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The impact of corporate social responsibility on financial performance and brand value.

1. Introduction

2. conceptual model and research assumptions, 2.1. conceptual model, 2.2. research assumptions, 2.2.1. the impact of corporate social responsibility on financial performance, 2.2.2. the impact of corporate social responsibility on brand value, 2.2.3. the regulatory role of horizontal social capital, 2.2.4. the regulatory role of vertical social capital, 3. model setting and data selection, 3.1. data sources, 3.2. variable setting, 3.2.1. dependent variables, 3.2.2. explanatory variables, 3.2.3. moderating variables, 3.2.4. control variables, 3.3. model settings, 4. analysis of empirical results, 4.1. descriptive statistics and correlation analysis, 4.1.1. descriptive statistical features, 4.1.2. correlation coefficient, 4.1.3. collinearity test, 4.2. empirical research results, 4.2.1. benchmark regression results, 4.2.2. robustness test results, replacing the dependent variable, changing the regression method, 4.2.3. discussion on endogeneity issues, 4.2.4. extensibility research, group testing of compliant disclosure and voluntary disclosure, group testing of heavy-polluting and non-heavily-polluting enterprises, 5. suggestions and prospects, 5.1. conclusions, 5.2. discussion of results, 5.3. research suggestions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Zhang, J.; Liu, Z. The Impact of Corporate Social Responsibility on Financial Performance and Brand Value. Sustainability 2023 , 15 , 16864. https://doi.org/10.3390/su152416864

Zhang J, Liu Z. The Impact of Corporate Social Responsibility on Financial Performance and Brand Value. Sustainability . 2023; 15(24):16864. https://doi.org/10.3390/su152416864

Zhang, Jing, and Ziyang Liu. 2023. "The Impact of Corporate Social Responsibility on Financial Performance and Brand Value" Sustainability 15, no. 24: 16864. https://doi.org/10.3390/su152416864

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• Published: 01 November 2021

The impact of corporate governance measures on firm performance: the influences of managerial overconfidence

• Tolossa Fufa Guluma   ORCID: orcid.org/0000-0002-1608-5622 1  

Future Business Journal volume  7 , Article number:  50 ( 2021 ) Cite this article

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The paper aims to investigate the impact of corporate governance (CG) measures on firm performance and the role of managerial behavior on the relationship of corporate governance mechanisms and firm performance using a Chinese listed firm. This study used CG mechanisms measures internal and external corporate governance, which is represented by independent board, dual board leadership, ownership concentration as measure of internal CG and debt financing and product market competition as an external CG measures. Managerial overconfidence was measured by the corporate earnings forecasts. Firm performance is measured by ROA and TQ. To address the study objective, the researcher used panel data of 11,634 samples of Chinese listed firms from 2010 to 2018. To analyze the proposed hypotheses, the study employed system Generalized Method of Moments estimation model. The study findings showed that ownership concentration and product market competition have a positive significant relationship with firm performance measured by ROA and TQ. Dual leadership has negative relationship with TQ, and debt financing also has a negative significant association’s with both measures of firm performance ROA and TQ. Moreover, the empirical results also showed managerial overconfidence negatively influences the relationship of board independence, dual leadership, and ownership concentration with firm performance. However, managerial overconfidence positively moderates the impact of debt financing on firm performance measured by Tobin’s Q and negative influence on debt financing and operational firm performance relationship. These findings have several contributions: first, the study extends the literature on the relationship between CG and a firm’s performance by using the Chinese CG structure. Second, this study provides evidence that how managerial behavioral bias interacts with CG mechanisms to affect firm performance, which has not been studied in previous literature. Therefore, the results of this study contribute to the theoretical perspective by providing an insight into the influencing role of managerial behavior in the relationship between CG practices and firm performance in an emerging markets economy. Hence, the empirical result of the study provides important managerial implications for the practice and is important for policy-makers seeking to improve corporate governance in the emerging market economy.

Introduction

Corporate governance and its relation with firm performance, keep on to be an essential area of empirical and theoretical study in corporate study. Corporate governance has got attention and developed as an important mechanism over the last decades. The fast growth of privatizations, the recent global financial crises, and financial institutions development have reinforced the improvement of corporate governance practices. Well-managed corporate governance mechanisms play an important role in improving corporate performance. Good corporate governance is fundamental for a firm in different ways; it improves company image, increases shareholders’ confidence, and reduces the risk of fraudulent activities [ 67 ]. It is put together on a number of consistent mechanisms; internal control systems and external environments that contribute to the business corporations’ increase successfully as a complete to bring about good corporate governance. The basic rationale of corporate governance is to increase the performance of firms by structuring and sustaining initiatives that motivate corporate insiders to maximize firm’s operational and market efficiency, and long-term firm growth through limiting insiders’ power that can abuse over corporate resources.

Several studies are contributed to the effect of CG on firm performance using different market developments. However, there is no consensus on the role CG on firm performance, due to different contextual factors. The role of CG mechanisms is affected by different factors. Prior studies provided different empirical evidence such as [ 14 ], suggested that the monitoring efficiency of the board of directors is affected by internal and external factors like government regulation and internal firm-specific factors; the role of board monitoring is determined by ownership structure and firm-specific characters Boone et al. [ 8 ], and Liu et al. [ 57 ] and Bozec [ 10 ] also reported that external market discipline affects the internal CG role on firm performance. Moreover, several studies studied the moderation role of different variables in between CG and firm value. Mcdonald et al. [ 63 ] studied CEO experience moderating the board monitoring effectiveness, and [ 60 ] studied the moderating role of product market competition in between internal CG and firm performance. Bozec [ 10 ] studied market disciple as a moderator between the board of directors and firm performance. As to the knowledge of the researcher, no study considered the influencing role of managerial overconfidence in between CG mechanisms and firm corporate performance. Thus, this study aims to investigate the influence of managerial overconfidence in the relationship between CG mechanisms and firm performance by using Chinese listed firms.

Managers (CEOs) were able to valuable contributions to the monitoring of strategic decision making [ 13 ]. Behavioral decision theory [ 94 ] suggests that overconfidence, as one type of cognitive bias, encourages decision-makers to overestimate their information and problem-solving capabilities and underestimates the uncertainties facing their firms and the potential losses from litigation associated with claims against them. Several prior studies reported different results of the manager's role in corporate governance in different ways. Previous studies claimed that overconfidence is a dysfunctional behavior of managers that deals with unfavorable consequences for the firm outcome, such as value distraction through unprofitable mergers and suboptimal investment behavior [ 61 ], and unlawful activities (Mishina et al. [ 64 ]). Oliver [ 68 ] argued the human character of individual managers affects the effectiveness of corporate governance. Top managers' behaviors and experience are primary determinants of directors' ability to effectively evaluate their managerial decision-making [ 45 ]. In another way, [ 47 , 58 ] noted managerial overconfidence can encourage some risk and make up for managerial risk aversion, which leads to suboptimal investment decisions. Jensen [ 41 ] suggested in the presence of free cash flow, the manager may overinvest and they can accept a negative net present value project. Therefore, the existence of CG mechanisms aims to eliminate or reduce the effect of agency and asymmetric information on the CEO’s decisions [ 62 ]. This means that the objectives of CG mechanisms are to counterbalance the effect of such problems in the corporate organization that may affect the value of the firms in the long run. Even with the absence of agency conflicts and asymmetric information problems, there is evidence documented for distortions such as the case of corporate investment. Managers will over- or under-invest regarding their optimism level and the availability of internal cash flow.

Agency theory by Jensen and Meckling [ 42 ] has a very clear vision of the problems that exist in the company to know the disagreement of interests between shareholders and managers. Irrational behavior of management resulting from behavioral biases of executive managers is a great challenge in corporate governance [ 44 ]. Overconfidence may create more agency conflict than normal managers. It may lead internal and external CG mechanisms to decisions which damage firm value. The role of CG mechanisms mitigating corporate governance results from agency costs, information asymmetry, and their impact on corporate decisions. This means the behavior of overconfident executives may affect controlling and monitoring role of internal/external CG mechanisms. According to Baccar et al. [ 5 ], suggestion is that one of the roles of corporate governance is controlling such managerial behavioral bias and limiting their potential effects on the company’s strategies. These discussions lead to the conclusion that CEO overconfidence will negatively or positively influence the relationships of CG on firm performance. The majority of studies in the corporate governance field deal with internal problems associated with managerial opportunism, misalignment of objectives of managers and stakeholders. To deal with these problems, the firm may organize internal governance mechanisms, and in this section, the study provides a review of research focused on this specific aspect of corporate governance.

Internal CG includes the controlling mechanism between various actors inside the firm: that is, the company management, its board, and shareholders. The shareholders delegate the controlling function to internal mechanisms such as the board or supervisory board. Effective internal CG is essential in accomplishing company strategic goals. Gillan [ 30 ] described internal mechanisms by dividing them into boards, managers, shareholders, debt holders, employees, suppliers, and customers. These internal mechanisms of CG work to check and balance the power of managers, shareholders, directors, and stakeholders. Accordingly, independent board, CEO duality, and ownership concentration are the main internal corporate governance controlling mechanisms suggested by various researchers in the literature. Thus, the study considered these three internal corporate structures in this study as internal control mechanisms that affect firm performance. Concurrently, external CG mechanisms are mechanisms that are not from the inside of the firm, which is from the outside of the firms and includes: market competition, take over provision, external audit, regulations, and debt finance. There are a lot of studies that examine and investigate the effect of external CG practices on the financial performance of a company, especially in developed nations. In this study, product market competition and debt financing have been taken as representatives of external CG mechanisms. Thus, the study used internal CG measures; independent board, dual leadership, ownership concentration, and product-market competition, and debt financing as a proxy of external CG measures.

