Intellectual capital (IC) has been proposed as an essential factor for organizational survivaland maintenance of competitive strength. However, there has been very limited consensuson what encompasses IC and how it can best be conceptualized and measured. Further, verylittle empirical work has specifically examined the relationship between IC and financialperformance. Given these shortcomings, this paper focuses first on the impact IC has onperformance and secondly on the role strategic alliances may have on this relationship.
While we argue that IC will impact performance, we anticipate this relationship willbe moderated by strategic alliances and other inter-firm collaborations. Findings revealinteresting relationships that suggest further effort should be placed on the conceptualizationand measurement of IC, specifically regarding its relationship to firm performance.
Keywords: High-tech firms; intellectual capital; new ventures.
Introduction
Intellectual capital (IC) has been proposed as an essential factor for organizationalsurvival, maintenance of competitive strength, and ultimately, firm performance(Nahapiet and Ghoshal, 1998; Leana and Van Buren, 1999). But while there is lit-tle theoretical debate about the relevance of IC to many contemporary firms, therehas been a very limited consensus on what encompasses this important conceptand how best it can be computed, reported, and empirically tested (Edvinsson andMalone, 1997). Further, because of the elusive, intangible nature of IC and its focuson issues such as employee competence building, firm culture and history, et cetera(Edvinsson and Sullivan, 1996), IC may not directly impact a firm’s bottom line
for many years, if ever. In response to such unresolved issues regarding IC, thispaper attempts to assess the impact IC has on the performance of new high-techfirms. While we argue that IC will impact performance, we anticipate this rela-tionship to be moderated by strategic alliances and other inter-firm collaborations,which are increasingly being utilized as key competitive strategies among high-tech firms (Duysters, Kok and Vaandrager, 1999; Kelly, Schaan and Joncas, 2002;PriceWater-houseCoopers, 2001). So although the existence of IC and its impacton new firm performance is an accepted premise (Bontis, 2002; Bontis, Keow andRichardson, 2000; Choo and Bontis, 2002), there is little empirical work support-ing this relationship. In particular, there is little support that utilizes the quantitativemeasurements currently being proposed in the literature. These and other issues arestudied in an exploratory fashion utilizing multiple variables and constructs of bothIC and performance.
We first review the limited literature available in this area to assess the cur-rent thinking regarding IC and its relationship to firm performance. Following thisreview, we focus on the role that strategic alliances and other interfirm collabora-tions may have on the IC-performance relationship. These discussions lead to thedevelopment of three hypotheses that are empirically tested in the final portions ofthe paper. Finally, the paper concludes with an in-depth discussion which includesthe limitations of the current study and areas for future research.
Theoretical Background and Hypotheses
Intellectual capital, as a business management topic of interest, is seemingly in itsembryonic stage of development. For the business practitioner, the first real acknowl-edgement of IC came with Skandia’s release of the world’s first intellectual capitalannual report in 1995 (Edvinsson and Malone, 1997). In academic research, IC hasbeen researched for a slightly longer period, primarily basing ideas and studieson social and/or human capital concepts (Coleman, 1990; Nahapiet and Ghoshal,1998; Leana and Van Buren, 1999). Although fairly young in its development,IC and related topics have recently increased in popularity, in both academic andpractitioner circles. Recognition of IC’s importance has increased as more and morefirms are creating value based on knowledge and other intangible assets rather thantangible assets such as buildings, equipment, and real estate. Progressively moreintangible assets are being flaunted as a key determinant of competitive advantage,even though organizations still do not fully understand the nature or value of them(Itami, 1987).
As IC receives more attention among practitioners, especially in the high-technology or knowledge-based industries (Darling, 1996; Edvinsson and Sullivan,1996; Saint-Onge, 1996), the challenge for organizational researchers lies in either
formulating new theories or extending and/or empirically testing existing theoriesin order to develop more rigorous conceptualizations, findings, and practical uses.
Intellectual capital
IC is not a one-dimensional concept and different authors have focused on dif-ferent facets of IC in the current management literature. In a very early work inthis field, Nelson and Winter (1982: 63) argued that “the possession of techni-cal ‘knowledge’ is an attribute of the firm as a whole … and is not reducible towhat any single individual knows, or even to any simple aggregation of the var-ious competencies and capabilities of all the various individuals, equipments andinstallations of the firm”. Spender (1996) expanded this line of thinking by com-bining the two dimensions of explicit/tacit and individual/collective knowledge toarrive at a matrix of four different elements of an organization’s intellectual capital:(1) individual explicit knowledge (conscious knowledge); (2) individual tacit knowl-edge (automatic knowledge); (3) social explicit knowledge (objective knowledge);and (4) social tacit knowledge (collective knowledge). Spender defines consciousknowledge as the knowledge typically available in the form of facts, concepts,and frameworks that can be stored and retrieved from memory or personal records.He defines automatic knowledge in terms of the many different forms of tacit know-ing, including theoretical and practical knowledge of people and the performanceof different kinds of artistic or technical skills. Objective knowledge represents theshared body of knowledge such as scientific communities and is often regardedas the most advanced form of knowledge. Collective knowledge, however, repre-sents the knowledge that is fundamentally embedded in the forms of social andinstitutional practices and resides in the tacit experiences and enactment of the col-lective. Collective knowledge and knowing capacity may remain relatively hiddenfrom individual actors but are accessible and sustainable through their interaction(Spender, 1996). Specifically, Spender (1996: 52) argues, “Collective knowledge isthe most secure and strategically significant kind of organization knowledge”.
