by
Rajiv Sabherwal
University of Missouri Curators Professor
Information Systems Area
CCB 206, College of Business Administration
University of Missouri, St. Louis
8001 Natural Bridge Road
St. Louis, MO 63121
Phone: (314) 516-6490
Fax: (314) 516-6827
E-mail: sabherwal@umsl.edu and
Sanjiv Sabherwal
Assistant Professor of Finance
College of Business Administration
University of Rhode Island
7 Lippitt Road
Kingston, RI 02881
Phone: (401) 874-4324
Fax: (401) 874-4312
E-mail: sabherwal@uri.edu
September 2003
*: We are grateful to Vandita Prabhu, Danai Tsotra, Chris Kang, and Anosh Wadia, for the assistance they provided during the collection of the secondary data used in this paper.
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"The power of intellectual capital is the ability to breed ideas that ignite value."
The above statement, which appeared in bold in the debut annual report of J.P. Morgan Chase, illustrates the importance of knowledge management (KM) in current business organizations (Stewart
2001). Knowledge is recognized as a key source of sustainable competitive advantage (Conner and
Prahalad 1996; Hansen, Nohria, and Tierney 1999). KM contributes to the performance of firms at several levels, including employees, processes, and products (Dyer and McDonnough 2001). Despite this recognition of the importance of KM, assessment of its bottom-line impacts is hindered by the difficulties in measuring the tangible and intangible benefits of KM efforts and then reliably attributing them to the KM efforts. This paper addresses this limitation by examining the impact of KM on the firm’s market value.
This goal is pursued by using an event study to assess the impact a KM announcement has on the market value of the firm.
The impacts of KM efforts on organizations would differ depending on factors such as the business strategy of the firm, the firm’s products, and so on (Spender 1996). Recognizing the importance of business strategy, we examine how the firm’s business strategy affects the impact KM has on firm value (Gupta and
Govindarajan 2000). Thus, instead of following the universalistic view that all KM announcements would contribute to firm value, this paper takes a contingency theoretic view, suggesting that the impact on firm value depends on whether the announced KM effort is appropriate for, or aligned with, the firm’s business strategy. Business strategy is examined in terms of Miles and Snow’s (1978) popular classification of
Defenders, Prospectors, and Analyzers. In viewing the alignment between the nature of the KM effort and the business strategy, this paper employs a theory-driven, multivariate approach, which utilizes prior research on organizational learning, knowledge management, and business strategy.
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The business strategy might also influence the extent to which there is a leakage of information about the KM effort before its announcement. Therefore, we examine the effect of business strategy on the timing of the impact on firm performance, i.e., the change in firm value in the two days immediately prior to the announcement, and in the two days immediately following the announcement. Two specific research questions are thus pursued in this paper:
1. Does the level of alignment between the firm’s business strategy and the nature of the KM announcement affect the firm value?
2. Does the firm’s business strategy affect the extent to which the impact on the firm value occurs before or after the announcement?
The next section utilizes prior literature on organizational learning and knowledge management to develop the theoretical background for the study. Section 3 examines the relationships between business strategy and knowledge management, and presents the research hypotheses. Section 4 describes the data collection and data analysis methods, followed by Section 5, which presents the results. Finally, Section 6 discusses the paper’s implications
Organization learning literature recognizes the importance of cognitive development (Argyris, 1977;
Stata, 1989), while differing with respect to whether behavioral change is also essential for learning to occur (Weick 1991). Concepts such as organizational memory (Argyris and Schon, 1978; Huber, 1991) and organizational mind (Sandelands and Stablein, 1987) indicate the value of considering learning as an organizational-level phenomenon. Although organizational learning frequently occurs through individuals
(Simon, 1991), it is not simply the cumulative result of each member's learning (Fiol and Lyles, 1985) and its eventual impact is on the organization (Slater and Narver, 1995;).
Organizations learn through the positive and negative outcomes their own members encounter for their behaviors (Argyris and Schon, 1978). In addition, organizations learn from other organizations, by
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interacting with them, using consultants, scanning the environment, and hiring new employees (Huber,
1991). This is called vicarious learning , in contrast to the more traditional experiential learning (Ginter and
Whyte, 1982; Huber, 1991). Organizational learning literature also distinguishes between single-loop learning , where the actions that produce negative outcomes are identified and either avoided or modified, without questioning the organizational norms, policies and other governing variables that lead to the problematic actions, and double-loop learning , where the organization responds to the undesirable situation by questioning and changing the governing variables that caused the actions taken (Argyris, 1977, 1982;
Argyris and Schon, 1978; Fiol and Lyles, 1985). Organizations that only use single-loop learning, while excluding double-loop learning, are likely to get trapped in suboptimal stability (March, 1991). By contrast, organizations that only use double-loop learning, while excluding single-loop learning, are "likely to find that they suffer the costs of experimentation without gaining many of its benefits" (March, 1991; p. 71).
According to Virany, Tushman, and Romanelli (1992), single-loop learning is more likely in stable, convergence situations while double-loop learning is more common during periods of reorientation.
Cognitive development, organizational impacts, and the role of individuals are also central to the literature on knowledge management (Kogut and Zander, 1992; Conner and Prahalad, 1996; Grant, 1996a,
1996b). The interplay between individual-level learning and organizational impacts is evident in the knowledge management literature. According to the knowledge-based theory of the firm, firms are superior to markets in their ability to integrate knowledge across individuals (e.g., Conner and Prahalad, 1996).
Indeed, this theory suggests that the primary reason for the existence of the firm is its superior ability to integrate multiple knowledge streams, for the application of existing knowledge to tasks as well as for the creation of new knowledge (Conner, 1991; Kogut and Zander, 1992; Conner and Prahalad, 1996; Grant,
1996a, 1996b). Knowledge is created and stored by individuals and the firm essentially acts as an institution for knowledge integration (Grant, 1996a, 1996b). Thus, according to the knowledge-based theory of the firm, knowledge starts with the individual, and the firm needs to integrate this knowledge using
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a combination of mechanisms and technology (Sabherwal and Becerra-Fernandez 2003). According to
Nonaka and his colleagues (Nonaka, 1994; Nonaka and Konno, 1998; Nonaka and Takeuchi, 1995), an organization cannot create knowledge by itself; instead, individual knowledge is the basis of organizational knowledge creation. Nonaka and Takeuchi (1995) differentiate between individual, group, organizational, and interorganizational levels in examining knowledge management.
Prior literature on organizational learning and KM has made several valuable contributions that are directly relevant in the context of this study. These are discussed in the remainder of this section.
The literature on organizational learning and knowledge management forms the basis for the definitions guiding this study. Based on this literature, knowledge is defined as the set of justified beliefs that enhance an entity’s capability for effective action (Nonaka, 1994; Alavi and Leidner 2001). It incorporates both explicit knowledge, which can be expressed in numbers and words and shared formally and systematically in the form of data, specifications, manuals, etc., and tacit knowledge, which includes insights, intuitions, and hunches, is difficult to express and formalize, and therefore difficult to share (e.g.,
Nonaka, 1994; Polanyi, 1996; Davenport and Prusak, 1998).
Based on the prior literature, knowledge management is defined as doing what is needed to get the most out of knowledge resources (Armbrecht et al., 2001; Sabherwal and Becerra-Fernandez 2003). It focuses on organizing and making available important knowledge, wherever and whenever it is needed.
The traditional emphasis in KM has been on explicit knowledge, but, increasingly, KM has also incorporated managing important tacit knowledge (Alavi and Leidner 2001).
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The literature on organizational learning and KM has also identified four important aspects 1 of the
KM effort: KM level, KM process, KM means, and knowledge source (Conner and Prahalad 1996; Grant
1996a, 1996b; Nonaka and Takeuchi 1995; Alavi and Leidner, 2001). These aspects reflect the hierarchical level at which the KM effort was focused (a sub-unit, the firm, or across firms), the primary goal with respect to KM (knowledge utilization, sharing, or creation), the means used to support KM (structure, technology, or both), and the source of knowledge (the firm, its partners, or both). They are discussed below.
KM level concerns the hierarchical grouping of individuals upon which the KM effort described in the announcement is focused. As discussed above, organizational learning and KM literature recognize that, ranging from the micro to the macro level, individuals who create, share or utilize knowledge are: (a) the individuals working in an organizational unit (which could be team, a functional area, a department, or a geographic location); (b) individuals working across organizational units; or (c) individuals working in the organization as well as individuals at the firm’s partners (i.e., its customers or suppliers). For example,
Grandori describes the role of KM at the interorganizational level as follows:
“Communities of shared knowledge do exist across firm boundaries … We would also find that knowledge exchanges are more intense in some cross-boundary relations (e.g., in co-makership subcontracting relations) than in some internal interunit relations (e.g., relations between divisions in pure M-forms)” (Grandori and Kogut 2002, p. 224).
Prior literature also recognizes that the KM effort may focus on one of several different KM processes. The KM process may involve the sharing, utilization, or creation of knowledge (Argote,
McEvily, and Reagans 2003). In knowledge utilization , the party that applies knowledge does not need to comprehend it (Conner and Prahalad 1996). All that is needed is for the knowledge to be somehow used
1 These aspects are not comprehensive, but they are the ones that made theoretical sense and could also be clearly identified from the announcements. One other KM attribute -- whether the knowledge being managed is tacit or explicit, and – was considered but later discarded, because clear theoretical links between this aspect and the business strategy could not be established and because almost all the announced KM efforts involved the management of both tacit and explicit knowledge.
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to guide decisions and actions. Therefore, knowledge utilization benefits from routines and direction, which do not involve the actual transfer or exchange of knowledge between the concerned individuals (Grant,
1996a). Knowledge sharing refers to the transfer of knowledge to other individuals. What is shared here is knowledge rather than recommendations based on the knowledge. Moreover, knowledge sharing may take place across individuals as well as across groups, departments, or organizations (Alavi and Leidner 2001).
Knowledge sharing occurs through exchange, externalization and internalization, and through training
(Grant 1996b). Finally, knowledge creation refers to the development of new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. The creation of explicit knowledge relies most directly on combination, while the creation of tacit knowledge relies most directly on socialization. Knowledge may also be created by hiring new employees or forming external alliances.
The above KM processes may be supported using several KM means. Structural arrangements and technologies are considered the primary means used to enable KM (Hansen et al. 1999; Earl 2001).
