thesis decision making

Think of yourself as a member of a jury, listening to a lawyer who is presenting an opening argument. You'll want to know very soon whether the lawyer believes the accused to be guilty or not guilty, and how the lawyer plans to convince you. Readers of academic essays are like jury members: before they have read too far, they want to know what the essay argues as well as how the writer plans to make the argument. After reading your thesis statement, the reader should think, "This essay is going to try to convince me of something. I'm not convinced yet, but I'm interested to see how I might be."

An effective thesis cannot be answered with a simple "yes" or "no." A thesis is not a topic; nor is it a fact; nor is it an opinion. "Reasons for the fall of communism" is a topic. "Communism collapsed in Eastern Europe" is a fact known by educated people. "The fall of communism is the best thing that ever happened in Europe" is an opinion. (Superlatives like "the best" almost always lead to trouble. It's impossible to weigh every "thing" that ever happened in Europe. And what about the fall of Hitler? Couldn't that be "the best thing"?)

A good thesis has two parts. It should tell what you plan to argue, and it should "telegraph" how you plan to argue—that is, what particular support for your claim is going where in your essay.

Steps in Constructing a Thesis

First, analyze your primary sources.  Look for tension, interest, ambiguity, controversy, and/or complication. Does the author contradict himself or herself? Is a point made and later reversed? What are the deeper implications of the author's argument? Figuring out the why to one or more of these questions, or to related questions, will put you on the path to developing a working thesis. (Without the why, you probably have only come up with an observation—that there are, for instance, many different metaphors in such-and-such a poem—which is not a thesis.)

Once you have a working thesis, write it down.  There is nothing as frustrating as hitting on a great idea for a thesis, then forgetting it when you lose concentration. And by writing down your thesis you will be forced to think of it clearly, logically, and concisely. You probably will not be able to write out a final-draft version of your thesis the first time you try, but you'll get yourself on the right track by writing down what you have.

Keep your thesis prominent in your introduction.  A good, standard place for your thesis statement is at the end of an introductory paragraph, especially in shorter (5-15 page) essays. Readers are used to finding theses there, so they automatically pay more attention when they read the last sentence of your introduction. Although this is not required in all academic essays, it is a good rule of thumb.

Anticipate the counterarguments.  Once you have a working thesis, you should think about what might be said against it. This will help you to refine your thesis, and it will also make you think of the arguments that you'll need to refute later on in your essay. (Every argument has a counterargument. If yours doesn't, then it's not an argument—it may be a fact, or an opinion, but it is not an argument.)

This statement is on its way to being a thesis. However, it is too easy to imagine possible counterarguments. For example, a political observer might believe that Dukakis lost because he suffered from a "soft-on-crime" image. If you complicate your thesis by anticipating the counterargument, you'll strengthen your argument, as shown in the sentence below.

Some Caveats and Some Examples

A thesis is never a question.  Readers of academic essays expect to have questions discussed, explored, or even answered. A question ("Why did communism collapse in Eastern Europe?") is not an argument, and without an argument, a thesis is dead in the water.

A thesis is never a list.  "For political, economic, social and cultural reasons, communism collapsed in Eastern Europe" does a good job of "telegraphing" the reader what to expect in the essay—a section about political reasons, a section about economic reasons, a section about social reasons, and a section about cultural reasons. However, political, economic, social and cultural reasons are pretty much the only possible reasons why communism could collapse. This sentence lacks tension and doesn't advance an argument. Everyone knows that politics, economics, and culture are important.

A thesis should never be vague, combative or confrontational.  An ineffective thesis would be, "Communism collapsed in Eastern Europe because communism is evil." This is hard to argue (evil from whose perspective? what does evil mean?) and it is likely to mark you as moralistic and judgmental rather than rational and thorough. It also may spark a defensive reaction from readers sympathetic to communism. If readers strongly disagree with you right off the bat, they may stop reading.

An effective thesis has a definable, arguable claim.  "While cultural forces contributed to the collapse of communism in Eastern Europe, the disintegration of economies played the key role in driving its decline" is an effective thesis sentence that "telegraphs," so that the reader expects the essay to have a section about cultural forces and another about the disintegration of economies. This thesis makes a definite, arguable claim: that the disintegration of economies played a more important role than cultural forces in defeating communism in Eastern Europe. The reader would react to this statement by thinking, "Perhaps what the author says is true, but I am not convinced. I want to read further to see how the author argues this claim."

A thesis should be as clear and specific as possible.  Avoid overused, general terms and abstractions. For example, "Communism collapsed in Eastern Europe because of the ruling elite's inability to address the economic concerns of the people" is more powerful than "Communism collapsed due to societal discontent."

Copyright 1999, Maxine Rodburg and The Tutors of the Writing Center at Harvard University

The effects of visualization on judgment and decision-making: a systematic literature review

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• Published: 25 August 2021
• Volume 73 , pages 167–214, ( 2023 )

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The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

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1 Introduction

A visualization is defined as a visual representation of information or concepts designed to effectively communicate the content or message (Padilla et al. 2018 ) and improve understanding in the audience (Alhadad 2018 ). This representation can manifest in a range of imagery, from quantitative graphs (Tang et al. 2014 ) to qualitative diagrams (Yildiz and Boehme 2017 ), to abstract visual metaphors (Eppler and Aeschimann 2009 ) or artistic imagery. Visualization design may also intend to promote a specific behavior in the audience (Correll and Gleicher 2014 ). The visualization of information is associated with effective communication in terms of clarity (Suwa and Tversky 2002 ), speed (Perdana et al. 2018 ), and the understanding of complex concepts (Wang et al. 2017 ). Research shows, for example, that visualized risk data require less cognitive effort in interpretation than textual alternatives and are therefore comprehended more easily (Smerecnik et al. 2010 ), and complex sentiment data visualized in a scatterplot improve the accuracy in law enforcement decisions compared to raw data (Cassenti et al. 2019 ).

Visual experiences are the dominant sensory input for cognitive reasoning in everyday life, business, and science (Gooding 2006 ). As Davis ( 1986 ) points out, image creation and perception are part of the “unique and quintessential competencies of homo sapiens sapiens”. Hence, the visualization of information is an integral research subject in the domains of cognitive psychology, education (Alfred and Kraemer 2017 ), management (Tang et al. 2014 ) including financial reporting, strategic management, and controlling, marketing (Hutchinson et al. 2010 ), as well as information science (Correll and Gleicher 2014 ).

Management researchers study visualizations from a business perspective. First, the field of financial reporting considers the effect of financial graphs on investor perception (Beattie and Jones 2008 ; Pennington and Tuttle 2009 ). Second, the potential consequences of visualizations on decision-making are examined in the area of managerial decision support, with a focus on judgments based on quantitative data such as financial decisions (Tang et al. 2014 ) and performance controlling (Ballard 2020 ). Finally, a small number of works investigate more complex decision-making based on qualitative, multivariate, and relational information (Platts and Tan 2004 ). Altogether visualizations fulfill a variety of functions, from focusing attention to sharing thoughts to identifying data structures, trends, and patterns (Platts and Tan 2004 ).

The vast majority of existing research in visualization, however, arises from the two domains of information science and cognitive psychology. Information science research on how to design visualizations for effective user cognition stretches back almost one century (Washburne 1927 ). While early research focuses on comparing tables and simple graphs, newer research on human–computer interfaces covers advanced data visualizations facilitated by computing power (Conati et al. 2014 ). For example, interactive visualization software enables users to manipulate data directly. While promising in terms of analytic capability, the potential for biases and overconfidence is suggested as a downside (Ajayi 2014 ). Equally, cognitive psychology research notes that visual information may be superior over verbal alternatives in certain cognitive tasks since they can be encoded in their original form, where spatial and relational data is preserved. Thereby, visual input is inherently richer than verbal and symbolic information, which is automatically reductionistic (Meyer 1991 ), but more suited for discrete information retrieval due to its simplicity (Vessey and Galletta 1991 ). However, the processes behind visual cognition remain largely unclear (Vila and Gomez 2016 ).

Despite the ubiquity of visualizations in research and practice, there is no comprehensive understanding of the potential and limits of information visualization for decision-making. Although at times converging, insights from research of different areas are seldom synthesized (Padilla et al. 2018 ), and there has been no effort for a systematic review or overarching framework (Zabukovec and Jaklič 2015 ). However, a synthesis of existing research is essential and timely due to three reasons. First, information visualization is ubiquitous both in the scientific and business community, yet there are conflicting findings on its powers and limits in support of judgment and decision-making. Second, cognitive psychology research provides several promising suggestions to explain observable effects of visualizations, yet these are rarely integrated into research in other domains, including strategic decision-making. Third, the barriers to using information visualization software have fallen to a minimum, making it available to a wide range of producers and users. This raises the issue of the validity of positive effects for various task and user configurations. The goal of this paper is therefore to provide an overview of the fragmented existing research on visualizations across the social and information sciences and generate insights and a timely research agenda for its applicability to strategic management decisions.

My study advances visualization research on three paths. First, I establish a framework to summarize the numerous effects and variable interactions surrounding the use of visualizations. Second, I conduct a systematic literature review across the social and information sciences and summarize and discuss this plethora of findings along with the aforementioned structure. Third, I utilize this work as a basis for identifying and debating gaps in existing research and resulting potential avenues for future research, with a focus on the area of strategic management decisions.

The structure of the article is as follows. The next chapter briefly describes the research field, followed by the methodology of my literature search. Next, I analyze the results of my search and discuss common insights. In the ensuing chapter, I develop an agenda for management research by building on particularly relevant ideas with conflicting or incomplete evidence. Finally, I conclude my review and discuss contributions and implications for practice.

2 Definition of the research field

2.1 definition of key terms.

Information visualizations support the exploration, judgment, and communication of ideas and messages (Yildiz and Boehme 2017 ). The term “graph” is often used as a synonym for information visualization in general (Meyer 1991 ) as well as describing quantitative data presentation specifically (Washburne 1927 ). As my review exhibits, these graphs constitute the prevalent form of information visualization. Common quantitative visualizations are line and bar charts, often showcasing a development over time and regularly used in financial reporting (Cardoso et al. 2018 ) and controlling (Hutchinson et al. 2010 ). In scientific literature, probabilistic charts such as scatterplots, boxplots, and probability distribution charts (Allen et al. 2014 ) frequently depict risk and uncertainty. More specialized charts include decision trees to depict conditional logic (Subramanian et al. 1992 ), radar charts to display complex multivariate information (Peebles 2008 ), or cluster charts and perceptual maps for marketing decision support (Cornelius et al. 2010 ).

Despite the breadth of existing visualization research, its application to strategic decisions is narrow and there is an abundance of research limited to elementary tasks and choices. To provide a clear distinction, I focus my search on decisions, judgments, and inferential reasoning as more advanced forms of cognitive processing. Decision-making can be broadly defined as choosing between several alternative courses of action (Padilla et al. 2018 ). On the other hand, reasoning and judgment refer to the evaluation of a set of alternatives (Reani et al. 2019 ), without actions necessarily being attached as for decision-making. Such efforts are cognitively demanding and complex when compared to more elementary tasks, such as a choice between options (Tuttle and Kershaw 1998 ), and include the rigorous evaluation of alternatives across a range of attributes, which is characteristic for strategic decisions (Bajracharya et al. 2014 ). For this reason, I include studies that examine the influence of visualizations on some form of decision or judgment outcome. Mason and Mitroff ( 1981 ) highlight that strategic decisions, in management and elsewhere, involve complex and ambiguous information environments. Information visualization may relate to decision quality in this context since one critical factor in the effectiveness of strategic decisions is the objective and comprehensive acquisition and analysis of relevant information to define and evaluate alternatives (Dean and Sharfman 1996 ).

2.2 Perspectives in literature

Visualization research exists within a range of domains in the social and information sciences, which reflects the diversity of the empirical application. I identify psychology (cognitive and educational), management (financial reporting, strategic management decisions, and controlling), marketing, and information science as the primary areas of research. This heterogeneity in terms of application area provides the first dimension in my literature review. Second, I classify existing studies along the type of variable interaction they primarily investigate. Based on the framework first introduced by DeSanctis ( 1984 ), I hereby differentiate four categories: Works principally focused on (1) the effects of visualizations on comprehension and decisions as dependent variables provide the basis of all research. This relationship is then investigated through: (2) User characteristics as moderators; (3) task and format characteristics as moderators; and (4) cognitive processing as mediator. An overview of this classification, including the prevalence of extant findings across domains, is given in Fig.  1 .

Visualization research structured by domain and variables primarily investigated

First, the investigation of visualization effects on decisions and judgments is established across all research areas mentioned, and primarily studies outcome variables such as decision accuracy (Sen and Boe 1991 ), speed (Falschlunger et al. 2015a ), and confidence (Correll and Gleicher 2014 ). While these studies contribute examples for graphs influencing observable decision effectiveness and efficiency across a range of contexts, they do not investigate moderating or mediating factors.