In a similar fashion, Nahapiet and Ghoshal (1997: 35) use the term intellectualcapital to refer to “the knowledge and knowing capability of a social collectivitysuch as an organization, intellectual community or professional practice”. Such acollective view of IC differs from some previous works that largely discounted thenotion of IC at the organizational level. For example, Simon (1991: 125) states “allorganizational learning takes place inside human heads” and that “an organizationlearns in only two ways: (a) by the learning of its members, or (b) by investingin new members who have knowledge the organization didn’t previously have”.So although Simon also acknowledges that individual learning is very much a socialand not merely a solitary phenomenon, he maintains the premise that knowledge
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300 N. Juma & G. T. Payne
resides in humans only and cannot be an organizational-level phenomenon. Morerecently, Bontis (2001) demonstrates a similar opinion by making the assertion thathuman capital cannot be owned by the organization.
IC is referred to here as a combination of two distinct, but interrelated, com-ponents: human capital and structural capital (Edvinsson and Malone, 1997). Thiscategorization of IC is adopted because of its holistic approach to knowledge con-ceptualization as it follows the works of Edvinsson and colleagues (Edvinsson andMalone, 1997; Edvinsson and Sullivan, 1996). As part of IC, human capital is com-posed of such organizational characteristics as knowledge, skills, and abilities thatreside in humans, while structural capital is regarded as the infrastructure that firmsdevelop to commercialize their human capital. In taking a more collectivist per-spective we include the social capital available to the organization as part of humancapital, specifically defining IC as an organization’s stock of social and human cap-ital that can be utilized to create value for the firm. This recognizes that a firm’s ICmay consist of more than the sum of its individual members’ knowledge and skills.
Intellectual capital’s influence on performance
Intellectual capital may be a relatively new theoretical concept, but only recentlyhave practitioners and scholars moved beyond the viewing of IC as a subjective,intangible, and internal element of an organization, to now being worthy of objec-tification and empirical study. Despite recent attention, little work has been done toquantify IC or empirically test its relationship to firm performance (Bontis, 2001).The economics literature has attempted to quantify IC in some cases by embeddingit in a company’s ratio of market value to book value. For instance, James Tobin andJohn Kenneth Galbraith compute market power as a ratio of the current market valueof a firm’s assets relative to the replacement cost of those assets (Dess and Picken,1999; Edvinsson and Malone, 1997). Such measures and their implications haveled to other questions regarding IC’s value to the firm and the noticeable changesthat are occurring in the current knowledge-based business environment.
Hamel and Prahalad (1996) state that in the present information age, things areancillary, and knowledge is central. They ascertain that the ratio of market value tobook value is changing to a multiple of 3, 5, 10 or more, for an increasing num-ber of firms, which indicates a larger ratio of intangible to tangible assets. Quinn,Baruch, and Zien (1997) express similar sentiments as they argue that the presenteconomy is increasingly driven by knowledge and information, and the returns tothe effective management of human and intellectual capital will be greater thanthose available from the more financially efficient management of physical andfinancial assets. Such changes can be readily seen among high-technology firmswhere huge investments flow into human capital as well as information technology.
However, these investments do not seem to translate into immediate positive valuesin traditional accounting standards; some studies have estimated a time lag of eightyears before related performance is evident (Biggadike, 1979; Edvinsson and Mal-one, 1997). IC, therefore, is a projective measure; it focuses on the future, whilemeasures of operating performance typically focus on the just previous or the mostcurrent performance measures of the firm. Such measurements are likely to be inopposition to what current IC may measure, particularly when using the market-to-book valuation method. Huseman and Goodman (1999) suggest that a balancesheet offers only a snapshot in time when measuring intangible assets and cannotrepresent the dynamic flow of an organization. This suggests that the indices usedto evaluate IC are indicative of how well the firm is preparing itself for the future,as IC is an investment whose ultimate return will be determined some time later(Edvinsson and Malone, 1997). IC is considered a debt issue in the same way asequity; it is borrowed from various stakeholders associated with the organization,such as customers, employees, and stockholders.
