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Trang 3School of Business and Technology
Disquisition Committee: Gary Renz, Ph.D Chairperson
Wilford Miles, Ph.D Douglas O’Bannon Ph.D
Trang 4Copyright 2001 by Walker, Dana Charles
All rights reserved
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Trang 6Disquisition Approval
To: Dr D Christopher Risker, Director Doctor of Management Program From: Doctoral Project Committee
Chair: Gary Renz, Ph.D Member: _ Wilford Miles, Ph.D Member: _Douglas O’ Bannon, Ph.D
Subject: Recommendation for the Award of the Degree of Doctor of Management
We, the Doctoral Project Committee, do certify that the Doctor of Management candidate:
Dana Charles Walker
has satisfactorily completed all requirements for the degree of Doctor of Management in the Doctoral Program at Webster University, and do, therefore, recommend that this candidate be
Chair: 7< Date: /„ |e) 2 enz„Ph Member: Pua, Date: of @/ Of Date: hs ‘gfe ⁄ awarded the degree Member: CONCURRENCE: I do concur with the recommendations of the Doctoral Project Committee as stated above 4 ⁄⁄ H2 ⁄ 2624 (mn 22 Borer Risker, Ph.D., Director
Trang 8University for their guidance and input from their respective academic disciplines I would like to especially thank Dr Edward Spillane and Dr Steve Hinson for their comments and review from the Finance and Economics perspective as well as Sue Meredith, Department
Associate, for providing logistical and continuity guidance to me throughout my doctoral studies at Webster
Real life examples of the value of human capital resides in my disquisition
committee, Dr Gary Renz, Dr Wilford Miles, and Dr Doug O’Bannon As I continue
postdoctoral research, I would like to thank Dr O’Bannon for kindling my interest in
Strategic Management Dr Miles provided me guidance, insight and the sharing of his time above and beyond the call of duty Thank you Dr Miles and Dr O’Bannon
Dr Renz moved me from a dazed and confused cacophony of thoughts into an
organized thinker and reader I want to thank Dr Renz for his guidance, encouragement,
Trang 9List of Tables ¬—— vi
List ofFigures -. ch nhìn nh nhe xi
.\›¬ic 0 NH(((Á đa eee ee ee eee ete eee e eee ee reer eee eeeee xi
Chapter L Introduction - {nà nh nh nh nhớ 1
Research Question . -SSn hhhhhh nn n n hx ng 1
Chapter II Literature Review and Propositlons . - l
Background . «cà nh nh nh nhớ 1
Dđnition of Constructs and Proposition Rationale - 6
Chapter II Methodology . -. - {nen nh nh nhe 9 Data Source . - {nh nh khe 9
Variables .-. - ch hon hi hư hi kinh 9
Independent Variables -ŸŸ {Sen sà 9
Dependent Variables -Ÿ{{{ŸŸằŸ nà 14
Control Variable . - << {Sen hs Xe 15
Hypotheses {nh nh nh he lồ
Within Industry Hypotheses -. - 16
Knowledge-Base, Across Industries Hypotheses 16
Statistical Methods for Testing Hypotheses -Ặ-SằsieeeH he nhà L7 Industry Extremes Dichotomy . .-. -Ÿ-Ÿ- 18 Sector Control Variable -{{-Ÿ- 19
Trang 10The Sample from Across Industries Perspective . . - 26
Within Knowledge-base Group Hypotheses Regression Models: . - 29
Results: Within/Total Asset Turnover/Low Knowledge-base group 32
Modification of Capital Intensity Variable .- -. esses eee e reece eees 36 Results: Within/Total Asset Turnover/High Knowledge-base group . 38
Results: Within/Return on Assets/Low Knowledge-base gT0up .- 42
Results: Within/Return on Assets/High Knowledge-base group - 46
Results: Within/Price to Book/Low Knowledge-base group - 51
Results: Within/Price to Book/High Knowledge-base group - 58
Results: Within/Human Capital Market Value/ Low Knowledge-base group «6 eee eee eee eee ee enters 62 Results: Within/Human Capital Market Value/ High Knowledge-base group -. - {nh ằnnnà 67 Summary of Results: Within Knowledge-base Industry Hypotheses 71
Results: Knowledge-base Across Industries Hypotheses - - - 72
Knowledge-Base, Across Industries Hypotheses - 73
Results: Across Knowledge-base/Total Asset TurnoveT - - - - 74
Results: Across Knowledge-base/Return on Assets - 78
Results: Across Knowledge-base/Price to Book - §2
Results: Across Knowledge-base/Human Capital Market Value 86
Trang 11Within Industries Discussion . . -ŸŸ nành nh nhe 9
Non Response Sample Bias Analysis -ŸŸŸ- 97 Alternatives to Remedy possible operational issues - - - 100
Sensitivity to Relevant Range . - se Ÿ nen nhe nhe 100 Across Knowledge-base Industries AnalysiS -ŸŸŸ- 102
Chapter VI Conclusion - - {cà nh hhh nh nh nh nh nhớ 103
Appendix A Industry Sector Codes and Standard Industry Classification 106 Appendix B_ Companies included in the SampÌes . - - - - 107
Appendix C Using Natural Logarithm Transformation Results . . - 110 Appendix D Fully Specified Interaction Variables for Coefficients
for Across Knowledge-base extended analysiS .- - 122
Appendix E Population and Sample Comparisons . - - - - - 123 Appendix F Results: Revised Total Asset Turnover/Low Knowledge-base 124
Trang 12Table 2 Table 3 Table 4 Table 5 Table 6 Tabie 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16 Table 17 Knowledge-basegTrOup - - s++sseerertrerree Industry Dummy Coding Within Industry for Low Knowledge-basegTOup -. - s+nnenneeerreerrsree Assets, Sales, and Employees by Sample and PopulatiOn 5s csssereeeiesrsesririrrrrrreereeee
