Knowledge Collaboration Among Professionals Protecting National ...

Organization Science

informs

Vol. 19, No. 2, March–April 2008, pp. 260–276 issn 1047-7039 eissn 1526-5455 08 1902 0260

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doi 10.1287/orsc.1070.0315 © 2008 INFORMS

Knowledge Collaboration Among Professionals Protecting National Security: Role of Transactive Memories in Ego-Centered Knowledge Networks Sirkka L. Jarvenpaa

Center for Business, Technology and Law, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712, [email protected]

Ann Majchrzak

Information and Operations Management, Marshall School of Business, University of Southern California, Los Angeles, California 90089, [email protected]

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urrent social cognition models of knowledge coordination based on transactive memory systems (TMS) theory have not generally considered conditions in which goals among partners are incongruent, and that those with specialized knowledge will not necessarily act to share their knowledge. As expected from previous literature, when facing a problem requiring inputs from others, an individual will draw on her personal or ego-centered network using the knowledge of her network’s TMS; however, we theorize that the mixed motives within her network will cause the individual to also take into account her perception of the level of distrust within the network when combining the received knowledge from others in the network. Moreover, an individual’s view of her network’s TMS will be shaped not by specific policies or enforcement mechanisms, but by semistructures for how knowledge is disseminated, owned, and discussed. Our theory is supported based on a survey of security professionals responding to national security threats. The findings encourage a reexamination of certain assumptions of TMS theory, as well as extending theories of ego-centered networks and social-cognitive information processing to include how individuals manage the knowledge-sharing/protection tension in interorganizational collaborations. Key words: interorganizational collaboration; transactive memories; security management; ego-centered network History: Published online in Articles in Advance January 25, 2008.

Introduction

organizational boundaries? This is a research area of national strategic importance but also has broader implications for theory development on knowledge networks. Individuals may generally trust members of their interorganizational ego-centered networks in terms of member competence, but distrust them in terms of their own interests. They may be watchful of others’ interests and vigilance fearing that others may expropriate valued knowledge (Heiman and Nickerson 2004, Norman 2002), leak a trade secret (Hannah 2005), or otherwise harm the long-term reputation of the individual or his organization (Scott and Walsham 2005). One way that individuals may manage this concern about mixed motives is through the use of a cognitive structure—transactive memory systems (TMS)—for who knows what and who knows who knows what (Wegner et al. 1991). A TMS facilitates task performance and learning through coordination efficiencies (Faraj and Sproull 2000, Lewis et al. 2005, Wegner 1986). It may help an individual to distinguish between helpful and potentially harmful combinations of pieces of information gathered from different individuals during different encounters in different contexts with different intentions.

Professionals are known to seek knowledge from their own personal networks—ego-centered networks—which extend beyond the formal organizational structures (Burt 1980, 1992; Cross and Cummings 2004; Cross and Sproull 2004) in response to the need for rapid ad hoc knowledge collaboration. The ego-centered networks are comprised of ties with whom the professionals (the egos) have had some prior professional contact (Wellman 1999). Sometimes, the network will consist of members of public and private organizations with multiple and often conflicting interests (Gal-Or and Ghose 2005). Early AIDs researchers were found not to share their knowledge with other institutions so as to hoard reputation rewards (Kramer 1999b). Similarly, studies of professionals protecting national security have identified the conflicting interests that create barriers when interorganizational collaboration is needed as threats dynamically evolve with short time horizons, even when professionals reach out to individuals in their ego-centered networks (GAO 2006, Goodman and Wilson 2000). How can knowledge of others be integrated in these short time horizon, ad hoc, mixed-motive collaborations that cross 260

Jarvenpaa and Majchrzak: Knowledge Collaboration Among Professionals Protecting National Security Organization Science 19(2), pp. 260–276, © 2008 INFORMS

Existing TMS literature has assumed first that members share the same goals, and second that those with certain expertise and assigned tasks will accept the responsibility to act based on that expertise (Brandon and Hollingshead 2004). This assumption of congruence in knowledge and action cannot be made in a mixedmotive situation with dynamically changing problems. Others may know something, but not have the necessary rights to share it for the specific context or problem, or may not share it because it may unduly harm their own organizations. What are the necessary factors for developing transactive memory of professionals when congruent interests cannot be assumed? Although others have studied TMS in large and volatile groups (Ren et al. 2006) and interdisciplinary teams (Akgun et al. 2006, Faraj and Xiao 2006), we know of no empirical research prior to ours that has studied the development and use of a professional’s TMS in mixed-motive interorganizational collaborations. In this paper we integrate research on knowledge networks, trust, and distributive cognition to delineate the semistructures that build awareness of others’ expertise and improve interorganizational collaborations involving security professionals. These semistructures encompass practices and procedures that affect how other parties interpret shared knowledge, whether other parties own the knowledge, and whether other parties agree to privacy, dissemination, and sensitivity protocols. Although we develop our theory in the context of security collaboration, it may be applicable to other mixed-motive interorganizational knowledge collaborations.

Theoretical Model and Hypotheses

Research on ad hoc knowledge collaboration has found that individuals often develop and rely on their own egocentered networks in deciding with whom to collaborate and how to collaborate (Reagans and McEvily 2003). Individuals’ ego-centered networks may have a large membership and diverse expertise, particularly among professionals with a long tenure and varied problem sets (Borgatti and Cross 2003, Burt 1992, Cross and Cummings 2004). When an individual must quickly draw on the knowledge of members in an ego-centered network with mixed motives, benevolence cannot be assumed. In fact, benevolent action in a mixed-motive situation may be interpreted as having harmful intentions (Korsgaard et al. 2002). The lack of benevolence complicates building the transactive memory and rendering it actionable in a knowledge collaboration. Definition and Perspective of TMS A TMS is traditionally defined as “(a) an organized store of knowledge contained entirely in the individual systems of group members, and (b) a set of knowledgerelevant transactive encoding, storage, and retrieval processes that occur among group members” (Hollingshead

