Distributing Value to Stakeholders across Industries: Influences of Power and Strategic Importance

ABSTRACT

Objective:  to investigate how stakeholder power and strategic importance influence value distribution to stakeholders in a U.S. liberal market economy, and to assess how this relationship varies across different industries.

Methods:  we conducted a content analysis and ordinary least squares regression on 755 IPO prospectuses from publicly traded American companies across seven industries between 2003 and 2019.

Results:  our findings indicate that strategic importance exerts a greater influence on value distribution than power. However, the impact of these factors varies by industry: strategic importance is more influential in technology, for example, whereas power has limited influence in finance. Industries such as energy and transportation display distinct patterns compared to manufacturing and life sciences.

Conclusions:  in the U.S. liberal market economy, companies prioritize stakeholders who contribute strategically to value creation over those who merely hold power, but this prioritization varies by industry. For managers and stakeholders, the results provide reflections on how to effectively involve and prioritize stakeholders, creating advantages and aligning with the specific needs of each industry. For literature, the study contributes by analyzing different industries and their specificities.

Keywords:
stakeholder theory; value distribution; power; strategic importance; IPOs

INTRODUCTION

In the realm of business strategy research, the discussion surrounding purpose has evolved over the decades. In this context, Freeman (1984) asserts the existence of a connection between companies and diverse groups of stakeholders. Moreover, stakeholder theory is open to the possibility that some informal relationships between stakeholders may be genuinely moral and thus irreducible to formal governance and contractual safeguards (Valentinov & Roth, 2024). Stakeholder-oriented management recognizes that decisions promoting long-term and sustainable value creation must balance stakeholders’ interests to meet their needs (Freeman et al., 2020).

Given the escalating complexity of the business environment, the traditional capitalist narrative, grounded in private ownership, vested interests, competition, and a free market, proves inadequate in addressing issues such as predatory competition and business ethics (Freeman, Martin et al., 2007). In response to these challenges, stakeholder capitalism emphasizes collaboration among participants in the firm’s value-creation process through freely established agreements with its stakeholders (Freeman et al., 2016), cultivating stakeholder relationships characterized by mutual trust and cooperation, grounded in fairness and reciprocity, enhancing resource exchange and improving overall firm performance (Jones et al., 2018).

Despite these collaborative advances, stakeholder pressures often converge toward universal adoption of sustainability initiatives, independent of economic context (Bello-Pintado et al., 2023). Recent trends, such as the increasing relevance of environmental, social, and governance considerations, reflect a shift in how companies define value and engage with stakeholders, reinforcing the importance of balancing strategic importance and power in stakeholder relationships (Santos Jhunior et al., 2025). Yet, as stakeholder groups become increasingly diverse and their demands more complex, businesses must develop more sophisticated and adaptive stakeholder management strategies (Mahajan et al., 2023).

Considering these strategic complexities, Harrison and Bosse (2013) recommend empirically examining the influence of the power and strategic importance of stakeholders in the firm’s value distribution. Power, in this perspective, is supported by the salience model (Mitchell et al., 1997), interpreted as stakeholders’ ability to negotiate their interests using coercive, utilitarian, and/or normative bases (Etzioni, 1964). Strategic importance, on the other hand, is understood as the ability of stakeholders to make a positive contribution to the overall value creation process of companies (Mitchell & Lee, 2019). Instrumental stakeholder theory (Donaldson & Preston, 1995; Jones et al., 2018) offers a valuable framework for balancing stakeholder power and strategic importance. By emphasizing trust-based and reciprocal relationships, this approach enables firms to strategically align stakeholders’ diverse interests, mitigating power imbalances and enhancing cooperation.

Previous studies have explored the relationship between stakeholder power, strategic importance, and value distribution through IPO prospectuses, such as Boaventura et al. (2020) and Araujo et al. (2024), who focused on Brazil and Canada respectively but with a limited sample size and without considering industry-specific contexts. The concepts used in the original model were expanded, and the sample scope was increased using the framework applied to initial public offerings (IPOs) of listed firms in Brazil (Boaventura et al., 2020). This framework was then extended to IPOs in the world’s largest capital market (Statista, 2024b).

Our study analyzes companies that went public in the United States. The United States, as the biggest liberal market economy (Hall & Soskice, 2001), not only boasts the most significant GDP globally (World Bank, 2023) but also hosts the two largest stock exchanges by market capitalization - the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotations Stock Market (NASDAQ), both regulated by the Securities and Exchange Commission (SEC). As of September 2023, the combined market capitalization of stocks listed on these exchanges exceeded $45 trillion - surpassing the total of the ten largest stock market operators combined (Statista, 2024a).

In this context, our study seeks to investigate the impact of power and strategic importance on the allocation of value among stakeholders in a unique institutional environment, in contrast to previous research. We will focus on the most prominent and advanced global market.

Initially, our study commences by acknowledging the findings of Boaventura et al. (2020), exploring the value of replicating their research in various markets. This initial focus sets the stage for our exploration into uncharted territories within the domain. Following this, we draw upon the insights of Torelli et al. (2020), who emphasize the impact of industry-specific contexts on stakeholder engagement and managerial decisions. Their work underscores the importance of considering external factors and industry-specific influences, aligning with our objective of delving deeper into these dynamics.

Building on this, we address the need, as pointed out by Clarkson (1995), to recognize and appropriately analyze the distinct characteristics inherent to each industry. This approach is critical in our research, especially when considering the proposal by Harrison and Bosse (2013) on how variables evolve in different industries. While studies have explored the industry’s influence on financial performance in U.S. IPOs, a comprehensive empirical analysis detailing the industry’s impact on value creation is notably absent. This gap is particularly evident given that most studies, including those by Boaventura et al. (2020), often relegate sectoral variables to a control status rather than a focal point of in-depth analysis.

Therefore, our research aims to fill this void by thoroughly exploring how the variables proposed by Harrison and Bosse (2013) develop across different industries, emphasizing nuanced differences and the industry’s role in value creation. Such an approach aligns with the perspective that multinational enterprises strategically engage stakeholders to enhance their corporate social innovation initiatives, highlighting the importance of structured stakeholder engagement in complex environments (Saka-Helmhout et al., 2024). This exploration is fundamental to advancing our understanding of the intricate relationship between industry-specific factors and stakeholder distribution.

The study had two main objectives. The first was to examine the phenomenon of value distribution to stakeholders and the relationship between power and strategic importance in a liberal market economy. The second was to understand the behavior of value distribution to stakeholders and the relationship between power and strategic importance across different industries.

Addressing this research gap explicitly, this paper contributes to the literature in two ways: first, it extends Boaventura et al.’s (2020) research to a different market. The second contribution is expanding the analysis by including the business industry as the primary variable, demonstrating and detailing the differences between them and how they influence the variables. By extending previous research on stakeholder value distribution, this study provides a novel perspective on the interplay between power and strategic importance in firms operating within a liberal market economy. Unlike prior studies focusing on specific national contexts, our research captures a broader economic environment where market mechanisms drive corporate decisions. This contribution is particularly relevant for managers and policymakers, as it offers insights into how firms prioritize stakeholders based on their strategic roles. Additionally, the study’s industry-level analysis underscores key sectoral differences, equipping practitioners with actionable knowledge to enhance stakeholder engagement strategies across different industries.

