TOP MANAGEMENT TEAM SOCIAL NETWORKS AND ORGANIZATIONAL INNOVATION: AN INFORMATION THEORY EXPLANATION OF TMT VALUE CREATION
Kevin D. Clark Villanova University College of Commerce and Finance Villanova University 800 Lancaster Avenue Villanova, PA 19085 Tel: 610-519-4326 e-mail:
[email protected] and Ken G. Smith University of Maryland Van Munching Hall Robert H. Smith School of Business University of Maryland College Park, MD 20742 Tel: 301-405-2250 e-mail:
[email protected]
Submitted to Administrative Science Quarterly Christine Oliver, Editor
TOP MANAGEMENT TEAM SOCIAL NETWORKS AND ORGANIZATIONAL INNOVATION: AN INFORMATION THEORY EXPLANATION OF TMT VALUE CREATION
Abstract This paper uses demography, group process, and network methods to investigate top management team (TMT) value creation. The TMT is cast as an important boundary-spanning mechanism that facilitates organization-level innovation by accessing, processing, and distributing information. Results from a field study of 72 TMTs of technology firms show TMT education level, social integration, internal networks, and external networks to be associated with increased organization-level innovation. The importance of measuring both internal and external networks is highlighted, as each impacts on innovation but in different ways. The relational view appears an appropriate lens for the study of TMTs, and researchers are encouraged to investigate additional TMT value-creation mechanisms.
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TOP MANAGEMENT TEAM SOCIAL NETWORKS AND ORGANIZATIONAL INNOVATION: AN INFORMATION THEORY EXPLANATION OF TMT VALUE CREATION Since Chester Barnard’s (1938) classic book The Functions of the Executive, scholars have attempted to explain how top management affects organizational outcomes. For example, Cyert and March (1963) introduced the “dominant coalition” concept and Child (1972) advanced the notion of “strategic choice” to explain how top management influenced firm survival. In contrast, scholars, such as Lieberson and O’Connor (1972), Aldrich (1979), and Astley and Van de Ven (1983), have argued that business environments are too complex for managers to matter in a significant way. In response to this debate, and spawned by Hambrick and Mason’s (1984) “upper echelon theory,” a long line of research ensued linking top management team composition, measured by demographic age, tenure, and education with organizational outcomes such as innovation (Bantel and Jackson, 1989; O’Reilly and Flatt, 1989), strategy (Finkelstein and Hambrick, 1990; Michel and Hambrick, 1992), strategic change (Grimm and Smith, 1991; Wiersema and Bantel, 1992) and performance (O’Reilly and Flatt, 1989; Hambrick and D’Aveni, 1992; Michel and Hambrick, 1992). The contention of much of this composition research was that top management’s experiences and values affects organizational outcomes through strategic decision-making. During the 90s, and drawing from team process research (e.g., Shaw, 1981), scholars began to examine attributes of top management process, such as how the team gets along or the formality of its operation. For example, Smith et al.(1994) found a positive relationship between top management social integration and performance and O’Reilly, Snyder, and Boothe (1993) reported a negative connection between top management cooperation with the extent of strategic
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change. Like the composition research, the process work contended that the manner by which top management interacted and communicated would influence organizational outcomes through strategic decision-making. The two streams of research have demonstrated that both top management composition and process are related to organizational outcomes such as innovation and profitability. Moreover, there is evidence that demography and process are interrelated, such that the composition of the team affects the process of interaction (Smith et al., 1994; Knight et al., 1999). Nonetheless, prevailing composition and process models have left us with a fragmented body of literature with inconsistent results (West and Schwenk, 1996; Lawrence, 1998), which Finkelstein and Hambrick characterize as lacking an “orderly, cumulative or a concise set of findings” (1996: 6). A number of scholars have urged a shift from attribute or atomistic study, which characterizes the composition and process research, to a relational approach (Lewin, 1951; White, 1962; Mitchell, 1969; Berkowitz, 1982; Burt, 1982; Granovetter, 1985; Gulati, Nohria and Zaheer, 2000). Relational or network approaches concentrate on the linkages between individual actors (or teams of actors) in a social system and the information that can be derived from such connections. Importantly, relationships are not reducible to the demographic characteristics of the individuals or group processes. As such, a key proposition of structural network theory is that a set of connections or relations is a valuable information-based resource for conducting an entire range of business affairs. Relational theory or network theory thus seeks to capture the essence of these connections and their impact on outcomes (Gulati et al., 2000). Mintzberg (1973) and Kotter (1982) were perhaps the first to observe that executives use their personal contacts or networks of relations to gather information and to access certain resources. From a top management team perspective, the value of a network is derived from the
