Market knowledge spillovers and differential ...

Market knowledge spillovers and differential absorption within marketing departments

Christophe Van den Bulte The Wharton School University of Pennsylvania 3730 Walnut Street Philadelphia, PA 19104-6340 U.S.A. [email protected]

Annouk Lievens Antwerp University - UFSIA Prinsstraat 13 2000 Antwerp Belgium [email protected]

Rudy K. Moenaert Tias Business School Tilburg University P.O. Box 90153 5000 LE Tilburg The Netherlands [email protected]

April 2004

Acknowledgement. We thank the management and employees of the three research sites for their cooperation, and George Day, Bruce Kogut, Gary Lilien, Hans Pennings, Lori Rosenkopf, Stefan Stremersch, Gabriel Szulanski, and Stefan Wuyts for comments on earlier drafts. We also benefited from comments by participants in the 2001 INFORMS Marketing Science Conference, the 2002 INSNA International Sunbelt Social Network Conference, and the doctoral seminar on Network Theory and Applications at Wharton. Correspondence. Christophe Van den Bulte, The Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104-6340, U.S.A.. Tel: 215-898-6532. Fax: 215-8982534. E- mail: [email protected]

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Market knowledge spillovers and differential absorption within marketing departments

Abstract We investigate how marketing professionals’ intra-departmental social network position is associated with their knowledge about customers, competitors, and technology. We also investigate how marketing professionals’ extant knowledge affects how much they absorb of the knowledge available through colleagues. Learning research suggests that the effect need not be positive: extant knowledge increases the ability to learn, but decreases the opportunity and motivation to do so. Using data from the central marketing departments of three banks, we present two key findings. First, marketers’ knowledge is associated with their social network exposure to others’ knowledge, even after controlling for more traditional metrics of social capital. Second, and more importantly, marketers’ extant knowledge has a positive effect on learning from peers in the technology domain, but a negative effect in the customer and competitor domains. This suggests that the professionals studied were realizing positive returns on their own intellectual capital for learning outside their primary domain of expertise, but negative returns for learning within their primary domain of expertise.

Key words: Market knowledge, social networks, social contagion, social capital.

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INTRODUCTION Marketers’ main responsibility is to manage the firm’s transactions and relationships with customers. To do so effectively, marketers gain knowledge about the ir markets and spread it within their organization. Market research and direct contact with customers, salespeople and distributors are important ways to stay abreast of market developments and to keep one’s knowledge up to date. Research on how consumers and R&D engineers use their social networks to gain information suggests that marketers’ network of marketing colleagues might also be a very effective source of market knowledge. Yet, while the importance of network ties in buying behavior, and especially in learning about new products, is well recognized in the marketing literature (e.g., Arndt 1967; Frenzen and Nakamoto 1993; Iacobucci 1996; Johanson and Mattsson 1994; Midgley et al. 1992; Wathne and Heide 2004), the same is not true for how marketing professionals learn through networks. Research in marketing has long emphasized the role of formal rather than informal systems to collect, interpret and disseminate information, and has considered marketers’ having and gaining market knowledge either as an organization- level phenomenon (e.g., Day 1991; Moorman 1995) or as an individual- level or dyadic phenomenon (e.g., Day and Nedungadi 1994; Deshpandé and Zaltman 1982; Moenaert and Souder 1996) without taking into consideration the structure of social networks linking individuals. The broader knowledge management literature has only recently started to focus on how informal networks affect knowledge development and dissemination (e.g., Lesser 2000). Another limitation in our current understanding of how marketers gain and share market knowledge is that the research on informal flows involving marketers has focused on social networks linking marketers to colleagues in other departments rather than on social networks within marketing departments (e.g., Griffin and Hauser 1992; Maltz and Kohli 1996; for an

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exception see Moorman et al. 1992). However, network effects are likely to be smaller within than across departments, because alternative mechanisms facilitating knowledge sharing— formal information systems, common culture, and hierarchical control—tend to be more prevalent or more effective within than across departments (Allen 1977; Axelrod 1986; Burt 1997; Simon 1991b). As a result, the present evidence of network effects in how marketers share knowledge across departmental boundaries may not generalize to sharing within departments. A third outstanding issue is what facets of network structure help one gain more knowledge. Prior research has focused on two facets: having a central brokerage position and having a set of direct contacts who are densely connected among themselves (e.g., Frenzen and Nakamoto 1993; Reagans and McEvily 2003). However, poor market intelligence made available by fellow marketers and other colleagues is not likely to be of much value (Deshpandé and Zaltman 1982, 1984; Maltz and Kohli 1996). Hence, the quality of one’s contacts’ knowledge that “spills over” along network ties can be expected to be a third important facet (Burt 2000; Lin 1999; Wuyts et al. 2002). Though central to some definitions of social capital and though well-accepted in the study of social contagion, the idea that the quality of one’s contacts’ knowledge matters has received only scant attention in prior research. A fourth issue about which our knowledge is rather limited is the effect of extant knowledge on learning. Over the last fifteen years, it has become accepted in the fields of innovation and organizational learning that one’s existing stock of knowledge contributes to one’s ability to absorb new knowledge (Cohen and Levinthal 1990). However, one of the fundamental ideas of social science is that behavior is driven not only by ability but also by opportunity and motivation (Elster 1989). To the extent that better informed marketers feel less need or opportunity to learn, those who are well informed may actually absorb less rather than more of

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the knowledge accessible via colleagues. The idea that prior knowledge may impede rather than foster learning has already found support in studies of education and consumer learning (e.g., Sørensen and Hallinan 1977; Wood and Lynch 2002). When it comes to how marketers and other employees learn about markets, however, little is known about how the positive ability effect and the negative opportunity and motivation effect balance out. The present study investigates how marketing professionals’ intra-departmental social networks are associated with their knowledge about customers, competitors, and technology. Specifically, we investigate whether marketers’ knowledge on those three dimensions is enhanced by having a portfolio of social ties to knowledgeable colleagues. In other words, we investigate whether the information quality of marketers’ contacts “spills over” and affects their own market knowledge. We also investigate to what extent knowledgeable marketers absorb more knowledge from their network contacts and whether the absorption differential is higher for technology knowledge, since it falls outside marketers’ primary domain of expertise.

