Introduction

Over the past dozen years, one of the most striking developments in business has been the rapid proliferation of knowledge management (KM) as a topic for research and as a phenomenon in practice. Organizations have launched KM initiatives that seek to consolidate, reconcile, and exploit their knowledge assets in order to be better able to compete in the dynamic global business environment. These initiatives involve processes comprised of various configurations of KM methods and technologies. Simultaneously, researchers have launched investigations that seek to clarify and deepen our understanding of the nature of KM, to devise KM methods and technologies, and to improve KM processes and their outcomes. There is considerable anecdotal evidence that KM initiatives have positively affected organizations' productivity, product and service quality, and internal processes.

There seems to be little doubt nowadays that KM is very important for smooth and successful operations in organizations (Drucker, 1993; Davenport et al., 1996; Staples et al., 2001). Moreover, it is widely accepted by practitioners and researchers that KM can also have important strategic consequences: improving organizations' competitive positions. More specifically, KM can be instrumental in the design and implementation of competitive strategies by promoting productivity (Wiig & Jooste, 2003), increasing agility (Dove, 2003), fostering customer loyalty (Housel & Bell, 2001), maximizing intellectual assets (Teece, 2003), and enhancing innovation (Amidon & Mahdjoubi, 2003). Furthermore, Bock et al. (2005) contend that KM initiatives can generate shareholder value.

Although such research indicates that KM can have significant impacts on firm performance, and therefore is of great value to the firm, empirical support for a link between well-designed KM initiatives and firm performance consists primarily of many individual case studies (e.g., Holsapple & Singh, 2001; Holsapple, 2003), analysis of practitioner focus group discussions (Smith & McKeen, 2003), and surveys of practitioner perceptions (Holsapple & Singh, 2005; Holsapple & Jones, 2007; Holsapple et al., 2007). However, aside from perceptions and special cases, we still do not know whether there is a systematic link between superior KM performance and a superior bottom-line. While they form a promising first step, we need to go beyond such anecdotal and opinion-oriented indicators. We need to investigate the association between a firm's relative KM performance and that same firm's relative performance as gauged by accounting measures. Across firms in various industries and of various sizes, is there a systematic association? If so, is it positive or negative, is it strong or weak? For which accounting measures of firm performance does such a link exist?

Although not focusing on accounting measures, several researchers have stressed the need for greater KM research regarding impacts on firm performance. For instance, Feng et al. (2004) state that while KM researchers tend to focus on the use of various technologies for knowledge acquisition and storage or on the conceptual nature of KM, they should not leave the impacts of KM initiatives on firm performance uninvestigated. The knowledge chain model calls attention to a link between KM and competitiveness by identifying and characterizing nine critical categories of KM activity that can be performed in ways that lead to competitive advantage through greater productivity, agility, innovation, and/or reputation (Holsapple & Singh, 2001, 2005; Holsapple & Jones, 2004, 2005). Tanriverdi (2005) emphasizes the need of theoretical and empirical work on understanding the relationships among information technology, KM capability, and firm performance. Our present focus on accounting measures of firm performance aims to yield insights that speak directly to the paramount, bottom-line concerns of top managers and firm shareholders.

Here, we develop a theoretical link between KM performance and business performance. We then proceed to discuss various issues that need to be considered when designing an empirical study for examining this link. For purposes of this paper, we define an organization's KM performance as the degree to which its KM methods and technologies succeed in harnessing organizational resources to achieve the goals or purposes of its KM initiatives. We define business performance in terms of standard accounting measures involving costs, income, returns, and market valuation.

The context of this study involves multi-market firms in diverse industries. Because knowledge is usually applicable beyond a single product market and there multiple external markets for efficient exchange of knowledge, any single market of a multi-market firm provides opportunities for exploiting knowledge in multiple product markets, creating knowledge-based synergies, and improving the overall performance of the firm (Tanriverdi, 2005). Because this context involves firms in various industries, it is likely that results from an empirical study are applicable across organizations and industries (Zacharia & Mentzer, 2004). Thus, multi-market firms in diverse industries provide a rich context for considering the missing link.

The continuing emergence of KM

The search for a missing link is predicated on the dual notions that KM is a sensible phenomenon and that KM is a non-trivial phenomenon of interest to modern business. If either of these conditions is not met, then the quest for a KM–firm performance connection is moot. Thus, we briefly discuss the emergence of KM as a field of study and practice.