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2001, p. 1080). A TMS has two components: (1) internal memory, or what the individual members know personally, and (2) external memory, or what the individuals know about what is known by other team members or can be located and retrieved from various storage devices (Wegner 1986). When a TMS has been developed for a network, individuals can specialize in different knowledge domains, yet locate and integrate the specialized expertise of others, leading to increased coordination efficiencies (Brandon and Hollingshead 2004, Lewis et al. 2005, Liang et al. 1995, Wegner 1986). TMS research has identified three indicators of the level of development of a TMS (Lewis 2003, Liang et al. 1995, Moreland and Argote 2003): (1) expertise specialization, (2) competence-based trust, and (3) expertise coordination. The more developed the TMS, (a) the greater the tendency for groups to delegate responsibility and specialize in different knowledge domains, (b) the higher the beliefs about the competence or the validity of a member’s expertise, and (c) the higher the ability of team members to coordinate their work efficiently based on the knowledge of who knows what. Even though TMS theory was originally developed for dyads in close relationships (Wegner 1986) and small, welldefined interacting groups (Liang et al. 1995), Anand et al. (1998) extended TMS theory to settings where knowledge is distributed among people who belong to groups both inside and outside organizational boundaries. In such settings, a TMS exists at individual, group, and organizational levels (Moreland and Argote 2003). We suggest that TMS may also be used to describe individuals’ mental models of their ego-entered networks. In ad hoc knowledge collaborations, Moreland and Argote (2003) suggest that TMS need to be developed not at the level of the problem-specific collaboration, but rather at a higher organizational level to provide more stability for understanding the expertise of future collaborators. For ad hoc collaborations, this higher-order level may be the ego-centered network. Because members of the network are only known to the focal individual (Macdonald and Piekkari 2005), it is the responsibility of the individual to learn who knows what and to use that knowledge to decide what knowledge can or cannot be shared with others in the network (Scott and Walsham 2004). This suggests that the TMS of an individual’s ego-centered network may affect her ability to coordinate with others in her network. The Effect of TMS on an Individual’s Ability to Combine Others’ Knowledge from the Mixed-Motive Network Knowledge collaboration requires what Kogut and Zander (1992) call combinative capabilities. Such capabilities include both the know-how about the process by which problems that involve combining different sources of expertise are solved, and the know-why of

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Jarvenpaa and Majchrzak: Knowledge Collaboration Among Professionals Protecting National Security

cause-effect relationships that explain what expertise is needed. One antecedent of combinative capabilities mentioned by several authors has been TMS among those from which knowledge is obtained (Borgatti and Cross 2003, Kogut and Zander 1992). In conditions of mixed motives, combinative capabilities are likely to be affected by perceived risks of exchanging knowledge with others. To reduce these risks, network research has focused on the role of trust at the individual and organizational levels (Carley 1991, McEvily et al. 2003, Tsai 2001, Zaheer et al. 1998). Trust is a multidimensional concept with different components facilitating different behaviors (Mayer et al. 1995, Zaheer et al. 1998). Whereas benevolence-based trust is fundamental for collaborative behavior (Blau 1964), in a mixed-motive situation, benevolence cannot be assumed. Although a well-developed TMS embeds trust in the source’s competence (competence-based trust), it does little to reduce uncertainty over the lack of benevolence or the conflicting interests to which the knowledge may be applied. The lack of benevolence necessitates wariness, skepticism, vigilance, and watchfulness over other’s interest (Lewicki et al. 1998), and has implications for combinative capabilities. In situations lacking benevolence, Lewicki et al. (1998) argue that people develop a situationally specific cognitive assessment, and label this assessment “distrust,” defined as “confident negative expectations regarding another’s conduct” (p. 439). Focusing on benevolencebased distrust, we define it as confident negative expectations about others’ interests that may harm or damage one’s own interest. Benevolence-based distrust keeps the professional alert for possible alternative motives, aware of the possibility of suspicious situational cues, and engaged in more sophisticated analyses of suspicious situations (Fein 1996). Kramer (1999a) argues that distrust can promote more active and mindful processing of information. At moderate levels, benevolencebased distrust can help an individual manage hazards in mixed-motive interorganizational knowledge collaboration. Therefore, we hypothesize: Hypothesis 1 (H1). In mixed-motive, interorganizational collaborations, an individual’s combinative capability will be higher when the individual has a more developed sense of the TMS of his ego-centered network and there is greater benevolence-based distrust of others in the network. Antecedents of TMS Development with Mixed Motives Existing TMS theories suggest that the best way to construct, evaluate, and use a TMS is through shared faceto-face experiences such as joint training (Liang et al. 1995, Moreland and Levine 2000). When team members are trained together, rather than apart, they are able

Organization Science 19(2), pp. 260–276, © 2008 INFORMS

to better locate, integrate, and use each other’s skills and knowledge. However, with ad hoc problem-specific collaborations, there is little time for joint training, and the collaborators may not have previously shared experiences (Moreland and Argote 2003). Although techniques other than training have been suggested (Hollingshead 2001, Moreland and Argote 2003), these techniques rest on the assumption that knowing what others know indicates how they will act. TMS literature has largely assumed that members have an interest in acting on the knowledge that the group believes they have. However, such congruence of knowledge and action cannot be assumed in mixed-motive networks where members often have no interest in participating in certain sharing activities. To protect themselves, they may act in ways that optimize a motive not known to others in the network. Therefore, in mixedmotive collaborations, antecedents for the development of a TMS must extend beyond joint training. According to Okhuysen and Eisenhardt (2002), predictability about others’ behaviors comes from “semistructures.” Semistructures are simple or minimalist rules that help members of a group organize their knowledge integration processes, yet remain flexible enough to adapt to an evolving situation. Problem-solving methods are an example of a semistructure. Such semistructures may be generated in ego-centered networks or imposed by powerful parties. We argue that three semistructures—dialogic practices, clarity of knowledge ownership, and knowledge dissemination protocols—may help reduce situational ambiguity and increase predictability about how others will act given what they know and hence facilitate development of a TMS in a mixedmotive network. Dialogic Practices. Intraorganizational teams struggle with ambiguity and differences in interpreting the same knowledge (Carlile and Rebentisch 2003, Dougherty 1992, Te’eni 2001). Professionals in mixed-motive interorganizational collaborations face these challenges to a greater extent because of different communication norms, thought worlds, practice sets, and domain principles in different organizations. The result can be unsurfaced differences in the interpretation of incoming information and unexpected differences in actions. Unless these differences surface, actions cannot be linked to expertise and the TMS updated. One semistructure for surfacing differences in interpretation may be the use of dialogic practices (Boland et al. 1994, Faraj and Xiao 2006, Te’eni 2001). Dialogic practices are semistructures that describe rules of conversation. Based on principles of hermeneutic inquiry, Boland et al. (1994) offer several “rules” of dialogic practices that include discussing sources of knowledge, encouraging knowledge emergence, comparing multiple perspectives, keeping knowledge indeterminant to be