KEY CONCEPTS, THEORY AND HYPOTHESES We view the firm as a network of individual actors, each being a pool of knowledge, and locate the organization’s market knowledge within these individuals (Arrow 1994; Grant 1996; Simon 1991a). Our analysis focuses on knowledge flowing across individuals through social networks. 1 This section presents definitions of key concepts and hypotheses.

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Some discussions of the social dimension of knowledge do not only address the association between individual knowledge and social relations, but also the notion that organizational cognitions may exist outside of individuals. These are two distinct ideas. Making the causal conjecture that individuals’ cognitions are affected by social structure is quite different from making the ontological claim or assumption that cognitions may exist outside individuals. Our research investigates only the former.

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Knowledge We investigate knowledge of three facets of the market environment, which we refer to as knowledge domains: customers, competitors and technology (e.g., Kohli et al. 1993). In this study, knowledge simply means being informed about what is. Such “know-what” (as opposed to procedural “know-how”) knowledge may include various elements (Merton 1973; Souder and Moenaert 1992): (a) facts, truths, or principles, (b) ideas validated by various tests, (c) findings of research, as well as (d) understandings derived from experience.

Social capital Several mechanisms can be posited to explain how one can mobilize knowledge and other resources through one’s network. The social capital literature has identified three distinct and complementary mechanisms, and has identified three specific facets of social networks that can lead to superior outcomes. The weak tie and structural holes arguments, associated with Granovetter (1973) and Burt (1992), emphasize the opportunities to access more information stemming from having a central brokering positio n within the flow of information. The network closure argument, associated with Coleman (1988), emphasizes that having a densely knit network makes it easier to create and enforce norms of cooperation and hence to gain support and get those who have the information to actually share it. The social resource or social contagion argument, associated with Lin (e.g., 2001), emphasizes the quality or value of secondorder resources, i.e. the resources that are not one’s own but that are embedded in and mobilizable through social networks, like the knowledge of one’s direct contacts (Bourdieu 1980; Lin 1999). While this third argument is less well known than the other two and not investigated as often in intra-organizational social capital research (Burt 2000), social contagion is obviously a key consideration when trying to understand knowledge acquisition via social

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networks. Enjoying access to high-quality second-order resources requires that one has identified resourceful others and that they are willing to make their resources available. As a result, Lin’s (1999) mediation argument notes, a central position or a densely knit network of direct contacts need not contribute much to one’s outcomes once second-order resources are taken into account. Accordingly, in this study, we focus on the effect of second-order resources while controlling for the potential effect of having a central position or a densely knit network of direct contacts.

Second-order resources If managers learn about their markets through colleagues, then their market knowledge should be affected not only by how many direct and indirect contacts they have and by whether their direct contacts are willing to share the knowledge they have, but also by how much knowledge these direct contacts have. For instance, poor market intelligence made available by fellow marketers and other colleagues is less likely to be of use than high quality intelligence (Deshpandé and Zaltman 1982, 1984; Maltz and Kohli 1996). Hence, one can conceive of actor i’s social capital as the set of resources y controlled by other actors j, weighted by the extent to which i interacts with each actor j (Borgatti et al. 1998; Snijders 1999): SNEi = Σ j wij yj where SNE stands for social network exposure and wij is a social weight variable indicating whether or how much i interacts with j. This operationalization clearly reflects the idea that social capital is decomposable into two elements, the social relationships allowing individuals to access resources possessed by others and the amount and quality of those resources (Bourdieu 1980; Lin 1999). This operationalization also is very similar to the way sociologists and marketers model influence and information diffusion processes in social networks (e.g., Friedkin

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1998; Van den Bulte and Lilien 2001). Unlike traditional accounts of how social networks act as a resource through brokerage or norm enforcement, the social-contagion-through-networkexposure account emphasizes the amount or quality of the resources that one can mobilize (Borgatti et al. 1998). Hence, the standard second-order resource argument implies: H1.Interacting with others who themselves are knowledgeable, i.e. having social network exposure to others’ knowledge, increases one’s own level of knowledge. While seemingly obvious, H1 need not hold since the knowledge made available through social contacts also has to be absorbed for actual learning to take place.

Extant knowledge and ability, opportunity and motivation to absorb knowledge Several researchers have discussed how extant knowledge may affect the ability, opportunity and motivation to learn, and hence the rate of absorption. Research on both individual and organizational learning suggests that the more one knows, the easier it is to recognize and assimilate new knowledge. In other words, learning may be subject to positive returns to extant knowledge (implying that knowledge may exhibit increasing returns to scale). Hence, similar to students of absorptive capacity in organizational learning (e.g., Cohen and Levinthal 1990), one might expect that marketers who are well informed absorb more of the information they have access to through colleagues, i.e., they benefit more from their second-order resources. It is important to note the causal mechanism leading to that claim: extant knowledge is posited to lead to more absorption because it improves one’s ability to learn. However, one of the fundamental ideas of social science is that behavior is driven not only by ability but also by opportunity and motivation (e.g., Elster 1989). To the extent that more knowledgeable marketers feel less need to learn from their colleagues (“I know enough to do my job”) or opportunity to do so (“I already know so much that there’s hardly anything left for me to learn”), they may actually

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absorb less rather than more of the ir contacts’ knowledge (March and Simon 1958; Sørensen and Hallinan 1977; Moorthy et al. 1997; Wood and Lynch 2002). Since the ability and the opportunity and motivation arguments make contradictory predictions about the effect extant knowledge on knowledge absorption, we posit: H2.The extant knowledge of an actor affects how much of his or her second-order knowledge resources that actor absorbs. Being non-directional, hypothesis H2 is mainly of descriptive interest. Taking into account differences across kno wledge domains allows us to develop sharper contingency predictions on whether the positive ability effect or the negative motivation/opportunity effect will dominate and hence on what the direction of the effect of extant knowledge will be.