LITERATURE REVIEW

Stakeholder theory

From its inception, stakeholder theory has been closely linked to the field of management strategy, systematically addressing interdependent relationships within the business environment (Freeman et al., 2020). Consequently, stakeholder-oriented management requires simultaneous consideration of the legitimate interests of all stakeholders (Donaldson & Preston, 1995).

In stakeholder-oriented management, a fundamental approach to mapping a company’s stakeholders is to categorize them into primary and secondary stakeholders (Clarkson, 1995). Primary stakeholders, including customers, employees, shareholders, suppliers, and communities, warrant greater attention from stakeholder-oriented management due to their potential to directly influence the company’s activities. On the other hand, secondary stakeholders, such as government, media, competitors, consumer protection agencies, and other special interest groups, can indirectly influence the company’s relationship with primary stakeholders (Freeman, Harrison et al., 2007).

Stakeholder theory posits that the primary purpose of companies is to meet the demands of groups interested in their activities and to channel them toward creating sustainable value (Harrison et al., 2020). Following the research by Harrison et al. (2010), the concept of value creation gained importance, with subsequent studies addressing the topic (Boaventura et al., 2020; Bridoux & Stoelhorst, 2014, 2016; Tantalo & Priem, 2016). As the idea evolved and was refined, experts recognized that stakeholder management strives to provide value beyond economic value (Murphy & Wilson, 2022). Building upon the significance of this notion, the next section will explore aspects of value creation.

Value for stakeholders

Strategies are employed to cater to various groups of stakeholders (Garriga, 2014). To formulate these strategies, it is essential to comprehend what each group deems important or its purpose, commonly referred to as value (Bridoux & Stoelhorst, 2022). This concept extends beyond economic value and encompasses well-being (Harrison & Wicks, 2013). Consequently, values vary among groups based on perceptions, attributes, and visions (Garriga, 2014).

To achieve this understanding, it is imperative to identify the value drivers for a company’s numerous stakeholders, encompassing both tangible and intangible dimensions (Harrison & Wicks, 2013). The identification process is executed through stakeholders’ utility functions, which reflect their preferences for various outcomes during a business’s activities (Harrison et al., 2010). These outcomes, also known as value drivers, encompass tangible and intangible characteristics drawn from legal, financial, or moral values (Phillips, 2003; Priem, 2007).

In this work, we define the terms used: the value generated is the sum of utility functions (Harrison & Wicks, 2013). Value represents everything significant to the stakeholder; utility is defined based on stakeholder perceptions, reflecting the value they derive and representing their preferences; value created is the total of utility functions (Harrison & Wicks, 2013).

Stakeholder value creation is grounded in notions of cooperation, engagement, and responsibility (Mitchell & Lee, 2019), and fair treatment of stakeholders by companies can generate virtuous circles of reciprocity (Bosse et al., 2009). Furthermore, companies that actively engage their stakeholders maximize their value creation (Pucci et al., 2020); thus, maintaining stakeholder engagement becomes a potential competitive advantage (Harrison et al., 2010).

In Table 1, we present Tantalo and Priem’s (2016) development with examples of tangible and intangible value drivers for a company’s primary stakeholders in their study. These examples are then employed to create a collection of terms and synonyms representing value for each stakeholder group (Boaventura et al., 2020).

Table 1
Stakeholder value drivers.

Stakeholders	Tangible value driver	Intangible value driver
Shareholders	Expected return (Fama & French, 1988). Investment time horizon (Fama & French, 1988). Corporate social responsibility (Aguilera et al., 2007).	Business risk (Amit & Wernerfelt, 1990).
Customers	Product’s price (Ackerman & Tellis, 2001). Accessibility - time required to purchase the product (Priem, 2007). Environmental corporate responsibility and ‘ecofriendly’ products (Bansal & Roth, 2000; Shrivastava, 1995).	Perceived value (Fornell et al., 1996). Time required to master using the new product (Priem, 2007). Perceived quality (Fornell et al., 1996).
Employees	Salary (Abu-Bader, 2000) and benefits (Sutton, 1985). Corporate social responsibility (Aguilera et al., 2007). Work-life balance policies (Haley-Lock, 2008).	Perceived fairness of the working environment (Aguilera et al., 2007; Colquitt, 2001). Job characteristics and skill variety (Glisson & Durick, 1988).
Suppliers	Ordering procedure and size (Essig & Amann, 2009). Long-term relationships (Kalwani & Narayandas, 1995). Price received (Kalwani & Narayandas, 1995). Client payment habits and payment terms (Wong, 2000).	Image and reputation of the customer (Essig & Amann, 2009). Possibility for cross selling and potential for follow-up business (Essig & Amann, 2009)
Communities	Number and types of jobs created (Porter & Kramer, 2011). Taxes to be paid (Buettner, 2001). Support infrastructure required (Porter & Kramer, 2011). Local clusters (Porter & Kramer, 2011).	Externalities linked to the business (e.g., noise or air pollution) (Bansal & Roth, 2000; Porter & Kramer, 2011).

• Note. Based on Tantalo, C., & Priem, R. L. (2016). Value creation through stakeholder synergy. Strategic Management Journal, 37(2), 314-329. https://doi.org/10.1002/smj.2337

The strategic importance and power of stakeholders involved in the value-creation process are critical criteria for allocating resources to a company’s stakeholders (Harrison & Bosse, 2013). Managers must assess the ideal quantity of value to offer stakeholders to optimize overall value creation; in other words, the benefits of stakeholder-oriented management must outweigh the costs (Harrison et al., 2010).

Strategic importance and power of stakeholders

A company’s long-term sustainability depends on the support of its stakeholders (Freeman & McVea, 2005), as the creation or destruction of value in the business environment is shaped by the interaction between firms and their stakeholders (Bosse & Coughlan, 2016). Managers can evaluate stakeholder relationships based on their strategic value and power (Friedman & Miles, 2006).

Various types of power can be studied independently or in combination (Mitchell et al., 1997). In this study, power is defined as stakeholders’ ability to extract value from the organization (Harrison & Bosse, 2013). All stakeholder groups possess the ability to impact the company positively or negatively through power (Harrison & Wicks, 2013), contingent on the type of support or dependence the company has with the actor (Harrison et al., 2010; Harrison & Bosse, 2013), which may involve economic, resource-related, capability-based, or informational aspects (Magee & Galinsky, 2008; Saffer et al., 2018). In this context, certain stakeholders manage to be prioritized in the value distribution process through power, which is not always positive and can involve impositions (Harrison et al., 2010; Harrison & Bosse, 2013).