Literature review and hypothesis building

Corporate governance and firm performance.

Corporate governance has got attention and developed as a significant mechanism more than in the last decades. The recent financial crises, the fast growth of privatizations, and financial institutions have reinforced the improvement of corporate governance practices in numerous institutions of different countries. As many studies revealed, well-managed corporate governance mechanisms play an important role in providing corporate performance. Good corporate governance is fundamental for a firm in several ways: OECD [ 67 ] indicates the good corporate governance increases the company image, reduces the risks, and boosts shareholders' confidence. Furthermore, good corporate governance develops a number of consistent mechanisms, internal control systems and external environments that contribute to the business corporations’ increase effectively as a whole to bring about good corporate governance.

The basic rationale of corporate governance is to increase the performance of companies by structuring and sustaining incentives that initiate corporate managers to maximize firm’s operational efficiency, return on assets, and long-term firm growth through limiting managers’ abuse of power over corporate resources.

Corporate governance mechanisms are divided into two broad categories: internal corporate governance and external corporate governance mechanisms. Supporting this concept, Keasey and Wright [ 43 ] indicated corporate governance as a framework for effective monitoring, regulation, and control of firms which permits alternative internal and external mechanisms for achieving the proposed company’s objectives. The achievement of corporate governance relies on the mechanism effectiveness of both internal and external governance structures. Gillan [ 30 ] suggested that corporate governance can be divided into two: the internal and external mechanisms. Gillan [ 30 ] described internal mechanisms by dividing into boards, managers, shareholders, debt holders, employees, suppliers, and customers, and also explain external corporate governance mechanisms by incorporating the community in which companies operate, the social and political environment, laws and regulations that corporations and governments involved in.

The internal mechanisms are derived from ownership structure, board structure, and audit committee, and the external mechanisms are derived from the capital market corporate control market, labor market, state status, and investors activate [ 26 ]. The balance and effectiveness of the internal and external corporate governance practices can enhance a better corporate operational performance [ 21 ]. Literature argued that integrated and complete governance mechanisms are better with multi-dimensional theoretical view [ 87 ]. Thus, the study includes both internal and external CG mechanisms to broadly show the connection of these components. Filatotchev and Nakajima [ 26 ] suggest that an integrated approach bringing external and internal mechanisms jointly enhances to build up a more general view on the effectiveness and efficiency of different corporate governance mechanisms. Thus, the study includes both internal and external CG mechanisms to broadly show the connection of these three components.

Board of directors and ownership concentration are the main internal corporate governance mechanisms and product market competition and debt finance also the main representative of external corporate governance suggested by many researchers in the literature that were used in this study. Therefore, the following sections provide a brief discussion of internal and external corporate governance from different angles.

Independent board and firm performance

Board of directors monitoring has been centrally important in corporate governance. Jensen [ 41 ] board of directors is described as the peak of the internal control system. The board represents a firm’s owners and is responsible for ensuring that the firm is managed effectively. Thus, the board is responsible for adopting control mechanisms to ensure that management’s behavior and actions are consistent with the interest of the owners. Mainly the responsibility of the board of directors is selection, evaluation, and removal of poorly performing CEO and top management, the determination of managerial incentives and monitoring, and assessment of firm performance [ 93 ]. The board of directors has the formal authority to endorse management initiatives, evaluate managerial performance, and allocate rewards and penalties to management on the basis of criteria that reflect shareholders’ interests.

According to the agency theory board of directors, the divergence of interests between shareholders and managers is addressed by adopting a controlling role over managers. The board of directors is one of the key governance mechanisms; the board plays a pivotal role in monitoring managers to reduce the problems associated with the separation of ownership and management in corporations [ 24 ]. According to Chen et al. [ 16 ], the strategic role of the board became increasingly important and going beyond the mere approval of strategic management decisions. The board of directors must serve to reconcile management decisions with the objectives of shareholders and stakeholders, which can at times influence strategic decisions (Uribe-Bohorquez [ 85 ]). Therefore, the board's responsibilities extend beyond controlling and monitoring management, ensuring that it takes decisions that are reliable with the corporations [ 29 ]. In the perspective of resource dependence theory, an independent director is often linked firm to outside environments, who are non-management members of the board. Independent boards of directors are more believed to be effective in protecting shareholders' interests resulting in high performance [ 26 ]. This focus on board independence is grounded in agency theory, which addresses inefficiencies that arise from the separation of ownership and control [ 24 ]. As agency theory perspective boards of directors, particularly independent boards are put in place to monitor managers on behalf of shareholders [ 59 ].

A large number of empirical studies are undertaken to verify whether independent directors perform their governance functions effectively or not, but their results are still inconclusive. Studies [ 2 , 50 , 52 , 56 , 85 ], reported the supportive arguments that independent board of directors and firm performance have a positive relationship; in other ways, a large number of studies [ 6 , 17 , 65 91 ], and findings indicated the independent director has a negative relation with firm performance. The positive relationship of independent board and firm performance argued that firms which empower outside directors may lead to their more effective monitoring and therefore higher firm performance. The negative relationship of independent board and firm performance results are based on the argument that external directors have no access to information about the internal business of the firms and their relation with internal management does not allow them to have a sufficient understanding of the firm’s day-to-day business activities or it may arise from the lack of knowledge of the business or the ability to monitor management actions [ 28 ].

Specifically in China, the corporate governance regulation code was approved in 2001 and required that the board of all Chinese listed domestic companies must include at least one-third of independent directors on their board by June 2003. Following this direction, many listed firms had appointed more independent directors, with a view to increase the independence of the board [ 54 ]. This proclamation is staying stable till now, and the number of independent directors in Chinese listed firms is increasing from time to time due to its importance. Thus, the following hypothesis is proposed.

Hypothesis 1

The proportion of independent directors in board members is positively related to firm performance.

Dual leadership and firm performance

CEO duality is one of the important board control mechanisms of internal CG mechanisms. It refers to a situation where the firm’s chief executive officer serves as chairman of the board of directors, which means a person who holds both the positions of CEO and the chair. Regarding leadership and firm performance relation, there are different arguments; there is not consistent conclusion among different researchers. There are two competitive views about dual leadership in corporate governance literature. Agency theory view proposed that duality could minimize the board’s effectiveness of its monitoring function, which leads to further agency problems and enhance poor performance [ 41 , 83 ]. As a result, dual leadership enhances CEO entrenchment and reduces board independence. In this condition, these two roles in one person made a concentration of power and responsibility, and this may result in busyness of CEO which affects the normal duties of a company. This means the CEO is responsible to execute a company’s strategies, monitoring and evaluating the managerial activities of a company. Thus, separating these two roles is better to avoid concentration of authority and power in one individual and separate leadership of board from the ruling of the business [ 72 ].

On the other hand, stewardship theory suggests that managers are good stewards of company resources, which could benefit a firm [ 9 ]. This theory advocates that there is no conflict of interest between shareholders and managers, if the role of CEO and chairman vests on one person, rather CEO duality would promote a clear sense of strategic direction by unifying and strengthening leadership.

In the Chinese firm context, there are different conflicting conclusions about the relationship between CEO duality and firm performance.

Hypothesis 2

CEO duality is negatively associated with firm performance.

Ownership concentration and firm performance

The ownership structure is which has a profound effect on business strategy and performance. Agency theory [ 81 ] argued that concentrated ownership can monitor corporate operating management effectively, alleviate information problems and agency costs, consequently, improve firm performance. The concentration of ownership as a large number of studies grounded in agency theory suggests that it has both the incentive and influence to assure that managers and directors operate in the interests of shareholders [ 19 ]. Concentrated ownership presence among the firm’s investors provides an important driver of good CG that should lead to efficiency gains and improvement in performance [ 81 ].

Due to shareholder concentrated economic risk, these shareholders have a strong encouragement to watch strictly over management, making sure that management does not engage in activities that are damaging the wealth of shareholders. Similarly, Shleifer and Vishny [ 80 ] argue that large share blocks reduce managerial opportunism, resulting in lower agency conflicts between management and shareholders.

In other ways, some researchers have indicated, block shareholders harmfully on the value of the firm, especially when majority shareholders can abuse their position of dominant control at the expense of minority shareholders [ 25 ]. As a result, at some level of ownership concentration the distinction between insiders and outsiders becomes unclear, and block-holders, no matter what their identity is, may have strong incentives to switch resources to the ways that make them better off at the cost of other shareholders. However, concentrated shareholding may create a new set of agency conflicts that may provide a negative impact on firm performance.

In the emerging market context, studies [ 77 , 90 ] find a positive association between ownership concentration and accounting profit for Chinese public companies. As Yu and Wen [ 92 ] argued, Chinese companies have a concentrated ownership structure, limited disclosure, poor investor protection, and reliance on the banking system. As this study argues, this concentration is more controlled by the state, institution, and private shareholders. Thus, ownership concentration in Chinese firms may be an alternative governance tool to reduce agency problems and enhance efficiency.

Hypothesis 3

The ownership concentration is positively related to firm performance.