Based on their scientometric study of 6,260 'KM' research publications in the INSPEC database through 2004, Prakasan et al. (2007) conclude that KM has experienced sudden growth and produced a substantial literature. They point out that there are varying definitions and conceptions of KM, but that whatever view is adopted should recognize that KM is of strategic importance to an organization's success. One such view is provided by the collaboratively engineered KM ontology, which defines KM 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' (Holsapple & Joshi, 2004). The ontology scopes out KM very broadly as any process of generating new knowledge, acquiring valuable knowledge from outside sources, selecting needed knowledge from internal sources, assimilating knowledge to alter the state of internal knowledge resources, embedding knowledge into organizational outputs, and/or leading, coordinating, controlling, and measuring these five kinds of activities.

While the KM ontology's conceptualization of KM generally unifies and subsumes the bulk of KM concepts and definitions advanced in the literature, it should be noted that there seem to be three camps differentiated by their stances on the role of technology in KM (Holsapple, 2005). One of these denies that technology has any substantial role to play in KM. At the other extreme are those who take the position that the central focus of KM is (or should be) on technologies. The middle path, which is the stance adopted here, acknowledges that both human and computer-based processors can be deployed in KM initiatives. In spite of different vantage points and ongoing development, it is fair to say that the field of KM has passed the 'tower of Babel' stage. It has reached a stage where researchers and practitioners have made considerable sense of KM phenomena, and are likely to continue doing so.

This progress is supported by the appearance of several scholarly journals that concentrate on the publication of cutting-edge KM research. Examples are shown in Table 1. According to Google Scholar, each of the two oldest of these journals has published articles that have been cited over 100 times by other scholarly publications. This is a very substantial citation level. The other two journals are too new to have had such citation impacts, but we should expect to see their articles exhibiting their greatest citation impacts in the 4-year to 7-year window following publication.

Table 1 - Major scholarly journals devoted to KM topics.

Full table

Also supporting progress of the KM field since the mid-1990s is a large number of special issues on KM. As shown in Table 2, these have appeared in several scholarly disciplines – information systems, strategy, computer science, information science, behavioral science – as well as various multi-discipline journals such as Decision Sciences, Management Science, Organization Science, and California Management Review. This is symptomatic of an ongoing interest in KM from diverse quarters. It also suggests that, as a field of study, KM has numerous reference disciplines and that KM itself can be a valuable reference discipline for other fields. Following on the heels of their special issues, many of the journals in Table 2 have become more open to publishing articles dealing with KM topics in their regular issues.

Table 2 - Special issues on KM.

Full table

Progress of and interest in the KM field can also be seen in overall publishing trends. Figure 1 illustrates the trend of publications pertaining to KM as tracked by Google Scholar (ISI Web of Science yields a similar trend, but is not nearly as broad in the publications that it tracks). For each year, the total number of publications referring to 'KM' in that year plus the four preceding years is shown. In the initial 5-year period (1991–1995), such publications averaged 340 per year, or about 6.5 per week. The most recent 5-year period (2002–2006) experienced an average of 9400 per year, or 180 per week, or more than 25 every day. To put this KM trend in perspective, compare it with the traditional business discipline of operations management, for which Google Scholar reports an average of 583 publications per year in the 1991–1995 window, ramping up to an average of 2936 per year in the 2002–2006 period.

Figure 1.

Trend of scholarly publications pertaining to 'Knowledge Management' (Source: Google Scholar, August, 10, 2007).

Full figure and legend (45K)

Critics of KM have characterized it as a temporary buzzword or hype, and predicted that KM would fall into the dustbin of management fads – or, at most, have a minor impact on research and practice. In retrospect, it seems that they have been mistaken. As shown above, KM has not been ephemeral. It has progressed, rather dramatically, into a substantial field of study that evokes considerable interest and contributions from diverse disciplines. It has spawned its own scholarly journals of record. It has been recognized by numerous non-KM journals of high stature as yielding research worthy of inclusion in their special issues, as well as their regular issues.

We conclude that KM is a sensible, non-trivial phenomenon of relevance to modern business. It is still a young field with various unanswered questions worthy of investigation. One of these concerns the missing link: Is there an association between superior KM performance and superior business performance as gauged by standard accounting measures?