KM structures include reorganization, co-operative projects across departments, employee rotation across departments, and creation of KM-related structures (e.g., KM center) and positions (e.g., Chief Knowledge
Officer). Examples of KM technologies include case-based reasoning systems, electronic discussion groups, computer-based simulations, databases, decision support systems, expert systems, expertise locator systems, video-conferencing, and best practices databases. For example, Malhotra et al. (2001) use the case of Boeing-Rocketdyne to show how IT can help inter-organizational teams to develop radically new products. In examining the KM means, we also consider the possibility of using a combination of both structures and technologies, such as through the establishment of a knowledge management center that utilizes information technology but also provides a forum for employees to get together and share ideas.
Thus, we distinguish among three situations: (a) structure; (b) technology; and (c) both.
Prior literature indicates that a KM effort may focus on external or internal sources of knowledge, and sometimes, both kinds of sources. Knowledge source reflects where the knowledge originates from in
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the KM effort described in the announcement. Recognizing the possibility of a KM effort focusing on the acquisition of knowledge from internal and external sources, this aspect is examined in terms of three kinds of knowledge sources: (a) the focal firm itself; (b) a partner (customer or supplier) of the focal firm;
(c) both the focal firm and its partner. Prior literature suggests that a firm’s members are more likely to value knowledge from external sources (Menon and Pfeffer 2003). Several benefits of seeking knowledge from external sources have been identified, including greater likelihood of getting the right answer to a given question (Friedkin 1982), greater number of alternative solutions (Constant et al. 1996), and more timely access to needed knowledge (Granovetter 1973).
KM can contribute to organizational performance at several levels: employees, processes, products, and the overall organization (Hansen et al. 1999; Senge 1990; Davenport and Prusak 1998;
Argote and Ingram 2000). KM facilitates the learning of the organization’s employees from each other and from external sources, about solutions to business problems that worked in the past, as well as those that did not. KM also provides employees with solutions to problems if the same problems were encountered earlier (Conner and Prahalad 1996). KM can help enhance innovativeness, effectiveness, and efficiency of organizational processes (Senge 1990; Davenport and Prusak 1998; Argote and Ingram 2000). KM can also contribute to a firm by enabling new products or improved products that provide a significant additional value as compared to earlier products, or by facilitating products that are inherently knowledge-based.
Thus, KM can impact organizational performance at the level of employees, processes, and products. Despite the intuitively obvious nature of these organizational impacts of KM, it is difficult to assess these impacts. The difficulty arises in part from problems in estimating KM’s costs and benefits.
One way of addressing this difficulty is to perform an event study, which examines the impact a certain kind of public announcement about a firm has on that firm’s stock-market value. Event studies have been used to examine the impacts of a variety of announcements, including joint-venture formation (Koh and
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Venkatraman 1991), IT investment decisions (Dos Santos et al. 1993; Im, Dow, and Grover 2001), the appointment of a Chief Information Officer (Chatterjee, Vernon, and Zmud, 2000), electronic commerce initiatives (Subramani and Walden 2001), on the value of a firm. Considering the above impacts of KM, a public announcement about a firm’s KM efforts may be expected to impact firm value.
Organizational learning and knowledge management are often aimed at improving organizational performance (Lee and Courtney 1989), but they do not always achieve this objective (Huber, 1991; March,
1991; March et al., 1991). Possible reasons for the lack of a positive impact on organizational performance include the new knowledge, which is believed to be true, being actually (i.e., objectively) incorrect (Nonaka,
1994), and changes in the contextual conditions surrounding the behavior.
"… learning does not always increase the learner's effectiveness, or even potential effectiveness. Learning does not always lead to veridical knowledge.
Sample data are not always representative and new findings sometimes overturn what was previously "known to be true." Entities can correctly learn that which is incorrect" (Huber, 1991; p. 89).
"Learning processes sometimes result in confusion and mistakes" (March et al.,
1991; p. 10).
Therefore, instead of following the universalistic view that all KM efforts would contribute to firm performance, this paper takes a contingency theoretic view, suggesting that the impact of a KM effort would depend on the characteristics of the firm. A similar contingency approach is implicit in some prior KM research (Argote et al. 2003; Das 2003). Moreover, the focus here is one specific dimension of the firm, i.e., its business strategy. The concept of business strategy, the rationale for considering it as important in examining the impact of a KM effort on firm value, and the specific nature of the relationship among business strategy, attributes of the KM effort, and firm value, are developed in the next section.
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Business strategy and organizational knowledge management affect each other (Ginter and Whyte
1982; Fiol and Lyles 1985). Argyris and Schon (1978) describe “modes of organizational knowing” (p. 317) as affecting how executives in the organization examine problems, model situations, and interpret events, and thereby influence the strategies they formulate. Executives learn from prior experiences and use this learning and understanding of past performance levels to adjust future business strategies (Lant and
Montgomery 1987). The knowledge base built through such organizational learning serves as the foundation for business strategy by influencing the way in which organizations identify strategies and make decisions (Duncan and Weiss 1979; Lant and Montgomery 1987; Ribbens 1997).
In addition to this affect of KM of business strategy, business strategy affects KM as well. Daft and
Weick (1984) propose that business strategy affects the organization’s openness to learning as well as the kind of information it acquires. Business strategy also influences KM through its effect on organization structure (Gold, Malhotra, and Segars 2001). According to Ribbens (1997): “The strategies, and their related structures and organization culture, provide the parameters for future organizational learning” (p.
62). Strategic decision-making models have been found to influence the nature of organizational learning systems (Shrivastava 1983).
Thus, business strategy and KM mutually affect each other (Ginter and Whyte 1982; Ribbens
1997). Although “a clear cause and effect relationship cannot be distinguished” (Ribbens 1997; p. 61), prior research clearly indicates the importance of mutually aligning business strategy and KM efforts (Earl 2001).
Also implicit in the literature described above is the notion that alignment between business strategy and
KM helps enhance organizational performance. Greater alignment between an organization's business strategy and its KM efforts indicates that these efforts are targeted on areas that are critical to its success.
Consequently, KM may be expected to contribute to the business performance to a greater extent than in
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organizations where they focuses on less important areas (Earl 2001). Alternatively, greater alignment between business strategy and KM indicates that the organization is pursuing the business strategy most suited for its KM capability (Gold et al. 2001). Therefore, when a firm announces a new KM effort, the greater the alignment between this KM effort and the firm’s business strategy, the more favorable the expected reaction from the stock market.
Hypothesis 1: Ceteris paribus , the cumulative abnormal stock market return (in the 5-day event window) due to a KM announcement is positively associated with the alignment between the firm’s business strategy and the attributes of the
KM initiative announced.
Business strategy is viewed in this paper in term of Miles and Snow’s (1978) typology of
Defenders, Analyzers, and Prospectors 2 . This typology of business strategies parsimoniously captures strategic differences in industry-independent terms (Hambrick 1983). Dent (1990) concludes that this typology provides the richest portrayal of business strategies. It has been empirically examined in numerous studies. Zahra and Pierce (1990) examined 17 empirical investigations of Miles and Snow’s business strategies, and several later publications have further examined this typology (e.g., Gilbert 1985;
Karimi et al. 1996a, 1996b; Miles and Snow 1996). These empirical studies provide a strong foundation for identifying the important characteristics of each strategy type. Moreover, several studies (Delery and Doty
1996; Doty et al. 1993; Segev 1989; Sabherwal and Chan 2001) have identified the theoretical profiles for
Miles and Snow’s business strategies.
2 : Miles and Snow (1978) also described a fourth type of organization (Reactors), but viewed it as one that either lacks a viable strategy or is in transition between two of the other strategies. We therefore decided to exclude Reactors, as done in most empirical studies on Miles and Snow’s typologies (e.g., Delery and Doty 1996; Hambrick 1981, 1983; Shortell and Zajac
1990). Doty et al. (1993) compared the effectiveness of the typology with and without Reactors, and found empirical support for excluding Reactors. Moreover, Daft and Weick (1984, p. 292) have argued that “the reactor strategy is not really a strategy at all,” Zahra and Pearce (1990, p. 752) have suggested that “reactors do not follow a conscious strategy,” and Miles and Snow have also excluded Reactors in their more recent (1984, 1996) descriptions of the typology.
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Based on a comprehensive review of this literature, especially the prior theoretical profiles of the three strategies and the books by Miles and Snow (1978, 1996), we identified 13 key attributes of the three business strategies. Seven of these attributes are used to classify the sample firms into Defenders,
Analyzers, and Prospectors. They include scope, product-market dynamism, firm-level uncertainty, liquidity, asset efficiency, fixed asset intensity, and long-range financial liability. The use of seven business strategy attributes to classify firms’ business strategies addresses Zahra and Pearce’s (1990) concern that prior attempts to classify firms in terms of their business strategies “are often based on very few criteria” (p. 755).
Prior research suggests that the seven business strategy variables differ across all three strategies. So the ideals for these variables are set at low, medium, and high for the three strategies. These ideal values for the three strategies, and the literature support for them, are discussed below and summarized in Table 1.
---------- Insert Table 1 about here ----------
The Defender is the most stable of the three. Stressing operational efficiency and economies of scale, it has greater asset efficiency (McDaniel and Kolari 1987; Segev 1989; Smith et al. 1989; Langerak et al. 1999) and fixed-asset intensity (Hambrick 1983; Segev 1989) than the other strategic types, with investments in highly cost-efficient but few core technologies (Doty et al. 1993). The Defender operates in a broad but stable business domain (Smith et al., 1989), and encounters least uncertainty (Doty et al. 1993).
Based on ratings by the original developers of this classification scheme (Miles et al. 1978), Doty et al.
(1993) characterize Defenders as being the most broad in scope, and the lowest in product-market dynamism. Due to its greater fixed-asset intensity, the Defender’s long-term financial liability is greater than the Prospector’s, but due to its more stable products and markets, its long-term financial liability is lower than the Analyzer’s (Segev 1989). However, because it does not pursue quick opportunities, the Defender has less need for liquidity than both Analyzers and Prospectors (Segev 1989).
The Prospector is very different. It continuously seeks new product-market opportunities (Doty et al. 1993), and is the creator of change in its market (Langerak et al. 1999). It seeks flexibility and