Low Knowledge-base group Firm and Sector
Frequency TabÌe 5-5 seeeeeerererrsrrsrrrsee
High Knowledge-base group Firm and Sector
Frequency Table sscscsscsessessesenssenseseenennenees
Descriptive Statistics of Key Independent variables
for the Low Knowledge-base gTOup
Descriptive Statistics of Key Independent variables for the High Knowledge-base group
Group Statistics for Two-Sample T Test
Levene Test and Independent Sample Test
Knowiledge-base Frequency Table .
Descriptive Statistics for full sample and subgroups
Descriptive Statistics for Paper & Forest Product Descriptive Statistics for Oil & Gas Sector
Descriptive Statistics for Telephone Long Distance Descriptive Statistics for Passenger Airlines Sector
Descriptive Statistics for Advertising Sector
Trang 13Table 20 Table 21 Table 22 Table 23 Table 24 Table 25 Table 26 Table 27 Table 28 Table 29 Table 30 Table 31 Table 32
Within Knowledge-base Total Asset Turnover Model,
Descriptive Statistics, Low Knowledge-base group 33
Within Knowledge-base Total Asset Turnover Model, Correlations, Low Knowledge-base group -.+ 34 Within Knowledge-base Total Asset Turnover Model
Regression, Low Knowledge-base group 35
Within Knowledge-base Total Asset Turnover Model Descriptive Statistics, High Knowledge-base group 40 Within Knowledge-base Total Asset Turnover Model,
Correlations, High Knowledge-base group -++- 40
Within Knowledge-base Total Asset Turnover Model,
Regression, High Knowledge-base group ++++ 41
Within Knowledge-base Return on Assets Model,
Descriptive Statistics, Low Knowledge-base group 43 Within Knowledge-base Return on Assets Model,
Correlations, Low Knowledge-base group +++ 44 Within Knowledge-base Return on Assets Model,
Regression, Low Knowledge-base group 44
Within Knowledge-base Return on Assets Model,
Descriptive Statistics, High Knowledge-base group 47
Within Knowledge-base Return on Assets Model,
Correlations, High Knowledge-base group -. 48
Within Knowledge-base Return on Assets Model,
Regression, High Knowledge-base group + 49 Within Knowledge-base Price to Book Model,
Trang 14Table 34 Table 35 Table 36 Table 37 Table 38 Table 39 Table 40 Table 41 Table 42 Table 43 Table 44
Within Knowledge-base Price to Book Model,
Regression, Low Knowledge-base group 34
Within Knowledge-base Price to Book Model,
Trimmed Firms, Low Knowledge-base group 34 Within Knowledge-base Price to Book Model,
Descriptive Statistics, Trimmed Sample,
Low Knowledge-base gTOup . - - <-eseerere 55 Within Knowledge-base Price to Book Model,
Correlations, Trimmed Sample,
Low Knowledge-base group -.-sssscssssseressererees 55 Within Knowledge-base Price to Book Model,
Regression, Trimmed Sample,
Low Knowledge-base group :cssssessssrssssesreennes 56 Within Knowledge-base Price to Book Model,
Descriptive Statistics, High Knowledge-base group 59 Within Knowledge-base Price to Book Model,
Correlations, High Knowledge-base group ++ 60
Within Knowledge-base Price to Book Model
Regression, on, High Knowledge-base group 60 Within Knowledge-base Human Capital Market Value
Model, Descriptive Statistics,
Low Knowledge-base gTOup -<-cseeeeee 64 Within Knowledge-base Human Capital Market Value Model, Correlations,
Low Knowledge-base gTOup - - se 64 Within Knowledge-base Human Capital Market Value
Model, Regression,
Trang 15Table 46 Table 47 Table 48 Table 49 Table 50 Table 51 Table 52 Table 53 Table 54 Table 55 Table 56 Table 57 Table 58 Within Knowledge-base Human Capital Market Value Model, Correlations,
High Knowledge-base øTOuP -s- se 69
Within Knowledge-base Human Capital Market Value Model, Regression,
High Knowledge-base grOUp - -<<->« 70
Summary of Results by Within Knowledge-base
Industry Models ssescssssscecsnsssssseessnsessoeernsees 71
Summary of Results by Within Knowledge-base
Trang 16Table 60 Table 61 Table 62 Table 63 Table 64 Table 65 Table 67
Across Knowledge-base Human Capital Market
Value Model, Descriptive Statistics - §7
Across Knowledge-base Human Capital Market
Value Model, Correlations - «5s - << <=> 88 Across Knowledge-base Human Capital Market
Value Model, Regression - «=> 89 Summary of results by Across Knowledge-base
Industries models .::.ssccssssossseneeseeseseneeeseessenees 90 Discussion, Summary of Results by Knowledge-base
Within Industry Models - 5s se 93
Discussion, Summary of Results by Knowledge-base
Trang 17Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12
for low knowledge-base øTOUp - -
Histogram of Total Asset Turnover, Productivity, for high knowledge-base gTOup - - Histogram of Return on Assets, Proftability,
for low knowledge-base group - -
Histogram of Return on Assets, Proftability,
for high knowledge-base øT0up - - - Histogram of Price to Book, Market evaluation, for low knowledge-base group - - 5< Histogram of Price to Book, Market evaluation for high knowledge-base grOup - Histogram of Human Capital Market Value, Market
premium, for low knowledge-base group
Histogram of Human Capital Market Value, Market
premium, for high knowledge-base group
Histogram of Total Asset Turnover, Productivity, for Knowledge-base Across Industries Histogram of Return on Assets, Profitability,