Given the nature of IC and its reflection on performance, we expect that measuresof operational performance (e.g., ROA, profitability), which are more historical innature, are likely to reflect a negative relationship with IC. As new ventures areoften debt heavy because of technology and other startup expenses, operationalaccounting measures will not demonstrate the value of intangible assets, especiallythose that are still being readied for utilization (e.g., chemical compound patentscurrently under clinical trials). Thus, we state
Hypothesis 1In new high-tech firms, there is a negative relationship between intellectualcapital and operating performance.
While it may be clear that in new ventures, IC and operating performance mea-sures are likely to be negatively related, IC’s relationship to market performancemay be less apparent and subject to some debate. Keats and Hitt (1988) argue thatoperating performance provides an evaluative referent and an indication of past andpresent organizational adaptation, while marketing performance provides a future-orientation of an organization’s adaptation. Thus, one may assume that investorsmay view heavy investments in IC as a positive indication of future returns. However,as Keats and Hitt (1988) also demonstrate, these measures reflect different dimen-sions of performance, but still tend to be interrelated. It seems that announcementsof a firm’s operating performance also influences an investor’s attitudes toward thefirm’s stocks and hence its price. Such dynamics create particular difficulties for newventures in the high technology industries because they are pressured to performfrom an accounting standpoint, but also maintain and even enlarge their stocks ofIC to ensure survival.
Given the interrelated nature of operating and market performance, we alsoexpect market performance measures (e.g., stock price changes) to also demon-strate a negative relationship to IC in new ventures. Although investments in ICare considered future investments in the organization and expectations of higherreturns, we suspect that the typical investor does not have the means to identify ICor objectify its value. Thus, market performance measures are likely to mirror oper-ating performance measures in young, high-tech firms because of the uncertaintyand long-term commitment needed before actual returns are seen. When operatingperformance measures are negative, investors’ attitudes toward a firm’s stock arelikely to be adversely affected, except for the well-informed or high-risk investor.However, because of uncertainty and due the lay-person’s inability to foresee theusefulness of high-tech products and innovations, even in cases of higher operatingperformance, some investors may actually anticipate a downturn since they mayperceive the current stock prices as peak prices (Keats and Hitt, 1988).
Exceptions exist where highly visible or highly anticipated innovations aremade that become readily understood and recognized by the lay investor (e.g.,Amazon.com). In such cases, one might speculate that market performance mayprecede operating performance. However, we expect that this is seldom the casedue to a lack of investor time and sophistication, few IC measurement tools, and ageneral uncertainty surrounding new, high-tech firms. Thus, we generally state,
Hypothesis 2In new high-tech firms, there is a negative relationship between intellectualcapital and operating performance.
Inter-firm collaborations
Inter-firm collaborative arrangements, including joint ventures, network structures,consortia, alliances, trade associations, and interlocking directorates (Barringer andHarrison, 2000; Gulati, 1995), have been recently used to define social capital asthe accrued resources of a firm through its network of inter-firm relationships (Kokaand Prescott, 2002).
Social capital is represented in inter-firm relationships through the flows of infor-mation and other resources that are based on reciprocity and equity among theparticipants (Koka and Prescott, 2002). These flows of information and resourcesare exchanged so that some form of competitive advantage may emerge. Therefore,social capital is not only a result of these relationships but can also serves as adriving motivation for forming inter-firm collaborations.
Social capital is defined here as the conglomeration of potentially beneficialrelationships with external firms that are currently held by the firm (Chung andSingh, 2000). This definition does not account for intra-firm networks (Tsai and
Ghoshal, 1998), but only external social capital gained from inter-firm collabora-tive relationships. The competitive advantages of such relationships, as documentedby Dyer and Singh (1998), seem to fall into four broad categories: (1) investments inrelation-specific assets; (2) substantial knowledge exchange, including the exchangeof knowledge that results in joint learning; (3) the combining of complementary, butscarce, resources or capabilities (typically through multiple functional interfaces),which results in the joint creation of unique new products, services, or technologies;and (4) lower transaction costs than competitor alliances, owing to more effectivegovernance mechanisms. Thus, apart from the transaction cost argument, intellec-tual property and knowledge development have shown to be the key reasons forforming inter-firm collaborations.
Inter-firm relationships and intellectual capitalof new venture high-tech firms
Knowledge-based firms, such as those found within high-technology industries,often seek to increase levels of intellectual capital through external sources. Oftenexternal efforts are focused on the development of alliances or other collaborativerelationships with outside organizations so as to quickly gain access to new ideas,patents, processes or other forms of intellectual capital (e.g., Hitt et al., 1991). Thispractice is particularly prevalent in hypercompetitive industries where extraordinaryturbulence exists and rapid change in technology and regulation are the norm (Brownand Eisenhardt, 1997; D’Aveni, 1994).