repeatedly revised in response to new information, and structuring discussions to move between summary-level knowledge and detailed analysis. Research has shown that the use of dialogic practices increases the amount of knowledge shared (Majchrzak et al. 2005), heedful interrelating (Weick and Roberts 1993) and joint sense making (Faraj and Xiao 2006) by causing participants to think about and reflect on the expertise of others and to critically assess motives for sharing unique knowledge. Therefore, the use of dialogic practices may increase predictability of others’ behaviors by helping individuals to better link others’ knowledge to their actions. In networks using such practices, the TMS may be perceived as more fully developed. Hypothesis 2A (H2A). In mixed-motive, interorganizational collaborations, the more that individuals use dialogic practices, the more the TMS of their ego-centered network will be perceived as developed. Clarity of Knowledge Ownership. The traditional view of TMS assumes that the location of knowledge (i.e., who knows what) implies expert ownership of that knowledge. When problems such as security threats are confronted by drawing on the knowledge of members in an interorganizational mixed-motive network, the knowledge that is shared is likely to evolve over time as new information about the problem is discovered and multiple interpretations of the incoming information are shared. This creates ambiguity over who has what responsibilities and rights to that knowledge (Ulmer and Sellnow 2000), such that an individual may know something but may not have the right to use or share that knowledge. However, what knowledge is considered “owned” and what ownership means varies across situations and different member intentions (Jarvenpaa and Tanriverdi 2006). Ideas generated exclusively within a company might be perceived as “owned” by the company, whereas ideas generated in collaboration with members of other companies may be perceived as sharable with those members (Jarvenpaa and Staples 2001). Making judgments about ownership is challenging, even in nonemergent situations. In dynamically evolving situations, such judgments are likely to be ambiguous which in turn reduces the ability to link knowledge and action. Policies that clarify ownership issues increase predictability by reducing divergence in views and increasing accountability (Ulmer and Sellnow 2000). In mixed-motive collaborations, clarity over ownership of knowledge should increase congruence between knowledge and action. When it is clear that a party owns knowledge, and what that ownership means, an inference is made that the party has the right and responsibility to act on that knowledge. Clarity over ownership may reduce fears of accidental or intentional infringement on another’s property when distributed knowledge is coordinated and integrated. Therefore, by increasing

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congruence between knowledge and action, ownership clarity may foster a more developed TMS. Hypothesis 2B (H2B). In mixed-motive, interorganizational collaborations, the more individuals perceive that knowledge ownership is clear, the more the TMS of their ego-centered network will be perceived as developed. Knowledge Dissemination Protocols. Finally, TMS theory assumes that when people own knowledge, they should share it (Lewis 2003). However, in mixed-motive settings, not all knowledge should be shared with every member of the network or in a collaboration. Withholding some knowledge may protect members of the network; conversely, withholding the wrong knowledge, or sharing sensitive knowledge too broadly, may hurt the network and individual firms within the network (GAO 2006, Kramer 1999a). Therefore, semistructures that clarify how knowledge should be disseminated within the network may be needed in such settings to reduce ambiguity and allow members to quickly identify discrepancies between knowledge and action. These semistructures would need to be fluid enough to adjust to an evolving situation, but rigid enough to increase predictability about how others will behave with their knowledge, thus helping to develop a TMS. Hypothesis 2C (H2C). In mixed-motive, interorganizational collaborations, the more individuals perceive that knowledge-dissemination protocols are adequate, the more the TMS of their ego-centered network will be perceived as developed. Because ego-centered networks are not formal organizations, these semistructures are unlikely to be imposed by external governing bodies, or to be contractually binding. They are not even going to take the shape of formal administrative coordination procedures (Faraj and Sproull 2000). Rather, the informal nature of the personal networks calls for flexible protocols that help to create the understanding of responsibilities, priorities, and risks to build the confidence to act. Dougherty (2007) describes such rules as enabling professionals to “go on” in their work life, even in unforeseen situations, and make the right calls. Thus, as suggested by Brown and Eisenhardt (1997) and Okhuysen and Eisenhardt (2002), these semistructures may be effective because they flexibly allow emergent approaches to task execution by providing simple general rules focused on knowledge integration rather than on the task. Such protocols would be adequate when others know the protocols and use them appropriately. Antecedents to Dialogic Practices Unlike the semistructures of knowledge ownership policies and dissemination protocols, dialogic practices require intensive effort and time to engage members within

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the network—time that takes members away from the responsibilities of their own organizations. Therefore, motivation and ease of communication must exist to encourage members to engage in dialogic practices. Motivation is critical in reducing ambiguity between knowledge and action within the firm (Argote 1999, Szulanski 1996). The prospect of acquiring new knowledge has been shown to motivate people to share their own knowledge on a corporate intranet (Kalman et al. 2002), in communications (Te’eni 2001), and in interfirm strategic alliances (Norman 2002). Professionals associated with organizations with a culture that embraces learning from outside sources may be more likely to engage in dialogic practices because they see such dialogue as not only potentially beneficial to them, but also to the organization. This learning intent may be manifested in organizational human resource practices that support permeable boundaries to learning from outside (Swart and Kinnie 2003) or from cultural or management practices that encourage learning. Hypothesis 3A (H3A). In mixed-motive, interorganizational collaborations, the greater the learning intent of the organization to which the ego party belongs, the more the individual will engage in dialogic practices. Dialogic practices may also be facilitated by ease of communicating with others in the network. In complex conditions in which diverse information is being conveyed and where a variety of media will be needed to transfer the knowledge (Boland et al. 2001), there is unlikely to be any one particular communication media that outperforms others in the collaboration (Massey and Montoya-Weiss 2006). Thus, ease of communication may be facilitated by the presence and use of multiple communication channels, including phone calls, e-mails, exchanges of documents, and face-to-face meetings. Hypothesis 3B (H3B). In mixed-motive, interorganizational collaborations, the greater the use of multiple channels of communication, the more the individual will engage in dialogic practices. Control Variables In addition to the antecedents identified in this paper that specifically focus on a mixed-motive condition, we include three controls. Two of these—task interdependence (Brandon and Hollingshead 2004, Hollingshead 2001) and network size (Moreland and Levine 2000, Ren et al. 2006)—are likely to affect the development of TMS. Length of time the individual has been associated with a network is included as a control on combinative capability because the longer the tenure in the network, the more the accumulated practice and expertise needed for combining knowledge of others from the network (von Hippel 1988).