Second, psychology research pushes this investigation further towards including moderating effects of user characteristics , such as domain expertise and training (Hegarty 2013 ), and measures of cognitive ability such as numeracy (Honda et al. 2015 ) or literacy (Okan et al. 2018a ). The relevance of these moderating factors is validated both in studies focusing on cognition as well as experiments in educational research, for example by providing evidence that the quality of a judgment made based on a graph may depend more on the user than the format itself (Mayer and Gallini 1990 ).

Similarly, human–computer interface research spearheads further insights into moderating factors of task and format characteristics, such as task type (Porat et al. 2009 ), task complexity (Meyer et al. 1997 ), data structure (Meyer et al. 1999 ), and the graphical saliency of features (Fabrikant et al. 2010 ) through rigorous user testing. At the same time, Vessey ( 1991 ) developed the theory of cognitive fit as a concept bridging cognitive and information systems research, stating that positive effects of graphs depend on a fit between task type and format type, differentiating between symbolic and spatial archetypes.

Finally, cognitive psychology research aims at explaining the observable effects of visualization in terms of mediating cognitive mechanisms . Here, cognitive load theory provides the foundation, stating that an individual’s working memory capacity is limited, and performance in a task or judgment depends on the cognitive load they experience while assessing information. According to this logic, cognitive load that is too high damages performance (Chandler and Sweller 1991 ). Reducing cognitive load by providing visualizations in complex environments is therefore often stated as a key goal of graph design (Smerecnik et al. 2010 ).

Importantly, the boundaries between these variable categories are fluid. Many studies investigate more than one relationship and the inclusion of moderating variables has become common. Various application areas covering these interdependencies attest to the heterogeneous nature of visualization research. However, previous reviews highlight that insights are seldom shared across fields and call for the integration of findings into new studies (Padilla et al. 2018 ). In particular, strategic management research does not yet follow such a holistic approach.

3 Method of literature search

3.1 search design.

The methodological basis of this paper is a systematic literature search as a means to collect and evaluate the existing findings in a systematic, transparent, and reproducible way on the specified topic (Fisch and Block 2018 ) in order to produce a more complete and objective knowledge presentation than in traditional reviews (Clark et al. 2021 ). I conduct a keyword search on the online search engines EBSCOhost and ProQuest, limited to English-language works that have been peer-reviewed, in order to ensure the quality of the sources. Gusenbauer and Haddaway ( 2020 ) identify both search engines as principal academic search systems as they fulfill all essential performance requirements for systematic reviews. On EBSCOhost, I use the databases Business Source Premier , Education Research Complete , EconLit , APA PsycInfo , APA PsycArticles , and OpenDissertations to search for empirical works; on ProQuest, I use the databases British Periodicals , International Bibliography of the Social Sciences (IBSS) , Periodicals Archive Online , and Periodicals Index Online with a filter on articles to cover the social sciences comprehensively. The keyword used is the concatenated term “(visualization OR graph OR chart) AND (decision OR judgment OR reasoning)”, searched for in abstracts. Footnote 1 The terms were chosen as “visualization” is commonly used as a category name for visualized information (Brodlie et al. 2012 ), and the “graph” is the focus of traditional visualization research (Vessey 1991 ). The term “chart” is a synonym for both quantitative and qualitative graphs which has seen increasing use particularly in the 2000s (Semmler and Brewer 2002 ). The terms “judgment OR decision OR reasoning” were added to ensure that studies examining observable outcomes of visualization use, as opposed to cognitive processes such as comprehension only, were highlighted. After a review of the evolution of visualization research over time, I focus my search to articles published from the year 1990 in order to capture the recent advancements covering modern modes of information visualization. Footnote 2 This search results in 1658 articles combined, after removing duplicates 1505 articles remain.

Next, I review all article abstracts based on the three content criteria defined in the following. I include all articles rooted in the (1) social sciences or information sciences , where the focus of the study lies on (2) how a visualization per se or a variation within related visualizations affects a user's or audience's decision or judgment in a given task , and the topic is studied through (3) original empirical works. Most articles are excluded in this process and 116 studies remain due to the prevalence of graphs as auxiliary means, not the subject of research, in various domains, particularly in medical research. I repeat this exclusion process by reading the full texts of all articles and narrow down the selection further to 81 papers.

Building on this systematic search, I conducted a supplementary search through citation and reference tracking, as well as supplementary search engines, such as JSTOR (Gusenbauer and Haddaway 2020 ). Footnote 3 This includes gray literature such as conference proceedings or dissertations, which lie outside of traditional academic publishing. In addition, I limit the inclusion of gray literature to studies by researchers included in my systematic search and completed within the last 10 years in order to gather a comprehensive and up-to-date overview of the findings of working groups particularly relevant to visualization research. Thereby I identify 52 additional articles, resulting in a total of 133 articles included.

3.2 Limitations of search

Due to the plethora of existing literature mentioning the topic of visualization in various contexts and degrees of quality, I subject my search to well-defined limitations. First, I only include peer-reviewed articles in my systematic search. These are studies that have been thoroughly validated and represent the major theories within a field (Podsakoff et al. 2005 ). However, I incorporate gray literature of comparable quality as part of my additional exploratory search.

Second, I limit the search to information and social sciences to deliberately omit results from the broad areas of medicine and natural sciences. In these, various specific concepts are visualized as a means within research, yet not investigating the visualization itself. For the same reason, I only apply the search terms to article abstracts, since the terms “graph” and “chart” in particular will result in a high number of results when searched for in the full text, due to the common use of graphs in presenting concepts and results.

Third, I only include original empirical work in order to enable the synthesis and critical validation of empirical findings across research areas. At the same time, I acknowledge the existence of several highly relevant theoretical works, which inform my search design and structure while being excluded from the systematic literature search and analysis.

4.1 Overview of results

I identify a total of 133 articles, published between 1990 and 2020. Interest in visualization research gained initial momentum in the early 1990s (Fig.  2 ). More recently, the number of studies rises starting around 2008, with the continued publication of five to ten papers per year since and a visible peak in interest around 2014/15. A significant share of recent works stems from the information science literature, and the wealth of publications around 2014 coincides with the advent of mainstream interest in big data (Arunachalam et al. 2018 ), which is closely linked to information visualization for subsequent analysis and decision-making (Keahey 2013 ). In addition, a cluster of publications by one group of authors (Falschlunger et al. 2014 , 2015a , c, b) in the financial reporting domain enhances the observed peak in publications, which is therefore not indicative of a larger trend. Instead, the continued wealth of publications in the last decade shows the contemporary relevance of and interest in visualization research.

Articles included in systematic search by publication year and area of research

Next to the information sciences, the largest share of the studies identified originates in cognitive psychology research. Furthermore, management literature discusses visualization and graphs continuously throughout the last three decades, with notable peaks in interest around the year 2000 in the domain of annual reporting (Beattie and Jones 2000 , 2002a , b ; Arunachalam et al. 2002 ; Amer 2005 ; Xu 2005 ) and internal management reporting with classic bar and line graphs around the year 2015 (Falschlunger et al. 2014 , 2015a , c ; Tang et al. 2014 ; Hirsch et al. 2015 ; Zabukovec and Jaklič 2015 ). Consumer research in marketing constitutes a further domain regularly discussing visualizations and their effect on decisions and judgment (Symmank 2019 ), albeit to a smaller extent. This heterogeneity in research areas is reflected by the journals identified in my search, where the 133 articles spread across 83 different journals, complemented by ten studies from conference proceedings and three papers included in doctoral dissertations (Table 1 ). Apart from the articles in conference proceedings added through the supplementary exploratory search, the studies were published in journals with a SCIMAGO Journal Rank indicator ranging from 0.253 (Informing Science) to 8.916 (Journal of Consumer Research). All but four journals received Q1 and Q2 ratings, which equals the top half of all SCImago rated journals. The h-index ranges from 6 (Journal of Education for Library and Information Science) to 332 (PLoS ONE) (Scimago Lab 2021 ).

In the 133 articles identified, experiments are by far the most common method for data collection, with 113 (85%) of studies conducting a total of 182 controlled experiments with over 28,000 participants (Fig.  3 ). In addition, I find seven instances of archival research covering over 600 companies, six instances of surveys with almost 1000 participants in total, four quasi experiments, two natural experiments, and one field experiment to complete the picture.

Articles included in systematic search by methodology

Of the 182 experiments conducted, the majority works with students as subjects (125 or 69%). The largest remaining share investigates a sample of the general (online) population (32 or 18%) and only 13% study the effect of visualization with practitioners in their respective domain (24). In contrast, four out of the six surveys were conducted with practitioners that were addressed explicitly. Besides, one survey each was conducted with students and subjects from the general population.

Following the advice by Fisch and Block ( 2018 ), I categorize the results from literature in a concept-centric manner, based on the primary variable interaction investigated. I further distinguish by the four application domains and seven subdomains discussed and present a structured overview at the end of each subchapter. The independent variable in all cases is the use of a visual representation designed for a specific use case, either as opposed to non-visual representation methods such as verbal descriptions [e.g. Vessey and Galletta ( 1991 )], or traditional visualizations that the research aims to improve on [e.g. Dull and Tegarden ( 1999 )].

4.2 Effects of visualizations on decisions and judgments

4.2.1 judgment/decision accuracy.

The most common dependent variable investigated in visualization research is the accuracy of the subjects on a given comprehension, judgment, or decision task. Most studies are in psychology research, with positive effects dominating. In cognitive psychology, experiments show that well-designed visualizations can improve problem comprehension (Chandler and Sweller 1991 ; Huang and Eades 2005 ; Nadav-Greenberg et al. 2008 ; Okan et al. 2018b ). For example, Dong and Hayes ( 2012 ) show in their experiment with 22 practitioners that a decision support system visualizing uncertainty improves the identification and understanding of ambiguous decision situations. Likewise, visualizations improve decision (Pfaff et al. 2013 ) and judgment accuracy (Semmler and Brewer 2002 ; Tak et al. 2015 ; Wu et al. 2017 ) and improve the quality of inferences made from data (Sato et al. 2019 ). Findings in educational psychology support this claim. In teaching, visual materials improve understanding and retention (Dori and Belcher 2005 ; Brusilovsky et al. 2010 ; Binder et al. 2015 ; Chen et al. 2018 ) in students, and support the judgment accuracy of educators when analyzing learning progress quantitatively (Lefebre et al. 2008 ; Van Norman et al. 2013 ; Géryk 2017 ; Nelson et al. 2017 ). Furthermore, Yoon’s longitudinal classroom intervention (2011) using social network graphs enables students to make more reflected and information-driven strategic decisions. However, other studies arrive at more mixed or opposing findings. In their experiment, Rebotier et al. ( 2003 ) find that visual cues do not improve judgment accuracy over verbal cues in imagery processing. Other experiments even demonstrate verbal information to be superior over graphs in comprehension (Parrott et al. 2005 ) as well as judgment accuracy (Sanfey and Hastie 1998 ). Some graphs appear unsuitable for specific content, such as bar graphs depicting probabilities (Newman and Scholl 2012 ) and bubble charts encoding information in circle area size (Raidvee et al. 2020 ). In addition, more complex charts like boxplots, histograms (Lem et al. 2013 ), and tree charts (Bruckmaier et al. 2019 ) appear less effective for the accurate interpretation of statistical data in some experiments, presumably as they elicit errors and confusion in insufficiently trained students.

Studies in management and business research arrive at further, more pessimistic results. While Dull and Tegarden ( 1999 ) find in their experiment with students that three-dimensional visuals can improve the prediction accuracy in financial reporting contexts, and Yildiz and Boehme ( 2017 ) observe in their practitioner survey that a graphical model of a corporate security decision problem improves risk perception when compared to a textual description, most other studies present a less positive picture. Several studies do not find graphs superior over tables in financial judgments (Chan 2001 ; Tang et al. 2014 ; Volkov and Laing 2012 ), and in consumer research (Artacho-Ramírez et al. 2008 ). In financial reporting, a dedicated school of research investigates the effect of distorted graphs lowering financial judgment accuracy (Arunachalam et al. 2002 ; Beattie and Jones 2002a , b ; Amer 2005 ; Xu 2005 ; Pennington and Tuttle 2009 ; Falschlunger et al. 2014 ), irrespective of whether the distortion is intended by the designer. Chandar et al. ( 2012 ) elaborate on the positive effect of the introduction of graphs and statistics in performance management for AT&T in the 1920s, but more recent case study examples are rare.

By contrast, several experimental studies from human–computer interaction research largely contribute evidence for a positive effect. Targeted visual designs lead to higher judgment accuracy in specific tasks (Subramanian et al. 1992 ; Butavicius and Lee 2007 ; Van der Linden et al. 2014 ; Perdana et al. 2018 ) and improve decision-making (Peng et al. 2019 ). For example, probabilistic gradient plots and violin plots enable higher accuracy in statistical inference judgments in the online experiment by Correll and Gleicher ( 2014 ) than traditional bar charts. However, experiments by Sen and Boe ( 1991 ) and Hutchinson et al. ( 2010 ) equally lack a significant effect on data-based decision-making quality. Amer and Ravindran ( 2010 ) find a potential for visual illusions degrading judgment accuracy similar to results from financial reporting, and McBride and Caldara ( 2013 ) find that visuals lower accuracy in law enforcement judgments when compared to raw data presentation (Table 2 ).

