36
Winter Is Coming: The Approaching Human Capital Management Storm
Solange Charas Ph.D
Board Advisor, Senior-Level HR Executive, Entrepreneur, and Adjunct Professor
Michael Young
Partner, Willkie Farr & Gallagher LLP
Introduction
“Winter is coming!” This phrase, originated in the popular series “Game of Thrones,” has become a watchword for constant vigilance. And for boards of directors, winter may be coming—on the topic of human capital management and disclosure. The harbingers of change are several. One is a statement by SEC Chairman Jay Clayton to Congress in December 2018 that “the SEC could do a better job around disclosure, specifically ‘driving disclosure toward human capital.’”1 Illustrative of momentum behind the subject, three months later the SEC's Investor Advisory Committee approved recommendations that the SEC “recognize the significance of HCM [Human Capital Management]” and incorporate the concept into the SEC's overall review of disclosure effectiveness.2
Another harbinger is the January 2019 release by the International Organization for Standardization (ISO) of a new standard on “Human Capital Management Reporting,”3 which provides guidelines on both internal and external human capital reporting. This is anticipated to impact more than 1 million ISO-certified organizations. Still another harbinger is a call by at least a dozen institutional investors and governance organizations, led by the Human Capital Management Coalition (HCMC),4 for increased disclosure about an organization's human capital efficiencies. But more important than any of these is that human capital metrics can enhance board-level decision-making. Research has shown that data diversification can “improve both the board's current and future strategic tasks performance”5 and enhance board governance quality. As SEC Chairman Clayton has noted, the use and disclosure of human capital metrics is a “shift from human capital being viewed, at least from an income statement perspective, as a cost” to being recognized as an “essential resource and driver of performance for many companies.”6
So the topic of HR data analytics is both timely and important. This chapter will focus on four areas in particular:
- The link between how an organization manages its human capital and corporate financial performance
- Who is calling for enhanced human capital disclosure and why
- The economic and governance-quality rationales for elevating human capital metrics to the board level
- A practical guide to specific human capital metrics that can enhance governance quality and competitive advantage7
The Link Between Human Capital and Firm Financial Performance
SEC Chairman Clayton's observation reflects a trend that has been building for years. With research showing the impact that human capital can have on firm performance,8 it was just a matter of time before the SEC and corporate governance organizations caught up. The evidence is overwhelming: A strong correlation exists between the way an organization manages its human capital and how well it performs financially.9 One observer has referred to human capital “as a combination of knowledge, skills and abilities embodied in people”10 that can be critical to a company's efforts to “[gain] competitive advantage over its rivals.”11 Other researchers have found that human capital “when combined with employees experiences through education and training [has] been viewed as the main drivers of organisational performance.”12 For many researchers, human capital is considered an “intangible” asset and, through the lens of the resource-based view (RBV) of the firm, is a critical component in economic value creation. According to Becker and Huselid (2006):
The resource-based view (RBV) of the firm has long provided a core theoretical rationale for HR's potential role as a strategic asset in the firm (Wright & McMahan, 1992). The notion that organizations can build competitive advantage, and as a result above-average financial performance, based on valuable and inimitable internal resources, offers an appealing rationale for HR's strategic importance. This integration tends to focus on human capital, or employee-level attributes, and the RBV's emphasis on recognizing existing strategic resources rather than the development of those strategic assets.13
The relationship between human resource programs (training, performance management, engagement) and a firm's performance has been the subject of study for more than 60 years. In keeping with the resource-based view of the firm14 Huselid identified a relationship between the totality of a firm's human resources efficacy (which he termed “high-performance work systems”) and financial performance. His analysis included the examination of close to 1,000 publicly traded firms with at least 100 employees and found that high-performance work systems had “an economically and statistically significant impact on both intermediate employee outcomes (turnover and productivity) and short- and long-term measures of corporate financial performance.”15 The financial measures he tracked included the Tobin Q measurement of market value compared to replacement cost and gross rate of return on capital. A 2015 report by Bernstein and Beeferman, entitled “The Materiality of Human Capital to Corporate Financial Performance,”16 examined more than 90 research studies on the link between human capital performance and corporate performance (including total shareholder return, return of assets, return on capital, and profitability) and concluded that the “evidence for human capital materiality is sufficiently compelling to warrant investor requests for companies to report systematically on their training and other HR policies with clarity and depth, which would enable investors to assess their alignment with the company's business strategy.”17 Their findings included a positive correlation between HR policies and investment performance. Other examples include:
- Portfolios of organizations with a focus on human capital management outperformed the S&P up to 11.9 percent, according to Bassi and McMurrer. “Superior human capital management is an extremely powerful predictor of an organization's ability to outperform its competition.”18
- Vesanen found that organizations that focus on employee engagement as an indicator of human capital health are “positively correlated with shareholder returns”—up to a 3.5 percent improvement over market index performance.19
An abundance of additional academic research has shown that “firms with superior human capital are better positioned to create resources and capabilities.”20 That means economic value for shareholders.