Product market competition and firm performance

Theoretical models have argued that competition in product markets is a powerful force for overcoming the agency problem between shareholders and managers [ 78 ]. Competition in product markets plays the role of a takeover [ 3 ], and well-managed firms take over the market from poorly managed firms. According to this study finding, competition helps to build the best management team. Competition acts as a substitute for internal governance mechanisms, practically the market for corporate control [ 3 ]. Chou et al. [ 18 ] provided evidence that product market competition has a substantial impact on corporate governance and that it substitutes for corporate governance quality, and they provide evidence that the disciplinary force of competition on the management of the firm is from the fear of insolvency. For instance, Ibrahim [ 39 ] reported firms to operate in competitive industries record more returns of share compared with the concentrated industries. Hart [ 33 ] stated that competition inspires managers to work harder and, thus, reduces managerial slack. This study suggests that in high competition, the selling prices of products or services are more likely to fall because managers are concerned with their economic interest, which may tie up with firm performance. Managers are more focused on enhancing productivity that is more likely to reduce cost and increase firm performance. Thus, competition in product market can reduce agency problems between owners and managers and can enhance performance.

Hypothesis 4

Product market competition is positively associated with firm performance.

Debt financing and firm performance

Debt financing is one of the important governance mechanisms in aligning the incentives of corporate managers with those of shareholders. According to agency theory, debt financing can increase the level of monitoring over self-serving managers and that can be used as an alternative corporate governance mechanism [ 40 ]. This theory argues two ways through debt finance can minimize the agency cost: first the potential positive impact of debt comes from the discipline imposed by the obligation to continually earn sufficient cash to meet the principal and interest payment. It is a commitment device for executives. Second leverage reduces free cash flows available for managers’ discretionary expenses. Literature suggests that when leverage increases, managers may invest in high-risk projects in order to meet interest payments; this action leads lenders to monitor more closely the manager’s action and decision to reduce the agency cost. Koke and Renneboog [ 48 ] have found empirical support that a positive impact of bank debt on productivity growth in German firms. Also, studies like [ 77 , 86 ] examine empirically the effect of debt on firm investment decisions and firm value; reveal that debt finance is a negative effect on corporate investment and firm values [ 69 ] find that there is a significant and negative relationship between debt intensity and firm productivity in the case of Indian firms.

In the Chinese financial sectors, banks play a great role and use more commercial judgment and consideration in their leading decision, and even they monitor corporate activities [ 82 ]. In China listed company [ 77 , 82 ] found that an increase in bank loans increases the size of managerial perks and free cash flows and decreases corporate efficiency, especially in state control firms. The main source of debts is state-owned banks for Chinese listed companies [ 82 ]. This shows debt financing can act as a governance mechanism in limiting managers’ misuse of resources, thus reducing agency costs and enhance firm values. However, in China still government plays a great role in public listed company management, and most banks in China are also governed by the central government. However, the government is both a creditor and a debtor, especially in state-controlled firms. Meanwhile, the government as the owner has multiple objectives such as social welfare and some national (political) issues. Therefore, when such an issue is considerable, debt financing may not properly play its governance role in Chinese listed firms.

Hypothesis 5

Debt financing has a negative association with firm performance

Influence of managerial overconfidence on the relationship of corporate governance and firm performance

Corporate governance mechanisms are assumed to be an appropriate solution to solve agency problems that may derive from the potential conflict of interest between managers and officers, on the one hand, and shareholders, on the other hand [ 42 ].

Overconfidence is an overestimation of one’s own abilities and outcomes related to one’s own personal situation [ 74 ]. This study proposed from the behavioral finance view that overconfidence is typical irrational behavior and that a corporate manager tends to show it when they make business decisions. Overconfident CEOs tend to think they have more accurate knowledge about future events than they have and that they are more likely to experience favorable future outcomes than they are [ 35 ]. Behavioral finance theory incorporates managerial psychological biases and emotions into their decision-making process. This approach assumes that managers are not fully rational. Concurrently, several reasons in the literature show managerial irrationality. This means that the observed distortions in CG decisions are not only the result of traditional factors. Even with the absence of agency conflicts and asymmetric information problems, there is evidence documented for distortions such as the case of corporate investment. Managers will over- or under-invest regarding their optimism level and the availability of internal cash flow. Such a result push managers to make sub-optimal decisions and increase observed corporate distortions as a result. The view of behavioral decision theory [ 94 ] suggests that overconfidence, as one type of cognitive bias, encourages decision-makers to overestimate their own information and problem-solving capabilities and underestimates the uncertainties facing their firms and the potential losses from proceedings related with maintains against them.

Researchers [ 34 ,  61 ] discussed the managerial behavioral bias has a great impact on firm corporate governance practices. These studies carefully analyzed and clarified that managerial overconfidence is a major source of corporate distortions and suggested good CG practices can mitigate such problems.

In line with the above argument and empirical evidence of several researchers, therefore, the current study tried to investigate how the managerial behavioral bias (overconfidence) positively or negatively influences the effect of CG on firm performance using Chinese listed firms.

The boards of directors as central internal CG mechanisms have the responsibility to monitor, control, and supervise the managerial activities of firms. Thus, the board of directors has the responsibility to monitor and initiate managers in the company to increase the wealth of ownership and firm value. The capability of the board composition and diversity may be important to control and monitor the internal managers' based on the nature of internal executives behaviors, managerial behavior bias that may hinder or smooth the progress of corporate decisions of the board of directors. Accordingly, several studies suggested different arguments; Delton et al. [ 20 ] argued managerial behavior is influencing the allocation of board attention to monitoring. According to this argument, board of directors or concentrated ownership is not activated all the time continuously, and board members do not keep up a constant level of attention to supervise CEOs. They execute their activities according to firm and CEO status. While the current performance of the firm desirable the success confers celebrity status on CEOs and board will be liable to trust the CEOs and became idle. In other ways, overconfidence managers are irrational behaviors that tend to consider themselves better than others on different attributes. They do not always form beliefs logically [ 73 ]. They blame the external advice and supervision, due to overestimating their skills and abilities, underestimate their risks [ 61 ]. Similarly, CEOs are the most decision-makers in the firm strategies. While managers are highly overconfident, board members (especially external) face information limitations on a day-to-day activities of internal managers. In other way, CEOs have a strong aspiration to increase the performance of their firm; however, if they achieve their goals, they may build their empire. This situation will pronounce where the market for corporate control is not matured enough like China [ 27 ]. So, this fact affects the effectiveness of board activities in strategic decision-making. In contrast, as the study [ 7 ] indicated, as the number of the internal board increases, the impact of managerial overconfidence in the firm became increasing and positively correlated with the leadership duality. In other ways, agency theory, many opponents suggest that CEO duality reduces the monitoring role of the board of directors over the executive manager, and this, in turn, may harm corporate performance. In line with this Khajavi and Dehghani, [ 44 ] found that as the number of internal board increases, the managerial overconfidence bias will increase in Tehran Stock Exchange during 2006–2012.

This shows us the controlling and supervising role of independent directors are less likely in the firms managed by overconfident managers than normal managers; conversely, the power of CEO duality is more salient in the case of overconfident managers than normal managers.

Hypothesis 2a

Managerial overconfidence negatively influences the relationship of independent board and firm performance.

Hypothesis 2b

Managerial overconfidence strengthens the negative relationships of CEO duality and firm performance.

An internal control mechanism ownership concentration believes in the existence of strong control against the managers’ decisions and choices. Ownership concentration can reduce managerial behaviors such as overconfidence and optimism since it contributes to the installation of a powerful control system [ 7 ]. They documented that managerial behavior affects the monitoring activities of ownership concentration on firm performance. Ownership can affect the managerial behavioral bias in different ways, for instance, when CEOs of the firm become overconfident for a certain time, the block ownership controlling attention is weakened [ 20 ], and owners trust the internal managers that may damage the performance of the firms in an emerging market where external market control is weak. Overconfidence CEOs have the quality that expresses their behavior up on their company [ 36 ]. In line with this fact, the researcher can predict that the impact of concentrated ownership on firm performance is affected by overconfident managers.

Hypothesis 2c

Managerial overconfidence negatively influences the impact of ownership concentration on firm performance.

Theoretical literature has argued that product market competition forces management to improve firm performance and to make the best decisions for the future. In high competition, managers try their best due to fear of takeover [ 3 ], well-managed firms take over the market from poorly managed firms, and thus, competition helps to build the best management team. In the case of firms operating in the competitive industry, overconfidence CEO has advantages, due to its too simple to motivate overconfident managerial behaviors due to being overconfident managers assume his/her selves better than others. Overconfident CEOs are better at investing for future investments like research and development, so it plays a strategic role in the competition. Englmaier [ 23 ] argues firms in a more competitive industry better hire a manager who strongly believes in better future market outcomes.

Therefore, the following hypothesis was proposed:

Hypothesis 2d

Managerial overconfidence moderates the effect of product market competition on firm performance.

Regarding debt financing, existing empirical evidence shows no specific pattern in the relation of managerial overconfidence and debt finance. Huang et al. [ 38 ] noted that overconfident managers normally overestimate the profitability of investment projects and underestimate the related risks. So, this study believes that firms with overconfident managers will have lower debt. Then, creditors refuse to provide debt finance when firms are facing high liquidity risks. Abdullah [ 1 ] also argues that debt financers may refuse to provide debt when a firm is having a low credit rating. Low credit rating occurs when bankers believe firms are overestimating the investment projects. Therefore, creditors may refuse to provide debt when managers are overconfident, due to under-estimating the related risk which provides a low credit rating.

However, in China, the main source of debt financers for companies is state banks [ 82 ], and most overconfidence CEOs in Chinese firms have political connections [ 96 ] with the state and have a better relationship with external financial institutions and public banks. Hence, overconfident managers have better in accessing debt rather than rational managers in the context of China that leads creditors to allow to follow and influence the firm investments through collecting information about the firm and supervise the firms directly or indirectly. Thus, managerial overconfidence could have a positive influence on relationships between debt finance and firm performance; thus, the following hypothesis is proposed:

Hypothesis 2e

Managerial overconfidence moderates the relationship between debt financing and firm performance.

