The Empirical Evidence for the Link between Knowledge Management
Performance and Firm Market Performance
Abstract
Although anecdotal evidence and individual case studies suggest that effective
knowledge management (KM) initiatives contribute to superior firm performance, quantitative
empirical investigations are scarce, and more to the point, most of them are based on perceptions
of survey participants embedded in the firms being studied. Moreover, studies analyzing the
question of whether superior KM performance can predict superior market-based valuation
appear to be non-existent. Findings of such studies would be of great value, not only to those
who champion and direct a firm’s KM efforts, but also to the firm’s strategists, planners, and
operational managers. Here, we empirically examine the relationship between KM performance
and firm valuation; the former is assessed by international panels of independent KM experts and
the latter is evaluated by market-based measures. The results based on data from 1998-2005
show that, in each of the eight years, superior KM performance has a statistically significant
positive association with firm valuation in terms of Tobin’s q, price-to-book ratio, and price-tosales ratio. This study contributes to the management literature by using independent expert
judges and archival data to substantiate the notion that KM competencies are an important
ingredient in a firm’s performance as indicated by market-based valuation.
Keywords: firm performance, KM, knowledge chain theory, knowledge management, marketbased valuation, organizational learning, price-to-book, price-to-earnings, price-to-sales,
resource-based view of the firm, Tobin’s q
1
Introduction
In today’s competitive and dynamic business environment, knowledge management
(KM) is one of the strategic applications that make operations more efficient and provide
competitive advantages in the marketplace (Gates 1999). Correspondingly, many organizations
have invested substantial resources in KM initiatives in the last decade (Haas 2006). For
example, International Data Corporation (IDC), a market research and analysis firm, estimates
that the global business spending on KM was $4.8 billion in 2007 (Babcock 2004), and the
spending in US reached $2.9 billion in 2006 (Motsenigos and Young 2002). Meanwhile,
researchers have devoted considerable attention to understanding KM, ranging from what are the
basic components of KM episodes to how to improve knowledge generation and realization
processes. Because KM is considered as a principle success factor, much of the growing
literature on KM in organizations embraces, explicitly or implicitly, the notion that KM can
bring important strategic consequences to organizations – accelerating business growth,
improving competitive position, achieving higher corporate valuation, and so forth (Edwards et
al. 2003).
Although anecdotal evidence and individual case studies are available on the link
between effective KM initiatives and superior firm performance, quantitative empirical
investigations are scarce, and more to the point, most of them are based on perceptions of
embedded participants. Focusing on KM-related capability evaluations in large multinational
firms, Denrell and colleagues (2004) suggest that individuals’ perceptions about their respective
firms’ abilities are not particularly reliable. Consequently, there remains lingering uncertainty
about the veracity of the notion that effective KM initiatives are indeed an antecedent or
predictor of superior firm performance. Specifically, while many people think the linkage exists,
2
it is still under a cloud or even dubious. Hence, scholars call for empirical research to resolve the
issue (Argote and Ingram 2000, Feng et al. 2004).
As a response to this call, the present study extends the relevant literature by empirically
examining the relationship between KM efficacy and firm valuation—the latter being a marketbased indicator of firm performance. To provide a solid theoretical basis for the link, this
research draws on the resource-based view of the firm, organizational learning theory, and
knowledge chain theory. Applying these theories to multi-industry organizations suggests that
firms with superior KM performance are likely to enjoy better bottom-line performance and
higher market value.
By KM performance, we mean the degree to which KM activities harness organizational
resources to achieve the goals or purposes of KM initiatives. This meaning is in line with
Darroch’s (2005) view that effective KM serves as a coordinating mechanism for transforming
all available resources into capabilities and thus allows a firm to glean greater value from the
resources (not just its knowledge resources). We adopt the common view that a firm’s marketbased valuation is indicative of its performance and performance prospects.
Our assessments of KM performance do not depend on perceptions of individuals
embedded within firms being studied, but rather on eight years of ratings by international panels
of KM experts independent of the assessed firms. Similarly, in measuring firm performance, we
do not rely on perceptions of those within the firms, but rather on market-based firm
performance figures. As an alternative to the standard accounting measures (e.g., return on assets
and return on sales), financial market-based measures reflect firms’ intangible assets, which are
pervasive in KM-intensive organizations and are often discounted by accounting-based ratios
(Bharadwaj et al. 1999, Lavie 2007). For this reason, market-based performance measures are
3
preferred over traditional accounting-based ones. Using an organizational level of analysis, we
assess the association between business effectiveness and successful KM initiatives within a
multi-industry context, suggesting that the results are likely to be applicable and generalizable
across organizations as well as industries (Zacharia and Mentzer 2004).
1.
Background
In the presentation of a collaboratively engineered KM ontology, Holsapple and Joshi
(2004) define knowledge management as “an entity’s systematic and deliberate efforts to
expand, cultivate, and apply available knowledge in ways that add value to the entity, in the
sense of positive results in accomplishing its objectives or fulfilling its purpose” (p. 593). This
definition is in keeping with that advanced by Ofek and Sarvary (2001): as a set of business
processes, KM captures and provides access to the firm’s collective experience that helps create
business solutions for strategic and competitive advantage. As noted by Bill Gates (1999), the
ultimate goal of KM is to increase institutional intelligence, or corporate IQ, which is greatly
needed by a company pursuing success in today’s dynamic markets.
Prior research suggests that KM can contribute to firm performance in several ways: (1)
enhancing the effectiveness and efficiency of business processes; (2) facilitating employees’
learning from internal and external sources; and (3) fostering the development of new
knowledge-based products or the improvement of existing ones that provide significant
additional value (Argote and Ingram 2000, Conner and Prahalad 1996). However, some other
studies contend that KM initiatives do not always positively impact business performance and
often fail to result in improved task outcomes in organizations (Sabherwal and Sabherwal 2007).
These inconsistent observations indicate that KM’s effects on firm performance are not yet well
understood and still open for investigation (Smith and McKeen 2005). Of course, addressing
4
these inconclusive effects is of great importance not only to researchers eager to identify crucial
drivers of superior business performance, but also to practitioners struggling to comprehend if,
and the extent to which, KM initiatives impact firm performance.
1.
A Resource-Based View of the Firm
Rooted in strategic management literature, the resource-based view (RBV) of the firm
suggests that a firm’s ability to create value is primarily based on its unique set of resources that
are valuable, rare, irreplaceable, and not readily reproduced (Barney 1991; Conner 1991).
Basically, the RBV focuses on the impacts of a firm’s internal characteristics on its performance
and explains why the firm is able to gain and sustain competitive advantages (Amit and
Schoemaker 1993; Wernerfelt 1984). The RBV embraces several important assumptions: (1) a
firm’s unique resources include not only tangible assets but also intangible ones that tied semipermanently to the firm (Spanos and Lioukas 2001); (2) the resources are usually rent-yielding
and likely to survive competitive imitation when protected by isolating mechanisms (Dierickx
and Cool 1989); and (3) the strategically important resources are heterogeneously distributed
across firms, and thus are expected to explain differences in firm performance (Grant 1991).
Previous research suggests that the unique firm resources involve brand names, financial
capital, skilled personnel, efficient business processes, in-house knowledge of technology, and so
forth (Bharadwaj 2000, Spanos and Lioukas 2001). More recently, several studies grounded in
the RBV assert that knowledge is a rare, valuable, and difficult-to-imitate resource and can be
applied in ways that add value to an organization (Feng et al. 2004, Uhlenbruck et al. 2006).
Specifically, the studies contend that knowledge is a basic source of competitive advantage and
performance differences among firms can be attributed to asymmetries in knowledge and its
management (Edwards et al. 2003, Lee and Choi 2003).
5
One step further, researchers have extended the RBV and developed the knowledgebased view (KBV) of the firm (Conner and Prahalad 1996, Decarolis and Deeds 1999).
Reflecting the RBV essence, the KBV posits that knowledge is the most strategically important
firm resource and KM is a key determinant of performance differences (Felin and Hesterly 2007,
Sabherwal and Sabherwal 2007). That is, the KBV highlights the importance of firm-specific
KM capabilities and focuses full attention on intangible resources that play a critical role in firm
valuation and performance. The KBV echoes calls for creating knowledge-based organizations
(Gupta and Sharma 2004).