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innovativeness in technology, and so has lower fixed asset intensity than other firms (Hambrick 1983;
Segev 1989). But this desire flexibility requires more liquidity and greater long-term financial liability (Segev
1989; Smith et al. 1989), and reduces control and operational efficiency (McDaniel and Kolari 1987;
Langerak et al. 1999). The Prospector has also been found to encounter the greatest uncertainty, followed by the Analyzer and then the Defender (Doty et al. 1993).
The Analyzer shares some characteristics of both other strategies. Unlike the Defender, it does not eschew change, but unlike the Prospector, it does not create change. Instead, combining the strengths of the other two types, it seeks to simultaneously minimize risk while maximizing opportunities for growth
(Miles and Snow 1978), and lies between the Defender and the Prospector in product-market dynamism
(Doty et al. 1993). Striving to address the conflicting demands of efficiency and innovation, the Analyzer maintains the greatest long-term financial strength, i.e., the lowest long-term financial liability (Segev 1989).
But in terms of liquidity, asset efficiency, and fixed asset intensity, Analyzers lie between Defenders and
Prospectors (Hambrick 1983; McDaniel and Kolari 1987; Segev 1989; Langerak et al. 1999).
In addition to the seven variables used to classify firms into Defenders, Analyzers, and
Prospectors, six variables – research and development (R&D) intensity, decentralization, firm size, and correlation between firm and market returns -- were used to validate the classification. This addresses
Zahra and Pearce’s (1990) concern that “very limited attention has been given to validation of group classification” (p. 755). Three of these six variables (executives’ age and tenure, and R&D intensity) are attributes of business strategy, but they were used only to validate the classification because prior literature only provides insights into their expected differences for one pair of business strategies but has produced either inconsistent or insufficient findings with respect to the third strategy. The other variables used to validate business strategies reflect the context (decentralization, firm size) or performance (correlation between firm and market returns) rather than business strategy.
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The lower portion of Table 1 summarizes the expected differences across the three business strategies in terms of the six validation variables. The executives managing the Prospector are younger and have lower experience than the executives managing the Defender, although no significant difference has been found between Analyzers and the other strategies in these aspects (Smith et al. 1989). The
Prospector has been found to have greater R&D intensity than the Defender (Hambrick 1983), although its differences with the Analyzer in R&D intensity have not been examined. The Defender is considered larger but less decentralized than the Prospector, with the Analyzer being in between the two in both aspects
(Doty et al. 1993). Moreover, due to the stable nature of Defenders, returns on their stock have lower correlation with the returns in overall stock market, as compared to the stocks of both Analyzers and
Prospectors (Snow and Hrebiniak 1980; Langerak et al. 1999). No such difference between Analyzers and
Prospectors are expected based on prior research.
Prior literature on the Defender, Analyzer, and Prospector strategies provides insights into the nature of KM effort most appropriate for each business strategy. The conclusions from this literature are described below and summarized in Table 2.
---------- Insert Table 2 about here ----------
KM Level: Being stable in their products and markets (Segev 1989), Defenders are low in product development or innovativeness (Sabherwal and Chan 2001). They do not actively seek new opportunities, and instead emphasize efficiency through routinization (Miles at al. 1978; Miles and Snow 1978; Doty et al.
1993). Therefore, they benefit little from knowledge creation, but considerably from knowledge utilization, which allows them to benefit from the lessons learnt from prior experiences. Knowledge utilization, through direction and routines (Connor and Prahalad 1996) offers efficiency benefits by supporting individuals to address problems based on solutions indicated by those possessing the knowledge instead of the more costly processes of creating or sharing knowledge. The circumstances needed for knowledge utilization –
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e.g., the knowledge user’s trust in the individual providing direction, and relevance of prior knowledge to current circumstances – are more likely in the Defender. Prospectors, in contrast, seek new opportunities and frequently develop new products (Hambrick 1983), and would therefore benefit the most from knowledge creation. The rapid change in Prospectors also considerably limits the value of knowledge utilization by reducing the potential applicability of prior knowledge due to changes circumstances.
Moreover, Defenders have mechanistic and formalized structures (Sabherwal et al. 2001), which implies that knowledge sharing would offer greater benefits than in Prospectors, which are organic and decentralized, with high levels of specialization (Doty et al. 1993). However, knowledge sharing would be most useful in Analyzers, which rely on a hybrid structure (Sabherwal et al. 2001) with “higher levels of interdependence than either prospectors or the defenders” (Doty et al. 1993; p. 1226). Based on the above, the ideal values for KM process for the three business strategies were set as shown in Table 2, which also indicates the ideal values for the other three KM attributes. This is consistent with Nonaka and Konno’s
(1998) view that innovative organizations (i.e., Prospectors) typically use combination and socialization to develop new concepts that are created and adopted at both organizational and inter-organizational levels.
KM Process: Being stable in their products and markets (Segev 1989), Defenders are low in product development or innovativeness (Sabherwal and Chan 2001). They do not actively seek new opportunities, and instead emphasize efficiency through routinization (Miles at al. 1978; Miles and Snow
1978; Doty et al. 1993). Therefore, they benefit little from knowledge creation, but considerably from knowledge utilization, which allows them to benefit from the lessons learnt from prior experiences.
Knowledge utilization, through direction and routines (Connor and Prahalad 1996) offers efficiency benefits by supporting individuals to address problems based on solutions indicated by those possessing the knowledge instead of the more costly processes of creating or sharing knowledge. The circumstances needed for knowledge utilization – e.g., the knowledge user’s trust in the individual providing direction, and relevance of prior knowledge to current circumstances – are more likely in the Defender. Prospectors, in
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contrast, are innovative (Langerak et al. 1999), continually seek new opportunities (Miles and Snow 1978), and frequently develop new products (Hambrick 1983). They would therefore benefit the most from knowledge creation. The rapid change in Prospectors also considerably limits the value of knowledge utilization by reducing the potential applicability of prior knowledge due to changes circumstances.
Moreover, Defenders have mechanistic and formalized structures (Sabherwal et al. 2001), which implies that knowledge sharing would offer greater benefits than in Prospectors, which are decentralized, with high levels of specialization (Doty et al. 1993). However, knowledge sharing would be most useful in Analyzers, which rely on a hybrid structure (Sabherwal et al. 2001) with “higher levels of interdependence than either prospectors or the defenders” (Doty et al. 1993; p. 1226). Therefore, knowledge utilization, sharing, and creation processes would be ideal for Defenders, Analyzers, and Prospectors, respectively.
The alignment between actual a firm’s KM process and its business strategy would be high when the KM process is consistent with these ideals, e.g., when a Defender firm aims to increase knowledge utilization. Alignment would be low when a Defender firm’s KM process is knowledge creation (due to
Defenders’ non-innovative nature) and when a Prospector firm’s KM process is knowledge utilization (due to the change encountered by Prospectors’). When either Defenders or Prospectors aim to increase knowledge sharing, alignment would be medium. This is consistent with the arguments above as well as the view of Analyzers as being midway between Defenders and Prospectors (Doty et al. 1993), as also seen in alignment being medium when Analyzers seek to increase knowledge utilization or creation. The alignment between the process of the KM effort and the business strategy was therefore set as shown in
Table 2, which also indicates the alignments for the other two KM attributes.
KM Means: Having a highly formalized (Doty et al. 1993) and mechanistic (Sabherwal et al. 2001) structure, Defenders would benefit most from KM efforts that focus on organization structure. The stable nature of Defenders also implies that KM benefits from structural changes would be more long-term in these organizations as compared to Prospectors, which encounter greater change and have an organic
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structure 3 . In contrast, KM technologies might enhance the flexibility of Prospectors, and technology-based
KM efforts would be more beneficial to them. This is consistent with Segev’s (1989) finding Prospectors to be much higher than Defenders in number of technologies, with Analyzers ranking in between them. Also,
Sabherwal et al. (2001) argue that Defenders use information technology only to keep costs down, while
Prospectors use them for differentiation, growth, alliance, and innovation. Combining features of Defenders and Prospectors, Analyzers would benefit from KM structures as well as KM technologies, but they would benefit most from KM efforts that effectively combine structural changes and KM technologies. Therefore, the ideal KM means for Defenders, Prospectors, and Analyzers would be structure, technology, and both structure and technology, respectively.
Knowledge Source: As discussed earlier, Prospectors rely much more than Defenders on information from external sources, whereas Defenders rely mainly on internal information, with Analyzers making use of both external and internal information. Prospectors’ employees are also likely to obtain greater help from individuals in other organizations due to their greater professional affiliations than
Defenders’ employees, with Analyzers again being between Prospectors and Defenders (Segev 1989).
Prospectors have also been characterized as learning from other organizations’ experiences to create new strategies, in contrast to Defenders, which rely on the organization’s own previous decisions and the associated outcomes 4 (Ribbens 1997). Therefore, knowledge source being internal, external, and both internal and external would be ideal for Defenders, Prospectors, and Analyzers, respectively.
The above conclusions regarding the alignment between the nature of KM announcement and business strategy are summarized in Table 2. As may be seen from the Table, the alignment patterns are
3
4
: Using a 7-point scale, Segev (1989) found Prospectors, Analyzers, and Defenders to average 6.77, 3.73, and 1.00 in number of technologies.
: Ribbens (1997) views organizations as “abstract,” which learn from others, or “concrete,” which learn from themselves, and considers Prospectors as abstract (specifically, abstract-random) and Defenders as concrete (specifically, concretesequential).
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similar across the three KM attributes, with high, medium, and low levels of alignment with Defender strategy being accompanied by medium, high, and medium levels of alignment, respectively, with Analyzer strategy, and low, medium, and high levels of alignment with Prospector strategy. Thus, in terms of each
KM attribute, the KM announcements for Defenders and Prospectors would have low, medium, or high levels of alignment, but the announcements for Analyzers would only have medium or high levels of alignment. This is consistent with the view of Analyzers as being midway between Defenders and
Prospectors, so that the Analyzer would have medium alignment with a KM attribute that is ideal for either
Defender or Prospector. However, this means that for Analyzers, the mean of alignment would be higher, and the standard deviation lower, as compared to Defenders and Prospectors.