for Knowledge-base Across Industries
Histogram of Price to Book, Market evaluation,
for Knowledge-base Across Industries
Trang 18Profitability, and the Market Evaluation of the Firm
by
Dana Charles Walker
Doctor of Management Webster University in St Louis, 2001
Gary Renz, Assistant Professor, Chairperson
Firm performance is a function of the organization’s ability to acquire and deploy
resources in such a way to develop a sustainable competitive advantage This study empirically investigates the relationship between a firm’s human capital and performance Firm performance was examined in four dimensions: productivity, profitability, market evaluation, and market premium Market evaluation and Market premium explores the market value of the firm in relationship to the book value and the role human capital may contribute to the market’s evaluation of the firm The major independent variable of the analysis is the value of human capital
For the within industry analysis two groups of firms were defined for contrasting multiple regression analysis using the different firm performances measurements defined as the dependent variables Group membership was determined by the firm’s primary source
of value creation The low knowledge-base group derived value from raw resource
extraction or significant fixed capital investments The high knowledge-base group derived value from the efforts of people A positive relationship between the value of human capital and firm performance was only supported by one performance measurement, the
market premium measurement In the low knowledge-base industry group the data
provided statistical support at the p < 05 level that there is a positive relationship between human capital and market premium measurement In the high knowledge-base group the data provided statistical support at the p < 01 level that there is a positive relationship between human capital and market premium measurement In neither the low knowledge-base group nor the high knowledge-base group, was there statistical support to reject the null hypothesis, and conclude that there is a positive relationship between the value of human capital and firm performance dimensions of productivity, profitability, or
market evaluation
A second series of analysis was performed “Across Industries” and proposed that certain industries rely more heavily upon the use of knowledge and intellectual capital in producing a firm’s goods or services For the across industries analysis the data did not
statistically support that the industry knowledge-base has a positive effect on the
Trang 19I Research Question
Firm performance is a function of the organization’s ability to acquire, develop, and use physical, financial, human, and intellectual capital Researchers have long studied
the relationship between physical and financial capital and firm performance, but very little research has been done to investigate the relationship between human capital and firm performance Human capital can create idiosyncratic organizational capabilities that may create sustainable competitive advantages, often by creating intellectual capital The
purpose of this research is to empirically investigate the relationship between human
capital and firm performance
Chapter II Literature Review and Propositions II Literature Review A Background
People direct, manage, and transform the physical, financial, and human resources of the firm into goods and services, which result in financial returns to the firm and its market evaluation This research focuses on the contribution of an organization's human
resources to the organization's performance Human resources (i.e., the employees) are
Trang 20complex structure and interactions of humans and is much more difficult to imitate than the competitive advantage based upon physical and financial capital, therefore creating a
sustainable competitive advantage (Teece 1998) As Pfeffer (1998) noted, human
resources, unlike physical and financial resources, are unique and scarce in a global
economy As such, they create a unique source of competitive advantage
Despite its theoretical importance, there has been little empirical research into the importance of human capital Several researchers have theorized about the relationship between human capital and organizational performance, each emphasizing different
aspects of this general proposition For example, from the strategic management literature, Geletkanycz and Hambrick (1997) noted that all corporate strategies require
humans to execute From the human resources management literature, in a conceptually
related stream, Becker and Gerhart (1996) explore the relationship of human resources
management to organizational performance Becker and Gerhart’s research is based upon Barney’s (1991) definitive article on using firm resources to create a sustained competitive advantage From the compensation perspective Levine (1992) explores the wages and benefits, and efficiency-wage issues relating to productivity Barney (1991) also
conceptualized that human capital is a source of sustainable competitive advantage His