A resource-based theory of KM and firm performance

With its root in management strategy literature, the resource-based theory of the firm aims to explain why firms are able to gain and sustain competitive advantages (Wernerfelt, 1984; Amit & Schoemaker, 1993). The theory asserts that the main driver of firm performance is a set of unique firm resources that are valuable, rare, difficult to imitate, and non-substitutable by other resources (Barney, 1991; Conner, 1991). According to the theory, such resources are usually rent-yielding and likely to survive competitive imitation when protected by isolating mechanisms such as time-compression diseconomies, historical uniqueness, embeddedness, and causal ambiguity (Rumelt, 1984; Dierickx & Cool, 1989; Barney, 1991). Moreover, an important assumption of the theory is that the resources needed to conceive, choose, and implement strategies are heterogeneously distributed across firms, which in turn are posited to account for differences in firm performance (Grant, 1991).

Advocates of the resource-based theory generally tend to define resources broadly, to include financial capital, physical assets, knowledge, brand image, human resources, organizational processes, and so forth (Bharadwaj, 2000). Based on the resource-based theory, one stream of research identifies knowledge as a basic source of competitive advantage and suggests that performance differences among firms can be attributed to asymmetries in their knowledge resources, the processors of those resources, and the processes that govern/guide the processors (Conner & Prahalad, 1996; Lee & Choi, 2003; Feng et al., 2004). The knowledge processors are human resources and/or physical resources (i.e., computers), while knowledge processes form a class of organizational processes oriented toward achieving KM objectives (Holsapple & Joshi, 2004).

Although knowledge resources have a broad definition, Tanriverdi & Venkatraman (2005) argue that the most strategic knowledge resources of a firm are product knowledge, customer knowledge, and managerial knowledge. Product knowledge refers to research, development, and operational knowledge employed by the firm to develop and produce its products and services (Markides & Williamson, 1994; Robins & Wiersema, 1995). Related to marketing and advertising skills and policies, customer knowledge refers to the knowledge of customer needs, preferences, and buying behaviors, and knowledge of the firm's markets (Woodruff, 1997). At corporate level managerial practices, policies, and processes, managerial knowledge refers to the knowledge required for governing the firm's business units (Prahalad & Bettis, 1986; Grant, 1988). These three types of knowledge resources complement each other (Tanriverdi & Venkatraman, 2005).

According to the foregoing stream of research, KM is of great importance to firm performance because it allows the firm to better leverage its knowledge resources. The knowledge chain theory furnishes a model for understanding where this leverage can occur and possible directions whereby firm performance can be enhanced. Grounded in a collaboratively engineered ontology (Holsapple & Joshi, 2002, 2004), the knowledge chain theory identifies and characterizes nine key classes of KM activities, and asserts that the ways in which a firm performs these can play fundamental roles in helping leverage its knowledge into better performance and competitiveness (Holsapple & Singh, 2001). The theory further asserts that such competitiveness can manifest in one or more of four directions: productivity, agility, innovation, and reputation (PAIR). The nine knowledge chain activity classes are comprised of five first-order activities and four second-order activities.

The first-order activities are concerned with basic types of operations that processors can undertake in operating on knowledge resources: generation, acquisition, selection, assimilation, and emission. Knowledge generation is an activity of deriving or discovering knowledge in the context of existing knowledge. Knowledge acquisition refers to the act of acquiring knowledge from external sources and making it suitable for subsequent use. Knowledge selection is an activity in which a processor selects needed knowledge from internal sources and makes it suitable for subsequent use. Knowledge assimilation refers to activities that alter an organization's state of knowledge by storing and distributing knowledge that has been generated, acquired, and/or selected. Finally, knowledge emission refers to activities that embed – directly or indirectly – knowledge into products or services for release into the organization's surroundings.

Second-order activities occur at a higher level, being concerned with processes involved in supporting, guiding, or governing the execution of first-order activities: measurement, control, coordination, and leadership. Knowledge measurement activities involve developing and using metrics for assessing knowledge resources, knowledge processors, and knowledge processes. Knowledge control is concerned with ensuring sufficient quantity, quality, and security of knowledge resources, knowledge processors, and knowledge processes. Knowledge coordination is concerned with managing the dependencies among knowledge resources, knowledge processors, and knowledge processes (e.g., allocations, incentives, work designs). Knowledge leadership activities are directed toward fostering an atmosphere in an organization that is conducive to knowledge work (e.g., motivating, inspiring, storytelling, setting examples).