To explore the impact of CG on firm performance and whether managerial behavior (managerial overconfidence) influences the relationships of CG and firm performance, the following research model framework was developed based on theoretical suggestions and empirical evidence.

Data sources and sample selection

The data for this study required are accessible from different sources of secondary data, namely China Stock Market and Accounting Research (CSMAR) database and firm annual reports. The original data are obtained from the CSMAR, and the data are collected manually to supplement the missing value. CSMAR database is designed and developed by the China Accounting and Financial Research Center (CAFC) of Honk Kong Polytechnic University and by Shenzhen GTA Information Technology Limited company. All listed companies (Shanghai and Shenzhen stock Exchange) financial statements are included in this database from 1990 and 1991, respectively. All financial data, firm profile data, ownership structure, board structure, composition data of listed companies are included in the CSMAR database. The research employed nine consecutive years from 2010 to 2018 that met the condition that financial statements are available from the CSMAR database. This study sample was limited to only listed firms on the stock market, due to hard to access reliable financial and corporate governance data of unlisted firms. All data collected from Chinese listed firms only issued on A shares in domestic stoke market exchange of Shanghai and Shenzhen. The researcher also used only non-financial listed firms’ because financial firms have special regulations. The study sample data were unbalanced panel data for nine consecutive years from 2010 to 2018. To match firms with industries, we require firms with non-missing CSRC top-level industry codes in the CSMAR database. After applying all the above criteria, the study's final observations are 11,634 firm-year observations.

Measurement of variables

Dependent variable.

• Firm performance

To measure firm performance, prior studies have been used different proxies, by classifying them into two groups: accounting-based and market-based performance measures. Accordingly, this study measures firm performance in terms of accounting base (return on asset) and market-based measures (Tobin’s Q). The ROA is measured as the ratio of net income or operating benefit before depreciation and provisions to total assets, while Tobin’s Q is measured as the sum of the market value of equity and book value of debt, divided by book value of assets.

Independent Variables

Board independent (bind).

Independent is calculated as the ratio of the number of independent directors divided by the total number of directors on boards. In the case of the Chinese Security Regulatory Commission (2002), independent directors are defined as the “directors who hold no position in the company other than the position of director, and no maintain relation with the listed company and its major shareholders that might prevent them from making objective judgment independently.” In line with this definition, many previous studies used a proportion of independent directors to measure board independence [ 56 , 79 ].

CEO Duality

CEO duality refers to a position where the same person serves the role of chief executive officer of the form and as the chairperson of the board. CEO duality is a dummy variable, which equals 1 if the CEO is also the chairman of the board of directors, and 0 otherwise.

Ownership concentration (OWCON)

The most common way to measure ownership concentration is in terms of the percentage of shareholdings held by shareholders. The percentage of shares is usually calculated as each shareholder’s shareholdings held in the total outstanding shares of a company either by volume or by value in a stock exchange. Thus, the distribution of control power can be measured by calculating the ownership concentration indices, which are used to measure the degree of control or the power of influence in corporations [ 88 ]. These indices are calculated based on the percentages of a number of top shareholders’ shareholdings in a company, usually the top ten or twenty shareholders. Following the previous studies [ 22 ], Wei Hu et al. [ 37 ], ownership concentration is measured through the total percentage of the 10 top block holders' ownership.

Product market competition (PMC)

Previous studies measure it through different methods, such as market concentration, product substitutability and market size. Following the previous work in developed and emerging markets [product substitutability [ 31 , 57 ], the current study measured using proxies of market concentration (Herfindahl–Hirschman Index (HHI)). The market share of every firm is calculated by dividing the firm's net sale by the total net sale of the industry, which is calculated for each industry separately every year. This index measures the degree of concentration by industry. The bigger this index is, the more the concentration and the less the competition in that industry will be, vice versa.

Debt Financing (DF)

The debt financing proxy in this study is measured by the percentage of a total asset over the total debt of the firm following the past studies [ 69 , 95 ].

Interaction variable

Managerial overconfidence (moc).

To measure MOC, several researchers attempt to use different proxies, for instance CEO’s shareholdings [ 61 ] and [ 46 ]; mass media comments [ 11 ], corporate earnings forecast [ 36 ], executive compensation [ 38 ], and managers individual characteristics index [ 53 ]. Among these, the researcher decided to follow a study conducted in emerging markets [ 55 ] and used corporate earnings forecasts as a better indicator of managerial overconfidence. If a company’s actual earnings are lower than the earnings expected by managers, the managers are defined as overconfident with a dummy variable of (1), and as not overconfident (0) otherwise.

Control variables

The study contains three control variables: firm size, firm age, and firm growth opportunities. Firm size is an important component while dealing with firm performance because larger firms have more agency issues and need strong CG. Many studies confirmed that a large firm has a large board of directors, which increases the monitoring costs and affects a firm’s value (Choi et al., 2007). In other ways, large firms are easier to generate funds internally and to gain access to funds from an external source. Therefore, firm size affects the performance of firms. Firm size can be measured in many ways; common measures are market capitalization, revenue volume, number of employments, and size of total assets. In this study, firm size is measured by the logarithm of total assets following a previous study. Firm age is the number of years that a firm has operated; it was calculated from the time that the company first appeared on the Chinese exchange. It indicates how long a firm in the market and indicates firms with long age have long history accumulate experience and this may help them to incur better performance [ 8 ]. Firm age is a measure of a natural logarithm of the number of years listed from the time that company first listed on the Chinese exchange market. Growth opportunity is measured as the ratio of current year sales minus prior year sales divided by prior year sales. Sales growth enhances the capacity utilization rate, which spreads fixed costs over revenue resulting in higher profitability [ 49 ].

Data analysis methods

Empirical model estimations.

Most of the previous corporate governance studies used OLS, FE, or RE estimation methods. However, these estimations are better when the explanatory variables are exogenous. Otherwise, a system generalized moment method (GMM) approach is more efficient and consistent. Arellano and Bond [ 4 ] suggested that system GMM is a better estimation method to address the problem of autocorrelation and unobservable fixed effect problems for the dynamic panel data. Therefore, to test the endogeneity issue in the model, the Durbin–Wu–Hausman test was applied. The result of the Hausman test indicated that the null hypothesis was rejected ( p  = 000), so there was an endogeneity problem among the study variables. Therefore, OLS and fixed effects approaches could not provide unbiased estimations, and the GMM model was utilized.

The system GMM is the econometric analysis of dynamic economic relationships in panel data, meaning the economic relationships in which variables adjust over time. Econometric analysis of dynamic panel data means that researchers observe many different individuals over time. A typical characteristic of such dynamic panel data is a large observation, small-time, i.e., that there are many observed individuals, but few observations over time. This is because the bias raised in the dynamic panel model could be small when time becomes large [ 75 ]. GMM is considered more appropriate to estimate panel data because it removes the contamination through an identified finite-sample corrected set of equations, which are robust to panel-specific autocorrelation and heteroscedasticity [ 12 ]. It is also a useful estimation tool to tackle the endogeneity and fixed-effect problems [ 4 ].

A dynamic panel data model is written as follows:

where y it is the current year firm performance, α is representing the constant, y it−1 is the one-year lag performance, i is the individual firms, and t is periods. β is a vector of independent variable. X is the independent variable. The error terms contain two components, the fixed effect μi and idiosyncratic shocks v it .

Accordingly, to test the impact of corporate governance mechanisms on firm performance and influencing role of the overconfident executive on the relationship between corporate governance mechanisms and firm performance, the following base models were used:

ROA / TQ i ,t  =  α  + yROA /TQ i,t−1  + β 1 INDBRD + β 2 DUAL + β 3 OWCON +  β 4 DF +  β 5 PMC +  β 6 MOC +  β 7 FSIZE + β 8 FAGE + β 9 SGTH + β 10–14 MOC * (INDBRD, DUAL, OWCON, DF, and PMC) + year dummies + industry Dummies + ή +  Ɛ it .

where i and t represent firm i at time t, respectively, α represents the constant, and β 1-9 is the slope of the independent and control variables which reflects a partial or prediction for the value of dependent variable, ή represents the unobserved time-invariant firm effects, and Ɛ it is a random error term.

Descriptive statistics

Descriptive statistics of all variables included in the model are described in Table 1 . Accordingly, the value of ROA ranges from −0.17 to 0.23, and the average value of ROA of the sample is 0.05 (5.4%). Tobin Q’s value ranges from 0.88 to 10.06, with an average value of 2.62. The ratio of the independent board ranges from 0.33 to 0.57. The average value of the independent board of directors’ ratio was 0.374. The proportion of the CEO serving as chairperson of the board is 0.292 or 29.23% over the nine years. Top 10 ownership concentration of the study ranged from 22.59% to 90.3%, and the mean value is 58.71%. Product market competition ranges from 0.85% to 40.5%, with a mean value of 5.63%. The debt financing also has a mean value of 40.5%, with a minimum value of 4.90% and a maximum value of 87%. The mean value of managerial overconfidence is 0.589, which indicates more than 50% of Chinese top managers are overconfident.

The study sample has an average of 22.15 million RMB in total book assets with the smallest firms asset 20 million RMB and the biggest owned 26 million RMB. Study sample average firms’ age was 8.61 years old. The growth opportunities of sample firms have an average value of 9.8%.