4.2.2 Response time

The next most common outcome variable investigated in visualization research is response time , often referred to as efficiency. Across the board, experimenters observe that information visualization lowers response time in various judgment and decision tasks. In psychology, this includes decision-making in complex information environments (Sun et al. 2016 ; Géryk 2017 ). The opposite effect emerges from only one study, where Pfaff et al. ( 2013 ) find that a decision support system visualizing complex uncertainty information requires a longer time to use than one omitting this graphical information. In management research, Falschlunger et al. ( 2015a ) find that visually optimized financial reports can speed up judgment both for students and practitioners. Studies originating in information science validate this picture, observing that well-designed visualizations reduce response time in quantitative (Perdana et al. 2018 ) as well as geospatial judgment tasks (MacEachren 1992 ). Furthermore, McBride and Caldara ( 2013 ) observe that students in their experiments arrive at faster judgments when provided with a network graph as opposed to a table (Table 3 ).

4.2.3 Decision confidence

Next to these directly observable metrics, experimenters regularly elicit measures of decision confidence in visualization research based on subjects’ self-assessment. From a cognitive psychology perspective, Andrade ( 2011 ) finds that subjects display excessive confidence in estimates based on visualizations, which biases subsequent decision-making. On the other hand, Dong and Hayes ( 2012 ) show that a visual decision support system depicting uncertainty in engineering design leads to marginally lower decision confidence, compared to traditional methods omitting uncertainty information. In management research, Tang et al. ( 2014 ) present an increase in confidence in the context of financial decision-making, and Yildiz and Böhme (2017) find in their practitioner survey that an appealing visual increases decision confidence in a managerial setting without changing the actual decision outcome. Similarly, further experiments in information science provide evidence for increased confidence with a link to increased judgment accuracy (Butavicius and Lee 2007 ) or without (Sen and Boe 1991 ; Wesslen et al. 2019 ). In the context of uncertainty, Arshad et al. ( 2015 ) once again report novice subjects having lower confidence in the use of graphs with uncertainty visualized, however, this effect does not occur for practitioners (Table 4 ).

4.2.4 Prevalence of biases

Several studies investigate the prevalence of biases by searching for distinct patterns of deviations in judgment and decision accuracy with largely mixed results. Through a total of seven cognitive psychology experiments, Sun et al. ( 2010 , 2016 ) and Radley et al. ( 2018 ) find that varying scale proportions in graphs change the resulting decision-making since data points are evaluated in a cognitively biased manner based on their distance to other chart elements. Furthermore, Padilla et al. ( 2015 ) demonstrate that uncertainty is understood to a disparate extent when it is encoded through spatial glyphs, color, or brightness. In human–computer interaction research, experiments observe similar framing biases through salient graphical features (Diamond and Lerch 1992 ) such as color schemes (Klockow-McClain et al. 2020 ). Lawrence and O’Connor ( 1993 ) also show that graph scaling affects judgment and relate this to the anchoring heuristic. Finally, financial reporting research extensively dedicates its field of impression management on the observation that such biases are prevalent and possibly intended in annual report graphics, including through distorted graph axes (Falschlunger et al. 2015b ) and an intentional selection of information to visualize (Beattie and Jones 1992 , 2000 ; Dilla and Janvrin 2010 ; Jones 2011 ; Cho et al. 2012a , b ). Two further experiments compare the prevalence of cognitive biases with graphs compared to text directly and find no difference for the recency bias in financial reporting (Hellmann et al. 2017 ) as well as for other heuristics in data-based managerial decision-making (Hutchinson et al. 2010 ) (Table 5 ).

4.2.5 Attitude change and willingness to act

Observations on attitude change and the willingness to act on information constitute the final category of outcome variables found in visualization research. Cognitive psychology research observes an effect of visualizations on risk attitude, where salient graphs can either enhance risk aversion (Dambacher et al. 2016 ) or risk-seeking (Okan et al. 2018b ), depending on the information that is highlighted most saliently. Similarly, varied financial graphs change investors’ risk perception and subsequent investment recommendations (Diacon and Hasseldine 2007 ). In the area of performance management, the visualization of KPIs motivates managers’ intention to act on the information when compared to text (Ballard 2020 ). Consumer research investigates such phenomena commonly, where brand attitude and the intention to purchase a product represent specific cases of judgment and decision-making. Miniard et al. ( 1991 ) were among the first to show that different pictures can result in different attitudes, while Gkiouzepas and Hogg ( 2011 ) extend this investigation to visual metaphors. Finally, information science research provides further insights. King Jr et al. (1991) find that visualizations are more persuasive in attitude change than text, and Perdana et al. ( 2018 ) increase student subjects’ willingness to invest in their experimental setting through visualization software. On the other hand, Phillips et al. ( 2014 ) find their subjects to be less willing to seek out additional information in ambiguous decision settings (Table 6 ).

4.3 User characteristics as moderating variables

4.3.1 expertise and training.

Common moderating variables investigated both in psychological and information science research are the users’ expertise or training experience in a given domain. Experimenters widely encounter a positive impact of experience on the influence of visualizations on judgment accuracy and efficiency. In cognitive psychology, Hilton et al. ( 2017 ) find that graphs of statistical risk improve decision quality for more experienced practitioners alone. On the other hand, some results from educational psychology point towards the opposite effect of experience. Mayer and Gallini ( 1990 ) find in their student experiments that learners with higher pre-test performance benefit less from visual aids than learners on a lower level. In the information sciences, Conati et al. ( 2014 ) find in their testing of computer interfaces that experience with visualizations leads to a pronounced advantage in judgment accuracy. Training sessions (Raschke and Steinbart 2008 ) and experience through task repetition (Meyer 2000 ) enhance the positive effects of graphs (Table 7 ).

4.3.2 Cognitive ability

Another user characteristic regularly investigated in the social sciences is the measurement of cognitive ability . In psychology studies, Honda et al. ( 2015 ) and Cardoso et al. ( 2018 ) find that reflective ability determines in part how well subjects translate visualizations into accurate judgments. Visual working memory (Tintarev and Masthoff 2016 ) and numeracy (Honda et al. 2015 ) are further traits related to cognitive ability in dealing with visualizations and found to enhance the benefits of visualizations on judgment effectiveness and efficiency. The only study presenting contrary results consists of three experiments by Okan et al. ( 2018a ), where subjects with higher graph literacy are more prone to specific biases when shown bar graphs of health risk data, and thereby make less accurate judgments. On the other hand, experiments in financial reporting (Cardoso et al. 2018 ) confirm the positive effect of the reflective ability. Conati and Maclaren ( 2008 ) and Conati et al. ( 2014 ) extend this idea to perceptual speed in the area of consumer research (Table 8 ).

4.3.3 User preferences

Finally, experimenters investigate user preferences at times. In the adjacent field of musical education, for example, Korenman and Peynircioglu ( 2007 ) demonstrate that the visual presentation of learning material is only helpful to students with the respective learning style. In cognitive psychology, Daron et al. ( 2015 ) observe a variation in user preferences when presented with visualization options, however without a significant effect on decision performance. This result is replicated in an online survey on human–computer interaction by Lorenz et al. ( 2015 ). O’Keefe and Pitt ( 1991 ) operationalize cognitive style from the MBTI framework and find a weak association with the subjects’ reported preferences for text or specific chart types. However, no relation to actual judgment accuracy or efficiency is found (Table 9 ).

4.4 Task and format characteristics as moderating variables

4.4.1 task type.

One common task characteristic identified as a moderating variable is the task type , originally defined in the information sciences. In her seminal theoretical paper, Vessey ( 1991 ) identifies spatial and symbolic tasks as the two archetypes, which correspond to spatial and symbolic types of cognitive processing and spatial (graphical) and symbolic (textual/numerical) representations. She hypothesizes that visualizations improve judgment effectiveness where these three manifestations align, which she defines as cognitive fit and validates through experiments (Vessey and Galletta 1991 ), including in the sphere of multiattribute management decisions (Umanath and Vessey 1994 ). Further research in information science widely supports this moderating effect by comparing tables and standard quantitative graphs in judgment tasks of increasing complexity (Coll et al. 1994 ; Tuttle and Kershaw 1998 ; Speier 2006 ; Porat et al. 2009 ). On the other hand, experiments in managerial forecasting (Carey and White 1991 ) and financial reporting (Hirsch et al. 2015 ) present the effectiveness of graphical displays in spatial decisions, based on cognitive fit theory. Fischer et al. ( 2005 ) provide further evidence from the domain of cognitive psychology, showing that bar graphs support spatial-numerical judgments particularly well when the chart orientation equals the cognitive processing by following a left-to-right direction (Table 10 ).

4.4.2 Level of data structure

I identify two other task characteristics investigated in the literature, albeit infrequently. First, the level of data structure has been investigated only once in the information science domain. Meyer et al. ( 1999 ) find line charts superior over tables in judgment tasks when the underlying data is structured, with the opposite effect for unstructured data (Table 11 ).

4.4.3 Task complexity

Second, two further experiments observe task complexity as a moderating effect. Meyer et al. ( 1997 ) demonstrate that the speed advantage they find for tables over bar graphs in their computer interface tasks becomes more pronounced with increasing task complexity. However, the same effect does not occur for line graphs. On the other hand, Falschlunger et al. ( 2015c ) find task complexity to be the main factor in predicting task efficiency and effectiveness in handling financial reports but do not observe interaction effects with the visualization (Table 12 ).

4.4.4 Graphical saliency of relevant data

Finally, various studies investigate modifications in the graph format as a variable, with a focus on the graphical saliency of relevant data . This area of research is bridging the two domains of cognitive psychology and information science with widely overlapping results. For example, Verovszek et al. (2013) observe in their information science experiment that colored visualizations are less effective in supporting laypeople’s judgments on urban planning than simple black-and-white line drawings since colorful, irrelevant features distract from the core information. Van den Berg et al. ( 2007 ) identify color as a more powerful feature to highlight salient information in graphs than other variables, such as size. Spence et al. ( 1999 ) find that variations in brightness lead to faster response times in comparison tasks than variations in color. Breslow et al ( 2009 ) demonstrate that the moderating effect of the use of color on judgment speed depends on the task type, with multicolored visuals ideal for identification tasks and black-and-white brightness scales preferable for comparison tasks. Finally, MacEachran et al. (2012) find colorless suited to represent uncertainty when compared to features such as fuzziness or transparency in their surveys with students and practitioners.

Next to color, three-dimensional depth cues have received attention in research. Several psychology experiments find that three-dimensional depth cues irrelevant to the information visualized lower judgment accuracy (Zacks et al. 1998 ; Edwards et al. 2012 ) as well as speed (Fischer 2000 ). Negative effects occur equally for other irrelevant visual cues lowering the saliency of actually relevant information (Fischer 2000 ). Further studies show that increasing the saliency of relevant features can enhance the tendency to make compensatory choices (Dilla and Steinbart 2005 ) and shorten response time (Fabrikant et al. 2010 ), while visual clutter decreases judgment accuracy and boosts response times (Ognjanovic et al. 2019 ). Several other studies test the suitability of a specific set of graphs for unique judgment areas such as uncertainty simulation in urban development (Aerts et al. 2003 ), risk communication (Stone et al. 2017 ; Stone et al. 2018 ), and performance management (Peebles 2008 ) (Table 13 ).

4.5 Cognitive aspects as mediating variables

4.5.1 cognitive load.

Cognitive psychology research introduces the idea of cognitive processes mediating the influence of visualizations on judgment performance, with a focus on cognitive load . Jolicœur and Dell’Acqua ( 1999 ) show in their experiment that the perception of visualizations is subject to structural constraints in working memory capacity, and Allen et al. ( 2014 ) manipulate cognitive load as a dependent variable to demonstrate that judgment accuracy and speed using visualizations decrease under higher cognitive load. Subsequently, psychology experiments provide evidence that visualizations improve decision performance by reducing cognitive load as a mediating factor, operationalized and measured either through pupil size and dilation (Smerecnik et al. 2010 ; Toker and Conati 2017 ) or self-reported load (Cassenti et al. 2019 ). In management research, Ajayi ( 2014 ) investigates this relationship in the context of a proprietary visualization tool for financial data but finds no effect of the visualization component on cognitive load or judgment accuracy. Two further experiments in human–computer interface research operationalize cognitive load based on subjective reporting (Anderson et al. 2011 ) and performance in a secondary task (Block 2013 ) and demonstrate that cognitive load mediates the relationship between visualization use and judgment accuracy and speed, with some types of graphics better suited than others (Table 14 ).