Differences across knowledge domains Customers, competitors, and technology are generally considered to be the three main facets of the market environment (e.g., Kohli et al. 1993; Souder and Moenaert 1992), but marketers do not relate to each equally. Customers and competitors are facets of the environment that the company’s marketing function is specialized in dealing with (Anderson 1982). Technology, in contrast, is typically more a central concern to other functional specialists and departments. Hence, one can expect marketers to have more experience and expertise in dealing with competitive and customer information, than with technological information, and to have developed more expertise and routines for codifying and classifying information to their key task domains (March and Simon 1958). As a result, marketers should experience less ambiguity when processing competitor and customer information and be able to maintain more complex cognitive schemata about these two domains compared to technology. As various strands of social information processing theory emphasize, actors are especially

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likely to rely on colleagues for knowledge domains outside their realm of confident expertise (e.g., Blau 1963). Direct social interaction allows the information to be tailored for the recipient and gives the latter the opportunity to ask clarification questions. Oral communication may also be very effective in quickly conveying very recent information and in sharing—or even jointly building—cognitive schemas to make sense of the data (Sims and Gioia 1986). Providing opportunities for customization, interactivity, and recency, in short, for rich information transfer, networking among colleagues may be particularly effective when dealing with complex or ambiguous knowledge domains (Daft and Lengel 1986). Note, it is precisely when issues are perceived as ill-structured that a better knowledge base and greater ability to recognize and assimilate relevant issues are likely to facilitate absorption (Cohen and Levinthal 1990). In contrast, within one’s own realm of specialization and expertise, one may perceive fewer opportunities to learn things one does not know yet and also feel less motivated to learn even more. Consequently, in such knowledge domains, the negative effect of extant knowledge on opportunity and motivation to learn is likely to be greater than in other domains. Hence: H3.The effect of one’s extant knowledge on how well one absorbs knowledge from one’s network is more positive for knowledge outside one’s primary domain of expertise than for knowledge inside one’s primary domain of expertise.

DATA Research setting and data collection The data were collected in the central marketing departments of three Belgian retail banks. Focusing on a single country and industry controls for differences in market dynamism, in complexity of regulation, and in other contextual elements affecting market learning. Ba nk A is a large traditional bank offering a wide range of financial products. It is the market leader in

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savings account products. It used to be a state owned bank. Its marketing department counted 100 full-time employees at the time of data collection. Ba nk B is also a large institution offering a full range of financial products. It has the reputation of being very innovative and was the first to launch a full range of electronic retail banking products in Belgium. Its marketing department counted 56 full- time employees. Bank C, finally, is a smaller institution, with 33 full- time employees in its marketing department. 2 Assuming that non-professionals such as low- level clerical staff did not notably affect marketing professionals’ market knowledge, and conscious of the need to maximize response when collecting network data, we limited the data collection to professionals. We identified them by using both formal job descriptions and nominations by each bank’s top marketing officer of employees making a substantial contribution in innovation processes. This resulted in networks of 42 professionals in bank A, 37 in bank B, and 16 in bank C. We collected data on all 95 professionals using a multi-part questionnaire containing questions regarding (a) market knowledge, (b) communication with colleagues in the marketing department, (c) extradepartmental communication and (d) experience and human capital (age, tenure within the bank, etc.). All network members in banks B and C, and all but one in bank A, responded.

Dependent variables: market knowledge We used self-reported measures of knowledge. Professionals were asked to rate their knowledge on customers, competitors and technology on multi- item 5-point Likert scales. The question was worded as: “Please indicate how strongly you agree with being well informed about

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As the size of their marketing departments indicates, these banks are not small organizations. At the time of data collection, A had over 8,500 employees, over 1,100 local agencies (i.e., branches), and assets under management of over 40 billion euro. B had close to 11,000 employees, over 950 agencies, and about 11 billion euro under management. C had over 1,300 employees, 330 agencies, and about 7 billion euro under management.

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the following items,” with the scale going from strongly disagree to strongly agree. The items and reliabilities are presented in Appendix A. The Cronbach α values indicate high reliability. To investigate the underlying structure and internal consistency of the multi- item scales for the three knowledge domains, we performed an exploratory factor analysis using data from the 91 respondents who answered all 12 items. A principal components procedure extracted three factors with latent roots (eigenvalues) larger than one. A scree plot indicated that no additional factors were necessary to account for the underlying factor structure. The three factors accounted for 74.8 % of the total variance. Items loaded highly on only one factor and the factor solution clearly corresponded to the three domains. We further assessed the factor structure with a confirmatory analysis (CFA) on the covariance matrix. Since our sample is small by CFA standards, we use the CFA merely as a complementary tool to detect possible problems in our measurement instrument, not as a means to confidently establish any type of general measurement validity. Even though the chi-square test of misfit was significant (p = .005), the normed chi-square (χ2 /df) was only 1.6, well within the recommended range between 1 and 2. Moreover, RMSEA was 0.081 and its 90% confidence interval (0.046, 0.11) included the 0.05 value, the goodness-of- fit index (GFI) was .87, close to the recommended .90 level, and the Tucker-Lewis index (TLI), an incremental fit measure, was .93, exceeding the recommended level of .90. The pattern of standardized residuals indicated no clear underfitting or overfitting. The factor inter-correlations, finally, ranged between 0.26 and 0.53 and were all at least 5 standard errors below unity, proving strong support for discriminant validity. Having found no reason to reject the reliability, internal consistency or discriminant validity of our knowledge scales, we constructed variables of customer knowledge, competitor knowledge and technological knowledge by summing the 4 items of each scale.