The strategic importance and power of stakeholders are the most significant aspects of this process. For this reason, Harrison and Bosse (2013) propose a value distribution matrix, where more value is allocated when a stakeholder possesses significant authority and strategic relevance, because the value contributed by such stakeholders can offset a higher cost of value allocation (Harrison & Bosse, 2013). This is due to the reciprocal relationship between what the stakeholder provides, whether through imposition or strategic importance, and the generated value (Harrison & Bosse, 2013).

This study was conducted in a liberal market, where free competition and minimal state intervention are pivotal for economic development and innovation. Consequently, the following hypotheses consider the reality of liberal market economies. The first hypothesis of the study is derived from the significance of the two variables:

Hypothesis 1: Power and strategic importance influence the value distribution to stakeholders.

The primary differentiation between power and strategic importance lies in their objectives. When a stakeholder wields power, it either gains benefit or compulsorily appropriates something from the corporation that would not typically be assigned to it (Mitchell et al., 1997). Consequently, receiving a quantity of value that would not typically be granted indicates that other priorities would be satisfied were it not for the exertion of force (Harrison & Wicks, 2013).

Regarding strategic importance, when a stakeholder actively contributes to the creation of value or aids in the development of strategies that foster the organization’s growth (Boaventura et al., 2020), the allocation of resources tends to yield a value return to the stakeholder, preventing losses (Harrison & Bosse, 2013). In essence, while strategically significant stakeholders actively contribute to value creation, stakeholders wielding power appropriate something without necessarily contributing to its creation. Given this distinction in relationship, stakeholders with greater strategic importance are likely to exert more influence on value creation compared to those with greater power (Boaventura et al., 2020; Harrison & Bosse, 2013). Based on this rationale, the subsequent hypothesis is proposed:

Hypothesis 2: Strategic importance has a more significant impact on the distribution of value than power.

This reflection suggests that the ability of stakeholders to actively contribute to value creation, as embodied by their strategic importance, holds greater sway in influencing the distribution of value compared to mere power dynamics. By focusing on strategic importance, which involves the positive contribution to the organization’s growth and value creation, the hypothesis underscores the potential for stakeholders actively engaged in the strategic development of the organization to wield a more substantial impact on the value distribution process. This aligns with the notion that stakeholders with strategic importance are likely to play a pivotal role in shaping the overall value landscape, surpassing the influence exerted by stakeholders primarily relying on power dynamics.

Stakeholders between industries

Recognizing that effective value creation hinges on the accurate identification and analysis of a company’s stakeholders (Mitchell & Lee, 2019), it becomes crucial to develop an in-depth understanding of the interests associated with each stakeholder group during the analysis process (Turcksin et al., 2011). It is noteworthy that companies operating in diverse industries contend with varying types and intensities of claims from their respective stakeholders (Fineman & Clarke, 1996).

Furthermore, the selection of a company’s organizational structure is believed to be influenced by the characteristics inherent to its industry (Brammer & Millington, 2003). Consequently, this paper has gathered theoretical and practical insights into the value drivers of stakeholders within each industry under examination. These insights are systematically categorized in Table 2.

Table 2
Stakeholders and their value drivers across industries.

Industries	Relevant stakeholders	Value drivers
Energy & Transportation	Mining and oil companies, biofuel farmers and producers, distributors, end user, car manufacturers, government (Turcksin et al., 2011), logistics operators, unions, cargo carriers, passenger carriers, end user (Macharis et al., 2010).	Productive capacity, balance in greenhouse gas emissions, energy availability (Turcksin et al., 2011), energy transition, environmental sustainability (Murray et al., 2008), accessibility, safety, environmental impact (Macharis et al., 2010).
Finance	Retail and/or wholesale banks, digital banks, credit operators, payment institutions (Mullan et al., 2017), institutional investors, hedge funds, central banks, unions, private and institutional clients, traditional shareholders, boards of directors (Fassin & Gosselin, 2011).	Customer demand, customer convenience, economic conditions, alliance formation, security (Mullan et al., 2017), financial protection, profit, continued financing, job preservation, financial system stability (Fassin & Gosselin, 2011).
Life Sciences	Pharmaceuticals, regulatory bodies, government, hospitals, health operators, physicians, research centers, patients (O’Riordan & Fairbrass, 2008), farmers, universities, development agencies (Zhang & Sun, 2022).	Biological safety, human health, environmental health, impacts on the aquatic ecosystem, treatment of used water, continued medical education (Doerr-MacEwen & Haight, 2006), food safety, biodiversity, genetic information, innovation (Zhang & Sun, 2022).
Manufacturing	Manufacturers of steel, copper, aluminum, packaging, paper and/or processed foods, communication equipment, electronic components (Heugens & Oosterhout, 2002).	Alignment of interests, commitment, conflict resolution, productive capacity, pricing, procedural reliability (Heugens & Oosterhout, 2002).
Real Estate & Construction	Real estate, hotels, motels (Dedeoğlu & Demirer, 2015), basic sanitation companies, construction companies of commercial and residential buildings, contractors (Bal et al., 2013).	Responsiveness, empathy, reliability, guarantees (Dedeoğlu & Demirer, 2015), sustainable development, integrity, knowledge transfer (Bal et al., 2013).
Technology	Software developers, providers of software as a product and as a service (SaaS and SaaP), communication operators, assemblers and sellers of microcomputers (Imran Babar et al., 2014).	Continuous improvement, requirements elicitation, information systems, technological infrastructure, data processing, performance and system acceptance (Imran Babar et al., 2014).
Trade & Services	Consumers, work teams, marketing executives, advertising agencies (Smith & Fischbacher, 2005), tour operators, visitors, native peoples, research centers (McCabe et al., 2012).	New services and products, satisfaction with the product/service, consumer preferences (Smith & Fischbacher, 2005), connective alliances, digital-technical development, shared responsibility (McCabe et al., 2012).

• Note. Developed by the authors.

While these insights do not encompass all value drivers within an industry, they reveal common considerations among active stakeholders. For instance, shared concerns include production capacity in the energy, transportation, and manufacturing industries (Heugens & Oosterhout, 2002; Turcksin et al., 2011), and the trustworthiness of real estate and manufacturing industries (Dedeoğlu & Demirer, 2015; Heugens & Oosterhout, 2002). Nonetheless, unique considerations emerge, such as biological and food safety in the life sciences industry (Doerr-MacEwen & Haight, 2006).

In the energy and transportation industries, stakeholder analysis typically employs frameworks like political, economic, social, technological, environmental, and legal (PESTEL) (Sircar et al., 2013; Widya Yudha & Tjahjono, 2019) and multi-actor multi-criteria analysis (MAMCA) (Macharis et al., 2010; Turcksin et al., 2011). On the other hand, the service industry utilizes the new service development (NSD) approach for stakeholder analysis (McCabe et al., 2012).