Various studies support the knowledge chain theory from standpoints of anecdotes (Holsapple & Singh, 2001) and field survey results (Holsapple & Singh, 2005; Holsapple & Jones, 2007). Together, the resource-based theory of the firm and the knowledge chain theory reinforce the claim that KM can affect firm performance. Here, we seek to build on this assertion by linking relative KM performance to relative firm performance in terms of various accounting measures. We do so by advancing a series of research hypotheses.

Research hypotheses – profit/cost ratios

Managing product, customer, and managerial knowledge all play important roles in firm performance. For example, product KM enables a firm to exploit existing research, development, and operational knowledge across multiple business units, and to reduce overall cost and time of developing and producing its products and services (Markides & Williamson, 1994; Laudon & Laudon, 1998). A firm that does not leverage such knowledge in new product design and development may suffer from high cost and excessive time delays (Laudon & Laudon, 1998; Tanriverdi, 2005). This is because new technologies and processes usually require not only considerable time to learn, but also major investments in research, design, engineering, and manufacturing (Nobeoka & Cusumano, 1997).

Customer KM enables a firm to exploit related customer knowledge across multiple business units, and thus provide better quality customer service and increase sales. Knowledge of customer needs, preferences, and buying behaviors makes it possible for a firm and its employees to provide fast, convenient, dependable, and consistent service (O'Brien & Marakas, 2005). Quality customer service contributes to sales because satisfied customers are more likely to return and even bring their friends with them (Parasuraman et al., 1985). Moreover, knowledge about expressed and latent customer needs of products and services can allow a firm to optimize cross-selling and up-selling. Cross-selling contributes to sales because it offers the firm an opportunity to push new products or services to current customers based on their past purchases (Tanriverdi, 2005). Similarly, up-selling may increase sales because it offers the firm an opportunity to sell a more substantial product than a new or existing customer is currently seeking (O'Brien & Marakas, 2005).

Finally, a firm can exploit its knowledge of related managerial practices, policies, and processes across multiple business units to more effectively govern the firm's business in the PAIR directions (Holsapple & Singh, 2001). By taking the advantage of existing managerial knowledge, productivity is enhanced by minimizing resource waste, avoiding repetition of mistakes, and preventing duplication of effort (Kamara et al., 2002). Such knowledge can also enhance agility, helping management respond quickly in an ever-changing business environment to cope with the growth and diversification of the firm's business activities. Thus, as with product and customer KM, managerial KM is a key performance driver (Tanriverdi, 2005).

It is not just a firm's product, customer, and managerial knowledge that differentiate it from competitors. At least as important as possession of unique knowledge, there are the firm's abilities and initiatives for leveraging its firm-specific knowledge resources. That is, a firm can design and execute its performance of knowledge chain activities in ways that differentiate it from competitors. Its processors and processes can be orchestrated via a mix of methods and technologies that are difficult for competitors to imitate in the near term, allowing the firm to enjoy relatively strong performance. Over time, firms can renew and refresh their knowledge resources and their approaches to knowledge chain activities. Such firms are likely to experience learning effects whereby they improve over time in their abilities for creating value and in their maintenance of a competitive edge.

Although it is challenging to accumulate and effectively deploy/leverage the three kinds of knowledge resources, being able to do so likely contributes to key aspects of firm performance, such as improved innovation, enhanced coordination of efforts, better decisional processes or outcomes, and rapid commercialization of new products (Gold et al., 2001; Holsapple & Singh, 2001). Ultimately, the contribution of KM should be reflected in a firm's bottom-line figures (Gold et al., 2001). Accordingly, we posit two main hypotheses that need to be investigated:

H1

Superior KM performance is positively related to higher profit ratios.

H2

Superior KM performance is positively related to lower cost ratios.

Backed by theory, these hypotheses contend that firms that are relatively successful in leveraging knowledge resources also enjoy superior financial performance in terms of:

• such profit ratios as return /assets, return/sales, or operating income/employee;
• such cost ratios as operating expense/sales or cost of goods sold/sales.

Values of these kinds of ratios are readily available for a large number of firms via Standard & Poor's COMPUSTAT database.