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Network data All 95 professionals received a structured sociometric questionnaire, based on the one used by Van den Bulte and Moenaert (1998). All but one professional responded. The questionnaire asked about the frequency at which they talked with each of the other professionals in their department. We measured oral communication very broadly, not constraining it to task-relevant information transfer. We did so for two reasons. Gossip, banter, chatter and idle talk on common but not task related interests can be effective in creating norms of information-sharing and mutual support and in closing gaps between members of different formal sub-groups (e.g., product managers versus market researchers), which in turn can facilitate task coordination, information sharing, and problem solving (e.g., Burt 1999; Kohli and Jaworski 1990). Measuring the network in broad terms rather than in terms specific to the dependent variable (e.g., “who do you turn to for information about customers/competitors/ technology?”) also has an important methodological advantage: it makes selection and reverse causality an unlikely threat to validity (Nieuwbeerta and Flap 2000). I.e., the network ties we measure do not exist simply because marketers seek to learn about markets from this or that colleague. Specifically, the questionnaire instructions were: “On the next pages, we have listed the personnel working in your department. Please indicate how much you talk on average with each of the mentioned persons. By talking, we understand whatever form of oral communication, irrespective of the content. Hence, 'talking' is not necessarily limited to information exchange concerning your work, but could also include any other topic that might emerge, e.g. during the lunch break or other informal gatherings. Please indicate below how often on average you talk with each of the persons mentioned below.” We measured communication frequency on a five-point scale (5-daily, 4-weekly, 3-monthly, 2-at least once every three months, 1-never or person unknown). Daily interaction occurred mostly only within formal sub-groups (e.g., market researchers), such that the daily network did

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not provide much information beyond sub-group membership. Conversely, the very great majority of pairs of actors interacted at least monthly, such that the monthly network was nearly fully connected and did not provide much information at all. Therefore, and following much research on intra-organizational communication (e.g., Allen 1977; Van den Bulte and Moenaert 1998), we dichotomized our network data at the weekly level. Hence, we represented a tie to be present if the answer was 4 or 5. The total network density, i.e. the number of ties reported divided by the total number of ties possible, was 37% in bank A, 52% in B, and 68% in C. We symmetrized each network matrix. That is, for each bank, we constructed a new, symmetric matrix with cell value aij = max{vij, vji}, where vij = 1 if i reported talking to j at least weekly (vij = 0 otherwise), and let vji =1 if j reported talking to i at least weekly. The rationale for symmetrization is twofold. The first reason is that, except for a few instances where one purposely ignores one’s communication partner, “talking to” is a naturally reciprocal relationship, so asymmetries are likely to stem from reporting errors, the impact of which can be reduced by symmetrizing (Alba and Kadushin 1976; Van den Bulte and Lilien 2001). Raw reciprocation rates were 79% in banks A and B and 77% in bank C. We also computed reciprocation rates based on the p1 statistical model (Holland and Leinhardt 1981). This model controls for random error and for each actor’s tendency to report ties, which may include a bias stemming from over- or under-estimating one’s own importance or popularity. The model-based reciprocation rates were 97% for A and B and 92% for C, very high values suggesting that the communication networks are genuinely symmetric. The second reason to symmetrize the network data is that the measure of centrality we use (described below) can be computed only for symmetric networks. Symmetrizing the network data before constructing any of the three measures of social capital prevents that our findings on the relative contribution of different

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social capital mechanisms stem from whether the network variable corresponding to each mechanism uses symmetric data or not.

Social network exposure Our social network exposure variable captures how knowledgeable the actors are whom one has access to: SNEi = Σ j wij yj where SNE stands for social network exposure, yj is the knowledge of actor j, and wij indicates whether i interacts at least weekly with j. The choice of social weights wij is important, as it corresponds to different information transfer and influence mechanisms (e.g., Burt 1987). In our study context, sharing knowledge operates through direct contact, so an obvious choice is wij = aij, such that one’s social capital is the sum of the knowledge of all one’s direct contacts. The resulting SNE measure, however, is affected not only by the quality of one’s contacts but also by their sheer number, and hence also captures to what extent one has a central position within one’s department. To better separate centrality from social network exposure and to avoid potential artifacts stemming from differences in size across banks, we follow prior research on social contagion processes (e.g., Burt 1987; Friedkin 1998; Van den Bulte and Lilien 2001) and normalize all rows by setting wij = aij / Σ j aij, such that (1) wii = 0, and (2) Σ j wij = 1 if wij ? 0 for some j, and Σ j wij = 0 otherwise. This normalization implies that professionals’ social capital is proportional to the average quality of knowledge among their direct contacts, irrespective of how many contacts they have. Note that this operationalization of SNE allows for knowledge to spill over from more distant sources: i’s knowledge is a function of the knowledge of her direct contacts, which in turn is a function of the knowledge of their own contacts, and so on. Hence, even though the social network exposure construct is formulated in terms of direct contacts only,

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it implies higher-order spillovers (Cliff and Ord 1981).

Control variables We did not collect data on bank-level phenomena such as quality of formal marketing information system, culture, incentive systems, organizational routines and other mechanisms for integrating individual- level knowledge, complexity of market and product mix, etc., but control for such differences across banks in our analyses using bank-specific dummies. As these dummies capture all cross-bank variance in knowledge, the remaining variables capture individuals’ deviations from their own bank’s average. We also use dummies to control for differences in task environment within the marketing department. Specifically, we differentiate among (a) top- level marketing executives, market/segment managers, and product managers, (b) market research and database specialists, (c) marketing communications specialists, and (d) administrative staff. This breakdown is based on the respondents’ job titles and information from the marketing managers, and is clearly reflected in the matrix of daily communication exhibiting a strong block-like structure. The questionnaire included several questions on communication beyond the marketing department, as the marketing literature emphasizes the importance of cross- functional information flows and coordination. One question inquired about the frequency of communication with colleagues from other departments within corporate headquarters, using the same 5-point scale as for the intra-departmental network data. Two similar questions inquired about the frequency of communication with local branches and independent agents, and with the affiliate bank in Luxemburg (Luxemburg is an independent country with separate tax legislation but with opportunities for legal forms of tax avoidance by investors with Belgian tax status). The answers to both questions were highly skewed, and after preliminary analyses, we dichotomized