ideas have been elaborated on by several researchers, including Wright and McMahan (1992), Lado and Wilson (1994), and Becker and Gerhart (1996)
Huselid and Becker (1996) and Huselid, Jackson & Schuler (1997) continued to
Trang 21Becker and Gerhart’s (1996) article by empirically and specifically exploring high
performance work systems and human resource management effectiveness Even though Huselid, Jackson & Schuler (1997) do identify an empirical link between human resource
management and firm performance, their source of evaluation of the effectiveness is
determined from the human resources organization, not by an objective external
measurement Another limitation to the Huseiid, Jackson & Schuler (1997) research is that
the sample firms are primarily manufacturing organizations Although human resource
management effectiveness is an important component of the organization and deployment
of human capital, it is but one of the components that should be explored in the search for the relationship of human capital to firm performance Previous literature and research has focused on individual components of the firm’s human resource systems (such as:
turnover, training, and recruitment) as well those component’s direct or indirect influence and effect on firm profitability The prior research has varied results, but each is limited to
their respective proportional contribution of human resource components rather than a
more unified view of the overall contribution of human capital to the firm Barney’s
original conceptual framework concentrated on the use of human capital as a more general proposition
Sveiby (1997) brings two important concepts and observations to the literature
that gives us reasons for re-exploring Barney’s (1991) conceptual model of human capital as a source of sustainable competitive advantage The first observation is that fifty percent of the fastest growing companies in the United States can be described as
Trang 22Samek did not conduct an empirical research study, but used their industry consulting exposure to observe that firms that concentrated on using human capital as a source of
value creation appeared to be industry leaders and more profitable than their direct
competitors These observations help us conclude that multiple industries should be considered in the analysis, specifically industries that derive their value from services as
well as the manufacturing of goods Secondly, with the growth of knowledge-based
companies, which are very dependent on intellectual capital in producing goods and services in their respective industry, the research should also explore the influence of human capital on firm performance
This research is largely based on Barney's model, as elaborated and refined by other researchers Barney’s model is consistent with other conceptual frameworks, including those asserting that human resources are a source of sustainable competitive
advantage; that human resource practices effect firm performance; that the purchase
(compensation) and acquisition of human capital influences performance; and that the growth of "knowledge-based" firms in the information-based economy demand increased human capital and the protection of intellectual capital Against this backdrop, this research will attempt to operationally define the value of human resources to a firm and then empirically demonstrate that human capital influences firm performance, specifically profitability and the market's evaluation of the firm
Trang 23factors on which the firm earns rents (Rents in the terminology of economics, and
revenues in the terminology of accounting and finance) Lindenberg and Ross categorized
the assets of the firm into three broad categories The first category was traditional assets such as plant and equipment and inventory The second category was comprised of
“special factors” that the firm possesses which lower the firm’s costs relative to those of a competitor The third category was another set of “special factors” of production The third category included patents
The construct of intellectual capital has emerged over the past decade as a key
factor in explaining organizational performance, especially in an information- or
knowledge-based economy Intellectual capital refers to the value of ideas and knowledge
acquired and used by an organization Each organization’s unique way of directing,
managing, and transforming its resources is one aspect of its intellectual capital Intellectual capital can be conceptualized different ways, but for the purposes of this research it is comprised of customer capital, structural capital, and human capital
(Stewart, 1997) Customer capital is the value derived from satisfied customers, reliable
suppliers, and other sources external to the organization This construct is similar to the construct of goodwill, although goodwill has a narrower accounting definition Structural capital refers to the knowledge captured and retained in the organizational systems and structures (examples include patents, intellectual property, and organizational memory)