In hypercompetitive environments, competitive advantage and long-term sur-vival are extremely difficult to sustain because they are so technology- andknowledge-intensive; time pressures are created so that most firms do not have theluxury of deriving internal innovations (Tushman and Rosenkopf, 1992). Further,long-term returns from single innovations typically have little ability to sustain com-petitive advantage because of the common occurrence of competence-destroyingdevelopments in technology (Brown and Eisenhardt, 1997; D’Aveni, 1994). Becauseof the environmental demands placed on high-tech firms, competitive strategy hasshown to be largely centered on collaboration with other firms. Collaboration ininnovative industries, like the biotechnology industry (Powell et al., 1996), oftenprospers at the network level, as opposed to the firm level, through learning networksand other vehicles of cooperation.
Arguments supporting the knowledge-development motivation behind inter-firmcollaborations should find support in high-technology industries. However, an alter-native explanation has been suggested for young, emerging firms in such industriesas opposed to older, more established organizations; Stuart, Hoang, and Hybels(1999) suggest that new ventures may seek inter-firm relationships to establish
reputations and increase legitimacy among more established organizations andinvestors within the industry. Therefore, increasing the levels of externally gainedsocial capital may encompass aspects other than that of simply improving intellec-tual capital.
This suggests that despite strong arguments and the general appeal of the socialcapital and IC relationship, increasing levels of social capital through multipleorganizational linkages has, in many cases, been financially detrimental to firm per-formance in the short term (Park and Ungson, 1997; Porter, 1987), but greatly bene-ficial to new ventures. Despite many negative findings, such collaborative strategiesare still regarded as highly viable options for organizations at all stages of devel-opment and have even very recently shown increased utilization (Duysters, Kokand Vaandrager, 1999; Kelly, Schaan and Joncas, 2002; PriceWaterhouseCoopers,2001). The financial reality of such relationships is perhaps most readily seen inthe mergers and acquisitions (M&As) literature, where the return on investment canbe directly measured. Organizations rarely recover their losses following an M&A(Hitt et al., 1991) because the acquiring organization must pay a premium for theassets and technologies of the target and the synergies and intellectual capital gaineddo not demonstrate returns greater than the premium paid (Sirower, 1997).
While M&As and collaborative relationships, such as strategic alliances,differ — specifically in terms of level of commitment and risk — the benefitsof inter-firm collaborations have been regarded by managers as similarly producinguncertainty in regards to financial performance outcomes (Kogut, 1989; Kale, Dyerand Singh, 2002). It is not unusual for organizations to enter into interorganizationalrelationships for periods of several years, thus a lag in financial returns from thoserelationships is eminent, if they result in returns at all. Although positive finan-cial returns and other positive outcomes from resource and knowledge exchangemay not be a direct result, status improvements to the focal firm may be a directconsequence of inter-firm relationships (Stuart, 2000).
Inter-firm relationships are often costly and very time consuming, especiallyas the number of partners increases. However, having only a few partners maydecrease the ability of the firm to remain flexible and adaptive to environmentalchanges, because of information asymmetry and over-reliance on existing relation-ships (Uzzi, 1997). This suggests that the optimal network structure may be com-posed of a mixture of tight and loose ties (Uzzi, 1997) and have only a moderatenumber of inter-firm relationships (Chung and Singh, 2000). Also, previous researchdemonstrates that alliance experience is essential to current or future alliance suc-cesses (Anand and Khanna, 2000).
There seems to be a clear performance relationship between the findings regard-ing inter-firm collaborations and IC. Intellectual capital, the knowledge capability ofa firm, is increasingly being recognized as a key to success in more information- and
knowledge-based industries. Likewise, inter-firm collaborations are also clearly rec-ognized as the primary method of gaining new knowledge and access to neededresources. Intellectual capital is created in two ways: (1) through the combinationof materials and resources to produce internally derived innovations or incrementaldevelopments, and (2) through the exchange of knowledge, resources and experi-ences between organizations (Nahapiet and Ghoshal, 1998). This suggests that notonly does social capital (through inter-firm relations) facilitate the development ofintellectual capital through the influence of knowledge and resources from otherfirms, but also that a two-way exchange exists between the dyads. This exchangeallows any internally created intellectual capital to be more easily dissipated outsidethe firm. Therefore, we argue that intellectual capital is an individual firm compo-nent, which is both added to and taken away from in the presence of inter-firmcollaborative agreements.