Table 2 presents the correlation matrix among variables in the regression analysis in the study. As a basic check for multicollinearity, a correlation of 0.7 or higher in absolute value may indicate a multicollinearity issue [ 32 ]. According to Table 2 results, there is no multicollinearity problem among variables. Additionally, the variance inflation factor (VIF) test also shows all explanatory variables are below the threshold value of 10, [ 32 ] which indicates that no multicollinearity issue exists.

Main results and discussion

Impact of cg on firm performance.

Accordingly, Tables 3 and 4 indicate the results of two-step system GMM employing the xtabond2 command introduced by Roodman [ 75 ]. In this, the two-step system GMM results indicated the CG and performance relationship, with the interaction of managerial overconfidence. One-year lag of performance has been included in the model and two to three periods lagged independent variables were used  as an instrument in the dynamic model, to correct for simultaneity, control for the fixed effect, and to tackle the endogeneity problem of independent variables. In this model, all variables are taken as endogenous except control variables.

Tables 3 and 4 report the results of three model specification tests to determine whether an appropriate estimation model was applied. These tests are: 1) the Arellano–Bond test for the first-order (AR (1)) and second-order correlation (AR (2)). This test indicates the result of AR (1) and AR (2) is tested for the first-order and second-order serial correlation in the first-differenced residuals, AR (2) test accepted under the null of no serial correlation. The model results show AR (2) test yields a p-value of 0.511 and 0.334, respectively, for ROA and TQ firm performance measurement, which indicates that the models cannot reject the null hypothesis of no second-order serial correlation. 2) Hansen test over-identification is to detect the validity of the instrument in the models. The Hansen test of over-identification is accepted under the null that all instruments are valid. Tables 3 and 4 indicate the p-value of Hansen test over-identification 0.139 and 0.132 for ROA and TQ measurement of firm performance, respectively, so that these models cannot reject the hypothesis of the validity of instruments. 3) In the difference-in-Hansen test of exogeneity, it is acceptable under the null that instruments used for the equations in levels are exogenous. Table 3 shows p-values of 0.313 and 0.151, respectively, for ROA and TQ. These two models cannot reject the hypothesis that the equations in levels are exogenous.

Tables 3 and 4 report the results of the one-year lag values of ROA and TQ are positive (0.398, 0.658) and significant at less than 1% level. This indicates that the previous year's performance of a Chinese firm has a significant impact on the current firm's performance. This study finding is consistent with the previous studies: Shao [ 79 ], Nguyen [ 66 ] and Wintoki et al. [ 89 ], which considered previous year performance as one of the significant independent variables in the case of corporate governance mechanisms and firm performance relationships.

The results indicate board independence has no relation with firm performance measured by ROA and TQ. However, hypothesis 1 indicated that there is a positive and significant relationship between independent board and firm performance, which is not supported. The results are conflicting with the assumption that high independent board on board room should better supervise managers, alleviate the information asymmetry between agents and owners, and improve the firm performance by their proficiency. This result is consistent with several previous studies [ 56 , 79 ], which confirms no relation between board independence and firm performance.

This result is consistent with the argument that those outside directors are inefficient because of the lack of enough information concerning the daily activities of internal managers. Specifically, Chinese listed companies may simply include the minimum number of independent directors on board to fulfill the institutional requirement and that independent boards are only obligatory and fail to perform their responsibilities [ 56 , 79 ]. In this study sample, the average of independent board of all firms included in this study has only 37 percent, and this is one of concurrent evidence as to the independent board in Chinese listed firm simple assigned to fulfill the institutional obligation of one-third ratio.

CEO duality has a negative significant relationship with firm performance measured by TQ ( β  = 0.103, p  < 0.000), but has no significant relationship with accounting-based firm performance (ROA). Therefore, this result supports our hypothesis 2, which proposed there is a negative relationship between dual leadership and firm performance. This finding is also in line with the agency theory assumption that suggests CEO duality could reduce the board’s effectiveness of its monitoring functions, leading to further agency problems and ultimately leads poor firm performance [ 41 , 83 ]. This finding consistent with prior studies [ 15 , 56 ] that indicated a negative relationship between CEO dual and firm performance, against to this result the studies [ 70 ] and [ 15 ] found that duality positively related to firm performance.

Hypothesis 3 is supported, which proposes there is a positive relationship between ownership concentration and firm performance. Table 3 result shows that there is a positive and significant relationship between the top ten concentrated ownership and ROA and TQ (0.00046 & 0.06) at 1% and 5% significance level, respectively. These findings are consistent with agency theory, which suggests that the shareholders who hold large ownership alleviate agency costs and information problems, monitor managers effectively, consequently enhance firm performance [ 81 ]. This finding is in line with Wu and Cui [ 90 ], and Pant et al. [ 69 ]. Concentrated shareholders have a strong encouragement to watch strictly over management, making sure that management does not engage in activities that are damaging to the wealth of shareholders [ 80 ].

The result indicated in Table 3 PMC and firm performance (ROA) relationship was positive, but statistically insignificant. However, PMC has positive ( β  = 2.777) and significant relationships with TQ’s at 1% significance level. Therefore, this result does not support hypothesis 4, which predicts product market competition has a positive relationship with firm performance in Chinese listed firms. In this study, PMC is measured by the percentage of market concentration, and a highly concentrated product market means less competition. Though this finding shows high product market concentration positively contributed to market-based firm performance, this result is consistent with the previous study; Liu et al. [ 57 ] reported high product market competition associated with poor firm performance measured by TQ in Chinese listed firms. The study finding is against the theoretical model argument that competition in product markets is a powerful force for overcoming the agency problem between shareholders and managers, and enhances better firm performance (Scharfstein and [ 78 ]).

Regarding debt finance and firm performance relationship, the impact of debt finance was found to be negative on both firm performances as expected. Thus, this hypothesis is supported. Table 3 shows a negative relationship with both firm performance measurements (0.059 and 0.712) at 1% and 5% significance level. Thus, hypothesis 5, which predicts a negative relationship between debt financing and firm performance, has been supported. This finding is consistent with studies ([ 86 ]; Pant et al., [ 69 ]; [ 77 , 82 ]) that noted that debt financing has a negative effect on firm values.

This could be explained by the fact that as debt financing increases in external loans, the size of managerial perks and free cash flows increase and corporate efficiency decrease. In another way, because the main source of debt financers is state-owned banks for Chinese listed firms, these banks are mostly governed by the government, and meanwhile, the government as the owner has multiple objectives such as social welfare and some national issues. Therefore, debt financing fails to play its governance role in Chinese listed firms.

Regarding control variables, firm age has a positive and significant relationship with both TQ and ROA. This finding supported by the notion indicates firms with long age have long history accumulate experience, and this may help them to incur better performance (Boone et al. [ 8 ]). Firm size has a significant positive relationship with firm performance ROA and negative significant relation with TQ. The positive result supported the suggestion that large firms get a higher market valuation from the markets, while the negative finding indicates large firms are more complex; they may have several agency problems and need additional monitoring, which results in higher operating costs [ 84 ]. Growth opportunity was found to be in positive and significant association with ROA; this indicates that a firm high growth opportunity can increase its performance.

Influences of managerial overconfidence in the relationship between CG measures and firm performance

It predicts that managerial overconfidence negatively influences the relationship of independent board and firm performance. The study findings indicate a negative significant influence of managerial overconfidence when the firm is measure by Tobin’s Q ( β  = −4.624, p  < 0.10), but a negative relationship is insignificant when the firm is measured by ROA. Therefore, hypothesis 2a is supported when firm value is measured by TQ. This indicates that the independent directors in Chinese firms are not strong enough to monitor internal CEOs properly, due to most Chinese firms merely include the minimum number of independent directors on a board to meet the institutional requirement and that independent directors on boards are only perfunctory. Therefore, the impact of independent board on internal directors is very weak, in this situation overconfident CEO becoming more powerful than others, and they can enact their own will and avoid compromises with the external board or independent board. In another way, the weakness of independent board monitoring ability allows CEOs overconfident that may damage firm value.

The interaction of managerial overconfidence and CEO duality has a significant negative effect on operational firm performance (0.0202, p  > 0.05) and a negative insignificant effect on TQ. Thus, Hypothesis 2b predicts that the existence of overconfident managers strengthens the negative relationships of dual leadership and firm performance has been supported. This finding indicates the negative effect of CEO duality amplified when interacting with overconfident CEOs. According to Legendre et al. [ 51 ], argument misbehaviors of chief executive officers affect the effectiveness of external directors and strengthen the internal CEO's power. When the CEOs are getting more powerful, boards will be inefficient and this situation will result in poor performance, due to high agency problems created between managers and ownerships.

Hypothesis 2c is supported

It predicts the managerial overconfidence decreases the positive impact of ownership concentration on firm performance. The results of Tables 3 and 4 indicated that the interaction effect of managerial overconfidence with concentrated ownership has a negative significant impact on both ROA and TQ firm performance (0.000404 and 0.0156, respectively). This finding is supported by the suggestion that CEO overconfidence weakens the monitoring and controlling role of concentrated shareholders. This finding is explained by the fact that when CEOs of the firm become overconfident for a certain time, the concentrated ownership controlling attention is weakened [ 20 ], owners trust the internal managers that may damage the performance of the firms in an emerging market where external market control is weak. Overconfident managers gain much more power than rational managers that they are able to use the firm to further their own interests rather than the interests of shareholders and managerial overconfidence is a behavioral biased that managers follow to meet their goals and reduce the wealth of shareholders. This situation resulted in increasing agency costs in the firm and damages the firm profitability over time.

It predicts that managerial overconfidence moderates the relation of product market competition and firm performance. However, the result indicated there is no significant moderating role of managerial overconfidence in the relationship between product market competition and firm performance in Chinese listed firms.