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information that is gained from mutual acquaintances, friendships, and from feelings of gratitude and obligation that are resultant from membership in certain groups or teams (Nahapiet and Ghoshal, 1998). Whereas the study of demographic composition may reflect the stocks of experiences and values held by the team (Becker, 1970, Hambrick and Mason, 1984), and the study of process is a sign of how the team transfers and manipulates knowledge and information (Shaw, 1981), the study of top management social networks exposes the types and amount of potential additional information that is available to team members through their connections to others. Like top management research on composition and process, the study of networks should enhance our understanding of the top-level decision making processes and how the TMT utilizes information in concert with organizational innovation efforts. The purpose of this research was to 1) investigate an alternative TMT value creation mechanism to strategic decision-making; 2) examine the relative contribution of top management social network characteristics versus composition and process on organizational innovation; and 3) study the specific dimensions of top management social networks and explore how they affect innovation. In adding top management social networks to composition and process perspectives, we seek to provide a more comprehensive and inclusive understanding of how top management teams affect organizational outcomes. By exploring how the alternative dimensions of top management team social networks impact innovation we hope to contribute to the development of a network or relational theory of top management. Technology firms pursue a variety of outcomes ranging from shorter-term financial returns to sales growth to product and market development. We focus on innovation as our outcome variable because this is the most important intermediate measure of performance for the sample of technology firms we studied. 1 .
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During interviews with the CEOs of firms in our sample, it became apparent that firms pursued a variety of performance outcomes. The theme that emerged from these conversations was the need for the firm to
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THEORY: DEMOGRAPHY, PROCESS AND THE RELATIONAL VIEW The top management team (TMT) is defined as a relatively small number of managers who are involved in the key decision making of the firm. The notion of a team of decision makers at the top has its origins in the work of Cyert and March (1963) and Thompson (1967) and was later bolstered in Hambrick and Mason’s (1984) upper echelons theory. In the following sections we use decision making theory, especially how decision makers access and process information, to propose links between TMT composition, process and social networks to organizational innovation. Information theory concentrates on uncertainty and the decision-maker’s need to gather and process information in order to reduce uncertainty (Galbraith, 1973). Galbraith (1973) contended that decision makers need to be able to find and collect the required information, process it, and make decisions (act) to counter environmental threats. Thompson (1967) argued that the management of uncertainty is the principal task of top-level decision-makers as they buffer and protect the organizational core. As key boundary spanners (Kotter, 1982; Luthans, Hodgetts, and Rosenkrantz, 1988), TMTs that are equipped to deal with uncertainty will be able to make better decisions regarding innovation (Thompson, 1967; Porat and Haas, 1967). Important factors in the TMT’s information processing capacity are the stocks of knowledge and skills team members hold, how the team processes knowledge and information, and the relational contacts that hold information that team members can draw upon (Tushman and Nadler, 1978). The decisions we focus on pertain to innovation, which we contend is a key response to environmental threats and changes. keep up with the high rate of change all firms experienced in their markets. Innovation was the chief mechanism by which firms dealt with environmental change. A regression demonstrated that innovation is
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TMT Composition As noted in the introduction, there is a long tradition of TMT composition research emphasizing demography. Two prominent variables in this research are TMT’s years of experience and level of education (Hambrick and Mason 1984). 