Taking these relationships into account we would expect inter-firm collabora-tive relationships to moderate the IC-performance relationship. In other words, thepresence of inter-firm relationships would only heighten the already present rela-tionships established between intellectual capital and the various forms of perfor-mance. However, we do recognize that the very presence of inter-firm collaborativerelationships may both be a positive factor as well as a negative factor regardingperformance. Legitimacy and knowledge may be gained, but the costs of inter-firmrelationships are often extremely high. New innovations may be developed, butreturns must be shared between two or more organizations, and IC may uninten-tionally dissipate to others. So as the number of inter-firm relationships increases,so does the cost of producing IC and the seemingly specific IC-performance returns.Thus, the following hypothesis represents our expectations:
Hypothesis 3Inter-firm collaborative relationships will moderate the relationship betweenintellectual capital and new, high-tech firm performance. Performance willdecrease with intellectual capital at a faster rate with increased levels of inter-firmcollaborations.
Methods
Data collection and sample
The data from the publicly traded firms used for this study were taken fromCOMPUSTAT and SEC (Securities and Exchange Commission) 10-K statements.The financial data were collected over a period of 6 years from 1996 through 2001,with the individual firm as the unit of analysis. Firms were limited to those thatwere new ventures (12 years of age or less) so that a significant time lapse couldoccur for many of these firms to realize returns from IC, but not too old to fully
overcome liabilities of newness. Industries represented in the sample were limitedto biotechnology, computer hardware, computer software, and telecommunicationsin an effort to better study knowledge-intensive firms that place more emphasis onIC and inter-firm collaborations. Similar industry samples have been utilized in ICand alliance research (e.g., Kale, Dyer and Singh, 2002), so that by examining arelatively controlled industry setting the fundamental relationships that exist amongour key constructs may be more readily exposed.
Measures
Multiple measures were used to test the hypotheses; these are discussed belowunder the headings of intellectual capital, inter-firm collaborations, operating per-formance, market performance, and control variables. Table 1 shows the variables’descriptive statistics and correlations.
Intellectual Capital (IC). Two measures of IC were used: (1) Economic ValueAddedTM (EVATM) and (2) Market Value Added (MVATM) (Bontis, 2001; Bontiset al., 1999). EVA was computed as the difference between net sales and the sumof operating expenses, taxes and capital charges. Stewart (1994) asserts that EVAis a superior measure of value. EVA charges management for using capital at anappropriate risk-adjusted rate and eliminates financial and accounting distortions.Thus, the EVA measure of intellectual capital properly accounts for all complextrade-offs involved in creating value. The EVA used in this study is modeled asper Stewart (1994), which is consistent with earlier research and theory building(Bontis et al., 1999; Bontis, 2001).
MVA was computed as the ratio between market value and book value of thefirm. Bontis et al. (1999) argue that maximizing the shareholders wealth is notthe same as maximizing the firm’s total market value. Simply investing as muchcapital as possible in a firm can maximize its total value, but shareholders’ wealthis maximized only by maximizing the difference between the firm’s total valueand the capital that the investors have committed to it. The difference is known asmarket value added (MVA). On an aggregate level, a firm’s MVA communicates themarket’s present judgment or sentiment on the net present value (NPV) of all currentand future capital investment projects (Bontis et al., 1999). On the other hand, EVAgoes further by emphasizing the significance of maximizing earnings above capitalcosts. Each of these measures were taken as an average over a multi-year periodand standardized to ensure normality.
We felt the need to utilize both the EVA and MVA measures because of thelimited use of either in previous studies. The ability to directly compare these mea-surements is a useful way to explore their utility and differences vis-à-vis several
performance measures. To our knowledge, no other study has directly comparedthese two measures empirically.
Inter-firm Collaborations. Each firms’ 2001 SEC 10-K statement was analyzedto determine the extent of on-going alliances, joint ventures, partnership and otherinterorganizational collaborative agreements. We searched each 10-K report forthe following key words: partnership, joint venture, alliance, contract, collabora-tion, and agreement. The score for each company represents the total number ofinter-firm collaborative agreements mentioned, both past and present. Contractsinvolving only the purchasing of licenses, financial services, loans or retainers werenot included as collaborative agreements. Any consulting or other individual (one-person) arrangements were also not included, as they do not represent organization-level relationships. Although Koka and Prescott (2002) validate that social capital isa three-dimensional construct that emerges from information volume, informationdiversity and information richness, their model also demonstrates that informa-tion volume (i.e., the sheer number of relationships) is the only dimension thatconsistently affects firm performance. So while we would like a more robust setof measures to truly test social capital’s relationship to IC and performance, weexpected that the number of inter-firm collaborations would serve adequately inthis case.
Operating Performance. For this construct, we used a moving three-year averagereturn on assets (ROA) and return on investment (ROI). Each was adjusted for riskby dividing by its standard deviation (Keats and Hitt, 1988). ROA was computed bythe net income from continuing operations, excluding extraordinary items, divideby total assets. ROI was computed by the net income before extraordinary itemsdivided by total invested capital. These data were obtained from COMPUSTAT.In cases where fewer than 3 years were available, the moving average for the lifeof the firm was taken.