Method Study Context We spent approximately 18 months building relationships with security personnel in the private and public sectors, and identified the U.S. Federal Bureau of Investigation (FBI) InfraGard program as a possible source of respondents for our survey. InfraGard encourages ad hoc private and public collaborations among security professionals. InfraGard consists of an e-mail distribution list of individuals cleared to receive high-security information about security-related threats or potential threats, as well as discussion threads and regional chapter meetings to encourage individuals from various organizations to collaborate and share knowledge in response to specific security threat information. The governing bodies of two InfraGard chapters agreed to allow us to conduct the study within their chapters. We initially interviewed the sponsor at each chapter and 10 other individuals involved in the security domain familiar with InfraGard to understand who they collaborated with, how they shared knowledge, and the consequences of sharing. We learned that even though InfraGard was initially set up to be a virtual institutional network for collaboration, members felt that it was just an e-mail distribution list of security alerts. InfraGard did not serve as an important collaborative forum for ad hoc security collaboration for any of the 12 people interviewed. One interviewee notes, “If there were a threat, the last thing I would do is inform anyone on InfraGard.” Interviewees relied on extensive personal networks across many organizations and individuals and included some members of InfraGard, but also other formal and informal sources. Interviewees felt the tension between knowledge sharing and protection such as “knowing what information to share and ensuring that others receiving the information know how to use it.” Professionals also feared knowledge leaks. One informant shared a bitter experience: “Sharing the wrong information is disastrous. At my company, blabbermouths published their work in the company newsletter, which got included in a proposal from another company that beat us out.” Another respondent reported how he had saved his own company $90 million. “What we learned from listening to them was that when improving a process, the incremental value they had gained from documenting their process in detail was marginal. So we took $10 M to do what they took $100 M to do. They gave away too much.” Several interviewees mentioned the important role of transactive memory in their networks. For example, one said, “I still prefer my informal network of experts. They may float from company to company, but I know them, they know me, and we can trust each other to keep secrets and know what to do in case of an event.” The need for structures that organize how professionals share and protect knowledge was echoed in


our interviews. One noted, “Policies and procedures are needed that assure reliable vetting make sure necessary agreements are in place MOA, [memo of understanding], nondisclosure agreements, corporate indemnification agreements, privacy policies, etc.” Another said, “For me to share, there needs to be assurance that information can be shared in a way that protects all parties from embarrassment, humiliation, or brand damage.” Others noted inadequate policies or procedures as reasons for not sharing. With regard to the use of multiple communication channels, interviewees mentioned using a variety of channels to communicate in an effort to overcome the shortcomings of any single channel. Survey Design The InfraGard sponsor for each chapter sent an e-mail on the chapter’s list server to the complete membership: 500 people in one region and 120 in another, requesting them to complete our survey, which was administered through a university, independent of the FBI. Respondents were informed that the survey would provide feedback to InfraGard on ways to improve knowledge sharing among public and private organizations, as well as within their own networks. The survey questions directed the respondents to describe their ego-centered network of security professionals. We defined this network as: “an informal personal network or circle of professionals interested in security generally, or interested in a specific aspect of security, with whom you have collaborated in the past to better understand new security information or confront a security risk.” Our pretests suggested that this definition provided a consistent frame of reference to personal networks. The survey collected data from the professionals about their perceptions of their ego-centered networks. To increase the reliability of responses, we asked the professionals to first think about collaborations they had engaged in when responding to security threats, then to think of their own “informal personal network or circle of professionals interested in security” that constitute their list of potential collaboration partners. We further grounded responses by asking them to think about how many people were in this network, and background information about the network (e.g., the percentage who were also members of InfraGard, the percentage new to the network this year, etc.). In this way, we felt we were able to encourage the respondents to think about their entire egocentric network, rather than specific names of individuals they could enumerate, as had been done in previous studies (e.g., Laumann 1966, Burt 1984, Campbell et al. 1991). Moreover, because we were not interested in the network’s structure or the influence or communication role of the individual vis-à-vis his network, we did not use the name generator technique to elicit the network.

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One hundred four security professionals completed the web-based survey. It is impossible to assess the representativeness of this sample. Owing to the sensitive nature of the e-mail distribution list, we had no access to survey respondents’ names, e-mail addresses, or other identifying information. We asked our InfraGard sponsors to ask key informants (i.e., those security professionals who collaborated with others and who had an interest in providing feedback to InfraGard) whether they had completed the survey. In addition, the sponsors conducted follow-up interviews with individuals who did not complete the survey. Reasons for not responding included that: they were not a security professional who acted on security information (i.e., only used the e-mail distribution list to stay informed), they did not collaborate with other security professionals outside their organization, or they did not feel any affiliation or identification with InfraGard. Therefore, we believe that our sample can be characterized as consisting of active security professionals engaged in security-related interorganizational collaborations, with some interest in developing further collaborations through InfraGard. However, as our objective is not to assess the prevalence of the conditions that foster TMS, but rather to examine the theoretical links between the key constructs, the representativeness of the sample should be less of an issue. Survey Measures Survey questions (see the appendix) directed the respondents to describe their ego-centered network of security professionals. The 104 respondents reported networks with sizes ranging from 4 to 500, with a mean of 103. On the average, 16%–30% of the members in the networks were new each year. These averages are comparable to other studies of personal networks (Hill and Dunbar 2003, Killworth et al. 1990, McCarty et al. 2001). The respondents identified, on average, fewer than 15% of the members of these networks as InfraGard members. Individuals reported collaborating in the past with an average of only 31%–45% of their network members, where collaboration was defined as working with another member as partners in a joint problemsolving process. On average, 46%–60% of the members in their network were geographically local (within driving distance). Despite the geographical closeness, the most common media for interaction among network members were one-on-one e-mail and group e-mail lists, followed by phone calling. Face-to-face meetings were the least-used mode of interaction, occurring on average “a few times per year.” Respondents had been members of their networks for 10 years, on average, with a substantial range (from 0.5 to 42 years). They came from a variety of organizations: 61% private (versus 39% public), 50% for-profit (versus 50% nonprofit), and 31% with security as the main line of business. They had held their security jobs, on average, for 15 years (range of 1 to 42 years).

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Combinative Capabilities. To develop our measure of combinative capabilities, we followed the procedure used by Cummings (2004). We first conducted interviews with the 12 security professionals to identify the types of know-how and know-why knowledge needed in ad hoc collaborative security responses, ensuring that the definitions of know-how and know-why were followed. We supplemented our interviews with a review of research to identify knowledge shared during security threat responses. Finally, extensive piloting yielded more refinements to our list of three know-how questions and three know-why questions. We asked respondents to report how frequently they had received each type of knowledge from their network using a one-to-seven scale. Transactive Memory Development. We measured transactive memory using the 10-item scale developed by Lewis (2003). We asked individuals to rate their network’s TMS based on their own personal interactions with others outside their employing organizations. Frequency of Use of Dialogic Practices. We adapted the 10-item scale measuring the use of dialogic practices during interactions among network members from Majchrzak et al. (2005) based on the five rules of dialogic practices identified by Boland et al. (1994)— source, easy travel, multiple perspectives, emergence, and indeterminance—measuring each element by two items. Because the original scale focused on information technology support for these elements, we changed the stem to have respondents focus on the frequency of elements among network members regardless of the media used. Organizational Learning Intent. We used Norman’s (2002) five-item measure of organizational learning objectives from strategic alliances. Knowledge-Dissemination Protocols. We developed a three-item scale of the adequacy of the policies indicating what knowledge to share. We measured the extent to which the respondents found such protocols to be adequate to protect sources, knowledge sharing, and information sensitivity. Clarity of Knowledge Ownership. We developed a three-item scale measuring the clarity about who owns the knowledge that is shared in the network. The items measured the clarity of the basic rights and responsibilities assumed with legal ownership (Barzel 1989). Benevolence-Based Distrust in Network Members. From the literature on interorganizational collaboration, we adapted a four-item scale measuring the perceived threat of other collaborators’ opportunism (Heiman and Nickerson 2004). A 0.01 correlation between this scale and the three items in the TMS instrument measuring competence-based trust indicated the divergent validity of the two forms of trust.