2 The level of experience and education captures the stocks of knowledge and skills represented within the TMT that may be used to guide innovation. According to Becker (1970), tenure in a firm or industry builds specific firm and industry knowledge. TMTs with extensive tenure in the industry and firm will be more able to use past experiences in the firm or industry to predict future events. Such experiences might be critical and useful when uncertainty is low and the firm requires efficiency and exploitation, but less valuable when uncertainty is high and the firm requires exploration and innovation. Hambrick and Mason (1984) specifically argue that executives who have spent their careers in one company or a single industry will have relatively limited knowledge base from which to develop new innovations. Particularly in “high-velocity environments,” it can be argued that experience in a single firm or industry will restrict the team’s ability to come up with creative solutions in response to changing environmental demands. The limited scope of the knowledge base of such a TMT would constrain their capacity to absorb novel information (Cohen and Levinthal, 1990). Moreover, long tenure in a firm or industry can be a proxy for risk aversion (Hambrick and Mason, 1984; MacCrimmon and Wehrung, 1990; Hitt and Tyler, 1991) and commitment to the a significant predictor of common stock price appreciation. 2 Another important compositional measure is TMT heterogeneity (Hambrick and Mason, 1984). For example, Bantel and Jackson (1989) found that TMT heterogeneity was related to organizational innovation. Heterogeneity is not included in this research for two reasons. First, there was not sufficient intra-TMT response to develop a good measure of heterogeneity. For example, use of the TMT tenure heterogeneity measure reduced the sample to a size (n = 65), which severely limited our degrees of freedom in conducting this study. Second, a regression performed with alternative heterogeneity measures (tenure and functional heterogeneity) did not significantly predict firm innovation. Given the already small sample size and our desire to include variables from three broad domains (composition, process and
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status quo (Child, 1974; Grimm and Smith, 1991; Hambrick, Geletkanycz, and Fredrickson, 1993). According to Hambrick and Mason, long-tenured TMTs demonstrate a reluctance to change past ways of operating. In support of the tenure-stability hypothesis, several researchers have found that increases in TMT organizational tenure are related to strategic persistence and a resultant absence of change (Katz, 1982; Finkelstein and Hambrick, 1990; Grimm and Smith, 1991; Wiersema and Bantel, 1992). Education, on the other hand, is an indicator of broad knowledge (Becker, 1970). Since formal years of education, for the most part, take place outside of the particular industry or organizational context, the knowledge that is developed is more general in nature. Furthermore, greater levels of education may be reflective of a desire for new learning and development (Bantel, 1993; Wally and Baum, 1994). In support of this reasoning, several studies have found positive relationships between TMT education level and receptivity to innovation (e.g., Becker, 1970; Rogers and Shoemaker, 1971). Finkelstein and Hambrick (1996) suggested that the value individuals place on education serves as an indicator of their cognitive complexity. It follows that a high level of education reflects a desire and willingness to grow and change. Consistent with this viewpoint, Kimberly and Evanisko (1981) found a positive relationship between education level and innovation in their study of hospital executives. Bantel and Jackson (1989) also found a positive relationship between the average level of education of the TMT and innovation in their study of commercial banks. This result was also supported in O’Reilly and Flatt (1989). Based on the above arguments, we predict: H1: TMT demography will be related to organizational innovation.
networks), we excluded heterogeneity.
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H1a: The level of TMT education will be positively associated with organizational innovation. H1b: The level of TMT experience will be negatively associated with organizational innovation. TMT Process Drawing from the work of O’Reilly, Caldwell, and Barnett (1989) and Smith et al., (1994), we examine two important aspects of TMT process: social integration and informality of communication. Smith et al., (1994) broadly defined social integration as including the extent to which members are cohesive and get along with one another, strive for consensus and avoid conflict. Communication informality concerns the extent to which top management teams favor less formal communication channels, such as spontaneous conversations and unstructured meetings, over more formal channels, such as highly structured meetings and written communication (Smith et al., 1994). In general, we expect that TMTs that get along well, strive for consensus, avoid conflict, and that use informal communication procedures will be more open to change and more capable for creative problem solving. Socially integrated TMTs will tend to be both friendly and cooperative and engage in a great deal of communication. Innovation will be more likely to occur in such teams because members are flexible and trusting. Consistent with these ideas, Eisenhardt and Bourgeois (1988) found internal political behavior, which can be seen as a measure of internal strife or conflict, to be negatively related to organizational performance in their study of high-velocity firms. Ouchi (1980) utilized transaction cost theory to make a similar argument. He argued that clans, which are highly socialized and cohesive collectives, have lower communication and coordination costs and are thus more efficient and flexible than formal inflexible bureaucracies.