In a sense, recognizing the importance of human capital is the easy part. The more difficult part is that, for many businesses, the productivity of labor can be exceedingly difficult to figure out. Humans can be irrational and unpredictable, making it hard to rely on assumptions about their productivity, longevity, performance consistency, and engagement levels. That's where the operational utility of HR analytics data can play a critical role. With new approaches to HR data analytics, it can be possible to observe HR and financial correlations and make better decisions about human capital investment.
The New Reality of Enhanced Information Disclosure
Given the relationship between human capital and financial performance, it is understandable that investors would want information showing how well human capital is managed. It is therefore no surprise that the calls for enhanced disclosure are voluminous and getting louder. The table below provides an overview of significant organizations seeking more human capital transparency (and their rationales):
The underlying reasons for these calls are varied, but they tend to center on several core concepts. Illustrative is a letter to the SEC by the HCMC. The HCMC's reasons for advocating better human capital disclosure include:
- Allowing investors to “adequately assess a company's business, risks and prospects, for investment, engagement or voting purposes”21
- Enhancing investor decisions by more efficiently investing direct capital with the resulting impact being the “lowering of the cost of capital for well-managed companies”22
- Obviating the need for organizations to respond piecemeal to investor requests for human capital data while making that information easier for all investors to collect and analyze23
- Leveling the playing field for investors that are not large enough to demand or otherwise access individualized disclosure24
- Allowing investors to focus on long-term investment strategies, which enhances the ability of capital markets to efficiently allocate capital25
The pressure on HR data analytics can be expected to build, and with it questions for boards of directors who want to keep up.
Economic and Decision-Quality Rationales for Including Human Capital Metrics as a Governance Issue
Two fundamental considerations support the inclusion of human capital metrics as a part of board governance. The first is economic. The second involves the quality of board governance itself.
Economic Rationale: Viewing the organization through the resource-based view (RBV) perspective, a business can be thought of as a blend of tangible and intangible resources. These resources include assets, processes, skills and capabilities, information, and knowledge. When these resources are combined, they generate a unique profile that drives the firm's business activity and ultimately market value. And it is this blend of tangible and intangible resources that provides an important competitive advantage for the firm.26
It is, of course, the board of directors that is accountable for the oversight of all this. And one benefit of an RBV perspective is that it can broaden the board's perspective in the economics of how it thinks about the firm. From an RBV perspective, firms can evolve from defining themselves by their products or services to thinking of themselves as a set of core competencies that hopefully create leadership in the sectors in which they choose to operate. The RBV perspective also highlights that not all economic resources are equal in value, and rare resources can be more economically valuable than resources that are abundant. Just as important, the RBV perspective emphasizes that the mere possession of resources without their deployment does not automatically create an economic advantage.27
On these last two points, it is easy to think of a company's human resources as “rare,” particularly as the Federal Reserve tells us the pool of available talent is shrinking.28 For almost any business, human capital is a key economic resource; hence the well-known utterance of so many CEOs that “People are our most important asset.” Zingales looked at it this way: “Corporate finance concerns the financing of enterprises, that is, unique combinations of physical and human capital.”29 Yet a fair question is: To what extent are boards of directors spending time on the economics of the human capital of the organization? Compared to the time devoted to strategy, management oversight, financial performance, operational problems, financial reporting, risk management, cybersecurity, and everything else, are boards doing enough? Or are the difficulties of human resource valuation and predictability helping to deflect board attention to other topics?