4.5.2 Gazing behavior

Another concept frequently operationalized to represent working memory capacity is gazing behavior , which more recent experiments observe through the use of eye-tracking technology, pioneered by the information sciences. Reani et al. ( 2019 ) observe in their experiment with 49 students that gazing behavior is associated with judgment accuracy, where subjects that pay more attention to relevant visual areas deliver more accurate answers. Similarly, Lohse ( 1997 ) finds that in the more complex decision environment of a budget allocation simulation, decision accuracy is related to efficient gazing behavior and can be improved through the use of colors to reduce the time subjects spend looking at the chart legend. Psychology experiments validate that well-designed graphs enable subjects to focus their attention on relevant information and subsequently improve decision accuracy (Huestegge and Pötzsch 2018 ) and response time (Vila and Gomez 2016 ) (Table 15 ).

4.5.3 Attention

Another variable operationalized at times in eye-tracking experiments is attention, which is elicited through metrics such as the average gazing duration on a specific visual element (Pieters et al. 2010 ). In their cognitive psychology experiment, Smerecnik et al. ( 2010 ) observe that graphs attract more attention in risk communication compared to tables and text and are associated with more accurate judgments. Applying this idea to consumer research, Pieters et al. ( 2010 ) study the consumer’s attention towards visual advertisements and observe that visual complexity based on features such as decorative color can hurt attention, while well-structured complexity such as arrangements of relevant information enhances attention and the attitude toward the brand (Table 16 ).

4.5.4 Affect

Finally, some research emerges into the potential mediating role of affect . Harrison ( 2013 ) shows in her large-scale online experiment that affective priming can significantly influence judgment accuracy in tasks supported visually and that the graphs themselves can cause a change in affect valence. Similarly, Plass et al. ( 2014 ) demonstrate in their educational research that color and shape in visualizations can evoke positive affect and are associated with better student learning (Table 17 ).

5 Discussion

In this paper, I have presented a systematic and integrative review of the current state of research on the effect of information visualization in the social and information sciences. I structured and summarized the results of my systematic literature review along the type of variable interactions present in experimental research. In order to discuss and synthesize the variety of literature insights, I categorize them into three groups: Descriptions of the positive effects for visualizations within decision-making, elaborations on moderators of this potential, and insights into negative effects of misguided visualization use. Table 18 highlights this categorization of results by application domain.

5.1 Positive Effect 1: Information visualization improves decision accuracy and quality

Research findings overwhelmingly confirm the hypothesis that visualizations enable the user to comprehend information more effectively, subsequently improving performance in judgments and decisions. The reason behind this effect is most commonly attributed to cognitive mechanisms. Suwa and Tversky ( 2002 ) point out that based on cognitive load theory, less working memory is needed when visuals provide external representations of concepts, which one can easily refer back to and thereby need not keep in mind, leading to improved judgments. Allen et al. ( 2014 ) show in their experiment that under externally induced cognitive load, well-designed charts suffer less than cluttered ones. Furthermore, graphs enable a simpler gazing pattern than text, which can be used as an indicator of cognitive effort (Smerecnik et al. 2010 ). Based on the concept of cognitive load reduction, visualizations are effectively used in various application areas including management research (Falschlunger et al. 2014 ) and more specifically managerial decision-making (Yildiz and Boehme 2017 ), next to psychology and information sciences more broadly.

5.2 Positive Effect 2: Information visualization steers attention towards uncertainty

A large share of studies identified points towards the strength of visualizations in enhancing uncertainty and risk features in a data set. Beyond increasing the awareness of uncertainty (Dong and Hayes 2012 ), the question of whether visualizations can also improve the reasoning with probabilistic information is studied extensively. Various studies show that visualizations can reduce typical comprehension issues, resulting in the more accurate use of probabilities from a statistical perspective (Allen et al. 2014 ; Wu et al. 2017 ; Stone et al. 2018 ). Positive effects in risk understanding are evaluated particularly in the contexts of safety, such as food safety (Honda et al. 2015 ) and violence risk (Hilton et al. 2017 ). Studies investigating the cognitive processes more closely provide evidence that simpler charts indeed perform best (Edwards et al. 2012 ) since they can reduce cognitive load (Anderson et al. 2011 ) and ultimately improve the internal processing of probabilistic models (Tak et al. 2015 ). As Quattrone ( 2017 ) points out, ambiguity and uncertainty are inherent in managerial decision-making and should be embraced by information visualization, but research on this insight in management is scarce.

5.3 Positive Effect 3: Information Visualization Speeds Up Cognitive Processing

There is evidence that graphs lead to faster processing, learning, and decision-making (Block 2013 ), as judgment and decision efficiency are measured and operationalized as the response time in various experiments. Utilizing eye-tracking technology, Reani et al. ( 2019 ) point out that different types of graphs result in varying gazing patterns in users and hypothesize a link to the reasoning processes. Based on the principle of saliency, multiple studies show that graphs optimally designed to focus attention on the most relevant information lead to more efficient and thereby faster gazing (Falschlunger et al. 2014 , 2015a ), since more time can be spent focusing on highly relevant information (Vila and Gomez 2016 ). Much of this existing work stems from the area of management reporting, investigating quantitative financial data. Overall, the evidence for visual aids speeding up cognitive processing and decision-making appears robust and applicable to management research.

5.4 Moderator 1: The effects of visualization depend on cognitive fit within the decision context

Cognitive fit is a moderator in the effectiveness of visualizations that has been well validated across psychological, management, and information science. Introducing cognitive fit theory, Vessey ( 1991 ) explains many existing research findings in the graph versus table literature claiming that graphs are not (always) more effective, most notably by DeSanctis ( 1984 ). Cognitive fit theory is validated widely (Vessey and Galletta 1991 ; Carey and White 1991 ; Coll et al. 1994 ; Meyer et al. 1997 ; Meyer 2000 ; Porat et al. 2009 ; Perdana et al. 2019 ). Padilla (2018) recognizes that this well-documented effect arises because a cognitive mismatch between data, task, and approach (format) requires more working memory, which negatively affects cognitive processing effectiveness and efficiency. Though highly reliable, many studies investigate elementary processing tasks with limited external validity for more complex decision-making in practice. Umanath and Vessey ( 1994 ) and others (Tuttle and Kershaw 1998 ; Hirsch et al. 2015 ) extend the original cognitive fit theory and successfully apply it to multi-attribute judgments—though at a potential time-accuracy tradeoff. Finally, the idea of matching task and format complexity can be seen as an extension to cognitive fit theory, where graphs are only helpful when they represent as much data complexity as necessary to complete the respective task, but as little as possible (Pieters et al. 2010 ; Van der Linden et al. 2014 ; Géryk 2017 ).

5.5 Moderator 2: Differences within users can be more relevant than the visualization design

Task complexity in relation to user ability needs to be strictly controlled for as a moderator of positive visualization effects. Early studies including individual differences hypothesize that graph potential may be limited to users with a high level of ability (Subramanian et al. 1992 ). Other studies claim that the positive effects of visualizations may be more significant for (McIntire et al. 2014 ) or even limited to (Mayer and Gallini 1990 ) less-skilled individuals. However, these seemingly conflicting results can be explained by the idea that since graphs are effective by requiring less working memory than other formats, improvements are only visible where working memory capacity is limited and needed elsewhere (Lohse 1997 ).

Furthermore, the majority of studies including user factors emphasize the importance of training and expertise, as opposed to inherent ability. Various studies support the claim that experience significantly enhances the contribution of visuals (Porat et al. 2009 ; Edwards et al. 2012 ; Falschlunger et al. 2015a ; Ognjanovic et al. 2019 ), with some claiming that training constitutes a requirement (Géryk 2017 ; Hilton et al. 2017 ) or that users without training are subject to stronger biases (Raschke and Steinbart 2008 ). Consequently, the training factor needs to be closely monitored particularly for a novel or complex visualization. However, extensive training of users is frequently time-consuming and costly. Therefore, the imperative arises for interactive visualization interfaces to accommodate for varying user needs in demanding decision situations. Interactive data visualization software is shown to improve investment decisions (Perdana et al. 2018 ) and judgments by reducing cognitive load (Ajayi 2014 ), for example with flexible performance management dashboards that reduce information load while hosting a full set of KPIs (Yigitbasioglu and Velcu 2012 ). Contrary to much of the early research on static visualizations, the progress in interactivity studies has been driven by practice and case studies, with calls for science to follow suit (Marchak 1994 ; McInerny et al. 2014 ). Overall, I conclude that a match in ability and training with format complexity and novelty, respectively, is a significant determinant of the effectiveness of visualizations. However, there has been little to no empirical research on the subject in the domain of management.

5.6 Negative Effect 1: Visualizations May Not Always Be Helpful: Risk to Impair Decision Making by Misguiding Attention

Several studies, including in management research, argue that visualizations misguide attention even in the presence of cognitive and user fit. For example, Hutchinson (2010) finds graphs to be as exposed to cognitive biases as tables in data-based managerial decision-making. Similarly, other studies identify graphical representations as equally or less effective than verbal formats in financial reports (Volkov and Laing 2012 ), forecasting (Chan 2001 ), probabilistic comprehension (Parrott et al. 2005 ), evidence evaluation (Sanfey and Hastie 1998 ), and communication (Rose 1966 ). The common denominator in these studies is the suboptimal use of salient visual elements, leading to distraction. For example, overly realistic visualizations encompassing color and higher complexity (DeSanctis 1984 ), may lead to visual clutter that decreases performance (Alhadad 2018 ). As Padilla et al. ( 2018 ) argue, visualizations are powerful because they attract fast cognitive bottom-up processing. However, when this superficial processing is focused on irrelevant elements, decision quality can suffer. A well-studied example of this effect is the addition of superfluous three-dimensional cues to quantitative graphs, which lowers accuracy in using the graph (Zacks et al. 1998 ; Fischer 2000 ).

5.7 Negative Effect 2: Visualizations can increase decision-maker overconfidence

The most documented cognitive bias in my review is overconfidence, which can be aggravated by the use of visualizations (O’Keefe and Pitt 1991 ). Multiple studies demonstrate that graph use can increase decision confidence without enhancing decision quality to the same extent in the context of management and finance (Tang et al. 2014 ; Yildiz and Boehme 2017 ; Wesslen et al. 2019 ). This may result from the perception that visualizations show more information at once (Miettinen 2014 ), thereby seemingly requiring less search for additional information (Phillips et al. 2014 ). In particular, this can be the case when graphs appear to visually simplify a problem and the decision-maker fails to adjust his confidence to the underlying complexity (Sen and Boe 1991 ). There is some research with inconclusive results (Pfaff et al. 2013 ), showing no difference in confidence (Hirsch et al. 2015 ) or even lowered confidence (Dong and Hayes 2012 ; Arshad et al. 2015 ). However, the majority of these studies deal with uncertainty communication, which is inherently tied to a decrease in confidence (Watkins 2000 ). Overall, the evidence demonstrates that unless highlighting uncertainty, visual aids result in higher decision confidence. The case of overconfidence is particularly well established in the area of management controlling and financial reporting but understudied for strategic decisions.

6 Research agenda

In summary, there is ample evidence for the potential of information visualization to improve decision-making in terms of effectiveness and efficiency, yet my review highlights possible limitations and risks where its use is misguided or inappropriate. I argue that several of these are particularly critical for further research since there is little to no application to the domain of strategic management decisions, despite the ubiquity of visualizations to support these in practice. Based on the summary of my insights by application domain in Table 18, I identify five research gaps in the field of strategic management decisions.

First, there is conflicting evidence regarding the effect of information visualization on decision-making under uncertainty, and existing research is mostly limited to information science (Aerts et al. 2003 ). Depending on the context and design, visualization use can increase or reduce risk-taking (Dambacher et al. 2016 ) but has the potential to improve probabilistic reasoning in an objective manner (Allen et al. 2014 ). Given the importance of uncertainty as a defining factor of strategic management decisions (Quattrone 2017 ), the possibility of information visualizations to improve risk understanding in the management context deserves closer evaluation. For example, the framing bias is a well-documented phenomenon in strategic decision-making (Hodgkinson et al. 1999 ), leading to different subjective risk interpretations and subsequent decisions based on the presentation of information. Naturally, the question arises whether information visualization can mitigate this bias and which salient visual features are beneficial. I suggest exploring this question through experiments with strategic management decision vignettes.

Research Gap 1: How can information visualization mitigate the framing bias and improve risk understanding in strategic management decisions?

Second, my review has made clear that the effectiveness of information visualization depends in large parts on user characteristics such as expertise (Hilton et al. 2017 ), numeracy (Honda et al. 2015 ), and graph literacy (Okan et al. 2018b ), yet there exists no transfer of this insight towards individual managerial traits. At the same time, well-established concepts such as the Upper Echelons Theory (Hambrick 2007 ) highlight the relevance of CEO characteristics, both observable and psychological for strategic managerial choices and, subsequently, company performance. While some concepts such as experience may be transferrable from existing visualization research (Falschlunger et al. 2015c ) requiring validation only, others, such as group position or individual values, present opportunities to extend theory substantially. I suggest exploring this area through a dedicated analysis of relevant CEO characteristics and corresponding empirical research with practitioner subjects.