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Informal clans can apply greater attention to problems that require innovative solutions, which may be particularly important in turbulent, high-velocity environments (Eisenhardt and Bourgeois, 1988). Ebadi and Utterback (1984) found that how well a team got along with one another was positively related to innovation. Versy (1991) uses Ford Motor Company’s “Team Taurus” as an example of how the pursuit of innovation requires collaboration and teamwork. Thus, although social integration may, in extreme cases, lead to “groupthink” (Janis, 1982), it can lead to very positive outcomes such as improved exploration, ownership of decisions and commitment to implementation (Smith et al., 1994). In general, we believe these positive benefits of social integration will facilitate the speedy development of new products and services. With regard to informality of TMT communication, organizational theory suggests that organizations that stress formal structures and processes are likely to respond to problems by utilizing standard operating procedures (Cyert and March, 1963). As a result, any innovation that results from standardized communication will be incremental rather than radical (Fredrickson, 1986). In contrast, research suggests that informal face-to-face contacts are key ingredients associated with successful R & D efforts (e.g., Katz and Allen, 1985; Utterback, 1974). Thus, H2:
TMT process will be related to organizational innovation.
H2a: TMT social integration will be positively associated with organizational innovation. H2b: TMT communication formality will be negatively associated with organizational innovation.
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TMT Networks Nohria (1992) argues that organizations do not operate in a vacuum; rather they are connected in complex ways to their environment. A network perspective allows us to measure and assess TMT linkages. In essence, the TMT network consists of everyone the team is in contact with for the purpose of achieving organizational goals. From a structural network theory perspective, the value and usefulness of an actor’s, or TMT’s, network stems from the position of the actor within the network (Burt, 1982). Boundary spanners who are central and who have many connections are able to access greater information than actors at the periphery of the network, or who are not as well-connected. The emphasis in structural theory is on the access and control of information or other resources. Recently, another group of theorists (Coleman, 1988; Gulati, 2000; Nahapiet and Ghoshal, 1998) have also focused on the social capital aspects of networks. Social capital incorporates a broader view of the value of networks to include elements of friendship, trust, and reciprocity. This research adopts primarily a structural view of the network where the size, structure and orientation of the TMT’s network allows it to access information. Because access to information may depend not only on the position of the actor, but also on the tenor of the relationship between actors, this research also incorporates an element of the social capital school – the strength of the tie (Granovetter, 1973; Hansen, 1999). Moreover, Adler and Kwon support the inclusion of what they term “content of social ties” in order for network research to be capable of fuller explanation and prediction (2002: 32). Accordingly, we examined five different characteristics of the TMT network: network size, network range,
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network strength of ties, network redundancy, and locus of orientation. 3
We describe each
in turn. Network Size. Network size refers to the number of contacts represented by the network (Scott, 1991). A TMT network can be said to be large or small by comparison to the TMT networks of other firms. Note that the size of the network is only concerned with the number of contacts available to the TMT, not the importance of the contacts or their diversity as such. Network size is important as it represents the potential of the team to utilize connections to gather additional information. In this research, we measure direct ties between TMT members and other actors, but do not measure ties these other actors may have to other actors. Such indirect ties have been shown to be sources of information, but also have been shown to be less important when a large number of direct ties are present (Ahuja, 2000). Network Range. Network range refers to the diversity of the contacts represented in the TMT network (Scott, 1991). Although larger networks tend to have greater range, this is not always the case. A TMT may have a great number of ties to one type of stakeholder (e.g., investors), but few ties to other stakeholders (e.g., suppliers, customers, universities, etc.). Another TMT may have relatively few ties, however, they may be connected to a greater diversity of stakeholders. Range is important because it reflects the TMT’s potential access to alternative information resources such as new opportunities in the environment.
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Another key concept in network theory is that of centrality, or prominence. An actor is central in a network when they are connected to many other actors. While clearly important, centrality can only be determined when the network is fully enumerated (e.g., where we have A’s relationship to B and C, and where we have B’s relationship with C). In this research we measure the relationships between the TMT and a variety of actors, however, we do not capture the relations between these other actors.
