6–8 million dogs and cats enter animal shelters every year, and 3–4 million of those animals are euthanized [ 1 ]. In other words, about 50% of the total canines and felines that enter animal shelters are put to death annually. Moreover, 10–25% of the total euthanized population in the United States is explicitly euthanized because of shelter overcrowding each year [ 2 ]. Though animal shelters provide incentives such as reduced adoption fees and sterilizing animals before adoption, only a quarter of total animals living in the shelter are adopted.

Animal adoption from shelters and rescues

There are various places to adopt an animal, and each potential owner must complete the adoption process and paperwork to take their new animal home [ 3 ]. Public and private animal shelters include animal control, city and county animal shelters, and police and health departments. Staff and volunteers run these facilities. Animals may also be adopted from a rescue organization, where pets are fostered in a home or a private boarding facility. These organizations are usually run by volunteers, and animals are viewed during local adoption events that are held at different locations, such as a pet store [ 3 ].

There could be several reasons for the euthanization of animals in a shelter, such as overcrowding, medical issues (ex. sick, disabled), or behavioral issues (ex. too aggressive). The causes for the overpopulation of animals include failure to spay or neuter animals leading to reckless breeding habits and abandonment or surrender of offspring, animal abandonment from owners who are no longer able to take care of or do not want the animal, and individuals still buying from pet stores [ 4 ]. With the finite room capacity for animals that are abandoned or surrendered, overpopulation becomes a key challenge [ 5 ]. Though medical and behavioral issues are harder to solve, the overpopulation of healthy adoptable animals in shelters is a problem that can be addressed through machine learning and predictive analytics.

Literature review

In this section, we describe the research conducted on animal shelters evaluating euthanasia and factors associated with animal adoption. The articles provide insights into factors that influence the length of stay and what characteristics influence adoption.

Studies have been conducted investigating the positive influence of pre-adoption neutering of animals on the probability of pet adoption [ 2 ]. The author investigated the impact of the cooperation of veterinary medical schools in increasing pet adoption by offering free sterilization. Results demonstrated that the collaboration between veterinary hospitals and local animal shelters decreased the euthanization of adoptable pets.

Hennessy et al. [ 6 ] conducted a study to determine the relationship between the behavior and cortisol levels of dogs in animal shelters and examined its effect in predicting behavioral issues after adoption. Shore et al. [ 7 ] analyzed the reasons for returning adopted animals by owners and obtained insights for these failed adoptions to attain more successful future approvals. The researchers found that prior failed adoption had led to longer-lasting future acceptances. They hypothesized that the failed adoptions might lead owners to discover their dog preferences by assessing their living situation and the type of animal that would meet that requirement.

Morris et al. [ 8 ] evaluated the trends in income and outcome data for shelters from 1989 to 2010 in a large U.S. metropolitan area. The results showed a decrease in euthanasia, adoption, and intake for dogs. For cats, a reduction in intake was observed until 1998, a decrease in euthanasia was observed until 2000, and the adoption of cats remained the same. Fantuzzi et al. [ 9 ] explored the factors that are significant for the adoption of cats in the animal shelter. The study investigated the effects of toy allocation, cage location, and cat characteristics (such as age, gender, color, and activity level). Results demonstrated that the more active cats that possessed toys and were viewed at eye level were more likely to impress the potential adopter and be adopted. Brown et al. [ 10 ] conducted a study evaluating the influence of age, breed, color, and coat pattern on the length of stay for cats in a no-kill shelter. The authors concluded that while color did not influence the length of stay for kittens, whereas gender, coat patterning, and breed were significant predictors for both cats and kittens.

Machine learning

Machine learning is one possible tool that can be used to identify risk factors for animal adoption and predict the length of stay for animals in shelters. Machine learning is the ability to program computers to learn and improve all by itself using training experience [ 11 ]. The goal of machine learning is to develop a system to analyze big data, quickly deliver accurate and repeatable results, and to adapt to new data independently. A system can be trained to make accurate predictions by learning from examples of desired input-output data. More specifically, machine learning algorithms are utilized to detect classification and prediction patterns from large data and to develop models to predict future outcomes [ 12 ]. These patterns show the relationship between the attribute variables (input) and target variables (output) [ 13 ].

Widely used data mining tasks include supervised learning, unsupervised learning, and reinforcement learning [ 14 ]. Unsupervised learning involves the use of unlabeled datasets to train a system for finding hidden patterns within the data [ 15 ]. Clustering is an example of unsupervised learning. Reinforcement learning is where a system is trained through direct interaction with the environment by trial and error [ 15 ]. Supervised learning encompasses classification and prediction using labeled datasets [ 15 ]. These classification and regression algorithms are used to classify the output variable with a discrete label or predict the outcome as a continuous or numerical value. Traditional algorithms such as neural networks, decision trees, and logistic regression typically use supervised learning. Figure  1 provides a pictorial of the steps for developing and testing a predictive model.

figure 1

Pictorial Representation of Developing a Predictive Model

Contributions to the literature

Although prior studies have investigated the impact of several factors, such as age and gender, on the length of stay, they focus on a single shelter, rather than multiple organizations, as in this study. The goal of this study is to investigate the length of stay of animals at shelters and the factors influencing the rate of animal adoption. The overall goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay. Machine learning algorithms are used to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). We address several objectives in this study that are listed below.

Identify risk factors associated with adoption rate and length of stay

Utilize the identified risk factors from collected data to develop predictive models

Compare statistical models to determine the best model for length of stay prediction

Exploratory Data results

From Fig.  2 , it is evident that the return of dogs is the highest outcome type at 43.3%, while Fig.  3 shows that the adoption of cats is the highest outcome type at 46.1%. Both figures illustrate that the euthanization of both cats and dogs is still prevalent (~ 20%). The results from Table 1 demonstrate that the longest time spent in the shelter is at 355 days by a male cat that is adopted and a female dog that is euthanized. Observing the results, adoption has the lowest variance among all animal types compared to the other outcome types. Adopted male cats have the lowest variance for days spent in the shelter, followed by female dogs. Female cats that are returned have the highest variance for days spent in the shelter.

figure 2

Distribution of Outcome Types for Dogs

figure 3

Distribution of Outcome Types for Cats

Figure  4 shows a comparison of cats and dogs for the three different outcome types. It is observed from the data that there are more dogs returned than cats. From Fig.  5 , it is observed that the number of days a dog stays in the shelter decreases as the age increases. This is not expected, as it is predicted that the number of days in a shelter would be lower for younger dogs and puppies. This observation could be due to having more data points for younger dogs.

figure 4

Comparison of Outcome Types for Cats and Dogs

figure 5

Age vs. Days in Shelter for Cats and Dogs

Machine learning results

Examining Table 2 , it is clear that the most proficient predictive model is developed by the gradient boosting algorithm for this dataset, followed by the random forest algorithm. The logistic regression algorithm appears to perform the worst with low precision, recall, and F1 score performance metrics for all categories of length of stay. For the prediction of low length of stay in a shelter, the random forest algorithm is the best performing model in comparison to the others at around 64–70% performance for precision, recall, and F1 score. The ANN algorithm is found to be the best when evaluating the precision and F1 score for medium length of stay, while the random forest algorithm is better for assessing recall. However, the performance of these models in predicting the medium length of stay for the given dataset is low for all three-performance metrics. The gradient boosting algorithm performs the best when predicting the high length of stay. Finally, the gradient boosting and random forest algorithms perform well when predicting the very high length of stay at around 70–80%.

Results from Table 2 also demonstrate that the model developed from the gradient boosting algorithm has a higher performance when predicting the high length of stay that leads to adoption, and when the outcome is euthanization. Evaluating the average of all three-performance metrics for all algorithms, the gradient boosting is the most proficient model at almost 60%, while logistic regression appears to be the worst. Table 2 also provides the computational time for each machine learning algorithm. For the given dataset, logistic regression runs the fastest at 9.41 s, followed by gradient boosting, artificial neural network, and finally, random forest running the longest. The gap in the performance measure ( pm ) is calculated by \( \frac{p{m}_{best}-p{m}_{worst}}{p{m}_{best}} \) , and is nearly 34, 39, and 32% for precision, recall, and F1 score, respectively.

Table 3 provides information on the top features or factors from each machine learning algorithm. Observing the table, we find that age (senior, super senior, and puppy), size (large and small), and color (multicolor) has a significant impact or influence on the length of stay. Specifically, we observe that older-aged animals (senior and/or super senior) appear as a significant factor for every algorithm. For the artificial neural network, older age is the #2 and #3 predictor, and super senior is the #2 predictor for the gradient boosting algorithm. Large and small-sized animals are also observed to be important features, as both are shown as the #1 predictor in the gradient boosting and ANN algorithms. The results also demonstrate that gender, animal type, other colors besides multicolor, middle age, and medium-sized animals did not significantly impact the length of stay.

Results from our study provided information on what factors are significant in influencing length of stay. Brown et al. [ 10 ] conducted research that found that age, breed designation, coat color, and coat pattern influenced the length of stay for cats in animal shelters. Similar to these studies, observations from our study also suggest that age and color have a significant impact or influence on the length of stay.

Determining which algorithm will develop the best model for the given set of data is critical to predict the length of stay and minimize the chances of euthanization. The goal of predictive analytics is to develop a model that best approximates the true mapping function for the relationship between the input and output variables. To approximate this function, parametric or non-parametric algorithms can be used. Parametric algorithms simplify the unknown function to a known form. Non-parametric algorithms do not make assumptions about the structure of the mapping function, allowing free learning of any functional form. In this study, we utilize both parametric (logistic regression and artificial neural network) and non-parametric (random forest and gradient boosting) algorithms on the given data. Observing the results from Table 2 , the gradient boosting and random forest (non-parametric algorithms) perform the best on the dataset. It is observed from the results that using a non-parametric approach leads to a better approximation of the true mapping function for the given records. These results also support prior studies on parametric versus non-parametric methods. Neely et al. [ 16 ] detailed the theoretical superiority of non-parametric algorithms for detecting pharmacokinetic and pharmacodynamic subgroups in a study population. The author suggests this superiority comes from the lack of assumptions made about the distribution of parameter values in a dataset. Bissantz et al. [ 17 ] discussed a resampling algorithm that evaluates the deviations between parametric and non-parametric methods to be noise or systematic by comparing parametric models to a non-parametric “supermodel”. Results demonstrate the non-parametric model to be significantly better. The use of algorithms that do not approximate the true function of the relationship between input and output provides better performance results for this application as well.

Current literature also supports the use of ensemble methods to increase prediction accuracy and performance. Dietterich [ 18 ] discussed the ongoing research into developing good ensemble methods as well as the discovery that ensemble algorithms are often more accurate than individual algorithms that are used to create them. Pandey, and S, T [ 19 ]. conducted a study to compare the accuracy of ensemble methodology on predicting student academic performance as research has demonstrated better results for composite models over a single model. This study applied ensemble techniques on learning algorithms (AdaBoost, Random Forest, Rotation Forest, and Bagging). For our study with the given records, the results support this claim. Both the gradient boosting and random forest algorithms are ensemble algorithms and performed the best on the animal shelter data.

Results from Table 2 demonstrate the best performance of the gradient boosting and random forest algorithm when the length of stay was classified as very high or the animal was euthanized. This is beneficial as the models can predict long stays where the outcome is euthanasia. This can lead to shelters identifying at-risk animals and implementing methods and solutions to ensure their adoption. These potential methods are the second phase of this research study, which will involve relocating animals to shelters where they will more likely be adopted. This phase is discussed in the future directions section.

Studies have been conducted evaluating euthanasia-related stress on workers (e.g., [ 1 ]). In other words, overpopulation not only leads to euthanasia but can, in turn, cause mental and emotional problems for the workers. For instance, Reeve et al. [ 20 ] evaluated the strain related to euthanasia among animal workers. Results demonstrated that euthanasia related strain was prevalent, and an increase in substance abuse, job stress, work causing family conflict, complaints, and low job satisfaction was observed. Predicting the length of stay for animals will aid in them being more likely to be adopted and will lead to fewer animals being euthanized, adding value not only to animals finding a home but also less stress on the workers.

The approach developed in this paper could be beneficial not only to reduce euthanasia but also to reduce overcrowding in shelters operated in countries where euthanasia of healthy animals is illegal, and all animals must be housed in shelters until adoption (or natural death). It is essential to develop an information system for a collaborative animal shelter network in which the entities can coordinate with each other, exchanging information about the animal inventory. Another benefit of this study is that it investigates applying machine learning to the animal care domain. Previous studies have looked into what factors influence the length of stay; however, this study utilizes these factors in addition to classification algorithms to predict how long an animal will stay in the shelter. Moreover, the use of a prescriptive analytics approach is discussed in this paper, where the predictions made by the machine learning algorithms will be used along with a goal programming model to decide in what shelter is an animal most likely to be adopted.

Limitations of this study include lack of behavioral data, limited sample size, and the use of simple algorithms. The first limitation, lack of behavioral data of the animal during intake and outcome, would be beneficial to develop a more comprehensive model. Though behavioral problems are harder to solve, having data would provide insight into how long these animals with behavioral issues are staying in shelters and what the outcome is. Studies have shown that behavioral problems play a significant role in preventing bonding between owners and their animals and one of the most common reasons cited for animal surrender [ 21 , 22 ]. These behavioral problems can include poor manners, too much energy, aggression, and destruction of the household. Dogs surrendered to shelters because of behavioral issues have also been shown to be less likely to be adopted or rehomed, and the ones that are adopted are more likely to be returned [ 21 ]. Studies have also been conducted to evaluate the effect of the length of time on the behavior of dogs in rescue shelters [ 23 , 24 , 25 ]. Most of them concluded that environmental factors led to changes in the behavior of dogs and that a prolonged period in a shelter may lead to unattractive behavior of dogs to potential owners. Acquiring information on behavioral problems gives more information for the algorithm to learn when developing the predictive model. This allows more in-depth predictions to be made on how long an animal will stay in a shelter, which could also aid in adoption. This approach can be used to shorten the length of stay, which makes sure that healthy animals are not developing behavioral problems in the shelters. It is not only crucial for the animal to be adopted, but also that the adoption is a good fit between owner and pet. Shortening the length of stay would also lessen the chance that the animal will be returned by the adopter because of behavior. Having this information will also allow shelters to find other shelters close by where animals with behavioral issues are more likely to be adopted. To overcome this limitation of the lack of data on behavioral problems, behavioral issues will be used as a factor and will be specifically asked for when acquiring data from shelters.

Another limitation includes collecting more data from animal shelters across the United States, allowing for more representative data to be collected and inputted into these algorithms. However, this presents a challenge due to most shelters being underfunded and low on staff. Though we reached out to shelters, most replied that they lacked the resources and staff to provide the information needed. Future work would include applying for funding to provide a stipend to staff for their assistance in gathering the data from respective shelters. With more data, the algorithm has more information to learn on, which could improve the performance metrics of the predictive models developed. There may also be other factors that show to be significant as more data is collected.

Finally, the last limitation is the use of simpler algorithms. This study considers basic ML algorithms. Nevertheless, in recent years, there has been development in the ML field of more complex networks. For instance, Zhong et al. [ 26 ] proposed a novel reinforcement learning method to select neural blocks and develop deep learning networks. Results demonstrated high efficiency in comparison to most of the previous deep network search approaches. Though only four algorithms were considered, future work would investigate deep learning networks, as well as bagging algorithms. Using more complex algorithms could ensure that if intricate patterns in the data are present, the algorithm can learn them.

Future direction

Phase 2: goal programming approach for making relocation decisions.

Using the information gathered in this study, we can predict the type of animals that are being adopted the most in each region and during each season of the year. To accomplish this, we utilize a two-phase approach. The first phase was leveraging the machine learning algorithms to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). Phase-2 involves determining the best shelter to transport adoptable animals to increase the adoption rates, based on several conflicting criteria. This criterion includes predicted length of stay from phase-1, the distance between where the animal is currently housed and the potential animal shelters, transportation costs, and transportation time. Therefore, our goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay, as well as determine the optimal animal shelter location where the animal will have the least amount stay in a shelter and most likely be adopted.

After predicting the length of stay of an incoming animal that is currently housed in the shelter l ′ using the machine learning algorithms, the next phase is to evaluate the potential relocation options for that animal. This strategic decision is specifically essential if the length of stay of the animal at its current location is high/very high. Nevertheless, while making this relocation decision, it is also necessary to consider the cost of transporting the animal between the shelters. For instance, if a dog is brought into a shelter in Houston, Texas, and is estimated to have a high/very high length of stay. Suppose if the dog is predicted to have a low length of stay at New York City and a medium length of stay at Oklahoma City, then a tradeoff has to be made between the relocation cost and the adoption speed. The objectives, length of stay, and relocation costs are conflicting and have to be minimized. Phase-2 attempts to yield a compromise solution that establishes a trade-off between these two criteria.

Goal programming (GP) is a widely used approach to solve problems involving multiple conflicting criteria. Under this method, each objective function is assigned as a goal, and a target value is specified for the individual criterion [ 27 ]. These target numbers can be fulfilled by the model with certain deviations, while the objective of the GP model is to minimize these deviations. Pertaining to this study, the desired values for the length of stay and relocation cost is pre-specified in the model and can be fulfilled with deviations. The GP model attempts to minimize these deviations. Thus, this technique attempts to produce a solution that is as close as possible to the targets, and the model solutions are referred to as the “most preferred solution” by prior studies (e.g., [ 28 , 29 ]).

As mentioned earlier, the primary task to be completed using this phase-2 goal programming approach is the relocation decisions considering the adoption speed and the cost of transporting the animal from the current location.

Model notations

Goal programming model formulation, goal constraints.

Objective 1: Minimize the overall length of stay of the animal under consideration (Eq. 1 ).

Goal constraint for objective 1: The corresponding goal constraint of objective 2 is given using Equation [ 30 ].

Objective 2: Minimize the overall relocation cost for transporting the animal under consideration (Eq. 3 ).

Goal constraint for objective 2: The corresponding goal constraint of objective 2 is given using Equation [ 18 ].

Hard constraints

Equation [ 9 ] ensures that the animal can be assigned to only one shelter.

The animal can be accommodated in shelter l only if there are a shelter capacity and type for that particular animal size category, and this is guaranteed using constraint [ 31 ]. It is important to note that both y and s are input parameters , whereas l is the set of shelters.

Equation [ 21 ] sets an upper limit on the length of stay category if the shelter l is assigned as the destination location. This prevents relocating animals to a shelter that might potentially have a high or very high length of stay.

Similarly, Equation [ 32 ] sets an upper limit on the relocation cost, if the shelter l is assigned as the destination location. This prevents relocating animals to a very far location. The current shelter location, l ′ , that is hosting the animal is an input parameter.

Objective function

Since the current problem focuses on minimizing the expected length of stay and relocation cost, the objective function of the goal programming approach is to reduce the sum of the weighted positive deviations given in Equations ([ 18 , 30 ], as shown in Equation [ 6 ].

where w g is the weight assigned for each goal g .

It is necessary to scale the deviation (since the objectives have different magnitudes as well as units) to avoid a biased solution.

If the scaling factors are represented by f g for goal g , then the scaled objective function is given in Equation [ 14 ].

Using this goal programming approach, the potential relocation options are evaluated considering the length of stay from phase-1. This phase-2 goal programming approach is useful, especially if the length of stay of the animal at its current location is high/very high, and a trade-off has to be made between relocation cost and length of stay. Phase-2 acts as a recommendation tool for assisting administrators with relocation decisions.

Nearly 3–4 million animals are euthanized out of the 6–8 million animals that enter shelters annually. The overall objective of this study is to increase the adoption rates of animals entering shelters by using key factors found in the literature to predict the length of stay. The second phase determines the best shelter location to transport animals using the goal programming approach to make relocation decisions. To accomplish this objective, first, the data is acquired from online sources as well as from numerous shelters across the United States. Once the data is acquired and cleaned, predictive models are developed using logistic regression, artificial neural network, gradient boosting, and random forest. The performance of these models is determined using three performance metrics: precision, recall, and F1 score.

The results demonstrate that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Followed closely in second is the random forest algorithm, then the artificial neural network, and then finally, the logistic regression algorithm is the worst performer. We also observed from the data that the gradient boosting performed better when predicting the high or very high length of stay. Further observing the results, it is found that age for dogs (e.g., puppy, super senior), multicolor, and large and small size are important predictor variables.