Within the technology industry, work structures are termed requirements engineering (RE) and value-based software engineering (VBSE), where requirements are interpreted as the needs of stakeholders interested in the software to be developed (Imran Babar et al., 2014). Consequently, analyzing stakeholder characteristics becomes imperative, as these stakeholders play a pivotal role in the success of the software being developed. Without their support, developing a high-quality system becomes unattainable (Imran Babar et al., 2014).

Given the industry-specific differences outlined above, the distribution of values can vary. Consequently, it is important to comprehend how different industries behave concerning the distribution of value. Guided by this reasoning, we now introduce the following hypothesis for further investigation:

Hypothesis 3: The behavior of value distribution and its relationship with stakeholder power and strategic importance differs among industries.

METHOD

The guidelines for the categorization of SEC are applied to study the phenomenon of value distribution to stakeholders in listed U.S. companies. In addition, a quantitative and qualitative approach is used, employing content analysis and statistical techniques.

Population and sample

This research selectively samples public companies from U.S. stock exchanges, primarily for three reasons: the United States has the world’s largest economy by GDP according to the World Bank (2023), it exemplifies a liberal market economy, and it hosts the two leading stock exchanges in market capitalization - the NYSE and the NASDAQ. These exchanges are home to numerous companies that have entered the market via IPOs, under the oversight of the SEC.

As a reference for other stock markets in the world, the NYSE is inextricably linked to the history of the entire United States, having been established in 1792 as one of the first stock exchanges (Mexmonov, 2020). The pioneering NASDAQ began trading on February 8, 1971, making it the world’s first electronic stock exchange (Library of Congress, 2023). Including domestic and foreign listings, these three exchanges unite more than six thousand listed companies. As of September 2023, the value of stocks listed on these two exchanges had a combined market capitalization of over $45 trillion - more than the next 10 largest stock market operators combined (Statista, 2024a).

We also included companies listed with the OTC Markets Group, established in 1913, which organizes stocks trading over-the-counter (OTC) into three marketplaces (OTCQX, OTCQB, and Pink) based on firm quality and disclosure practices. Only SEC-reporting companies were included in our sample because non-SEC-reporting OTC firms do not consistently meet the disclosure standards necessary for reliable data, as highlighted by recent studies on modern OTC market structures (Davis et al., 2023).

To obtain the necessary information, the prospectus documents of the listed companies were obtained from the website of the regulatory authority. Listing refers to the process by which a company’s shares are officially traded on stock exchanges. When offering securities in the U.S., companies must comply with SEC Form S-1, a registration form based on the 1933 Securities Act, enacted to provide greater transparency in securities offerings. With Form S-1, companies are required to disclose data about their characteristics, business operations, management, and financial statements certified by independent auditors.

Given that this process is costly, heavily regulated, and monitored by the SEC (Statista, 2024b), the information contained in these IPO prospectuses, even if their primary purpose differs, has the necessary reliability to compile the data for this project.

Between 2003 and 2019, the population of the stock exchanges was 3,493 IPOs. The data was collected until 2019 due to the COVID-19 pandemic. With the advent of the pandemic, there was initially a pause in the launch of IPOs. Subsequently, IPOs launched between 2021 and 2023 were strongly influenced by the crisis, which could impact the actions and stakeholders affected in the creation of value. This is because the focus during this period was on employees and customers. Our sample included 850 prospectuses, with 50 IPO prospectuses chosen at random each year.

This sample was chosen based on Hair et al. (2014), who suggest that the ideal size should be at least 5 to 10 times the number of variables analyzed to ensure the robustness of the statistical analyses and the accuracy of the model estimates. This means that, even in cases of complex samples, such as that of the present study, the number of 850 companies is adequate to provide a representative and valid analysis. The division of the sample into 50 companies per year was established to ensure that each period was equally represented, allowing more balanced comparisons between the different years analyzed (Hair et al., 2014). After analysis, some prospectuses had to be discarded for the reasons listed below:

Data processing identified 18 instances of ambiguity, where an IPO form was rejected and resubmitted because information was missing on the first attempt. Twenty-four IPO prospectuses of special purpose acquisition companies (SPACs) were also removed because they were different in nature (in terms of structure, objectives, and regulatory processes) from companies already operating in the market and had no history of activities, thus not meeting the requirements of this research. Finally, 53 prospectuses were withdrawn because they did not contain all the sections that should be analyzed according to the procedures described above.

As a result, 755 IPOs comprised the final sample within the period specified, divided between the seven different industries pre-established by the SEC. Table 3 displays the number of companies in each industry that comprise the sample.

Table 3
Sample composition of IPOs by industry.

Industry	Total	%
Energy & Transportation	84	11%
Finance	52	7%
Life Sciences	204	27%
Manufacturing	117	16%
Real Estate & Construction	26	3%
Technology	122	16%
Trade & Services	150	20%
Total	755	100.0%

• Note. Developed by the authors.

Data collection

The dependent and independent variables were gathered from IPO prospectuses. The initial stage in creating a database for quantitative analysis involved a qualitative phase using content analysis. The purpose of content analysis is to comprehend meanings by allowing inferences to be drawn from observable snippets via word association (Bardin, 1977). The content analysis validated by Boaventura et al. (2020) was utilized: words were formed for each variable and counted using the NVivo program, resulting in the database used for the quantitative step. Creswell and Creswell (2017) mention that one of the great advantages of software like NVivo is the ability to organize large volumes efficiently, allowing the researcher to classify, code, and store data in an accessible way, which facilitates the analytical process.

IPO prospectuses must include details about the distribution of shares, financial information of the company, and any legal matters related to the offering. In addition, the company presents its future strategy, i.e., how the funds acquired through the capital opening will be used. For the purposes of this study, only the sections of the prospectuses that contain information about the company’s operations, its market, indications of future strategy, and risks related to the company and the market were considered. These sections follow similar standards between the U.S. Securities and Exchange Commission (SEC) and the Comissão de Valores Mobiliários (CVM) in Brazil.

They are suitable for use in SEC-regulated IPO prospectuses: (1) offering summary - a summary of the company and its strategy, located at the beginning of the prospectus; (2) analysis and discussion of financial and operating results; (3) risk factors - risks that the company poses in relation to its business and the offering of shares; and (4) description of business and market - more detailed information about the company’s operations and its market. Therefore, after validation between IPOs from the U.S. and Brazil, it is possible to use the same sections employed in the study by Boaventura et al. (2020) for the measurement of the variables.

Assignment of variables

To analyze the different variables, a list of words previously defined by Boaventura et al. (2020) was used, which correspond to value. The following synonym table, formulated based on the examples grouped by Tantalo and Priem (2016), is used when searching for words in IPOs to account for the frequency with which these words appear in relation to the targeted stakeholder group.

Boaventura et al.’s (2020) study provides a list of synonyms for stakeholders. As a result of this advancement and refinement of the process, a lexicon with synonyms of stakeholders was created, to which mutually exclusive actors from other industries, such as patients and foundries, were included. The newly created list is shown in Table 4.