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Strength of network ties. The strength of ties in the TMT network is a key dimension of network theory (Granovetter, 1973; Krackhardt, 1992). Network ties are said to be strong when they are exercised frequently, have been in existence for some time, and when there is an emotional component to the link (Granovetter, 1973). Tie frequency refers to the number of times a contact is made with the other actor during a stated time period. Tie duration represents the amount of time that has elapsed since the tie was first established. Emotional intensity captures the closeness of the relationships that exist between the TMT and other actors (e.g., a friendship tie would be closer than a business-only relationship). Network Redundancy. Redundancy represents the extent to which more than one member of the TMT has a link to the same stakeholder. Highly redundant networks are those in which nearly every team member is connected to the same stakeholders; hence the connections are redundant. In contrast, a non-redundant network would entail no overlap in connections. In other words, each team member would be connected to different sets of stakeholders. In network theory, highly redundant networks are considered inefficient. Locus of Orientation. Locus of orientation refers to the extent to which the TMT network is connected internally or externally. A TMT may concentrate its ties within the firm (internal locus) or may attempt to build extensive ties with external actors (external locus). Though not a traditional network concept, the locus of orientation is relevant to the study of TMTs because it reflects the extent to which the team is focused on the external environment (e.g., boundary-spanning and buffering activities) versus internal actors (e.g., coordination and control) (Thompson, 1967). Overall, we expect each of these TMT network variables to be related to the level of innovation. James Thompson (1967) and Henry Mintzberg (1973) argued that top managers
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play a crucial boundary-spanning role serving as a conduit for information flows between internal and external stakeholders. In their role as boundary spanners, these managers have connections outside and inside of the organization that potentially provide value through access to new information and in the efficient processing of this information (Thompson, 1967). Boundary spanners’ networks may also provide the firm with information on the availability of new resources and technologies, as well as provide advantages from learning, scale and scope economies (Gulati et al., 2000). In developing our propositions linking the five characteristics of social networks to innovation, we treat networks as conduits of information that aid organization members in the search and discovery of opportunities to innovate (March, 1991). TMTs with large networks will be able to obtain information at a faster rate, be capable of accessing a richer set of data, and will draw from a broader set of referrals (Nahapiet and Ghoshal, 1998). The ability of an organization to innovate is dependent on the availability of new knowledge and information. TMTs with large networks will be more able to utilize these networks to search and discover new knowledge and information, which can be used in the innovation process (Nahapiet and Ghoshal, 1998). A boundary spanner may have a broad range of different contacts, for example, with banks, investment houses, universities, technology centers, and business associations or they may all be of one type, for example, contacts only with suppliers or customers of the firm. Boundary spanners with a narrow range of contacts will be limited in the types of information and knowledge they can search for and discover, whereas networks composed with a broader range of different contacts will have greater potential to use these network contacts to search and discovery of opportunities to innovate.
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The popular adage ‘it’s not what you know, but who you know’ is a cynical commentary on the nature of organizational life. Instead, this research posits that who you know (and how you know them) impacts what you know. Although weak ties may provide certain efficiency benefits, especially where the meaning of information is not problematic (Nahapiet and Ghoshal, 1998), strong ties will be critical when the information is uncertain and ambiguous (Hansen, 1999). There is significant evidence that when ties are strong, individuals will be more willing to exchange information and cooperate for mutual benefit (Mishira, 1996). This may be especially true in high velocity environments where information will be more complex and may be tacit (Hansen, 1999). We expect TMT members with strong ties to be able to garner richer information and to be more confident in that information than will TMTs with weak ties. Moreover, the richer and more accurate information gleaned from strong ties will be important to speedy development of successful innovations. According to the weak ties argument, the TMT should maximize the size and range of its contacts, and this is facilitated by the utilization of the less costly to develop and maintain weak ties (Granovetter, 1973). Extending this argument, overlap or redundancy in the individual networks of executives compromises the possible size and range of the total TMT network that can be used for search and discovery. Therefore from a weak ties perspective, redundancy in TMT network is sub-optimal because it will make search and discovery repetitive and inefficient. In other words, highly redundant teams would all have the same information and would not be efficient in the development of new products or services; low network redundancy in TMTs would enhance efficiency and increase the likelihood that search would lead to more significant discovery.