The findings from this study can be utilized to predict how long an animal will stay in a shelter, as well as minimize their length of stay and chance of euthanization by determining which shelter location will most likely lead to the adoption of that animal. For future studies, we will implement phase 2, which will determine the best shelter location to transport animals using the goal programming approach to make relocation decisions.

Data description

A literature review is conducted to determine the factors that might potentially influence the length of stay for animals in shelters. These factors include gender, breed, age, and several other variables that are listed in Table 4 . These features will be treated as input variables for the machine learning algorithms. Overall, there are eight input or predictor variables and one output variable, which is the length of stay.

Animal shelter intake and outcome data are publicly made available by several state/city governments on their website (e.g., [ 33 , 34 ]), specifically in several southern and south-western states. These online sources provide datasets for animal shelters from Kentucky (150,843 data rows), California (334,016), Texas (155,115), and Indiana (4132). Since there is no nationwide database for animal shelters, information is also collected through individual animal shelters that conduct euthanization of animals. We contacted over 100 animal shelters across the United States and inquired for data on the factors mentioned in Table 4 . We received responses from 20 of the animal shelters that were contacted. Most responses received stated there was not enough staff or resources to be able to provide this information. From the responses that were received back, only four shelters were able to provide any information. Of those four, only two of the datasets contained the factors and information needed, which are Colorado (8488 data rows) and Arizona (4, 667 data rows).

The data that is collected from the database and animal shelters included information such as animal type, intake and outcome date, gender, color, breed, and intake and outcome status (behavior of animal entering the shelter and behavior of animal at outcome type). These records also included information on several types of animals, such as dogs, cats, birds, rabbits, and lizards. For this study, the focus is on dogs and cats. After filtering through these records, we found that only California, Kentucky, Colorado, Arizona, and Indiana had all of the factors needed for the study. Upon downloading data from the database and receiving data from the animal shelters, the acquired data underwent data integration, data transformation, and data cleaning (as detailed in Fig.  1 ). After data pre-processing, there are over 113,000 animal records.

Data cleaning methods

Next, data cleaning methods are utilized to detect discrepancies in the data, such as missing values, erroneous data, and inconsistencies. Data cleaning is an essential step for obtaining unbiased results [ 35 , 36 ]. In other words, identifying and cleaning erroneous data must be performed before inputting the data into the algorithm as it can significantly impact the output results.

The following is a list of commonly used data cleaning techniques in the literature [ 11 ]:

Substitution with Median: Missing or incorrect data are replaced with the median value for that predictor variable.

Substitution with a Unique Value: Erroneous data are replaced with a value that does not fall within the range that the input variables can accept (e.g., a negative number)

Discard Variable and Substitute with a Median: When an input variable has a significant number of missing values, these values are removed from the dataset, and the features that remain with missing or erroneous values are replaced with the median.

Discard Variable and Substitute with a Unique Value: Input variables with a significant number of missing values are removed from the dataset, and the features that remain with missing or erroneous values are coded as − 1.

Remove Incomplete Rows Entirely: Incomplete Rows are removed from the dataset.

Data preprocessing

Some animal breeds are listed in multiple formats and are changed to maintain uniformity. An example of this is a Russian Blue cat, which is formatted in several ways such as “Russian”, “Russian Blue”, and “RUSSIAN BLUE”. Animals with multiple breeds such as “Shih Tzu/mix” or “Shih Tzu/Yorkshire Terr” are classified as the first breed listed. Other uncommon breeds are classified as “other” for simplicity. Finally, all animal breeds are summarized into three categories (small, medium, or large) using the American Kennel Clubs’ breed size classification [ 37 ]. Part of the data cleansing process also includes categorizing multiple colors found throughout the sample size into five distinct color categories (brown, black, blue, white, and multicolor). We classified age into five categories for dogs and cats (puppy or kitten, adolescent, adult, senior, super senior). The puppy or kitten category includes data points 0–1 year, adolescence includes data points 2–3 years old, adulthood includes animals 4–7 years of age, and senior animals are 8–10 years of age. Any animal that is older than ten years are categorized as a super senior, based on the recommendations provided in Wapiti Labs [ 38 ].

As mentioned previously, the output variable is the length of stay and is classified as low, medium, high, and very high/euthanization. The length of stay is calculated by taking the difference between the intake date and outcome date. To remove erroneous data entries and special cases, the number of days in the animal shelter is also capped at a year. The “low” category represents animals that are returned (in which case, they are assigned the days in the shelter as 0) or spent less than 8 days before getting adopted. It is important to keep these animals at the shelter so that the owner may find them or they are transferred to their new homes. Animals that stayed in a shelter for 9–42 days and are adopted are categorized as “medium” length of stay. The “high” category is given to animals that stayed in the shelter for 43–365 days. Finally, animals that are euthanized are categorized as “very high”.

After integrating all data points from each animal shelter, the sample size includes 119,691 records. After the evaluation of these data points, 5436 samples are found to have miscellaneous (such as a negative length of stay) or missing values. After applying data cleaning techniques, the final cleaned dataset includes 114,256 data points, with 50,466 cat- and 63,790 dog-records.

Machine learning algorithms to predict the length of stay

The preprocessed records are then separated into training and testing datasets based on the type of classification algorithm used. Studies have demonstrated the need for testing and comparing machine learning algorithms, as the performance of the models depends on the application. While an algorithm may develop a predictive model that performs well in one application, it may not be the best performing model for another. A comparison between the statistical models is conducted to determine the overall best performing model. In this section, we provide a description as well as the advantages of each classification algorithm that is utilized in this study.

Logistic regression

Logistic regression (LR) is a machine learning algorithm that is used to understand the probability of the occurrence of an event [ 39 ]. It is typically used when the model output variable is binary or categorical (see Fig.  6 ), unlike linear regression, where the dependent variable is numeric [ 40 ]. Logistic regression involves the use of a logistic function, referred to as a “sigmoid function” that takes a real-valued number and maps it into a value between 0 and 1 [ 41 ]. The probability that the length of stay of the animal at a specific location will be low, medium, high, or very high, is computed using the input features discussed in Table 4 .

figure 6

Pictorial Representation of the Logistic Regression Algorithm

The linear predictor function to predict the probability that the animal in record i has a low, medium, high, and very high length of stay categories is given by Equations ( 11 ) –[ 3 ], respectively.

Where β v , l is a set of multinomial logistic regression coefficients for variable v of the length of stay category l , and x v , i is the input feature v corresponding to data observation i .

Artificial neural network

Artificial Neural Network (ANN) algorithms were inspired by the brain’s neuron, which transmits signals to other nerve cells [ 40 , 42 ]. ANN’s were designed to replicate the way humans learn and were developed to imitate the operational sequence in which the body sends signals in the nervous system [ 43 ]. In an ANN, there exists a network structure with directional links connecting multiple nodes or “artificial neurons”. These neurons are information-processing units, and the ties that connect them represent the relationship between each of the connected neurons. Each ANN consists of three layers - the input layer, hidden layer, and the output layer [ 32 , 44 ]. The input layer is where each of the input variables is fed into the artificial neuron. The neuron will first calculate the sum of multiple inputs from the independent variables. Each of the connecting links (synapses) from these inputs has a characterized weight or strength that has a negative or positive value [ 32 ]. When new data is received, the synaptic weight changes, and learning will occur. The hidden layer learns the relationship between the input and output variables, and a threshold value determines whether the artificial neuron will fire or pass the learned information to the output layer, as shown in Fig.  7 . Finally, the output layer is where labels are given to the output value, and backpropagation is used to correct any errors.

figure 7

Pictorial Representation of the Artificial Neural Networks

Random Forest

The Random Forest (RF) algorithm is a type of ensemble methodology that combines the results of multiple decision trees to create a new predictive model that is less likely to misclassify new data [ 30 , 45 ]. Decision Trees have a root node at the top of the tree that consists of the attribute that best classifies the training data. The attribute with the highest information gain (given in Eq. 16 ) is used to determine the best attribute at each level/node. The root node will be split into more subnodes, which are categorized as a decision node or leaf node. A decision node can be divided into further subnodes, while a leaf node cannot be split further and will provide the final classification or discrete label. RF algorithm uses mtree and ntry as the two main parameters in developing the multiple parallel decision trees. Mtree specifies how many trees to train in parallel, while ntry defines the number of independent variables or attributes to choose to split each node [ 30 ].. The majority voting from all parallel trees gives the final prediction, as given in Fig.  8 .

figure 8

Pictorial Representation of the Random Forest Algorithm

Gradient boosting

Boosting is another type of ensemble method that combines the results from multiple predictive algorithms to develop a new model. While the RF approach is built solely on decision trees, boosting algorithms can use various algorithms such as decision trees, logistic regression, and neural networks. The primary goal of boosting algorithms is to convert weak learners into stronger ones by leveraging weighted averages to identify “weak classifiers” [ 31 ]. Samples are assigned an initial uniformed weight, and when incorrectly labeled by the algorithm, a penalty of an increase in weight is given [ 46 ]. On the other hand, samples that are correctly classified by the algorithm will decrease in weight. This process of re-weighing is done until a weighted vote of weak classifiers is combined into a robust classifier that determines the final labels or classification [ 46 ]. For our study, gradient boosting (GB) will be used on decision trees for the given dataset, as illustrated in Fig.  9 .

figure 9

Pictorial Representation of Boosting Algorithm

Machine learning model parameters

The clean animal shelter data is split into two datasets: training and testing data. These records are randomly placed in the two groups to train the algorithms and to test the model developed by the algorithm. 80% of the data is used to train the algorithm, while the other 20% is used to test the predictive model. To avoid overfitting, a tenfold cross-validation procedure is used on the training data. There are no parameters associated with the machine learning of logistic regression algorithms. However, a grid search method is used to tune the parameters of the random forest, gradient boosting, and artificial neural network algorithms. This allows the best parameter in a specific set to be chosen by running an in-depth search by the user during the training period.

The number of trees in the random forest and gradient boosting algorithms is changed from 100 to 1000 in increments of 100. A learning rate of 0.01, 0.05, and 0.10 is used based on the recommendations of previous studies [ 47 ]. The minimum observations for the trees’ terminal node are set to vary from 2 to 10 in increments of one, while the splitting of trees varies from 2 to 10 in increments of two. A feed-forward method is used to develop the predictive model using the artificial neural network algorithm. The feed-forward algorithm consists of three layers (input, hidden, output) as well as backpropagation learning. The independent and dependent variables represent the input and output layers. Since the input and output layers are already known, an optimal point is reached for the number of nodes when between 1 and the number of predictors. This means that for our study, the nodes of the hidden layer vary from 1 to 8. The learning rate values used to train the ANN are 0.01, 0.05, and 0.10.

To find the optimal setting for each machine learning algorithm, a thorough search of their corresponding parameter space is performed.

Performance measures

In this study, we use three performance measures to evaluate the ability of machine learning algorithms in developing the best predictive model for the intended application. The measures considered are precision, F1 score, and sensitivity/recall to determine the best model given the inputted data samples. Table 5 provides a confusion matrix to define the terms used for all possible outcomes.

Precision evaluates the number of correct, true positive predictions by the algorithm while still considering the incorrectly predicted positive when it should have been negative (Eq. 17 ). By having high precision, this means that there is a low rate of false positives or type I error. Sensitivity or recall evaluates the number of true positives that are correctly predicted by the algorithm while considering the incorrectly predicted negative when it should have been positive (Eq. 18 ). Recall is a good tool to use when the focus is on minimizing false negatives (type II error). F1 score (shown in Eq. 19 ) evaluates both type I and type II errors and assesses the ability of the model to resist false positives and false negatives. This performance metric evaluates the robustness (low number of missed classifications), as well as the number of data points that are classified correctly by the model.

Availability of data and materials

Most of the datasets used and/or analyzed during the current study were publicly available online as open source data. The data were available in the website details given below:

We also obtained data from Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter. No administrative permission was required to access the raw data from these shelters.


Logistic Regression

Artificial Neural Network

Gradient Boosting

Goal Programming

Coefficient of Variation

Anderson KA, Brandt JC, Lord LK, Miles EA. Euthanasia in animal shelters: Management's perspective on staff reactions and support programs. Anthrozoös. 2013;26(4):569–78. .

Article   Google Scholar  

Clevenger J, Kass PH. Determinants of adoption and euthanasia of shelter dogs spayed or neutered in the University of California veterinary student surgery program compared to other shelter dogs. J Veterinary Med Educs. 2003;30(4):372–8.

Animal Humane Society. (n.d.). Retrieved November 2019, from .

Home. (2016, July 15). Retrieved November 2019, from .

Rogelberg SG, DiGiacomo N, Reeve CL, Spitzmüller C, Clark OL, Teeter L, et al. What shelters can do about euthanasia-related stress: an examination of recommendations from those on the front line. J Appl Anim Welf Sci. 2007;10(4):331–47. .

Article   CAS   PubMed   Google Scholar  

Hennessy MB, Voith VL, Mazzei SJ, Buttram J, Miller DD, Linden F. Behavior and cortisol levels of dogs in a public animal shelter, and an exploration of the ability of these measures to predict problem behavior after adoption. Appl Anim Behav Sci. 2001;73(3):217–33.

Shore ER. Returning a recently adopted companion animal: Adopters' reasons for and reactions to the failed adoption experience. J Appl Anim Welf Sci. 2005;8(3):187–98.

Article   CAS   Google Scholar  

Morris KN, Gies DL. Trends in intake and outcome Data for animal shelters in a large U.S. metropolitan area, 1989 to 2010. J Appl Anim Welf Sci. 2014;17(1):59–72. .

Fantuzzi JM, Miller KA, Weiss E. Factors relevant to adoption of cats in an animal shelter. J Appl Anim Welf Sci. 2010;13(2):174–9.

Brown WP, Morgan KT. Age, breed designation, coat color, and coat pattern influenced the length of stay of cats at a no-kill shelter. J Appl Anim Welf Sci. 2015;18(2):169–80.

Srinivas, S., & Rajendran, S. (2017). A Data-driven approach for multiobjective loan portfolio optimization using machine-learning algorithms and mathematical programming. In big Data analytics using multiple criteria decision-making models (pp. 175-210): CRC press.

Waller MA, Fawcett SE. Data science, predictive analytics, and big Data: a revolution that will transform supply chain design and management. J Bus Logist. 2013;34(2):77–84.

Kantardzic M. DATA MINING: concepts, models, methods, and algorithms. 2nd ed: IEEE: Wiley; 2019.

Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.

Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and Data mining methods in diabetes research. Computational Structural Biotechnol J. 2017;15:104–16. .

Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW. Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R. Ther Drug Monit. 2012;34(4):467–76. .

Article   PubMed   PubMed Central   Google Scholar  

Bissantz N, Munk A, Scholz A. Parametric versus non-parametric modelling? Statistical evidence based on P-value curves. Mon Not R Astron Soc. 2003;340(4):1190–8. .

Dietterich TG. Ensemble methods in machine learning. Berlin: Heidelberg; 2000.

Book   Google Scholar  

Pandey M, S, T. A comparative study of ensemble methods for students&apos; performance modeling. Int J Computer ApplS. 2014;103:26–32. .

Reeve CL, Rogelberg SG, Spitzmüller C, Digiacomo N. The caring-killing paradox: euthanasia-related strain among animal-shelter Workers1. J Appl Soc Psychol. 2005;35(1):119–43. .

Gates MC, Zito S, Thomas J, Dale A. Post-adoption problem Behaviours in adolescent and adult dogs rehomed through a New Zealand animal shelter. Animals : an open access journal from MDPI. 2018;8(6):93. .

Weiss E, Gramann S, Drain N, Dolan E, Slater M. Modification of the feline-Ality™ assessment and the ability to predict adopted Cats' behaviors in their new homes. Animals : an open access journal from MDPI. 2015;5(1):71–88. .

Normando S, Stefanini C, Meers L, Adamelli S, Coultis D, Bono G. Some factors influencing adoption of sheltered dogs. Anthrozoös. 2006;19(3):211–24.

Protopopova A, Mehrkam LR, Boggess MM, Wynne CDL. In-kennel behavior predicts length of stay in shelter dogs. PLoS One. 2014;9(12):e114319.

Wells DL, Graham L, Hepper PG. The influence of length of time in a rescue shelter on the behaviour of Kennelled dogs. Anim Welf. 2002;11(3):317–25.

CAS   Google Scholar  

Zhong G, Jiao W, Gao W, Huang K. Automatic design of deep networks with neural blocks. Cogn Comput. 2020;12(1):1–12.

Rajendran S, Ravindran AR. Multi-criteria approach for platelet inventory management in hospitals. Int J Operational ResS. 2020;38(1):49–69.

Bastian ND, McMurry P, Fulton LV, Griffin PM, Cui S, Hanson T, Srinivas S. The AMEDD uses goal programming to optimize workforce planning decisions. Interfaces. 2015;45(4):305–24.

Rajendran S, Ansaripour A, Kris Srinivasan M, Chandra MJ. Stochastic goal programming approach to determine the side effects to be labeled on pharmaceutical drugs. IISE Transactions on Healthcare Systems Engineering. 2019;9(1):83–94.

Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. Random forests for classification in ECOLOGY. Ecology. 2007;88(11):2783–92.

Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat. 2000;28(2):337–407.

Ge Z, Song Z, Ding SX, Huang B. Data mining and analytics in the process industry: the role of machine learning. IEEE Access. 2017;5:20590–616.

Open Data: City of Austin Texas: Open Data: City of Austin Texas. (n.d.). Retrieved March 2019, from .

County of Sonoma: Open Data: Open Data. (n.d.). Retrieved March 2019, from .

Kambli A, Sinha AA, Srinivas S. Improving campus dining operations using capacity and queue management: a simulation-based case study. J Hosp Tour Manag. 2020;43:62–70.

Rajendran S, Zack J. Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach. Transport Res Part E: Logistics and Transportation Review. 2019;128:470–505.

American Kennel Club. (n.d.). Retrieved November 2019, from .

Elk Antler Supplements & Chews: Wapiti Labs, Inc. (n.d.). Retrieved November 2019, from .

Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code for Biol Med. 2008;3(1):17.

Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med. 2005;34(2):113–27.

Kim A, Song Y, Kim M, Lee K, Cheon JH. Logistic regression model training based on the approximate homomorphic encryption. BMC Med Genet. 2018;11(4):83.

Google Scholar  

Srinivas S, Ravindran AR. Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: a prescriptive analytics framework. Expert Syst Appl. 2018;102:245–61. .

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436.

Shih H, Rajendran S. Comparison of time series methods and machine learning algorithms for forecasting Taiwan blood Services Foundation’s blood supply. Journal of healthcare engineering. 2019;2019.

Srinivas S, Salah H. Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: a data analytics approach. Int J Med Inform. 2020;145:104290.

Rokach L. Ensemble-based classifiers. Artif Intell Rev. 2010;33(1):1–39.

Srinivas S. A machine learning-based approach for predicting patient punctuality in ambulatory care centers. Int J Environ Res Public Health. 2020;17(10):3703.

Download references


We would like to thank the Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter for providing the length of stay reports in order to complete this study.

This research was not funded by any agency/grant.

Author information

Authors and affiliations.

Department of Bioengineering, University of Missouri Columbia, Columbia, MO, 65211, USA

Janae Bradley

Department of Industrial and Manufacturing Systems Engineering, University of Missouri Columbia, Columbia, MO, 65211, USA

Suchithra Rajendran

Department of Marketing, University of Missouri Columbia, Columbia, MO, 65211, USA

You can also search for this author in PubMed   Google Scholar


JB performed data mining, data cleaning and analyses of the animal shelter data and machine learning algorithms. JB was also a major contributor in writing the manuscript. SR performed data mining, cleaning, and analyses of the machine learning algorithms, as well as the goal programming. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Suchithra Rajendran .

Ethics declarations

Ethics approval and consent to participate.

Most of the datasets used in this study are open source and are publicly available. The remaining data was collected from animal shelters with their consent to use the data for research purposes.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit . The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bradley, J., Rajendran, S. Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation. BMC Vet Res 17 , 70 (2021).