More than that, a particular difficulty for boards of directors can be getting useful data to begin with. Financial professionals are trained to focus on finance. HR professionals, for their part, are not trained in analytic approaches and not normally expected to produce analytical data about human capital performance. The result can be a failure of human resource metrics to make their way to the CEO, let alone to the board. Adding to the difficulty is that, for the most part, the economics of people management are difficult to evaluate, especially at the individual level, and a cost-benefit analysis can make a conversation on each element of human capital seem more costly than beneficial. Human capital analytics can provide the data to make the discussion more feasible.
Governance-Enhancing Rationale: Zhang framed his research around an obvious but important question: “How can boards improve the performance of their strategic tasks by using their information advantages?”30 Relying on the RBV of the firm, Zhang's examination included both a static and dynamic view of board-level information and extended beyond what directors know to how their information is put to use. He specifically looked at the “practices of using diverse information such as open discussion and active search” and concluded that “using diverse information is likely to have a significant effect on the current strategic tasks performance.” Among his conclusions was that “possessing diverse information is likely to have a significant effect” on both current and future strategic task performance.31 According to Zhang, the process to enhance the integration (dynamic) aspects of any kind of new information will typically include open discussion, effective leadership, and active search.32 A key aspect is to search out (active search) new information to be brought to the board for deliberation. This would include data about human capital.
Other Theories About Information Impact on Decision-Making
Beyond the RBV model and Zhang's thesis, other theories speak to the relationship between information and decision-making quality. A recent study by Saaty addresses a heuristic about the decision-making process.33 He observes that managerial decisions are made through a series of trade-offs that require the consideration of tangible and intangible factors (like human capital). Based on a theory of “analytic hierarch process” (AHP), pairwise comparisons can be made to derive “priority scales.” These priority scales measure intangibles in relative terms. Saaty explains:
Comparisons are made using a scale of absolute judgements that represents how much more one element dominates another with respect to a given attribute. The judgements may be inconsistent, and how to measure inconsistency and improve the judgements when possible to obtain better consistency is a concern of the AHP. The derived priority scales are synthesized by multiplying them by the priority of their parent nodes and adding for all such nodes.34
This process relies on robust information so that pairwise comparisons can be made about each element in the decision-making process. Directors may intuitively employ such a hierarchical process—weighing and prioritizing objectives and synthesizing available data to make decisions. But without information about the performance of human capital, critical information can be absent. The availability of human capital information, which for many businesses will be the most important intangible, can enhance governance quality.
Human Capital Metrics: Using “Big Data” Approaches
The past two decades have been characterized by a proliferation of “big data” approaches to business analysis and governance. Eckerson tells us that the new science of “big data” involves “a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events.…It is forward-looking, using past events to anticipate the future.”35 Big data approaches have been, for the most part, adopted by thoughtful boards, as evidenced by the kind of data provided to directors in their deliberations.