Research Gap 2: How do CEO characteristics influence the effectiveness of information visualization in strategic management decisions?

Third, while the prevalence of visualization use for impression management in financial reporting is well-established (Falschlunger et al. 2015b ), there is a complete lack of transfer of this phenomenon to the realm of strategic management decisions. As Whittington et al. ( 2016 ) highlight, strategy presentations can be seen as an effective tool for CEO impression management. Given the popularity of visualizations in this communication medium – both through quantitative charts and schematic diagrams (Zelazny 2001 ), the question arises to what degree impression management also takes place in this case, for example through the reporting bias (Beattie and Jones 2000 ). I suggest investigating this subject empirically, for example through archival studies.

Research Gap 3: To what extent does CEO impression management occur through visualization use in strategy presentations?

Fourth, while overconfidence in managerial decision-making is a commonly reported issue with significant efforts to develop corrective feedback as a remedy (Chen et al. 2015 ), there is little understanding of the role of information visualization in this matter. My review has demonstrated that visual aids often increase decision confidence as much as they improve the judgment itself (Yildiz and Boehme 2017 ) or even more (Sen and Boe 1991 ), but can also reduce confidence, particularly where uncertainty information is depicted (Dong and Hayes 2012 ). However, the latter effect was only studied for topics unrelated to management. Therefore, there is a complete lack of understanding of the effects of visualizations on managerial overconfidence, and I suggest exploring this research gap empirically with practitioners.

Research Gap 4: How do visual aids influence overconfidence in managerial decision-making?

Finally, a large share of cognitive psychology research discusses the effectiveness of visualization use through the reduction of cognitive load, yet they usually start off with low-load contexts, which is the opposite of high-stress managerial decision-making (Laamanen et al. 2018 ). Allen et al. ( 2014 ) find evidence that the effectiveness of distinct graph types changes with the level of externally induced cognitive load, raising the question to what extent previous insights on helpful visual aids are applicable to managerial decisions in a high-stakes environment filled with distractions and parallel issues requiring attention. Therefore, I suggest studying visualization use in experimental environments with varying levels of cognitive load as the independent variable, ideally with management practitioners and a realistic strategic task setting.

Research Gap 5: How does cognitive load influence the effectiveness of information visualization in strategic management decisions?

7 Conclusion

Information visualization has become ubiquitous in our daily professional and private lives, even more so with the advent of accessible and powerful computer graphics. However, the impact that visualizations have on human cognition and ultimately decisions stills remains unclear to a large extent. While the prevalence of visualization research across a plethora of application domains shows its pertinence, the decentralized approach has led to a scattered and unstructured field of theories and empirical evidence. My literature review thus sought to provide a far-reaching overview of this work and a detailed research agenda. As a result, three contributions arise from my review.

First, I provide an overarching structure to summarize the range of effects and interacting variables that can be found surrounding visualization research. This includes a wide set of dependent variables ranging from decision quality and speed to confidence and attitudes, as well as complex moderating and mediating effects that are crucial to understanding the overall power of visualizations. This precise framework is paramount to a holistic and comprehensive review of the scattered existing literature.

Second, to the best of my knowledge, my systematic literature review is the first on visualizations spanning the whole of social and information sciences simultaneously. While some previous reviews such as the one by Yigitbasioglu and Velcu ( 2012 ) utilize a multidisciplinary approach, they usually define the visualization type investigated more narrowly, for example by focusing on dashboards only. I believe that my integrative overview provides a valid contribution to the ongoing work to synthesize the mixed results in visualization research.

Third, I demonstrate that despite the plethora of evidence at first sight, visualization research is far from complete due to its multitude of moderating variables and at times conflicting results. Building on my systematic review of existing literature, I specify an agenda of potential research directions for future studies to follow in order to advance our understanding of the cognitive implications of visualizations in the context of managerial decision making in particular.

This paper also has direct implications for management practice. As Zhang ( 1998 ) points out, managerial decision-making is particularly well-positioned to profit from good visualizations since it often utilizes unstructured, large sets of information that are computer-centered, dynamic, and need to be interpreted constantly under time pressure. However, the interaction of visualization use with various factors should not be underestimated in the design of computer graphics for decision support. The high validity of the cognitive fit theory and the contingency on user characteristics found in the literature demonstrates that the designer should spend extensive time on clarifying for whom and what the visualization is intended. Furthermore, the potential for overconfidence and automatic processing based on visualized information may result in decision-makers skipping on more elaborate thought, which may be desirable in some, but certainly not all situations.

Availability of data and material

Not applicable.

Code availability

Thanks to the anonymous reviewer for encouraging me to extend my keyword search.

Thanks to the anonymous reviewer for this valuable impulse.

Thanks to the anonymous reviewer for pointing me towards additional, highly relevant articles.

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Eberhard, K. The effects of visualization on judgment and decision-making: a systematic literature review. Manag Rev Q 73 , 167–214 (2023). https://doi.org/10.1007/s11301-021-00235-8

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Received : 26 October 2020

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Published : 25 August 2021

Issue Date : February 2023

DOI : https://doi.org/10.1007/s11301-021-00235-8

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Developing a Thesis Statement

Many papers you write require developing a thesis statement. In this section you’ll learn what a thesis statement is and how to write one.

Keep in mind that not all papers require thesis statements . If in doubt, please consult your instructor for assistance.

What is a thesis statement?

A thesis statement . . .

• Makes an argumentative assertion about a topic; it states the conclusions that you have reached about your topic.
• Makes a promise to the reader about the scope, purpose, and direction of your paper.
• Is focused and specific enough to be “proven” within the boundaries of your paper.
• Is generally located near the end of the introduction ; sometimes, in a long paper, the thesis will be expressed in several sentences or in an entire paragraph.
• Identifies the relationships between the pieces of evidence that you are using to support your argument.

Not all papers require thesis statements! Ask your instructor if you’re in doubt whether you need one.

Identify a topic

Your topic is the subject about which you will write. Your assignment may suggest several ways of looking at a topic; or it may name a fairly general concept that you will explore or analyze in your paper.

Consider what your assignment asks you to do

Inform yourself about your topic, focus on one aspect of your topic, ask yourself whether your topic is worthy of your efforts, generate a topic from an assignment.

Below are some possible topics based on sample assignments.

Sample assignment 1

Analyze Spain’s neutrality in World War II.

Identified topic

Franco’s role in the diplomatic relationships between the Allies and the Axis

This topic avoids generalities such as “Spain” and “World War II,” addressing instead on Franco’s role (a specific aspect of “Spain”) and the diplomatic relations between the Allies and Axis (a specific aspect of World War II).

Sample assignment 2

Analyze one of Homer’s epic similes in the Iliad.

The relationship between the portrayal of warfare and the epic simile about Simoisius at 4.547-64.

This topic focuses on a single simile and relates it to a single aspect of the Iliad ( warfare being a major theme in that work).

Developing a Thesis Statement–Additional information

Your assignment may suggest several ways of looking at a topic, or it may name a fairly general concept that you will explore or analyze in your paper. You’ll want to read your assignment carefully, looking for key terms that you can use to focus your topic.

Sample assignment: Analyze Spain’s neutrality in World War II Key terms: analyze, Spain’s neutrality, World War II

After you’ve identified the key words in your topic, the next step is to read about them in several sources, or generate as much information as possible through an analysis of your topic. Obviously, the more material or knowledge you have, the more possibilities will be available for a strong argument. For the sample assignment above, you’ll want to look at books and articles on World War II in general, and Spain’s neutrality in particular.

As you consider your options, you must decide to focus on one aspect of your topic. This means that you cannot include everything you’ve learned about your topic, nor should you go off in several directions. If you end up covering too many different aspects of a topic, your paper will sprawl and be unconvincing in its argument, and it most likely will not fulfull the assignment requirements.

For the sample assignment above, both Spain’s neutrality and World War II are topics far too broad to explore in a paper. You may instead decide to focus on Franco’s role in the diplomatic relationships between the Allies and the Axis , which narrows down what aspects of Spain’s neutrality and World War II you want to discuss, as well as establishes a specific link between those two aspects.

Before you go too far, however, ask yourself whether your topic is worthy of your efforts. Try to avoid topics that already have too much written about them (i.e., “eating disorders and body image among adolescent women”) or that simply are not important (i.e. “why I like ice cream”). These topics may lead to a thesis that is either dry fact or a weird claim that cannot be supported. A good thesis falls somewhere between the two extremes. To arrive at this point, ask yourself what is new, interesting, contestable, or controversial about your topic.

As you work on your thesis, remember to keep the rest of your paper in mind at all times . Sometimes your thesis needs to evolve as you develop new insights, find new evidence, or take a different approach to your topic.

Derive a main point from topic

Once you have a topic, you will have to decide what the main point of your paper will be. This point, the “controlling idea,” becomes the core of your argument (thesis statement) and it is the unifying idea to which you will relate all your sub-theses. You can then turn this “controlling idea” into a purpose statement about what you intend to do in your paper.

Look for patterns in your evidence

Compose a purpose statement.

Consult the examples below for suggestions on how to look for patterns in your evidence and construct a purpose statement.

• Franco first tried to negotiate with the Axis
• Franco turned to the Allies when he couldn’t get some concessions that he wanted from the Axis

Possible conclusion:

Spain’s neutrality in WWII occurred for an entirely personal reason: Franco’s desire to preserve his own (and Spain’s) power.

Purpose statement

This paper will analyze Franco’s diplomacy during World War II to see how it contributed to Spain’s neutrality.

• The simile compares Simoisius to a tree, which is a peaceful, natural image.
• The tree in the simile is chopped down to make wheels for a chariot, which is an object used in warfare.

At first, the simile seems to take the reader away from the world of warfare, but we end up back in that world by the end.

This paper will analyze the way the simile about Simoisius at 4.547-64 moves in and out of the world of warfare.

Derive purpose statement from topic

To find out what your “controlling idea” is, you have to examine and evaluate your evidence . As you consider your evidence, you may notice patterns emerging, data repeated in more than one source, or facts that favor one view more than another. These patterns or data may then lead you to some conclusions about your topic and suggest that you can successfully argue for one idea better than another.

For instance, you might find out that Franco first tried to negotiate with the Axis, but when he couldn’t get some concessions that he wanted from them, he turned to the Allies. As you read more about Franco’s decisions, you may conclude that Spain’s neutrality in WWII occurred for an entirely personal reason: his desire to preserve his own (and Spain’s) power. Based on this conclusion, you can then write a trial thesis statement to help you decide what material belongs in your paper.

Sometimes you won’t be able to find a focus or identify your “spin” or specific argument immediately. Like some writers, you might begin with a purpose statement just to get yourself going. A purpose statement is one or more sentences that announce your topic and indicate the structure of the paper but do not state the conclusions you have drawn . Thus, you might begin with something like this:

• This paper will look at modern language to see if it reflects male dominance or female oppression.
• I plan to analyze anger and derision in offensive language to see if they represent a challenge of society’s authority.

At some point, you can turn a purpose statement into a thesis statement. As you think and write about your topic, you can restrict, clarify, and refine your argument, crafting your thesis statement to reflect your thinking.

As you work on your thesis, remember to keep the rest of your paper in mind at all times. Sometimes your thesis needs to evolve as you develop new insights, find new evidence, or take a different approach to your topic.

Compose a draft thesis statement

If you are writing a paper that will have an argumentative thesis and are having trouble getting started, the techniques in the table below may help you develop a temporary or “working” thesis statement.

Begin with a purpose statement that you will later turn into a thesis statement.

Assignment: Discuss the history of the Reform Party and explain its influence on the 1990 presidential and Congressional election.

Purpose Statement: This paper briefly sketches the history of the grassroots, conservative, Perot-led Reform Party and analyzes how it influenced the economic and social ideologies of the two mainstream parties.

Question-to-Assertion

If your assignment asks a specific question(s), turn the question(s) into an assertion and give reasons why it is true or reasons for your opinion.

Assignment : What do Aylmer and Rappaccini have to be proud of? Why aren’t they satisfied with these things? How does pride, as demonstrated in “The Birthmark” and “Rappaccini’s Daughter,” lead to unexpected problems?

Beginning thesis statement: Alymer and Rappaccinni are proud of their great knowledge; however, they are also very greedy and are driven to use their knowledge to alter some aspect of nature as a test of their ability. Evil results when they try to “play God.”

Write a sentence that summarizes the main idea of the essay you plan to write.

Main idea: The reason some toys succeed in the market is that they appeal to the consumers’ sense of the ridiculous and their basic desire to laugh at themselves.

Make a list of the ideas that you want to include; consider the ideas and try to group them.