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extra-departmental communication at the daily level (1 if yes, 0 if no) and branch and affiliate communication at the weekly-or-higher level (1 if yes, 0 if no). 3 Finally, we also collected data on the proportion of time the professionals spent working in the office versus in the field, visiting customers or local branches, as well as data on four human capital variables: age, years of work experience within the bank and the department, respectively, and level of education. After controlling for years of experience within the department, none of these variables contributed to explain market knowledge, and they are omitted from the analysis reported here. As we mentioned in the theory section, we also include control variables fo r two alternative social capital arguments. The network closure argument (Coleman 1988) emphasizes the benefit of having a dense ego-network, i.e. having ties to peers who are densely interconnected among themselves. The argument is that members of a closely-knit network are more likely to honor requests for help and to support each other. The reason is that in such networks, an actor whose request for help is denied will find it easier to damage a refuser’s reputation among the latter’s peers. If the request is honored, however, the actor can easily boost the helper’s reputation. In other words, the presence of common third parties makes it easier to create and enforce norms of cooperation (e.g., Greif et al. 1994). In addition, network density is also thought to foster identification with the group, further facilitating cooperation (Portes and Sensenbrenner 1993). We control for this network closure argument by including ego-network density as a covariate in the model. This is a standard construct in social network research. We take the ego-network of each professional (i.e., the focal individual and all the other members of the marketing department he or she talks to), count the number of actual ties, and divide it by the number of possible ties.

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We use communication with other departments and with branches as control variables, and use communication with Luxemburg affiliates as an instrumental variable.

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The weak tie and structural hole arguments, in contrast, posit that when it comes to having opportunities to access new, non-redundant information, there is an advantage in having many contacts who do not interact very much with each other but have many unique (non-shared) contacts (e.g., Burt 1992; Granovetter 1973; Rindfleisch and Moorman 2001). Being exposed to multiple, weakly interconnected colleagues may not only give one access to information about more events, but may also provide one with multiple interpretations of the same events, allowing one to cross- validate bits of information and to come to more reliable knowledge (Burt 1999). To capture the extent to which each professional is centrally located within the flow of oral communication within his or her department, we use the Stephenson-Zelen measure of information centrality. 4 While a central brokering position provides one with more opportunities to access, network density can be instrumental in turning these opportunities into actual shared knowledge. Hence, both features of one’s network may facilitate different but necessary sub- mechanisms for one to actually learn from one’s colleagues (Burt 2000; Granovetter 1982; Hansen 1999), such that the presence of one feature should increase the effect of the other. We therefore also include the product of ego- network density and information centrality as a control variable. Because both ego-network density and information centrality are sensitive to network size, and hence the size of the bank’s department, we normalized these two network variables by

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We considered several measures of network centrality and brokerage and concluded that, for this study’s purposes, information centrality best captures the idea that actors located between otherwise disconnected colleagues have an advantage in information access. Information centrality is a generalization of the more widely known Freeman betweenness centrality measure and is more sensitive to peripheral actors, which can be important in diffusion of knowledge (Wasserman and Faust 1994, p. 217). Like Burt’s (1992) constraint measure of social capital, information centrality reflects whether one is a unique bridge between otherwise disconnected colleagues, but unlike the constraint measure, information centrality looks beyond one’s own direct contacts. Measuring brokerage positions by focusing on direct contact is quite appropriate when studying the role of social network capital in processes involving the direct exchange of resources (e.g., trade) or operating across close relationships (e.g., mentoring), but not when studying knowledge flows. Readers interested in a formal definition of the measure and technical details are referred to Stephenson and Zelen (1989) and Wasserman and Faust (1994).

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bank. Table 1 reports the descriptive statistics of the variables retained for final analysis. Note that we have missing data about knowledge for three observations. Note, the meaning and theoretical range of the values of the SNE variables are, by construction, the same as those of the knowledge variables (being knowledgeable measured on 1-5 scale).

MODEL AND ESTIMATION We face the issue of specifying a model that allows one to study spillovers when one has only cross-sectional data. In addition, we face estimation issues: how to estimate statistically valid coefficients in a model where the dependent variable is knowledge and two of the explanatory factors, social network exposure and absorption ability/motiva tion, are also based on knowledge. This section addresses each issue in turn.

Inferring knowledge spillovers in a static model Even though learning and spill-overs are dynamic concepts, their antecedents can be studied using cross-sectional data under the assumption of equilibrium (e.g., Cliff and Ord 1981; Friedkin 1998; Land and Deane 1992). We make the following assumptions: (1) the social network (reflected in the density measures, the information centrality measures and the social influence weights wij) is stable, (2) personal attributes are constant or change only very slowly compared to the speed at which social learning occurs so that they can be assumed constant, and (3) the knowledge system observed is in equilibrium, so that a person’s knowledge is constant. Equilibrium does not imply the absence of learning dynamics. It implies only that learning compensates for depreciation in knowledge stocks due to forgetting or to market changes, such that market knowledge remains constant (Appendix B). A model of a person i’s market

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knowledge can then be written as a standard network autoregression model: (1)

yi

=

α xi + ρ Σ j wij yj + ε i , where

yi

=

knowledge of i;

xi

=

vector of human capital, social capital that is not a function of knowledge, and control variables;

wij

=

social weight;

α, ρ

=

vector of parameters and parameter, respectively, to be estimated;

εi

=

error term, independently and normally distributed.

In matrix notation, the model is: (2)

Y

=

αX +

ρWY

+ E

Since SNE variables cannot be computed if any of the observations has missing values for the y variables, we imputed the one missing value for competitor knowledge and the two values for technology knowledge (Table 1) using auxiliary regression (Burt 1987). We first regressed yi on xi using all complete observations, computed predicted values for all observations, and replaced the three missing values by their predicted values to compute the SNE variables.

Switching regression to test for knowledge-based differences in absorption We want to investigate whether existing knowledge affects the extent to which one absorbs knowledge from one’s contacts. In other words, we want to assess whether the ρ coefficient is associated with knowledge: (3)

yi

=

α xi + ρi Σ j wij yj + ε i .