2.
Organizational Learning Theory
Rooted in management and economics literature, the organizational learning theory
(OLT) aims to provide a framework for interpreting the processes of learning and their outcomes
at an organizational level. Organizational learning is comprised of the ways firms build,
supplement, and organize knowledge and routines around their activities and within their
cultures, and adapt and develop organizational efficiency by improving use of the broad skills of
their workforces (Dodgson 1993). The generic term “routines” includes not only the conventions,
procedures, strategies, and policies under which firms are constructed and operated, but also the
structure of codes, beliefs, cultures, and frameworks that shape and define the firms (Levitt and
March 1988).
The concept of organizational learning can be characterized in terms of four attributes:
existence, breadth, elaborateness, and thoroughness (Huber 1991). Existence of organizational
learning is associated with the assertion that an organization learns if any of its units (no matter
how many) acquires knowledge that is potentially useful to the organization. Breadth of
organizational learning asserts that more organizational learning occurs when more of the
6
organization’s units obtain the knowledge and recognize it as potentially useful. Elaborateness of
organizational learning is concerned with the claim that more organizational learning occurs
when more and more varied interpretations are developed. Finally, thoroughness of
organizational learning corresponds to the statement that more organizational learning occurs
when more organizational units develop uniform comprehensions of the various interpretations.
Friedlander (1983) argues that “the crucial element in learning is that the organism be
consciously aware of differences and alternatives and have consciously chosen one of these
alternatives. The choice may be not to construct behavior but, rather, to change one’s cognitive
maps or understandings” (p. 194). In line with this argument, Dodgson (1993) contends that
organizational learning can be perceived as the highest form of adaptation, raising the probability
of survival in changing environments. Accordingly, many studies grounded in the OLT assert
that learning can enhance firm performance by keeping the firm in tune with trends and
developments in its business.
For example, Bennet and Bennet (2003) suggest that as a purposive quest, organizational
learning helps retain and improve productivity and innovativeness to cope with dynamic business
situations. Similarly, Uhlenbruck and colleagues (2006) argue that organizational learning leads
to new routines to deploy resources and to identify and respond quickly to new opportunities.
Focusing on both learning processes and outcomes, Dodgson (1993) states that learning is a key
feature in the process whereby firms accumulate technology to possess leading competitive
positions. As noted by Jack Welch, former chairman and CEO of General Electric, “An
organization’s ability to learn, and translate that learning into action rapidly, is the ultimate
competitive advantage.” (Gates 1999, P.237).
7
From a KM perspective, organizational learning is an integral part of KM strategy and
reflects an organization’s KM capacity (Bennet & Bennet, 2003). KM helps define and specify
what should be learned, why and when it should be learned, and who should be learning it.
Therefore, KM efforts can influence employees to develop positive attitudes toward learning.
Such attitudes are likely to stimulate organizational learning because individuals and teams come
to believe that learning can help their firm handle change, uncertainty, and complexity in the
ever-changing business environment. KM can also create a culture of peer collaboration and
open communication (Liebowitz 2008). Both of these lead to a setting conducive to
organizational learning. Moreover, organizational learning is often facilitated by such KM
activities as knowledge acquisition, distribution, assimilation, and storage (Huber 1991). In
particular, knowledge acquisition promotes organizational learning by motivating individuals to
obtain new knowledge from external sources and to make it suitable for future use. In summary,
the OLT indicates that through organizational learning, KM can be reasonably linked to firm
performance and valuation.
3.
Knowledge Chain Theory
Employing Porter’s (1985) analysis approach, Lee and Yang (2000) have developed a
“knowledge value chain” (KVC) framework. It identifies infrastructure and process activity as
two KM components that enable a firm to provide valuable products and services to its
customers. Resembling the value chain model, their framework is comprised of four categories
of KM infrastructure and five categories of KM process activity. They argue that these nine
elements can be performed in ways that increase a firm’s competency with respect to one or
more of Porter’s five primary activities (e.g., inbound logistics). These focal points for value
creation are thus capable of being shaped by the firm’s KM performance. Maintaining that it is
8
difficult to gauge this KM performance via accounting measures, they suggest two alternatives.
First, they propose learning measures such as the size of communities of practice or the level of
training for employees. Second, they propose that KM performance can be measured in terms of
operating performance metrics such as lead time or level of customer satisfaction.
In a separate research stream, Holsapple and Singh (2001) have advanced the knowledge
chain theory (KCT). Grounded in a descriptive, collaboratively-engineered KM ontology, the
KCT identifies and characterizes nine KM activities: five first-order activities that an
organization performs in manipulating knowledge resources and four second-order activities that
support and guide performance of the primary activities. The KCT posits that these nine KM
activities combine to yield two kinds of organizational outcomes: learning and projections. That
is, how well an organization learns and how well it projects are key determinants of the
organization’s viability and success in a competitive environment. The theory asserts that the
nine activities enable a firm to leverage its knowledge into a competitive advantage. Specifically,
any of these activities can be carried out in ways that improve a firm’s competitiveness and
performance. The theory further asserts that such superior firm performance can be achieved in
four main directions: superior productivity, agility, innovation, and/or reputation (PAIR).
4.
Prior Empirical Studies
Employing both the resource-based and the knowledge-based views of the firm, Bogner
and Bansal (2007) argue that a sustained competitive advantage stems from a firm’s ability to
develop rare and valuable knowledge through learning, and to subsequently build upon and
spread that knowledge throughout the organization. They deconstruct a firm’s complex KM
capability into three key components: the firm’s ability to produce new knowledge, its ability to
build on that knowledge, and its effectiveness in capturing the potential value of the knowledge.
9
To examine the impacts of the KM capability components on firm performance, they develop
patent-citation measures for each of them: cited patents, citing patents, and citing patents owned
by the innovator. They analyze the data from 30,022 patent records of 42 firms and find that a
firm’s growth rate is positively associated with its ability to generate rare and valuable
knowledge, and to build on that knowledge.
Darroch (2005) develops a model that includes both tangible and intangible knowledge
resources as being enablers of a firm’s routines (e.g., knowledge dissemination and
responsiveness to knowledge) which, in turn, are positively related to the firm’s innovations and
financial performance. Using a multi-item scale, she surveys 443 CEOs from New Zealand firms
for their perceptions about their respective firm’s status regarding innovation, comparative and
internally reflective performance, and its KM initiatives and practices (i.e., knowledge
acquisition and dissemination, and responsiveness to knowledge). She finds that KM initiatives
and practices are important determinants of a firm’s ability to innovate, and knowledge
acquisition has a significant impact on firm performance through responsiveness to knowledge.
Arguing that there is little empirical evidence that KM is a profitable enterprise, Castillo
(2003) empirically tests the linkage between firm performance and KM initiatives. His data
analysis suggests that there is little payoff from organizational KM efforts and KM initiatives are
not significantly related to any of the three accounting-based ratios: return on sales, return on
assets, and return on equity. Based on these findings, he argues that companies emphasizing KM
may not excel over similar firms within their specific industries and managing knowledge may
be a tricky business.
In a survey of 131 major firms in Korea, Choi and colleagues (2008) investigate the
relationship between KM strategies and firm performance by drawing on complementarity
10
theory from economics. Categorizing KM strategies based on KM focus and KM source, they
suggest that KM strategies can be explicit- and tacit-oriented, as well as internal- and externaloriented. While explicit-oriented strategy emphasizes knowledge codification and reuse to
improve organizational efficiency, tacit-oriented strategy focuses on a personalization approach
where tacit knowledge is shared through interpersonal interactions and socialization processes.
While external-oriented strategy brings in knowledge from outside sources via acquisition,
internal-oriented strategy accentuates the need to generate new knowledge within a firm. Their
results show that superior firm performance can be achieved by implementing explicit-, external, or internal-oriented strategy, and an organization employing both external- and internaloriented strategies can achieve even better performance.