Use of Multiple Communication Channels. We asked respondents to indicate the degree to which they used six different communication channels to interact with the people in their networks. These communication channels included: (1) meeting one on one or in small-group faceto-face meetings, (2) phone calls, (3) attending local chapter meetings and conferences, (4) using e-mail one on one, (5) using group e-mail lists or list servers, (6) logging in to a portal. Channels used at least once a month were included in the count. Controls. Task interdependence was measured using a standardized four-item scale (Goodhue and Thompson 1995). Years in the network was measured as a singleitem question. We measured personal network size via a single global measure, which is considered an efficient, reliable, and valid way to differentiate respondents, and has been found to correspond well with more complex network measures (Bernard et al. 1990, Fu 2005, Marsden 1990, McCarty et al. 2001, Marin and Hampton 2007). Analysis Strategy Preliminary analyses of the model variables across the two InfraGard chapters showed no significant differences. Accordingly, we pooled data across the two chapters and used partial least squares (PLS), a latent structural equation modeling technique that uses a correlational, principle component-based approach to estimation (Chin 1998). Each multi-item construct was modeled as reflective (rather than formative) of the latent variable because we expected the items measuring each construct to covary. For example, the items corresponding to organizational learning objectives measured the underlying construct of learning. Our model exceeded Chin’s (1998) sample-size recommendation of five to ten times the largest number of structural paths to any one construct. To estimate the significance of the path coefficients, we used bootstrapping with a sample size of 200, as recommended by Chin (1998). We included the three controls: the person’s tenure in the network, task interdependence, and network size.

Results

The results are divided into a discussion of the measurement model to confirm convergent and divergent validity of constructs, and a discussion of the structural model to test the hypothesized relationships between the constructs. Measurement Model Results of the PLS component-based analysis, correlations among the constructs, alpha coefficients, reliability tests, PLS-computed variability for each construct, and interconstruct correlations are presented in Tables 1 and 2. Of the 10 items in the TMS instrument, 4 were


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Table 1

Item Discriminant Analysis (Cross-Loadings)

TMS

Dialogic practices

Knowledgedissemination protocol

Knowledge ownership

Organizational learning intent

Benevolencebased distrust

Interdependence

077 084 081 086 087 085

023 034 046 039 034 030

030 024 039 037 037 025

018 020 024 038 030 023

−009 −001 009 013 009 013

028 026 030 026 028 023

017 022 018 015 028 024

020 017 024 012 018 007

TMA TMB TMC TMD TME TMF

021 032 035 032 028 040

059 078 085 087 071 077

037 054 042 042 039 037

027 033 037 042 041 045

008 012 022 030 041 045

021 031 025 026 014 029

001 007 001 −006 −006 0

025 020 010 008 021 016

DIALA DIALB DIALC DIALD DIALE DIALF DIALG DIALH DIALI DIALJ

030 037 026 018 046 034 034 032 026 031

049 045 042 044 055 042 042 048 039 036

076 069 085 086 086 084 082 082 081 079

026 032 021 020 037 031 025 034 025 020

012 004 004 009 018 015 013 026 009 011

027 027 044 034 033 033 041 031 027 041

007 022 015 012 018 014 009 011 016 013

025 028 029 026 025 020 027 027 026 021

DISSEMA DISSEMB DISSEMC

022 026 033

046 040 044

019 030 037

083 092 088

025 041 032

0 009 021

−010 −004 −004

021 016 017

OWNA OWNB OWNC

0 004 019

023 035 025

009 016 012

021 033 039

086 090 060

−008 −004 006

−031 −024 −013

−017 −015 007

LEARNA LEARNB LEARNC LEARND LEARNE

024 030 023 028 036

024 025 028 030 034

035 039 033 039 040

003 004 014 016 015

−007 −007 0 −003 0

089 086 091 090 086

−003 010 007 008 −003

010 003 011 022 026

BEN.-BASED DISTRUST A BEN.-BASED DISTRUST B BEN.-BASED DISTRUST C BEN.-BASED DISTRUST D

021

006

010

003

−012

−004

078

020

014

−011

017

−018

−037

002

081

037

017

−007

009

−011

−030

001

083

033

029

008

019

003

−020

014

088

017

010 017 024 017

017 020 022 013

026 027 030 027

014 021 019 019

−016 −004 −012 −013

019 009 019 010

028 028 027 032

091 091 094 075

Combinative capabilities

CAPEA CAPEB CAPEC CAPED CAPEE CAPEF

Items

DEPENDA DEPENDB DEPENDC DEPENDD

Notes. Boldface numbers are loadings (correlations) of indicators to their own construct; other numbers are cross-loadings. To calculate cross-loadings, a factor score for each construct was calculated based on the weighted sum, provided by PLS-Graph, of that factor’s standardized and normalized indicators. Factor scores were correlated with individual items to calculate cross-loadings. Boldface item loadings should be greater than cross-loadings. See the appendix for actual item wording in surveys.

dropped for low loadings on the TMS construct, following suggestions for trimming by Gray and Meister (2004). Table 1 provides the correlations of each item to its intended construct (i.e., loadings) and to all other perceptual constructs (i.e., cross-loadings). Although there

is some cross loading, all items load more highly on their own construct than on other constructs, and all constructs share more variance with their own measures than with other constructs. Table 2 shows that the alpha coefficients for the items within each construct are sufficiently high, as are the more accurate compos-

0.89 0.95

0.91

0.84 0.95 0.89

0.93 NA NA NA

0.91

0.84 0.94

0.85

0.70 0.93 0.85

0.90

Combinative capabilities TMS Dialogic practices Knowledgedissemination Ownership Learning intent Ben.-based distrust Depend Multichannels Network size Network years 0.78

0.64 0.78 0.68

0.77

0.59 0.66

0.70

AVE

0.20 0.18 0.03 0.01

0.09 0.33 0.27

0.31

0.43 0.39

0.83

Combinative capabilities

022 015 −011 010

038 032 002

050

077 055

TMS

031 054 −011 010

016 042 017

033

081

Dialogic

021 007 −013 007

039 012 −005

088

Knowledgedissemination

−011 −009 −004 003

080 −003 −026

Ownership

017 020 0 008

088 006

Learning intent

029 026 010 016

083

Benevolencebased distrust

0.88 0.38 0.03 0.13

Dependence

NA 0 001

Multichannels

NA −010

Network size

Notes. Boldface numbers on diagonal are the square root of the average variance shared between the constructs and their measures. Off-diagonal elements are correlations among constructs. For single-item constructs, only correlations are presented. For discriminant validity, diagonal elements should be larger than off-diagonal elements.