Specifying ρi = g(yi) and assessing whether ∂g(yi )/∂yi > 0 is obviously problematical since the same variable would enter in both sides of the equation. Instead, one might consider splitting the

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data into two groups, high yi versus low yi, and assess whether the ρ parameter is higher for individuals who have higher market knowledge using switching regression models. However, because yi is partly affected by ε i, breaking the set of professionals into groups based on the dependent variable yi restricts the range of the error term ε i, so one cannot just use OLS or IV (instrumental variables) estimation on the high and low groups and compare estimates. Switching regression models circumvent this problem, by explicitly taking into account that high versus low group membership is partly stochastic (Maddala 1983, pp. 223-228). The estimation is easy. One first estimates the probabilities of group membership using a probit regression, such that Pˆ [yi = high] = F ( βˆ xi ) = ˆF i, where F is the standard normal c.d.f., and then uses the estimated probability of being highly knowledgeable ˆF i, its derivative f ( βˆ xi ) = ˆf i, and the product of ˆF i with social network exposure as additional explanatory variables in a traditional least squares regression. If the interaction is significant and positive, one can conclude that, holding everything constant, knowledgeable people absorb more knowledge from direct contacts (i.e., have a higher absorption rate ρ) than less knowledgeable people do.

Network autoregression Equations 1 and 2 are often referred to as a network autoregression model, because the value of the dependent variable for one individual i is regressed against a weighted average of the values of the same variable for other individuals connected to i. Even though wii = 0, such that yi is not regressed against itself, the model does exhibit circularity for most forms of the W matrix. The value of the dependent variable yi is influenced by the va lues of the dependent variable for all direct contacts through the social network exposure variable SNE = Σ j wij yj. But in turn, a yj value entering in i’s SNE is itself a function of j’s social network exposure in which yi, and

19

therefore its random component ε i, enters. As a result, the value of an individual’s social network exposure, a regressor, will be partly affected by her own random error, and therefore the two are correlated. Such correlation violates the conditions for unbiased and consistent OLS regression estimates. We therefore use instrumental variables (IV) estimation instead. This is a standard procedure to deal with errors- in-variables problems in general, and can be used for network autoregression models as well (Anselin 1988; Land and Deane 1992). Following recommendations by Hordijk and Nijkamp (1978) and Anselin (1988), we not only considered standard covariates as instruments but also constructed social network exposure to exogenous variables (i.e., of the type WZ or Σ j wij zj ). We use five instruments apart from the regressors already in the model: a dummy indicator for weekly communication with affiliate banks, social network exposure to colleagues often cited as a communication partner (computed as Σ j wij zj with zj = Σ i vij ), and the product of the ˆF i for each of the three knowledge domains with social network exposure to colleagues with weekly contact with local branches or independent agents. Since we estimate not one but three regression equations (one per knowledge domain) over the same individuals, we can gain efficiency by taking into account the correlations among error terms across equations. To do so and also allow for heteroscedasticity (e.g., due to differences in the reliability of people’s knowledge self-assessment ), we use the generalized method of moments (GMM). This approach reduces to the full- information instrumental variable estimator (FIVE) under conditional homoscedasticity, which in turn reduces to three-stage least squares (3SLS) if the set of instruments is common to all equations (Hayashi 2000).

RESULTS Table 2 reports the estimates and t-statistics of the coefficients in the system of three

20

equations. The first key finding is that the effect of social network exposure varies depending on whether one has high or low knowledge. This is so for all three knowledge domains. Hence, the hypothesis that how much an actor absorbs of the second-order knowledge resources available to him or her depends on how well- informed the actor is (H2), is supported for all three domains (p < 0.05). The sign of the effect, however, varies across knowledge domains. For knowledge about customers and competitors, more knowledgeable marketers absorb less from their colleagues, whereas for knowledge about technology, more knowledgeable marketers absorb more from their colleagues. Hence, the hypothesis that the effect of one’s knowledge on how well one absorbs knowledge from one’s network is larger outside one’s primary task domain (H3) is strongly supported. The presence of a significant interaction effect between SNE and extant knowledge means that one cannot use the SNE coefficients to test hypothesis H1 positing that interacting with others who themselves are well informed, i.e., that having social network exposure to others’ information increases one’s own level of information. Because of the interaction, whether that statement is supported or not depends on one’s extant knowledge level: the marginal effect ∂y/∂SNE is 2.84 – 9.88 ˆF i for customer knowledge, 2.38 – 8.69 ˆF i for competitor knowledge, and -2.84 + 6.87 ˆF i for technolo gical knowledge. The SNE coefficients reported in Table 2 pertain to the situation where ˆF i = 0.5 Marketers with very little knowledge of a particular domain such ˆ i = 0, do benefit from exposure to knowledgeable colleagues for both customer (t = 2.11, p that F

= 0.039) and competitor knowledge (t = 2.43, p = 0.018). In contrast, marketers with very little technological knowledge do not benefit from their peers’ knowledge (t = -1.55, p = 0.124). In additional analyses, we estimated the marginal effects ∂y/∂SNE and their significance level when

21

ˆF i = 1 for each knowledge domain. The estimated effect for customer knowledge was -7.01 (t = -

1.83, p = 0.071) and that for competitor knowledge was -5.89 (t = -3.18, p = 0.002). So, marketers with very high customer and competitor knowledge absorb not more but less from their peers. The reverse is true for technological knowledge: there, very knowledgeable marketers do benefit from exposure to other knowledgeable marketers (coefficient = 4.83, t = 2.62, p = 0.011). 6 The control variables do not exhibit many strong effects consistent across the knowledge domains. Of theoretical interest is that the interaction between ego-network density and information centrality is positive, and significantly so, for customer knowledge (t = 1.94, p = 0.057) and competitor knowledge (t = 2.21, p = 0.030). So, we find that the joint presence of a central position and network closure does have an association with market knowledge in these two primary domains, even when the effect of social network exposure to knowledgeable colleagues is accounted for. Even though the effect size is about equally large for technological knowledge, it is not significant. DISCUSSION Key findings This study examines the impact social network exposure to direct colleagues’ knowledge on three domains of market knowledge—customers, competitors, and technology. It also examines the effect of one’s extant knowledge on the rate of absorption through network exposure. This study offers three sets of results in this regard. ˆ i ranges between 0.02 (or lower) and 1, so taking the 0 and 1 values as points of reference For all three domains, F does not amount to extrapolating outside the observed range. 5