But overwhelming a board of directors with large volumes of “big data” is not necessarily productive. According to Muller (2017), “In the digital era, the board of directors has an additional task: information governance. While storing data is no longer the problem, handling the data is.”36 Human capital metrics is the science of making HR data manageable by transforming an organization's aggregate human capital experience into standardized measures. This is a relatively new discipline despite the fact that professionals at Arthur Andersen in the mid-1990s created a consulting protocol to directly measure the ROI of human capital.37 In 2011, McKinsey issued a special briefing asking if organizations “are using their ‘people data’ to create value,”38 and the last five years have seen a boom in the proliferation of efforts to transform the “soft science” of human resources to a “hard science” of statistical analysis.39 One indicator of the growing popularity of data analytics is the rapid growth of professional groups addressing the topic. At present, LinkedIn includes more than 200 listings for “human resources” or “human capital” analyst. The three largest LinkedIn groups focused on data analytics include:
- Big Data & Analytics with 350,032 members
- Data Science Central with 385,945 members
- Data Mining, Statistics, Big Data, Data Visualization & Data Science with 180,050 members
In New York City, more than 1,400 professionals are members of the New York Strategic HR Analytics Meetup.
The concept underlying the statistics of HR analytics is straightforward. Within any corporation, there is data about the company's human capital—that is, data about the behavior and performance of its people—that may correlate to indicators of financial performance (e.g., earnings, return on assets, return on investment, return on capital employed, profitability). Unlike the “soft” HR data historically available, HR analytics should make available information that is objective, reliable, and easily validated through statistical approaches. Ultimately, this data-metrics approach translates human capital data into financial ratios that allow the drivers of economic value creation to be more readily understood, measured, and compared. Doing so need not be prohibitively expensive or even complicated. The relevant data may already reside in a typical business's computer. Its deployment can simply be a matter of applying human capital metrics algorithms and monitoring those metrics and ratios as closely as other financial indicators are monitored.
The next question for a board of directors, of course, is what data to look at. The research indicates that several broad human capital areas are strongly correlated to financial performance. These areas, and 13 metrics representing suggested measures, include:40
- General financial performance indicators. The relevance of these indicators includes the contribution of human capital to financial outcomes. As indicated earlier, there are direct correlations between these human capital trends and financial performance.
- Retention/Attrition/Mobility. The relevance of these indicators goes to the stability of the workforce. Research corroborates the entirely logical proposition that a direct relationship exists between a stable workforce (especially management) and firm performance. Extending tenure and reducing attrition (especially regrettable or voluntary attrition) is shown to be a positive factor in firm performance.41 In addition, a recent meta-analysis has shown that enhanced employee mobility correlates to firm performance through enhanced knowledge transfer.42
- Training investments. A robust body of research43 shows the relationship between a company's investment in training and developing its workforce and financial outcomes.
- Workforce diversity. Performance can be enhanced through a diversity of gender, generation, ethnicity, and other demographics.
Any or all of these areas can be quantified in terms of human capital metrics and ratios and then monitored against firm performance to optimize return on human capital investment. Which metrics are of greatest use to a particular business can be a function of the business's unique operating and competitive characteristics.
General Financial Performance Indicators: Low-lying fruit may be found in monitoring the highest-level indicators of human capital's contribution to financial performance. Metrics, which can be compared over different periods or benchmarked against competitors, may include a productivity factor, an expense factor, human capital return on investment (HCROI), and human capital value added (HCVA). The table below provides the definition, algorithm, appropriate application, and relevance, as well as potential “signals” of human capital health:
| Metric | Definition | Algorithm | Application | Signal |
| Productivity (or Revenue) Factor | Revenue per Employee | Revenue/ Full-Time Equivalents (FTEs) (including contracted employees calculated as FTEs) |
A fundamental measure of the productivity of employees—how much revenue is attributable to each full-time employee unit. This can be combined with an expense factor to understand the relationship between revenue and expense per FTE. | A decline in productivity may signal either that revenue has declined on the same number of FTEs or that the composition or number of employees should be reviewed |
| Expense Factor | Expense per Employee | Expense/ FTE (including contract employees calculated as FTE) |
A fundamental measure of the expense attributable to each employee. This can be combined with a revenue factor to understand the relationship between revenue, expense and income per FTE. | An increase in expense per employee may be due to labor or other expenditures. Investment in business processes or capital may be justified even if this factor increases. |
| Human Capital ROI | HCROI can be an efficiency measure of human capital. | (Revenue-Operating Expense (excluding Human Capital Costs)/Human Capital Costs Human capital costs include compensation, benefits, training, recruiting, and all labor costs and expenses. |
Similar to its financial counterpart, ROI, which measures the total cost to generate each dollar of revenue, HCROI captures the gross operating margin generated for each dollar of total human capital expense. |
This measure captures an employee's contribution to overall corporate costs and payback since the metric looks at staff productivity in ways that complement ROI analysis. |