The alignment of announced KM effort with business strategy is argued to affect the abnormal stock market return associated with the announcement for firms pursuing any of the three business strategies. However, inherent differences among the three business strategies imply that these firms may differ in the levels of alignment. For example, Segev (1989) included two kinds of analysis, internal and external, and found only the Analyzer to be high in both internal and external analyses. Sabherwal and
Chan (2001) also considered only the Analyzers to be high in analysis, representing "the organization’s overall problem-solving behavior, including the tendency to search deeper for the roots of problems, and to generate the best possible solution alternatives” (Venkatraman 1989a; p. 948). Moreover, Miles et al.
(1978) viewed planning at Analyzers, Defenders, and Prospectors, as both comprehensive and intensive, intensive but not comprehensive, and comprehensive but not intensive, respectively. Thus, the Analyzer may be expected to make the greatest use of knowledge. It would therefore seek greater alignment between KM strategy and business strategy, and would also be more likely to obtain, as compared to
Defenders and Prospectors, greater benefits from a KM effort.
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As compared to Analyzers, Defenders and Prospectors would make less efforts to seek alignment between KM efforts and business strategy. Defenders would consider KM strategy to be less important and
KM efforts as compromising efficiency. This might be because the Defenders’ stable nature enables the gradual development of an administrative structure wherein large-scale KM efforts are not needed; Miles et al. (1978) seem to agree: “(the Defender’s) administrative system is ideally suited for generating and maintaining efficiency” (p. 551). Prospectors would find it difficult to align business and KM strategies due to the dynamic nature of their industry environment and their lack of formalization (Doty et al. 1993). They would also be somewhat reluctant to make the long-term investments that may be needed in KM efforts.
Furthermore, as compared to Analyzers, when a Defender or Prospector announces a KM effort, the stock market reaction is likely to be less positive, due to the perceived compromise in organizational efficiency or the perception that the benefits would be short-term and uncertain, respectively. Thus, as compared to
Defenders and Prospectors, Analyzers are expected to exhibit greater alignment and obtain greater cumulative abnormal returns from the KM announcement.
Hypothesis 2: Ceteris paribus , the alignment between a firm’s business strategy and the attributes of the KM initiative announced is greater for firms pursuing an
Analyzer business strategy firms than for firms pursuing: (a) Prospector and (b)
Defender business strategies.
Hypothesis 3: Ceteris paribus , the cumulative abnormal stock market return (in the 5-day event window) with KM announcements is higher for firms pursuing an
Analyzer business strategy firms than for firms pursuing: (a) Prospector and (b)
Defender business strategies.
Differences are also expected across business strategies in the timing of the impact the KM announcement has on firm value. As compared to other organizations, Prospectors invest more heavily in promotional activities and marketing (Hambrick 1983; McDaniel and Kolari 1987). They also more closely monitor their product/market trends (Miles et al. 1978; Snow and Hrebiniak 1980), and more commonly take externally-oriented actions (Miles 1982; Thomas and McDaniel 1990). In contrast, Defenders are internally focused (Miles et al. 1978; McKee, Varadarajan, and Pride 1989). They have less external
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linkage than Analyzers and Prospectors, and devote less efforts to monitoring market developments or understanding customer needs (Langerak et al. 1999). Meyer (1982) found Defender, Analyzer, and
Prospector hospitals to rank low, medium, and high in boundary spanning. Similarly, in insurance industry,
Hambrick (1982) has found Prospectors and Analyzers to significantly exceed Defenders in the frequency of interest in, and hours of, entrepreneurial scanning. Due to the relative lack of external linkages,
Defenders would receive environmental information earlier than Analyzers and Prospectors (Hambrick
1983).
Prospectors have been argued, and empirically found, to closely monitor their product/market trends and have greater marketing expenditures than Defenders (Hambrick 1983). Miles et al. (1978) agree that “the Prospector’s administrative system is well suited to maintain flexibility” (p. 553). Analyzers also observe the market avidly, and respond very quickly to market changes. According to Miles and Snow
(1978), “(Analyzers’) successful imitation is accomplished through extensive marketing surveillance mechanisms ” (p. 73; emphasis in original unless otherwise indicated). In sharp contrast is the Defender, for which the “primary risk is that of … being unable to respond to a major shift in the market environment”
(Miles et al. 1978; p. 551).
While these arguments have focused on the flow of information from the environment into the organization, we posit that the relative absence of these communication and boundary spanning channels in Defenders would also inhibit leakage of firm information to the environment. Consequently, news about the firm’s actions would be less likely to be available to the external market earlier, and hence leakage of information about the KM effort prior to its announcement would be less likely. This leads us to propose that, as compared to Prospectors and Analyzers, Defenders would encounter lower impact of KM announcement on firm value prior to the announcement. In contrast, Prospectors and Analyzers would encounter greater information leakage prior to the firm’s decisions, and very little of the decision’s impact on firm value would occur after the public announcement. Therefore, in Prospectors, as compared to
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Defenders and Analyzers, the impact of the KM announcement on firm value would be lower during the period following the announcement.
Hypothesis 4: Ceteris paribus , the abnormal stock market return associated with KM announcements in the two days preceding the KM announcement is lower for firms pursuing a Defender business strategy firms than for firms pursuing: (a) Prospector, and (b) Analyzer business strategies.
Hypothesis 5: Ceteris paribus , the abnormal stock market return associated with KM announcements in the two days following the KM announcement is lower for firms pursuing a Prospector business strategy than for firms pursuing:
(a) Defender, and (b) Analyzer business strategies.
We define the event as a public announcement of a firm’s KM effort. The sample of events was constructed from search of announcements related to KM during the period from January 1, 1989, to
December 31, 2001. The online search features of Lexis/Nexis were used to search five leading news sources -- Business Wire , PR Newswire , The New York Times , The San Francisco Chronicle , and USA
Today -- for announcements containing the words: (a) “knowledge management,” “knowledge integration,”
“knowledge creation,” “knowledge sharing,” “knowledge transfer,” or “knowledge synthesis”; and (b)
“culture,” “social,” “structure,” “information technology,” or “information system” in headline and lead paragraphs. This search revealed 6,082 potentially relevant articles, from which 721 abstracts were shortlisted, excluding announcements concerning: general trends in KM; a partnership between two or more KM vendors; or the sales or performance of a specific KM product from a vendor’s perspective. After reading the 721 complete announcements, 142 of them were selected, excluding those related to a not-forprofit or government organization, a non-U.S. firm, a subsidiary of a company, or a KM vendor.
The final study sample of 71 announcements was then selected by excluding announcements when: (a) there was a confounding announcement (e.g., earnings, dividends, merger, acquisition, divestiture, changes in top management, or new product launch) on the same firm during the period from
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two days before the KM announcement to two days after it; (b) there were more than one KM announcements on the same firm such that the earlier announcement was within period starting 300 days before the later announcement (in this case the earlier announcement was included but the later announcement was dropped); (c) the company was not publicly traded so that returns data was not available; (d) the returns data was not available for one or more business days starting from 300 days before the announcement. As shown in Appendix A, which provides more information on the identification of the sample of 71 announcements, the final sample does not include any announcement from years 1989 to 1994. Appendix B identifies the announcements.
Objective data from several secondary sources -- Standard and Poor's COMPUSTAT service, and the Center for Research in Security Prices (CRSP), and the firms’ annual reports, 10K statements, and proxy statements -- was used to measure the variables used to classify and validate business strategies.
Table 3 summarizes these measures. KM attributes were identified from the text of the announcements.
Two raters 5 independently read each announcement and identified the KM attributes. Satisfactory interrater reliability was indicated by Cohen’s kappa, which was significant at p < 0.001 level for each KM attribute. Disagreements were resolved as follows. Rater 1 marked the rationale for each KM attribute on each announcement. Rater 2 copied specific portions of each announcement that were used to form these conclusions into an electronic document, and provided this document to Rater 1. Rater 1 resolved all disagreements by again reading the relevant announcements and the reasoning used by both raters 6 .
---------- Insert Table 3 about here ----------
5
6
: The raters were one of the authors (Rater 1) and a Ph.D. student (Rater 2), both with prior research experience.
: When Rater 1 reexamined the announcements in the light of the reasons used by both the raters, changes were made from his initial ratings in 26 (i.e., 9.2 percent) of the 284 ratings.
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The event study method measures the effect of an unanticipated event (the KM announcement in this paper) on stock prices. The standard approach is based on estimating a market model for each firm using an estimation window, which is a period before the event could have had an effect, and then calculating abnormal returns for an event window, which is the period during which the event is believed to affect the stock price (Brown and Winter 1985; Koh and Venkatraman 1991). These abnormal returns are assumed to reflect the stock market's reaction to the arrival of new information. Data on stock prices, used to compute the cumulative abnormal returns, or CAR, were collected from CRSP.
The announcement day was numbered as t = 0, the days preceding it as t = -1, -2, etc., and the days following the announcement as t = +1, +2, etc. To model the normal return, we used the market model 7 , which is specified as: R i,t
= α i
+ β i
R m,t
+ ε i,t
; where R i,t
= return of stock i on day t , R m,t
= return on the market portfolio on day t ; α i
and β i
are the ordinary-least-squares estimates of intercept and slope parameters for firm i , and ε i,t
= disturbance term for stock i on day t . In the event-study literature, the two common choices for the market portfolio are an equally-weighted CRSP index and a value-weighted
CRSP index. We performed the analyses using both these indexes 8 . For estimating the market model, we used an estimation window of 255 days prior to the event, starting from t = -300 and ending on t = -46 9 .
Prior research has used a variety of event windows, ranging from two days (t = -1 to 0) to 181 days
(t = -90 to +90) (McWilliams and Siegel 1997). It is generally agreed, however, that the nature of the event being studied should determine the length of the event window (Ryngaert and Netter, 1990). To capture the
7 Market model improves over the constant-mean-return model by reducing the variance of the abnormal return, which could increase the ability to detect event effects (MacKinlay, 1997). The variance reduction and the consequent benefit are greater when the R 2 of the market-model regression is higher.
8
9
The value-weighted index is potentially better in this study since both average and median R 2 values of the market model regressions across our sample firms are better when using it as compared to the equally-weighted market index. However, since the equally-weighted index is also commonly used in event studies, we performed the analyses using both indices.
For seven of the sample firms, the return data was not available for all the way back to t = -300. Therefore, a shorter window had to be used for these firms. The estimation window for these firms ranged from 100 to 246 days.
--- Page 23 ---