Figure 2 puts the hypotheses into perspective; they are concerned with the two shaded boxes and connecting arrow. Observe that KM performance is affected by an organization's KM initiatives, or more specifically by the design, execution, and evaluation of knowledge processes whose ingredients are knowledge resources, knowledge processors, and the ways in which knowledge chain activities are implemented. KM performance is also affected by various environmental forces and conditions, some of which may be quite unforeseen. The degree of success realized in leveraging knowledge and knowledge-processing resources to fulfill organizational purposes impacts firm performance. Two ways of assessing firm performance are in terms of competitive performance and financial performance. It seems likely that the former (as stipulated by the PAIR directions of knowledge chain theory) affects the latter. Be that as it may, our focus here is on the little-explored link between KM performance and financial performance. As Figure 2 indicates, we advance another hypothesis for this link beyond H₁ and H_2.

Figure 2.

Context of the link between KM performance and financial performance.

Full figure and legend (104K)

Research hypothesis – market/book value

Another way to measure the value of KM involves Tobin's q theory. Tobin's q is the ratio of the market value of a firm to the replacement value of its total assets (Tobin, 1978). According to theory, the long-run equilibrium market value of a firm should be equal to the replacement value of its total assets; and a q ratio larger than 1.0 indicates the unmeasured source of value attributed to the intangible assets. Thus, Tobin's q can be viewed as a measure of a firm's intangible assets. Moreover, it is a forward-looking measure for the reason that it provides a market-based view of investor expectations of a firm's future financial performance (Rao et al., 2004). Because KM resources comprise the main part of intangible assets (Strassmann, 1999; Bontis, 2001), a firm that is successful in leveraging knowledge resources in turn enjoys high value of intangible assets, and thus is likely to have a relatively large q ratio. Therefore, we hypothesize:

H3

Superior KM performance is positively related to higher Tobin's q ratio.

Tobin's q has been used in finance to study managerial performance (Lang & Litzenberger, 1989) and equity ownership (McConnell & Servaes, 1990), in marketing to study marketing communication's credibility (Luo & Donthu, 2006) and brand equity (Simon & Sullivan, 1993), and in information systems to study IT capability (Bharadwaj et al., 1999) and e-commerce competence (Saeed et al., 2005).

Chung & Pruitt (1994) introduced a method that is widely used to calculate Tobin's q ratio. Using 10 years of data, they have shown that their relatively easy method of calculating the q ratio explains at least 96.6% of the variance in Tobin's q obtained via Lindenberg and Ross's (1981) more theoretically correct model. The Chung and Pruitt technique is as follows:

where MVE the (Closing price of share at the end of the financial year) (Number of common shares outstanding); PS the liquidating value of the firm's outstanding preferred stock; Debt the (Current liabilities-Current assets)+(Book value of inventories)+(Long term debt); and TA is the book value of total assets.

Values for calculating these parameters are readily available for a large number of firms via the COMPUSTAT database.

Testing the hypotheses

There are several issues that need to be addressed in order to test the hypotheses. Here, we outline these issues and suggest approaches for dealing with them.

First, as noted above, measures for dependent variables can be readily obtained from Standard & Poor's COMPUSTAT for large numbers of firms in North America or globally. These measures are consistently applied across the included firms. However, such measures are not readily available for private firms and may be difficult to obtain for firms not listed in tracked equity markets. Moreover, the dependent variables are not applicable to not-for-profit organizations. Thus, it makes sense to begin investigating the hypotheses with respect to the tens of thousands of firms tracked by COMPUSTAT, and with the understanding that results may not be applicable to privately held firms or not-for-profit organizations. Alternative financial databases such as Thomson's Worldscope may also be considered as a source of dependent variable values.

Second, with respect to the independent variable, we need a way to distinguish between relative degrees of KM performance – particularly, regarding a threshold for 'superior' KM performance. Doing so requires the identification of criteria for judging a firm's KM performance, an approach to scoring firms with respect to these criteria, a protocol for aggregating a firm's scores on the criteria, and a decision rule for designating those firms that exhibit superior KM performance (i.e., exceeding some aggregate score level). Ideally, the criteria should span the important dimensions of KM performance as recognized in prior research. The criteria may be established by the researcher investigating the hypotheses, or by an independent party. Similarly, scoring firms on the various criteria may be done by the researcher, or by a reputable independent party. The latter is preferable. To the extent that objective measures of KM performance are available, they may be preferable to subjective judgments by KM experts. However, in the near term, interpretive scoring is likely to be the most feasible route. The simplest protocol for aggregating a firm's scores is to evenly weight them; deviations from such weighting need to be justified. Examples of a decision rule are (a) setting a cut-off point above which aggregate scores indicate superior KM performance, or (b) setting a cut-off percentile such that firms with aggregate scores in this top percentile are deemed to demonstrate superior KM performance.