| Human Capital Value Added |
An indicator of the financial value (profit) an average employee brings to an organization. | (Revenue-Operating Expense (excluding human capital costs)/Total FTE |
This metric can be used in conjunction with other indicators of profit contribution, including investment in plant, property, equipment, marketing, and R&D, to better understand the contribution that human capital makes to earnings. | This measure shows the average profit per employee or the extent to which the average employee contributes to the bottom line. |
Retention/Attrition/Mobility: A more in-depth use of HR analytics can examine the stability of the workforce, the rate at which employees leave the organization (and potentially why), and how quickly talent is deployed. Three potentially useful metrics indicators are employee attrition, employee tenure, and mobility. They can be reported to the board of directors on a regular basis as described below:
| Metric | Definition | Algorithm | Application | Signal |
| Employee Attrition | The number of employees leaving (for any reason) during the measurement period as a percentage of total headcount | (# of employees who left during the period)/average headcount for period | This metric can be compared to added employees during the period to see how quickly employee populations can be stabilized. In addition, this metric can be combined with average tenure to understand if long- or short-tenured employees are leaving the organization. Further insight might be gained by an analysis of the cause of turnover (voluntary or involuntary, retirement, etc.) as well as FTE performance level and demographics. | Turnover is costly. Research shows that today's voluntary turnover can increase overall HR costs by 30% in the subsequent year. Additional costs can include impact on the corporate brand and productivity. |
| Employee Tenure | The average length of employment. This is an indicator of the stability of the workforce and can also indicate the level of employee engagement as a “more satisfied” employee may stay longer. | Sum (over all employees) of years since joining the organization)/(number of employees) | Swings in average tenure can help explain the demographic composition of the workforce. If average tenure is declining, this can be compared to overall headcount to understand who is leaving and why. Declines in average tenure may be an indicator of acceleration in new hires, so caution should be used in coming to conclusions. | Research shows that a more stable workforce is associated with higher levels of corporate performance. This metric allows decision maker to understand who is leaving the organization and at what point in their career. Interventions can be developed to help extend employee tenure including “just-in-time” programs. |
| Mobility Metric | Mobility is the path that an employee takes within an organization that allows them to have different work experiences. Mobility includes: promotions and demotions, lateral moves, role or department changes, rotational assignments, status changes (part-time to full-time or vice versa) | (Number of employees who had a “mobility” event)/(total number of employees) | Mobility achieves two objectives. It is a vehicle to transfer skills and technical “know how” to different parts of the organization. It can also help satiate the needs of Millennials who are focused on mobility as a career objective. Mobility can also be used as an organizational tool. Wild swings in mobility can indicate an inefficient human resources strategy or administration. | Research supports that mobility has a generally positive impact on morale, especially in organizations with Millennial employees who value mobility and the opportunity to work in different parts of the organization. Companies can test the relationship between mobility and financial performance. A focus of mobility analytics is tenure—with longer tenure generally being more desirable. |
Training Investment: Two training metrics can provide information about an organization's absolute and relative investment in training. Given that training is often the biggest HR expense after compensation and benefits, metrics that can quantify the impact of training on organizational performance can be particularly useful. The main question is whether the organization can validate and quantify the positive impact of training on financial outcomes. The first step is to understand trends in training investment, both in terms of hard-dollar costs and in terms of training hours delivered to employees. The two training metrics are described below:
| Metric | Definition | Algorithm | Application | Signal |
| Training Hours per FTE | The average length of time (in hours) that employees spend in training during a period | (Total number of hours [of all employees participating in training] in the period)/(average number of FTEs for the period) | A direct relationship between training and financial outcomes is well documented. Training can also greatly enhance a desirable corporate culture. | In addition to the number of hours provided for training, an evaluation of the cost of training might be considered. This metric can be compared to future productivity. Research suggests a time lag between training and the recognition of benefits. Patterns can be explored to identify time lags. |
| Training Costs per FTE | The total cost of training during the period | (Total cost for training during the period)/(average number of FTEs for the period). Costs include both hard-dollar and soft-dollar costs (including the hourly cost based on wages of those attending training and the cost of HR staff in administering training programs.) |
See above. | See above. |
Workforce Diversity: An additional area involves the diversity of an organization's workforce. A broad spectrum of perspectives can improve performance and decision-making (especially when that spectrum parallels the customer base), and an abundance of research suggests that greater workforce diversity has a direct relationship with competitive effectiveness.44 The metrics listed below are the most basic of organizational diversity—gender, generation, and ethnicity. The list of metrics can be expanded to take into account diversity within, for example, particular levels of management or particular divisions or functions. The level of diversity can be monitored regularly.