Market Performance. Two measures were also utilized here: market return andstock price change over time. The market return construct included a moving three-year average of return and was also adjusted for market risk and capital marketperformance (Keats and Hitt, 1988). The second measure reflects the differencein the closing stock price average for years 1996 and 2001. In the event the firmwas less than 5 years of age, the stock price change for the life of the firm wastaken. The moving three-year average used for market return and the change overtime for the closing stock prices were selected to avoid the possibility of erroneousinferences based on a single year performance (Goll and Rasheed, 1997). Suchmeasures also reflect a reasonable lag period for the impact of IC and interfirmrelationships’ effects over the life of the firm.
Control Variables. We controlled for age, size and industry effects. We computedfirm size by taking the logarithm of number of employees. This transformation was
necessary to normalize these data. Jackson and Morgan (1982) suggested that themeasure of size used depends on the objectives and subject of a study; followingAgarwal (1979) we considered sales, number of employees, and assets as potentialmeasures. Since we found these measures to be highly intercorrelated, we chose touse number of employees because the use of sales or assets seemed inappropriategiven the nature of new ventures and knowledge-intensive industries.
To control for direct industry effects, we classified all firms into one-digitStandard Industrial Classification (SIC) codes, obtained from COMPUSTAT.Dummy variables were utilized to categorize each group. The results for these clas-sifications were not reported in the analyses unless significant results were found.Finally, we controlled for age by measuring the number of years elapsed from thefirm’s initial public offering. We did not expect this variable to have much impact onthe results as the sample was already restrained to young firms. However, we wishedto ensure that there was no extreme relationships unaccounted for in relation to age.
Among the independent and control variables, only one of pair of variables(EVA and size) had an unusually high correlation. A variance inflation factor(VIF) test confirmed that multicollinearity is not problematic since no VIF score isgreater than 2.
Analyses
We used a series of moderated regression analyses to test the stated hypotheses.As seen in Tables 2 and 3, each of the two measures of intellectual capital (EVAand MVA) was regressed on both the market and operation performance measures.Moderated regression was deemed the most appropriate approach to these analy-ses due to the number of dependent variables being utilized and the hypothesizedmoderating effects of alliances.
The original regression model for each dependent variable (labeled Model 1) uti-lizes all the independent variables except for the IC-Alliance interaction. The secondmodel for each dependent variable (labeled Model 2) includes the IC-Alliance inter-action variable. We include the results of this second model although no significantchanges occurred between the first and second model. Thus Hypothesis 3 is notsupported by these data. Given these results, we will now primarily focus on theregression results concerning the first two hypotheses and the implications theysuggest.
Results and Discussion
The results of the analyses demonstrate that IC is related to firm performance.However, this relationship is varying and difficult to interpret. Perhaps the clear-est and most significant relationship is found for the EVA measure of IC and the
August 14, 2004 0:2 WSPC/150-IJIM 00107
310 N. Juma & G. T. Payne
Tabl
e2.
Inte
llec
tual
capi
tal(
EV
A)
mod
eled
tope
rfor
man
ce:M
ulti
ple
regr
essi
onre
sult
s.∗
Dep
ende
ntV
aria
bles
Inde
pend
ent
Var
iabl
esR
OA
aR
OIa
Mar
ketR
etur
n(3
yr)a
Stoc
kPr
icea
Mod
el1
/Mod
el2
Mod
el1
/Mod
el2
Mod
el1
/Mod
el2
Mod
el1
/Mod
el2
EV
A−3
.158
**−2
.737
**2.
427*
*2.
056*
*−2
.727
**−2
.300
**−1
.356
−1.1
49(−
0.74
2)(−
0.72
4)(0
.656
)(0
.625
)(−
0.83
0)(−
0.81
0)(−
0.36
1)(−
0.33
4)E
VA
22.
346*
*2.
342*
*−1
.838
†−1
.840
†1.
893†
1.88
5†0.
640
0.65
1(0
.477
)(0
.478
)(−
0.43
0)(−
0.43
2)(0
.483
)(0
.486
)(0
.146
)(0
.149
)A
llia
nces
−0.7
49−0
.637
0.23
40.
135
−0.1
94−0
.104
0.42
40.
482
(−0.
053)
(−0.
049)
(0.0
19)
(0.0
12)
(−0.
021)
(−0.
013)
(0.0
37)
(0.0
45)
All
ianc
e−0
.149
0.21
9−0
.119
−0.2
45X
EV
A(−
0.02
2)(0
.036
)(−
0.02
6)(−
0.03
7)
Size
b7.
913*
**7.
885*
**−3
.664
***
−3.6
50**
*2.
893*
*2.
856*
*2.
909*
*2.
900*
*(0
.806
)(0
.806
)(−
0.42
9)(−
0.42
9)(0
.446
)(0
.444
)(0
.359
)(0
.359
)A
geb
0.79
40.
804
0.54
30.
804
0.91
80.