price effect of announcements that occurred after as well as before the KM announcement (due to leakage of information about the firm’s planned KM endeavor) (Subramani and Walden 2001), a five-day event
window was used, from t = -2 to t = +2. This is consistent with some prior studies (e.g., Friedman and
Singh 1989), and was considered appropriate here because a longer event window would increase the likelihood of stock prices being affected by confounding events and reduce the power of the test statistic
(Brown and Winter 1985), while a shorter window would increase the possibility that the effect of the KM announcement is not adequately captured (Friedman and Singh 1989; Im et al. 2001).
The normal return for the firm i on the day t of the event window if the event had not occurred is computed as α ˆ i
+ ˆ
β i
R m, t
, using estimates α i
and
ˆ
β i
, from the market model regression. The abnormal return was then computed as AR i t,
= R i t,
− ( α ˆ i
+ β
ˆ i
R m, t
) . The abnormal returns were cumulated over the event window ( t = -2 to t = +2) to compute the cumulative abnormal return for each firm: CAR i
= t
2
= − 2
AR i t,
.
The sample firms were classified into the three business strategy types based on the proximity of each firm’s business strategy attributes to the ideal profiles for Defenders, Prospectors, and Analyzers. To do so, we first identified the ideal business strategy profile for Defenders, Prospectors, and Analyzers, based on the theoretical profiles of the three strategy types in terms of the seven business strategy attributes, as discussed earlier and summarized in Table 1. High, medium, and low values for the ideal business strategy attributes were operationalized as normalized scores of +0.5, zero, and –0.5, respectively 10 (Govindarajan 1988; Sabherwal and Chan 2001). The Euclidian distance between each firm’s business strategy and the three ideal business strategies was next computed. Three distance
10 In other words, ideal scores of high, medium, and low on a business strategy attribute imply that in terms of that attribute, the ideal value is half standard deviation greater than, the same as, and half standard deviation less than, respectively, the mean score for the firms in the sample.
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measures were thus obtained for each firm, indicating its distances from the ideal profiles for Defenders,
Prospectors, and Analyzers. Each firm was classified into one of the three business strategies based on distance from which ideal strategy was the lowest for that firm 11 . The validity of this classification was then examined by using Duncan’s Multiple Range Test to test for the differences in the means of six validation variables across the three business strategies.
Three tasks were involved in this step. First, the ideal profiles of KM profiles for Defenders,
Prospectors, and Analyzers were constructed in terms of the four KM characteristics. This was done based on the theoretical profiles which were discussed earlier and summarized in Table 2. Again, ideal values of high, medium, and low, were operationalized as 0.5, 0, and –0.5, respectively.
Second, we computed the Euclidian distance between each firm’s KM initiative and the ideal KM profile for the business strategy type to which it belonged. For example, if a firm had been classified as a
Defender, the distance was computed from the ideal KM profile for Defenders.
Third, alignment was computed by subtracting the above distance from one. Smaller Euclidian distance indicates that the firm’s actual KM profile is closer to the ideal profile, and the degree of alignment is higher. Subtracting the distance measure from one thus helps convert it into a measure of alignment.
Hypothesis 1 was tested using hierarchical regression analyses with value-weighted CAR and equally-weighted CAR as the dependent variables. The assumption of constant error variance across firms, or homoskedasticity, may be violated in cross-sectional regression models with CAR as the dependent variable, causing the standard errors of the estimated regression coefficients to be biased estimators of the true standard errors of the estimated coefficients, and the tests of hypotheses to be invalid. To address this
11 : We had decided that if two of the three distances were tied for the lowest value, we would exclude that firm from further analyses related to different business strategies. No such case was however encountered.
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problem, we did not assume homoskedasticity, and instead derived heteroskedasticity-consistent standard errors and t-statistics using the approach employed by White (1980).
To ascertain the robustness of our regression results, we conducted the analyses with valueweighted CAR and equally-weighted CAR, and for the entire sample of 71 announcements as well as a smaller sample of 67 after excluding four outlier announcements, which had value-weighted CAR more than two standard deviations above or below the sample mean. Thus, four hierarchical regressions were conducted, with the two dependent variables, and with and without the outliers. We controlled for firm size and time (measured as the natural log of the number of days between January 1, 1995, and the announcement), and also included two dummies to represent Prospector and Analyzer business strategies.
Hypotheses 2 through 5 were tested using t-tests. To test Hypothesis 2, we conducted a t-test comparing alignment across Analyzers and Prospectors (H2a) and Analyzers and Defenders (H2b). To test
Hypothesis 3, we compared value-weighted and equally-weighted measures of CAR across Analyzers and
Prospectors (H3a) and across Analyzers and Defenders (H3b). Finally, we conducted similar t-tests across pairs of business strategies indicated in Hypotheses 4 and 5, but compared the abnormal stock market return associated with KM announcements in the two days preceding the KM announcement (H4) and the abnormal stock market return associated with KM announcements in the two days following the KM announcement (H5). The following process was followed for all the t-tests for Hypotheses 3 through 5.
The computation of abnormal returns for firm i o n day t , AR i,t
, was described earlier in the paper.
The variances for abnormal returns were calculated as follows (Subramani and Walden, 2001): var( AR i t,
) =