In a study of 222 Spanish firms in biotechnology and telecommunications industries,
Marques and Simon (2006) survey senior business managers about their respective firms’ KM
practices and firm performance. They view KM practices as innovative organizational activities
that bring important changes to business processes and to traditional management paradigms.
Using a 7-point Likert scale, they assess KM practices in terms of six dimensions (e.g.,
knowledge transfer, continuous learning) and measure firm performance via profitability,
growth, efficiency, stakeholder satisfaction, and competitive position. Analysis of the survey
data leads to their conclusion that there is a strong and positive relationship between the
implementation of KM practices and firm performance.
5.
Summary
Synthesizing prior work on the relationship between KM and firm performance, we
observe several major themes. First, it appears that KM assets (e.g., KM initiatives, resources,
and practices) are a key component for organizations’ efforts to create value. Second, while some
11
studies find a significant link between some aspect(s) of KM performance and some aspect(s) of
firm performance, other studies fail to do so. The mixed results indicate the linkage is worthy of
further investigation. Third, most of the empirical findings are based on perceptions of survey
participants embedded in the firms being studied. Denrell and colleagues (2004) suggest that
individuals’ perceptions on their respective firms’ KM capabilities may not accurately reflect
reality. Thus, they imply that researchers should devise and employ more reliable methods to
measure firm performance and KM performance. Fourth, few studies examine whether a firm’s
actual market-based performance is related to its KM performance. To better appreciate the
market-based effects of superior KM performance, we offer the following quantitative empirical
evidence.
3. Research Hypotheses
As a market-based firm performance measure, Tobin’s q is the ratio of the market value
of a firm and the replacement value of its total assets (Tobin 1978). Unlike traditional accounting
ratios (e.g., return on assets and return on sales), Tobin’s q reflects the contribution of intangible
assets to firm value and is also a forward-looking measure because it provides a market-based
view of investor expectations of a firm’s future performance (Bharadwaj et al. 1999, Rao et al.
2004). A higher Tobin’s q ratio indicates greater unmeasured firm value attributed to the
intangible assets, suggesting better market-based firm performance. Tobin’s q has been used in
finance to study managerial performance (Lang and Litzenberger 1989) and equity ownership
(McConnell and Servaes 1990), in marketing to study marketing communication’s credibility
(Luo and Donthu 2006) and brand equity (Simon and Sullivan 1993), and in information systems
to study IT capability (Bharadwaj et al. 1999) and e-commerce competence (Saeed et al. 2005).
12
According to the RBV, KM resources should play an important role in improving longrun firm performance, because they are rare, valuable, and inherently difficult to imitate. In line
with the OLT, firms with superior KM performance should experience a learning effect in which
they improve over time in their abilities for creating value (Galunic and Rodan 1998, Gold et al.
2001). Similarly, the KCT asserts that a firm potentially can execute any of the knowledge chain
activities in ways that boost productivity, increase agility, enhance innovation, and/or augment
reputation to provide sustainable competitive advantage (Holsapple and Singh 2001). Consistent
with these theories and aforementioned research findings, we argue that firms, which are
relatively successful in leveraging KM resources, in turn enjoy superior market-based firm
performance. Formally, we hypothesize:
Hypothesis 1. Superior KM performance is positively related to a higher Tobin’s q ratio.
To provide a multi-angle and in-depth analysis of the focal relationship, we also measure
market-based firm performance with three other ratios: price-to-book (P/B), price-to-sales (P/S),
and price-to-earnings (P/E). Calculated as the market price to book value per share, the P/B
measure indicates how much value the market expects a firm to be able to extract from its pool
of assets (Tuschke and Sanders 2003). The P/B measure is also an indication of the valuation of
intangible assets and the existence of any hidden reserves (e.g., undervalued assets). Like
Tobin’s q, the higher the P/B ratio, the greater the potential and value of a firm as viewed by its
investors. A P/B ratio less than 1.0 indicates the firm’s lack of potential and the presence of
obsolete assets (Li et al. 2004). Accordingly, we hypothesize:
Hypothesis 2. Superior KM performance is positively related to a higher P/B ratio.
As the ratio of stock price to revenue per share, the P/S measure shows how much the
market values each dollar of a company’s sales. A high P/S ratio means that investors are willing
13
to pay more for every dollar of sales, indicating that they expect relatively high profits from that
dollar (Nathan et al. 2001). In other words, it often signals a company’s competitive advantage in
terms of high profit margins. The P/E measure, which is the ratio of stock price to net profit per
share, reflects the money investors are willing to pay for the earnings a company makes.
Logically, a high P/E ratio means that the market is ready to pay more for the earnings,
suggesting that there is high expectation of fast earnings growth for the company in the future
(Sudarsanam and Mahate 2003). On the other hand, a low P/E ratio indicates that investors have
less confidence in the company’s growth potential. Regarding both P/S and P/E as useful
measures of market-based firm valuation and expected future performance, we hypothesize:
Hypothesis 3. Superior KM performance is positively related to a higher P/S ratio.
Hypothesis 4. Superior KM performance is positively related to a higher P/E ratio.
Although our hypotheses for P/S and P/E are as above, three important distinctions
between the two should be noted at the outset. First, prior research suggests that the P/S measure
is highly related to profitability experienced by a firm (Nathan et al. 2001). That is, a company
that can generate substantial profit from every dollar in sales usually has a higher price-to-sales
ratio than a company that generates very little profit from each dollar in sales (Carlson 2000). In
contrast, the P/E measure is strongly associated with investors’ expectations of earnings growth
in the near future (Shroff 1995). Compared with P/S, the P/E ratio is a more future-oriented
market-based valuation, reflecting something relatively uncertain and elusive (i.e., growth
expectation). The P/S ratio is comparatively present-oriented and tells more about the status quo
of a firm’s performance (i.e., current profitability). Second, compared with earnings, revenues
are more stable and predictable, and thus are more difficult to be manipulated (Senchack and
Martin 1987). Consequently, the P/S measure is less subject to accounting gimmickry and serves
14
as a more reliable indicator of how the market values firm performance than the P/E measure
(Nathan et al. 2001). Third, the P/S ratio provides a meaningful relative measure of how the
market values firm performance, even when the firm is losing money – in which case the P/E
ratio is meaningless (Senchack and Martin 1987).
4. Methodology
1.
Matched Sample Comparison Group Method
We employ the Matched Sample Comparison Group (MSCG) method to test the research
hypotheses. The MSCG method involves statistical analysis that compares the levels of a
variable of interest across two samples over some time period (Megginson and Weiss 1991). In
this study, a treatment sample is comprised of firms with superior KM performance. A carefully
selected control sample is comprised of firms that match the treatment sample in terms of firm
size and industry type. The variables of interest are the foregoing market-based ratios used to
compare how the market values firm performance for each of the two groups. The time period
for analysis is a five-year window.
The major direct benefit of the MSCG method lies in its capability to allow the
performance of the matched control sample of firms to serve as a benchmark and help remove
the confounding effects of extraneous factors and market forces that could influence firm
performance (Bharadwaj 2000). Here, the method thus enables us to decide whether the effects
(i.e., higher Tobin’s q, P/B, P/S, and P/E) are really associated with the independent variable –
superior KM performance. As an important statistical procedure, the MSCG method has been
applied in a variety of fields. For instance, it has been adopted in management research (Garby
2002), finance research (Jain and Kini 1995), marketing research (Kalwani and Narayandas
15
1995), information systems research (Feng et al. 2004), and accounting research (Balakrishanan
et al. 1996).
2.
Most Admired Knowledge Enterprises
As the treatment sample of firms with superior KM performance, we use the Most
Admired Knowledge Enterprises (MAKEsm). Annually, since 1998, these organizations are
determined and publicly reported by Teleos and its KNOW Network
(http://www.knowledgebusiness.com). Teleos is an independent KM and intellectual capital
research company based in the United Kingdom. The company operates the KNOW Network –
an international Web-based professional knowledge-sharing network that aims to help
practitioners achieve the best possible levels of KM performance.