Trang 24responsible for its day-to-day productivity, as well as its future innovations (Drucker, 1993) In summary, human capital creates new ideas and relationships, perpetuates past ideas and relationships, and refines and develops existing products, services, and ideas
Human capital is what develops and sustains an organization's unique competitive
advantage, which in tum results in better firm performance For these reasons, this
research will empirically investigate whether increases in human capital are positively
correlated with different indicators of firm performance
B Definition of Constructs and Rationale for Propositions
Human capital is defined as the knowledge that a firm's human resources possess and generate, including their skills, experience, and creativity The knowledge, skills, and
abilities of employees largely define an employee's human capital, but in different
organizations an employee's knowledge, skills, and abilities may be more or less valuable,
and thus the value of human capital varies by organization For example, each
organization's unique strategy for deploying and organizing its human resources to achieve
its goals, called Strategic Human Resource Management (or SHRM) (Wright & McMahan
1992), is a form of structural capital that helps a firm increase its human capital (Stewart 1997)
Trang 25dimension is commonly referred to as profitability, or the degree to which a firm's revenues exceed its costs (although the accounting definition is more complicated) The third dimension has no accepted name or label, but refers to the degree to which a firm's
market value exceeds its book value, or market-to-book This last dimension is related to firm performance because if the firm was not operating well (i.e., not "performing"), then its market value should be limited to the net value of its physical and financial assets after
deducting financial liabilities In this sense, the additional value over and above the net value of the firm's assets can be attributed to the operations of the firm (the "operating
value" of the firm) Another way of conceptualizing this dimension is to think of it asa
premium resulting from intangible factors, such as a firm's intellectual capital, or monopoly rents
Although the focus of this research is on the contribution of human capital to firm performance, physical capital is also an important factor of production The type and amount of physical assets and technology (i.e., the physical capital) used to produce goods
and services varies both across industries, and among firms within an industry The better
Trang 26Given the importance of human capital in creating and sustaining an organization's competitive advantages and capabilities, which in turn influence firm performance, the
following three propositions are advanced:
Within a given industry, and controlling for differences in physical capital, the greater the value of a firm's human capital:
e the greater the firm's productivity e the greater the firms profitability, and
e the greater the market's evaluation of the firm relative to the value of
its financial and physical assets
While the propositions above focus on within industry differences in human capital, a related issue is whether similar relationships between the value of human capital and firm performance exist across different industries This is more difficult to address because different industries use different combinations of human, physical, and financial resources To the extent that human capital derived from human resources is more or less valuable in creating an industry's product or service, the relationships proposed above within an industry may not exist Even if the relationships between human capital and firm performance exist, the magnitude of those relationships will probably differ across industries
To investigate the relationship between human capital and performance across industries, this research proposes that industries can be classified according to the degree to which their goods and services are "knowledge-based." Distinguishing among
Trang 27services (consulting) and goods (software and pharmaceuticals) Conversely, "low" knowledge-based industries produce goods and services using relatively little intellectual
capital, and include such industries as the passenger airlines, chemical production, and mineral extraction industries Although this idea that industries vary in the importance of knowledge and intellectual capital is not well-defined presently, this research should help
clarify how this construct can be used in the future
Logically, the degree to which an industry relies on intellectual capital (i.e., the
degree to which it is a "knowledge-based" industry) in producing its goods and services
moderates the strength of the relationship between the value of human capital and frm performance In other words, the relationship between the value of human capital in a firm and a) the firm's productivity, b) its profitability, and c) the market evaluation of the
firm relative to the book value of assets is moderated by the importance of knowledge and intellectual capital (i.e., its knowledge base) in the production of goods and services This reasoning leads to the following propositions across different industries:
The more important that knowledge and intellectual capital are to producing an industry's goods or services, and controlling for physical capital:
e the greater the contribution a firm's human capital will make to its
productivity
e the greater the contribution a firm's human capital will make to its
profitability, and
e the greater the contribution a firm's human capital will make to the market value of the firm relative to the book value of its assets As mentioned above, there is relatively little empirical research on the value of
Trang 28support the propositions, this will be evidence that human capital is positively related to firm performance Second, the magnitude of the effect of human capital will be
Trang 29Chapter III Methodology
A Data Source
This research will use a secondary database from Standard and Poor’s 1999-2000
Research Insights (commonly referenced in the literature as CompuStat) to test the hypotheses This database consists of 7,000 publicly traded firms Of the total of 7,000
firms, 1836 firms reported the key variable of total employee compensation
B Variables
1 Value of Human Capital (Independent Variable)
The fundamental issue in this research is whether an increase in the value of a firm's human capital increases the firm's performance Therefore, the critical issue is how to create a variable that measures the value of human capital There are several possible
ways to measure the value of human capital, none of which are completely satisfactory
The discussion below explains how this key independent variable was defined for this
research
The approach used in this research to assess the value of a firm's human capital to
calculate the average cost of compensation per employee in a firm Using compensation
levels to value human capital is based on labor economics and theories of compensation The reasoning is that if the labor market functions as theorized, then employees will be paid according to the value of their human capital The database contains information on
the total compensation costs for a firm (TC) and the number of employees (EE) The
Trang 30by the number of employees (TC/EE) This variable gives an indication of the average employee's human capital based on average compensation costs
An alternative measure would be to simply use the firm's total compensation costs
(TC) as an indicator for its stock of human capital However, firm size would dramatically
influence this number, with large firms showing a greater stock of human capital than smaller firms Dividing total compensation by the number of employees essentially standardizes this variable so that firm size is controlled
Another potential problem with this variable is the accuracy with which
compensation levels match employees’ human capital Theoretically, no firm would pay more than the value of an employee's production However, human resources
management systems vary in their effectiveness and labor markets are not perfectly
competitive As a result, employees may be overpaid (or underpaid) relative to the value of their human capital in different firms Because of this potential mismatch between compensation and productivity levels, compensation costs might not accurately reflect the value of human capital However, the extent to which firms can pay employees
inefficiently is constrained by the realities of competition Eventually, firms that radically
overpay or underpay will be driven out of business Of course, this would not be true for monopolies, regulated industries, or the public sector, which might be able to overpay employees Therefore, this variable is valid only for firms in the for-profit sector (Note
that no firm can systematically and consistently underpay employees by very much because
Trang 312 Knowledge-Base of Industry (Independent Variable)
The knowledge-base construct is relatively novel, and there is no operational
definition of the construct in the research literature However, the construct is relatively clear conceptually, and it is easy to think of industries at the extreme ends of what is undoubtedly a continuum (e.g., consulting and software versus mineral extraction and
passenger air travel) Therefore, rather than attempt to create a continuous variable measuring the knowledge-base of all industries, this research will investigate only industries at the extreme ends of the knowledge-base continuum
The appropriate industries were selected based on their production processes and
the types of goods and services they produced Four industries, two from each end of the knowledge-base continuum were initially selected for analysis The consulting and
software industries were chosen to represent the category called "high knowledge-based"
industries (Firms in these two industries will be coded as 'l' on the dummy variable representing this "knowledge-base" variable.) The mineral extraction and passenger airline industries were chosen to represent the "low knowledge-based" industries (Firms in these
two industries will be coded as 0 on the "knowledge-base" dummy variable.)