• nature = peaceful
• war matériel = violent (competes with 1?)
• need for time and space to mourn the dead
• war is inescapable (competes with 3?)

Use a formula to arrive at a working thesis statement (you will revise this later).

• although most readers of _______ have argued that _______, closer examination shows that _______.
• _______ uses _______ and _____ to prove that ________.
• phenomenon x is a result of the combination of __________, __________, and _________.

What to keep in mind as you draft an initial thesis statement

Beginning statements obtained through the methods illustrated above can serve as a framework for planning or drafting your paper, but remember they’re not yet the specific, argumentative thesis you want for the final version of your paper. In fact, in its first stages, a thesis statement usually is ill-formed or rough and serves only as a planning tool.

As you write, you may discover evidence that does not fit your temporary or “working” thesis. Or you may reach deeper insights about your topic as you do more research, and you will find that your thesis statement has to be more complicated to match the evidence that you want to use.

You must be willing to reject or omit some evidence in order to keep your paper cohesive and your reader focused. Or you may have to revise your thesis to match the evidence and insights that you want to discuss. Read your draft carefully, noting the conclusions you have drawn and the major ideas which support or prove those conclusions. These will be the elements of your final thesis statement.

Sometimes you will not be able to identify these elements in your early drafts, but as you consider how your argument is developing and how your evidence supports your main idea, ask yourself, “ What is the main point that I want to prove/discuss? ” and “ How will I convince the reader that this is true? ” When you can answer these questions, then you can begin to refine the thesis statement.

Refine and polish the thesis statement

To get to your final thesis, you’ll need to refine your draft thesis so that it’s specific and arguable.

• Ask if your draft thesis addresses the assignment
• Question each part of your draft thesis
• Clarify vague phrases and assertions
• Investigate alternatives to your draft thesis

Consult the example below for suggestions on how to refine your draft thesis statement.

Sample Assignment

Choose an activity and define it as a symbol of American culture. Your essay should cause the reader to think critically about the society which produces and enjoys that activity.

• Ask The phenomenon of drive-in facilities is an interesting symbol of american culture, and these facilities demonstrate significant characteristics of our society.This statement does not fulfill the assignment because it does not require the reader to think critically about society.

Drive-ins are an interesting symbol of American culture because they represent Americans’ significant creativity and business ingenuity.

Among the types of drive-in facilities familiar during the twentieth century, drive-in movie theaters best represent American creativity, not merely because they were the forerunner of later drive-ins and drive-throughs, but because of their impact on our culture: they changed our relationship to the automobile, changed the way people experienced movies, and changed movie-going into a family activity.

While drive-in facilities such as those at fast-food establishments, banks, pharmacies, and dry cleaners symbolize America’s economic ingenuity, they also have affected our personal standards.

While drive-in facilities such as those at fast- food restaurants, banks, pharmacies, and dry cleaners symbolize (1) Americans’ business ingenuity, they also have contributed (2) to an increasing homogenization of our culture, (3) a willingness to depersonalize relationships with others, and (4) a tendency to sacrifice quality for convenience.

This statement is now specific and fulfills all parts of the assignment. This version, like any good thesis, is not self-evident; its points, 1-4, will have to be proven with evidence in the body of the paper. The numbers in this statement indicate the order in which the points will be presented. Depending on the length of the paper, there could be one paragraph for each numbered item or there could be blocks of paragraph for even pages for each one.

Complete the final thesis statement

The bottom line.

As you move through the process of crafting a thesis, you’ll need to remember four things:

• Context matters! Think about your course materials and lectures. Try to relate your thesis to the ideas your instructor is discussing.
• As you go through the process described in this section, always keep your assignment in mind . You will be more successful when your thesis (and paper) responds to the assignment than if it argues a semi-related idea.
• Your thesis statement should be precise, focused, and contestable ; it should predict the sub-theses or blocks of information that you will use to prove your argument.
• Make sure that you keep the rest of your paper in mind at all times. Change your thesis as your paper evolves, because you do not want your thesis to promise more than your paper actually delivers.

In the beginning, the thesis statement was a tool to help you sharpen your focus, limit material and establish the paper’s purpose. When your paper is finished, however, the thesis statement becomes a tool for your reader. It tells the reader what you have learned about your topic and what evidence led you to your conclusion. It keeps the reader on track–well able to understand and appreciate your argument.

Writing Process and Structure

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Getting Started with Your Paper

Interpreting Writing Assignments from Your Courses

Generating Ideas for

Creating an Argument

Thesis vs. Purpose Statements

Architecture of Arguments

Working with Sources

Quoting and Paraphrasing Sources

Using Literary Quotations

Citing Sources in Your Paper

Drafting Your Paper

Generating Ideas for Your Paper

Introductions

Paragraphing

Developing Strategic Transitions

Conclusions

Revising Your Paper

Peer Reviews

Reverse Outlines

Revising an Argumentative Paper

Revision Strategies for Longer Projects

Finishing Your Paper

Twelve Common Errors: An Editing Checklist

How to Proofread your Paper

Writing Collaboratively

Collaborative and Group Writing

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A framework of evidence-based decision-making in health system management: a best-fit framework synthesis

Tahereh shafaghat.

1 School of Management and Medical Informatics, Health Human Recourses Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

2 Department of Health Care Management, School of Public Health, Health Policy and Management Research Center, Shahid Saoughi University of Medical Sciences, Yazd, Iran

Peivand Bastani

3 Faculty of Health and Behavioral Sciences, School of Dentistry, University of Queensland, QLD 4072 Brisbane, Australia

Mohammad Hasan Imani Nasab

4 Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran

Mohammad Amin Bahrami

Mahsa roozrokh arshadi montazer.

5 Student Research Committee, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran

Mohammad Kazem Rahimi Zarchi

Sisira edirippulige.

6 Faculty of Medicine, Center for Health Services Research, The University of Queensland, Brisbane, Australia

Associated Data

All data in a form of data extraction tables are available from the corresponding author on a reasonable request.

Scientific evidence is the basis for improving public health; decision-making without sufficient attention to evidence may lead to unpleasant consequences. Despite efforts to create comprehensive guidelines and models for evidence-based decision-making (EBDM), there isn`t any to make the best decisions concerning scarce resources and unlimited needs . The present study aimed to develop a comprehensive applied framework for EBDM.

This was a Best-Fit Framework (BFF) synthesis conducted in 2020. A comprehensive systematic review was done via six main databases including PUBMED, Scopus, Web of Science, Science Direct, EMBASE, and ProQuest using related keywords. After the evidence quality appraisal, data were extracted and analyzed via thematic analysis. Results of the thematic analysis and the concepts generated by the research team were then synthesized to achieve the best-fit framework applying Carroll et al. (2013) approach.

Four thousand six hundred thirteen studies were retrieved, and due to the full-text screening of the studies, 17 final articles were selected for extracting the components and steps of EBDM in Health System Management (HSM). After collecting, synthesizing, and categorizing key information, the framework of EBDM in HSM was developed in the form of four general scopes. These comprised inquiring, inspecting, implementing, and integrating, which included 10 main steps and 47 sub-steps.

Conclusions

The present framework provided a comprehensive guideline that can be well adapted for implementing EBDM in health systems and related organizations especially in underdeveloped and developing countries where there is usually a lag in updating and applying evidence in their decision-making process. In addition, this framework by providing a complete, well-detailed, and the sequential process can be tested in the organizational decision-making process by developed countries to improve their EBDM cycle.

Globally, there is a growing interest in using the research evidence in public health policy-making [ 1 , 2 ]. Public health systems are diverse and complex, and health policymakers face many challenges in developing and implementing policies and programs that are required to be efficient [ 1 , 3 ]. The use of scientific evidence is considered to be an effective approach in the decision-making process [ 3 – 5 ]. Due to the lack of sufficient resources, evidence-based decision-making ( EBDM) is regarded as a way to optimize costs and prevent wastes [ 6 ]. At the same time, the direct consequence of ignoring evidence is poorer health for the community [ 7 ].

Evidence suggests that health systems often fail to exploit research evidence properly, leading to inefficiencies, death or reduced quality of citizens’ lives, and a decline in productivity [ 8 ]. Decision-making in the health sector without sufficient attention to evidence may lead to a lack of effectiveness, efficiency, and fairness in health systems [ 9 ]. Instead, the advantages of EBDM include adopting cost-effective interventions, making optimal use of limited resources, increasing customer satisfaction, minimizing harm to individuals and society, achieving better health outcomes for individuals and society [ 10 , 11 ], as well as increasing the effectiveness and efficiency of public health programs [ 12 ].

Using the evidence in health systems’ policymaking is a considerable challenging issue that many developed and developing countries are facing nowadays. This is particularly important in the latter, where their health systems are in a rapid transition [ 13 ]. For instance, although in 2012, a study in European Union countries showed that health policymakers rarely had necessary structures, processes, and tools to exploit research evidence in the policy cycle [ 14 ], the condition can be worse among the developing and the underdeveloped ones. For example, evidence-based policy-making in developing countries like those located in the Middle East can have more significant impacts [ 15 , 16 ]. In such countries resources are generally scarce, so the policymakers' awareness of research evidence becomes more important [ 17 ]. In general, low and middle-income countries have fewer resources to deal with health issues and need quality evidence for efficient use of these resources [ 7 ].

Since the use of EBDM is fraught with the dilemma of most pressing needs and having the least capacity for implementation especially in developing countries [ 16 ], efforts have been made to create more comprehensive guidelines for EBDM in healthcare settings, in recent years [ 18 ]. Stakeholders are significantly interested in supporting evidence-based projects that can quickly prioritize funding allocated to health sectors to ensure the effective use of their financial resources [ 19 – 21 ]. However, it is unlikely that the implementation of EBDM in Health System Management (HSM) will follow the evidence-based medicine model [ 10 , 22 ]. On the other hand, the capacity of organizations to facilitate evidence utilization is complex and not well understood [ 22 ], and the EBDM process is not usually institutionalized within the organizational processes [ 10 ]. A study in 2005 found that few organizations support the use of research evidence in health-related decisions, globally [ 23 ]. Weis et al. (2012) also reported there is insufficient information on EBDM in local health sectors [ 12 ]. In general, it can be emphasized that relatively few organizations hold themselves accountable for using research evidence in developing health policies [ 24 ]. To the best of our knowledge, there isn`t any comprehensive global and practical model developed for EBDM in health systems/organizations management. Accordingly, the present study aimed to develop a comprehensive framework for EBDM in health system management. It can shed the light on policymakers to access a detailed practical model and enable them to apply the model in actual conditions.

This was a Best Fit Framework (BFF) synthesis conducted in 2020 to develop a comprehensive framework for EBDM in HSM. Such a framework synthesis is achieved as a combination of the relevant framework, theory, or conceptual models and particularly is applied for developing a priori framework based on deductive reasoning [ 25 ]. The BFF approach is appropriate to create conceptual models to describe or express the decisions and behaviors of individuals and groups in a particular domain. This is distinct from other methods of evidence synthesis because it employs a systematic approach to create an initial framework for synthesis based on existing frameworks, models, or theories [ 25 ] for identifying and adapting theories systematically with the rapid synthesis of evidence [ 25 , 26 ]. The initial framework can be derived from a relatively well-known model in the target field, or be formed by the integration of several existing models. The initial framework is then reduced to its key components that have shaped its concepts [ 25 ]. Indeed, the initial framework considers as the basis and it can be rebuilt, extended, or reduced based on its dimensions [ 26 ]. New concepts also emerge based on the researchers' interpretation of the evidence and ongoing comparisons of these concepts across studies [ 25 ]. This approach of synthesis possesses both positivist and interpretative perspectives; it provides the simultaneous use of the well-known strengths of both framework and evidence synthesis [ 27 ].

In order to achieve this aim the following methodological steps were conducted as follows:

Searching and selection of studies

In this step, we aimed to look for the relevant models and frameworks related to evidence-based decision-making in health systems management. The main research question was “what is the best framework for EBDM in health systems?” after defining the research question, the researchers searched for published studies on EBDM in HSM in different scientific databases with relevant keywords and constraints as inclusion and exclusion criteria from 01.01.2000 to 12.31.2020 (Table ​ (Table1 1 ).

Search strategy for the review

Inclusion and exclusion criteria

Inclusion criteria were determined as the studies that identify the components or develop a model or framework of EBDM in health organization in the form of original or review articles or dissertations, which were published in English and had a full text. The studies like book reviews, opinion articles, and commentaries that lacked a specific framework for conducting our review were excluded. During the search phase of the study, we attempted as much as possible to access studies that were not included in the search process or gray literature by reviewing the references lists of the retrieved studies or by contacting the authors of the articles or experts and querying them, as well as manually searching the related sites (Fig.  1 ).

The PRISMA flowchart for selection of the studies in scoping review

Quality appraisal

The quality of the obtained studies was investigated using three tools for assessing the quality of various types of studies considering types and methods of the final include studies in systematic review. These tools were including Critical Appraisal Skills Program (CASP) for assessing the quality of qualitative researches [ 28 ], Scale for the Assessment of Narrative Review Articles (SANRA) [ 29 ], and The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers [ 30 ] (Table 3- Appendix ).