Internationally recognized, the MAKEsm study has a dual aim: identification of
organizations that are leaders (most admired) in the practice of KM and learning from these
organizations. Employing a Delphi-oriented approach, the study uses a large panel of KM
experts to determine those organizations that have demonstrated superior KM initiative and
performance over the most recent year. Members of the panel are high-level executives from
Fortune’s Global 500 companies (i.e., CEO, CFO, CIO), about 300 chief knowledge officers
from a broad base of organizations around the world, and various prominent knowledge
management practitioners.
The initial Delphi round constructs a worldwide set of candidate organizations based on
nominations elicited from the panelists. In the study’s second round, each panelist selects up to
three candidates that he/she deems to have exhibited superior KM performance within the main
business sector of his/her organization. Each panelist also selects – across the entire set of
candidate organizations – up to three more that he/she deems to have exhibited superior KM
16
performance. Every organization that is selected by at least 20% of the panelists is designated as
being a finalist. In the last round, panelists are given eight criteria that Teleos and its KNOW
Network have found to be keys for characterizing strong KM performance:
●
success in establishing an enterprise knowledge culture
●
top management support for managing knowledge
●
ability to develop and deliver knowledge-based goods/services
●
success in maximizing the value of the enterprise’s intellectual capital
●
effectiveness in creating an environment of knowledge sharing
●
success in establishing a culture of continuous learning
●
effectiveness in managing customer knowledge to increase loyalty and value
●
ability to manage knowledge in ways that create shareholder value
For each finalist, each panelist uses a 1 to 10 scale (poor to excellent) to provide a rating
on each criterion. With the eight criteria being equally weighted, eighty is the highest possible
score that a panelist can give to a nominated organization. For each finalist, scores are summed
across all panelists’ ratings. The twenty finalists receiving the highest totals are designated as
MAKEsm study winners of the year.
3.
Sample Selection
The procedure for creating the treatment sample is as follows. We first construct a sample
that includes all organizations recognized as MAKEsm study winners in any of the eight years
from 1998 through 2005. This initial sample consists of 42 organizations, as many of them have
been winners in multiple years. Eleven of these are organizations for which no COMPUSTAT
data are available (i.e., firms not traded in a U.S. stock exchange – some foreign firms, privately
held firms, not-for-profit organizations).Because our study requires financial data from
17
COMPUSTAT, the eleven are dropped. This gives 31 firms in the final treatment sample – each
of which has been collaboratively determined to exhibit superior KM performance by a large
panel of experts.
A widely-accepted procedure (see Barber and Lyon 1996, Bharadwaj 2000, Feng et al.
2004, and Przasnyski and Tai 2002) is employed to establish the control sample. First, we obtain
the primary four-digit Standard Industrial Classification (SIC) code for each of the 31 superior
KM performers. Second, for each treatment firm, we identify a set of candidate control firms that
have the same primary four-digit SIC code as the treatment firm. Third, we reduce each
candidate set by keeping only those firms whose eight-year average sales are within a range of
70% to 130% of the corresponding treatment firm. Fourth, if more than one candidate control
firm satisfies the 30%-range target, we choose the one whose eight-year average sales are closest
to the treatment firm; if no candidate control firm satisfies the target, we identify a corresponding
control firm (that meets the 30%-range target) at the treatment firm’s primary three-digit or twodigit SIC code. Finally, if no firm satisfies the 30%-range target at primary four-, three-, or twodigit SIC code, we then apply the treatment firm’s non-primary SIC code to identify a
corresponding control firm that meets the 30%-range target. Most firms in this study have one
primary SIC code and several non-primary SIC codes. For example, Canon has the primary SIC
code of 3577 (computer peripheral equipment) and two non-primary SIC codes: 3861
(photographic equipment and supply) and 3559 (special industry machinery). Appendix A lists
the treatment firms (superior KM performers) and their control firms (not superior KM
performers).
The procedure described above enables us to match pairs of firms by size and type. The
paired firms are of similar size in that the eight-year average sales of the control firm are within
18
70% to 130% of its treatment firm. Previous research asserts that this 30%-range target is a
fundamental specification for defining comparison groups (Barber and Lyon 1996, Bharadwaj
2000). The firms in each pair are also drawn from the same industry type due to the same four-,
three-, or two-digit SIC code they have.
The underlying rationale of the procedure is (1) an appropriate industry benchmark can
explain cross-category and firm-size-based variations in organizational performance (Denis and
Denis 1993; Kaplan 1989), and (2) similar-sized firms in the same industry usually employ
similar accounting methods, and thus their financial ratios are directly comparable (Bharadwaj
2000). The whole procedure also provides a solid foundation for this study’s basic assertion that
potential differences between the two groups of firms in market-based ratios are attributed to the
primary between-group distinction: firms in treatment group have been recognized as superior
KM performers, while firms in control group have not.
4.
Comparability Test and Choice of Statistical Tests
To find out how well the two groups are matched, we test their comparability by using
several general firm size measures: sales, total assets, and number of employees. Also, as both
the treatment and control firms are relatively large, they tend to be highly diversified (Bharadwaj
2000). To ensure their degrees of diversification are not significantly different, we also compare
these 31 pairs of firms by using the entropy measure of related diversification (Choi and Russell
2005).1 Given that none of the data sets for firm size and entropy are normally distributed (see
Table 1), the non-parametric Wilcoxon Signed Ranks Test is used to examine the comparability
of the two groups. The normality tests are performed using the Shapiro-Wilk method, which
1
Entropy measure of related firm diversification is defined as the sum of related diversification (RD) and unrelated
diversification (UD) where,, and.
is defined as the sales share of the business segment of the industry group ’s total sales; is defined as the sales share
of industry group of the firm’s total sales; and is a function of .
19
compares sample distributions with a normal distribution and is especially suitable for small
samples (Ezzamel et al. 1987).
Descriptive statistics and results of comparability tests are shown in Table 2. These are
based on eight-year averages (1998 – 2005). For the comparability test, averages should be more
reliable than single-year observations (the averages are more normal and less variable). Mean
sales for control firms are about 94% of the treatment firm level. Because the non-parametric
tests find no statistically significant differences between the two groups for any of the four
variables in Table 2, we conclude that the two groups of firms are well matched with respect to
both firm size and diversification.
--------------------------------Insert Table 1 and 2 about here
---------------------------------5. Financial Performance Halo Effect Test
Prior research suggests that the perception-based identifications of MAKEsm study
winners may suffer from a financial performance halo effect (Bharadwaj 2000). As a major
threat to the validity of this study, a halo effect is a cognitive bias whereby the perception of a
particular trait is influenced by a general impression (Rosenzweig 2007). For the current study,
the halo effect refers to a possible bias that the recognition of MAKEsm study winners has been
influenced by their past financial performance. If such bias exists, then it may be that selection of
winner firms is actually due to prior financial performance that is superior, rather than
superiority of their KM performance.
To address this critical issue, we use the approach developed by Brown and Perry (1994)
to examine whether there is a financial performance halo effect in the MAKEsm study data. The
approach is applicable to various contexts and especially useful for a data set derived from largescale survey results that may be heavily influenced by factors extraneous to the constructs of
20
interest. The Brown and Perry halo technique involves analysis of the following financial
measures: average return on assets (AROA), sales, growth, risk, and relative value (RV).2 Here,
the analysis tests a logistic regression model: Y = B0 + B1AROA + B2SALES + B3GROWTH +
B4RISK + B5RV + e. The dependent variable in this model is binary, with Y = 1 for winning
firms and Y = 0 for control firms. The five financial measures serve as independent variables. If
a halo effect exists, we would observe one or more statistically significant p-values for the
independent variable coefficients.
In keeping with prior research, we use five-year averages of these measures to examine
the halo effect. The five years are those immediately preceding the first recognition of each
MAKEsm study winner. For instance, if a firm was first recognized as a MAKEsm study winner in
1998, then financial performance data from 1993 to 1997 are used for that firm; if a firm was
first recognized as a MAKEsm study winner in 2005, then financial performance data from 2000
to 2004 are used to test the logistic regression model for that firm.