 s i
2


 1


+
1
T i
+
( R m t,
T i
τ
( R m , τ
− R m
− R m
) 2
) 2







--- Page 26 ---

where s i
2 is the residual return variance from the estimation of market model over the estimation window comprising of T i
days, R m
is the mean market return on the market index over the estimation window, and
R m t, is the return on the market index on day t in the estimation window. The length of the estimation window, T i
, is 255 days for most of the sample firms.
The abnormal returns were cumulated over the event window (t = -2 to t = +2), to compute the cumulative abnormal return (CAR) for each firm. The variances of abnormal returns were also cumulated:
CAR i
= t
2
= − 2
AR i t,
These were aggregated across firms as follows: var( CAR i
) = t
2
= − 2 var( AR i t,
)
CAR =
1
N i
N
= 1
CAR i var( CAR ) =
1
N 2 t
2
= − 2 var( CAR i
)
The t-statistics were computed using the following formula for examining whether the mean CAR over the event period differs significantly between two groups consisting of N
1
and N
2
firms, respectively: t =
CAR
1
− CAR
2 var( CAR
1
N
1
)
+ var( CAR
2
N
2
)
Table 4 summarizes the key characteristics of the 71 KM efforts. As may be shown from the Table, these efforts were usually at the organizational level (38 cases), with frequent use of knowledge sharing (37 cases), technology (56 cases), and internal knowledge (46 cases). The classification of the 71 firms produced 28 Defenders, 27 Analyzers, and 16 Prospectors. Table 5 provides the means of the validation variables for each business strategy, the expected differences, and the actual significant differences. As may be seen from the Table, eleven of twelve expected differences were empirically found; the expected
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difference in the size of Analyzers and Prospectors was not supported. Thus, the validation tests using these six variables, that had not been used for the classification, support the empirical classification.
---------- Insert Tables 4, 5 about here ----------
Hypothesis 1 is supported by all four regressions (with value-weighted and equally-weighted measures of CAR, and with and without the outliers), as shown in Table 6. The addition of alignment to the independent variables produces an R 2 change ranging from 5.19 percent to 10.81 percent, with all corresponding F-values and the standardized betas for alignment being significant (at p < 0.05 or better).
---------- Insert Table 6 about here ----------
The t-tests, reported in Table 7, provide fairly strong support to Hypotheses 2, 3, 4, and 5. The only exception is the lack of support for Hypothesis 4a, which proposed the abnormal returns in the two days prior to the announcement to be less in Defenders as compared to Analyzers. All other hypotheses (2a, 2b,
3a, 3b, 4b, 5a, and 4b) are supported by the t-tests.
This paper was motivated by two broad research questions. We sought to examine: (a) whether the stock-market reaction to a firm’s public announcement of a KM effort is affected by the alignment between the KM effort and the firm’s business strategy; and (b) whether greater information leakage of information about the KM effort occurs prior to the announcement in the case of firms pursuing certain business strategies. Five specific research hypotheses were proposed based on prior literature on organizational learning, knowledge management, and business strategy, and tested using a variety of secondary data on 71 KM announcements from 1995 to 2001. This section examines the implications of our findings, but before we do so, some inherent limitations of the study should be recognized.
First, although we started our search for KM announcements using a very broad set of keywords and a large number of potentially relevant announcements, a careful review of the announcements
--- Page 28 ---