915
0.88
60.
909
(0.0
55)
(0.0
56)
(0.0
43)
(0.0
56)
(0.0
93)
(0.0
93)
(0.0
75)
(0.0
78)
Indu
stry
b
SIC
0000
−1.9
95†
−1.9
83†
(−0.
209)
(−0.
209)
SIC
2000
−3.4
57**
−3.4
52**
(−0.
363)
(−0.
364)
SIC
3000
−1.9
32†
−1.9
32†
(−0.
194)
(−0.
195)
F7.
936*
**7.
168*
**2.
429*
*2.
198*
*1.
560
1.40
41.
157
1.05
0R
0.59
30.
594
0.37
80.
378
0.38
10.
381
0.27
50.
276
R2
0.35
20.
352
0.14
30.
143
0.14
50.
145
0.07
60.
076
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August 14, 2004 0:2 WSPC/150-IJIM 00107
312 N. Juma & G. T. Payne
Intellectual Capital (IC)
PerformanceMeasures
ROA & Market Return
ROI
Fig. 1. The curvilinear relationships of IC to alternative performance measures.
dependent variable ROA. In Table 2, we see that there is a negative curvilinearrelationship because of the negative relationship to EVA, but positive relationshipto EVA squared. This suggests that as EVA increases, ROA decreases in a curved,downward pattern. This supports Hypothesis 1, but we do not find similar resultswith ROI. Contrary to our expectations, ROI shows a rather slight positive curvi-linear relationship to EVA. These opposing relationships are visually demonstratedin Fig. 1.
These results may be partially attributed to our operationalization of IC. Partof the problem with previous empirical IC studies is the inability to adequatelyquantify IC. The EVA and MVA measures were promoted as proxy measures byprevious studies (Bontis et al., 1999; Bontis, 2001), and we are not aware of othermeasurements that are applicable across industries. Since our sample consisted ofhigh-tech new ventures, we considered using citation-weighted patents, howeverthis was not appropriate given our sample, which includes such firms as softwareproducers and some manufacturing firms. Future research should compare theseand other measures within one or a very few industries to more specifically addressthis issue.
In addition to IC’s unexpected relationship to ROI, size exhibited a significantnegative affect on ROI, although it showed a significant positive relationship inthe ROA model. We presume that these results are due to the measurement of IC,ROA, and ROI. ROI emphasizes invested capital rather than tangible assets as inROA. Therefore, new ventures may not have had enough time to recover from assetinvestments and smaller firms may have less need for assets and derive smaller levelsof invested capital so that returns are positive in more cases. This finding may be best
August 14, 2004 0:2 WSPC/150-IJIM 00107
New Venture High-Tech Firms 313
understood through the examination of the two significant industry controls, whichare negatively associated to ROI. SIC codes in the 2000 and 3000 range representmanufacturing firms only. Manufacturing firms are typically more reliant on tangibleassets than service firms so that their initial capital investments in land, buildingsand machinery will tend to inflate this measurement. It is interesting though, thatthese same industry control variables did not show an opposite significance in theROA model. Accounting and measurement differences may also be playing a rolein these relationships.
EVA also demonstrates a curvilinear relationship with market return and stockprice, lending some support to Hypothesis 2. However, the IC-stock price relation-ship showed no significance. As previously suggested, it is likely that IC has not yetbeen unraveled by capital markets institutions, and therefore IC is not yet translatablewith any degree of confidence into predictions of stock price behavior (Mouritsen,1998). We find it interesting that market return mirrors the ROA patterns so closely.As suggested previously, this may relate to the way investors valuate companies.For instance, if an investor desires to understand a firm’s financial performance priorto making an investment, one key indicator would be ROA. Such concrete and read-ily available financial tools such as ROA, P/E, or EPS would tend to overshadowany perceptions of IC to any but the most knowledgeable investor.
As hypothesized in the study, the results primarily suggest that there is an inverserelationship between intellectual capital and performance in new venture high-techfirms. However, IC shows totally opposite effects on ROA and ROI, and IC showsan inverse relationship to market return, but no significance in relation to stockprice change. We also find that the EVA measure of IC demonstrates a significantrelationship to ROA performance, but MVA does not. All of these relationships,however, pale in comparison to the one clear relationship that exists across all ofthe models: Size clearly accounts for much of the variance in each case and ispositively related to performance in all cases except the ROI measurement, whereit is negatively associated.
Size has been a long-term element of study in organizations and both advantagesand disadvantages have been associated with different levels. Large size allows foreconomies of scale, increased brand recognition, and market power (Hambrick,MacMillan and Day, 1982), while small size is typically argued to be more flexible,quick, and risk-seeking (Chen and Hambrick, 1995). These basic associations seemto be supported in this study, principally in relation to how performance is measured.For instance, the stock price seems to be largely a reflection of the visibility oflarger, older firms. Thus, stock price seems to be a less appropriate measurement ofperformance in this particular sample.