Results of the logistic regression analysis, as shown in Table 3, suggest that none of the
p-values for the independent variable coefficients is statistically significant. Also, the p-value
(0.36) for the model chi-square (7.68) is not significant. Lacking statistically significant p-values,
we conclude that the five financial measures are neither individually, nor collectively, factors in
the designation of MAKEsm winners. Thus, there is no evidence of a financial halo effect that
could introduce bias into our study.
----------------------------------------------Insert Table 3 about here
------------------------------------------2
Return on assets = net income/total assets;
Sales = logarithm of the average sales for past five years;
Growtht = (%change in sales t-1 + … + %change in salest-5)/5;
Risk = debt/equity; where t is year in which a firm is first recognized as a MAKE sm winner;
Relative value = (market value/shareholder equityfirm)/(market value/shareholder equityindustry).
21
5. Results
Multiple methods have been proposed for calculating Tobin’s q ratio. This study employs
the method developed by Chung and Pruitt (1994) (Appendix B shows the details), which has
been adopted by various business disciplines such as finance (Weber and Dudney 2003),
information systems (Saeed et al. 2005), marketing (Fang et al. 2008), and management (Youndt
et al. 2004).
The key advantage of this method is that it uses a simple formula that relies on financial
and accounting information available from a firm’s financial reports (Bharadwaj et al. 1999). As
a very effective method, it is a good surrogate for the more traditional and complex method
developed by Lindenberg and Ross (1981). For example, by using 10 years of data, Chung and
Pruitt find that their method of calculating q ratio can explain at least 96.6% of the variance in
Tobin’s q obtained via Lindenberg and Ross’s model.
Consistent with prior research (Lubatkin and Shrieves 1986, Olson and Pagano 2005,
Otchere and Abou-Zied 2007), we employ a five-year window method to decide whether a
winner firm should be included in the data analysis for a specific year. That is, if a firm is
recognized as a winner in year t, then it will be included in the data analysis for each of the five
years from year t-1 to year t+3. This five-year window method has been widely used in
accounting, finance, and management to study issues related to firm performance (Hoskisson and
Hitt 1988, Lavie and Rosenkopf 2006, Rosner 2003).
Observing that the four market-based ratios are not normally distributed, we once again
use the non-parametric Wilcoxon Signed Ranks Test to evaluate the research hypotheses. Table 4
shows the Shapiro-Wilk normality test results for these ratios by using the data of treatment
firms from 1998 to 2005.
22
--------------------------------Insert Table 4 about here
-----------------------------As shown in Table 5, the results indicate that in each of the eight years, Tobin’s q, P/B,
and P/S are significantly higher for superior KM performers than for control firms, thus
supporting hypotheses 1-3. Figure 1 depicts means of Tobin’s q ratios of treatment firms and
control firms in all eight years. The P/E ratio is higher for superior KM performers in six of the
eight years, with statistical significance found in the year of 1999 and 2004. Overall, the results
are consistent with the predictions, suggesting that firms with superior KM performance are
likely to achieve better firm performance in terms of market-based valuations.
--------------------------------Insert Table 5 about here
----------------------------------------------------------------Insert Figure 1 about here
--------------------------------6. Discussion
As KM has attracted tremendous attention from both academy and industry, its
relationship with market valuation of firm performance is a highly important and timely research
topic. Drawing on three relevant theories and prior research findings, we argue that KM is a core
driver of competitive advantage and empirically investigate the relationship between superior
KM performance and superior market-based valuation, signaling competitive advantage and
reflecting relatively strong firm performance in terms of Tobin’s q, P/B, P/S, and P/E.
1.
Insights into the Results
Although Tobin’s q and the P/B ratio are calculated differently, they are very close to
each other (Li et al. 2004). The positive results for them suggest that KM assets (i.e., knowledge
resources, knowledge processors, knowledge processes) can indeed be captured by forward23
looking, market-based measures and that superior KM performers enjoy higher firm market
value. Such higher valuation is explained by investors’ confidence that the MAKEsm study
winner firms will gain/sustain a competitive advantage and glean greater value from their
tangible and intangible assets. Such confidence makes the investors more willing to provide
capital to the firms at a relatively lower cost, something that is ultimately reflected in higher
market value. In other words, the stock market rewards firms having superior KM performance.
The results are also consistent with the notion that as strategically important applications, welldesigned KM initiatives create an intangible asset for the implementing firm and strengthen the
firm’s future performance potential.
The positive result for the P/S ratio indicates that firms successful in leveraging
knowledge resources receive higher market valuation for each dollar of their sales. Such higher
valuation is based on the expectation that the firms can generate more profit for each dollar of
products they sell than firms of comparable size in the same industry. Thus, the result suggests
that superior KM performance is positively associated with higher profitability. This is in accord
with Bogner and Bansal’s (2007) finding that a firm’s profitability is impacted by its ability to
generate rare and valuable knowledge, and to apply that knowledge to business activities.
The inconclusive result for Hypothesis 4 shows that superior KM performance may not
be significantly related to higher P/E ratio, suggesting that MAKEsm study winner firms do not
necessarily benefit from investors’ willingness to pay more for the earnings they make. This
means that, even if there exists an expectation of fast earnings growth for superior KM
performers, such expectation is not necessarily significantly higher for these performers than for
firms of comparable size in the same industry. It may well be that, within a particular industry,
the market tends to overvalue firms with relatively low earnings per share. Such overvaluation
24
boosts stock prices of the firms and thus makes the P/E ratios higher than they might otherwise
be. However, this statistically insignificant result is to some extent in line with the argument that,
compared with P/S, the P/E ratio is a somewhat less reliable indicator of firm performance due to
the relative ease with which it may be manipulated.
There are three perspectives for interpreting the positive relationship between KM
performance and firm performance in terms of market-based valuation: (1) undertaking the
strategies and behaviors that yield superior KM performance constitute an antecedent for
superior market-based valuation; (2) superior market-based valuation motivates a firm to design
and execute superior KM initiatives; or (3) superior market-based valuation and superior KM
performance are joint consequences of some undetermined third factor. We prefer the first over
the other two because it is in line with the three theories, reported anecdotes, and individual case
studies. The second perspective would be inconsistent with all of these logical relations and
theoretical rationales. While the third perspective is a possibility, we adopt the first until some
such factor can be determined. In short, the findings suggest that superior KM performers have a
knack for managing knowledge assets in ways that produce superior firm performance.
2.
Implications
The findings of this study have important implications for practice. By substantiating the
link between KM performance and firm performance, this study provides support to managers
who have invested considerable resources in KM, especially to those who have implemented
successful KM strategies and initiatives. The results suggest that it is not enough for firms to
merely have some form of KM practice; they must develop a clear strategy for making their KM
performance superior (e.g., along the lines of realizing the MAKEsm study’s eight criteria).
25
Although there is little well-developed or one-size-fits-all guidance for managers on how
to achieve superior KM performance, some insights can be drawn from the literature. First, an
important step toward achieving superior KM performance is self-assessment, which requires
firms to effectively assess their own KM capability by looking broadly and deeply. Second,
management support is indispensable for superior KM performance because the implementation
of KM initiatives is resource-intensive and the necessary resources are more likely to be
allocated when top management support is available. Finally, effective management of KM
assets involves various facets, from developing a culture of continuous learning to implementing
a computer-based system for knowledge creation and sharing.
By identifying the importance of KM, this study also encourages practitioners to pay
careful attention to the management of their unique and valuable resources. To enable the
resources to survive competitive imitation, firms must protect them by isolating mechanisms
such as time-compression diseconomies, historical uniqueness, embeddedness, and causal
ambiguity. In this way, the firms can maximize the rent-yielding advantage of the resources and
achieve and sustain a superior competitive position – resulting in superior firm performance.
Our empirical results also provide important implications to researchers. Although the
current study indicates that superior KM performance leads to higher market valuation in
regocnition/anticipation of improved firm performance, the underlying mechanisms whereby this
is achieved are not fully clear. Previous research suggests that KM may impact firm performance
indirectly through other variables such as innovation, product leadership, and customer intimacy
(McKeen et al. 2006), or through the knowledge chain theory’s PAIR dimensions of
productivity, agility, innovation, and reputation (Holsapple and Singh 2001). Therefore, an
alternative conceptualization would be a two-stage model that examines the effects of KM on
26
intermediate variables, which in turn are positively associated with superior firm valuation,
performance, or competitiveness. Future research is encouraged to develop and empirically test
such a model.