22

Our first key finding is that social network exposure to others’ knowledge is associated with one‘s knowledge, even after controlling for standard measures of social capital such as access to many parts of the network and ego- network density. The social contagion, “second-order resources” or “spillover” mechanism and its operationalization using a network autocorrelation structure have been applied in several studies of innovation adoption and have strong theoretical appeal, since they closely capture the crux of social capital as being the value of the resources embedded in and mobilizable through social networks. Yet, the social contagion mechanism has not attracted much attention in research on social networks and knowledge diffusion within organizations. Our results support Burt’s (2000) suggestion that it warrants more consideration. The second finding worth highlighting is the two significant interaction effects between information centrality and density. These are consistent with key theoretical arguments (e.g., Granovetter 1982) and replicate recent findings that effective network structures combine both brokerage (facilitating identification of opportunities) with cohesion (facilitating actual transfer of resources) (e.g., Gargiulo and Rus 2001; Reagans and McEvily 2003; Reagans and Zuckerman 2001). Also noteworthy is that these effects are found after controlling for secondorder resources, indicating that the latter did not fully mediate the effects of centrality and density (compare Lin 1999). That the interaction effect is not significant in the technological knowledge domain is puzzling. It raises the possibility that for knowledge that tends to be more outside both one’s own and one’s direct marketing colleagues’ expertise, one’s general structural location within one’s own department hardly matters. Rather, the only relevant form of social

ˆF i = Pˆ [yi = low] rather than ˆF i = Pˆ [yi = high]. The resulting effect sizes are close to the values one would obtain by using the results in Table 2 and setting ˆF i = 1, 6

We obtained these effect sizes by re-estimating the model using

but are not identical to them, especially for technology (4.83 vs. 4.03, a difference of about 20%). Conditioning analysis indicates the presence of moderate ill-conditioning involving the intercept and the SNE coefficient in the re estimated model, so the difference in effect sizes most likely stems from mere inaccuracy in the point estimates.

23

capital might be being connected to those few experts who actually are knowledgeable, e.g. because of former job stints or connections to other departments (compare Allen 1977). Our third key finding is that marketers’ extant knowledge affects their learning from fellow marketers, but not always positively. For technological knowledge, we find that more knowledgeable marketers absorb more of their colleagues’ knowledge available through direct contacts. However, for customer and competitor knowledge, we find the reverse. In these two areas which are most central to marketers’ job, we even find a negative effect of extant knowledge on learning from one’s peers. This indicates that marketers’ intellectual capital exhibited negative returns to scale for learning about their core task environments, but positive returns to scale for learning outside their area of expertise.

Contribution and implications for future research Social contagion and second-order resources. Our first contribution consists in documenting that social network exposure to others’ knowledge affects marketers’ own knowledge, even after controlling for standard measures of social capital such as having access to many parts of the network and enjoying social closure (high density) in one’s network of direct contacts. This is the first study to provide such evidence for marketing knowledge. Though novel to marketing and motivated mostly from social capital theory, this effect of second-order knowledge operationalized as the average knowledgeability of those colleagues one talks to at least weekly is consistent with prior research by Deshpandé and Zaltma n (1982; 1984) documenting that whether managers use a particular piece of market research is affected by the degree of interaction between the managers and their market researchers and by the quality of the market research report. While the studies differ in both context and specific concepts and measures, they have the common result that both the nature of the source-recipient tie and the quality of the

24

resource shared by the source matter. Similarly, our findings are consistent with the notion advanced by Rindfleisch and Moorman (2001) and Wuyts et al. (2002) that how much technological knowledge firms absorb is likely to be a function not only of the strength, cohesion, and range of their network ties but also of the quality of the knowledge resources controlled by their contacts. Future network research in marketing may benefit from moving beyond focusing on network centrality and network closure, and considering second-order resources as a relevant form of social capital that can improve managers’ and organizations’ outcomes. Unlike having a central brokerage position or having a dense ego-network, social network exposure to resources is not a generic construct but can be tailored to the outcome variable. If knowledge is the outcome, then the resource one tries to access through one’s network is also knowledge (e.g., this study). If the outcome is marketers’ ability to design and implement successful programs and projects, then the resource one might seek to mobilize through one’s network may not be knowledge but discretionary budgets and political clout within the company (e.g., Workman 1993). If the outcome is the speed of promotion of minorities, then access to mentors’ personal experience and clout may be the key social capital resource (e.g., Ibarra 1995). Compared to the two standard network measures of social capital, SNE allows marketing researchers to more precisely tailor our operational constructs to the processes we seek to understand. Our evidence of contagion of second-order knowledge supports Burt’s (2000) suggestion that the social contagion or second-order resources mechanism warrants more consideration in research on how social networks can benefit actors. Marketing scientists have the potential to contribute to such research advances since the concept of social contagion is already well accepted and extensively modeled in the context of the diffusion of innovations (e.g., Putsis et al.