Our findings suggest several areas for future research. First and foremost, wesuggest that future studies relating IC to performance should attempt to develop new,
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314 N. Juma & G. T. Payne
more adequate or complete measures of IC. Although both practitioners and schol-ars would desire a quantifiable and readily available measurement of IC, the EVAand MVA variables do not seem to differentiate themselves enough from normalmeasures of performance to be used as a predictive indicator. The use of more qual-itative sources seems to be more appropriate to determine such “un-quantifiable”and intangible variables. Utilizing education levels of employees, R&D spendingor output (e.g., patent numbers), or some such combination of variables may be amore appropriate way to deal with the IC measurement issue. Future studies shouldattempt a comparative study of these and other measurements to determine if onerepresents IC better than another.
An additional finding involves the utilization of inter-firm relationships as mod-erators in the analyses. Although we did not find significance, perhaps a non-findingsuggests that external social capital may already be accounted for in other variableswithin the equation. In other words, these relationships may already feed into themeasurements of IC, thus having no other impact beyond those already accountedfor by operating expenses and/or investor perceptions.
Finally, we are surprised that organizational size played such a large role inthe findings, especially given our focus on new ventures. This also may reflectsome overlap in the measurements of IC, performance, or both. Given the assetsportion of each of these indicators, we are again left to ponder the viability of theIC measurements. In short, it seems that simple archival quantifications may beuseful, but tend to lack the robustness desired in relation to standard organizationalperformance measures.
Conclusion
The primarily purpose of this study was to investigate the impact of intellectual cap-ital on firm performance in new ventures of high-tech industries. These exploratoryresults indicate that there is a relationship between IC and firm performance, but itis an unclear one at best. So although the existence of IC and its impact on new firmperformance is an accepted premise (Bontis, 2002; Bontis, Keow and Richardson,2000; Choo and Bontis, 2002), there seems to be little empirical work support-ing this relationship, or at least support that utilizes the quantitative measurementscurrently being proposed in the literature.
Although there seems to be conceptual, as well as some empirical evidence,to support the existence of IC, the established measurements of IC are less thanideal and remain controversial. Bontis et al. (1999) argue that although EVA doesnot explicitly relate to the management of intangible resources, the effective man-agement of knowledge assets will increase EVA. However, Bontis (2001) laterasserted that one of the greatest limitations of EVA is a lack of empirical research
to “conclusively demonstrate” that it is a predicator of stock price or its variation.Empirically, Lehn and Makhija (1996) found a correlation between EVA and stockprices and claimed that EVA is better at explaining stock prices than alternativemeasures, but Chen and Dodd (1997) suggest that EVA has not demonstrated anyadditional explanatory power over accounting profit in explaining stock price vari-ation. In summary, although we have been able to lend some support to an EVA-ICrelationship, it is clearly not a good measure of IC in this context and does notseem to lend itself to conclusively demonstrate any definitive relationship to firmperformance.
According to Bontis (2001), MVA’s greatest limitation stems from the nature ofactivities it evaluates. MVA tends to aggregate the gains and losses accrued fromhistoric activities on a one-to-one basis with the previous year results plus presentmoods as reflected in the market price. Thus, a company with a successful historywill keep on showing positive and high MVA even when the present and futuresituation is grim. Such weaknesses of MVA as a measurement criterion have beenempirically demonstrated in this study.
As stated in the introduction of our paper, IC is an academic research field still inits early stages. Although much discussion and interest has been expressed recently,little definitive answers have been obtained because both scholars and practitionersseem to be unable to conceptualize and measure IC effectively. For example, in asample of 253 companies among the US Fortune 500 and Canada Post 300 firms,Joyce and Striver (1999) found an interesting gap in those firms that thought IC wasimportant verses those that actually computed and used IC measurements; 63 percentof the top executive respondents felt that measuring innovation was important, butonly 14 percent actually measured it and only 10 percent used the measures forstrategy development.
These findings suggest new challenges for organizational researchers toformulate new theories or extend and/or empirically test existing theories in IC.But before IC theories can be tested, researchers must establish measures that accu-rately measure the intangible IC construct. We need to develop and test variousmeasures rather than simply re-labeled measurements of similar constructs (Bontis,2001). Some of this testing should focus on several of the more prominent mod-els that have been established thus far in the IC field, such as: Skandia Navigator,IC-index, Technology Broker, Intangible Asset Monitor, citation-weighted patents,Human Resource Accounting (HRA), Tobin’s Q ratio, and the Balanced Score Card(Bontis, 2001; Bontis et al., 1999; Brummet, Flamholtz and Pyle, 1968; Kaplan andNorton, 1992). Testing should come through longitudinal and comprehensive meansto ensure advancement of this relatively new area of study.
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