As noted earlier, the third possible perspective for interpreting the positive results of this
study is that superior firm and KM performances are joint consequence of an X factor. In light of
this, another useful direction of future research would be to determine whether there exists such a
factor and what would be it. Addressing this important and interesting research question will
allow researchers to enrich studies of effective KM, extend the literature on organizational
competitiveness, and arrive at a better understanding of the relationship between KM and firm
performance.
3.
Contributions and Limitations
This study contributes to the growing literature on organizational effects of KM by using
a forward-looking approach to evaluating firm performance. Specifically, in line with the notion
that KM is mainly an organization’s intangible asset, we measure firm performance with marketbased ratios that effectively reflect the value of such asset. Furthermore, in gauging KM
performance, we adopt ratings by international panels of KM experts. Thus, our empirical data
avoid possible bias due to reliance on perceptions of individuals embedded within firms being
studied. Finally, we collect and analyze eight years’ data from 1998-2005 to examine the
relationship between KM performance and firm performance. Therefore, this longitudinal study
should not only yield reliable results on the focal relationship but also enable researchers to
effectively track the organizational effects of KM over a long time period.
The results and implications of this study should also be considered in light of its
limitations. As described in the methodology section, we use MAKEsm study results to identify
27
firms with superior KM performance. Because the MAKEsm study’s determination of superior
KM firms is based on perceptions of KM experts from around the world, its results may reflect
some bias. Another limitation is the selection of some of the control firms. That is, for some
winner firms, an appropriate control firm of similar size is not found at the primary four-digit
SIC level, but at a primary three-/two-digit or non-primary SIC code. Moreover, both the
treatment and control samples consist of relatively large organizations, so it is possible that the
relationship between KM performance and firm performance found in this study is limited to
these relatively large firms. Finally, the generalizability of our findings may be limited by the
moderately small sample size.
References
Amit, R., P. J. Schoemaker. 1993. Strategic assets and organizational rent. Strategic
Management J. 14(1) 33-46.
Argote, L., P. Ingram. 2000. Knowledge transfer in organizations: a basis for competitive
advantage in firms. Organ. Behavior and Human Decision Processes 82(1) 150-169.
Babcock, P. 2004. Shedding light on knowledge management. HR Magazine 49(5) 46-50.
Balakrishnan, R., T. J. Linsmeier, M. Venkatachalam. 1996. Financial benefits from JIT
adoption: effects of customer concentration and cost structure. Accounting Rev. 71(2) 183-205.
Barber, B. M., J. D. Lyon. 1996. Detecting abnormal operating performance: the empirical
power and specification of test statistics. J. of Finance Econom. 41(3) 359-399.
Barney, J. 1991. Firm resources and sustained competitive advantage. J. Management 17(1) 99120.
Bennet, A., D. Bennet. 2003. The rise of the knowledge organization. C. W. Holsapple, ed.
Handbook on Knowledge Management 1: Knowledge Matters, Springer Verlag, Berlin,
Germany.
Bharadwaj, A. S. 2000. A resource-based perspective on information technology capability and
firm performance: an empirical investigation. MIS Quart. 24(1) 169-196.
Bharadwaj, A. S., S. G. Bharadwaj, B. R. Konsynski. 1999. Information technology effects on
firm performance as measured by Tobin’s q. Management Science 45(6) 1008-1024.
Bogner, W. C., P. Bansal. 2007. Knowledge management as the basis of sustained high
performance. J. Management Studies 44(1) 165-188.
Brown, B., S. Perry. 1994. Removing the financial performance halo from Fortune’s most
admired companies. Acad. Management J. 37(5) 1347-1359.
28
Carlson, C. B. 2000. Price-To-Sales Ratio.
http://www.buyandhold.com/bh/en/education/Carlson/2000/price_to_sales.html
Castillo, J. 2003. Challenging the knowledge management mystique: an exploratory study on the
performance of knowledge managing companies. J. Management Res. 3(3) 152-172.
Choi, B., S. K., Poon, J. G. Davis. 2008. Effects of knowledge management strategy on
organizational performance: a complementarity theory-based approach. Omega 36 235-251.
Choi, J., J. S. Russell. 2005. Long-term entropy and profitability change of United States public
construction firms. J. Management Engrg 21(1) 17-26.
Chung, K. H., S. W. Pruitt. 1994. A simple approximation of Tobin’s q. Financial Management
23(3) 70-74.
Conner, K. R. 1991. A historical comparison of the resource-based theory and five schools of
thought within industrial organization economics. J. Management 17(1) 121-154.
Conner, K. R., C. K. Prahalad. 1996. A resource-based theory of the firm: knowledge versus
opportunism. Organ. Sci. 7(5) 477-501.
Darroch, J. 2005. Knowledge management, innovation and firm performance. J. Knowledge
Management 9(3) 101 – 115.
Decarolis, D. M., D. L. Deeds. 1999. The impact of stocks and flows of organizational
knowledge on firm performance: an empirical investigation of the biotechnology industry.
Strategic Management J. 20 953-968.
Denis, D. J., D. K. Denis. 1993. Managerial discretion, organization structure, and corporate
performance: a study of leveraged capitalizations. J. Accounting and Econom. 16(1-3) 209-236.
Denrell, J., N. Arvidsson, U. Zander. 2004. Managing knowledge in the dark: an empirical study
of the reliability of capability evaluations. Management Sci. 50(11) 1491-1503.
Dierickx, I., K. Cool. 1989. Asset stock accumulation and sustainability of competitive
advantage. Management Sci. 35(11) 1504-1511.
Dodgson, M. 1993. Organizational learning: a review of some literatures. Organ. Stud. 14(3)
375-394.
Edwards, J. S., M. Handzic, S. Carlssson, M. Nissen. 2003. Knowledge management research &
practice: visions and directions. Knowledge Management Res. & Practice 1(1) 49-60.
Ezzamel, M., C. Mar-Molinero, A. Beecher. 1987. On the distributional properties of financial
ratios. J. Bus. Finance & Accounting. 14(4) 463-481.
Fang, E., R. W., Palmatier, J. E. M. Steenkamp. 2008. Effect of service transition strategies on
firm value. J. Marketing 72(5) 1-14.
Felin, T., W. S. Hesterly 2007. The knowledge-based view, nested heterogeneity, and new value
creation: philosophical considerations on the locus of knowledge. Acad. Management Rev. 32
195-218.
Feng, K. C., E. T. Chen, W. C. Liou. 2004. Implementation of knowledge management systems
and firm performance: an empirical investigation. J. Comput. Inform. Systems 45(2) 92-104.
29
Friedlander, F. 1983. Patterns of individual and organizational learning. S. Srivastva and
Associates, eds. The Executive Mind: New Insights on Managerial Thought and Action. San
Francisco: Jossey-Bass.
Galunic, D. C., S. Rodan. 1998. Resource recombinations in the firm: knowledge structures and
the potential for Schumpeterian innovation. Strategic Management J. 19(12) 1193-1201.
Garby, E. 2002. Alternative measures of performance for e-companies: a comparison of
approaches. J. Bus. Strategies 19(1) 1-17.
Gates, B. 1999. Business @ the Speed of Thought: Using a Digital Nervous System. New York:
Warner Books, Inc.
Gold, A. H., A. Malhotra, A. H. Segars. 2001. Knowledge management: an organizational
capabilities perspective. J. Management Inform. Systems 18(1) 185-214.
Grant, R. M. 1991. The resource-based theory of competitive advantage: implications for
strategy formulation. California Management Rev. 33(3) 114-135.
Gupta, J. N. D., S. K. Sharma. 2004. Creating Knowledge Based Organizations. Idea Group Inc.
Publishing, Hershey, PA.
Haas, M. R. 2006. Knowledge gathering, team capabilities, and project performance in
challenging work environments. Management Sci., 52 1170-1184.