It proposed that overconfidence managers moderate the relationship of debt financing and performance in Chinese listed firm: The study finding is unobvious; it negatively influenced the relation of debt financing with accounting-based firm performance measure ( β  = −0.059, p  < 0.01) and positively significant market base firm performance ( β  = 0.735, p  < 0.05). The negative interaction results could be explained by the fact that overconfident leads managers to have lower debt due to overestimate the profitability of investment projects and underestimate the related risks. This finding is consistent with [ 38 ] finding that overconfident CEOs have lower debt, because of overestimating the investment projects. In another perspective, the result indicated a positive moderating role of overconfidence managers in the relationship of debt financing and market-based firm performance. This result is also supported by the suggestion that overconfident managers have better in accessing debt rather than rational managers in the context of China because in Chinese listed firms most of the senior CEOs have a better connection with the external finance institutions and state banks to access debt, due to their political participation than rational managers.

The main objectives of the study were to examine the impact of basic corporate governance mechanisms on firm performance and to explore the influence of managerial overconfidence on the relationship of CGMs and firm performance using Chinese listed firms. The study incorporated different important internal and external corporate governance control mechanisms that can affect firm performance, based on different theoretical assumptions and literature. To address these objectives, many hypotheses were developed and explained by a proposing multi-theoretical approach.

The study makes several important contributions to the literature. While several kinds of research have been conducted on the relationships of corporate governance and firm performance, the study basically extends previous researches based on panel data of emerging markets. Several studies have investigated in developed economies. Thus, this study contributed to the emerging market by providing comprehensive empirical evidence to the corporate governance literature using unique characteristics of Chinese publicity listed firms covering nine years (2010–2018). The study also extends the developing stream of corporate governance and firm performance literature in emerging economies that most studies in emerging (Chinese) listed companies give less attention to the external governance mechanisms. External corporate governance mechanisms like product market competition and debt financing are limited from emerging market CG literature; therefore, this study provided comprehensive empirical evidence.

Furthermore, this study briefly indicated how managerial behavioral bias can influence the monitoring, controlling, and corporate decisions of corporate firms in Chinese listed firms. Therefore, as to the best knowledge of the researcher, no study investigated the interaction effect of managerial overconfidence and CG measures to influence firm performance. Thus, the current study provides an insight into how a managerial behavioral bias (overconfidence) influences/moderates the relationship between corporate governance mechanisms and firm performance, in an emerging market. Hence, the study will help managers and owners in which situation managerial behavior helps more for firm’s value and protecting shareholders' wealth (Fig. 1 ).

Generally, the previous findings also support the current study's overall findings: Phua et al. [ 71 ] concluded that managerial overconfidence can significantly affect corporate activities and outcomes. Russo and Schoemaker [ 76 ] found that there is opposite relationship between overconfidence managers and quality of decision making, because overconfident behavioral bias reduces the ability to make a rational decision. Therefore, the primary conclusion of the study is that it attempts to understand the strength of the effect of corporate governance mechanisms on firm performance, and managerial behavioral bias must be taken into consideration as one of the influential moderators.

Proposed research model framework

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I declare that all data and materials are available.

Abbreviations

China accounting and finance center

Chief executive officer

• Corporate governance

Corporate governance mechanisms

China Stock Market and Accounting Research

China Securities Regulatory Commission

Generalized method of moments

• Managerial overconfidence

Research and development

Return on asset

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Ph.D Thesis: Impact of Corporate Social Responsibility on Financial Performance and Competitiveness of Business: A Study of Indian Firms

Posted: 16 Apr 2013 Last revised: 1 Dec 2014

Rupal Tyagi

Indian Institute of Technology, Roorkee

Date Written: July 19, 2012

The past two decades have witnessed a remarkable change in the way businesses run and operate, with the quest for excellence and all-round growth the primary objective of corporations. Pursuit of financial growth does not always lead to social advancement, and is often detrimental to the environment, resulting in unhealthy workplaces, exposure to toxic substances, urban decay and other similar issues. Managers and practitioners have often been criticised for being single minded about value maximisation. The turn of events has pressurised firms to put serious efforts into a wide range of Corporate Social Responsibility (CSR) activities. CSR has become a critical aspect in strategic decision making of a firm primarily due to financial scandals and a drop in investors’ confidence. CSR has emerged as a view that can add to the financial performance of a company and suggests that corporate decision-makers must take care of a range of social and environmental affairs in order to maximise long-term financial returns. Every firm differs in the way it implement CSR in strategic business practices, with its size, operating industry, stakeholder demands, historical CSR engagement, level of diversification, research and development and labour market conditions a few of the factors that determine this decision making. One side of the coin confirms the benefits colligated with good reputation, while the other indicates that a firm’s costs of adhering to ethical standards will translate into higher product prices, a competitive disadvantage and lower profitability. Even after deep exploration of the Corporate Financial Performance (CFP)- Corporate Social Performance (CSP) relationship, empirical evidence to date is somewhat conflicting. Globalisation and liberalisation in the Indian economy has shifted corporate goals from a socio - economic focus towards increasing shareholders value to the benefit of various stakeholders. Although extensive research on CSR-CFP has been carried out in developed countries, there is a paucity of such studies in India. The main thrust of the current study is to get intimate with this issue or devise a problem along with attaining new insights into it. This study intends to get to grips with and derive the perceptivity of corporate social behaviour towards its stakeholders along with justifying its triple bottom line benefits while filling the literary gap through replicating and extending previous findings on social and financial performance of firms. In doing so, this study also attempts to analyse in detail the aforesaid relationship and discuss the effectiveness of social and financial performance along with competitive performance of sample Indian companies. The results identify critical Indian CSR factors and determine their importance in shaping the CSP-CFP relationship, on the basis of which further research in sectors identified as weak may be carried out.

Keywords: Corporate Social Responsibility, Corporate Financial Performance, Corporate Social Performance, Competitiveness, globalisation, financial growth, stakeholder relationship, India, transparency and disclosure, environment, business ethics

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Management Decision

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The authors aim to develop and test a theory of dual responsibility to explain the relationship between corporate social responsibility (CSR) and firm performance. The authors empirically examine whether firms that meet their economic and social responsibilities simultaneously perform better than firms that fail to do so. In doing so, the authors theoretically extend and empirically test Barney's (2018) call to incorporate the stakeholder perspective with resource-based view (RBV). The authors also examine the moderating effects of firm status on this relationship.

Design/methodology/approach

The authors use a longitudinal panel sample of 137 S&P 500 firms and data for the years between 2004 and 2013 collected from multiple data sources. The authors use stochastic frontiers analysis to measure firm capabilities in the areas of R&D, operations and marketing. These capability measures are then used along with CSR measures and a measure of firm status to test the hypotheses of this study. The authors also conducted several robustness checks and various supplementary analyses using different econometrics techniques and different operationalizations of the key variables of interests.

The results show that firm CSR is positively related to firm performance and that the effect of CSR on performance is stronger for firms with higher levels of R&D capability and operational capability. The authors also find support for the three-way interaction between CSR, economic responsibility and firm status, suggesting that firms high in both social and economic responsibilities and status will enjoy the highest levels of performance.

Research limitations/implications

The findings of this study are based on large, publicly listed firms in North America. Therefore, their generalizability to other contexts and other types of firms require additional research. The reliance on KLD measures is also a limitation, especially because they have not reported CSR ratings after 2013.

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For practicing managers, the main implication of this study is that an optimal balance between market and nonmarket strategies is key for superior performance.

Social implications

The continued debate regarding the firm's purpose can be understood by focusing equally on the two main responsibilities of firms: nonsocial responsibility and social responsibility toward all stakeholders.

Originality/value

The study answers the call to incorporate stakeholder theory into the RBV of the firm by highlighting the critical role of firm capabilities in the relationship between CSR and performance. The study also highlights the role that firm status plays in the relationship between market and nonmarket strategies and firm performance.

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Al-Shammari, M.A. , Banerjee, S.N. and Rasheed, A.A. (2022), "Corporate social responsibility and firm performance: a theory of dual responsibility", Management Decision , Vol. 60 No. 6, pp. 1513-1540. https://doi.org/10.1108/MD-12-2020-1584

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The effect of corporate governance on bank performance: evidence from Turkish and some MENA countries banks

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• Berna Doğan Başar   ORCID: orcid.org/0000-0001-7134-3930 1 ,
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This paper examines the relationship between the structure of corporate governance and performance in the banking sector. A sample has been selected using the generalized method of moment (GMM) approach. The sample consists of data from a total of 33 Turkish, Tunisia, Morocco and Lebanon banks which are listed in stock during the period between 2012 and 2017. Since this paper emphasizes corporate governance, a “Board Characteristics Index” was developed based upon four different aspects of the board composition—board leadership structure, board member characteristics and board committee structure. It also demonstrates how the overall index relates to banking performance. The results show that the governance index, which aggregates with the four sets of board attributes relate significantly and positively with return on assets.

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Berna Doğan Başar, Burak Büyükoğlu & İbrahim Halil Ekşi

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Başar, B.D., Bouteska, A., Büyükoğlu, B. et al. The effect of corporate governance on bank performance: evidence from Turkish and some MENA countries banks. J Asset Manag 22 , 153–162 (2021). https://doi.org/10.1057/s41260-021-00223-3

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• 04 Jun 2024
• Cold Call Podcast

How One Insurtech Firm Formulated a Strategy for Climate Change

The Insurtech firm Hippo was facing two big challenges related to climate change: major loss ratios and rate hikes. The company used technologically empowered services to create its competitive edge, along with providing smart home packages, targeting risk-friendly customers, and using data-driven pricing. But now CEO and president Rick McCathron needed to determine how the firm’s underwriting model could account for the effects of high-intensity weather events. Harvard Business School professor Lauren Cohen discusses how Hippo could adjust its strategy to survive a new era of unprecedented weather catastrophes in his case, “Hippo: Weathering the Storm of the Home Insurance Crisis.”