Download citation

Received : 07 January 2020

Accepted : 22 December 2020

Published : 05 February 2021


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Animal shelter
  • High euthanization rates
  • Machine learning algorithms
  • Prediction models
  • Goal programming approach
  • Decision support tool

BMC Veterinary Research

ISSN: 1746-6148

research articles about adoption

Bridging the gap: a systematic analysis of circular economy, supply chain management, and digitization for sustainability and resilience

  • Published: 13 May 2024

Cite this article

research articles about adoption

  • Bhawna   ORCID: 1 , 2 ,
  • Parminder Singh Kang 2 , 3 &
  • Sanjeev Kumar Sharma 1  

The primary objective of this research paper is to conduct a comprehensive and systematic literature review (SLR) focusing on Sustainable Supply Chain Management (SSCM) practices that promote Circular Economy (CE), sustainability, and resilience through adopting emerging digital technologies. A SLR of 130 research articles published between 1991 and 2023 was used to analyze emerging trends in CE, supply chain management (SCM), and digitalization. This study meticulously examined research publication patterns, the intricate themes explored, influential scholars, leading countries, and substantial scientific contributions that have shaped this multifaceted domain. This paper contributed to the collective understanding of how SSCM practices, driven by the principles of CE and empowered by the adoption of digital technologies, foster sustainability, resilience, and innovation within contemporary SCs. The research findings presented herein are primarily based on an analysis of the current literature from only Scopus and Web of Science (WoS) databases, which may restrict the generalizability of implementing these results. Based on this study, organizations and practitioners can assess the maturity of their SCM practices, gauge the resilience and digitalization levels of their SCs, and align them with academic literature trends. This enables practitioners to bridge the gap between scholarly advancements and real-world SCM implementation. Through its systematic review, the study provides a structured literature review that offers a collective understanding of SSCM practices driven by CE principles and empowered by digital technologies. This understanding enables sustainability, resilience, and innovation within contemporary SCs, benefiting academicians and practitioners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

research articles about adoption

Data availability

Data can be provided upon request.

Alamelu R, Jayanthi M, Dinesh S, Nalini R, Shobhana N, Amudha R (2023) Sustainable supply chain and circular economy ingenuities in small manufacturing firms-a stimulus for sustainable development. Mater Today: Proc 92:17–23

Google Scholar  

Bag S, Pretorius JHC (2022) Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework. Int J Organ Anal 30(4):864–898.

Article   Google Scholar  

Bag S, Wood LC, Xu L, Dhamija P, Kayikci Y (2020) Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycl 153:104559.

Birkel H, Hohenstein N-O, Hähner S (2023) How have digital technologies facilitated supply chain resilience in the COVID-19 pandemic? An exploratory case study. Comput Ind Eng 183:109538.

Branke J, Farid SS, Shah N (2016) Industry 4.0: a vision for personalized medicine supply chains? Cell Gene Therapy Insights 2(2):263–270.

Carter CR, Rogers DS (2008) A framework of sustainable supply chain management: moving toward new theory. Int J Phys Distribution Logistics Manage 38(5):360–387

Centobelli P, Cerchione R, Esposito E, Passaro R, Shashi (2021) Determinants of the transition towards circular economy in SMEs: a sustainable supply chain management perspective. Int J Prod Econ 242:108297.

Cerqueira-Streit J, Endo G, Guarnieri P, Batista L (2021) Sustainable supply Chain Management in the Route for a circular economy: an integrative literature review. Logistics 5(4):81.

Chadegani AA, Salehi H, Yunus MM, Farhadi H, Fooladi M, Farhadi M, Ebrahim NA (2013) A comparison between two Main Academic Literature collections: web of Science and Scopus databases.

Chari A, Niedenzu D, Despeisse M, Machado CG, Azevedo JD, Boavida-Dias R, Johansson B (2022) Dynamic capabilities for circular manufacturing supply chains—exploring the role of industry 4.0 and resilience. Bus Strategy Environ 31(5):2500–2517.

Chauhan C, Singh A (2019) A review of industry 4.0 in supply chain management studies. J Manuf Technol Manage 31(5):863–886

Dentoni D, Pinkse J, Lubberink R (2021) Linking Sustainable Business models to Socio-Ecological Resilience through Cross-sector partnerships: a Complex Adaptive systems View. Bus Soc 60(5):1216–1252.

Donthu N, Kumar S, Pattnaik D (2020) Forty-five years of Journal of Business Research: a bibliometric analysis. J Bus Res 109:1–14

Edwin Cheng TC, Kamble SS, Belhadi A, Ndubisi NO, Lai K, Kharat MG (2022) Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms. Int J Prod Res 60(22):6908–6922.

El Baz J, Tiwari S, Akenroye T, Cherrafi A, Derrouiche R (2022) A framework of sustainability drivers and externalities for industry 4.0 technologies using the best-worst method. J Clean Prod 344:130909.

Esmaeilian B, Sarkis J, Lewis K, Behdad S (2020) Blockchain for the future of sustainable supply chain management in industry 4.0. Resour Conserv Recycl 163:105064.

Ferasso M, Beliaeva T, Kraus S, Clauss T, Ribeiro-Soriano D (2020) Circular economy business models: the state of research and avenues ahead. Bus Strategy Environ 29(8):3006–3024.

Fisher O, Watson N, Porcu L, Bacon D, Rigley M, Gomes RL (2018) Cloud manufacturing as a sustainable process manufacturing route. J Manuf Syst 47:53–68.

Gaur V, Gaiha A (2020) Building a transparent supply chain blockchain can enhance trust, efficiency, and speed. Harvard Business Rev 98(3):94–103

Ghisellini P, Cialani C, Ulgiati S (2016) A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J Clean Prod 114:11–32.

Golicic SL, Smith CD (2013) A meta-analysis of environmentally sustainable supply chain management practices and firm performance. J Supply Chain Manage 49(2):78–95

Govindan K, Hasanagic M (2018) A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. Int J Prod Res 56(1–2):278–311.

Graham G, Hardaker G (2000) Supply-chain management across the Internet. Int J Phys Distrib Logist Manag 30(3/4):286–295.

Hendry LC, Stevenson M, MacBryde J, Ball P, Sayed M, Liu L (2019) Local food supply chain resilience to constitutional change: the Brexit effect. Int J Oper Prod Manage 39(3):429–453.

Hofmann E, Rüsch M (2017) Industry 4.0 and the current status as well as future prospects on logistics. Comput Ind 89:23–34.

Ingemarsdotter E, Jamsin E, Kortuem G, Balkenende R (2019) Circular strategies enabled by the internet of things—A framework and analysis of current practice. Sustainability 11(20):5689

Jabbarzadeh A, Fahimnia B, Sabouhi F (2018) Resilient and sustainable supply chain design: sustainability analysis under disruption risks. Int J Prod Res 56(17):5945–5968.

Lopes de Sousa Jabbour AB, Jabbour CJ, Godinho Filho M, Roubaud D (2018) Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations. Ann Oper Res 270:273–286

Kähkönen A-K, Lintukangas K, Hallikas J (2018) Sustainable supply management practices: making a difference in a firm’s sustainability performance. Supply Chain Management: Int J 23(6):518–530

Karmaker CL, Aziz A, Ahmed R, Misbauddin T, Moktadir MA (2023) Impact of industry 4.0 technologies on sustainable supply chain performance: the mediating role of green supply chain management practices and circular economy. J Clean Prod 419:138249

Lu HE, Potter A, Sanchez Rodrigues V, Walker H (2018) Exploring sustainable supply chain management: a social network perspective. Supply Chain Management: Int J 23(4):257–277

Luthra S, Mangla SK (2018) Evaluating challenges to industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Saf Environ Prot 117:168–179

MacArthur E (2013) Towards the circular economy. J Ind Ecol 2(1):23–44

MacArthur E (2017) A new textiles economy: redesigning fashion’s future. Ellen MacArthur Foundation 1–150

Merli R, Preziosi M, Acampora A (2018) How do scholars approach the circular economy? A systematic literature review. J Clean Prod 178:703–722.

Morali O, Searcy C (2013) A review of sustainable supply Chain Management practices in Canada. J Bus Ethics 117(3):635–658.

Nandi S, Sarkis J, Hervani AA, Helms MM (2021) Redesigning Supply chains using blockchain-enabled circular economy and COVID-19 experiences. Sustainable Prod Consum 27:10–22.

Obeidat SM, Abdalla S, Al Bakri AAK (2023) Integrating green human resource management and circular economy to enhance sustainable performance: an empirical study from the Qatari service sector. Empl Relations: Int J 45(2):535–563.

Pagell M, Wu Z (2009) Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J Supply Chain Manage 45(2):37–56

Pettit TJ, Fiksel J, Croxton KL (2010) Ensuring supply chain resilience: development of a conceptual framework. J Bus Logistics 31(1):1–21.

Ren Y, Li R, Wu K-J, Tseng M-L (2023) Discovering the systematic interlinkages among the circular economy, supply chain, industry 4.0, and technology transfer: a bibliometric analysis. Clean Responsible Consum 9:100123.

Schmidt CVH, Kindermann B, Behlau CF, Flatten TC (2021) Understanding the effect of market orientation on circular economy practices: the mediating role of closed-loop orientation in German SMEs. Bus Strategy Environ 30(8):4171–4187.

Shin N, Park S (2021) Supply chain leadership driven strategic resilience capabilities management: a leader-member exchange perspective. J Bus Res 122:1–13.

Singh G, Singh S, Daultani Y, Chouhan M (2023) Measuring the influence of digital twins on the sustainability of manufacturing supply chain: a mediating role of supply chain resilience and performance. Comput Ind Eng 186:109711

Stock T, Seliger G (2016) Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP 40:536–541.

Talla A, McIlwaine S (2022) Industry 4.0 and the circular economy: using design-stage digital technology to reduce construction waste. Smart Sustainable Built Environ.

Tavera Romero CA, Castro DF, Ortiz JH, Khalaf OI, Vargas MA (2021) Synergy between circular economy and industry 4.0: a literature review. Sustainability 13(8):4331.

Tortorella G, Fogliatto FS, Gao S, Chan T-K (2022) Contributions of industry 4.0 to supply chain resilience. Int J Logistics Manage 33(2):547–566

Yadav G, Luthra S, Jakhar SK, Mangla SK, Rai DP (2020) A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: an automotive case. J Clean Prod 254:120112.

Download references

This research paper is part of a funded research project under Mitacs, Canada (Funding Ref. FR106245).

Author information

Authors and affiliations.

University Institute of Applied Management Sciences (UIAMS), Panjab University, Chandigarh, India

Bhawna & Sanjeev Kumar Sharma

Department of Decision Sciences, School of Business, MacEwan University, Edmonton, AB, Canada

Bhawna & Parminder Singh Kang

School of Engineering and Sustainable Development, Faculty of Technology, De Montfort University, Leicester, UK

Parminder Singh Kang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Bhawna .

Ethics declarations

Conflict of interest.

There are no relevant financial or non-financial competing interests to report.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Bhawna, Kang, P.S. & Sharma, S.K. Bridging the gap: a systematic analysis of circular economy, supply chain management, and digitization for sustainability and resilience. Oper Manag Res (2024).

Download citation

Received : 12 October 2023

Revised : 20 March 2024

Accepted : 16 April 2024

Published : 13 May 2024


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Sustainable supply chain management
  • Circular economy
  • Supply chain sustainability
  • Supply chain digitalization
  • Research trends
  • Find a journal
  • Publish with us
  • Track your research
  • Open access
  • Published: 13 May 2024

An investigation into the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies for birth companionship in Tehran: a qualitative inquiry on mitigating mistreatment of women during childbirth

  • Marjan Mirzania 1 ,
  • Elham Shakibazadeh 1 , 2 ,
  • Sedigheh Hantoushzadeh 3 ,
  • Zahra Panahi 4 ,
  • Meghan A. Bohren 5 &
  • Abdoljavad Khajavi 6  

BMC Public Health volume  24 , Article number:  1292 ( 2024 ) Cite this article

Metrics details

A birth companion is a powerful mechanism for preventing mistreatment during childbirth and is a key component of respectful maternity care (RMC). Despite a growing body of evidence supporting the benefits of birth companions in enhancing the quality of care and birth experience, the successful implementation of this practice continues to be a challenge, particularly in developing countries. Our aim was to investigate the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies for birth companions to mitigate the mistreatment of women during childbirth in Tehran.

This exploratory descriptive qualitative study was conducted between April and August 2023 at Valiasr Hospital in Tehran, Iran. Fifty-two face-to-face in-depth interviews were conducted with a purposive sample of women, birth companions, and maternity healthcare providers. Interviews were audio-recorded, transcribed verbatim, and analyzed using content analysis, with a deductive approach based on the Implementation Outcomes Framework in the MAXQDA 18.

Participants found the implemented program to be acceptable and beneficial, however the implementation team noticed that some healthcare providers were initially reluctant to support it and perceived it as an additional burden. However, its adoption has increased over time. Healthcare providers felt that the program was appropriate and feasible, and it improved satisfaction with care and the birth experience. Participants, however, highlighted several issues that need to be addressed. These include the need for training birth companions prior to entering the maternity hospital, informing women about the role of birth companions, assigning a dedicated midwife to provide training, and addressing any physical infrastructure concerns.

Despite some issues raised by the participants, the acceptability, adoption, appropriateness, feasibility, and fidelity of the implementation strategies for birth companions to mitigate the mistreatment of women during childbirth were well received. Future research should explore the sustainability of this program. The findings of this study can be used to support the implementation of birth companions in countries with comparable circumstances.

Peer Review reports

Despite every woman’s right to have a positive birth experience, the mistreatment during childbirth has been documented worldwide in health facilities [ 1 , 2 , 3 , 4 ]. Recent studies from Iran have reported a high rate of mistreatment, including verbal abuse, frequent and painful vaginal examinations, neglect and abandonment, lack of supportive care, physical abuse [ 5 ], denial of mobility [ 5 , 6 , 7 ], and pain relief [ 5 , 8 ]. Additionally, women are typically not allowed to choose their labour positions [ 6 ] or have a birth companion [ 7 ].

A powerful mechanism to prevent mistreatment during childbirth, as demonstrated in previous research, is the presence of a birth companion [ 6 , 9 , 10 ]. The World Health Organization (WHO) recommends ensuring the presence of a chosen companion during labour and childbirth, as outlined in three guidelines [ 11 , 12 , 13 ]. This practice is recognized as a significant strategy for enhancing the quality of care and the birthing experience [ 12 ], and is considered a crucial element of respectful maternity care (RMC) [ 14 ]. Evidence shows that having birth companions is associated with reduced pain intensity and duration of labour, increased likelihood of spontaneous vaginal birth, decreased need for analgesia, episiotomy, and cesarean section, improved birth experience, early initiation of breastfeeding, and reduced postpartum depression [ 15 , 16 , 17 ]. Despite recognizing these benefits, the successful implementation of birth companions remains a challenge. Many women in health facilities across the world, particularly in developing countries, are denied this right [ 18 , 19 , 20 , 21 , 22 , 23 ].

Addressing the research-to-practice gap and scaling up evidence-based interventions (EBIs) are key goals of implementation science (IS). IS is a multidisciplinary field defined as “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and hence, to improve the quality and effectiveness of health services” [ 24 ]. A wide range of implementation frameworks has been published. The implementation outcomes framework, introduced by Proctor et al. (2011), is one of these frameworks. This evaluation framework includes eight outcomes that serve as indicators of successful implementation: acceptability, adoption, appropriateness, feasibility, fidelity, implementation costs, penetration, and sustainability [ 25 ].

In Iran, the Ministry of Health and Medical Education (MOHME) implemented a policy in 2014 to promote maternal and newborn health by encouraging vaginal childbirth in public hospitals. One strategy of this policy to enhance the childbirth experience is the redesign of maternity wards to allow for the presence of birth companions [ 26 ]. However, public hospitals do not always support the implementation of birth companionship. As part of a large implementation research project, we have identified the challenges of implementing a birth companion as a formative research. The results showed that the major challenges include the lack of knowledge of companions, interference of companions in the clinical duties of staff, cultural issues, staff unwillingness, lack of supervision, and structural characteristics such as lack of physical space [ 27 ]. To address these issues, we developed and implemented strategies for birth companions. To the best of our knowledge, no comprehensive study has examined the implementation outcomes of birth companions in Iran. Therefore, this study aimed to investigate the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies for birth companions to mitigate the mistreatment of women during childbirth in Tehran.

Study design and setting

This study was part of a larger implementation research project examining the development and implementation of a context-specific intervention to reduce disrespectful maternity care and evaluation of strategies to improve implementation. This project, initiated in October 2021, consists of five phases: (1) needs assessment, (2) identifying the interventions to reduce mistreatment of women during childbirth, (3) identifying the implementation challenges of interventions, (4) designing implementation strategies for the intervention, and (5) testing implementation strategies in a real-life setting. The results of phases 1 and 3 of the project are presented in detail elsewhere [ 5 , 27 , 28 ]. This study used an exploratory descriptive qualitative design. It employed face-to-face in-depth interviews as data collection methods. Data was analyzed according to content analysis with a deductive approach.

Study context

This study was conducted between April and August 2023 at Valiasr Hospital in Tehran, Iran. We selected this hospital because it is a major, tertiary referral hospital in Tehran that offers a wide range of obstetric services to diverse groups of women. The maternity ward, which supports approximately 200 women giving birth per month, consists of a 12-bed hall for the first stage of labour and a separate room with one bed for the active stage of labour.

Designing implementation strategies of birth companions

In response to the challenges identified for the presence of birth companions in phase 3 of the project, we designed implementation strategies. These strategies include: (1) determining the implementation team, (2) training midwives, (3) conducting orientation sessions for obstetricians and residents, (4) training birth companions, (5) allowing birth companions to accompany women during labour and childbirth, and (6) continuously monitoring the implementation process. The implementation of these strategies spanned an 8-week period from April to June 2023. Our study focused on the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies for birth companions during the early implementation phase. These indicators are crucial for the initial stages of implementing health interventions [ 25 ]. According to the implementation outcomes framework of Proctor et al. (2011), acceptability is defined as “the perception among implementation stakeholders that a given treatment, service, practice, or innovation is agreeable, palatable, or satisfactory”; adoption as “the intention, initial decision, or action to employ an innovation”; appropriateness as “the degree of compatibility or perceived fit of the innovation”; feasibility as “the degree of successful implementation of the innovation in a setting”; and fidelity as “the degree of implementation of the innovation as intended” [ 25 ]. The details of implementation strategies of birth companions are provided below.

Determining the implementation team

The team consisted of members of the study team, the head of obstetrics, and maternity healthcare providers (MHCPs). The members of the study team (first and second authors) held a meeting with the head of obstetrics and the matron-in-charge to explain the purpose of the study.

Training midwives

All midwives received training from the matron-in-charge ( n  = 30, five midwives in each session). The training focused on the purpose of the study, the benefits of having birth companions during labour and childbirth, and specifically on providing training to birth companions. A member of the study team (the lead researcher) participated in the sessions.

Conducting orientation sessions for obstetricians and residents

The head of obstetrics held a meeting with obstetricians and residents to explain the purpose of the study and the benefits of having birth companions during labour and childbirth.

Training birth companions

Each birth companion received a 10-minute training session from midwives on supportive labour techniques, their roles and responsibilities during labour and childbirth, and the maternity regulations upon arrival at the maternity hospital for birth.

Allowing birth companions to accompany women during labour and childbirth

Any female birth companion that labouring women wanted was allowed to stay with her during labour and childbirth.

Continuously monitoring the implementation process

Supervisory visits to the maternity hospital were conducted by the study team, the matron-in-charge, and a team from the MOHME to oversee the implementation. The first author was present at the maternity hospital every day during both morning and evening shifts. The matron-in-charge visited the maternity hospital daily, and the third author visited the maternity hospital on a weekly basis, specifically on Fridays.

Recruitment and participants

Three groups of participants were identified for this study: (a) women, (b) birth companions, and (c) MHCPs (midwives, residents, and head of obstetrics). The eligibility criteria were as follows: women who had a vaginal birth, regardless of the outcome; female birth companions stayed with women during labour and childbirth; residents who had completed at least one semester (six months) in the maternity hospital; and midwives and head of obstetrics with at least one year of work experience in their role and involvement in the birth companion study. Women who had a labour progress disorder and cesarean section were excluded from this study. A purposive sampling technique with maximum variation was used to recruit participants. This technique aimed to include individuals with diverse characteristics, such as age, education, socioeconomic status for women and birth companions, and age, work experience, and shift for MHCPs.

Following prior coordination and permission from the hospital authorities, the first author (M.M.) invited participants to contribute in person. The purpose and reasons for conducting the study were explained to participants. All participants provided written consent to participate in the study and audio recordings p the interviews. They were also aware that their participation was voluntary, and that they could decline or stop the interviews at any time without facing any consequences.