3 Physical Capital (Independent Variable)
Trang 32of money invested in physical assets, including equipment and real property And, even
though the variable does not directly measure the level of technology or the quality of the fixed assets, higher quality and newer technology are probably more expensive, thus
increasing this ratio
4 Productivity (Dependent Variable)
Simply using total revenues (TR) as a measure of productivity is inadequate because productivity refers to whether that revenue was produced efficiently However,
by dividing total revenues by total assets (TA), this creates a simple measure of
productivity, asset turnover (as measured by dollar revenues) This ratio represents the
efficiency with which physical and human resources convert inputs into the goods and
services that were subsequently sold
An alternative productivity measure that is tied closer to a firm's human resources
is to divide the firm's total revenues (TR) by the number of employees (EE) The resulting
variable (TR/EE) indicates the average productivity per employee, as measured by
revenues However, this variable does not reflect the cost of human capital, or any other
capital, and thus does not measure cost efficiency
5 Profitability (Dependent Variable)
The variable measuring firm profitability will be the firm's Return on Assets
(ROA), not its profit or net income Return on Assets will be calculated as the ratio
between the firm's profit and its total assets (TA), comprised of both financial and physical
Trang 336 Market value in excessive of Book value (Dependent Variable)
This construct is well defined in the research literature It is the ratio of a stock’s market price to its book value
The formula used in this research is:
Book Value per share = Common equity divided by Common shares outstanding
Market/book ratio (M/B) = Market price per share divided by Book Value per share
Formulas from Brigham (1995)
7 Industry Dummy Variable (Within Industry Control Variable)
To increase the sample size for the "within industry" propositions, data from several industries will be combined into one cross-sectional statistical analysis Because there may be significant differences in the production functions, and thus the relative importance of human and physical capital, a dummy variable will be used to control for
inter-industry differences This variable will not be used in the "across industry"
Trang 34C Hypotheses
The hypotheses are simply the theoretical propositions with the operational definitions of the constructs (i.e., the variables) substituted for the constructs, with the
relevant control variables added These are restated as follows:
1 Within Knowledge-base (Industry) Hypotheses
Within a given industry (measured by industry dummy variable), and controlling for
differences in physical capital (measured by CI), the greater the value of its human capital
(measured by the average compensation per employee (TC/EE))
e the greater the firm's productivity (measured by total asset turnover and noted as
(TR’TA))
e the greater the firms profitability (measured by return on assets (ROA)), and e the greater the market's evaluation of the firm relative to the value of its financial
and physical assets (measured by M/B)
These hypothesized relationships will be estimated as follows:
Productivity (TR/TA) = a + biIndustry + 52Capital Intensity + 6s Compensation
Profitability (ROA) = a + bsindustry + b2Capital Intensity + bs Compensation Market Value (W/B) = a+ b¡Industry + ¿zCapital Intensity + 5s Compensation For each hypothesis:
e The null hypothesis (Ho) is that b3 <0
e The alternative hypothesis (Ha) is that 53> 0 (i.e., a right-tailed test that there is a positive relationship between human capital and performance)
(Because there are no hypotheses about the industry moderating the effect of human capital, interaction terms will not be estimated.)
2 Knowledge-Base (Across Industries) Hypotheses
The more important that knowledge and intellectual capital are to producing an industry's goods or services (measured by knowledge base dummy variable), and controlling for physical capital (measured by C1):
e the greater the contribution a firm's human capital will make to its productivity
Trang 35e the greater the contribution a firm's human capital will make to its profitability (measured by return on assets (ROA)), and
e the greater the contribution a firm's human capital will make to the market value of the firm relative to the net value of its assets (measured by M/B)
These hypothesized moderated relationships will be estimated as follows:
Productivity (TR/TA) = a + b: Knowledge Base + 52 Capital Intensity +
bs Compensation + 5+ (Knowledge Base x Compensation) Profitability (ROA) = a + b: Knowledge Base + 52 Capital Intensity +
bs Compensation + bs (Knowledge Base x Compensation)
Market Value (M/B) = a + 5: Knowledge Base + 62 Capital Intensity +
bs Compensation + 5s (Knowledge Base x Compensation) For each hypothesis:
e The null hypothesis (Ho) is that bs < 0
e The alternative hypothesis (Ha) is that bs > 0 (i.e., a right-tailed test that being
a high knowledge base firm has a positive effect on the relationship between
human capital and performance in relation to the low knowledge base firms)
D Statistical Methods for Testing Hypotheses
Because the research model proposes functional relationships among multiple
constructs, multiple regression will be used to test the hypotheses Both simultaneous and
hierarchical regressions will be used to test the hypotheses Any transformations
necessary to meet the assumption of linearity will be made after examining the data Any
Trang 36Industry Dichotomy
The high knowledge-base industry sectors selected were: Pharmaceuticals,
Biotechnology, and Advertising The primary source of value creation of the corporation in the high knowledge-base sectors is expected to be derived from the efforts of people
(human capital) and reflected in the human effort in structures such as intellectual capital
The low knowledge-base industry sectors selected were: Paper and Forest