Holsapple, C. W., K. D. Joshi. 2004. A formal knowledge management ontology: Conduct,
activities, resources, and influences. J. Amer. Soc. Inform. Sci. Tech. 55(7) 593-612.
Holsapple, C. W., M. Singh. 2001. The knowledge chain model: Activities for competitiveness.
Expert Systems Appl. 20(1), 77-98.
Hoskisson, R. E., M. A. Hitt. 1988. Strategic control systems and relative R&D investment in
large multiproduct firms. Strategic Management J. 9(6) 605-621.
Huber, G. P. 1991. Organizational learning: the contributing processes and the literatures. Organ.
Sci. 2(1) 88-115.
Jain, B. A., O. Kini. 1995. Venture capitalist participation and the post-issue operating
performance of IPO firms. Managerial Decision Econom. 16(6) 593-606.
Kalwani, M., N. Narayandas. 1995. Long-term manufacturer-supplier relationship relationships:
do they pay off for supplier firms. J. Marketing 59(1) 1-17.
Kaplan, S. 1989. The effect of management buyout on operating performance and value. J.
Financial Econom. 24(2) 217-254.
Lang, L. H. P., R. H. Litzenberger. 1989. Dividend announcements: cash flow signaling vs. free
cash flow hypothesis? J. Financial Econom. 24(1) 181-191.
Lavie, D. 2007. Alliance portfolios and firm performance: a study of value creation and
appropriation in the U.S. software industry. Strategic Management J., 28 1187-1212.
Lavie, D., L. Rosenkopf. 2006. Balancing exploration and exploitation in alliance formation.
Acad. Management J. 49(4) 797-818.
Lee, H., B. Choi. 2003. Knowledge management enablers, processes, and organizational
performance. J. Management Inform. Systems 20(1) 179-228.
30
Lee, C. C., J. Yang. 2000. Knowledge value chain. J. Management Development 19(9) 783-793.
Levitt, B., J. G. March.1988. Organizational learning. Annual Rev. of Sociology 14(3) 319-340.
Li, M. Z. F., T. H., Oum, Y. Zhang. 2004. Tobin’s q and airline performances. Public Works
Management & Policy 9(1) 51-65.
Liebowitz, J. 2008. ‘Think of others’ in knowledge management: making culture work for you.
Knowledge Management Res. & Practice. 6(1) 47-51.
Lindenberg, E. B., S. A. Ross. 1981. Tobin’s q ratio and industrial organization. J. Bus. 54(1) 132.
Lubatkin, M., R. E. Shrieves. 1986. Towards reconciliation of market performance measures to
strategic management research. Acad. Management Rev. 11(3) 497-512.
Luo, X., N. Donthu. 2006. Marketing’s credibility: a longitudinal investigation of marketing
communication productivity and shareholder value. J. Marketing 70(4) 70-91.
Marques, D. P., F. J. G. Simon. 2006. The effect of knowledge management practices on firm
performance. J. Knowledge Management 10(3) 143-156.
McConnell, J. J., H. Servaes. 1990. Additional evidence on equity ownership and corporate
value. J. Financial Econom. 27(2) 595-612.
Mckeen, J. D., M. H. Zack, S. Singh. 2006. Knowledge management and organizational
performance: An exploratory study. Proc. Hawaii Internat. Conf. System Sci., Hawaii, January.
Megginson, W. L., K. A. Weiss. 1991. Venture capitalist certification in initial offerings. J.
Finance 46(3) 879-903.
Motsenigos, A., J. Young. 2002. KM in the U.S. Government Sector. KMWorld.
http://www.kmworld.com/Articles/Editorial/Feature/KM-in-the-U.S.-government-sector9397.aspx
Nathan, S., K. Sivakumar, J. Vijayakumar. 2001. Returns to trading strategies based on price-toearnings and price-to-sales ratios. J. Investing 10(2) 17-28.
Ofek, E., M. Sarvary. 2001. Leveraging the customer base: creating competitive advantage
through knowledge management. Management Sci. 47 1441-1456.
Olson, G. T., M. S. Pagano. 2005. A new application of sustainable growth: a multi-dimensional
framework for evaluating the long run performance of bank mergers. J. Bus. Finance &
Accounting 32(9&10) 1995-2036.
Otchere, I., K. Abou-Zied. 2008. Stock exchange demutualization, self-listing and performance:
the case of the Australian Stock Exchange. J. Banking & Finance 32 512-525.
Porter, M. 1985. Competitive Advantage. New York: Free Press.
Przasnyski, Z. H., L. S. Tai. 2002. Stock performance of Malcolm Baldrige National Quality
Award winning companies. Total Quality Management 13(4) 475-488.
Rao, V. R., M. K. Agarwal, D. Dahlhoff. 2004. How is manifest branding strategy related to the
intangible value of a corporation? J. Marketing 68(4) 126-141.
31
Rosenzweig, P. 2007. Misunderstanding the nature of company performance: the halo effect and
other business delusions. California Management Rev. 49(4) 6-20.
Rosner, R. L. 2003. Earnings manipulation in failing firms. Contemporary Accounting Res. 20(2)
361-408.
Sabherwal, R., S. Sabherwal. 2007. How do knowledge management announcements affect firm
value? A study of firms pursuing different business strategies. IEEE Trans. Engrg Management,
54 409-422.
Saeed, K. A., V. Grover, Y. Hwang. 2005. The relationship of e-commerce competence to
customer value and firm performance: an empirical investigation. J. Management Inform.
Systems 22(1) 223-256.
Senchack, A. J., J. D. Martin. 1987. The relative performance of the PSR and PER investment
strategies. Financial Analysts J. 43(2) 46-56.
Shroff, P. K. 1995. Determinants of the returns-earnings correlation. Contemporary Accounting
Res. 12(1) 41-55.
Simon, C. J., M. W. Sullivan. 1993. The Measurement and determinants of brand equity: a
financial approach. Marketing Sci. 12(1) 28-52.
Smith, H. A., J. D. McKeen. 2005. Developments in practice XVII: a framework for KM
evaluation. Comm. Association Inform. System. 16(9) 233-246.
Spanos, Y. E., S. Lioukas. 2001. An examination into the causal logic of rent generation:
contrasting Porter’s competitive strategy framework and the resource-based perspective.
Strategic Management J., 22 907-934.
Sudarsanam, S., A. A. Mahate. 2003. Glamour acquirers, method of payment and postacquisition performance: the UK evidence. J. Bus. Finance & Accounting 30(1&2) 299-341.
Tobin, J. 1978. Monitary policies and the economy: the transmission mechanism. Southern
Econom. J. 44(3) 421-431.
Tuschke, A., W. G. Sanders. 2003. Antecedents and consequences of corporate governance
reform: the case of Germany. Strategic Management J. 24 (7) 631-649.
Uhlenbruck, K., M. A. Hitt,, M. Semadeni. 2006. Market value effects of acquisitions involving
Internet firms: a resource-based analysis. Strategic Management J. 27 899-913.
Weber, M., & Dudney, D. 2003. A reduced form coefficients analysis of executive ownership,
corporate value, and executive compensation. The Financial Review 38 (3) 399-413.
Wernerfelt, B. 1984. A resource-based view of the firm. Strategic Management J. 5(2) 171-180.
Youndt, M. A., M. Subramaniam, S. A. Snell. 2004. Intellectual capital profiles: an examination
of investments and returns. J. Management Stud. 41(2) 335-361.
Zacharia, Z. G., J. T. Mentzer. 2004. Logistics salience in a changing environment. J. Bus.
Logist. 25(1) 187-210.
32
Table 1: Shapiro-Wilk Normality Test Results for Firm Size and Entropy
Measurement
Statistic (W-Value) P-Value Accept Null Hypothesis Normally Distributed
Sales
0.78
<0.01
No
No
Total Assets
0.86
<0.01
No
No
Number of Employees
0.76
<0.01
No
No
Entropy
0.85
<0.01
No
No
Note. The null hypothesis of Shapiro-Wilk test is that there is no difference between the distribution of a sample
data set and a normal one.