Data collection

A semi-structured interview guide and face-to-face in-depth interviews were used to collect data. The interview guides were developed based on the framework of Proctor et al. [ 25 ] and then pilot-tested by conducting three initial interviews with participants, but were not analyze (Additional file 1 : Interview guides). For women and birth companions, the study examined the acceptability and adoption of having a birth companion. Meanwhile the MHCPS were asked about the acceptability, adoption, appropriateness, feasibility, and fidelity of having a birth companion. Each interview started with an overarching question such as “Please describe your overall experience with the implementation of the birth companion program at this hospital”. The interview process continued with questions such as “Are you satisfied or dissatisfied with the current implementation of the program or intervention?”, “How appropriate is the implementation of this program or intervention in the hospital?”, “What are your thoughts on integrating this program or intervention into your hospital?”. Probing questions, such as ‘“Can you explain more?”, “Why do you think that is?” and ‘What would need to change?, were used. All interviews were conducted in Persian by the first author (M.M.), a PhD candidate in Health Education and Promotion with experience in conducting qualitative research. No prior relationships existed between her and any of the other participants. Interviews with the women and birth companions were conducted before discharge in a quiet and private place in the postpartum ward. Interviews with MHCPs were conducted in a private room with no one else present at the maternity hospital. The interviews lasted approximately 30–40 minutes, and field notes were taken. Each participant was contacted once during the study. At the end of each interview, demographic information of the participants was collected. Data saturation was achieved through interviews with 22 women, 14 birth companions, and 16 MHCPs, after which, no new major themes emerged.

Data analysis

Data analysis was conducted simultaneously with data collection, using content analysis with a deductive approach [ 29 ]. First, M.M. listened to the recorded interviews repeatedly and transcribed them verbatim in Persian. Anonymity was ensured using numerical labels for each transcript file. The transcripts were checked for accuracy by the second author (E.Sh., a female professor in health education and promotion with experience in qualitative research). They were then independently coded by M.M. and E.Sh. We marked the segments of interest in the text and color-coded them. We then put these color-coded text segments together and assigned codes to them. We grouped the various codes according to their similarities and differences and linked them to pre-determined categorizations in different themes and sub-themes. The differences among coders regarding coding were discussed until a consensus was reached. Data management and analysis were performed using MAXQDA 18 software [ 30 ]. The selected quotations were translated into English to complement the findings of the study.

The trustworthiness of the study was assessed using Lincoln and Guba’s criteria [ 31 ]. Credibility was ensured through the triangulation of participants, including women, birth companions, and MHCPs. Additionally, the initially extracted codes were provided to three participants for approval, further enhancing credibility. Confirmability ensured by utilizing multiple data sources such as field notes, observations, audio recordings, and transcripts. Additionally, the data analysis process was reviewed and confirmed by an expert qualitative researcher who was not involved in the study. To enhance dependability, two authors independently analyzed the interviews. Furthermore, a detailed description of the research process was provided to ensure the transferability of the results. This allows for the evaluation and application of the study in different contexts. The study was reported according to the consolidated criteria for reporting qualitative research (COREQ) checklist [ 32 ] (Additional file 2 : COREQ Checklist).

Review author reflexivity

The authors maintained a reflexive stance throughout the study from study selection to data synthesis. The author team represents diverse international academic and professional backgrounds (health education and promotion, reproductive health, obstetrics and gynecology, and health services management) with a range of research focus areas and expertise. We are mindful that the authors’ perspectives might have affected the manner in which the data were collected, analyzed, and interpreted. The different perspectives of the authors could be related to their subject expertise, professional backgrounds, and knowledge of birth companionship and respectful care. As a multidisciplinary team, the authors challenged and critiqued their preconceived assumptions through reflective dialogue and supported each other to understand how these assumptions affected the analysis or interpretation of the findings. We believe that the diversity in our team helped us to critique and challenge our biases and develop the findings of the study.

Socio-demographic characteristics of participants

A total of 52 interviews were conducted, including 22 with women, 14 with birth companions and 16 with MHCPs. The socio-demographic characteristics of the participants are summarized in Tables  1 and 2 . None potential participants declined to participate in this study. Most of the women in this study were Iranians housewives with secondary education. More than one-third of the birth companions were mothers of women and most of the support was provided only during labour. We reported on the acceptability, adoption, appropriateness, feasibility, and fidelity of birth companions’ implementation strategies, using direct quotations from the participants (Table  3 ).


Participants shared opinions on the acceptability of implementing birth companion strategies in three sub-themes: perceived value of birth companions, relative advantage, and credibility.

Perceived value of birth companions

Women and birth companions had overall positive experiences with the implementation of birth companions. They believed that the implementation of the program was a good idea, which resulted in continuous support from companions, satisfaction with care, and an improved birth experience. As one woman explained:

“It was my first delivery, and I was feeling very stressed. The healthcare providers were busy and unable to give me the attention I needed, but having my sister there made a significant difference. She massaged my back, used a hot water bag, assisted me with walking and exercising, and contacted healthcare providers when I required assistance. If my companion was not there, I would have had a difficult birth.” (Woman 2, 25 years old).

Another person noted that: “It was a positive experience for me, and I am content with how everything went, particularly because my mother was present in the delivery room. For example, when I was in pain, she would hold my hand and say, ‘send blessings’ or during childbirth, she would say, ‘well done, push, it’s great, I can see the baby’s head’, and it was encouraging … Thank you for making it possible for companions to be with us even during childbirth.” (Woman 19, 16 years old)

“The presence of birth companions at this hospital was a good idea; we were satisfied with this program. In the public hospitals of our city, the companions are not allowed to enter the maternity hospital. However, here I had no barrier to my presence …” (Birth companion 7, 26 years old).

Relative advantage

Women were asked if they would be more inclined to choose a hospital for giving birth if it offered birth companions as a standard practice in the maternity ward, and all of them responded affirmatively. One woman stated:

“When I gave birth a few years ago, they did not allow me to have a companion. This hospital was recommended to me by a friend. She said that last week, my sister gave birth there, and she had a companion… I came here only because I could have a companion, and I was satisfied with having a companion by my side.” (Woman 14, 21 years old).


Both women and their companions described the quality of program implementation and training provided by midwives as beneficial:

“I think this program is being implemented well… The midwife taught me support techniques. I did them for my daughter and tried not to interfere with the clinical work of the providers… They were effective in relieving her pain.” (Birth companion 14, 50 years old).
“When I was in pain, my companion used a hot water bag, asked me to take deep breaths, or used Entonox gas… They were very helpful.” (Woman 12, 24 years old).

While providers also acknowledged the usefulness of implementing birth companions, the implementation team felt that some were initially reluctant to support the program and perceived it as an added burden. However, this reluctance changed over time due to positive outcomes, such as increasing women’s satisfaction, greater participation of companions, and reducing the workload of providers. Several providers also mentioned concerns about limited physical space, violation of women’s privacy, overcrowding, and the transmission of infection:

“Some of us initially did not support the implementation of this program, because it was perceived as an additional burden. However, after some time of implementation of the program, we observed positive outcomes, such as increased satisfaction among women during childbirth, participation of companions, and a reduction in workload… Now I can confidently say that all the providers have accepted it.” (Midwife 1, 40 years old).

In this study, adoption of implementation of birth companion strategies was discussed in two sub-themes: uptake and actual use.

The providers’ responses to the program were positive. They stated that they allow companions to accompany women during labour and childbirth. Upon entering, they provided explanations about the regulations of the maternity hospital, the role and responsibilities of the companion during labour and childbirth. They also taught emotional support techniques such as praying, using calming verbal expressions, encouraging, and comforting. Additionally, they taught physical support techniques including helping with walking, feeding, massaging, and breathing exercises.

“Upon entering, we ask women if they would like to have a companion. If they wish, we allow their companion to enter the maternity hospital. We teach her (companion)… Finally, we ask her to sign the form to receive training from the midwife.” (Midwife 14, 29 years old).
“We allow the companion to be present. We offer training to birth companions led by midwives. The midwife teaches… Most companions also perform well, according to the training they receive.” (Resident 10, 31 years old).

Providers’ adoption of the program increased over time as they gained a clearer understanding of how the program was intended to work. However, a few providers also raised concerns that the program may not be sustainable after its initial phase ends. These concerns have contributed to doubts about the program’s full adoption.

“This program cannot be expected to be sustainable within a few months of implementation… I believe it requires additional time and ongoing monitoring to be effectively integrated into the work tasks of our providers.” (Head of obstetrics, 49 years old).


Participants reported three sub-themes related to the appropriateness of implementation of birth companion strategies, including perceived usefulness, integration into existing workflows, and informing women about the possibility of having a birth companion.

Perceived usefulness

Most of the participants agreed that implementation of the program in this maternity hospital was appropriate. One birth companion stated:

“I believe it is necessary to have a companion in this maternity hospital due to the overcrowding and insufficient staff. The healthcare providers do not have enough time to provide back massages of comfort a woman in labour. As companions, we can fulfil this role for them.” (Birth companion 4, 46 years old).

Integration into existing workflows

Some providers agreed that birth companions could be integrated into the existing workflows:

“I think that these implementation strategies for birth companions can be very helpful… they are simple and low cost. If we use these strategies correctly, there will be no problems in our workflow.” (Midwife 3, 41 years old).

Informing women about the possibility of having a birth companion

Some women mentioned that if they had been informed in advance (e.g., in childbirth preparation classes) about the possibility of having a birth companion, they could have chosen a more suitable person to accompany them.

“… If I had known that I could have a companion, I would have brought someone with me who would be more comfortable, trained, or at least had experience with vaginal delivery.” (Woman 1, 43 years old).


The providers felt that the routine use of birth companions was feasible in this maternity hospital and described three sub-themes that would contribute to improving feasibility: training birth companions in prenatal care, recruiting a fixed midwife, and improving the physical infrastructure.

Training birth companions in prenatal care

The providers commented on the importance of training birth companions and preparing them to play a role in prenatal care. Most providers stated that in order for the few minutes of training upon entering the maternity hospital to be more effective, it is important to give attention to the training of birth companions in childbirth preparation classes.

“I think the important thing is to train… It is necessary to provide training for labouring women and their companions before they enter maternity hospitals.” (Midwife 8, 40 years old).
“… Unfortunately, most of the companions were not trained here. Well, how much time do I have to explain to her during labour?” (Midwife 13, 37 years old).
“I believe that training at the maternity hospital can be more effective if the companion is already trained, and our training includes a review component.” (Resident 11, 30 years old).

Recruiting a fixed midwife

Similarly, providers discussed the importance of recruiting a fixed midwife to improve the feasibility of birth companions in maternity hospitals. The majority of providers stated that, in light of the overcrowding and understaffing, successful implementation of the program relied on recruit a fixed midwife who could provide training to labouring women and their companions.

“… I believe it is necessary to have a permanent midwife for training in order to consistently implement this program.” (Head of obstetrics, 49 years old).

Improving the physical infrastructure

Improving the physical infrastructure of maternity hospitals was also suggested by some providers as a factor related to feasibility:

“… Yes, routine use of this program is possible, but it is also important to ensure that the physical environment is suitable. We have limited physical space here. The burden of visiting is also high, and we are concerned about overcrowding and the transmission of infection.” (Resident 2, 30 years old).

Two sub-themes related to the fidelity of implementation of birth companion strategies were identified: adherence and participant responsiveness.

Almost all providers agreed that they had implemented the program as intended by the project developers. However, several of them stated that as the implementation progressed, other women (those who were scheduled for a caesarean section or had an abortion) also requested the presence of their companions, which posed a challenge at times. This is because providers had to spend time explaining and justifying their decisions.

“I believe the providers implemented the program according to the original protocol. I noticed a significant improvement in the conditions at the maternity hospital after the implementation of this program.” (Midwife 15, 34 years old).
“… The women who were scheduled for a caesarean section or had an abortion also requested the presence of their companions. If there are also companions, the maternity hospital will become very crowded, which will hinder the provision of proper care.” (Resident 16, 28 years old).
“Anyway, when a program starts to reach the ideal, it faces challenges. However, I believe that the providers who were directly involved in the implementation process adhered to the plan…” (Head of obstetrics, 49 years old).

Participant responsiveness

The level of participant engagement in the program was reported to be high, as one provider remarked:

“I think almost all providers were involved in this program. We may not have had a good participation at the beginning of the program, but it increased over time …” (Midwife 8, 40 years old).

Furthermore, providers’ statements showed that the reception of women and their companions in the presence of a birth companion was positive:

“Both women and their companions were receptive to this program. When we informed women that they could have a companion, even during their childbirth, they would be happy…” (Midwife 4, 41 years old).

This was the first qualitative study in Iran to examine the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies of birth companions based on the experience of women, birth companions, and MHCPs. In summary, the findings of this study indicated strategies for effectively implementing birth companions in public hospitals in Tehran.

In our study, the sub-themes associated with the acceptability of implementing birth companion strategies from the participants’ perspectives included perceived value, relative advantage, and credibility. We found that the implementation strategies used by the birth companion were acceptable to most participants. Our findings are consistent with those of previous studies [ 33 , 34 ]. Overall, women and their companions greatly appreciated the provision of a birth companion in the hospital, as it improved satisfaction with care and the birth experience [ 22 , 33 , 35 , 36 ]. Similarly, providers have described the benefits of implementing birth companions, such as continuous support and a reduced workload [ 16 , 20 , 34 , 37 ]. Furthermore, in our study, women and their companions mentioned the benefits of the quality of the program implementation and training provided by midwives. Similar findings have been reported by Kabakian-Khasholian et al. [ 34 ].

Our findings showed that although the presence of birth companions was not initially supported by some providers, its acceptance grew over time with an increased understanding of the program as well as the positive outcomes that followed for both women and providers. Another study on birth companions in the labour ward of a center in India showed that providers were initially hesitant to allow birth companions due to overcrowding and the potential disruption of their duties and decision-making [ 20 ]. The experience reported by our providers is not surprising. This is an important finding for implementation, and demonstrates that immediate acceptance of new programs after introduction cannot be expected, as research has shown that the acceptability of any program increases with knowledge of that program [ 25 ]. A possible explanation for the higher acceptability of birth companions in our study could be attributed to the continuous monitoring of the implementation team and the provision of feedback throughout the implementation process.

The uptake and actual use were perceived as important aspects of adoption of implementing birth companion strategies. Despite the fact that providers adopted the program and responded positively to its use, a few expressed doubts about the program’s sustainability beyond the initial phase. Our findings are consistent with those of a previous study conducted in Arab countries, which reported that obstetric residents expressed uncertainty regarding about the long-term viability of the labour companionship model [ 34 ]. Although examining the sustainability of the program was not the goal of our study, it is important to note this issue, which should be explored in the future.

Our study findings showed that the appropriateness of implementation of birth companion strategies refers to the perceived usefulness, integration into existing workflows, and informing women about the possibility of having a birth companion. Providers found that birth companions could be integrated into workflows. Though studies in LIMCs show that providers were reluctant to incorporate birth companions into routine maternity services for reasons such as women’s disobedience to provider instructions, companion interference in care, and the transmission of infections [ 36 , 38 , 39 ]. Some women in our study expressed the desire to be informed about the option of having a birth companion during antenatal care. This finding aligns with a study on birth companionship in Tanzania [ 33 ].

This study suggests that the implementation of birth companion strategies in this maternity hospital is feasible, but several potential factors should be considered. Some of our providers pointed out the importance of training birth companions through childbirth preparation classes for the effectiveness of their support upon entering maternity hospitals, as highlighted by Kabakian-Khasholian et al. [ 34 ]. Providers also emphasized that recruiting fixed midwives to provide training to women and their birth companions in the maternity hospital was important to support the feasibility of the program. Women and companions have also criticized the infrastructure of the maternity hospital. It is important to note that in this study, any strategy for the reconstruction of physical space (such as the lack of suitable space for the accommodation of companions) was considered but opposed by the management of the maternity hospital, despite it being an important component in the implementation of birth companions.

Our study has several practical implications. Despite the recommendations of the WHO regarding the choice of a companion during labour and childbirth, as well as existing policies, there is a need for the presence of a birth companion in Iran. Increased efforts by policy-makers and managers of maternal health programs are necessary to ensure women’s access to this right and to effectively and sustainably implement it in maternity hospitals. This will help to improve the quality of maternity care and enhance positive childbirth experiences. Furthermore, the collaboration of MHCPs in the implementation of birth companions and the establishment of continuous monitoring systems in maternity hospitals is important. It is also necessary to include training for birth companions in childbirth preparation classes, educating them about their expected role in supporting women.

Strengths and limitations

To our knowledge, this is the first study to examine the implementation outcomes of birth companions in Iran. This study encompasses a wide range of perspectives and experiences from women, birth companions, and MHCPs. This study has several limitations. First, due to the sensitive nature of the mistreatment issue, participants may have underreported some of their experiences with the companionship program possibly influenced by social desirability bias. We attempted to reduce this bias by conducting interviews in a private room and ensuring the anonymity of the participants’ identities. Second, this study was conducted solely at a public teaching hospital in Tehran, which restricts the generalizability of the findings to private hospitals in Iran. Nonetheless, this study adds to the literature on implementation strategies for birth companion’s support by incorporating implementation research (IR). The findings of this study will be useful for health policymakers in supporting the implementation of birth companions to reduce mistreatment of women during labour and childbirth. However, we recommend continuous monitoring of the actual collaboration among MHCPs during the program implementation process.

Our study found that the implementation strategies for birth companions in Tehran public hospitals are acceptable, appropriate, and feasible. These strategies improve satisfaction with care and the birth experience, seek continuous support from companions, and reduce provider workloads. However, there are several issues that need to be addressed regarding birth companions in maternity hospitals. These include training birth companions prior to the arrival, informing women about the presence of birth companions, assigning a dedicated midwife to provide training, and improving the physical infrastructure. The findings of this study can be utilized to support the implementation of birth companions in countries with comparable circumstances.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions of the participants but are available from the corresponding author on reasonable request.


World Health Organizaion

Respectful Maternity Care

Evidence-Based Interventions

Implementation Science

Ministry of Health and Medical Education

Maternity Healthcare Providers

Consolidated Criteria for Reporting Qualitative Research

Implementation Research

Shemelis D, Gelagay AA, Boke MM. Prevalence and risk factor for mistreatment in childbirth: in health facilities of Gondar city, Ethiopia. PLoS ONE. 2022;17(5):e0268014. .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Tobasía-Hege C, Pinart M, Madeira S, Guedes A, Reveiz L, Valdez-Santiago R, Pileggi V, Arenas-Monreal L, Rojas-Carmona A, Piña-Pozas M. Irrespeto Y maltrato Durante El Parto Y El aborto en América Latina: revisión sistemática y metaanálisis. Rev Panam Salud Publica. 2019;43. .

Sharma G, Penn-Kekana L, Halder K, Filippi V. An investigation into mistreatment of women during labour and childbirth in maternity care facilities in Uttar Pradesh, India: a mixed methods study. Reprod Health. 2019;16(1):1–16. .

Article   Google Scholar  

Lukasse M, Schroll AM, Karro H, Schei B, Steingrimsdottir T, Van Parys AS, Ryding EL, Tabor A, Group BS. Prevalence of experienced abuse in healthcare and associated obstetric characteristics in six European countries. Acta Obstet Gynecol Scand. 2015;94(5):508–17. .

Article   PubMed   Google Scholar  

Mirzania M, Shakibazadeh E, Bohren MA, Hantoushzadeh S, Babaey F, Khajavi A, Foroushani AR. Mistreatment of women during childbirth and its influencing factors in public maternity hospitals in Tehran, Iran: a multi-stakeholder qualitative study. Reprod Health. 2023;20(1):79. .

Article   PubMed   PubMed Central   Google Scholar  

Hajizadeh K, Vaezi M, Meedya S, Mohammad Alizadeh Charandabi S, Mirghafourvand M. Prevalence and predictors of perceived disrespectful maternity care in postpartum Iranian women: a cross-sectional study. BMC Pregnancy Childbirth. 2020;20(1):1–10. .

Shakibazadeh E, Taherkhani F, Yekaninejad MS, Shojaeizadeh D, Tajvar M. Prevalence of disrespectful maternity care in hospitals affiliated with TUMS and its associated factors. Hayat. 2021;27(3):262–77.

Google Scholar  

Ghanbari-Homayi S, Fardiazar Z, Meedya S, Mohammad-Alizadeh-Charandabi S, Asghari-Jafarabadi M, Mohammadi E, Mirghafourvand M. Predictors of traumatic birth experience among a group of Iranian primipara women: a cross sectional study. BMC Pregnancy Childbirth. 2019;19:1–9. .