Table 4
Synonyms for primary stakeholders.

Stakeholder	Synonyms
Customers	Customer, customers, client, clients, consumer, consumers, buyer, buyers, user, users, shopper, shoppers, tenant, tenants, patient, patients.
Communities	Community, communities, society, societies, population, ‘members of the public’, citizen, citizens, government, governments, authority, authorities, nation.
Employees	Employee, employees, attendant, attendants, laborer, laborers, ‘staff member’ ‘staff members’, worker, workers, personnel, ‘team member’, ‘team members’, crew, ‘work force’, staff, staffs, team, teams.
Suppliers	Supplier, suppliers, provider, providers, manufacturer, manufacturers, contractor, contractors, foundry, foundries, ‘third party’, ‘third parties’.
Shareholders	Shareholder, shareholders, investor, investors, shareowner, shareowners, bondholder, bondholders, stockholder, stockholders, unitholder, unitholders, holder, holders.

• Note. Developed by the authors.

Count of variables

First, for the dependent variable value, the count was performed as follows: each time, in the same paragraph, a value synonym (Table 5) appears together with its stakeholder (Table 6) in three sections: (1) offering summary; (2) analysis and discussion of financial and operating results; and (3) description of business and market. For the independent variable power, the number of times the stakeholder (Table 6) is mentioned in the risk factors section is recorded. And for the independent variable strategic importance, the count was the number of times the stakeholder (Table 6) is mentioned in the business description.

Table 5
Correlation of variables and descriptive statistics.

Correlation	Power	Strategic importance	Value
Power	1
Strategic importance	0.541	1
Value	0.584	0.743	1
Descriptive statistics	Power	Strategic importance	Value
Mean	0.358	0.183	0.352
Median	0.355	0.166	0.322
Standard deviation	0.123	0.101	0.164
Minimum	0	0.006	0.054
Maximum	1	1	1
Observations	3775	3775	3775

• Note. Developed by the authors.

Table 6
Descriptive statistics by stakeholder.

Descriptive statistics	Stakeholder	Obs.	Mean	Standard deviation	Min.	Max.
Power	Customers	755	0.248	0.198	0	1
	Communities		0.116	0.123	0	1
	Employees		0.241	0.139	0	1
	Suppliers		0.246	0.183	0	1
	Shareholders		0.169	0.100	0	1
Strategic importance	Customers	755	0.223	0.180	0	1
	Communities		0.067	0.073	0	1
	Employees		0.133	0.105	0	1
	Suppliers		0.144	0.128	0	1
	Shareholders		0.058	0.108	0	1
Value	Customers	755	0.201	0.147	0	1
	Communities		0.109	0.122	0	1
	Employees		0.243	0.154	0	1
	Suppliers		0.109	0.102	0	1
	Shareholders		0.148	0.125	0	1

• Note. Developed by the authors.

Data analysis

For the analysis, after collecting the number of words that appeared for each variable in the prospectuses, scores were created and normalized to remove any existing discrepancies. To test the hypotheses, four ordinary least squares regressions were used (Hair et al., 2014), presented below.

• Model 1: Value = β0 + Industry + Year

• Model 2: Value = β0 + β1 Power + Industry + Year

• Model 3: Value = ß0 + ß2 Strategic Importance + Industry + Year

• Model 4: Value = β0 + β1 Power + β2 Strategic Importance + Industry + Year

Where:

• (a) Value = Value distributed to stakeholders.

• (b) Power = Stakeholder’s power.

• (c) Strategic importance = Stakeholder’s strategic importance.

• (d) β0 = Constant’s coefficient.

• (e) ßi = Independent variables coefficients.

• (f) Industry = Dummy variable for industry.

• (g) Year = Dummy variable for IPOs’ year.

In analyses based on OLS regression, it is necessary to perform tests for collinearity and homoscedasticity to determine whether the model used is working adequately. The collinearity test uses the variance inflation factors (VIF), which indicate the correlations between the different variables and the inflation of their variances. To check for homoscedasticity - i.e., constant error variance across different observations - the Breusch-Pagan LM test is used (Breusch & Pagan, 1979).

RESULTS

Correlations and descriptive statistics

The study sample included the IPOs of 755 companies. As shown in Table 6, most of the sample is represented by companies from the life sciences industry (27%). The next most frequently represented industries are trade and services (20%), technology (16%), manufacturing (16%), and energy and transportation (11%). The correlations of the variables and their descriptive statistics - mean, standard deviation, minimum value, maximum value, and number of observations - are shown in Table 5. Since there are five groups of stakeholders, the number of observations is five times larger than that of companies.

The independent variables power and strategic importance are less correlated with each other compared to their correlations with the dependent variable. The correlation between strategic importance and value is the highest among all variables. A preliminary analysis of the behavior of the variables shows that strategic importance has the lowest mean and standard deviation, followed by value, which has a medium mean and the highest standard deviation among the variables, and finally power, with a higher mean and a medium standard deviation.

Table 6 shows the descriptive statistics - mean, standard deviation, minimum value, maximum value, and number of observations - of the variables among the stakeholder groups of all sample companies. Customers, suppliers, and employees, in that order, were the stakeholders with the highest averages for the variables power and strategic importance.

As for the value variable, there was a change in order compared to the variables mentioned above: shareholders stepped in, and suppliers dropped out. The stakeholders with the highest average values and standard deviations are, in descending order, employees, customers, and shareholders, while communities and suppliers have the lowest average values and standard deviations. Table 7 shows the descriptive statistics - mean, standard deviation, and number of observations - of the variables across the industries of all companies in the sample.

Table 7
Descriptive statistics by industry.

Descriptive statistics	Industry	Obs.	Mean	Standard deviation
Power	Energy & Transportation	420	0.273	0.132
	Finance	260	0.283	0.088
	Life Sciences	1020	0.354	0.086
	Manufacturing	585	0.368	0.115
	Real Estate & Construction	130	0.306	0.102
	Technology	610	0.452	0.140
	Trade & Services	750	0.361	0.111
Strategic importance	Energy & Transportation	420	0.111	0.091
	Finance	260	0.152	0.070
	Life Sciences	1020	0.159	0.063
	Manufacturing	585	0.191	0.087
	Real Estate & Construction	130	0.193	0.175
	Technology	610	0.256	0.112
	Trade & Services	750	0.201	0.102
Value	Energy & Transportation	420	0.236	0.152
	Finance	260	0.284	0.116
	Life Sciences	1020	0.310	0.107
	Manufacturing	585	0.369	0.126
	Real Estate & Construction	130	0.303	0.120
	Technology	610	0.489	0.192
	Trade & Services	750	0.384	0.171

• Note. Developed by the authors.

The industry with the highest average for the three variables was technology. The trade and service industry had the second-highest average for the variables strategic importance and value, and the third-highest average for power. The manufacturing industry also gained prominence by presenting the second-highest average for the power variable and the third-highest average for the value variable. When the three variables are considered together, the industries with the lowest average values are finance and energy and transportation.