produced a somewhat small sample of 71 announcements. Consequently, we have a small number of firms pursuing each strategy, especially Prospectors. Our results should therefore be viewed with some caution.
Second, our findings may be limited because we focused on publicly announced KM efforts. Thus, we took a limited view of the firm’s overall KM endeavors instead of examining the focal effort’s relationship with the firm’s prior KM practices.
Third, the discussion of the three business strategies as pure strategies is a simplification. The ideal Analyzer, Defender and Prospector business strategies are archetypes that may sometimes be combined. Similarly, the same KM effort may sometimes involve more than one KM processes or more than one KM levels. In order to avoid complicating our analysis and discussion, we refrained from discussing and testing such hybrids of business strategies, KM processes, or KM levels in this study.
Finally, although the profiles for business strategies could be derived readily due to the substantial prior literature on the area and the availability of the theoretical ideal profiles in prior studies (Segev 1989;
Doty et al. 1993; Sabherwal and Chan 2001), the development of the ideal profiles for the KM efforts was more difficult due to the somewhat limited attention to KM in the prior literature on Miles and Snow's (1978) typology. We developed the ideal profiles of KM efforts based on a review of this literature and the literature on organizational learning and knowledge management.
Our results should be considered in the light of the above limitations. However, they have several potential implications. First, the empirical support for Hypothesis 1 lends further support to the argument that greater alignment between the announced KM effort and the firm’s business strategy would lead to more favorable stock-market reaction. This impact on firm value implies that alignment may be more important than we could conclude by only examining the relationship between alignment and a survey respondent’s perception of KM effectiveness. This also suggests to practitioners that it is not enough to simply monitor the amount of investment in KM efforts within an organization but that it is necessary also to
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understand the thrust of this investment (e.g., the KM process primarily supported). In Table 2, we identified the kinds of KM efforts that would be most appropriate for Defenders, Analyzers, and
Prospectors, and the empirical support for Hypothesis 1 increases the confidence in these expectations based on prior literature. This suggests, for example, that the Defender would find it more beneficial to target its KM efforts at individuals working within an organizational sub-unit, concentrate on knowledge utilization, rely on KM structures, and draw upon internal knowledge. Moreover, this finding suggests that simply replicating a competitor’s KM efforts would not benefit a firm, unless there are strong similarities in the two firms’ business strategies.
Second, the support for Hypotheses 2 and 3, combined with the support for Hypothesis 1, suggests that Analyzers announce KM efforts that are better aligned to their business strategy (Hypothesis
2), and this greater alignment produces more favorable reaction from the stock market (Hypothesis 1).
Consequently, Analyzers receive greater increase in firm value due to the announcement of KM efforts
(Hypothesis 3). However, the lack of significance of the dummy variable representing Analyzer strategy
(Table 6) indicates that the Analyzer business strategy does not have a direct effect on stock-market reaction, over and above the indirect effect through alignment. Thus, Analyzers better align their KM efforts to their business strategy, and therefore receive more favorable stock-market reactions to their KM announcements. Indeed, based on the mean CAR’s reported in Table 7, the announcement of KM efforts generates a negative reaction. While this might be due to the perception that the KM efforts would not be valuable given the dynamic nature of the Prospector, or would compromise the efficiency desired by the
Defender, the lack of significance for the strategy dummies in the regressions suggests the need for caution and further analysis before reaching this conclusion. If further supported, these findings would have potentially radical management implications that KM efforts are perceived as valuable only to Analyzers.
Third, the t-tests related to Hypotheses 4 and 5 indicate that there may be greater leakage of information about the KM effort prior to its announcement in Analyzers, as compared to Defenders and
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Prospectors. These findings may be because of the Analyzers’ greater boundary spanning and interorganizational linkages than Defenders and their greater stability as compared to Prospectors. The lack of support for Hypothesis 4b indicates that Prospectors may not have greater information leakage, probably because their greater boundary spanning may be offset by the Defender’s greater stability and size. The support for Hypothesis 5, indicating that there is less favorable stock-market reaction in the case of Prospectors following the announcement might reflect their more open nature as well as the overall negative reaction Prospectors’ KM announcements receive. Further investigation of these results – which have been validated using regressions with two-day abnormal returns as dependent variables and similar independent variables as in Table 6 – is currently in progress, with a five-day window being used both before and after the announcement.
Finally, the paper provides further insights into Miles and Snow's Defenders, Prospectors, and
Analyzers. This typology of business strategy is a well-established one, and this paper contributes by operationalizing the three strategies in terms of attributes that can be measured using secondary data.
Thus, it provides a way of using secondary data to assess the three strategies based on theoretically and empirically supported variables. The paper also contributes to the KM literature by identifying the profiles of
KM efforts most suitable for each business strategy.
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--- Page 36 ---

Variables Defenders Analyzers ProspectorsReferences
Classification Variables
1 Scope High Medium Low Porter (1985);
Smith et al. (1989);
Doty et al. (1993)
2 Product-market dynamism Low Medium High Segev (1989);
Smith et al. (1989);
Shortell and Zajac (1990);
Doty et al. (1993)
3 Firm-level uncertainty
4 Liquidity
5 Asset efficiency
Low Medium High Doty et al. (1993)
Low Medium High Segev (1989)
High Medium Low McDaniel and Kolari (1987);
Segev (1989);
Langerak et al. (1999)
6 Fixed asset intensity
Validation Variables
1 Firm size
High
7 Long-range financial liability Medium
Medium
Low
Low Hambrick (1983);
Segev (1989)
High Segev (1989);
Smith et al. (1989)
Large Medium Small Doty et al. (1993)
2 Decentralization
3 Executives’ age
4 Executives’ tenure
5 R&D Intensity
Low Medium High Segev (1989);
Doty et al. (1993)
High ? Low Smith et al. (1989)
High
Low
?
?
Low Smith et al. (1989)
High Hambrick (1983);
Smith et al. (1989)
6 Correlation between firm and market returns
Low ? High Snow and Hrebiniak (1980);
Langerak et al. (1999)
--- Page 37 ---

Defenders Analyzers ProspectorsReferences
KM Process
Knowledge utilization
Knowledge sharing
Knowledge creation
KM Means
High Medium Low
Medium High
Low
Medium
Medium High
Knowledge Source
Focal organization High Medium Low
Both focal organization and partners Medium High
Knowledge Users
Individuals working in an organizational sub-unit
Medium
Individuals across the organization Low
Individuals across the organization as well as its partners
Medium High
Medium High Medium
--- Page 38 ---

Measures
Classification Variables
1 Scope
3 Firm-level b
COMPUSTAT
2 Product-market dynamism COMPUSTAT
CRSP
Natural log of the average of the number of the four-digit SIC codes a in the year of the announcement and the number of the four-digit SIC codes in the year before the announcement.
Sum of: (a) the change in SIC codes from two years prior to the year of the announcement to the year before the announcement; and (b) the change in SIC codes from the year prior to the year of the announcement to the year of the announcement.
Variability of firm’s return, computed as the standard deviation of the daily idiosyncratic returns c of the firm for the year, with the daily idiosyncratic return being measured as the residual from the OLS regression of the firm’s daily return on the daily return on the equally-weighted market portfolio
(Demsetz and Lehn 1985; Bhushan 1989).
4 Liquidity
5 Asset efficiency
COMPUSTAT
COMPUSTAT
6 Fixed asset intensity COMPUSTAT
7 Long-range financial liability COMPUSTAT
Validation Variables
1 Firm size
2 Decentralization
3 Executives’ f
4 Executives’ tenure
COMPUSTAT
COMPUSTAT and
Annual reports/ 10K statements/ Proxy statements
Current ratio = Current assets/current liabilities.
Total asset turnover (or sales/total assets).
Fixed assets/total assets.
Debt/equity ratio, or total liability/total equity
Natural log of market value equity e d .
Computed as the number of executive officers/number of employees.
Natural log of average age of executive officers.
Proportion of executives with over five years experience in the firm.
5 R&D Intensity COMPUSTAT
6 Correlation between firm and market returns
CRSP
R&D expense/net sales
The square root of the r-squared of the ordinary-least-squares regression of the firm’s daily return on the daily return on the equally-weighted market portfolio. a b
: The number of four-digit SIC codes is a proxy for the number of lines of business of a firm, which is commonly used in accounting and economics literature to measure firm diversification (e.g., Bhushan 1989; Campa and Kedia 2002).
: These variables were computed as the means of the values reported at the end of the year of the announcement and at the end of the year preceding the announcement. The resulting values were standardized across the sample. d c : This measure was highly correlated with, but preferred to, an alternative measure using standard deviations of firm returns.
: This measure was highly correlated with, but preferred to, an alternative measure using total debt ratio = total liability/total assets. f e : This measure was highly correlated with, but preferred to, an alternative measure using natural log of total assets.
: These measures were computed based on the document immediately preceding the announcement.
--- Page 39 ---

KM Attribute
KM level
Categories
Individuals within a sub-unit
Frequency
18
Individuals across the organization 38
Individuals in the organization and its partners
15
KM Process
KM Means
Knowledge utilization
Knowledge sharing
Knowledge creation 13
Structure 8
Knowledge Source Focal organization
Partners
Both
21
37
Technology 56
Both 7
46
7
18
--- Page 40 ---

1 Firm size
3 Executives’ age
Means F-value Significant
Defenders Analyzers Prospectors Differences a
D>A, D>P, A>P 8.14 6.69 6.69 4.26* D>A, D>P
D<A,
A<P
D>P 3.94
(51.51 years)
3.87
(48.07 years)
3.85
(47.41 years) tenure 0.81 0.61 0.55 5.01**
5 R&D Intensity b D<A, D<P 3.05 10.33 8.92 6.65** D<A, D<P
6 Correlation between firm and market returns
Significance Levels (one-tailed)
*: p < 0.05
**: p < 0.01
***: p < 0.001 a b
: Duncan’s Multiple Range Test is used, with pre-specified significance level of p < 0.05 (one-tailed).
: For the analysis for R&D intensity, n = 42 due to missing data, with 13 Defenders, 13 Analyzers, and 12 Prospectors. For all other variables, n=71, with 28 Defenders, 27 Analyzers, and 16 Prospectors.
--- Page 4 1 ---