Third, there is the issue of the time frame for the investigation, including possible time lags. The researcher must determine the period over which KM performance is to be judged and use dependent variable measures that are consistent with this time period. It may be that a firm's superior KM performance does not immediately manifest as superior financial results, but takes time to have a noticeable effect. On the other hand, there can be delays in recognizing superiority of KM performance, such that the financial effects of KM initiatives are already being realized. A window for dependent variable measurement that brackets the period used for judging KM performance may be appropriate.

A fourth issue involves the selection of a sample of firms from among the tens of thousands of candidates for which financial performance data are available and about which KM performance evaluations might be made. A random sampling strategy may be inappropriate because care must be taken to ensure that firms with superior KM performance are included.

Moreover, care must be taken to eliminate confounding effects that extraneous variables (e.g., firm size) or environmental forces (e.g., conditions of an industry segment) could have on a firm's financial performance. The matched sample comparison group methodology is helpful for testing research hypotheses in such cases (Bharadwaj, 2000). Furthermore, some firms may be highly diversified, while others are not. Tests should be conducted to ensure comparable degrees of diversification. This can be done with the entropy measure of related diversification (Robins & Wiersema, 1995; Choi & Russell, 2005).

Fifth, validity of these hypotheses tests is threatened by a financial performance halo effect. This refers to a bias in which judgments about superior KM performance are influenced by the firms' past financial performance. If such a bias exists, then an alternative explanation for a firm being recognized as a superior KM performer is that the firm has outstanding past financial performance. One way to address this threat is the logistic regression analysis approach developed by Brown and Perry (1994) to examine if the financial performance halo effect exists in the collected data. If a halo effect is found in the data, then its use for testing the hypotheses is highly suspect.

Provided the foregoing issues are successfully addressed, the way is clear for testing hypotheses H_1, H_2, and H_3. Doing so will tell us whether there is indeed a link between superior KM performance and superior financial performance. Finding such a link would be a substantial milestone for the KM field. It would greatly buttress the proposition that KM initiatives can affect firm performance. It would help practitioners make the case to top management for supporting KM in their organizations. It would justify further research related to better understanding the linkage and the surrounding context illustrated in Figure 2.

Conclusion

Broadly, this study contributes to the growing body of literature that takes a resource-based view of KM. To set the stage for this paper, we offer a brief retrospective analysis of the KM field, arguing that KM is a phenomenon of growing importance to modern organizations. Then, drawing on the resource-based theory of the firm, we call attention to a missing link: the relationship between a firm's KM performance and that firm's financial performance. We develop three hypotheses about this link and situate them within a contextual framework for understanding how KM may be appropriately viewed as a key driver of firm performance. Important issues involved in testing the hypotheses are discussed, including guidance on how to address these hypotheses. Future research will undertake a testing of the hypotheses. If the hypothesized associations are found to exist, then yet other research can explore why the link exists, how it can be enhanced, and the direction of causality (if any) in the associations.

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About the authors

Clyde W. Holsapple holds the University of Kentucky's Rosenthal Endowed Chair in Management Information Systems. His research activity has concentrated on the subjects of knowledge management, decision support systems, intelligent systems, and electronic commerce. Professor Holsapple's publication credits include 15 books and over 200 scholarly articles in journals and books. His research has been published in such journals as Decision Sciences, Decision Support Systems, Operations Research, Communications of the ACM, The Computer Journal, Policy Sciences, Organization Science, Human Communication Research, Group Decision and Negotiation, Computer-Supported Cooperative Work, Journal of Strategic Information Systems, IEEE Transactions on Systems, Man, and Cybernetics, Expert Systems with Applications, IEEE Expert, Expert Systems, Knowledge Acquisition, Journal of Knowledge Management, Knowledge and Policy, Knowledge and Process Management, and International Journal of Knowledge Management. He is editor of the basic reference works Handbook on Knowledge Management (Springer-Verlag, 2003) and Handbook on Decision Support Systems (Springer-Verlag, 2008). He serves as Editor-in-Chief of the Journal of Organizational Computing and Electronic Commerce.

Jiming Wu is a doctoral candidate in Decision Science and Information Systems at the University of Kentucky. His research interests include knowledge management, hedonic information technology and IT adoption, and computer and network security. His research has been accepted for publication in such journals as Database, Journal of Electronic Commerce Research, and the Journal of International Technology and Information Management.