research proposal on corporate governance pdf

International corporate governance: A review and opportunities for future research

• Review Article
• Published: 03 April 2019
• Volume 50 , pages 457–498, ( 2019 )

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• Ruth V. Aguilera 1 , 2 ,
• Valentina Marano 1 &
• Ilir Haxhi 3  

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We review four decades of research about the corporate governance of multinational corporations (MNCs), which we label International Corporate Governance (ICG). We identify and discuss three main streams of research that draw on different conceptualizations and theoretical lenses of (corporate) governance. After synthesizing their respective findings, we propose several avenues for future research that integrate these three streams of research with the goal of developing a more nuanced understanding of ICG. We hope this review article will inspire international business scholars to continue examining how corporate governance can be an effective tool for MNC success.

Dans cet article, nous passons en revue quatre décennies de recherche sur la gouvernance d’entreprise des firmes multinationales (FMN), que nous appelons Gouvernance internationale des sociétés (GIS). Nous identifions et discutons trois grands courants de recherche qui s’appuient sur différents angles conceptuels et théoriques de la gouvernance (d’entreprise). Après avoir résumé leurs résultats respectifs, nous proposons plusieurs pistes futures de recherche qui intègrent ces trois axes de recherche dans le but de développer une compréhension plus nuancée de la GIS. Nous espérons que cet article de synthèse inspirera les spécialistes de l’ international business pour continuer à examiner comment la gouvernance d’entreprise peut être un outil efficace pour le succès des FMN.

En este artículo, revisamos cuatro décadas de investigación sobre el gobierno corporativo de una empresa multinacional (EMN), que etiquetamos como Gobierno Corporativo Internacional (GCI). Identificamos y discutimos tres corrientes de investigación que se basan en diferentes conceptualizaciones y lentes teóricos de gobierno (corporativo). Después de sintetizar sus hallazgos respectivos, proponemos diferentes avenidas para la investigación futura que integra estas tres corrientes de investigación con el objetivo de desarrollar una comprensión más matizada del GCI. Esperamos que este artículo de revisión inspire a los académicos de negocios internacionales a continuar examinando como el gobierno corporativo puede ser una herramienta efectiva para el éxito de las EMN.

Neste artigo, revisamos quatro décadas de pesquisas sobre a governança corporativa de corporações multinacionais (MNCs), as quais denominamos Governança Corporativa Internacional (ICG). Identificamos e discutimos três principais correntes de pesquisa que se baseiam em diferentes conceituações e lentes teóricas da governança (corporativa). Depois de sintetizar suas respectivas conclusões, propomos vários caminhos para futuras pesquisas que integram essas três correntes de pesquisa com o objetivo de desenvolver uma compreensão mais sutil da ICG. Esperamos que este artigo de revisão inspire acadêmicos de negócios internacionais a continuar examinando como a governança corporativa pode ser uma ferramenta eficaz para o sucesso das MNCs.

在本文中,我们综述了四十年来关于跨国公司(MNCs)公司治理的研究, 我们将其称为国际公司治理(ICG)。我们确定并讨论了三个主要的利用了(公司)治理不同概念和理论视角的研究分支。在综合了它们各自的研究结果后, 我们提出了未来研究的几种途径, 将这三个研究分支整合在一起, 旨在开发对ICG更细微的理解。我们希望这篇综述文章能启发国际商务学者继续研究公司治理如何能成为跨国公司成功的有效工具。

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Acknowledgements

The authors gratefully acknowledge comments and suggestions from Professor Benito (Editor) and three fantastic anonymous referees, conference participants at the Academy of Management’s 2018 Annual Meeting in Chicago, the International Corporate Governance Society’s 2018 Annual Meeting in Shanghai, and the 2018 Strategic Management Society’s Special Conference in Oslo. All errors remain ours.

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Aguilera, R.V., Marano, V. & Haxhi, I. International corporate governance: A review and opportunities for future research. J Int Bus Stud 50 , 457–498 (2019). https://doi.org/10.1057/s41267-019-00232-w

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DOI : https://doi.org/10.1057/s41267-019-00232-w

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Corporate Governance Research: A Review of Qualitative Literature

The focus on corporate governance has increased since the past few decades in most countries around the world, especially in the wake of corporate collapses in early 2000s. These corporate collapses have leads to the growing volume of academic and practical research in this area. Although the quantitative literature on corporate governance is diverse and extensive, the qualitative research in this area is rather limited. This paper provides a review of prior qualitative research on the corporate governance to guide future research on corporate governance. Qualitative study method allows the researcher to gather rich information in this area.

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The Making and Meaning of ESG

U of Penn, Inst for Law & Econ Research Paper No. 22-23

European Corporate Governance Institute - Law Working Paper No. 659/2022

Harvard Business Law Review, Forthcoming

53 Pages Posted: 15 Sep 2022 Last revised: 8 Apr 2024

Elizabeth Pollman

University of Pennsylvania Carey Law School; Co-Director, University of Pennsylvania Carey Law School - Institute for Law and Economics; European Corporate Governance Institute

Date Written: October 31, 2022

ESG is one of the most notable trends in corporate governance, management, and investment of the past two decades. It is at the center of the largest and most contentious debates in contemporary corporate and securities law. Yet few observers know where the term comes from, who coined it, and what it was originally aimed to mean and achieve. As trillions of dollars have flowed into ESG-labeled investment products, and companies and regulators have grappled with ESG policies, a variety of usages of the term have developed that range from seemingly neutral concepts of integrating “environmental, social, and governance” issues into investment analysis to value-laden notions of corporate social responsibility or preferences for what some have characterized as “conscious” or “woke” capitalism. This Article makes three contributions. First, it provides a history of the term ESG that was coined without precise definition in a collaboration between the United Nations and major players in the financial industry to pursue wide-ranging goals. Second, it identifies and examines the main usages of the term ESG that have developed since its origins. Third, it offers an analytical critique of the term ESG and its consequences. It argues that the combination of E, S, and G into one term has provided a highly flexible moniker that can vary widely by context, evolve over time, and collectively appeal to a broad range of investors and stakeholders. These features both help to account for its success, but also its challenges such as the difficulty of empirically showing a causal relationship between ESG and financial performance, a proliferation of ratings that can seem at odds with understood purposes of the term ESG or enable “sustainability arbitrage,” and tradeoffs between issues such as carbon emissions and labor interests that cannot be reconciled on their own terms. These challenges give fodder to critics who assert that ESG engenders confusion, unrealistic expectations, and greenwashing that could inhibit corporate accountability or crowd out other solutions to pressing environmental and social issues. These critiques are not necessarily fatal, but are intertwined with the characteristic flexibility and unfixed definition of ESG that was present from the beginning, and ultimately shed light on obstacles for the future of the ESG movement and regulatory reform.

Keywords: environmental, social, governance, ESG, sustainability, corporate social responsibility, corporate purpose, stakeholder capitalism, socially responsible investment, impact investing, corporate law, securities regulation, SEC, climate disclosure, board diversity, human capital management

JEL Classification: D21, G39, K22, L21

Suggested Citation: Suggested Citation

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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