| Metric | Definition | Algorithm | Application | Signal |
| Gender Diversity | The ratio of male and female workers. A category has been recently added for employees not identifying with either gender. | (Female headcount)/(total headcount) (Male headcount)/(total headcount) (Other or no gender identity headcount)/(total headcount) |
Detail about gender diversity in specific divisions or functional areas (e.g., IT, engineering, etc.) may reveal bias. | Gender diversity metrics combined with employee attrition may offer insight into who is leaving the firm and why. |
| Generational Diversity | The ratio of each of five generations in the workplace: Silent (born between 1930 and 1948) Baby Boomers (born between 1949 and 1965) GenXers (born between 1965 and 1985) Millennials (born between 1986 and 1998) and GenZs (born after 1999). |
(Number of employees in selected generation)/(total headcount) | The observation of trends may help in understanding workforce attitudes and enhance responsiveness to workforce needs or desires. | An organization with a majority of Baby Boomers may face issues of succession and talent replacement. An organization heavily populated by Millennials may face employee desire for social impact, training and development, mobility, and work/life balance. |
| Ethnic Diversity | A ratio regarding each of the seven identified ethnicities: American Indian or Alaska Native; Asian; Black or African American; Native Hawaiian or Other Pacific Islander; White; Does not Identify with any Particular Race; and Hispanic/Latino(a). | (Number of employees in ethnic category)/(total headcount) | The additional perspective of a broad spectrum may help decision-making particularly if it better reflects the customer base. | Research supports a direct relationship between HR diversity and improved financial performance. |
Conclusion
The broad point is this: Boards of directors take into account a panoply of data in their deliberations and decision-making. With the addition of human capital metrics, directors can be armed with measurable data about their workforce and the extent to which human capital investments are generating enhanced corporate financial performance. The goal is not to enable the board to make managerial decisions—that's management's job. The goal is to allow the board to ask informed questions, probe, and understand how trends in the organization's human capital are either contributing to, or detracting from, economic value creation. Given the trend to enhanced human capital disclosure, boards may increasingly be called upon to understand the quality and implications of HR data metrics. In other words, winter is coming. Boards of directors will want to be ready.
About the Authors
Dr. Solange Charas is a board director, senior-level HR executive, and adjunct professor. She provides advisory services to boards and C-suite executives on optimizing the return on investment in human capital. She has served as the chairperson of the audit committee as well as the rumuneration committee of two public companies, and as a director on three private for-profit companies, a nonprofit organization, and a higher-education institution.
In her client work, she specializes in human capital metrics, using proprietary analytical approaches to identify the drivers of economic value creation which allow organizations to improve corporate leadership effectiveness, employee productivity, M&A success, and advises boards and C-suite executives on the proposed SEC disclosure requirements as well as other required disclosures reflecting human capital performance.