Table 2: Descriptive Statistics and Comparability Test Results
Descriptive
MAKEsm Study
Control
Wilcoxon Signed Ranks Test
Variables
Winners
Firms
for Paired Samples
Variable
Median
Mean
Median
Mean
Z-Value
Sales (billion $)
Assets (billion $)
Employees
Entropy
25.66
26.86
72,746
0.44
42.78
49.34
95,802
0.63
19.95
27.54
81,546
0.47
40.15
42.68
96,825
0.66
-1.31
-1.10
-0.01
-0.34
Table 3: Results of Halo Effect Test
Statistic
PValue
0.19
0.27
0.99
0.74
AROA Sales Growth
Coefficient (B Value)
0.051 0.197 0.000
P-Value
Model Chi-Square:
7.68
0.35 0.54
Risk RV
0.001
0.001
0.86 0.88
1.00
P Value for Model:
0.36
Model Degree of
Freedom: 7
Table 4: Shapiro-Wilk Normality Test Results for the Four Ratios
Ratio
Statistic (W-Value) P-Value Accept Null Hypothesis
Tobin’s q
0.78
<0.01
No
Price to Book
0.43
<0.01
No
Price to Sales
0.61
<0.01
No
Price to Earnings
0.71
<0.01
No
Normally Distributed
No
No
No
No
Table 5: Statistical Results
The First
Four
Ratios
Year
1998
N=18
33
Firm
Winners
Control
Tobin’s
q
P/B
Mean
Median
4.24
1.89
3.36
1.41
P/S
P/E
Z
Z
Z
Z
Mean Median
Mean Median
Mean Median
Value
Value
Value
Value
-2.69a
9.15
4.87
8.53
2.98
-2.10b
6.03
1.87
3.33
1.14
-2.79a
31.3
49.6
26.2
30.9
-0.04
1999
N=19
Winners
Control
5.75
2.35
4.65
2.04
-2.51b
11.08
5.64
9.60
3.72
-1.87c
8.50
2.24
5.19
1.56
-2.84a
49.7
0.11
40.8
31.1
-2.23b
2000
N=22
Winners
Control
3.26
1.65
2.61
1.07
-2.61a
7.21
4.11
5.45
2.76
-2.11b
4.19
1.67
2.82
1.22
-2.85a
51.9
39.9
39.9
32.4
-0.28
2001
N=24
Winners
Control
2.20
1.34
1.38
1.16
-3.03a
5.61
3.49
3.78
2.37
-2.14b
2.94
1.21
1.60
0.95
-2.97a
24.6
43.0
29.9
22.6
-0.11
2002
N=24
Winners
Control
2.16
0.74
1.49
0.64
-3.25a
10.40
1.94
3.13
1.33
-2.42b
2.57
1.20
1.61
0.67
-2.80a
35.1
27.1
27.4
17.6
-1.29
2003
N=23
Winners
Control
2.68
0.79
1.67
0.76
-3.39a
2.19
2.25
3.62
2.04
-1.87c
2.95
1.17
1.65
0.68
-3.02a
13.5
-12.6
21.6
-3.10
-1.49
2004
N=25
Winners
Control
2.67
1.09
1.87
0.81
-2.79a
0.68
2.39
3.79
1.57
-1.93c
3.13
1.56
1.74
0.85
-2.94a
40.3
22.5
24.1
18.2
-1.76c
2005
Winners
N=24
Control
a
Significant at
0.01;
b
Significant at
0.05;
c
Significant at
0.10.
P/B—price to
book; P/S—
price to sales;
P/E—price to
earnings; N—
number of
observations.
2.79
1.09
2.38
0.70
-3.62a
9.84
2.28
4.16
1.64
-3.77a
3.25
1.39
1.89
0.71
-3.75a
25.0
62.3
18.7
22.9
-0.37
Appendix A: Treatment Firms and Corresponding Control Firms
Superior KM
Control Firms
Performers
Company Name
SIC
Industry Description
Company Name
SIC
Industry Description
3M CO
2670
GEORGIA-PACIFIC
2600
1
ACCENTURE LTD
7371
Convert paper, paperboard
Computer programming
services
COMPUTER SCIENCE
7370
Paper and allied products
Computer programming,
data process
AMAZON.COM INC
5961
Catalog, mail-order houses
BP PLC
2911
Petroleum refining
1
3861
CHEVRONTEXACO
1
CISCO SYSTEMS INC
DELL INC
GENERAL ELECTRIC COPRE FASB
CANON INC
2911
3661
3571
Photographic equipment &
Supply
Petroleum refining
Tele & telegraph apparatus
electronic computers
LIBERTY MEDIA
CAPITAL GROUP
CONOCOPHILLIPS –
PRO FORMA
FUJIFILM HOLDINGS
CORP -ADR
TOTAL SA -ADR
ALCATEL -ADR
NEC CORP
9997
Conglomerate
GOOGLE INC-REDH
7370
HEWLETT-PACKARD CO
3570
1
INTL BUSINESS MACHINES
CORP
3570
INFOSYS TECHNOLOGIES
7371
INTEL CORP
3674
34
Computer programming,
data process
Computer & office
equipment
Computer & office
equipment
Computer programming
services
Semiconductor related
5961
Catalog, mail-order houses
2911
Petroleum refining
2911
3661
3571
Photographic equipment &
Supply
Petroleum refining
Tele & telegraph apparatus
electronic computers
E.ON AG
9997
Conglomerate
YAHOO INC
7370
Computer programming,
data process
FUJITSU LTD
3571
electronic computers
HITACHI LTD -ADR
3570
KEANE INC
7371
ERICSSON (L M) TEL -
3663
3861
Computer & office
equipment
Computer programming
services
Radio, TV broadcast,
device
Pharmaceutical
preparations
JOHNSON & JOHNSON
2834
LUCENT TECHNOLOGIES
INC
3661
Tele & telegraph apparatus
MICROSOFT CORP
7372
Prepackaged software
MONSANTO CO
2870
NOKIA CORP
3663
PFIZER INC
2834
Agricultural chemicals
Radio, TV broadcast,
Communication equipment
Pharmaceutical
preparations
ROYAL DUTCH/SHELL GRP
COMB
2911
SCHLUMBERGER LTD
1389
SCIENCE APPLICATIONS
INTL CORP
8700
Petroleum refining
Oil and gas field
exploration services
Engineering, accounting,
research, management,
public relations services
Electric, other electronic
equipment
Household audio & video
equipment
ADR
MERCK & CO
NORTEL NETWORKS
CORP
ELECTRONIC DATA
SYSTEMS CORP
SYNGENTA AG
2834
3661
7370
2870
GLAXOSMITHKLINE
PLC -ADR
2834
EXXON MOBIL CORP
2911
HALLIBURTON CO
1389
VNU NV -ADR
8700
MATSUSHITA
ELECTRIC -ADR
3600
TOSHIBA CORP
3600
6311
Life insurance
electronic computers
Motor vehicles & car
bodies
SONY CORP
3651
SUN LIFE FINANCIAL INC
6311
Life insurance
NORTHWESTERN
MUTUAL LIFE INS
SUN MICROSYSTEMS
3571
3711
electronic computers
Motor vehicles & car
bodies
APPLE INC
GENERAL MOTORS
CORP-PRE FASB
3571
TOYOTA MOTOR CORP
UNILEVER PLC
2000
GROUPE DANONE
2000
WIPRO LTD
7373
CACI INTL INC -CLA
7373
XEROX CORP
3577
Food and kindred products
Computer integrated system
design
Computer peripheral
equipment
SANYO ELECTRIC CO
3579
Control firm identified based on
the winner firm’s non-primary
SIC code.
35
Computer programming,
data process
Agricultural chemicals
Radio, TV broadcast,
Communication equipment
Pharmaceutical
preparations
3663
3600
1
Tele & telegraph apparatus
MOTOROLA INC
1
SIEMENS AG
Communication equipment
Pharmaceutical
preparations
3711
Petroleum refining
Oil and gas field
exploration services
Engineering, accounting,
research, management,
public relations services
Electric, other electronic
equipment
Electric, other electronic
equipment
Food and kindred products
Computer integrated system
design
Office Machines