Hypothesis 3
Hypothesis 4
Hypothesis 5
Value-weighted sum of abnormal returns for all five days
Equally-weighted of abnormal returns for all five days
Value-weighted sum of abnormal returns for days t-1, t-2
Equally-weighted sum of abnormal returns for days t+1, t+2
Value-weighted sum of abnormal returns for days t+1, t+2
Equally-weighted of abnormal returns for days t+1, t+2
Significance levels (one-tailed):
*: p < 0.05
**: p < 0.01
***: p < 0.001
-0.18%
-0.45%
-0.76%
-0.89%
0.44%
0.36%
0.65%
1.35%
0.87%
1.15%
-2.53%
-1.32%
-0.64%
-0.26%
0.30%
0.58%
-2.96%
-2.43%
P < A
P < A
D < A
P < A
D < A
D < P
D < A
D < P
D < A
P < D
P < A
P < D
P < A
7.85***
4.63***
-4.09***
-1.78*
-3.44**
-3.80***
-0.24
-6.96***
-1.34
-8.63***
-7.24***
-6.62***
-5.95***
-6.12***
--- Page 42 ---

a
Independent Variables b
Entire Sample (n = 71) No Outliers c (n = 67)
CAR CAR
Value-weighted Equally-weighted Value-weighted Equally-weighted
Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2
Constant 0.064
(0.085)
0.141
(0.104)
0.097
(0.085)
0.164
(0.095)
-0.013
(0.071)
0.032
(0.065)
0.032
(0.076)
0.078
(0.067)
Time -0.008
(0.011)
Firm Size -0.001
(0.004)
Strategy Dummy #1
(Prospectors)
Strategy Dummy #2
(Analyzers)
Alignment
-0.025
(0.027)
0.007
(0.013)
-0.011
(0.013)
-0.002
(0.004)
-0.041
(0.029)
-0.042
(0.022)
0.113**
(0.040)
-0.013
(0.011)
-0.001
(0.004)
-0.011
(0.023)
0.016
(0.014)
-0.016
(0.012)
-0.002
(0.004)
-0.025
(0.022)
-0.026
(0.022)
0.099**
(0.040)
0.001
(0.009)
0.001
(0.003)
-0.001
(0.017)
0.006
(0.011)
-0.001
(0.008)
0.000
(0.003)
-0.011
(0.017)
-0.020
(0.015)
0.059*
(0.027)
-0.004
(0.009)
-0.001
(0.004)
0.004
(0.020)
0.018
(0.013)
-0.006
(0.009)
-0.001
(0.004)
-0.006
(0.018)
-0.008
(0.018)
0.060*
(0.034)
R 2 (%) 3.44 14.25 3.81 13.32 0.44 7.04 2.83 8.02
F-statistic 0.68 2.18* 0.61 1.94* 0.10 0.90 0.41 1.01
∆ R 2 (%)
∆ F-statistic
10.81 9.51 6.60 5.19
7.88** 7.03** 4.08* 3.36* a Unstandardized regression coefficients are shown, with the standard errors, adjusted using the heteroskadasticity-consistent covariance matrix developed by White (1980), in parentheses. b One-tailed significance levels are reported for hypothesized relationships, while two-tailed significance levels are reported for nonc hypothesized relationships. Significance levels are indicated as: *** p < 0.001; ** p < 0.01; and * p < 0.05.
In these analyses, we excluded four outliers that were more than two standard deviations away from the mean in terms of valueweighted CAR.

Year
Abstracts
Identified
Abstracts
Shortlisted 1
1994 20 2
1995 44 2
1996 110 14
1997 412 33
1998 932 64
1999 1,281 113
2000 1,570 169
2001 1,681 319
Total 6,082 721
Announcements
Shortlisted 2
Announcements in
The Final Sample 3
0
0
0
0
4
16
0
4
0
1
0
20
11
50
36
142
0
0
0
0
3
1
8
12
8
24
15
71
1
2
: After reading the abstracts, we excluded articles that were about: (a) general trends in KM; (b) a partnership between two or more
KM vendors; or (c) the sales or performance of a specific KM product from a vendor’s perspective.
: In this step, we excluded articles that focused on: (a) a not-for-profit or government organization; (b) a global company; (c) a
3 subdivision or geographic region of a company; or (d) a specific KM vendor or performance of a particular KM product.
: In this step, we excluded articles due to four reasons: (i) there was a confounding announcement (e.g., earnings, dividends, merger, acquisition, divestiture, changes in top management, or new product launch) on the same firm during the period from two days before the KM announcement to two days after it; (ii) there were more than one KM announcements on the same firm such that the earlier announcement was within period starting 255 days before the later announcement (in this case the earlier announcement was included but the later announcement was dropped); (iii) the company was not publicly traded so that returns data was not available; (iv) the returns data was not available for every business day of the period starting 255 days before the announcement.

16 21-Apr-98
17 6-Jul-98
18 30-Jul-98
19 4-Aug-98
20 14-Sep-98
21 5-Oct-98
22 23-Nov-98
23 7-Dec-98
24 10-Dec-98
25 9-Aug-99
26 30-Aug-99
27 9-Sep-99
28 20-Sep-99
29 27-Sep-99
30 6-Oct-99
31 18-Oct-99
32 19-Oct-99
33 11-Jan-00
34 2-Feb-00
35 14-Feb-00
Announcement Date Firm
1 25-Jul-95
2 14-Sep-95
3 7-Dec-95
4 11-Dec-96
5 3-Feb-97
6 2-Jul-97
Amdahl
Hewlett Packard Company
Symbol
WMX
INFR
Bell Atlantic Corp. (Verizon Wireless) BEL
Exchange
NYSE
AMH AMEX
BLUD NASDAQ
HWP
NASDAQ
NYSE
NYSE
7 7-Jul-97
8 17-Jul-97
9 29-Sep-97
10 17-Nov-97
11 1-Dec-97
12 17-Dec-97
13 26-Feb-98
Versant Object Technology Corp. VSNT
Cambridge Technology Partners
BLS
MYG
SVU
CATP
BAY
REGI
NYSE
NASDAQ
NYSE
NYSE
NASDAQ
NYSE
NASDAQ
14 23-Mar-98
15 7-Apr-98
Northern Telecom (Now Nortel
Networks Corp.) NT NYSE
CIEN NASDAQ
Prosoft I-Net Solutions (Now
Merck & co.
Data General Corp
Florida Power and Light
Cambridge Technology Partners
Phillips Peroleum Company
Ford Motor Company
Sprint
General Electric Company
American Eagle Outfitters, Inc.
Cisco Systems, Inc.
Computer Sciences Corp.
CompuCom Systems, Inc.
Hewlett Packard Company
MRK
DGN
FPL
CATP
IRF
EMC
P
F
LMT
FON
MOB
GE
AEOS
PG
CSCO
CSC
CMPC
INIT
HWP
NYSE
NYSE
NYSE
NASDAQ
NYSE
NYSE
NYSE
NYSE
NYSE
NYSE
NYSE
NYSE
NASDAQ
NYSE
NASDAQ
NYSE
NASDAQ
NASDAQ
NYSE
Announcement Date Firm
36 23-Feb-00 Compaq Computer Corporation
Proxicom 37 21-Mar-00
38 24-Mar-00
39 31-Mar-00
40 8-May-00
41 16-May-00
Allegiance Telecom, Inc.
Keane
42 17-May-00
43 31-May-00
44 14-Jun-00
45 6-Jul-00
46 17-Jul-00
47 7-Aug-00
48 15-Aug-00
49 28-Aug-00
50 12-Oct-00
51 25-Oct-00
52 26-Oct-00
53 30-Oct-00
Ford Motor Company
The BFGoodrich Company
54 9-Nov-00
55 11-Dec-00
56 13-Dec-00
57 1-Feb-01
58 9-Apr-01
59 16-Apr-01
60 23-Apr-01
61 2-May-01
62 14-May-01
63 14-May-01
Delphi Automotive Systems
Briggs & Stratton
AFLAC
Best Buy Co., Inc.
US Xpress Enterprises, Inc.
National TechTeam Inc.
Armstrong Holdings, Inc.
Eastman Chemical Company
NCR or NCR Corporation
Northwest Natural Gas
64 21-May-01
65 27-Jun-01
66 3-Jul-01
67 23-Jul-01
68 6-Aug-01
69 8-Aug-01
70 13-Aug-01
71 13-Dec-01
VeriSign
American Tower Corporation
Dow Chemical Company
BellSouth
Bristol Myers Squibb
ZAMBA Solutions
Symbol
CPQ
Exchange
NYSE
PXCM NASDAQ
TIBX NASDAQ
ALGX
KEA
UAL
NASDAQ
AMEX
NYSE
EMC
MSFT
VIAN
F
NYSE
NASDAQ
NASDAQ
NYSE
HOOV NASDAQ
CERN
R
BA
GR
ITT
NASDAQ
NYSE
NYSE
NYSE
NYSE
DPH
BGG
AFL
BBY
NYSE
NYSE
NYSE
NYSE
NM:SPRSA NASDAQ
TEAM NASDAQ
ACK NYSE
NU
EMN
FDX
NCR
NWN
DJ
VRSN
AMT
DOW
BLS
BMY
ZMBA
MER
NYSE
OTC
NYSE
NYSE
NYSE
NYSE
NYSE
NYSE
NYSE
NASDAQ
NYSE
NYSE
NYSE