0.93

Alpha

Construct

Composite reliabilities

Table 2 Interconstruct Correlations: Consistency and Reliability

268 Jarvenpaa and Majchrzak: Knowledge Collaboration Among Professionals Protecting National Security Organization Science 19(2), pp. 260–276, © 2008 INFORMS


269


Figure 1

Results

Use of multiple channels for communication

0.47

**

0.27** Dialogic practices

Organizational learning intent

Distrust in network members

Knowledge dissemination protocols

0.33**

0.45**

Level of network’s TMS development R2 = 0.50

0.27**

0.43**

Combinative capabilties R2 = 0.25

0.18* Clarity of knowledge ownership Control: Task interdependence

ite reliabilities. Table 2 also presents average variance extracted as well as correlations between constructs, including the control variables. Comparing the square root of the average variance extracted (AVE) (i.e., the diagonals in Table 2 representing the average association of each construct to its measures) with the correlations among constructs (i.e., the off-diagonal elements in Table 2 representing the overlap association among constructs) indicates that each construct is more closely related to its own measures than to those of other constructs. Moreover, all AVEs are above the 0.50 recommended level (Chin 1998). In sum, these results support the convergent and discriminant validity of our constructs. Structural Model Figure 1 is a graphical depiction of the PLS results, and Table 3 contains the outermodel loadings of the items on each construct. The hypothesized paths of predictors of the use of dialogic practices are significant, accounting for 39% of the variance. The hypothesized paths between the three TMS antecedents and TMS development are also significant, accounting for 50% of the variance in TMS development. Finally, distrust and TMS development accounted for 25% of the variance in combinative capability. Tenure in the network was not a significant control variable for combinative capability, nor was task interdependence a significant control for TMS development. Finally, network size was a significant control, negatively related to TMS development. Because hypothesized, combinative capability is explained in part by TMS development coupled with distrust, TMS development is explained in part by three semistructures; the use of dialogic practices is explained by the communication channels and the learning intent of the ego-party’s organization. To rule out alternative plausible explanations, we examined the direct paths between TMS antecedents and combinative capability. These are not significant. We

– 0.08 0.07

– 0.23**

Control: Network size

Control: Years in network

also examined paths between the use of multiple channels of communication and TMS and found all paths to be insignificant. We checked whether task interdependence or size moderated the relationship between TMS development and combinative capabilities and found no significant relationships. We eliminated the 12 cases with networks greater than 150 (a network size that Hill and Dunbar 2003 argue is the cognitive limit for human information processing), and the 10 cases with networks smaller than 10 (the size of small teams), and obtained results similar to those of the full sample. Finally, we examined the potential for our results to be explained by common method variance. We followed suggestions by Podsakoff et al. (2003) to reduce this potential in the design of the survey by ensuring anonymity, which reduces the likelihood of bias from social desirability and respondent acquiescence. We also separated the predictor and criterion variables psychologically, proximally, and through varying scales. Respondents were not psychologically primed to connect our predictors because the survey was introduced as focusing on collaborations in general, not specifically on combinative capability (Podsakoff et al. 2003). In addition, we tested for the effect of common method bias in three ways. First, we used the partial correlation approach, a method recommended by Lindell and Whitney (2001) and used in a variety of studies assessing common method variance (cf. Pavlou and El Sawy 2006). In the partial correlation approach, the researcher partials out for the effect of a method variance that would be equivalent to the lowest correlation among the variables of interest. Disattentuation did not affect the significance of the relationships among the variables, which suggests that the results cannot be accounted for by common method variance. Second, we performed Harman’s one-factor test by entering all the principal constructs into a principal components factor analysis (Podsakoff and Organ 1986). Eight factors resulted. The


270 Table 3


common method latent construct, and rerunning the structural model. The results did not change.

Outer Model Loadings Original sample estimate

Mean of subsamples

Standard error

t-stat

Combinative capabilities CAPEA 077 CAPEB 083 CAPEC 081 CAPED 086 CAPEE 087 CAPEF 085

077 083 080 087 089 085

007 006 006 003 002 003

132 174 180 245 426 223

TM development TMA TMB TMC TMD TME TMF

060 078 085 087 071 076

061 078 085 086 071 077

010 005 005 004 005 004

64 153 218 213 122 144

Dialogic practices DIALA DIALB DIALC DIALD DIALE DIALF DIALG DIALH DIALI DIALJ

076 069 085 086 086 084 082 082 081 079

076 069 086 086 085 084 082 082 080 078

005 008 003 002 003 003 004 004 005 004

126 79 309 334 295 269 203 254 181 190

Knowledge dissemination protocols DISSEMA 083 082 DISSEMB 092 091 DISSEMC 088 089

007 003 003

126 375 279

Knowledge ownership clarity OWNA 086 OWNB 090 OWNC 059

085 088 059

013 012 018

77 90 37

Organizational learning intent LEARNA 089 LEARNB 086 LEARNC 091 LEARND 090 LEARNE 086

088 086 090 089 085

004 004 005 005 005

200 219 159 151 180

Benevolence-based DISTRUSTA DISTRUSTB DISTRUSTC DISTRUSTD

087 076 078 082

005 011 011 009

60 51 59 106

088 088 091 074

011 011 010 014

79 88 96 42

distrust 088 078 081 083

Task interdependence DEPENDA 091 DEPENDB 090 DEPENDC 094 DEPENDD 075

first accounted for 30% of the variance. The other seven (with eigenvalues greater than one) contributed to the remaining 40% of the variance, each accounting for 3%–11%. This suggests that while there is likely to be some common method variance, the effect is small. Finally, following Podsakoff et al. (2003), we performed a single-method factor approach in PLS by having indicators measure both their theoretical constructs and a