Data extraction

After searching the studies from all databases and removing duplicates, the studies were independently reviewed and screened by two members (TS and MRAM) of the research team in three phases by the title, abstract, and then the full text of the articles. At each stage of the study, the final decision to enter the study to the next stage was based on agreement and, in case of disagreement, the opinion of the third person from the research team was asked (PB). Mendeley reference manager software was used to systematically search and screen relevant studies. The data from the included studies were extracted based on the study questions and accordingly, a form of the studies’ profile including the author's name, publication year, country, study title, type of study, and its conditions were prepared in Microsoft Excel software (Table 4- Appendix ).

Synthesis and the conceptual model

In this step, a thematic analysis approach was applied to extract and analyze the data. For this purpose, first, the texts of the selected studies were read several times, and the initial qualitative codes or thematic concepts, according to the determined keywords and based on the research question, were found and labeled. Then these initial thematic codes were reviewed to achieve the final codes and they were integrated and categorized to achieve the final main themes and sub-themes, eventually. The main and the sub-themes are representative of the main and sub-steps of EBDM. At the last stage of the synthesis, the thematic analysis was finalized with 8 main themes and all the main and the sub-themes were tabulated (Table 5- Appendix ).

Creation of a new conceptual framework

For BFF synthesis in the present study, we compared the existing models and tried to find a model that fits the best. Three related models that appeared to be relatively well-suited to the purpose of this study to provide a complete, comprehensive, and practical EBDM model in HSM were found. According to the BFF instruction in Carroll et al. (2013) study [ 25 ], we decided to use all three models as the basis for the best fit because any of those models were not complete enough and we could give no one an advantage over others. Consequently, the initial model or the BFF basis was formed and the related thematic codes were classified according to the category of this basis as the main themes/steps of EBDM in HSM (Table 5- Appendix ). Then, the additional founded thematic codes were added and incorporated to this basis as the other main steps and the sub-steps of the EBDM in HSM according to the research team and some details in the form of sub-steps were added by the research team to complete the synthesized framework. Eventually, a comprehensive practical framework consisting of 10 main steps and 47 sub-steps was created with the potentiality of applying and implementing EDBM in HSM that we categorized them into four main phases (Table 6- Appendix ).

Testing the synthesis: comparison with the a priori models, dissonance and sensitivity

In order to assess the differences between the priori framework and the new conceptual framework, the authors tried to ask some experts’ opinions about the validity of the synthesized results. The group of experts has included eight specialists in the field of health system management or health policy-making. These experts have been chosen considering their previous research or experience in evidence-based decision/policy making performance/management (Table ​ (Table2). 2 ). This panel lasted in two three-hour sessions. The finalized themes and sub-themes (Table 6- Appendix ) and the new generated framework (Fig.  3 ) were provided to them before each session so that they could think and then in each meeting they discussed them. Finally, all the synthesized themes and sub-themes resulted were reviewed and confirmed by the experts.

The demographic characteristic of the experts that participated in the synthesis

The main steps and sub-steps of the framework of EBDM in health system management

Ethical considerations

To prevent bias, two individuals carried out all stages of the study such as screening, data extraction, and data analysis. The overall research project related to this manuscript was approved by the medical ethics conceal of the research deputy of Shiraz University of Medical Sciences with approval number IR.SUMS.REC.1396–01-07–14184, too.

The initial search across six electronic databases and the Cochrane library yielded 4613 studies. After removing duplicates, 2416 studies were assessed based on their titles. According to the abstract screening of the 1066 studies that remained after removing the irrelevant titles, 291 studies were selected and were entered into the full-text screening phase. Due to full-text screening of the studies, 17 final studies were selected for extracting the components and steps of EBDM in HSM (Fig.  1 ). The features of these studies were summarized in Table 4- Appendix (see supplementary data). Furthermore, according to the quality appraisal of the included studies, the majority of them had an acceptable level of quality. These results have been shown in Table 3- Appendix .

Results of the thematic analysis of the evidence (Table 5- Appendix ) along with the concepts proposed and added by the research team according to the focus-group discussion of the experts were shown in Table 6- Appendix . Accordingly, the main steps and related sub-steps of the EBDM process in HSM were defined and categorized.

After collecting, synthesizing, and categorizing thematic concepts, incorporating them with the initial models, and adding the additional main steps and sub-steps to the basic models, the final synthesized framework as a best-fit framework for EBDM in HSM was developed in the form of four general phases of inquiring, inspecting, implementing, and integrating and 10 main steps (Fig.  2 ). For better illustration, this framework with all the main steps and 47 sub-steps has been shown in Fig.  3 , completely.

The final synthesized framework of evidence-based decision-making in health system management

In the present study, a comprehensive framework for EBDM in HSM was developed. This model has different distinguishing characteristics than the formers. First of all, this is a comprehensive practical model that combined the strengths and the crucial components of the limited number of previous models; second, the model includes more details and complementary steps and sub-steps for full implementation of EBDM in health organizations and finally, the model is benefitted from a cyclic nature that has a priority than the linear models. Concerning the differences between the present framework and other previous models in this field, it must be said that most of the previous models related to EBDM were presented in the scope of medicine (that they were excluded from our SR according to the study objectives and exclusion criteria). A significant number of those models were proposed for the scope of public health and evidence-based practice, and only a limited number of them focused exactly on the scope of management and policy/decision making in health system organizations.

Given that the designed model is a comprehensive 10-step model, it can be used in some way at all levels of the health system and even in different countries. However, there will be a difference here, given that this framework provides a practical guide and a comprehensive guideline for applying evidence-based decision-making approach in health systems organizations, at each level of the health system in each country, this management approach can be applied depending on their existing infrastructure and the processes that are already underway (such as capacity building, planning, data collection, etc.), and at the same time, with a general guide, they can provide other infrastructure as well as the prerequisites and processes needed to make this approach much more possible and applicable.

It is true that evidence-based management is different from evidence-based medicine and even more challenging (due to lack of relevant data, greater sensitivity in data collection and their accuracy, lack of consistency and lack of transparency in the implementation of evidence-based decision-making in management rather than evidence-based medicine, etc.). Still, the general framework provided in this article can be used to help organizations that really want to act and move forward through this approach.

Furthermore, based on the findings, most of the previous studies only referred to some parts of the components and steps of the EBDM in health organizations and neglected the other parts or they were not sufficiently comprehensive [ 31 – 40 ]. Most of the previous models did not mention the necessary sub-steps, tools, and practical details for accurate and complete implementation of the EBDM, which causes the organizations that want to use these models, will be confused and cannot fully implement and complete the EBDM cycle. Among the studies that have provided a partly complete model than the other studies, were the studies by Brownson (2009), Yost (2014), and Janati (2018) [ 3 , 41 , 42 ]. Consequently, the combination of these three studies has been used as the initial framework for the best-fit synthesis in the present study.

Likewise, the models presented by Brownson (2009) and Janati (2018) were only limited to the six or seven key steps of the EBDM process, and they did not mention the details required for doing in each step, too [ 3 , 4 , 42 ]. Also, the model presented in the study of Janati (2018) was linear, and the relationships between the EBDM components were not well considered [ 42 , 43 ]; however, the model presented in this study was recursive. Also, in Yost's study (2014), despite the 7 main steps of EBDM and some details of each of the steps, the proposed process was not schematically drawn in the form of a framework and therefore the relationships between steps and sub-steps were not clear [ 41 ]. According to what was discussed, the best-fit framework makes the possibility of concentrating the fragmented models to a comprehensive one that can be fully applied and evaluated by the health systems policymakers and managers.

In the present study, the framework of EBDM in HSM was developed in the form of four general scopes of inquiring, inspecting, implementing, and integrating including 10 main steps and 47 sub-steps. These scopes were discussed as follows:

In the first step, “situation analysis and priority setting”, the most frequently cited sub-step was identifying and prioritizing the problem. Accordingly, Falzer (2009), emphasized the importance of identifying the decision-making conditions and the relevant institutions and determining their dependencies as the first steps of EBDM [ 44 ]. Aas (2012) has also cited the assessment of individuals and problem status and problem-finding as the first steps of EBDM [ 34 ]. Moreover, the necessity of identifying the existing situation and issues and prioritizing them has been emphasized as the initial steps in most management models such as environmental analysis in strategic planning [ 45 ].

Despite considering the opinions and experience of experts and managers as one of the important sources of evidence for decision-making [ 42 , 46 – 50 ], many studies did not mention this sub-step in the EBDM framework. Hence, the present authors added the acquisition of experts’ opinions as a sub-step of the first step because of its important role in achieving a comprehensive view of the overall situation.

In the second step, “quantifying the issue and developing a statement”, “Developing the conceptual model for the issue” was more addressed [ 37 , 41 , 47 ]. In addition, the authors to complete this step added the fourth sub-step, “Defining the main statement of issue”. This is because that most of the problems in health settings may have a similar value for managers and decision-makers and quantifying them can be used as a criterion for more attention or selecting the problem as the main issue to solve.

The third step, “Capacity building and setting objectives”, was not seen in many other included studies as a main step in EBDM, however, the present authors include this as a main step because without considering the appropriate objectives and preparing necessary capacities and infrastructures, entering to the next steps may become problematic. Moreover, in numerous studies, factors such as knowledge and skills of human resources, training, and the availability of the essential structures and infrastructures have been identified as facilitators of EBDM [ 51 – 55 ]. According to this justification, they are included in the present framework as sub-steps of the third step.

Considering the third step and based on the knowledge extracted from the previous studies, the three sub-steps of “understanding context and Building Culture” [ 56 , 57 ], “gaining the support and commitment of leaders” [ 39 , 57 , 58 ], and “identifying the capabilities required by employees and their skills weaknesses” [ 58 – 60 ] were the most important sub-steps in this step of EBDM framework. In this regard, Dobrow (2004) has also stated that the two essential components of any EBDM are the evidence and context of its use [ 32 ]. Furthermore, Isfeedvajani (2018) stated that to overcome barriers and persuade hospital managers and committees to apply evidence-based management and decision-making, first and foremost, creating and promoting a culture of "learning through research" was important [ 61 ].

The present findings showed that in the fourth main step, “evidence acquisition and integration”, the most important sub-step was “finding the sources for seeking the evidence” [ 39 – 41 , 60 , 62 , 63 ]. Concerning the sources for the use of evidence in decision-making in HSM, studies have cited numerous sources, most notably scientific and specialized evidence such as research, articles, academic reports, published texts, books, and clinical guidelines [ 39 , 64 , 65 ]. After scientific evidence, using the opinions and experiences of experts, colleagues, and managers [ 42 , 46 , 49 , 66 ] as well as the use of census and local level data [ 49 , 66 , 67 ], and other sources such as financial [ 67 ], political [ 42 , 49 ] and evaluations [ 49 , 68 ] data were cited.

The fifth step of the present framework, “evidence appraising”, was emphasized by previous literature; for instance, Pierson (2012) pointed to the use of library services in EBDM [ 69 ]. Appraising and selecting the evidence according to appropriate appraisal tools/methods was cited the most. International and local evidence is confirmed that ignoring these criteria can lead to serious faults in the process of decision and policy-making [ 70 , 71 ].

Furthermore, the sixth step, “analysis, synthesis, and interpretation of data”, was mentioned in many included studies [ 36 , 39 , 41 , 42 , 57 , 59 , 72 ]. This step emphasized the role of analysis and synthesis of data in the process of generation applied and useful information. It is obvious that the local interpretation according to different contexts may lead to achieving such kind of knowledge that can be used as a basis for local EBDM in HSM.

Implementing

The third scope consisted of the seventh and eighth steps of the EBDM process in HSM. In the seventh step, “developing evidence-based alternatives”, the issue of involving stakeholders in decision-making and subsequently, planning to design and implementation of the process and evaluation strategies had been focused by the previous studies [ 58 , 60 , 62 , 63 , 73 ]. Studies by Belay (2009) and Armstrong (2014) had also emphasized the need to use stakeholder and public opinion as well as local and demographic data in decision-making [ 49 , 67 ].

“Pilot-implementation of selected alternatives” was the eighth step of the framework. Some key sub-steps of this step were resources allocation [ 58 ], Pre-implementation and pilot change in practice and assessing barriers and enablers for implementation [ 40 ] that indicated the significance of testing the strategies in a pilot stage as a pre- requisition of implementing the whole alternatives. It is obvious that without attention to the pilot stage, adverse and unpleasant outcomes may occur that their correction process imposes many financial, organizational, and human costs on the originations. In addition, a study explained that one of the strategies of the decision-makers to measure the feasibility of the policy options was piloting them, which had a higher chance of being approved by the policymakers. Also, pilot implementation in smaller scales has been recommended in public health in cases of lack of sufficient evidence [ 74 ].