• 22 Apr 2024
• Research & Ideas

When Does Impact Investing Make the Biggest Impact?

More investors want to back businesses that contribute to social change, but are impact funds the only approach? Research by Shawn Cole, Leslie Jeng, Josh Lerner, Natalia Rigol, and Benjamin Roth challenges long-held assumptions about impact investing and reveals where such funds make the biggest difference.

• 23 Jan 2024

More Than Memes: NFTs Could Be the Next Gen Deed for a Digital World

Non-fungible tokens might seem like a fad approach to selling memes, but the concept could help companies open new markets and build communities. Scott Duke Kominers and Steve Kaczynski go beyond the NFT hype in their book, The Everything Token.

• 12 Sep 2023

How Can Financial Advisors Thrive in Shifting Markets? Diversify, Diversify, Diversify

Financial planners must find new ways to market to tech-savvy millennials and gen Z investors or risk irrelevancy. Research by Marco Di Maggio probes the generational challenges that advisory firms face as baby boomers retire. What will it take to compete in a fintech and crypto world?

• 17 Aug 2023

‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy Crypto

Bitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain?

• 17 Jul 2023

Money Isn’t Everything: The Dos and Don’ts of Motivating Employees

Dangling bonuses to checked-out employees might only be a Band-Aid solution. Brian Hall shares four research-based incentive strategies—and three perils to avoid—for leaders trying to engage the post-pandemic workforce.

• 20 Jun 2023

Elon Musk’s Twitter Takeover: Lessons in Strategic Change

In late October 2022, Elon Musk officially took Twitter private and became the company’s majority shareholder, finally ending a months-long acquisition saga. He appointed himself CEO and brought in his own team to clean house. Musk needed to take decisive steps to succeed against the major opposition to his leadership from both inside and outside the company. Twitter employees circulated an open letter protesting expected layoffs, advertising agencies advised their clients to pause spending on Twitter, and EU officials considered a broader Twitter ban. What short-term actions should Musk take to stabilize the situation, and how should he approach long-term strategy to turn around Twitter? Harvard Business School assistant professor Andy Wu and co-author Goran Calic, associate professor at McMaster University’s DeGroote School of Business, discuss Twitter as a microcosm for the future of media and information in their case, “Twitter Turnaround and Elon Musk.”

• 06 Jun 2023

The Opioid Crisis, CEO Pay, and Shareholder Activism

In 2020, AmerisourceBergen Corporation, a Fortune 50 company in the drug distribution industry, agreed to settle thousands of lawsuits filed nationwide against the company for its opioid distribution practices, which critics alleged had contributed to the opioid crisis in the US. The $6.6 billion global settlement caused a net loss larger than the cumulative net income earned during the tenure of the company’s CEO, which began in 2011. In addition, AmerisourceBergen’s legal and financial troubles were accompanied by shareholder demands aimed at driving corporate governance changes in companies in the opioid supply chain. Determined to hold the company’s leadership accountable, the shareholders launched a campaign in early 2021 to reject the pay packages of executives. Should the board reduce the executives’ pay, as of means of improving accountability? Or does punishing the AmerisourceBergen executives for paying the settlement ignore the larger issue of a business’s responsibility to society? Harvard Business School professor Suraj Srinivasan discusses executive compensation and shareholder activism in the context of the US opioid crisis in his case, “The Opioid Settlement and Controversy Over CEO Pay at AmerisourceBergen.”

• 16 May 2023
• In Practice

After Silicon Valley Bank's Flameout, What's Next for Entrepreneurs?

Silicon Valley Bank's failure in the face of rising interest rates shook founders and funders across the country. Julia Austin, Jeffrey Bussgang, and Rembrand Koning share key insights for rattled entrepreneurs trying to make sense of the financing landscape.

• 27 Apr 2023

Equity Bank CEO James Mwangi: Transforming Lives with Access to Credit

James Mwangi, CEO of Equity Bank, has transformed lives and livelihoods throughout East and Central Africa by giving impoverished people access to banking accounts and micro loans. He’s been so successful that in 2020 Forbes coined the term “the Mwangi Model.” But can we really have both purpose and profit in a firm? Harvard Business School professor Caroline Elkins, who has spent decades studying Africa, explores how this model has become one that business leaders are seeking to replicate throughout the world in her case, “A Marshall Plan for Africa': James Mwangi and Equity Group Holdings.” As part of a new first-year MBA course at Harvard Business School, this case examines the central question: what is the social purpose of the firm?

• 25 Apr 2023

Using Design Thinking to Invent a Low-Cost Prosthesis for Land Mine Victims

Bhagwan Mahaveer Viklang Sahayata Samiti (BMVSS) is an Indian nonprofit famous for creating low-cost prosthetics, like the Jaipur Foot and the Stanford-Jaipur Knee. Known for its patient-centric culture and its focus on innovation, BMVSS has assisted more than one million people, including many land mine survivors. How can founder D.R. Mehta devise a strategy that will ensure the financial sustainability of BMVSS while sustaining its human impact well into the future? Harvard Business School Dean Srikant Datar discusses the importance of design thinking in ensuring a culture of innovation in his case, “BMVSS: Changing Lives, One Jaipur Limb at a Time.”

• 18 Apr 2023

What Happens When Banks Ditch Coal: The Impact Is 'More Than Anyone Thought'

Bank divestment policies that target coal reduced carbon dioxide emissions, says research by Boris Vallée and Daniel Green. Could the finance industry do even more to confront climate change?

The Best Person to Lead Your Company Doesn't Work There—Yet

Recruiting new executive talent to revive portfolio companies has helped private equity funds outperform major stock indexes, says research by Paul Gompers. Why don't more public companies go beyond their senior executives when looking for top leaders?

• 11 Apr 2023

A Rose by Any Other Name: Supply Chains and Carbon Emissions in the Flower Industry

Headquartered in Kitengela, Kenya, Sian Flowers exports roses to Europe. Because cut flowers have a limited shelf life and consumers want them to retain their appearance for as long as possible, Sian and its distributors used international air cargo to transport them to Amsterdam, where they were sold at auction and trucked to markets across Europe. But when the Covid-19 pandemic caused huge increases in shipping costs, Sian launched experiments to ship roses by ocean using refrigerated containers. The company reduced its costs and cut its carbon emissions, but is a flower that travels halfway around the world truly a “low-carbon rose”? Harvard Business School professors Willy Shih and Mike Toffel debate these questions and more in their case, “Sian Flowers: Fresher by Sea?”

Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide

More companies are bringing seemingly unrelated businesses together in new ways, challenging traditional stock categories. MarcAntonio Awada and Suraj Srinivasan discuss how applying machine learning to regulatory data could reveal new opportunities for investors.

• 07 Apr 2023

When Celebrity ‘Crypto-Influencers’ Rake in Cash, Investors Lose Big

Kim Kardashian, Lindsay Lohan, and other entertainers have been accused of promoting crypto products on social media without disclosing conflicts. Research by Joseph Pacelli shows what can happen to eager investors who follow them.

• 31 Mar 2023

Can a ‘Basic Bundle’ of Health Insurance Cure Coverage Gaps and Spur Innovation?

One in 10 people in America lack health insurance, resulting in $40 billion of care that goes unpaid each year. Amitabh Chandra and colleagues say ensuring basic coverage for all residents, as other wealthy nations do, could address the most acute needs and unlock efficiency.

• 23 Mar 2023

As Climate Fears Mount, More Investors Turn to 'ESG' Funds Despite Few Rules

Regulations and ratings remain murky, but that's not deterring climate-conscious investors from paying more for funds with an ESG label. Research by Mark Egan and Malcolm Baker sizes up the premium these funds command. Is it time for more standards in impact investing?

• 14 Mar 2023

What Does the Failure of Silicon Valley Bank Say About the State of Finance?

Silicon Valley Bank wasn't ready for the Fed's interest rate hikes, but that's only part of the story. Victoria Ivashina and Erik Stafford probe the complex factors that led to the second-biggest bank failure ever.

• 13 Mar 2023

What Would It Take to Unlock Microfinance's Full Potential?

Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better.

Cintas: More Dangerous Overvaluation

• Cintas has outperformed significantly, generating a TSR of 42.16% since the last article was published.
• The company is a class-leading business service provider with strong financial performance and profitability.
• I believe that the current valuation is excessive and consider CTAS to be overvalued, recommending a "Hold" stance.
• Looking for a helping hand in the market? Members of iREIT® on Alpha get exclusive ideas and guidance to navigate any climate. Learn More »

Dear readers/followers,

In the last article on Cintas ( NASDAQ: CTAS ), which today was exactly a year ago, I made the decision to stay neutral and a "HOLD" for Cintas - and this worked out well if you were already a Cintas shareholder. Unfortunately, this wasn't the case if you weren't already an investor in Cintas - because the company outperformed quite significantly, generating a TSR of 42.16% since my last piece - which you can find here.

What caused this outperformance, and why was my assessment wrong? When it comes to companies like this, it's always a matter of looking at how "high things" may go before they drop back down. I am in no way averse to the fact that Cintas deserves a high premium - it does.

Where I'm having a disagreement with "the market" is how high exactly this is supposed to be - and I am hardly alone in this sentiment either, with at least one article coming out with a full-fledged "SELL" rating at this particular time.