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Finally, we predict that locus of orientation (e.g., internal vs. external) of team members’ networks will be an important predictor of innovation. March (1991) uses the concepts of exploration and exploitation to describe two ways in which organizations search, discover, and learn. Exploration refers to search and discovery related adaptation and radical innovation. In contrast, exploitation refers to refinement, production, efficiency, selection, implementation, execution, and incremental innovation. Generally speaking, we would expect internally-oriented TMT networks to be related to increased efficiency, sales growth, and return on sales. Internally-oriented networks would not have the external contacts to search and discover new information from outside the organization. Such teams may focus on modification of existing products and services. In contrast, externally-oriented TMT networks would reflect an “open systems” orientation towards innovation and adaptation. As suggested above, we believe that networks will add value over and above TMT composition and process through exploitation of network ties and structure, and through the trusting relationships that evolve from enjoying strong ties. Thus, boundary spanners with large externally-oriented networks, containing a broad range of different and non-redundant contacts, and composed of strong ties will have more information and knowledge to bring to the innovation process. This network structure will enhance the likelihood that the boundary spanners will be able to access a wide range of ideas and/or informational contacts that will facilitate innovation. H3:
Top management team (TMT) networks will be associated with inc reased organizational innovation. H3a: TMT network size will be positively associated with organizational innovation.
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H3b: TMT network range will be positively associated with organizational innovation. H3c: TMT strength of ties will be positively associated with organizational innovation. H3d: TMT network redundancy will negatively associated with organizational innovation. H3e: TMT external locus of orientation will be positively associated with organizational innovation.
SAMPLE AND METHOD Field data were collected using: (1) on-site structured interviews with each firm’s CEO, (2) in-depth questionnaires completed by the CEO and members of the firm’s TMT, and (3) archival sources of secondary data (corporate records, SEC filings, and other publications, etc.). The sample was selected according to two criteria. First, given the intensive nature of the data collection process, all firms had to be local or within driving distance of the researchers. Second, in order to ensure that the firms faced similar competitive environments (to have a focused sample), the companies had to conform to the definition of high-technology organizations. Milkovich (1987, p. 80) defined high-technology industries as being populated by “firms that emphasize invention and innovation in their business strategy, deploy a significant percentage of their financial resources to R & D, employ a relatively high percentage of scientists and engineers in their workforce, and compete in worldwide, short-life-cycle product markets.”4 4
Rogers and Larsen (1984) offered a similar definition suggesting that industries that meet this definition include guided missiles and spacecraft, radio and TV receiving equipment, communications equipment, electronic components, aircraft and parts, office and computing machines, ordnance and accessories, drugs and medicines, industrial inorganic chemicals, professional and scientific instruments, engines and turbines, plastic material and synthetics, computer programming, data-processing, research and development laboratories, and management consulting.