Balde MD, Nasiri K, Mehrtash H, Soumah A-M, Bohren MA, Irinyenikan TA, Maung TM, Thwin SS, Aderoba AK, Vogel JP. Labour companionship and women’s experiences of mistreatment during childbirth: results from a multi-country community-based survey. BMJ Glob Health. 2022;5(Suppl 2):e003564. .

Diamond-Smith N, Sudhinaraset M, Melo J, Murthy N. The relationship between women’s experiences of mistreatment at facilities during childbirth, types of support received and person providing the support in Lucknow, India. Midwifery. 2016;40:114–23. .

World Health Organization. WHO recommendations on health promotion interventions for maternal and newborn health 2015. Geneva: WHO. 2015. , accessed 10 July 2023.

World Health Organization. WHO recommendations: intrapartum care for a positive childbirth experience. Geneva: WHO. 2018. , accessed 10 July 2023.

World Health Organization. WHO recommendations for augmentation of labour. Geneva: WHO. 2014. , accessed 10 July 2023.

Shakibazadeh E, Namadian M, Bohren MA, Vogel JP, Rashidian A, Nogueira Pileggi V, Madeira S, Leathersich S, Tunçalp Ӧ, Oladapo OT. Respectful care during childbirth in health facilities globally: a qualitative evidence synthesis. BJOG. 2018;125(8):932–42. .

Article   CAS   PubMed   Google Scholar  

Bohren MA, Hofmeyr GJ, Sakala C, Fukuzawa RK, Cuthbert A. Continuous support for women during childbirth. Cochrane Database Syst Rev 2017(7). .

Sarwal T, Sarwal Y, Tyagi S, Sarwal R. Opinion of health care providers on birth companions in obstetrics department of a tertiary Care Hospital in North India. medRxiv 2021:2021.2006. 2024.21259462. .

Kazemi Robati A, Molaei B, Motamed N, Hatami R, Gholami H, Ahmadi Birjandi A, Esmkhani M. Effects of the presence of the doula on pregnant women’s anxiety and pain during delivery: a randomized controlled trial. J Adv Med Biomedical Res. 2020;28(131):316–22.

Hunie Asratie M, Mohammed Assfaw H, Abuhay M. Labor companionship in labor and delivery at Debremarkos town public health facilities: magnitude and associated factors. Int J Womens Health 2021:815–27. .

Maknojia AS, Malik A. Why can’t I have a choice of companion during labor? Barriers to implementation of companion presence. J Asian Midwives (JAM). 2021;8(2):39–45.

Bharti J, Kumari A, Zangmo R, Mathew S, Kumar S, Sharma AK. Establishing the practice of birth companion in labour ward of a tertiary care centre in India—a quality improvement initiative. BMJ Open Qual. 2021;10(Suppl 1):e001409. .

Diniz CSG, d’Orsi E, Domingues RMSM, Torres JA, Dias MAB, Schneck CA, Lansky S, Teixeira NZF, Rance S, Sandall J. Implementation of the presence of companions during hospital admission for childbirth: data from the birth in Brazil national survey. Cad Saude Publica. 2014;30:140–53. .

Afulani P, Kusi C, Kirumbi L, Walker D. Companionship during facility-based childbirth: results from a mixed-methods study with recently delivered women and providers in Kenya. BMC Pregnancy Childbirth. 2018;18(1):1–28. .

Moridi M, Pazandeh F, Hajian S, Potrata B. Midwives’ perspectives of respectful maternity care during childbirth: a qualitative study. PLoS ONE. 2020;15(3):e0229941. .

Eccles M, Mittman B. Welcome to implementation science. Implement Sci. 2006;1(1):1. .

Article   PubMed Central   Google Scholar  

Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, Griffey R, Hensley M. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76. .

Shahshahan Z, Heshmati B, Akbari M, Sabet F. Caesarean section in Iran. Lancet. 2016;388(10039):29–30. .

Mirzania M, Shakibazadeh E, Hantoushzadeh S, Khajavi A, Rahimi Foroushani A. Challenges to the implementation of a multi-level intervention to reduce mistreatment of women during childbirth in Iran: a qualitative study using the Consolidated Framework for Implementation Research. Under review.

Mirzania M, Shakibazadeh E, Bohren MA, Babaey F, Hantoushzadeh S, Khajavi A, Rahimi Foroushani A. Knowledge, attitude and practice of healthcare providers on mistreatment of women during labour and childbirth: a cross-sectional study in Tehran, Iran, 2021. Under review.

Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88. .

Software VERBI. MAXQDA Software for qualitative data analysis.:Berlin, Germany: Sozialforschung GmbH; 1998–2018.

Lincoln Y, Guba E. Naturalistic inquiry. Newbury Park, CA: Sage; 1985.

Book   Google Scholar  

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57. .

Chaote P, Mwakatundu N, Dominico S, Mputa A, Mbanza A, Metta M, Lobis S, Dynes M, Mbuyita S, McNab S. Birth companionship in a government health system: a pilot study in Kigoma, Tanzania. BMC Pregnancy Childbirth. 2021;21:1–17. .

Kabakian-Khasholian T, Bashour H, El‐Nemer A, Kharouf M, Elsheikh O. Implementation of a labour companionship model in three public hospitals in arab middle‐income countries. Acta Paediatr. 2018;107:35–43. .

Bohren MA, Berger BO, Munthe-Kaas H, Tunçalp Ö. Perceptions and experiences of labour companionship: a qualitative evidence synthesis. Cochrane Database Syst Rev 2019(3):18. .

Banda G, Kafulafula G, Nyirenda E, Taulo F, Kalilani L. Acceptability and experience of supportive companionship during childbirth in Malawi. BJOG. 2010;117(8):937–45. .

Singh S, Goel R, Gogoi A, Caleb-Varkey L, Manoranjini M, Ravi T, Rawat D. Presence of birth companion—a deterrent to disrespectful behaviours towards women during delivery: an exploratory mixed-method study in 18 public hospitals of India. Health Policy Plan. 2021;36(10):1552–61. .

Maimbolwa MC, Sikazwe N, Yamba B, Diwan V, Ransjö-Arvidson A-B. Views on involving a social support person during labor in Zambian maternities. J Midwifery Womens Health. 2001;46(4):226–34. .

Kabakian-Khasholian T, El-Nemer A, Bashour H. Perceptions about labor companionship at public teaching hospitals in three arab countries. Int J Gynecol Obstet. 2015;129(3):223–6. .

Download references


This study was part of a PhD dissertation. The authors would like to thank the officials and maternity healthcare providers of Valiasr Hospital in Tehran as well as all the women and birth companions for their valuable contribution to this study.

This study was funded by the Health Information Management Research Center, Tehran University of Medical Sciences, Iran (grant number 1401-3-208-62407). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and affiliations.

Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Marjan Mirzania & Elham Shakibazadeh

Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran

Elham Shakibazadeh

Department of Obstetrics and Gynecology, School of Medicine, Vali-E-Asr Reproductive Health research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Sedigheh Hantoushzadeh

Department of Obstetrics and Gynecology, Maternal-Fetal Neonatal Research Center, Tehran University of Medical Sciences, Valiasr Hospital, Tehran, Iran

Zahra Panahi

Gender and Women’s Health Unit, Nossal Institute for Global Health, School of Population and Global Health, University of Melbourne, Carlton, VIC, Australia

Meghan A. Bohren

Department of Social Medicine, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

Abdoljavad Khajavi

You can also search for this author in PubMed   Google Scholar


E.Sh., M.M., S.H., M.B., A.Kh., and Z.P. designed the study. M.M. and E.Sh. developed the interview guide. M.M. conducted the interviews. M.M. and E.Sh. analyzed the data. M.M. drafted the manuscript, and E.Sh. reviewed and edited it. All authors have read and approved the final manuscript. All authors have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Corresponding author

Correspondence to Elham Shakibazadeh .

Ethics declarations

Ethics approval and consent to participate.

This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval for this study was obtained from the Tehran University of Medical Sciences Ethics Committee (code number: IR.TUMS.SPH.REC.1400.169). Participation was voluntary, and all participants provided written informed consent. For any participant under 16 years of age, we obtained informed consent to participate in the study from parents or legal guardians.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit . The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Mirzania, M., Shakibazadeh, E., Hantoushzadeh, S. et al. An investigation into the acceptability, adoption, appropriateness, feasibility, and fidelity of implementation strategies for birth companionship in Tehran: a qualitative inquiry on mitigating mistreatment of women during childbirth. BMC Public Health 24 , 1292 (2024).

Download citation

Received : 12 November 2023

Accepted : 30 April 2024

Published : 13 May 2024


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Birth companion
  • Implementation research
  • Implementation strategies
  • Implementation outcomes
  • Mistreatment
  • Qualitative research
  • Respectful maternity care

BMC Public Health

ISSN: 1471-2458

research articles about adoption

  • Open supplemental data
  • Reference Manager
  • Simple TEXT file

People also looked at

Original research article, linking farmers’ perceptions and management decision toward sustainable agroecological transition: evidence from rural tunisia.

research articles about adoption

  • 1 International Center for Agricultural Research in The Dry Areas (ICARDA), Tunisia, Tunisia
  • 2 Institut National de la Recherche Agronomique de Tunisie (INRAT), Ariana, Tunisia

Global food systems face sustainability challenges like undernourishment, inequity, resource degradation, and pollution. Food production and consumption drive environmental change with greenhouse gas emissions, biodiversity loss, and land-system shifts. The climate change crisis has intensified concerns about the ecological impact of these systems. Sustainable food networks, such as community-supported agriculture, are promoting sustainable production and consumption through short supply chains. International bodies like the Food and Agriculture Organization (FAO) and the Consultative Group for International Agricultural Research (CGIAR) are also spearheading initiatives for more equitable and sustainable food systems. In Tunisia, where dryland areas predominate, the ongoing implementation of the Agroecology Initiative provides the context for this study, which explores the drivers and barriers of agroecological transformation in this challenging environment. The research focuses on stakeholder engagement, with a gender perspective to explore farmer perceptions. The study, conducted in the northwest of Tunisia in 2022–2023, involved focus groups, workshops, surveys, and questionnaires with various stakeholders. Findings highlight farmer organizations’ potential in promoting sustainable farming, with clear goals, diversified systems, and collaborations. However, challenges such as input scarcity, water shortage, low income, and marketing must be addressed. Results also indicate that over 90% of farmers who received assistance with agroecological practices reported a change in their ideas and practices. Fifty seven percent of the workshops participants identified the olive oil value chain as having the greatest potential for agroecological transformation, but it faces constraints such as climate, lack of policy incentives, training, funding, and difficulty in adopting technical innovations. Women’s inclusion in agriculture, environmental, social, and economic challenges were also highlighted. Despite these obstacles, key drivers for agroecological transition were identified. These include the compatibility of many agroecological practices with existing farmer capabilities, their cultural and economic benefits, and the positive outcomes for environmental sustainability and health. The study advocates for a socio-technical systems analysis to address the root causes hindering Tunisia’s agroecological transformation. A participatory approach is crucial to understanding priorities and developing a sustainable and resilient food system. Furthermore, the research underscores the importance of considering diverse farmer perspectives and tailoring strategies to support this critical transition effectively.

1 Introduction

Global food systems are struggling to achieve sustainable development goals, contributing to undernourishment, inequity, natural resource degradation, and environmental pollution. Current food systems are vulnerable to multiple shocks, such as climate change, economic crises, and pandemics, which can have cascading effects on smallholder food security. The rising prices of fertilizers and food imports resulting from these shocks have rekindled interest in the call for a policy shift toward agroecology ( 1 ). Food production and consumption are major contributors to global environmental change, including greenhouse gas emissions, biodiversity loss, and land-system change ( 2 ).

Alternative food networks, such as food cooperatives and community-supported agriculture, aim to promote sustainable production and consumption through short supply chains and connections between consumers and producers. These networks also foster social interactions and collective mindfulness for a sustainable food system. Producers face both pressure and opportunities to incorporate sustainability into their business practices to meet consumers’ expectations. The agroecological transition is a promising approach to create more equitable and ecologically sustainable food systems ( 3 ). Agroecology is the application of ecological principles to agricultural systems, offering solutions to farming and food security challenges such as drought, hunger, poverty, and inequality ( 4 ). It supports small-scale farmers in diversity and ensures a long-term balance between food production and the sustainability of natural and environmental resources. It also transforms food systems and ensures resilience by balancing between socio-economic and environmental facets.

According to Dagunga et al. ( 5 ), promoting agroecology in smallholder farming communities faces both challenges and opportunities. Some of the opportunities for promoting agroecology, include the potential for increased productivity, improved soil health, and enhanced biodiversity. However, there are many challenges to this transition, such as institutional, social, technical, economic, and environmental factors. These challenges include limited access to resources, such as land, water, and capital, as well as inadequate policy support and institutional frameworks. Additionally, there may be cultural and social barriers to the adoption of agroecological practices ( 6 ). Previous research also highlights the importance of participatory approaches and knowledge sharing in promoting agroecology among smallholder farmers ( 5 ).

International bodies like the Food and Agriculture Organization (FAO) and the Consultative Group for International agricultural Research (CGIAR) are introducing initiatives to promote more equitable and ecologically sustainable food systems. The agroecological transformation initiative, 1 which promotes good governance of natural resources, input reduction and biodiversity, as well as social and cultural inclusion, equity, and knowledge sharing, is seen as an opportunity for a shift toward more sustainable, inclusive, and resilient food systems ( 7 ).

This study is part of the “Agroecological Transformation in Food, Land and Water Systems” initiative launched by the CGIAR and implemented in Tunisia by the International Centre for Agricultural Research in the Dry Areas (ICARDA). This research contributes to addressing the climate change crisis and to enhancing the resilience of food systems. This research aims to investigate the barriers of agroecological transformation in the dryland context based on the involvement of the different stakeholders with a special emphasis on farmers’ beliefs, experiences, and characteristics. Farmers, perception is analyzed, considering the gender perspective. Focusing on dryland areas is crucial due to their unique challenges and characteristics such as water scarcity, erratic rainfall, and fragile ecosystems. Contrasting with more temperate or humid regions, the dryland context requires tailored solutions that consider the specific needs and constraints of farmers operating in these environments.

2 Conceptual framework

Conventional expert-led change assessment methods based on top-down approaches generate quantifiable indicators that allow regional or national comparisons. However, they have certain shortcomings, such as alienating local communities and failing to capture the views of diverse stakeholders ( 8 ). Involving the community in evaluation procedures means that indicators are more relevant and specific to the context and evolve over time with the community. Participation leads to the empowerment and capacity building of communities to address emerging challenges in their local environment ( 8 ). The agroecological transition is a process that involves the adoption of innovative practices that aim to balance productivity with environmental protection. These practices require a significant change in the way farmers manage their crops and natural resources. Therefore, the adoption of agroecological innovations is subject to various uncertainties and risks, which can influence farmers’ perceptions of the innovation ( 9 ). Perceptions, which refer to individuals’ interpretations and understanding of received information, play a crucial role in the agroecological transition. In this context, farmers’ perceptions of innovation can greatly shape their willingness to adopt it. These perceptions can be influenced by various factors, such as the perceived advantages and drawbacks of the innovation, the compatibility with existing practices, the level of information and experience, and the social and cultural context ( 10 ).

To understand the role of perceptions in the agroecological transition, researchers use various experimental methods, such as surveys, interviews, or focus groups. These methods help identify the factors that influence farmers’ perceptions of innovation and how these perceptions impact their decision-making process ( 11 , 12 ). According to Roussy et al. ( 12 ), the factors that influence the adoption of agricultural innovations by farmers are observable and unobservable. Three main categories are identified as observable: external factors, internal factors, and innovation-specific factors. External factors include market conditions, policy environment, and social networks. Internal factors include farmer characteristics, farm characteristics, and risk attitudes. Innovation-specific factors include characteristics of the innovation, information sources, and adoption process ( Figure 1 ).

Figure 1 . Factors influencing farmers’ adoption of agroecological innovations, adapted from Roussy et al. ( 12 ).

Considering farmers’ perceptions of these factors in the agroecological transition can help researchers and policymakers design and promote innovations that are more likely to be accepted and adopted by farmers ( 13 ). By understanding farmers’ perceptions and addressing the factors that influence them, it is possible to accelerate the transition toward more sustainable and environmentally friendly agricultural practices. Understanding farmers’ perceptions and strategies highlights the need to involve multiple actors in co-constructing policies and plans to address challenges in food systems. Additionally, farmers’ perception-centered approach emphasizes the significance of integrating and sharing knowledge from different sources to enhance agricultural productivity and improve the delivery of agricultural extension services to small-scale farmers ( 14 ). The literature underscores the importance of stakeholder engagement, innovation management, and entrepreneurship development. It emphasizes the need for a systematic and integrative approach to understand the relationship between these concepts and foster sustainable innovation while considering the interests and concerns of various stakeholders in decision-making processes ( 15 – 17 ).

Another classification of the factors influencing the agroecological transition is revealed according to many studies ( Figure 2 ). These factors are categorized into personal, technical, economic, and social factors. Personal factors pertain to the specific characteristics and beliefs of individual farmers, while technical factors include the knowledge, skills, and resources required for agroecological practices. Economic factors encompass the availability of funds and economic incentives to support the transition. Social factors, on the other hand, are influenced by external factors such as access to grants, markets, and community attitudes ( 3 , 9 , 18 ). These factors are interconnected and can collectively shape the success or barriers to the agroecological transition. Understanding and weighing these factors is crucial when developing strategies to promote sustainable and resilient food systems.

Figure 2 . Categories of the factors influencing the agroecological transition.

3 Methodology

The research methodology is based on a participatory approach supplemented by quantitative and qualitative analysis. The case study is conducted in the northwest region of Tunisia characterized by a mixed tree-crop-livestock farming system.

3.1 Study site

Located in northwest semi-arid zone of Tunisia, the Kef-Siliana transect ( Figure 3 ) has been designated a priority zone by the Agroecology Initiative ( 19 ) due to its vulnerability to both soil erosion and climate change ( 20 ). While Siliana and Kef governorates both experience a continental climate, their rainfall and temperature ranges differ slightly. Siliana receives between 350 and 550 mm of rain annually with temperatures ranging from 3.2 to 35.7°C, whereas Kef experiences an average annual rainfall of 350 mm to 450 mm and temperatures varying from 7.3 to 26.5°C. These predominantly rural regions face socioeconomic challenges such as high poverty rates, unemployment, and limited access to basic services, leading to significant outmigration, particularly among young people. Despite these challenges, the transect boasts a diversified agricultural system, including cereal crops, livestock farming, and olive tree cultivation. This agricultural diversity reflects the complex interdependence of various sectors and the complexity of the regions’ resource utilization patterns ( 21 , 22 ).

Figure 3 . The Tunisian Transect Kef-Siliana localization in the northwest of Tunisia (Source: ( 19 )).

3.2 Data collection

The research involved semi-structured interviews, focus group discussions, workshops, a survey, and a closed-ended questionnaire. The participants were identified based on their expertise, involvement in the initiative, and their roles in the agroecological transition landscape. The selection process has involved reaching out to academic and research institutions, governmental bodies, extension services and other relevant stakeholders to ensure a diverse representation of expertise and perspectives in the study. Table 1 summarizes the different sources of the collected data, the details of the respondents, the research questions, and the methods of analysis. The data were collected in ( 7 ) through semi-structured interviews with four professional farming organizations, workshops with farmers, technicians, researchers, public and private stakeholders from various value chains, and an open-ended survey carried out among 69 farmers belonging to farmers’ organizations. Additionally, a questionnaire about the perception of the agroecology transformation barriers and drivers was conducted with 35 farmers engaged in the initiative.

Table 1 . Overview of data Sources, participants, research questions, and methodology.

3.3 Data sources

The semi-structured interviews and the focus group discussions were conducted with four farmers organizations included in the Tunisian agroecological living landscape in the transect Kef-Siliana. The agroecology initiative is built around the concept and approach of living landscape to integrate the socioeconomic-system and ecosystems in one site to implement and test the agroecological transition ( 19 ). The Tunisian living landscape is characterized by the urgent need to enhance natural resource management, foster agricultural innovation, and address climate change impacts effectively. The main objectives of the interviews were to describe the key characteristics of each farmers’ organization and their main activities. To explore the diversity of the key partners and to discuss the main issues/challenges and their propositions to see how the agroecology approach could satisfy their needs.