In this article, I mean to update my thesis on Cintas and show you why I am still not adding to my position, despite what can only be described as a very solid sort of outperformance the company is now responsible for.

Cintas - 40%+ annual TSR is superb, no doubt - but where do we go from here?

A false equivalency that is fairly common in the market is equating good historical performance with the company delivering the same going forward. However, it would be flawed of me to not clearly acknowledge this company's outperformance in direct opposition to my thesis - in short, admitting that I was wrong here. But that is the key word - was.

Will I be wrong again, maintaining my stance here?

Time will tell as to that.

Cintas is, without a doubt in my mind, a class-leading company in the business service industry. It does boring things in an inherently boring industry - which is typically what I love. Looking at operating margins, net margins, returns on assets, equity, invested capital, and capital employed, this company is a "monster", between the 86th to 95th percentile in the entire sector. Profitability over a 10-year period is unbroken, making it better than 99.9% of all companies here (Source: GuruFocus, Morningstar)

This is a very strong reason why you should invest in a quality business, and why you should be hesitant to let them go. Also, a good reason I go "HOLD" here, and have in the past, and not "SELL". This latter rating is one I reserve for companies that, I believe, have reached the "end" of their journey.

Also, even in my last article, I never considered the company excessively overvalued. Specifically, I said:

And let me be clear - while I do consider the company overvalued, I do not consider it excessively so. To put it simply, if I had a $100k Cintas position, which I do not, I probably would not sell it - at least not yet. I would hold it and let it "ride". (Source: Cintas article)

The company reported 3Q24 not that long ago. Despite being at a good level already, the company recorded revenue improvements in the double digits, with a continued gross margin of over 49% - meaning the company managed to increase GM's in this sort of operating environment, by no less than 200+ bps.

Operating income was also up over 16% YoY, with margin improvements that kept the company's OM above 21.5% here - a very solid and impressive level, all things considered.

Even more impressive, was a 22% increase in net income, in part but not entirely due to a lower effective tax rate. Cintas also increased its dividend on par with this, bumping it by 17.1%, a gift to shareholders not small in scope. Management was rightly pleased with its performance.

Every single operating segment where Cintas works or operates has increased or improved, reflecting very strong execution. The company confirmed full-year guidance and even raised it, now expecting upward of $9.6B in revenues and an EPS rise to a high-end range of $15, based on relatively flat or unchanged interest rate expenses, and continued stability in effective tax rates.

The company's strengths remain, without a doubt, its scale and vertical integration of its services. The company operates in an otherwise fragmented industry, where its advantages in terms of size have led it to be able to dominate and manage margins in ways that smaller companies are mostly unable to.

The company has a superb track record of growing adjusted EPS for more than 51 years out of the last 53 and has increased its annual dividend since before I was born, in 1983.

Perhaps my biggest mistake was expecting the company to really experience a powerful downward reversal after COVID-19, forecasting companies to dial down their cleaning and similar services. This has not happened. Also, I overestimated competition.

While competition for Cintas does exist, this company clearly manages to stay ahead. The company is A-rated, has a market cap that's now well in excess of $50B, and an otherwise quality and profitability that's impressive, based on current operating, net margins, and return metrics. Despite macro pressures and inflation, the company has held up its ROIC well in the face of increasing WACC. Readers would be right in questioning why I have been neutral for as long as I have been - but I stand by my opinion that now is not the time to suddenly turn around here.

Why is that?

Well, first of all, institutional investors and "gurus" have been offloading shares for over a year now. Ray Dalio is one I follow closely, and he sold it about a year ago. Also, insiders for this company are doing only one thing over the past 3 years - and that's selling their shares, not buying new ones. So, no doubt that the company is a qualitative business - but massive upside?

That's where we'll have a disagreement, if that's your stance, and I'll show you why.

Cintas Valuation is even worse going into 2024-2025

Really, I should only need to show you a single picture to illustrate the depth of what I would consider borderline "Insanity" of the company's valuation at this particular time.

F.A.S.T graphs Cintas Upside (F.A.S.T graphs Cintas Upside)

Cintas now yields less than 0.8%, and trades at over 45x P/E. This is a company that provides very basic support services, which means that when you invest in Cintas, you're paying 45x+ earnings for a company that cleans and provides basic services. This is not in any way a snub to the company or what it does - because they do so extremely well. It's a reminder as to what exactly you're investing in, and why I consider this so very dangerous if you were to invest here.

Let's say that you, like me, wanted to have 15% annualized from your investments. Do you realize that even with a 10% annualized EPS growth, at current valuations, this would require Cintas to go above 47x P/E , which the company has never done or traded at? It also has never really been above 40x for any consistent time.

If we use the 20-year average here as a proxy, which, is around 24x P/E, then that's a negative RoR potential of 35% at this valuation of above 45x.

Cintas Upside F.A.S.T. Graphs (Cintas Upside F.A.S.T. Graphs)

While I am bumping my price target as of this article, it's nowhere close to what the company is trading at right now. It's, in fact, closing in on twice my current price target. And while the company has absolutely superb beating records, at some point this exuberance and this valuation journey comes to an end - that much I believe. And when it does, the ensuing hangover won't be pretty - especially if you're still "left at the party", so to speak.

I remain inherently valuation-focused. At this point, there is nothing in this company's fundamentals or performance that could convince me to pay this sort of price for this sort of company - that is simple fact.

Other analysts? Are they still as exuberant?

Yes, sort of. The low range target for the company is $450/share, with a high of $800/share. This should show you how uncertain analysts are in trying to assign a valuation to this business. 15 analysts follow, but only 5 of those are at "BUY", with 7 at "HOLD" and the rest at different "Underperform", "SELL" or similar recommendations.

A very tricky situation with very little concrete guidance to be found here, with an average PT of around $700/share. I would not give this much credence from where I sit - especially because less than 2 months ago, the average was $100/share less than it is currently.

This is, ironically enough, a very boring company that has caught the attention of many analysts, and who are assigning it some extremely un-boring and highbrow sort of valuations.

Me, I cannot get on board with that. I have looked a few times if this failure on my part was something that could have somehow been forecasted or expected - but every time I have looked over the results, the fundamentals and where the company could have gone, my conclusion is that "no", I could not have somehow forecasted this behaviour on the company's side when it comes to the market.

So while my forecasts were wrong - in the way that the company has outperformed, I view the company as more dangerous than ever. I don't short companies, and I would never short a business like this one.

For my update here, I believe the company is clearly overvalued here, and I would not own the company at this price. If I owned shares, I would sell them, and here is my thesis for the company as of 2024E.

My current thesis for Cintas is:

• The company is fundamentally an excellent services company with a high premium - and is better bought, as proven by history, at cheap valuations.
• With cheap prices and fear, this company can easily generate triple-digit returns, even if the dividend is comparatively low.
• At current valuations, even a forward premium of 35X+ results in potential market underperformance, or a bare-bone upside close to the market. This is a no-go in terms of what you "should" invest in at this particular time.
• Given current trends, I continue to consider Cintas a "HOLD". It is, in my opinion, a financially lethal investment as of right now. I would also say that if you hold Cintas, it is time to consider rotating.
• I give Cintas a PT of $385/share as of June 2024, giving the company both leeway and credence for improvements, but I will refuse to go beyond that here.

Remember, I'm all about:

1. Buying undervalued - even if that undervaluation is slight, and not mind-numbingly massive - companies at a discount, allowing them to normalize over time and harvesting capital gains and dividends in the meantime.

2. If the company goes well beyond normalization and goes into overvaluation, I harvest gains and rotate my position into other undervalued stocks, repeating #1.

3. If the company doesn't go into overvaluation, but hovers within a fair value, or goes back down to undervaluation, I buy more as time allows.

4. I reinvest proceeds from dividends, savings from work, or other cash inflows as specified in #1.

Here are my criteria and how the company fulfills them ( italicized ).

• This company is overall qualitative.
• This company is fundamentally safe/conservative & well-run.
• This company pays a well-covered dividend.
• This company is currently cheap.
• This company has a realistic upside based on earnings growth or multiple expansion/reversion.

It's a great company but lacks cheapness and a realistic upside to an attractive fair valuation. For that, I give this a "HOLD".

The company discussed in this article is only one potential investment in the sector. Members of iREIT on Alpha get access to investment ideas with upsides that I view as significantly higher/better than this one. Consider subscribing and learning more here.

This article was written by

Wolf Report is a senior analyst and private portfolio manager with over 10 years of generating value ideas in European and North American markets.

He is a contributing author and analyst for the investing group iREIT on Alpha and Wide Moat Research LLC where in addition to the U.S. market, he covers the markets of Scandinavia, Germany, France, UK, Italy, Spain, Portugal and Eastern Europe in search of reasonably valued stock ideas.

Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. While this article may sound like financial advice, please observe that the author is not a CFA or in any way licensed to give financial advice. It may be structured as such, but it is not financial advice. Investors are required and expected to do their own due diligence and research prior to any investment. Short-term trading, options trading/investment and futures trading are potentially extremely risky investment styles. They generally are not appropriate for someone with limited capital, limited investment experience, or a lack of understanding for the necessary risk tolerance involved. I own the European/Scandinavian tickers (not the ADRs) of all European/Scandinavian companies listed in my articles. I own the Canadian tickers of all Canadian stocks i write about. Please note that investing in European/Non-US stocks comes with withholding tax risks specific to the company's domicile as well as your personal situation. Investors should always consult a tax professional as to the overall impact of dividend withholding taxes and ways to mitigate these.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

McKinsey Live Event: Unlocking the full value of gen AI

Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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