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The focus on this sample dictated we study innovation as our key dependent variable. Two hundred thirteen firms were identified as meeting the study criteria. Initially, an introductory packet consisting of a letter from the research team and three endorsements: the Dean of the Business School, the Director of the University’s Center for Entrepreneurship, and the editor of a technology-based trade publication was mailed to the CEO of each identified firm. The research team then phoned each CEO to provide additional information regarding the study, to answer any specific questions or concerns of the CEO, to ask for the firm’s participation, and to schedule a time for an initial site visit and interview. Of the 213 firms contacted, 85 companies agreed to be interviewed, yielding an initial participation rate of 39%. Each CEO or President was interviewed for approximately one hour. There were three main purposes for the on-site CEO interview. First, the interview enabled us to gain the CEO’s support for the study. Second, the interview was used to collect information on the level of innovation within each firm and other contextual information. Finally, CEOs were asked to identify the members of their top management group. As part of this interview, each CEO was asked to sign a letter encouraging identified team members to complete questionnaires and to identify an internal contact that could help the research team distribute and collect surveys. Of the 85 firms that were interviewed, we obtained complete sets of data on 73 firms (participation rate = 35%). The average number of top management team members responding to the study was 3, for an intra-TMT response rate of 59%. Approximately 57% of the firms in this sample were publicly-traded. The companies agreeing to participate were not significantly different from those not participating in terms of reported sales (t = 1.364, p > .05), or number of employees (t = 1.695 , p > .05). The sample was composed of firms from multiple high
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technology industries including computer software, semi-conductor equipment, information technology and integration services, engineering services, and communications and included firms ranging in size from $320,000 to $8.4 billion in sales revenues (mean = $362 million). Variable Definition and Measurement TMT composition, process and social networks were assessed through questionnaires distributed to each team member. The questionnaire was extensively pre-tested with a) several MBA students who had extensive experience working in technology intensive firms, and b) four CEOs from firms that were not part of the study. For the most part, the composition, process and network measures were collected at the individual level and then aggregated to the group level of analysis. Where appropriate, three tests were performed to determine statistical support for aggregation. First, ANOVA was performed to determine if variation in a particular measure was significantly greater across sampled organizations than within. James (1982) and Bliese (1998) developed two additional aggregation tests. ICC(1) is used to determine whether a significant proportion of the variance in the measure can be explained by membership in a particular TMT. ICC(2) is used to determine the stability of the team level means. Appendix A contains a full discussion of aggregation issues and the results of the tests. The actual scale items used in the analysis are found in appendix B. TMT Composition Experience. Becker (1970) found that years of experience in a job, or industry represented specific knowledge of an individual. For this study, years of experience is an index of mean TMT tenure in job, tenure with employer, and tenure in industry. First, the individual years of experience were standardized. A linear additive index measure was then constructed for each individual in the TMT. Finally, the aggregation from the individual to group level of
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analysis was accomplished by computing the mean experience index of the team members. Education. Following Becker (1970), years of education is used as the measure of the general knowledge residing in the TMT. Respondents were asked how many years of post-high school education they had completed. The aggregation to the group level of analysis was accomplished by computing the mean years of education for TMT members. TMT Process Social Integration. The TMT social integration scale is comprised of 15 different items as borrowed from Smith et al. (1994, α = .9233). More specifically and based on a principal components factor analysis, the 15 items that comprise three scales (cohesiveness, consensus, and conflict) were pulled together into an overall social integration measure. 5 First, individual responses to the items were collapsed to create individual scores for each component of social integration. Second, an additive index was calculated for each individual in the TMT. Finally, the aggregation from the individual to group level of analysis was accomplished by averaging the individual social integration scores of the TMT members. Communication Formality. Formality of TMT interactions was measured with four items that were aggregated from the individual to team levels. A principal components factor analysis with the items that made up social integration index above indicated that communication formality is a distinct process factor. First, individual responses to the items were collapsed to form a formality score for each member of the TMT. The aggregation from the individual to group level of analysis was accomplished by averaging the formality scores of each of the TMT members. TMT Social Network 5
As a further check, we ran the analysis with the separate measures of TMT cohesion (α=.8074), consensus (α=.7257), and conflict (α=.8849). The results with regard to the relationships of interest were similar.
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Based on extensive pre-testing with CEOs and executives from this industry, TMT members were asked about the relationships that they had with actors from nine general external categories (e.g., financial institutions, suppliers, customers, competitors, alliance partners, government agencies, trade associations, board of directors, and other) and four general internal categories (e.g., operations, marketing/sales, research and development, and other) of actors. We then asked specific questions about each of these relationships. From these procedures we estimated each respondent’s network size, network range and strength of ties. The specific network items can be found in Appendix B. Network Size. Network size is a count of the number of contacts represented by the combined networks of the TMT members. First, the count of all contacts listed by individual TMT members who responded to the survey was computed. Second, this group-level total was divided by the number of respondents to generate the mean number of contacts per TMT member. Finally, to account for non-response, this mean was multiplied by the size of the team as indicated by the CEO. The resultant measure represents the total number of contacts of the TMT. 6 Network Range. Network range refers to the diversity of contacts in the TMT’s social network. Range has been operationalized as the number of different status groups accessed (Walker, Wasserman, and Wellman, 1993). In this research, range is operationalized as the proportion of actor categories (e.g., customers, financial institutions, alliance partners, etc..) to which the TMT is linked. Strength of Ties. Tie strength is a multifaceted construct consisting of frequency of interaction, duration of the relationship, and emotional intensity of the bond (Granovetter, 1973).