Dr. Charas's PhD research focused on innovative approaches to select, develop, and manage passionate high-performing interdisciplinary teams at the board and C-suite level through a proprietary instrument she developed, which allows her to quantify the impact these teams have on corporate financial performance.
She is currently the founder and CEO of HCMoneyball—a software company providing automated human capital metrics calculations and benchmarking to subscribers. Prior to receiving her PhD, she was the CEO of Charas Consulting, CHRO at Praetorian Financial Services Group, Benfield, and Havas Advertising, and in her client-serving endeavors, held senior-level positions at Arthur Andersen, Ernst & Young, and GE Capital.
She is an adjunct professor at Columbia University, University of Southern California, and NYU, teaching in the Master's programs in human capital Management. She delivers courses in Quantitative Methods and Model Building for Decision-Makers, Finance for Human Capital Management, HR Analytics, and Total Rewards.
Dr. Charas has a BA from UC Berkeley, an MBA from Cornell University, and a PhD from Case Western Reserve University. Her work has appeared in more than 25 academic and over 150 practitioner journals, including Harvard Business Review, The Conference Board's Director Notes, Corporate Board magazine, Forbes magazine, FastCompany, Entrepreneur magazine, and the International Journal of Disclosure and Governance. She is a contributor to The Art of M&A: A Merger, Acquisition, and Buyout Guide (5th ed.),45 The Glass Company,46 Delivering on the Promise: How to Attract, Manage and Retain Human Capital,47 and the first edition of The Handbook of Board Governance.48
Named by Accounting Today as one of the “top 100 most influential people in accounting,” Michael R. Young is a litigation partner at New York's Willkie Farr & Gallagher LLP where he chairs the firm's securities litigation practice.
Mike advises and defends boards of directors, audit committees, accounting firms, public companies, and company officers on issues of corporate governance and financial reporting. His trial victories include the landmark jury verdict for the defense in the first class action tried to a jury pursuant to the Private Securities Litigation Reform Act of 1995. He has served as a member of FASB's Financial Accounting Standards Advisory Council, as chair of the New York City Bar Association's Financial Reporting Committee, and as counsel to the American Institute of Certified Public Accountants and the Center for Audit Quality.
A prolific author on the subjects of financial reporting, audit committee effectiveness, and the roles and responsibilities of the independent auditor, Mike's books include The Financial Reporting Handbook (Wolters Kluwer 2003), Accounting Irregularities and Financial Fraud (Harcourt 2000), and, most recently, Financial Fraud Prevention and Detection: Governance and Effective Practices (Wiley 2014). Mike is a much-sought speaker and commentator on financial reporting issues, and has been regularly quoted in such publications as the Wall Street Journal, the New York Times, Fortune, Forbes, USA Today, the Washington Post, and the National Law Journal. He has also appeared as an invited guest on Fox Business News, CNBC, MSNBC, CNN, and BNN (Canada).
Mike is a graduate of Allegheny College and the Duke University School of Law, where he was research and managing editor of the Duke Law Journal.
Notes
- 1. SEC Today, Volume 2018-238, Wednesday, December 12, 2018, p. 2. https://www.sec.gov/news/public-statement/clayton-remarks-investor-advisory-committee-meeting-121318.
- 2. https://www.sec.gov/spotlight/investor-advisory-committee-2012/iac032819-investor-as-owner-subcommittee-recommendation.pdf, p. 3.
- 3. https://www.iso.org/standard/69338.html.
- 4. http://www.uawtrust.org/hcmc.
- 5. Zhang, P. (2010), Board Information and Strategic Tasks Performance. Corporate Governance: An International Review, 18(5), 473–487. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.891.7279&rep=rep1&type=pdf.
- 6. https://www.sec.gov/news/public-statement/clayton-remarks-investor-advisory-committee-call-020619.
- 7. https://www.researchgate.net/publication/230660474_Boards_Governance:and_Value_Creation_The_Human_Side_of_Corporate_Governance.