Test of hypotheses

The next step is to check whether the variables fit the (OLS) regression. In the variance inflation factor (VIF) test, where values above 10 may indicate a collinearity problem, the independent variables power and strategic importance had a result below 2. The Breusch-Pagan LM test identified heteroskedasticity in the sample. However, it is common and recommended in econometric analysis to use a standard error correction that is robust to heteroskedasticity (Wooldridge, 2015), which was used in all OLS regression models in this study.

Table 11 shows the results of the models that include the distribution of the dependent variable and the independent variables power and strategic importance, in the sum of the stakeholder groups. The models used the dummy variables industry trade and services and year 2019 as the industry and year of reference, so they are omitted from the display in Table 8.

Table 8
Result of regressions.

	Model 1	Model 2	Model 3	Model 4
Variables	Coefficient	Coefficient	Coefficient	Coefficient
Constant	0.384***	0.112***	0.161***	0.068***
Power		0.681***		0.327***
Strategic Importance			1.085***	0.899***
Energy & Transportation	−0.146***	−0.091***	−0.053***	−0.043***
Finance	−0.108***	−0.053***	−0.049***	−0.033**
Life Sciences	−0.074***	−0.065***	−0.026**	−0.03***
Manufacturing	−0.019	−0.025	−0.008	−0.013
Real Estate & Construction	−0.082***	−0.046*	−0.075***	−0.059***
Technology	0.106***	0.045***	0.046***	0.027**
2003	0.042	0.06**	0.0198	0.0321
2004	0.035	0.062**	0.0282	0.0421**
2005	0.033	0.068**	0.0161	0.0357
2006	0.013	0.052*	0.0105	0.0297
2007	−0.0005	0.028	0.0044	0.0171
2008	0.014	0.065**	0.0361	0.0569**
2009	0.009	0.041	0.0083	0.0241
2010	−0.0382	−0.015	−0.0076	−0.0018
2011	−0.0327	0.007	0.0006	0.0139
2012	−0.0122	0.007	−0.0026	0.0048
2013	−0.0037	0.031	0.0209	0.0335
2014	−0.0027	0.026	0.0038	0.0164
2015	0.015	0.051*	0.0048	0.0239
2016	−0.0094	0.016	−0.0161	−0.0029
2017	−0.0308	−0.024	−0.0387*	−0.0342
2018	−0.0219	−0.022	0.0025	−0.0016
N	3775	3775	3775	3775
R²	0.231***	0.439***	0.596***	0.633***
* p < 0.1 ** p < 0.05 *** p < 0.01

• Note. Developed by the authors.

Model 1 of our regression analysis included only the constant and control variables. In this model, the constant and the dummy variables representing different industries showed statistically significant parameters at the 1% significance level. However, an exception was noted in the manufacturing industry, where these parameters were not statistically significant. Additionally, none of the years included in the model showed statistical significance. The R-squared (R²) value of this model was 0.231, also significant at the 1% level. This means that Model 1 explains 23.1% of the total variation in the dependent variable, which in this case is value.

In Model 2, the independent variable power, the constant, and the dummy variables were used. In this model, the constant had a positive coefficient of 0.112, and the independent variable power had a positive coefficient of 0.681, both statistically significant at the 1% significance level. Among the dummy variables significant at the 1% level - energy and transportation, finance, life sciences, and technology - only technology had a positive coefficient. In this model, the R² at the 1% significance level was 0.439, which means that 43.9% of the overall variation in the dependent variable value can be explained by Model 2.

In Model 3, the independent variable strategic importance, the constant, and the dummy variables were used for regression. In this model, the constant had a positive coefficient of 0.161, and the independent variable strategic importance had a positive coefficient of 1.085, both with statistically significant parameters at the 1% significance level. Among the dummy variables significant at the 1% level, only energy and transportation, finance, real estate and construction, and technology had positive coefficients. In this model, the R² at the 1% significance level was 0.596, which means that 59.6% of the total variation in the dependent variable value can be explained by Model 3.

In Model 4, the two independent variables power and strategic importance, along with the constant and dummy variables, were used for regression. In this model, the constant had a positive coefficient of 0.068, and the independent variables power and strategic importance had positive coefficients of 0.327 and 0.899, respectively, with all three parameters being statistically significant at the 1% level. All dummy variables significant at the 1% level - energy and transportation, life sciences, and real estate and construction - had negative coefficients. In this model, the R² at the 1% level was 0.633, meaning that 63.3% of the overall variation in the dependent variable value can be explained by Model 4.

The outcomes from Models 2 and 3 reveal that one independent variable was statistically significant and had an impact on the variable value. Furthermore, in Model 4, both the strategic importance and power variables displayed positive and significant coefficients when included together in the same model. These results collectively lend support to Hypothesis 1 (H1).

Table 9 shows the results of the OLS method regressions for each group of stakeholders separately, i.e., five separate regressions combined in the same table to avoid redundancy. As mentioned earlier, a robust standard error correction was used, and the VIF test yielded a value of less than 10.

Table 9
Result of regressions for each group of stakeholders.

	Power		Strategic importance		R²
	Coefficient	p-value	Coefficient	p-value
Customers	0.168	***	0.547	***	0.755***
Communities	0.078	***	1.259	***	0.655***
Employees	0.245	***	0.725	***	0.434***
Suppliers	0.050	***	0.501	***	0.507***
Shareholders	0.251	***	0.497	***	0.348***
* p < 0.1	** p < 0.05		*** p < 0.01

• Note. Developed by the authors.

The regressions run separately for each stakeholder group showed consistent results compared to those in Model 4, where stakeholders were grouped together. The two independent variables, power and strategic importance, had statistically significant parameters at the 1% level for all stakeholder groups, with the coefficient for strategic importance being larger than that of power in all cases. The R² for all models also showed significance at the 1% level. Among the R² values, the consumers group presented the highest coefficient, with the model explaining 75.5% of the influence on value creation, followed by the community, with 65.5%.

Employees and consumers were the stakeholder groups that stood out the most in both variables, ranking second and third with the highest coefficients. The group with the most influence shifted: in power, the most influential group was shareholders; in strategic importance, it was the community.

Considering Models 2 and 3, where strategic importance (1.085) had a higher coefficient than power (0.681), and the stakeholder group analyses, where the coefficient for strategic importance was higher for all groups compared to power - with the largest differences observed for community and suppliers - we confirm H2, that strategic importance has more influence on value creation.

Industries analysis: Understanding the behavior

The industry was used as a control variable in the previous models to assess general impact. However, to understand behavior and differences in greater depth, a regression was performed for each industry to assess how the relationship between value, strategic importance, and power operates in different sectors. Table 10 summarizes the outcomes. As before, a robust standard error correction was used, and the VIF test yielded a value of less than 10.

Table 10
Result of regressions for each industry.