The workshops were instrumental in identifying the opportunities and challenges to the agroecological transformation and selecting the main value chains with the greatest potential for boosting this transformation. The selection was based on a global evaluation matrix prioritizing the value chains according to a set of predefined criteria based on agroecological principles and their economic, social, and environmental dimensions or criteria ( 20 ). These selection criteria are summarized in Table 2 . The research by Di Vita et al. ( 24 ) and Spina et al. ( 25 ) underscores the importance of employing value chain methodologies. Through a holistic approach that involves establishing a focus group with thematic nodes and topics, involving national-level actors and experts, collecting data via interviews, and rigorously processing the gathered information, a comprehensive framework is developed to enhance understanding and decision-making in the field.

Table 2 . Dimensions for the selection of the value chains.

The survey explores the influences of farmers’ organization on innovative farming practices. It includes questions on the impact of agricultural demonstrations on farmers’ understanding and practices, on trade between farmers, on collective investment, on the perception of the organization of farmers in the community, and on the inclusiveness, exchange of information, commitment, and participation of women within the farmer organization, as well as on contracts and services between the farmer organization and farmers.

The questionnaire on perception is designed based on the factors that were identified in the theoretical framework as influencing the agroecological transformation. It is structured into several sections. The first section focuses on the socio-economic characteristics of the farmers, including age, gender, location, education level, land ownership, main farming activities, and years of experience. The second part of the questionnaire explores the farmers’ perceptions of the agroecological transformation in Tunisia. This section is further divided into four subsections. The first subsection addresses the effects of agroecological practices, the second subsection focuses on the farmers’ capabilities, and the third subsection delves into the difficulties and challenges associated with transformation. The fourth subsection of the questionnaire deals specifically with technical barriers. It is important to note that the active participation in the agroecological transformation was a selection criterion for all the respondents.

3.4 Respondents’ characteristics

An overview of the characteristics of the farmers included in the survey and in the questionnaire is included in Table 3 . The survey was conducted on a total of 69 farmers, with 38 female and 31 male farmers, while the questionnaire on perception was conducted on 35 farmers, with 6 female and 29 male farmers. The farmers are in Transect Kef-Seliana and Kairouan, with a primary focus on livestock, cereal crops, and olive trees as their main crops.

Table 3 . Characteristics of the farmers.

The land holdings of the farmers range from 1 to 50 hectares in the survey and 0–100 hectares in the questionnaire, with an average of 9 and 17 hectares, respectively. The age range of the farmers is between 22 and 73 years, with an average age of 48 years for the survey and 51 years for the questionnaire.

3.5 Analytical methods

Descriptive statistical analysis was conducted using various basic statistical measures, including mean, standard deviation, maximum, minimum, frequencies, and percentages. In addition, several analytical techniques were employed, such as SWOT analysis, Chi 2 , correlation, Kendall W and Kruskal-Wallis tests, Bayesian Belief Network (BBN) visualization, and factorial analysis. These methods were performed to accomplish several objectives: determining the level of engagement of local communities, prioritizing value chains with high agroecological potential, evaluating the progress toward an agroecological system through project interventions and farmers’ organizations, and assessing and categorizing the different drivers and barriers in the agroecological transformation of the Tunisian food system. The software tools SPSS and Stata were utilized for these analyses.

3.5.1 The SWOT analysis

The SWOT analysis is a strategic tool that helps identify the strengths, weaknesses, opportunities, and threats associated with projects and businesses ( 26 , 27 ). Its primary purpose is to evaluate both external and internal factors that either support or hinder the progress and successful implementation of projects or programs, aiding in making informed operational decisions ( 28 ). This analysis provides a framework for the strategic development of programs or projects, and it has been widely used to explore the internal and external environments, enabling the formulation of strategies and decision-making approaches for projects and programs ( 29 ). However, in the context of agroecology research, the SWOT analysis does encounter certain limitations. These limitations encompass subjectivity, the absence of quantifiable metrics hindering precise numerical assessments and comparisons, the dynamic nature of factors necessitating ongoing updates, and the limited focus on interactions, which may not fully consider how different factors in agroecosystems interact and influence each other. This can overlook important connections and complexities within agricultural systems, which are crucial for sustainability and resilience ( 30 , 31 ). It is crucial to consider these limitations to ensure a comprehensive and balanced evaluation of agroecosystems. Despite the SWOT analysis limitations, it remains relevant in the literature due to its usefulness in exploring possibilities during the decision-making process and its flexibility in combination with other approaches ( 32 – 34 ).

3.5.2 Bayesian belief network

A Bayesian Belief Network (BBN) is a graphical model that represents the probabilistic relationships between different variables. It is a powerful tool for understanding the complex interdependencies among variables and their influence on each other. BBNs are particularly useful for analyzing and visualizing data in fields such as decision analysis, risk assessment, and machine learning ( 35 ). In the context of this study, the BBN was used to visualize the relationships between different variables related to perceived changes. It helped to identify and understand how changes in one variable were connected to changes in other variables, providing insights into the overall impact of project interventions.

3.5.3 Factorial analysis

A principal component analysis with a varimax (orthogonal) rotation method is applied to perform exploratory factor analysis. The aim of this analysis was to obtain a factor structure of Agroecological transition perceived drivers and barriers, with both empirical and conceptual support ( 36 ). To determine the applicability of factor analysis, Bartlett’s test of sphericity ( p  < 0.05) was used. The number of factors to retain was decided by applying the criteria of eigenvalues greater than 1 ( 37 ). Finally, the extracted factors were labeled to give each factor a meaningful definition and meaning for interpretation.

4 Results and discussion

4.1 level of engagement of local communities.

The general characterization of the four farmers ‘organizations is summarized in Table 4 . The farmer organizations have diverse social and technical histories, allowing for the study of agroecological transition dynamics under various social and policy configurations. Farmer Organization 1, established in 2015, focuses on livestock and diverse agricultural production on smaller land holdings, with an exclusively female membership. In contrast, Farmer Organization 4, founded in 2017, specializes in cereal cultivation and livestock farming on larger land areas, boasting a more gender-balanced composition (50% women). Farmer Organization 3, established in 2020, centers its activities around olive trees, fruit trees, and beekeeping on moderate-sized land holdings. Farmer Organization 2, founded in 2022, is primarily involved in livestock farming and cereal crop cultivation on medium-sized land areas, with the lowest number of members and only 11% female representation. These organizations often develop common projects and actions, and their area and number of beneficiaries reflect their radius of action and capacity for scaling out.

Table 4 . General characterization of the farmers’ organizations.

The SWOT Analysis is performed to assess the agroecological transition potential of the farmer organizations in the transect Kef-Seliana. The findings show that the farmer organizations promote diversified and sustainable farming systems that align with agroecological principles and facilitate a variety of agroecological practices. The key points identified from the SWOT analysis are included in Table 5 .

Table 5 . SWOT analysis assessing the agroecological transition potential of the farmer organizations.

The interviewed farmer organizations have successful projects and collaborations with various key partners, such as The German International Cooperation (GIZ), The International Center for Agricultural Research in the Dry Areas (ICARDA), and the Regional Agricultural Development Commissariat (CRDA), to access resources, expertise, and funding opportunities. They have implemented various activities, such as local food artisanal production, conservation agriculture practices, crop rotation, forage mixtures (cereal-legumes), mechanization, forage seeds distribution, access to finance, and capacity building, which contribute to environmental and farming sustainability and connectivity. The diversified membership, with a focus on women and young farmers, aligns with the agroecological principle of social equity and justice. While Farmer Organization 1 had 100% female adherents and Farmer Organization 3 had 70% women adherents, Farmer Organization 2 only had 11% women members. Similarly, the percentage of members less than 35 years old varied across the organizations, with Farmer Organization 1 having 20%, Farmer Organization 2 having 11%, Farmer Organization 3 and 4 having 40%. This diversity in gender and age representation highlights that not all farmer organizations in Tunisia exhibit the same level of inclusion of women and young farmers. However, all the studied organizations encourage economic diversity and have a clear purpose in contributing to good governance. According to many studies, farmers’ collectives have different approaches for supporting agroecological transitions, including funding, advice, capacity building, experimentation with new practices, and information exchange ( 38 , 39 ). Diversified Farming Systems include functional biodiversity in farming practices to maintain ecosystem services like soil fertility, pest and disease control, water use efficiency, and pollination ( 40 ). Besides, crop rotation and legumes were identified as the most adequate diversification strategies for intensive rainfed cereal-based cropping systems ( 41 ).

4.2 High-potential value chains for agroecological transition

During the workshops conducted with the different stakeholders, many potential value chains were identified including the olive oil, honey, and sheep value chains. Among 33 and 30 participants, respectively in Kef and Siliana, 18 participants in both locations have selected the olive oil value chain as the value chain with the highest potential to integrate agroecology principles, as indicated in Figure 4 .

Figure 4 . Stakeholder preference for value chains integrated with agroecology principles in Kef and Siliana.

The prioritization of value chains for the agroecological transition in Tunisia highlights the olive oil sector as the most promising for development, considering economic, social, and environmental factors. Table 6 presents the participants motivations regarding the selection of the olive oil value chain.

Table 6 . Olive oil value chain selection dimensions and arguments.

4.3 Agroecological assessment of the olive oil value chain

The stakeholders present in the workshops were asked if the olive oil value chain can integrate the agroecological principles. The 13 principles of agroecology ( 42 ) applied to the selected value chain are presented in Table 7 .

Table 7 . The agroecological principles applied to the olive oil value chain.

Several research studies have backed the views of different stakeholders and considered the multi-stakeholder perspective to identify the obstacles and prospects in the food products’ value chains ( 24 ). The goal is to identify potential innovations that align with the needs and perceptions of the stakeholders ( 16 , 43 , 44 ). According to Torquati et al. ( 45 ), short extra virgin olive oil supply chains enhance agricultural products’ sustainability, with no real trade-offs when considering value chain results and environmental impact. In the context of the Tunisian olive oil supply chain, an optimal configuration incorporating organic farming, biodynamic growing techniques, and a two-phase extraction system using wet pomace for compost preparation is recommended ( 46 ). Circular economy principles can be implemented in the olive oil supply chain, but overcoming technological barriers and knowledge gaps is crucial for advancing circularity in the Mediterranean region’s agroecological systems ( 47 ).

4.4 Farmers’ perceptions of change

The aim of the survey was to understand how farmers perceive the change toward an agroecological farming system based on project interventions, and what is the influence of the organizational factor in this transformation in the Tunisian context. The descriptive analysis reveals that over 91.3% of farmers who received training and assistance with agroecological practices as part of ICARDA projects reported a change in their ideas and practices, while around 8.7% reported no change at all. These results confirm the findings of Oppong et al. ( 48 ), indicating that farmers in Ghana’s semi-deciduous region face challenges in adopting climate-smart agricultural practices due to lack of training, government support and extension officers. According to Šūmane et al. ( 49 ) redesigning the farming systems, necessitates farmer engagement in practices and local knowledge production. Integrating researcher and support-oriented strategies to bridge theory and practice is crucial for sustainable agroecological farming systems development ( 50 ).

Table 8 illustrates the number and percentage of farmers adopting and not adopting new agroecological practices by age and gender. The project suggests incorporating agroecological practices such as intercropping, direct seeding, minimal tillage, and crop rotation. The total percentage of female respondents is higher than male respondents, with 55 and 45%, respectively. The highest percentage of adopting farmers is in the 41–60 age group, with 36.5% of female respondents and 20.6% of male respondents.

Table 8 . Farmer’s adoption of agroecological practices by age and gender.

The Pearson chi-squared (chi 2 ) test showed no significant association between location and the adoption of new practices (with a Pearson chi 2 statistic of 0.3570 and a p -value of 0.550) or between gender and the adoption of new practices (Pearson chi 2  = 0.3570, Pr = 0.550). These results could be explained by the high level of adopting farmers among the respondents. The correlation coefficient between the adoption of new practices and farmer’s age is −0.051, indicating a very weak negative correlation. However, the p -value (0.677) suggests that this correlation is not statistically significant. However, many studies reveal that age of farmers have a negative effect on the adoption of sustainable agriculture practices ( 51 – 54 ).

Farmers’ perceptions of the change after research and development projects reveal varying levels of endorsement. In terms of motivation and engagement, change in farming comprehension and practices, and improved information exchange between farmers, these aspects are perceived very positively (Mean = 0.95, 0.92, and 0.91, respectively), indicating strong support for agroecological initiatives ( Supplementary Appendix 1 ). Factors related to inclusiveness of small farmers (Mean = 0.87), participation of women (Mean = 0.78), and commercial exchange between farmers (Mean = 0.70) are viewed more moderately. On the other hand, perception of investment in collective activities (Mean = 0.56) and better services and contracts between the farmers’ organization and agricultural producers (Mean = 0.49) are comparatively lower, suggesting a more nuanced view or potential challenges. Understanding these nuanced perspectives is crucial in tailoring interventions and promoting sustainable agricultural practices.

Through the chi-squared test, statistically significant linkages between various aspects of the perceived change are identified ( Supplementary Appendix 2 ). The visualization via the Bayesian Belief Network (BBN) allows for understanding the complex interdependencies between the different variables ( Figure 5 ). The farmer’s perception of changes in motivation and engagement is linked to the perception of changes in women’s participation and to the enhancement of services and contracts with farmers’ organizations. Likewise, the perception of a better understanding of farming practices is connected to the change of farming practices and to a better information exchange between farmers. Information exchange between farmers is related to the perception of a better commercial exchange that also associated to the enhancement of services and contracts with farm organizations. Only the perception of inclusiveness and collective investments are not connected to other aspects of change. The identification of these interlinks helps prioritizing the intervention areas where interventions had the most significant impact. A higher perceived motivation and engagement suggests the effectiveness of interventions in that domain and may impact women participation and the enhancement of services and contracts with farmers’ organizations. The project’s interventions were also effective leading to a high perceived understanding of farming practices that improves the information exchange between farmers and farming practices change. This insight can guide the design of future interventions based on the identified associations, leading to more targeted and impactful interventions.

Figure 5 . Bayesian Belief Network illustrating the interconnected perceptions of change among farmers.

The findings suggest a significant association between gender and the perceptions of motivation and engagement in agricultural projects ( Supplementary Appendix 3 ). A strong association is identified between gender and the women’s participation perception and the perception of better services and contracts between farmers and farmer organizations. The study shows that the perception of change on motivation and engagement increases from 95 to 100% if all respondents are women, while the women’s participation perception increases from 78 to 98% ( Figure 6 ). These results are consistent with several studies that have explored the role of gender in agricultural projects. Cloete et al. ( 55 ) found that rural Nicaraguan women’s motivations change from initial to sustained forms, enabling them to sustain community-led projects and build social capital, self-efficacy, and agency. Amran and Fatah ( 56 ) studied women’s empowerment in agriculture in Malaysia and found that access to extension services and effective decision-making are key factors, but limited leadership, motivation and engagement challenges, and restricted community group participation hinder women’s empowerment. Meinzen-Dick et al. ( 57 ) emphasized the importance of integrating gender into agricultural research, development, and extension to enhance food security and promote innovation in developing countries. Gender perceptions can significantly influence smallholder farmers’ adoption of resilient or sustainable farming practices. Studies have shown that women, who are often the most vulnerable smallholder farmers, are bound to benefit from this agricultural technology, mostly because of its attributes (i.e., climate smart practices) ( 58 ). Additionally, women have less access to productive resources, financial capital, and advisory services compared to men which may explain women’s high positive perception of motivation, engagement, and participation in projects’ activities ( 59 ).

Figure 6 . Gender influence in the motivation and engagement perception and in women participation perception of farmers.

4.5 Farmers’ organizations influence in the adoption of innovative farming practices and decision-making change

Table 9 presents the results of farmers’ perceptions of the effects of farmers’ organizations on changing practices and decisions on the farm. The items in the survey included the effect of farmers’ organizations on “changing input purchasing behavior,” “changing practices and techniques for crop management and/or breeding,” “changing sales and marketing behavior,” “changing relationships with other farmers,” and “changing vision for the farm in 10 years.” The results of the reliability analysis using Cronbach’s alpha for a scale composed of the five items show that the average interitem covariance is 1.58, indicating that the items in the scale are positively correlated. The scale reliability coefficient is 0.93, which is considered high and suggests that the scale has good internal consistency. This means that the items in the scale are measuring the same construct and are reliable for measuring that construct.

Table 9 . Responses on farmer’s perceptions of farmers’ organization effects on changing practices and decisions on the farm ( n  = 69).

The weighted average decision score is the sum of the mean values for the five items, divided by the total number of the items. It was 3.70, indicating an overall positive perception of the effects of farmers’ organizations on changing practices and decisions on the farm. The results show that the highest levels of agreement were observed for changing relationships with other farmers and changing the vision of the farm in 10 years. The Kendall W test shows that the five variables presenting the effects of farmers’ organization have similar mean ranks, ranging from 2.69 to 3.27 ( Supplementary Appendix 4 ). This suggests a general agreement that all effects hold some importance. However standard deviations are relatively high, indicating variation in perceived importance among respondents. Kendall’s Coefficient of Concordance (W) was estimated at 0.064 and statistically significant at 10%, indicating a weak level of agreement in the ranking of effects across respondents. The weak concordance suggests individual differences in how they prioritize these effects. There is not a strong consensus on which effect is most or least important.

The findings are consistent with previous studies that highlighted the significance of behavioral, social, and cognitive factors in influencing farmers’ decisions. Spina et al. ( 60 ) found that farmers’ attitudes strongly influence their intention to adopt, followed by social norms and perceived control. According to Addai et al. ( 61 ), the membership in farmer organizations affects the decision to adopt farm technologies by rice farmers in Ghana. The household head’s decision to adopt new farming practices such as machinery use and row planting increases upon joining a farmer organization. A scoping review of the literature on farmers’ organizations impacts on small-scale producers in sub-Saharan Africa and India found that farmers’ organizations, such as associations, cooperatives, and women’s groups, provide services that contribute to income and productivity for small-scale producers ( 62 ). Most reviewed studies reported positive impacts on farmer income, but much fewer reported positive impacts on crop yield and production quality. Environmental benefits, such as resilience-building and improved water quality and quantity, were documented in 24% of the studies. The review suggests that farmers’ organizations could be integrated into policy by having access to markets through information, infrastructure, and logistical support at the center of farmers’ organizations design ( 62 ).

To understand if there are any gender disparities in how farmers’ organizations shape farm management, a Kruskal-Wallis’s test was performed. The Kruskal Wallis test is a non-parametric test that compares the medians of two or more groups, and it is used when the data do not meet the assumptions of normality and equal variances required by parametric tests. Results showed that there were significant differences between the two groups in all five variables ( p  < 0.05) ( Supplementary Appendix 5 ). Specifically, women had higher mean ranks than men in the perception of the farmer organization effect on changing input purchasing behavior, on changing practices, on changing sales and marketing behavior, and on changing their vision for the farm in 10 years. The higher mean ranks for the female group suggest they generally perceived these effects as more important than the male farmers. Men had a higher mean rank only in the perception of the farmer organization effect on changing relationship with other farmers. The overall assessment suggests that, on average, females tend to provide higher ratings for the farmer organization effects on changing practices and decisions on the farm compared to males. However, the variability in responses is higher among males, indicating that there might be more diverse opinions among males. Women could be more aware of the farmer organization roles and influences because of the important gap in productivity, income, and resources that women are experiencing. According to Bello et al. ( 63 ), a disparity between men and women with a gender performance gap of about 11% in favor of men, is partially explained by factors such as the men access to improved varieties, membership of farmer-based organizations, extension services, and quantity of seeds sown.

Farmers’ organizations play a significant role in influencing the adoption of farming innovative practices and decision-making change. The positive perceptions of the effects of farmers’ organizations on changing practices and decisions on the farm, particularly in relation to changing relationships with other farmers and the long-term vision for the farm, underscore the importance of collaborative and supportive networks in promoting sustainable farming practices. However, the lower levels of agreement regarding changing sales and marketing behavior, as well as input purchasing behavior and crop management practices, suggest that there may be specific areas where farmers’ organizations could focus on enhancing their support and influence.

4.6 Farmers perception of agroecological transformation

The findings derived from the perception analysis provide valuable information regarding the farmers’ perception of agroecological transformation drivers and barriers. The descriptive analysis of the sample reveals that most of the participants in the study are male farmers, comprising 83% of the sample. In terms of education level, a significant proportion of the participants have completed secondary education (37%), followed by those with a university level of education (20%). The primary activities of the participants are dominated by olive tree cultivation (43%), with field crop cultivation (28%) and livestock farming (14%) also being prevalent. The participants’ age ranges from 21 to 72 years, with a mean of 52 years. Land ownership among participants varies widely, ranging from no land to 100 hectares, with a mean of 17 hectares. There is only one young farmer (27 years old) who does not own any land. On average, the participants have 28 years of experience as farmers, and their primary activity contributes about 63% of their income, with some variation across individuals ( Supplementary Appendix 6 ).