Integrating

This last scope consists of the ninth and tenth steps. The main sub-step of the ninth step, “evaluating alternatives”, was to evaluating process and outcomes and revise. After a successful implementation of the pilot, this step can be assured that the probable outcomes may be achieved and this evaluation will help the decision and policymakers to control the outcomes, effectively. Also, it impacts the whole target program and proposes some correcting plans through an accurate feedback process, too. Pagoto (2007) explained that a facilitator for EBDM would be an efficient and user-friendly system to assess utilization, outcomes, and perceived benefits [ 55 ].

Also, the tenth step, “integrating and maintaining change in practice”, was not considered as a major step in previous models, too, while it is important to maintain and sustain positive changes in organizational performance. In this regard, Ward (2011) also suggested several steps to maintain and sustain the widespread changes in the organization, including increasing the urgency and speed of action, forming a team, getting the right vision, negotiating for buy-in, empowerment, short-term success, not giving up and help to make a change stick [ 35 ]. Finally, the most important sub-steps that could be mentioned in this step were the dissemination of evidence results to decision-makers and the integration of changes made to existing standards and performance guidelines. Liang (2012) had also emphasized the importance of translating existing evidence into useful practices as well as disseminating them [ 47 ]. In addition, the final sub-step, “feedback and feedforward towards the EBDM framework”, was explained by the authors to complete the framework.

Some previous findings showed that about half and two-thirds of organizations do not regularly collect related data about the use of evidence, and they do not systematically evaluate the usefulness or impact of evidence use on interventions and decisions [ 75 ]. The results of a study conducted on healthcare managers at the various levels of an Iranian largest medical university showed that the status of EBDM is not appropriate. This problem was more evident among physicians who have been appointed as managers and who have less managerial and systemic attitudes [ 76 ]. Such studies, by concerning the shortcomings of current models for EBDM in HSM or even lack of a suitable and usable one, have confirmed the necessity of developing a comprehensive framework or model as a practical guide in this field. Consequently, existing and presenting such a framework can help to institutionalize the concept of EBDM in health organizations.

In contrast, results of Lavis study (2008) on organizations that supported the use of research evidence in decision-making reported that more than half of the organizations (especially institutions of health technology assessment agencies) may use the evidence in their process of decision-making [ 75 ], so applying the present framework for these organizations can be recommended, too.

Limitations

One of the limitations of the present study was the lack of access to some studies (especially gray literature) related to the subject in question that we tried to access them by manual searching and asking from some articles’ authors and experts. In addition, most of the existing studies on EBDM were limited to examining and presenting results on influencing, facilitating, or hindering factors or they only mentioned a few components in this area. Consequently, we tried to search for studies from various databases and carefully review and screen them to make sure that we did not lose any relevant data and thematic code. Also, instead of one model, we used four existing models as a basis in the BFF synthesis so that we can finally, by adding additional codes and themes obtained from other studies as well as expert opinions, provide a comprehensive model taking into account all the required steps and details. Also, the framework developed in this study is a complete conceptual model made by BFF synthesis; however, it may need some localization, according to the status and structure of each health system, for applying it.

The present framework provides a comprehensive guideline that can be well adapted for implementing EBDM in health systems and organizations especially in underdeveloped and developing countries where there is usually a lag in updating and applying evidence in their decision-making process. In addition, this framework by providing a complete, well-detailed, sequential and practical process including 10 steps and 56 sub-steps that did not exist in the incomplete related models, can be tested in the organizational decision-making process or managerial tasks by developed countries to improve their EBDM cycle, too.

Acknowledgements

This research, derived from Proposal No. 96-01-07-14184, was conducted by Mrs. Tahereh Shafaghat as part of the activities required for a Ph.D. degree in health care management at the Shiraz University of Medical Sciences. The authors wish to express their sincere gratitude to the research administration of Shiraz University of Medical Sciences for its financial and administrative support and to the English editorial board of Research Editor Institution for improving the native English language of this work.

Abbreviations

Tables ​ Tables3, 3 , ​ ,4, 4 , ​ ,5 5 and ​ and6 6 .

Quality assessment of the included studies

Summary of characteristics of included studies

The steps and sub-steps of the EBDM framework resulted from thematic analysis

The finalized steps and sub-steps of the EBDM framework resulted from evidence synthesis and the research team analysis

a The numbers in parentheses indicates the frequency of references that include the concept

b RTS stands for the concepts synthesized, proposed and added by the research team and confirmed by the experts

Authors' contributions

PB and TSH designed the study and its overall methodology. BP also edited and finalized the article. TSH searched all the databases, with the help of MRAM retrieved the sources, scanned, and screened all the articles in 3 phases. TSH also prepared the draft of the article. MAB and MKRZ contributed to data analysis and synthesis. Also, the study was under consultation and supervision by ZK and MHIN as advisors. All the authors have read and approved the final manuscript.

As the overall study was an approved research project of Shiraz University of Medical Sciences and it was conducted by Mrs. Tahereh Shafaghat as part of the activities required for a Ph.D. degree in the health care management field, the Shiraz University of Medical Sciences supported this study. This study was sponsored by Shiraz University of Medical Sciences under code (96‑01‑07‑14184). The funding body was not involved in the design of the study, data collection, analysis, and interpretation, as well as in writing the manuscript.

Availability of data and materials

Declarations.

Since at this study a scoping review was conducted and then the best-fit framework synthesis was used for developing a comprehensive EBDM framework in HSM, there was no human or animal participant in this study. However, the overall research project related to this manuscript was approved by the medical ethics conceal of the research deputy of Shiraz University of Medical Sciences with approval number IR.SUMS.REC.1396–01-07–14184.

Not applicable.

The authors declare that they have no competing interests.

Publisher's Note

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

Tahereh Shafaghat and Peivand Bastani have equal participation as first authors.

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Four Pillars of Decision-driven Analytics

May 14, 2024 • 5 min read.

In an excerpt from their new book, Wharton’s Stefano Puntoni and co-author Bart De Langhe argue that the power of data can only be realized by leveraging human intelligence.

In a new book titled Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data , professors and behavioral scientists Bart De Langhe and Stefano Puntoni challenge the idea that our decisions should be driven by data. Rather, they argue that the power of data can only be realized by putting data in the background.

In this excerpt from their book, De Langhe and Puntoni draw from their own research and teaching to offer four pillars of decision-driven analytics.

In the mid-1850s, astronomers figured that the orbit of the planet Uranus was not like it should be according to the laws of physics. A French astronomer, Alexis Bouvard, thought that perhaps that was because we didn’t know about a planet further out in the solar system that exerted an influence on Uranus’s orbit. People started searching the sky for it. Soon enough, Urbain Le Verrier, another Frenchman, found the missing planet. It was named Neptune.

This was a great victory for the power of observation. Investing in data collection saved the day. It taught astronomers that the key to unraveling the mysteries of the cosmos was more and better data.

The rationale for making decisions with input from analytics rests on similar principles. Without data we navigate blind, while with data we can make decisions rooted in evidence. The implication is that good thinking means thinking with data.

The story doesn’t end here, though. An anomaly was soon observed also in the orbit of another planet: Mercury. The same Urbain Le Verrier who had found Neptune now hypothesized the existence of a missing planet lying between Mercury and the Sun. He called this missing planet Vulcan. Again, people started looking for it, only this time nobody could find it. Astronomers kept looking for Vulcan in the subsequent decades but the missing planet remained missing, and the mystery of Mercury unsolved.

The anomaly in the orbit of Mercury could be explained only half a century later. The explanation had to wait for Albert Einstein’s publication of a new theory of gravitation, called the theory of general relativity. This theory revolutionized our understanding of the universe by placing space and time in a four-dimensional continuum.

Although nobody knew that before Einstein entered the scene, all planetary orbits were in fact not conforming to Isaac Newton’s laws. Nobody knew that because the difference between the predictions of the two theories are smaller and smaller as you move away from the Sun. Only in the case of Mercury, which is the planet closest to the Sun, the curvature in space-time caused by the mass of the Sun was large enough for the divergence between the predictions based on Newton’s and Einstein’s theories to be detected by the telescopes of the time.

The mystery of Mercury was solved in a very different way from the mystery of Uranus. While the latter could be solved with better observations, the former could only be solved with better theory, by thinking without data.

Managers are like astronomers, looking to solve problems and find solutions in a complex world, where data is abundant but often hard to make sense of. The message is clear: Data and algorithms are crucial to making good decisions. But human judgment and intelligence are crucial, too.

The Four Pillars of Decision-driven Analytics

Many companies are witnessing an expanding gap between data and decisions, even with the goal of being a “data-driven organization.” The increasing complexity of data and algorithms can make it harder for decision-makers to collaborate with data analysts. For a business to thrive, it’s essential for both groups to understand and value each other’s expertise.

Many businesses find themselves overwhelmed by the sheer volume of data at their disposal. Putting decisions firmly at the center of the analytics process can be transformative. Starting with decisions and working back to the data will improve the quality of decision-making, improve the collaboration between managers and data analysts, and ultimately foster an organizational culture that is action oriented and that prizes the quality of decisions over ego or politics.

Here are the four core principles of decision-driven analytics:

• Decisions. Identify controllable, relevant decision alternatives. Consider diverse perspectives and a wide array of solutions. Prioritize feasible and impactful alternatives to achieve important business outcomes.
• Questions. Formulate precise questions that will help rank the identified decision alternatives. Ambiguous questions can lead to miscommunication and poor decisions.
• Data. Evaluate the data-generating mechanism. While Big Data can be tempting, the emphasis should be on collecting relevant data.
• Answers. When the earlier steps are done right, determining the best action becomes straightforward. Remember, acknowledging uncertainty and sidestepping overconfidence are key for informed decisions.

Decision-driven analytics is about making informed choices, not just processing data or flooding presentations with graphs. It emphasizes gleaning actionable insights from pertinent data. Embracing this approach means letting go of the notion that every data point is vital and not being distracted by the newest tools.

Data is just a means to an end. What matters is the decisions we make.

Excerpted and adapted from Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data, by Bart De Langhe and Stefano Puntoni, copyright 2024. Reprinted by permission of Wharton School Press.

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Functional Reasoning Support for Nuclear Power Plant Field Operators

Transient operations at Nuclear Power Plants (NPPs), involving system startups or shutdowns, are critical yet challenging phases. These operations are characterized by rapid changes in system conditions and an increased potential for unexpected equipment behaviors. In such settings, the mental models that operators rely on become essential. Mental models enable operators to diagnose the current state of the plant and anticipate future events. However, these mental models are vulnerable to flaws during transient operations. Such flaws can significantly misalign an operator's perception with the actual state of the plant, potentially leading to operational errors that compromise safety. Therefore, this study investigates the flawed mental models of field operators during transient operations at NPPs.

Chapter 2 aimed to understand the mechanisms behind flawed mental models among field operators at NPPs. This chapter utilized document analysis to identify that field operations consist of six task stages: pre-job briefing, procedure walk-down, place-keeping, operator transit, response planning, and response implementation. An expert panel survey was conducted to explore and classify the components of mental models within NPP operations into three critical areas: workspace dynamics, workflow prognostics, and hazard identification. This analysis revealed that operators needed to continually activate and update their mental models to meet the specific demands of each task stage and to maintain alignment with the actual state of the plant. The chapter also identified that flaws in mental models frequently arose from a lack of comprehensive system dynamics information and inadequate guidance provided by existing operational procedures.

Chapter 3 aimed to develop a formal framework based on functional reasoning to enhance the capture and utilization of system dynamics information within NPP operations. The chapter began by developing an ontology that formalized the connectivity, states associated with components, and the mapping relationships between these states and components' capabilities. This ontology served as a foundation, enabling the structured interpretation of component function types and behaviors. Building on the ontology, chapter 3 then developed algorithms for functional analysis and behavioral analysis that translated observable sensor readings into detailed descriptions of component functions and behaviors. Lastly, this chapter utilized a batch plant to demonstrate the implementation outcomes of the developed framework.

Chapter 4 assessed the impact of the generated system dynamics information on operator decision-making. This chapter detailed the design and execution of human-subject experiments involving industrial professionals from the nuclear power sector. These experiments aimed to measure how the provision of system dynamics information affected operators' decision-making across different operational scenarios. Findings from the study revealed that while providing system dynamics information improved decision-making accuracy and efficiency in handling complex tasks, it also increased the perceived workload among operators. The study noted that operators required a period of adaptation to effectively integrate and utilize the increased volume of information provided.

The culmination of this research underscored the critical importance of mental models in ensuring operational safety. One of the key practical implications of this study is the potential integration of advanced decision-support systems into NPP operations. These systems aim to deepen

operators' understanding of plant dynamics, thereby enhancing operational safety. Looking forward, future research should investigate innovative methods of information delivery that could reduce cognitive load and improve the assimilation of complex information. This exploration might also extend these concepts to other high-stakes industries, broadening the impact of this work and offering new avenues for enhancing safety and decision-making across various fields

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