	Power		Strategic importance		R²	N
Industry	Coefficient	p-value	Coefficient	p-value
Energy & Transportation	0.317	***	1.123	***	0.747***	420
Finance	0.084		1.209	***	0.681***	260
Life Sciences	0.159	**	0.959	***	0.455***	1020
Manufacturing	0.395	***	0.714	***	0.556***	585
Real Estate & Construction	0.659	*	0.044		0.572	130
Technology	0.214	**	1.238	***	0.694***	610
Trade & Services	0.389	***	1.008	***	0.676***	750
* p < 0.1	** p < 0.05		*** p < 0.01

• Note. Developed by the authors.

Table 11
Comparison of results in the countries studied.

		Brazil	Canada	United States
Variables analyzed in each model		Beta	Beta	Beta
1.	Strategic importance	0.418	0.650	1.085
2.	Power	0.327	0.465	0.618
3.	Strategic importance	0.242	0.537	0.899
	Power	0.253	0.221	0.327

• Note. Developed by the authors.

The only industry where the general model was not statistically significant was real estate and construction. Power was not statistically significant in the finance industry, showed significance at the 1% level in three of them (energy and transportation, manufacturing, and trade and services) and at the 5% level in two (life sciences and technology). The independent variable strategic importance showed statistically significant parameters at the 1% level in all industries. Comparing the betas of the independent variables, in all industries the strategic importance variable had a considerably higher coefficient than power.

Regarding the explanatory power of the models by industry, the R² values were high. The lowest explanatory power was for the life sciences industry, at 45.5%; and the highest was for energy and transportation, at 74.7%. Therefore, it is legitimate to confirm Hypothesis 3, which states that the behavior of value distribution and its relationship with stakeholder power and strategic importance differs across industries.

DISCUSSION

The United States is considered the standard model of a liberal market economy, where companies rely on competitive market arrangements to coordinate their actions. The allocation mechanism is primarily based on market supply and demand, as noted by Hall and Soskice (2001) and Fainshmidt et al. (2018). In contrast, other countries, such as Brazil, have wealthy and dominant families and groups as central ordering agents of economic life. These groups take center stage in ownership, resource allocation, and management. This divergence in economic structures implies that the institutional context shapes not only governance practices but also the mechanisms of value distribution. Despite similarities with the Canadian case, which was also studied earlier, insights about the U.S. hold particular importance due to its global representation in the world economy.

In sum, after analyzing the results, it can be concluded that strategic importance holds more significance than stakeholder power in the U.S. institutional context when assigning value to stakeholders. This result is in line with the findings of Boaventura et al. (2020), who analyzed Brazil, and Araujo et al. (2024), who analyzed Canada, allowing an even greater level of empirical inference due to the strength of the U.S. market.

In this regard, power and strategic importance significantly influence the distribution of value to stakeholders in publicly traded companies, particularly in the context of the United States. This finding aligns with the dynamics observed in the relationships between companies and their stakeholders, as discussed by Bosse and Coughlan (2016). Specifically, companies that distribute value to stakeholders tend to increase stakeholder commitment to overall value creation.

The results presented in this study not only confirm but also strengthen, with empirical evidence, the arguments of other studies applying similar frameworks, where the results for Models 2 and 3 showed significance at the 1% level (Araujo et al., 2024; Boaventura et al., 2020). Moreover, our findings extend the theoretical framework by demonstrating that the integration of power and strategic importance is essential for value creation - that is, neither factor in isolation suffices to optimize total value (Harrison et al., 2010; Harrison & Bosse, 2013).

With the validation of the predictions regarding variable behavior, it was feasible to compare the results across the three previously investigated countries. Table 9 summarizes the coefficient results from the models. In this approach, we observed, regarding the strategic importance variable, that although it is significant in all three models, its coefficient increases with economic development. This rise in strategic relevance also appears in the model analyzing all variables together. We can conclude that there is a connection between economic progress and the increased value attributed to stakeholders who contribute strategically.

However, we observed that with the power variable, in the model in which the variable is studied independently, there is growth based on the economies of the nations, albeit not as expressive as in strategic significance. The difference in coefficients between countries is considerably lower in the study of the power variable in the general model, indicating that the difference in value distribution between countries, based on their development, is comparable. Thus, stakeholders with higher power, regardless of the country, obtain a comparable allocation, sometimes due to power.

When we compare the distribution of value between different countries in the whole model, we see that the gap between strategic importance and power grows with economic development. As a result, economically less developed countries may face increased pressure from stakeholders who have some form of authority to receive value, regardless of their contribution to its creation. In more economically developed countries, the stakeholder who truly contributes to producing value receives more than the stakeholder who merely has power. This difference empirically expresses that interactions with groups of stakeholders have different approaches and are influenced by the nature and degree of interdependence between companies and their stakeholders (Freeman et al., 2020).

Building upon this, our findings suggest that the institutional and political context of economies may play an essential role in shaping how companies distribute value to stakeholders. In developed economies, characterized by robust market institutions and strict regulatory frameworks, merit-based and value-driven stakeholder relationships appear to prevail over relationships based primarily on power or political influence. Conversely, weaker institutional contexts, common in developing markets, might encourage or allow political influence and power to play a greater role in stakeholder value distribution (Santos Jhunior et al., 2025).

From the results of the comparison of Models 2, 3, and 4, it appears that the two independent variables, power and strategic importance, are more appropriate when they are used together (Mitchell et al., 2017), in line with the argument that the value distributed to stakeholders is increased when they have a high combination of power and strategic importance to the companies (Harrison & Bosse, 2013). This is because the strategic importance or power of the stakeholders by themselves may not represent the optimal set for creating total value (Harrison et al., 2010).

When comparing various industries against the variables of power and strategic importance, a notable pattern emerges: stakeholders with high strategic importance consistently have a more significant influence on value creation than those who hold power. This trend is observed across all industries, with the sole exception being the real estate and construction industries. Remarkably, in these industries, the influence of strategically important stakeholders is at least three times greater than that of their powerful counterparts. Of the three industries that showed significance at the 1% level for the independent variables, the amplitude of the predominance of strategic importance over power ranged from 81% in manufacturing to 254% in energy and transportation. If we look at the five industries where power has statistical significance at least at the 5% level, the magnitude of the difference is even larger, ranging from 81% in manufacturing to 503% in life sciences.

These variations likely reflect underlying structural differences in the value drivers unique to each industry. For instance, in manufacturing, value creation is largely driven by factors such as alignment of interests, commitment, and conflict resolution, factors that account for an 81% difference between the effects of power and strategic importance. In the energy and transportation industry, key value drivers like productive capacity, energy availability, and environmental sustainability amplify the relative impact of strategic importance (Macharis et al., 2010; Turcksin et al., 2011), resulting in a 254% difference. In life sciences, where factors such as biological safety, human health, and innovation are critical (Doerr-MacEwen & Haight, 2006; Zhang & Sun, 2022), the gap can reach up to 503%. These empirical findings suggest that industry-specific factors - such as production processes, regulatory environments, and technological dependencies - play an important role in shaping stakeholder value distribution. With that, the influence of the industry on the stakeholder engagement of companies (Torelli et al., 2020), as well as the intensity of the different requirements between them (Fineman & Clarke, 1996), becomes clear.