Respondents’ perceptions about challenges and barriers of adopting agroecological practices are varying from strong agreement to total disagreement. The percentages of respondents for each category, means, standard deviations, decisions, and the ranking of the perceived barriers and motivating factors to the adoption of agroecological practices by farmers are summarized in Supplementary Appendix 7 . The highest perceived barriers are the lack of financing and credit opportunities, the lack of encouragement from the government, water shortages, soil erosion, and other environmental problems, the absence of encouraging legislation and laws, the lack of infrastructure and supporting systems, the lack of training on ecological farming, and the lack of production inputs. Improved water conservation and enhanced soil quality are indeed key benefits of the agroecological transition. However, water shortages and soil erosion can still be perceived as barriers due to the initial challenges and adjustments required during the transition process. Despite the eventual benefits, the transition to agroecology may initially pose challenges in adapting to new practices and overcoming existing environmental issues.

Indeed, the most motivating factors perceived by farmers are that agroecological practices contribute to preserving the environment and natural resources, reduce the cost of production, contribute to improved food quality, are compatible with culture and values, contribute to improved production and income, and are compatible with farmers’ knowledge and experience. The most motivating items of the agroecological transformation can be the entry points for the transition. However, the respondents agree less with the facts that agroecological practices and activities are compatible with the financial, economic, technical, and logistical capabilities of farmers. These results are confirmed by Kendall’s W test. The test has been used to assess the level of agreement among respondents’ rankings of various statements related to agroecological practices and their associated motivations, challenges, and barriers. The value of Kendall’s W is 0.20 and the p -value is 0.000 ( Supplementary Appendix 7 ). This indicates that there is a statistically significant weak level of agreement among the respondents’ rankings of the various statements related to agroecological practices and their associated challenges and barriers. The mean ranks for each statement provide insight into the relative importance or perception of each item. For example, “Agroecological practices contribute to preserve the environment and natural resources” has the highest mean rank of 21.87, indicating that, on average, respondents ranked this statement as more important or more in agreement compared to other statements. Conversely, “Constraints and complexity of agroecological transition consist of the lack of consumer demand for ecological products” has a lower mean rank of 9.31, indicating that, on average, respondents ranked this statement as less important compared to other statements. The Cronbach’s alpha value is 0.763, indicating an acceptable level of reliability and suggesting a satisfactory level of internal consistency among the items.

4.7 Key driver and barrier factors of the agroecological transformation in Tunisia

Factorial analysis is conducted to understand the structure of the main drivers and barriers of the agroecological transformation considering the current perceptions of the Tunisian farmers. The factorial analysis conducted on 30 factors (items) related to agroecological practices reveals a nuanced understanding of the complexities and challenges surrounding their adoption. The analysis delineates 9 key components (explaining 78% of the total variance), each capturing distinct aspects of the agroecological transition process ( Supplementary Appendix 8 ).

• Component 1: captures financial, and economic considerations, alongside logistical and technical feasibility, that emerge as crucial determinants of this first factor labeled as “Compatibility with farmers’ capabilities and knowledge and capacity building needs.” This component also focusses on technical difficulties facing ecological transformation, such as the lack of training, technical knowledge, and experience.

• Component 2: highlights key constraints such as the absence of encouraging legislation and laws, and the lack of government support, the delayed results to enhance incomes and the lack of exchange of experiences and of cooperation between farmers. The second factor more related to the perception of barriers is labeled as “Political, institutional, and communication barriers and risk perception.”

• Component 3: includes constraints such as the high cost of transition, difficulties in changing production habits and lack of cooperation between the different stakeholders. This factor is labeled as “Stakeholder cooperation and implementation challenges.”

• Component 4: emphasizes the alignment of agroecological practices with cultural and economic expectations, including initial productivity changes, cost reduction, and long-term production and income improvement. This component can be interpreted as “Cultural and economic benefits.”

• Component 5: highlights logistical difficulties such as input unavailability, the lack of infrastructure and supporting systems and challenges in scaling up agroecological practices. This factor is summarized as a barrier and labeled as “Logistical difficulties and scaling-up challenges.”

• Component 6: focuses on environmental aspects, including the contribution of agroecological practices to preserve the environment and natural resources, and constraints related to water shortages, soil erosion, and other environmental problems. This factor can be interpreted as “Environmental sustainability and mitigation in agroecological practices,” highlighting the role of environmental challenges and mitigation factors as drivers of the agroecological transformation.

• Component 7: encompasses factors related to access to both economic and non-economic aspects such as access to information, credit, and financial support. This component considered as a barrier and is identified as “Access to information and financial services.”

• Component 8: highlights constraints such as the lack of consumer demand for ecological products, marketing difficulties, and market access challenges. The component is identified as “Market-related factors.”

• Component 9: suggests that agroecological practices contribute to improved food quality and hygiene and can be interpreted as “Health Determinants” factor.

Figure 7 presents drivers and barriers in agroecological transitions in Tunisia according to the local farmers involved. The total explained variance by the extracted components reached 78%. The drivers include compatibility with farmers’ capabilities (17.71%), cultural and economic benefits (8.75%), environmental sustainability (6.07%), and health determinants (3.55%). Political, institutional, and communication barriers (15.54%), stakeholder cooperation challenges (9.76%), logistical difficulties (8.15%), access to information and financial services (5.58%), and market-related factors (3.83%) are identified as barriers. Consistent with these results, the literature highlights the complexity of the factors involved as barriers of agroecological transitions ( 64 ). Furthermore, the sustainability of transitions to agroecology is linked to factors such as capacity building, social capital, and farmer knowledge, emphasizing the multifaceted nature of these transitions ( 65 ).

Figure 7 . Key drivers and barriers of agroecological transition.

5 Conclusion and implications

In examining the potential for agroecological transitions in Tunisia, specifically the Kef-Siliana transect, this study has revealed valuable insights. The SWOT analysis demonstrates that farmer organizations have clear goals, diversified farming systems, and partnerships in collaboration with various organizations and institutions. The study emphasizes the significant potential of these farmers’ organizations in advancing sustainable farming practices. However, it also underscores the need for targeted efforts to address specific challenges in farming practices and decision-making. Outlined obstacles include the unavailability of seeds and fertilizers, water shortage, limited income, diseases, and marketing issues. To prioritize value chains for agroecological transition in Tunisia, the study identifies the olive oil sector as the most promising for development, considering economic, social, and environmental factors. Implementing recycling and input minimization principles in the olive oil supply chain and bridging the gap between theoretical agroecological concepts and farming practice implementation are recommended to cultivate sustainable agroecological farming systems. The survey’s results indicate that farmers who received training and assistance with agroecological practices reported positive changes in their ideas and practices. Therefore, the study emphasizes the importance of farmer engagement, knowledge production, and multi-stakeholder collaboration in promoting agroecological transitions in Tunisia. The Bayesian Belief Network (BBN) visualization highlights complex interdependencies between different factors, emphasizing the significance of women’s participation, improved services and contracts with farmers’ organizations, and a better understanding of farming practices to facilitate agroecological transitions. The study identifies various challenges and barriers, including political, institutional, and communication barriers, logistical difficulties, and market-related factors. To address these challenges and facilitate agroecological transitions, the study emphasizes the need for farmer engagement, knowledge production, and multi-stakeholder collaboration. Furthermore, it suggests targeted efforts to address specific aspects of farming practices and decision-making. The study’s findings also underscore the influence of gender perceptions on the adoption of resilient and sustainable farming practices among smallholder farmers, emphasizing the importance of integrating gender into agricultural research, development, and extension to enhance food security and foster innovation in Tunisia. At the political and institutional level, the study recommends the increase of public incentives and supportive legislation to support agroecological practices. Additionally, the study suggests offering innovative financing and credit opportunities to farmers to overcome the lack of production inputs and limited access to microfinancing. Recognizing the lack of training on ecological farming as a significant barrier, the study proposes the development of capacity building programs to equip farmers with the necessary knowledge and skills to embrace agroecological practices.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the studies involving humans because the farmers participated voluntarily and provided their consent to answer the survey questions. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AS: Conceptualization, Formal analysis, Methodology, Writing – original draft, Data curation, Investigation, Software. BD: Conceptualization, Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing, Project administration. AO: Validation, Writing – review & editing. RM: Data curation, Investigation, Writing – original draft. AF: Funding acquisition, Project administration, Resources, Writing – review & editing. MZ: Investigation, Writing – review & editing. MD: Investigation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is part of the Agroecology Initiative “Transformational Agroecology across Food, Land and Water Systems” under a grant agreement (#200302) with the International Center for Agricultural Research in the Dry Areas (ICARDA - https://www.icarda.og/ ). We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund: .

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The opinions expressed here belong to the authors and do not necessarily reflect those of ICARDA or CGIAR.

Supplementary material

The Supplementary material for this article can be found online at:

1. ^ More about this initiative can be read here: .

1. Anderson, CR, Bruil, J, Chappell, MJ, Kiss, C, and Pimbert, MP. Agroecology now! Transformations towards more just and sustainable food systems . Cham: Springer Nature (2021) Available at: .

Google Scholar

2. Kretschmer, S, and Kahl, J. Sustainable development goal drivers in food systems. Front Sustain Food Syst . (2021) 5:536620. doi: 10.3389/fsufs.2021.536620

Crossref Full Text | Google Scholar

3. Padel, S, Levidow, L, and Pearce, B. UK farmers’ transition pathways towards agroecological farm redesign: evaluating explanatory models. Agroecol Sustain Food Syst . (2019) 44:139–63. doi: 10.1080/21683565.2019.1631936

4. Conde, YQ, Locatelli, B, Vallet, A, and Sevillano, RB. Agroecology for food security and against climate change in Peru. Econo Agr Recur Nat . (2022) 22:5–29. doi: 10.7201/earn.2022.01.01

5. Dagunga, G, Ayamga, M, Laube, W, Ansah, IGK, Kornher, L, and Kotu, BH. Agroecology and resilience of smallholder food security: a systematic review. Front Sustain Food Syst . (2023) 7:1267630. doi: 10.3389/fsufs.2023.1267630

6. Altieri, MA, and Nicholls, CI. Agroecology: challenges and opportunities for farming in the anthropocene. Int J Agric Nat Resour . (2020) 47:204–15. doi: 10.7764/ijanr.v47i3.2281

7. CGIAR Initiative on Agroecology (2023). The agroecology transition: different pathways to a single destination - eight country experiences . Available at:

8. Fraser, EDG, Dougill, AJ, Mabee, WE, Reed, M, and McAlpine, P. Bottom up and top down: analysis of participatory processes for sustainability indicator identification as a pathway to community empowerment and sustainable environmental management. J Environ Manag . (2006) 78:114–27. doi: 10.1016/j.jenvman.2005.04.009

PubMed Abstract | Crossref Full Text | Google Scholar

9. Soini Coe, E, and Coe, R. Agroecological transitions in the mind. Elementa . (2023) 11:26. doi: 10.1525/elementa.2022.00026

10. Rizzo, G, Migliore, G, Schifani, G, and Vecchio, R. Key factors influencing farmers’ adoption of sustainable innovations: a systematic literature review and research agenda. Org Agr . (2023) 14:57–84. doi: 10.1007/s13165-023-00440-7

11. Gaba, S, and Lamine, C. Social–ecological experiments to foster agroecological transition. People Nat . (2020) 2:317–27. doi: 10.1002/pan3.10078

12. Roussy, C, Ridier, A, and Chaib, K. Farmers’ innovation adoption behaviour: role of perceptions and preferences. Int J Agric Resour Gov Ecol . (2017) 13:138–61. doi: 10.1504/IJARGE.2017.086439

13. Chikezie, NP, Omolehin, RA, and Fadiji, TO. Socio-economic and institutional factors influencing adoption of community-based agriculture and rural development (CBARD) crop production interventions project in Kaduna and Bauchi states, Nigeria. Open Access J Agric Res . (2019) 4:215. doi: 10.23880/oajar-16000215

14. Martin, G. A conceptual framework to support adaptation of farming systems – development and application with forage rummy. Agric Syst . (2015) 132:52–61. doi: 10.1016/j.agsy.2014.08.013

15. Ayuso, S, Rodríguez, M, García-Castro, R, and Ariño, M. Does stakeholder engagement promote sustainable innovation orientation? Ind Manag Data Syst . (2011) 111:1399–417. doi: 10.1108/02635571111182764

16. Leonidou, E, Christofi, M, Vrontis, D, and Thrassou, A. An integrative framework of stakeholder engagement for innovation management and entrepreneurship development. J Bus Res . (2018) 119:245–58. doi: 10.1016/J.JBUSRES.2018.11.054

17. Thabrew, L, Wiek, A, and Ries, R. Environmental decision making in multi-stakeholder contexts: applicability of life cycle thinking in development planning and implementation. J Clean Prod . (2009) 17:67–76. doi: 10.1016/J.JCLEPRO.2008.03.008

18. Dupré, M, Michels, T, and Le Gal, PY. (2017). Diverse dynamics in agroecological transitions on fruit tree farms. Eur J Agron . 90:23–33.

19. Alary, V, Frija, A, Ouerghemmi, H, Idoudi, Z, Rudiger, U, Rekik, M, et al. Context assessment for agroecology transformation in the Tunisian living landscape . Beirut, Lebanon: International Center for Agricultural Research (2022) in the Dry Areas (ICARDA). Working Paper, 71.

20. Attiaoui, I, and Boufateh, T. (2019). Impacts of climate change on cereal farming in Tunisia: a panel ARDL–PMG approach. Environmental Science and Pollution Research . 26:13334–45.

21. CRDA Kef (2020). Commissariat Régional de Développment Agricole . Technical Report in Kef.

22. ODNO. (2020). Gouvernorat de Siliana en chiffre . Ministère de l’Economie et de la Planification. Office de Développement du Nord-Ouest. Available at:

23. Jochem, S. (2015). Les lignes directrices pour sélectionner des chaînes de valeur. Intégration des critères économiques, environnementaux, sociaux et institutionnels. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH sur mandat du Ministère fédéral de la Coopération économique et du Développement (BMZ) et Organisation internationale du Travail(OIT). 66 Available at:

24. Di Vita, G, Spina, D, De Cianni, R, D’Amico, M, and Zanchini, R. Enhancing the extended value chain of the aromatic plant sector in Italy: a multiple correspondence analysis based on stakeholders’ opinions. Agric Econ . (2023) 11:15. doi: 10.1186/s40100-023-00257-8

25. Spina, D, Barbieri, C, Carbone, R, Hamam, M, D’Amico, M, and Di Vita, G. Market trends of medicinal and aromatic plants in Italy: future scenarios based on the Delphi method. Agronomy . (2023) 13:1703. doi: 10.3390/agronomy13071703

26. Agyekum, EB, Ansah, MNS, and Afornu, KB. Nuclear energy for sustainable development: SWOT analysis on Ghana’s nuclear agenda. Energy Rep . (2020) 2020:107–15.

27. Rauch, P. SWOT analyses and SWOT strategy formulation for forest owner co-operations in Austrias. Eur J For Res . (2007) 2007:413–20.

28. Kangas, J, Tikkanen, J, Leskinen, P, Kurttila, M, and Kajanus, M. Developing hybrid SWOT methodologies for choosing joint bioeconomy co-operation priorities by three Finnish universities. Biofuels . (2017) 8:459–71. doi: 10.1080/17597269.2016.1271625

29. Ghazinoory, S, Abdi, M, and Azadegan-Mehr, M. Swot methodology: a state-of-the-art review for the past, a framework for the future/SSGG Metodologija: Praeities ir Ateities Analizė. J Bus Econ Manag . (2011) 12:24–48. doi: 10.3846/16111699.2011.555358

30. Oladele, OI, Lepetu, J, Subair, SK, and Obuh, J. SWOT analysis of extension systems in southern African countries. J Agri Enviro Int Dev . (2009) 103:309–20.

31. Tryphone, G, and Thomas, SP. Evaluating limitations of Agroecological practices and stakeholders’ response: a case of Uluguru Mountains landscape in Morogoro municipality, Tanzania. East Afr J Sci Technol Innov . (2023) 4. doi: 10.37425/eajsti.v4i3.676

32. Dyson, RG. Strategic development, and SWOT analysis at the University of Warwick. Eur J Oper Res . (2004) 152:631–40. doi: 10.1016/S0377-2217(03)00062-6

33. Helms, MM, and Nixon, JC. Exploring SWOT analysis—where are we now? J Strat Manag . (2010) 3:215–51. doi: 10.1108/17554251011064837

34. Oladele, OI, and Sakagami, JI. SWOT analysis of extension systems in Asian and west African countries. Food Agric Environ . (2004) 2:232–6.

35. Heckerman, D, Geiger, D, and Chickering, DM. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn . (1995) 20:197–243. doi: 10.1007/BF00994016

36. Costello, AB, and Osborne, J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval . (2019) 10:1–9. doi: 10.7275/jyj1-4868

37. Riitters, KH, O’Neill, RV, Hunsaker, CT, Wickham, JD, Yankee, DH, Timmins, SP, et al. A factor analysis of landscape pattern and structure metrics. Landsc Ecol . (1995) 1995:23–39.

38. Ryschawy, J., Sarthou, J., Chabert, A., and Thérond, O. (2019). The key role of actors in the agroecological transition of farmers: a case-study in the Tarn-Aveyron Basin: agroecological transitions: from theory to practice in local participatory design . Springer. 149–173.

39. Slimi, C, Prost, M, Cerf, M, and Prost, L. Exchanges among farmers’ collectives in support of sustainable agriculture: from review to reconceptualization. J Rural Stud . (2021) 83:268–78. doi: 10.1016/J.JRURSTUD.2021.01.019

40. Kremen, C, Iles, A, and Bacon, C. Guest editorial, part of a special feature on A social-ecological analysis of diversified farming systems: benefits, costs, obstacles, and enabling policy frameworks diversified farming systems: an Agroecological, systems-based alternative to modern industrial. Agriculture . (2012) 17:19–44. doi: 10.5751/ES-05103-170444

41. Bene, C, Gómez-López, M, Francaviglia, R, Farina, R, Blasi, E, Martínez-Granados, D, et al. Barriers and opportunities for sustainable farming practices and crop diversification strategies in Mediterranean cereal-based systems. Front Environ Sci . (2022) 10:16. doi: 10.3389/fenvs.2022.861225

42. HLPE. Agroecological and other innovative approaches for sustainable agriculture and food systems that enhance food security and nutrition . Rome: A report by the High-Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security (2019) Available at: .

43. Pisanelli, A, Consalvo, C, Russo, G, Ciolfi, M, Martini, E, Lauteri, M, et al. Agroforestry systems and innovation in extra-virgin olive oil chain (EVOO) in Central Italy: a multi-stakeholder perspective. Innov Appr Appl Sustaina Rural Dev . (2017) 85–99. doi: 10.1007/978-3-030-02312-6_5

44. Tyl, B, Vallet, F, Bocken, N, and Real, M. The integration of a stakeholder perspective into the front end of eco-innovation: a practical approach. J Clean Prod . (2015) 108:543–57. doi: 10.1016/J.JCLEPRO.2015.07.145

45. Torquati, B, Cecchini, L, Paffarini, C, and Chiorri, M. The economic and environmental sustainability of extra virgin olive oil supply chains: an analysis based on food miles and value chains. Econ Agro Alimen Food Econ Open Access . (2021) 23:1–28. doi: 10.3280/ECAG1-2021OA11391

46. Jellali, A, Hachicha, W, and Aljuaid, A. Sustainable configuration of the Tunisian olive oil supply chain using a fuzzy TOPSIS-based approach. Sustain For . (2021) 13:722. doi: 10.3390/SU13020722

47. Stempfle, S, Carlucci, D, Gennaro, B, Roselli, L, and Giannoccaro, G. Available pathways for operationalizing circular economy into the olive oil supply chain: mapping evidence from a scoping literature review. Sustain For . (2021) 13:9789. doi: 10.3390/su13179789

48. Oppong, E, Opoku, A, Tuffour, H, Snr, A, and Kyere, C. Climate change and climate-smart agricultural practices: opportunities and challenges in the semi-deciduous region of Ghana. Int J Environ Clim Change . (2021) 11:100–10. doi: 10.9734/ijecc/2021/v11i630426

49. Šūmane, S, Kunda, I, Knickel, K, Strauss, A, Tisenkopfs, T, Ríos, I, et al. Local and farmers' knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. J Rural Stud . (2017) 59:232–41. doi: 10.1016/J.JRURSTUD.2017.01.020

50. Lacombe, C, Couix, N, and Hazard, L. Designing agroecological farming systems with farmers: a review. Agric Syst . (2018) 165:208–20. doi: 10.1016/J.AGSY.2018.06.014

51. Awan, S, Ashfaq, M, Naqvi, S, Hassan, S, Kamran, M, Imran, A, et al. Profitability analysis of sustainable cotton production: a case study of cotton wheat farming system in Bahawalpur district of Punjab. Bulgarian J Agr Sci . (2015) 21:251–6.