Products, Oil and Gas extraction, Telephone Long Distance, and Passenger Airline sectors The primary source of value creation of the corporation in the low
knowledge-base sector is expected to be derived from the value of the raw resource
extracted (examples: Forest Products, Oil and Gas) or from significant fixed capital
investments (examples: Commercial Airplanes or Long Distance Telephone Infrastructure
and capacity)
Two additional sectors from low knowledge-base industry sectors (Gold Mining and Metals Mining) were dropped from the analysis because of the low number of firms
within these sectors that reported data values for the key variables required for the
analysis
Industry groupings were determined by Sector alignment rather than Standard
Industry Classification (SIC) Sectors were used for three reasons, one theoretical reason
Trang 37From analytical perspective, the Standard Industry Classification 4 digit
classification was extremely narrow for the sample Some 4 digit SIC codes created
industry groups with only one member The 4 digit SIC code would consume a large number of degrees of freedom to implement The 2 digit general SIC codes were too
broad and included industries that were related by the kindred products, but did not reflect the industry classification germane to the human capital analysis Also from the analytical
perspective, the Industry Sector Code provided a method to comparison in future analysis
and research
Although Industry Sector Codes are not parallel to Standard Industry
Classification an analysis was performed to identify alignment and exclusions (see
Appendix A for detailed comparison / contrast analysis of the sample for Sector and SIC)
Sector Dummy Variable (Within Industry Control Variable)
The original proposal included a “within industry” analysis which was modified to a “within knowledge-base” analysis because of the difficulty in a obtaining sufficient
number of firms reporting the compensation independent variable within a specific single industry To increase the sample size for the "within industry" propositions, data from several industries will be combined into one cross-sectional statistical analysis Because there may be significant differences in the production functions, and thus the relative
importance of human and physical capital, a dummy variable is used to control for
Trang 38- | = number of independent control variables to represent the unique industry within each
of the two groups
Therefore for the high knowledge-base group consisting of Pharmaceutical,
Biotechnology, and Advertising kc=3 Industry sector dummy variables (within industry
control variables) of INDMY1 and INDMY2 were coded as follows:
Table |
Pharmaceutical
B
The low knowledge-base group consisting of Paper and Forest Products; Oil and Gas;
Telephone Long Distance; and Passenger Airlines giving a kg = 4 Industry sector dummy
variables (within industry control variables) were coded as follows: Table 2 INDMY4 INDMY6 Oil and Gas 0 0 T Distance 0 0 Airlines 1 0
and Forest Products 0 1
Trang 39Chapter IV Research Results
A The Sample
A total of 117 firms were included in the final data set A complete list of firms included in the final data set is listed in Appendix B The low knowledge-base group contained 89 firms The high knowledge-base group contained 28 firms
The final data set contained a total of seven sectors comprised of four sectors for the low knowledge-base group and three sectors for the high knowledge-base group
The final data set of 117 firms represents 12.1% of the 971 firms from COMPUSTAT database within the seven sectors
Total Assets, Total Net Sales, and Total Number of Employees are key variables
used in this study Total Assets and Total Net Sales data was available for all 971 firms
from the COMPUSTAT database for the seven sectors Total Number of Employees was
available from 897 firms from the COMPUSTAT database for the seven sectors
Table 3 Assets, Sales, Number of Employees for all 7 sectors
Key Variable Total in the Sector Total in Sample Percentage of Sector All firms listed in 117 Total Firms represented by the COMPUSTAT Sample Total Assets 2,613,829 1,072,542 41.0% Total Sales 1,525,231 623,812 40.9% Total Employees 4,842 2,745 56.7% Note: Assets in millions (SUS), Sales in millions (SUS), Employees in thousands (see Appendix B for analysis by sector
The low knowledge-base group contained four sectors:
Table 4 Low knowledge-base sectors and frequency Sector Name Sector ID Frequency Percentage Cumulative Percentage Paper & Forest Products 1050 8 9.0% 9.0%
Oil & Gas 4060 9 10.1% 19.1%
Telephone Long Distance 8630 47 52.8% 71.9%
Passenger Airlines 9520 25 28.1% 100.0%
Total 89
Trang 40The high knowledge-base group contained three sectors:
Table 5 High knowledge-base group sectors and frequency Sector Name Sector ID Frequency Percentage Cumulative Percentage Advertising 2400 12 42.9% 42.9% Pharmaceuticals 3530 9 32.1% 75.0% Biotechnology 3590 7 25.0% 100.0% Total 28
Each group and each sector within each group was reviewed for data
reasonableness Firms with omitted key variable values or misreported data values (such as zero sales, or zero employees) were excluded from the data set Omitted firms included one from the advertising sector, one from the pharmaceutical sector, and two from the passenger airlines sector
An important assumption in the analysis is that the within-group data are samples
from normal populations with the same variance Histograms were reviewed for normal
distribution of data and the Kolmogorov-Smirnov and Shapiro-Wilk tests of normality was performed on the key variable Value of Human Capital (Total Compensation divided by
Number of Employees) The findings were that normality could not be rejected for the
variable Value of Human Capital Normality of distribution was noted for both knowledge-based groups and all seven sectors for the Value of Human Capital The
graphic histogram review for both knowledge-based groups and all seven sectors for the