The strategic importance variable is higher in the technology industry compared to other industries; and compared to the manufacturing industry, it is almost double. This occurs due to the difference in the products offered: technology companies depend, most of the time, on the intellectual capital of the stakeholders - those who help to create it. On the other hand, in manufacturing companies, where the product is often mass-produced and has low added intellectual value, the influence of stakeholders who assist in the process tends to be lower. As for the power variable, differences between industries exist; however, they are smaller by comparison. This is because, regardless of the industry, stakeholders with greater power have the coercive capacity to exercise their will, independent of their contribution alongside the company (Mitchell et al., 2017).

From a practical perspective, these results provide managers with clear guidance on tailoring stakeholder value distribution strategies according to industry-specific value drivers. For example, in the technology industry, relevant stakeholders such as software developers, providers of software, communication operators, and microcomputer assemblers are critical for ensuring continuous improvement, effective requirements elicitation, robust technological infrastructure, and efficient data processing (Imran Babar et al., 2014). This suggests that technology firms should prioritize relationships with stakeholders who actively contribute to innovation and system performance. In contrast, in the manufacturing industry, where value creation depends more on operational efficiency and process reliability, driven by factors such as alignment of interests and conflict resolution, companies should focus on ensuring robust process management and operational consistency.

CONCLUDING REMARKS

This study pursued two primary objectives: (1) to investigate how power and strategic importance influence value distribution to stakeholders within a liberal market economy, and (2) to assess how these relationships vary across industries.

Listed companies in the world’s largest capital market (Statista, 2024a) strategically consider the value drivers of primary stakeholders (Harrison et al., 2010; Harrison & Wicks, 2013; Tantalo & Priem, 2016) when seeking cooperation and engagement. This finding aligns with evidence from Brazilian (Boaventura et al., 2020) and Canadian (Araujo et al., 2024) markets.

From a theoretical standpoint, this study contributes to instrumental stakeholder theory (Donaldson & Preston, 1995; Jones et al., 2018) by showing that stakeholders with greater strategic importance (e.g., innovation, intellectual capital, knowledge sharing) are prioritized over those relying primarily on power (Harrison et al., 2010; Harrison & Bosse, 2013; Mitchell et al., 2017). These findings enhance the understanding of how institutional environments (Fainshmidt et al., 2018) shape stakeholder engagement and value distribution (Freeman et al., 2020).

By adopting a sectoral approach, this study highlights industry-specific differences in stakeholder engagement. Stakeholders with high power exert limited influence in finance, while strategic importance significantly drives value creation in technology and finance, enabling managers to prioritize stakeholders more effectively (Mitchell et al., 2017).

This study expands stakeholder theory by emphasizing that stakeholder management requires diverse strategies adapted to each industry’s dynamics, including stakeholder expectations, power relations, and economic impacts. There is no single approach to managing stakeholders; rather, strategies must reflect industry-specific realities.

From a practical perspective, businesses should develop stakeholder management policies that prioritize strategic contributions rather than relying solely on power-based dynamics (Harrison & Bosse, 2013). Managers can benefit from fostering cooperation, trust-based engagement, and reciprocal relationships to enhance value creation (Freeman et al., 2020; Jones et al., 2018).

Managers in multinational enterprises should also balance stakeholder expectations, adapting their engagement strategies to reflect the specific institutional contexts of different regions (Saka-Helmhout et al., 2024). For instance, technology firms should prioritize innovation-driven stakeholders, while manufacturing firms should focus on operational efficiency (Mitchell et al., 2017; Torelli et al., 2020).

A key methodological limitation of this study is the reliance on IPO prospectuses, which may introduce bias due to strategic disclosures made during public offerings. Future research could mitigate this limitation by using more diverse data sources, such as interviews and financial reports, to validate these insights. Future studies should also explore other factors influencing stakeholder value distribution, such as labor coordination, ownership concentration, indirect value capture, and political influences. Examining the long-term relationship between stakeholder value distribution and corporate performance could further refine stakeholder theory and provide valuable insights for managers and policymakers. These recommendations aim to advance stakeholder theory and offer clearer guidance for managing stakeholder relationships across diverse industries and market conditions.

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• How to cite:
  Araújo, M. M., Santos Jhunior, R. O., Uchôa, M., Mello, A. A. R., & Boaventura, J. M. G. (2025). Distributing value to stakeholders across industries: Influences of power and strategic importance. BAR-Brazilian Administration Review, 22(3), e240216. DOI: https://doi.org/10.1590/1807-7692bar2025240216
• Funding:
  Fundação de Amparo à Pesquisa do Estado de São Paulo (2020/06015-3). Conselho Nacional de Desenvolvimento Científico e Tecnológico (308687/2022-1 and 422628/2021-2), and Fundação Instituto de Administração (FIA).
• Peer Review Report:
  The Peer Review Report is available at this external URL
• Data Availability:
  Mendonça de Araújo, Maurício; Oliveira Santos Jhunior, Ronaldo; Uchoa, Mariana; Mello, André ; Boaventura, João Mauricio Gama. (2025), Data for “Distributing Value to Stakeholders across Industries: Influences of Power and Strategic Importance” published by BAR - Brazilian Administration Review, Mendeley Data, V1. doi: https://doi.org/10.17632/b89xw5jwtz.1. BAR - Brazilian Administration Review encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects.
• Plagiarism Check:
  BAR maintains the practice of submitting all documents received to the plagiarism check, using specific tools, e.g.: iThenticate.
• JEL Code:
  M10, C12, L25

Edited by

• Editors-in-Chief:
  Ricardo Limongi https://orcid.org/0000-0003-3231-7515
  (Universidade Federal de Goiás, Brazil)

• Associate Editor:
  Marina Gama https://orcid.org/0000-0003-2154-1984
  (Pontifícia Universidade Católica do Paraná, Brazil)

Edited by

• Editorial assistants:
  Eduarda Anastacio and Simone Rafael (ANPAD, Maringá, Brazil).

Data availability

Mendonça de Araújo, Maurício; Oliveira Santos Jhunior, Ronaldo; Uchoa, Mariana; Mello, André ; Boaventura, João Mauricio Gama. (2025), Data for “Distributing Value to Stakeholders across Industries: Influences of Power and Strategic Importance” published by BAR - Brazilian Administration Review, Mendeley Data, V1. doi: https://doi.org/10.17632/b89xw5jwtz.1. BAR - Brazilian Administration Review encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects.

Publication Dates

• Publication in this collection
  20 Oct 2025
• Date of issue
  2025

History

• Received
  03 Dec 2024
• Accepted
  01 May 2025
• Published
  30 July 2025