52. Hina, T, and Naseer, M. Impact of better management practices on sustainable cotton production: evidence from South Punjab. J Econ Impact . (2019) 1:92–7. doi: 10.52223/jei0103194

53. Lastiri-Hernández, M, Álvarez-Bernal, D, Moncayo-Estrada, R, Cruz-Cárdenas, G, and Garcia, J. Adoption of phytodesalination as a sustainable agricultural practice for improving the productivity of saline soils. Environ Dev Sustain . (2020) 23:8798–814. doi: 10.1007/s10668-020-00995-5

54. Olawuyi, S. Farmers' preference for soil and water conservation practices in Nigeria: analytic hierarchic process approach. J Econ Behav Stud . (2018) 10:68–80. doi: 10.22610/jebs.v10i4(j).2408

55. Cloete, E, House, A, Velásquez, L, Calderon, M, López, J, Rivera, R, et al. I left my shyness behind: sustainable community-led development and processes of motivation among rural Nicaraguan women. J Community Psychol . (2022) 51:860–79. doi: 10.1002/jcop.22926

56. Amran, F, and Fatah, F. Insights of women’s empowerment and decision-making in rice production in Malaysia. Food Res . (2020) 4:53–61. doi: 10.26656/fr.2017.4(s5).013

57. Meinzen-Dick, R, Quisumbing, A, and Behrman, J. A system that delivers: integrating gender into agricultural research, development, and extension In: A Quisumbing, R Meinzen-Dick, T Raney, A Croppenstedt, J Behrman, and A Peterman, editors. Gender in agriculture . Dordrecht: Springer (2014). 373–91.

58. Murage, AW, Pittchar, JO, Midega, CAO, Onyango, CO, and Khan, ZR. Gender specific perceptions and adoption of the climate-smart push–pull technology in eastern Africa. Crop Prot . (2015) 76:83–91. doi: 10.1016/j.cropro.2015.06.014

59. Jost, C, Kyazze, F, Naab, J, Neelormi, S, Kinyangi, J, Zougmore, R, et al. Understanding gender dimensions of agriculture and climate change in smallholder farming communities. Clim Dev . (2015) 8:133–44. doi: 10.1080/17565529.2015.1050978

60. Spina, D, Caracciolo, F, Chinnici, G, Di Vita, G, Selvaggi, R, Pappalardo, G, et al. How do farmers plan to safeguard the environment? Empirical evidence on farmers’ intentions to adopt organic pest management practices. J Environ Plan Manag . (2023):1–21. doi: 10.1080/09640568.2023.2218021

61. Addai, K, Temoso, O, and Ng’ombe, J. Review for "participation in farmer organizations and adoption of farming technologies among rice farmers in Ghana". Int J Soc Econ . (2021) 529–45. doi: 10.1108/ijse-06-2021-0337/v3/review1

62. Bizikova, L, Nkonya, E, Minah, M, Hanisch, M, Turaga, RMR, Speranza, CI, et al. A scoping review of the contributions of farmers’ organizations to smallholder agriculture. Nat Food . (2020) 1:620–30. doi: 10.1038/s43016-020-00164-x

63. Bello, L, Baiyegunhi, L, Danso-Abbeam, G, and Ogundeji, A. Gender decomposition in smallholder agricultural performance in rural Nigeria. Sci Afr . (2021) 13:e00875. doi: 10.1016/j.sciaf.2021.e00875

64. Ying, TW, and Wenying, L. Agroecological transitions: A mathematical perspective on a transdisciplinary problem. Front Sustain Food Syst . (2020) 4:2020. doi: 10.3389/fsufs.2020.00091

65. Schwarz, G, Vanni, F, Miller, D, Helin, J, Pražan, J, Albanito, F, et al. Exploring sustainability implications of transitions to agroecology: a transdisciplinary perspective. EuroChoices . (2022) 21:37–47. doi: 10.1111/1746-692X.12377

Keywords: participatory approach, agroecological transformation, perceptions, resilience, value chain, North Africa

Citation: Souissi A, Dhehibi B, Oumer AM, Mejri R, Frija A, Zlaoui M and Dhraief MZ (2024) Linking farmers’ perceptions and management decision toward sustainable agroecological transition: evidence from rural Tunisia. Front. Nutr . 11:1389007. doi: 10.3389/fnut.2024.1389007

Received: 20 February 2024; Accepted: 25 April 2024; Published: 13 May 2024.

Reviewed by:

Copyright © 2024 Souissi, Dhehibi, Oumer, Mejri, Frija, Zlaoui and Dhraief. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Asma Souissi, [email protected]

This article is part of the Research Topic

Sustainable and Resilient Food Systems in Times of Crises

research articles about adoption

Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work

May 8, 2024 | Jared Spataro - CVP, AI at Work

  • Share on Facebook (opens new window)
  • Share on Twitter (opens new window)
  • Share on LinkedIn (opens new window)

Illustration showing Microsoft Copilot prompts

One year ago, generative AI burst onto the scene and for the first time since the smartphone, people began to change the way they interact with technology. People are bringing AI to work at an unexpected scale — and now the big question is, how’s it going?

As AI becomes ubiquitous in the workplace, employees and businesses alike are under extreme pressure. The pace and intensity of work, which accelerated during the pandemic, has not eased, so employees are bringing their own AI to work. Leaders agree AI is a business imperative — and feel the pressure to show immediate ROI — but many lack a plan and vision to go from individual impact to applying AI to drive the bottom line.

At the same time, the labor market is set to shift and there’s a new AI economy. While some professionals worry AI will replace their job, the data tells a more nuanced story — of a hidden talent shortage, more employees eyeing a career change, and a massive opportunity for those willing to skill up.

“AI is democratizing expertise across the workforce,” said Satya Nadella, Chairman and Chief Executive Officer, Microsoft. “Our latest research highlights the opportunity for every organization to apply this technology to drive better decision-making, collaboration — and ultimately business outcomes.”

For our fourth annual Work Trend Index, out today, we partnered with LinkedIn for the first time on a joint report so we could provide a comprehensive view of how AI is not only reshaping work, but the labor market more broadly. We surveyed 31,000 people across 31 countries, identified labor and hiring trends from LinkedIn, analyzed trillions of Microsoft 365 productivity signals and conducted research with Fortune 500 customers. The data points to insights every leader and professional needs to know — and actions they can take — when it comes to AI’s implications for work.

1. Employees want AI at work — and won’t wait for companies to catch up.

Three in four knowledge workers (75%) now use AI at work. Employees, overwhelmed and under duress, say AI saves time, boosts creativity and allows them to focus on their most important work. While 79% of leaders agree AI adoption is critical to remain competitive, 59% worry about quantifying the productivity gains of AI and 60% worry their company lacks a vision and plan to implement it. While leaders feel the pressure to turn individual productivity gains into organizational impact, employees aren’t waiting to reap the benefits: 78% of AI users are bringing their own AI tools to work. The opportunity for every leader is to channel this momentum into ROI.

2. For employees, AI raises the bar and breaks the career ceiling .

We also see AI beginning to impact the job market. While AI and job loss are top of mind for some, our data shows more people are eyeing a career change, there are jobs available, and employees with AI skills will get first pick. The majority of leaders (55%) say they’re worried about having enough talent to fill open roles this year, with leaders in cybersecurity, engineering, and creative design feeling the pinch most.

And professionals are looking. Forty-six percent across the globe are considering quitting in the year ahead — an all-time high since the Great Reshuffle of 2021 — a separate LinkedIn study found U.S. numbers to be even higher with 85% eyeing career moves. While two-thirds of leaders wouldn’t hire someone without AI skills, only 39% of users have received AI training from their company. So, professionals are skilling up on their own. As of late last year, we’ve seen a 142x increase in LinkedIn members adding AI skills like Copilot and ChatGPT to their profiles and a 160% increase in non-technical professionals using LinkedIn Learning courses to build their AI aptitude.

In a world where AI mentions in LinkedIn job posts drive a 17% bump in application growth, it’s a two-way street: Organizations that empower employees with AI tools and training will attract the best talent, and professionals who skill up will have the edge.

3. The rise of the AI power user — and what they reveal about the future.

In the research, four types of AI users emerged on a spectrum — from skeptics who rarely use AI to power users who use it extensively. Compared to skeptics, AI power users have reoriented their workdays in fundamental ways, reimagining business processes and saving over 30 minutes per day. Over 90% of power users say AI makes their overwhelming workload more manageable and their work more enjoyable, but they aren’t doing it on their own.

Power users work for a different kind of company. They are 61% more likely to have heard from their CEO on the importance of using generative AI at work, 53% more likely to receive encouragement from leadership to consider how AI can transform their function and 35% more likely to receive tailored AI training for their specific role or function.

“AI is redefining work and it’s clear we need new playbooks,” said Ryan Roslansky, CEO of LinkedIn. “It’s the leaders who build for agility instead of stability and invest in skill building internally that will give their organizations a competitive advantage and create more efficient, engaged and equitable teams.”

The prompt box is the new blank page

We hear one consistent piece of feedback from our customers: talking to AI is harder than it seems. We’ve all learned how to use a search engine, identifying the right few words to get the best results. AI requires more context — just like when you delegate work to a direct report or colleague. But for many, staring down that empty prompt box feels like facing a blank page: Where should I even start?

Today, we’re announcing Copilot for Microsoft 365 innovations to help our customers answer that question.

YouTube Video

  • Catch Up, a new chat interface that surfaces personal insights based on your recent activity, provides responsive recommendations , like “You have a meeting with the sales VP on Thursday. Let’s get you prepared — click here to get detailed notes.”

Screenshot of prompt publishing in Copilot Lab

These features will be available in the coming months, and in the future, we’ll take it a step further, with Copilot asking you questions to get to your best work yet.

LinkedIn has also made free over 50 learning courses to empower professionals at all levels to advance their AI aptitude.

Head to WorkLab for the full Work Trend Index Report , and head to LinkedIn to hear more from LinkedIn’s Chief Economist, Karin Kimbrough, on how AI is reshaping the labor market.

And for all the blogs, videos and assets related to today’s announcements, please visit our  microsite .

Tags: AI , LinkedIn , Microsoft Copilot , Work Trend Index

  • Check us out on RSS

research articles about adoption


  1. (PDF) Parenting adopted children and supporting adoptive parents

    research articles about adoption

  2. Philadelphia PA Adoption Agency

    research articles about adoption

  3. International Advances in Adoption Research for Practice

    research articles about adoption

  4. Adoption Today Features Article on Angels in Adoption™

    research articles about adoption

  5. 8 Awesome Facts About Adoption

    research articles about adoption

  6. Adoption Today-May 2015 Magazine

    research articles about adoption


  1. still doing adoption research? #adoption


  1. (PDF) The Effects of Adoption on Foster Children's Well-Being: A

    In this integrative review, research pertaining to the physical, cognitive, socioemotional, and psychological effects of adoption on foster children was examined. A systematic review of the ...

  2. Adoption Research

    Adoption Research. We provide accurate, reliable, and up-to-date reports that inform and. equip professionals, policymakers, and the public at large to improve. and strengthen adoption. In 2021, we conducted the largest survey ever of adoptive parents. NCFA explored the profile of adoptive parents, their experiences, and what has changed in ...

  3. Review: Adoption, fostering, and the needs of looked-after and adopted

    Throughout the review, we draw from three literatures: research on foster children and foster care; research on domestic adoption; and research on internationally adopted children. It is important to acknowledge that, although these children share similar vulnerabilities, the experiences of foster and domestically adopted children differ from ...

  4. Understanding adoption: A developmental approach

    They gradually develop a self-concept (how they see themselves) and self-esteem (how much they like what they see) ( 2 ). Ultimately, they learn to be comfortable with themselves. Adoption may make normal childhood issues of attachment, loss and self-image ( 2) even more complex. Adopted children must come to terms with and integrate both their ...

  5. Attachment across the Lifespan: Insights from Adoptive Families

    Abstract. Research with adoptive families offers novel insights into longstanding questions about the significance of attachment across the lifespan. We illustrate this by reviewing adoption research addressing two of attachment theory's central ideas. First, studies of children who were adopted after experiencing severe adversity offer ...

  6. Family environment and development in children adopted from

    Clinical Research Article; Open access; Published: 26 May 2021; Family environment and development in children adopted from institutionalized care. Margaret F. Keil 1, Adela Leahu 1, Megan Rescigno 2,

  7. Adoption and the effect on children's development

    Adoption, whether formal or informal, has always been a superior method of assuring survival for children whose parents are unwilling or unable to care for them. However, adoption can also affect child development in profound ways. Data collected over the past three decades support adoption as a superior means of promoting normal development in ...

  8. Full article: Adoption, Communication, and Family Networks: Current

    View PDF View EPUB. This special issue of the Journal of Family Communication highlights the role of communication processes in adoption placements. Six original, peer-reviewed, empirical articles comprise this special issue. Together, these articles represent cutting-edge research on the formative dialogue that sustains adoption kinship networks.

  9. (PDF) Review: Adoption research: Trends, topics, outcomes

    The current article provides a review of adoption research since its inception as a field of study. Three historical trends in adoption research are identified: the first focusing on risk in ...

  10. Review: Adoption research: Trends, topics, outcomes

    The current article provides a review of adoption research since its inception as a field of study. Three historical trends in adoption research are identified: the first focusing on risk in adoption and identifying adoptee—nonadoptee differences in adjustment; the second examining the capacity of adopted children to recover from early adversity; and the third focusing on biological ...

  11. Understanding Technology Adoption: Theory and Future Directions for

    How and why individuals adopt innovations has motivated a great deal of research. This article examines individuals' computing adoption processes through the lenses of three adoption theories: Rogers's innovation diffusion theory, the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance and Use of Technology.

  12. The effectiveness of psychological interventions with adoptive parents

    Future research should consider using valid and reliable adoption-sensitive measures that account for the additional complexities in presentation such as the Brief Assessment Checklists (BAC-C, BAC-A) developed specifically for use among children and adolescents in foster, kinship, residential and adoptive care (Tarren-Sweeney, 2013a).

  13. Adoption research, practice, and societal trends: Ten years of ...

    This article summarizes the social trends and research related to adoption over the last 10 years, including longitudinal and meta-analytic studies, increased research and conceptualization of ethnic and racial identity development, research on microaggressions, and research on diverse adoptive families, including those with gay and lesbian ...

  14. PDF The Hidden Impact of Adoption

    of adoption. From the beginnings of modern adoption research in the late 1960s and 1970s, researchers have found adoption to be an emancipatory institution for children who faced pre-adoption trauma or high-risk future life trajectories (Bohman, 1971; Rutter, Brodzinsky, & Palacios, 2005). However, adoption research also demonstrates that

  15. Full article: Examining the Intersection of Ethics and Adoption

    JaeRan Kim. Ethics are implicitly embedded in nearly every aspect of adoption. They are at the heart of our professional practice - including, but not exclusive to, educators, medical practitioners, lawyers, mental health providers, adoption advocates, researchers, and genetic counselors. Since Babb's ( 1999) book on Ethics in American ...

  16. Experiences of Adoption Disruption: Parents' Perspectives

    The authors would like to acknowledge the adoptive parents who generously shared their experiences during the research; Adoption UK for enabling access to them; the Health and Social Care Board (HSCB) who funded this study and for their inspiration, expertise and support. Special thanks to my practice assessor Una Lernihan at the HSCB; the ...

  17. Living in Adoption's Emotional Aftermath

    Adoptees reckon with corruption in orphanages, hidden birth certificates, and the urge to search for their birth parents. By Larissa MacFarquhar. April 3, 2023. "In order to be adopted you first ...

  18. Mental health and behavioural difficulties in adopted children: A

    This review seeks to summarise the post-adoption variables associated with adopted children's mental health or behavioural difficulties to inform future research and shape interventions. A search for publications that assess associated risk and protective factors using Web of Science, Psychinfo, Medline and Sociological Abstracts identified ...

  19. Adoption and Trauma: Risks, Recovery, and the Lived Experience of

    In the past, most researchers attributed greater developmental and mental health risks for adopted individuals primarily to the vulnerabilities and adversities they experienced prior to adoption (e.g., genetics, prenatal complications, neglect, abuse, multiple foster care placements, orphanage life). More recently, the role of post-adoption ...

  20. Adoption study links child behavior issues with mother's trauma

    The research team, led by Leslie Leve, a professor in the UO College of Education and scientist with the Prevention Science Institute, found a link between birth mothers who had experienced stressful childhood events, such as abuse, neglect, violence or poverty, and their children's behavior problems. This was true even though the children ...

  21. Increasing adoption rates at animal shelters: a two-phase approach to

    Background Among the 6-8 million animals that enter the rescue shelters every year, nearly 3-4 million (i.e., 50% of the incoming animals) are euthanized, and 10-25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal ...

  22. Bridging the gap: a systematic analysis of circular economy ...

    The primary objective of this research paper is to conduct a comprehensive and systematic literature review (SLR) focusing on Sustainable Supply Chain Management (SSCM) practices that promote Circular Economy (CE), sustainability, and resilience through adopting emerging digital technologies. A SLR of 130 research articles published between 1991 and 2023 was used to analyze emerging trends in ...

  23. An investigation into the acceptability, adoption, appropriateness

    Despite some issues raised by the participants, the acceptability, adoption, appropriateness, feasibility, and fidelity of the implementation strategies for birth companions to mitigate the mistreatment of women during childbirth were well received. Future research should explore the sustainability of this program.

  24. Adoption Quarterly

    Adoption Quarterly is an unparalleled forum for examining the issues related to adoption as viewed from a lifespan perspective, and of the psychological and social meanings of the word "family." This international, multidisciplinary journal features conceptual and empirical work, as well as book reviews from the fields of the social sciences, humanities, biological sciences, law, and social ...

  25. Frontiers

    The study's findings also underscore the influence of gender perceptions on the adoption of resilient and sustainable farming practices among smallholder farmers, emphasizing the importance of integrating gender into agricultural research, development, and extension to enhance food security and foster innovation in Tunisia.

  26. <em>British Educational Research Journal</em>

    The British Educational Research Journal is an interdisciplinary journal publishing the best educational research from across the globe. Abstract The metaverse is rapidly reshaping our understanding of education, yet identifying the public's beliefs, emotions and sentiments towards its adoption in education remains largely uncharted...

  27. Ethical Considerations in Adoption Research: Navigating Confidentiality

    The past several decades have seen a shift in attention in adoption research. While much of the early research was concerned with differences between adopted and nonadopted individuals and the effects of early adversity (e.g. Juffer & van IJzendoorn, 2005; Kumsta et al., 2015), there is currently a growing focus on the individual experiences and circumstances that shape the adjustment of ...

  28. RESEARCH ARTICLE Nutrition impacts of non-solid cooking fuel adoption

    1.Introduction. The latest statistics show that a significant number of children under five across the world suffer from malnutrition, especially those in developing countries (Liu et al. 2019; Qin et al. 2019).For example, FAO (2021) estimates that in 2020,149.2 million and 45.4 million under-five children were stunted and wasting, respectively. WHO (2021b) reports the prevalence of anemia in ...

  29. Adoption & Fostering: Sage Journals

    Adoption & Fostering is a quarterly peer reviewed journal which has been at the cutting edge of debate on childcare issues for over 50 years. It is the only UK journal dedicated to adoption and fostering issues, providing an international forum for a wide range of professionals: academics and practitioners in social work, psychology, law, medicine, education...

  30. Microsoft and LinkedIn release the 2024 Work Trend Index on the state

    Employees, overwhelmed and under duress, say AI saves time, boosts creativity and allows them to focus on their most important work. While 79% of leaders agree AI adoption is critical to remain competitive, 59% worry about quantifying the productivity gains of AI and 60% worry their company lacks a vision and plan to implement it.