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As a check, the analysis was run using two other measures of network size, the mean number of contacts
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For each category of potential contacts, respondents were asked to indicate how long, on average, they had known the individuals in that category, how frequently they interacted, and how close were these relationships. The scores for each category were then combined to create an average duration, frequency, and emotional intensity (closeness) score for the network of each TMT member. Because the scales for each of these components of strength are different, the responses were standardized. The standardized scores for tie duration, frequency, and emotional intensity were then used to develop an index (linear combination) that represented the strength of ties for each TMG members’ network. Finally, the aggregation to the group level of analysis was accomplished by averaging the strength measures of the TMT members. Redundancy of Ties. A fully redundant network is one in which the networks of each TMT member are identical. That is, where the contacts one executive has with different types of contacts mirror those of the other executives on the TMG. In a non-redundant network, some executives would have unique ties, or could serve as structural bridges (Burt, 1992). Our measure captures the extent to which TMT members are linked to the same stakeholder categories. Specifically, the measure of redundancy used in this research is the proportion of categories to which at least two members of the TMT are linked to the total possible number of categories (13). Redundancy is a group-level construct and thus aggregation tests are not appropriate. Network Locus. Network locus has not been a key area of research in network theory, however, a review of information theory (Galbraith, 1973; Mintzberg, 1973; Thompson, 1967) suggests that this may be an important factor in TMT functioning. Network locus refers to whether the network tends to be comprised of connections to actors inside the company versus connections to actors outside of the company. First, the number of external contacts listed by per TMT member and the unadjusted sum of TMT member contacts, and the results were similar. 22
TMT members was summed. Second the total number of contacts (external plus internal) listed by TMT members was summed. The measure was the ratio of the number of external ties to the total number of ties. Thus, a locus score of greater than .50 indicates an externally-oriented TMT network. Organizational Innovation During the pilot testing of the instrument, it became clear that firms innovate in a variety of ways. In keeping with the findings of Damanpour (1991), innovation is operationalized as the number of new innovations (e.g., products, services, markets, processes) developed by a firm during the last year. Since the composition of this innovation measure deviates slightly from Damanpour (1991), construct validity was confirmed by comparing this measure to Damanpour’s and firm R & D spending (Scherer and Ross, p. 652). Both the simpler new product and services count measure (r = .885, p < .01) and R & D spending (r = .644, p < .01) were strongly correlated with the measure used in this research. The innovation measure was collected from CEO interviews and the R & D spending measure was obtained from annual reports. Organization Size. Because larger firms may have more resources (financial capital, organizational slack, etc.) to devote to innovation efforts, organizational size was controlled in the analysis (Cohen and Levin, 1989). Organizational size was measured as annual sales revenue as collected from the CompuStat database, annual reports and, for private firms, during CEO interviews. Statistical Analysis Table 1 is the correlation matrix of all variables used in the analysis. Hypotheses 1, 2 and 3 were tested using hierarchical regression analysis with forced forward selection of blocks of
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variables. First, net sales revenue was entered as a control for organizational size. The blocks were then entered consistent with the presentation of concepts in this paper (e.g., composition first, process second, networks last). Entering the network block of variables last provides a test of the value of the TMT network, relative to composition and process, facilitating organizational innovation. 7 The sub-hypotheses concern the directional impact of specific demographic, process, and network characteristics. The test for each sub hypothesis is a significant beta (one-tailed t-test) in the predicted direction in the regression containing all of the measures. ---------------------------------Insert Table 1 about here ----------------------------------
RESULTS Table 2 shows the regression of firm-level innovation on TMT composition, TMT process, and TMT networks while controlling for firm size. After controlling for firm size, the TMT composition block (education and experience) was entered into the regression. A change in model R2 of .064 (p