Discussion and Implications

Our approach focuses on professionals who, faced with a difficult problem, must be able to engage others in ad hoc collaborations and quickly combine knowledge from these sources to solve the problem. Professionals in such situations often draw on their interorganizational ego-centered networks of personal contacts. These networks often involve members with different interests and motives. By integrating research and theory on knowledge networks, trust, and distributive cognition, we find support for the role of TMS development and benevolence-based distrust in explaining an individual’s ability to combine knowledge from others drawn from these mixed-motive networks. In mixed-motive situations, TMS achieves its coordination benefits by indicating not only what should be shared (because others do not know what you might know) and what need not be shared (because others already know it), but also what should not be shared (since others may act in a harmful way with that knowledge). In a context where knowing what others know is not equivalent to action, the hypothesized semistructures help match action and knowledge. Finally, the results suggest that the timeintensive activity of dialogic practices can be encouraged by an organization’s perceived learning intent, as well as by providing multiple channels for communication. This study makes a contribution by identifying ways to increase combinative capabilities of professionals who must draw on interorganizational ego-centered networks where mixed motives may prevail, and where solutions must be arrived at too quickly for classic trustbuilding and expertise-building sessions. Although our hypotheses have been tested exclusively with professionals protecting our national security, there are broader implications, which are discussed later. Limitations Our study suffers from several limitations. We used our own measure of ego-centered networks. Our data were perceptual and retrospective, captured from a single source. Our measure of ego-centered network data is subject to recall and estimation errors as well as ambiguity of the network boundary and of the subpopulation in the eyes of the respondents. These are not uncommon problems in ego-centered network research (McCarty et al. 2001, Marin and Hampton 2007). Although common method variance did not appear to be a major contributor to the results, these limitations suggest that this study should be considered exploratory. In addition, causal direction is problematic to infer from cross-sectional survey designs. For example, it is possible to revert the direction of the arrows in Figure 1. That is, combinative capabilities may facilitate the development of TMS,


and the more developed the TMS of the network, the more likely that professionals will use dialogic practices, find knowledge ownership clear, and perceive knowledge dissemination protocols as adequate. Lewis et al. (2005) research on group TMS development suggests that causal directions are likely to be reciprocal over time. Thus, TMS development would support and be supported by both combinative capabilities and the simple semistructures. Although a longitudinal field study of security professionals may help to sort out the causal relationships, the difficulty of accessing this population and securing adequate research participation may make such a study unlikely. Another limitation to consider is the sample that may render findings unique. Professionals protecting national security may be classified as “collectively paranoid” (Kramer 1999a), characterized as hypervigilant in processing information, dwelling on negative interpretations of events, overattributing hostile intentions to others, and exaggerating conspiracy theories. Nevertheless, it is possible that the sample may not be as unique as one might initially believe. Lewicki et al. (1998) argue, for example, that the high-trust/high-distrust condition of the ego-centered networks of security professionals, is “the most prevalent form of multiplex working relationships in modern organizations” (p. 447). Other examples of ad hoc collaborations with mixed motives across organization include emergency response teams (Majchrzak et al. 2007), software developers who temporarily work together on open-source code (Grand et al. 2004), some forms of cross-firm, temporary new product development groups (Engwall and Svensson 2001), and virtual network organizations that are quickly assembled to respond to a client’s need for manufacturing a product (Hagel and Brown 2005). Therefore, although security professionals may be unique in their level of paranoia, the temporary, interorganizational, mixed-motive nature of ad hoc collaborations can be observed in a range of other settings. Knowledge Networks in Mixed-Motive, Interorganizational Collaborations In the interorganizational knowledge network literature, there has been little discussion of structures except those of a formal nature. Formal contractual governance policies between organizations may be delineating mechanisms that constrain the boundaries or periphery of these networks, but fail to account for the richness of behavioral and cognitive choices made by participants. Policies that formally protect the intellectual property between two companies in a joint venture may ignore the reality that participants from those two companies may be drawing on external sources to solve a difficult problem. In addition, corporate policies limiting release of sensitive information may be largely irrelevant in an ad hoc brainstorming session when external sources

271

are called upon to solve an urgent problem (Majchrzak and Jarvenpaa 2005). Moreover, and perhaps most disturbing, organizational policies discouraging interorganizational collaboration to protect leaks also discourage knowledge sharing in a way that hampers interorganizational coordination and national security (GAO 2006). Thus, by creating constraints on boundaries, existing research not only ignores the behavioral realities of interorganizational collaborations (such as their ad hoc nature), but perpetuates their shortcomings. Our findings suggest that although ego-centered networks are not governed by formal contracts, professionals do structure their social context to match action and knowledge. In promoting combinative capabilities, benevolence-based distrust helps professionals decide what not to share and what can be shared for task completion. Dialogic practices, adequate knowledge-dissemination protocols, and knowledge ownership clarity provide structure for professionals to develop awareness of others’ expertise in a mixed-motive situation. These semistructures help individuals identify discrepancies in expectations in interpreting incoming information (by dialogic practices), in knowledge dissemination (with protocols), and in how knowledge will be used (with clarity over ownership). Together, these semistructures increase expectations about what others in the network know and do not know, and how they will act given their knowledge. Conceptualization of TMS in Interorganizational, Mixed-Motive, Ego-Centered Networks Suggesting that a network (rather than a group or organization) has a TMS exposes researchers to a richer array of expertise that individuals may draw on when the need for collaboration arises (Moreland and Argote 2003). Although the negative correlation between network size and TMS development suggests that TMS may be harder to develop in larger networks, the positive effects of the three semistructures suggest ways of increasing the likelihood of achieving the intended value from these networks despite the negative effects of size. Conceptualizing networks as having TMS may have an additional implication beyond ad hoc collaborations. TMS theory has relied on the interdependence assumption that TMS develops only when “one person relies on another to know different information necessary for completing a joint task” (Lewis 2003, p. 600). However, in our study, fewer than half of the network members, on average, had been engaged in any joint task with the responding professionals. Yet the professionals were able to develop a TMS for the network (with help from the semistructures). Moreover, task interdependence was not significantly related to TMS development. We believe the respondents developed a TMS in the absence of joint tasks because they based their dependence on future potential opportunities rather than current task needs. Moreland and Argote’s (2003) research

272


demonstrating that information about an imminent personnel turnover negatively affects a TMS, and Lewis’ (2003) study showing that a TMS grows stronger as time passes, could be interpreted as support for the suggestion that expectations about future dependencies may impact TMS development. We therefore suggest that limiting TMS applicability to current dependencies may be too constraining. Temporary membership in dynamic organizations reduces opportunities for shared experiences. This is a problem for existing TMS theory because shared experiences are a key precursor of TMS development (Lewis et al. 2005, Moreland and Levine 2000). Moreland and Argote (2003) suggest that, in the absence of shared experiences among all organizational members, “information of the type that shared experience provides” (p. 139) should be circulated, including publicity about what other workers do. Our research extends this theorizing about antecedents to TMS when shared experiences are not possible. We suggest that semistructures that clarify expectations may help to provide a basis for TMS development in the absence of shared experiences. Dialogic practices, for instance, reveal tacit assumptions that help each member to understand how others interpret incoming information. We argue that by helping to understand others’ interpretations, dialogic practices not only complement TMS, as argued by Faraj and Xiao (2006), but also foster it because they make clear who knows what. Applying TMS to ego-centered networks also questions the assumption commonly made in TMS research that “who knows what” translates to “who acts on what” (Brandon and Hollingshead 2004). In interorganizational contexts, people may know something, but not have the ownership rights and responsibilities to act on that knowledge. Alternatively, they may know something that leads them to take strategic actions that confuse competitors (or the enemy), or to incite reactions from others to see what they know (Brown and Eisenhardt 1997). Thus, the assumption of a tightly linked relationship between knowledge and action has meant that TMS could not be applied to mixed-motive, interorganizational relationships. Our research suggests that this assumption may not be needed if semistructures are in place for assessing the degree to which actions and knowledge converge. An extension is needed to TMS theory, then, that explicitly includes the degree of congruence between knowledge and action. Role of Trust in Mixed-Motive Knowledge Collaborations Trust has long been considered fundamental for cooperation in risky situations (Mayer et al. 1995) including interorganizational settings (Zaheer et al. 1998). Trust is multifaceted (Mayer et al. 1995), including dimensions such as competence and benevolence. TMS