- 8. Becker, B. E., & Huselid, M. A. (2006), Strategic Human Resource Management: Where Do We Go from Here? Journal of Management, 32, 898–925; Bowen, D. E., & Ostroff, C. (2004), Understanding HRM–Firm Performance Linkages: The Role of “Strength” of the HRM System, Academy of Management Review, 29, 203–221; Huselid, M. A. (1995), The Impact of Human Resource Management Practices on Turnover, Productivity, and Corporate Financial Performance, Academy of Management Journal, 38, 635–672; Le, H., Oh, I. S., Shaffer, J., & Schmidt, F. (2007), Implications of Methodological Advances for the Practice of Personnel Selection: How Practitioners Benefit from Meta-analysis. Academy of Management Perspectives, 21, 6–15; Subramony, M., Krause, N., Norton, J., & Burns, G. N. (2008), The Relationship Between Human Resource Investments and Organizational Performance: A Firm-level Examination of Equilibrium Theory, Journal of Applied Psychology, 93, 778–788.
- 9. Crook, T. R., Todd, S. Y., Combs, J. G., Woehr, D. J., & Ketchen Jr, D. J. (2011), Does Human Capital Matter? A Meta-analysis of the Relationship Between Human Capital and Firm Performance. Journal of Applied Psychology, 96(3), 443.
- 10. Adelowotan, M. O., & Olaoluniyi, M. O. (2015), Significance of Human Capital to Organisational Value Creation. In 1st Academic Conference of Accounting and Finance, 2015, p. 845.
- 11. https://blogs.oracle.com/oraclehcm/create-great-employee-experiences.
- 12. Adelowotan, M. O., & Olaoluniyi, M. O. (2015), Significance of Human Capital to Organisational Value Creation. In 1st Academic Conference of Accounting and Finance, 2015, p. 843.
- 13. Becker, B. E., & Huselid, M. A. (2006), Strategic Human Resources Management: Where Do We Go from Here? Journal of Management, 32(6), 898–925, p. 900.
- 14. Barney, J. (1991), Firm Resources and Sustained Competitive Advantage, Journal of Management, 17(1), 99–120.
- 15. Huselid, M. A. (1995), The Impact of Human Resource Management Practices on Turnover, Productivity, and Corporate Financial Performance, Academy of Management Journal, 38(3), p. 635.
- 16. Bernstein, A., & Beeferman, L. (2015), The Materiality of Human Capital to Corporate Financial Performance, available at SSRN 2605640.
- 17. Ibid., p. 47.
- 18. https://www.td.org/insights/human-capital-management-predicts-stock-prices.
- 19. Vesanen, V. (2011), Does the Stock Market Fully Value Intangible? Brands and Global Equity Prices. Master's Thesis, Aalto University, School of Economics (August).
- 20. Khan, E. A., & Quaddus, M. (2018), Dimensions of Human Capital and Firm Performance: Micro-firm Context, IIMB Management Review, 30(3), 229–241.
- 21. https://www.sec.gov/rules/petitions/2017/petn4-711.pdf, p. 1.
- 22. Ibid.
- 23. https://www.sec.gov/rules/petitions/2017/petn4-711.pdf.
- 24. Ibid.
- 25. Ibid.
- 26. Becker, B. E., & Huselid, M. A. (2006), Strategic Human Resources Management: Where Do We Go from Here? Journal of Management, 32(6), 898–925.
- 27. Eisenhardt, K. M., & Martin, J. A. (2000), Dynamic Capabilities: What Are They? Strategic Management Journal, 21: 1105–1121.
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- 31. Ibid., p. 474.
- 32. Ibid.
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- 34. Ibid., p. 83.
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- 38. https://www.mckinsey.com/business-functions/organization/our-insights/question-for-your-hr-chief-are-we-using-our-people-data-to-create-value.
- 39. https://www.isixsigma.com/new-to-six-sigma/what-six-sigma/.
- 40. These 13 metrics are not exhaustive but a small sample of the human capital metrics that can provide valuable information.
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