incorporates competence-based trust via the subindicator of credibility (beliefs about the reliability of members’ expertise). Levin and Cross (2004) argue that benevolence-based trust is necessary in all knowledge collaborations because concerns over harm would make one reluctant to learn from the knowledge source. Our results challenge their argument and suggest that professionals can still learn from others as long as they are aware of the lack of benevolence and manage accordingly. Our findings show that professionals’ combinative capabilities are improved with the level of benevolencebased distrust in the network. Although some of the trust literature portrays distrust as the inverse of trust, conveying an avoidable negative evaluation of a social relationship (Kramer 1999b), other literature portrays distrust as a necessary dimension of any mixed-motive situation, conveying not a negative evaluation of the relationship, but rather a level of certainty about others’ actions (Lewicki et al. 1998). With a greater certainty level, professionals can act to protect themselves while successfully collaborating with others. Future work needs to validate our findings on benevolence-based distrust and examine the dynamics that lead to it in ego-centered networks. Conclusion Our research tentatively suggests that a paradigm shift is needed to develop an information environment for national security collaborations. The paradigm used today is centered on policy and process design with numerous audit functions to ensure congruence with those policies and processes (GAO 2006). The paradigm recognizes the need for localization of such policies to the formal organizational level, but fails to recognize the critical role of the individual. Our findings suggest that an alternative to this hierarchical and rigid paradigm may be the use of semistructures that serve as organizing principles in informal peer-based structures. Such semistructures form a critical role in developing the necessary TMS, which in turn facilitates security collaboration. Because security collaborations increasingly rely on a professional’s personal network, which lies outside the control of the organization, formal policies and processes, even if localized, are an unrealistic prescription. Acknowledgments

The authors are grateful to security professionals who gave their time for the conduct of this study. Clearly, they are not responsible for the contents of this manuscript. The authors also appreciate the insightful and constructive comments of the Organization Science Editor-in-Chief and the reviewers. Encouraging and useful feedback was also received from the discussant and the attendees of the International Conference of Information Systems (ICIS) in Las Vegas, NV, December 12, 2005.


Appendix. Survey Items Label

Item

Combinative capabilities: How frequently have you received the following types of knowledge from the other organizations involved [in your network]? (1 = almost never to 7 = nearly always) CAPEA Know-how about how a threat was identified CAPEB Know-how about steps taken to respond to a threat CAPEC Know-how about how to prevent future similar threats CAPED Reasons behind decisions others made in responding to the security threat CAPEE Reasons behind involving certain people in the security response CAPEF Reasons behind decisions made for not pursuing certain security responses TMS development: Based on your personal interactions with the members of this network, (1 = strongly disagree to 7 = strongly agree) TMA Each member has highly specialized knowledge of some aspect of security TMB I am comfortable accepting security-related suggestions from the other members TMC I trust that other members’ knowledge about security is credible TMD I am confident relying on the information that other members bring to a discussion TME Members in this network know each other and work together in a well-coordinated fashion TMF Members respond to security problems smoothly and efficiently Dialogic practices: When you think of discussions you have had with others in your network, how frequently do the following happen? (1 = never to 7 = daily) DIALA Develop several options for interpreting information or responding to a threat DIALB Describe problems at both the summary level as well as the detailed level DIALC Discuss alternative scenarios for a problem DIALD Brainstorm about ideas or possible solutions DIALE Describe detailed context of threat information DIALF Understand how information changes over time DIALG Discuss sources of ideas for handling threat DIALH Discuss how time is affecting information DIALI Revisit decisions or interpretations about security issues made earlier DIALJ Discuss source of threat information Adequacy of knowledge dissemination protocols: How adequate are the following administrative procedures used in your network for meeting your security needs? (1 = completely inadequate to 7 = completely adequate) DISSEMA Norms and procedures for informing others about security threat information DISSEMB Procedures for identifying what information is sensitive DISSEMC Safeguards to protect the privacy of the source Clarity of knowledge OWNA OWNB OWNC

ownership: Regarding your network: (1 = completely to 7 = strongly important) It is often unclear who owns the knowledge that is shared among members (reverse) There is a lot of ambiguity about who owns the solutions created among members (reverse) The policy is clear about who owns what rights to knowledge, inventions, or discoveries.

Org learning intent: When participating in your community of security professionals, to what degree are these objectives especially important to your employer? (1 = completely unimportant to 7 = strongly important) LEARNA Learn about new technology LEARNB Learn about new management techniques LEARNC Learn about new ways to prevent security problems LEARND Learn about new ways to respond to security threats LEARNE Access to others’ skills and knowledge Benevolence-based distrust. How frequently do you worry about people in other organizations: (1 = almost never to 7 = all the time) DISTRUSTA Acquiring too much knowledge DISTRUSTB Not sharing necessary knowledge with you DISTRUSTC Aggressively protecting some of their knowledge from you DISTRUSTD Probing you for valuable knowledge lying outside the scope of your agreement with them

273

274


Appendix. (cont’d.) Label

Item

Multiple channels of communication: # of different communication media used at least once a month: (1) Meet one-on-one or small group face-to-face meetings; (2) Call people on the phone; (3) Attend local chapter meetings, conferences’ (4) Use e-mail one-on-one; (5) Use Group e-mail lists or list serves; (6) Log in to a portal Control: Task interdependence. My security responsibilities in my organization: (1 = strongly disagree to 7 = strongly agree) DEPENDA Requires me to talk with staff from other organizations DEPENDB Often involves me sharing information with staff at other organizations DEPENDC Often involves using information and solutions from other organizations DEPENDD Creates results that are dependent on the efforts of others from other organizations Control: Network size. Approximately how many people are in your network? Control: Years in network. For how many years have you been a member of this network?

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