25

1997; Van den Bulte and Stremersch in press). Hence, our study points to a promising venue to apply a set of methods familiar in one area of marketing science to a substantive issue that has long been of central interest to the discipline. That social contagion affects market knowledge even after taking into account central brokerage and density, dimensions of network structure which are already well accepted in the marketing management literature on market knowledge further underscores that there is a formerly unrecognized opportunity to apply well known modeling tools to a long-established managerial research question. Brokerage and density. Studies on information sharing among consumers have documented the “weak tie” benefit of having a portfolio of contacts allowing one to tap indirectly across a very wide network and the “strong tie” benefit of having dense cliques of close ties to some people ensuring that they actually share their valuable information (e.g., Brown and Reingen 1987; Frenzen and Nakamoto 1993). Our results involving information centrality and density extend these findings to the realm of ma rketing professionals’ competitor and customer knowledge. Of greater theoretical interest is that we also document an interaction between the two. This is consistent with both theoretical arguments and recent empirical research in sociology that effective network structures combine both brokerage (facilitating identification of opportunities) with cohesion (facilitating actual transfer of resources). Networks within departments. Our results show that that key theoretical predictions on second-order resources, brokerage and density are supported even for purely intra-departmental networks. While the importance of cross-functional social ties has long been documented (e.g., Allen 1977; Maltz and Kohli 1996; Moenaert and Souder 1996), network effects are expectedly weaker within than across departments since alternative mechanisms facilitating knowledge sharing—formal information systems, common culture, and hierarchical control—tend to be

26

more prevalent or more effective within than across departments (Allen 1977; Axelrod 1986; Burt 1997; Simon 1991b). However, our findings clearly indicate that even intra-departmental social networks affect marketers’ knowledge of customers, competitors, and technology. Hence, research on how marketers learn about their markets would benefit from taking into account intra-departmental networks. Non-linear effects of extant knowledge. Our most novel finding is that marketers’ extant knowledge affects their learning from fellow marketers, but not always positively. For technological knowledge, we find that more knowledgeable marketers absorb more of their colleagues’ knowledge available through direct contacts. However, in the two knowledge domains which are most central to marketers’ job, customers and competitors, we find a nega tive effect of extant knowledge on learning from one’s peers. This indicates that marketers’ intellectual capital exhibited negative returns to scale for learning about their core task environments, but positive returns to scale for learning outside their area of expertise. This finding conflicts with equating extant knowledge and absorptive capacity, but is in accordance with classic models of search and learning where how much one learns is not only facilitated by what one already knows but is also bounded by a “performance gap” between one’s actual knowledge and aspired knowledge. The more one knows, the greater one’s ability to learn, but the smaller the performance gap, and hence the smaller one’s opportunities and motivation to learn (March and Simon 1958; March et al. 2000; Moorthy et al. 1997; Sørensen and Hallinan 1977; Wood and Lynch 2002). While the present study is the first to combine the idea of non- linear effects of extant knowledge with that of market learning through networks, several prior studies have pointed to essentially the same ability vs. opportunity/motivation mechanisms inducing either positive or

27

negative returns to extant knowledge, including Boulding and Staelin (1995) in the context of R&D, and both Wuyts and his associates (2002) and Stremersch and his associates (2003) in the context of industrial buying behavior. The same rationale was put forward by Slotegraaf and her associates (2003, p. 298) in their study on how firm resources affect returns to market deployment. Like all these studies, our work does not actually measure ability vs. opportunity/motivation directly, but model non- linearities consistent with their expected effects. Clearly, future research efforts that directly measure the constructs rather than infers them from positive or negative returns to scale would allow one to be more confident about the underlying mechanisms at work.

Managerial implications The presence of positive spillovers among fellow marketers for knowledge of customers and competitors suggests companies might benefit from fostering stronger intra-departmental interaction. Interventions to foster impromptu face-to-face contact such as locating marketers in propinquity to each other, creating common areas, having social mixers, and so on might he lp (Allen 1977; Van den Bulte and Moenaert 1998). Purposive rather than impromptu interaction might be facilitated by making it easier for marketers to identify who knows what. Deploying collaboration software and databases can be instrumental in this regard (e.g., Kaihla 2004). The presence of negative marginal returns to extant knowledge about customer and competitors implies that social learning is of little use to those marketers who are already well informed. Yet, they remain valuable interaction partners to others. As a result, attempts to foster more interaction among marketers may lead to a high burden on the most knowledgeable ones without them getting much knowledge in return. However, returns do not have to be in kind. Those who provide advice typ ically enjoy higher status and reference power (Blau 1963), and

28

organizations should not leave these spontaneous give-and take processes entirely unattended and hope for the best. Rather, they can take steps to recognize knowledge sharing as good organizational citizenship. Fostering a cooperative culture in which knowledge sharing is expected should be helpful as well. For technological knowledge, the challenge seems quite different as here we found not negative but positive marginal returns to knowledge. That means that for those who do not know much about technology, but need to learn, exposure to knowledgeable fellow marketers may be insufficient. Possibly, more structured learning modes may be required.

Limitations Future research may also be able to avoid a few other limitations of the present study. One advance would consist in using longitudinal data to estimate functional specifications allowing for switches from positive to negative marginal returns (i.e., from increasing to decreasing returns), such as S-curve growth models. When only cross-sectional data are available, the switching regression methodology used in this study should prove useful. Yet, one must bear in mind that this allows one to retrieve the parameter values of a dynamic process only under the assumption of steady state, and as Solow (1970, p. 7) remarked, “the steady state is not a bad place for the theory of growth to start, but may be a dangerous place for it to end.” Using longitudinal data on knowledge outcomes would allow one to relax the auxiliary assumption of equilibrium. Another limitation of our cross-sectional design is that it raises the question of causality. First, there is the specter of reverse causality, i.e., the possibility that the network ties are not the cause of a knowledge process but the outcome. That would occur if the network ties resulted from marketers’ search efforts. This, however, is unlikely. Since we measur ed the network in

29

broad terms rather than in terms specific to the dependent variable (e.g., “who do you turn to for information about customers/competitors/ technology?”), the network ties do not exist simply because marketers seek to learn about markets from this or that colleague. This makes reverse causality an unlikely threat to validity (Nieuwbeerta and Flap 2000). The low correlations between knowledge on the one hand and information centrality on the other hand (Table 1) also suggest that reverse causality in unlikely: if being highly knowledgeable really affected the odds of being heavily connected either directly or indirectly, then one would expect stronger correlations than 0.20, and certainly no negative correlations. The second potential problem is that of spurious correlation: can’t our results be explained by the homophily principle, in this case the idea that employees tend to interact more with colleagues having similar levels of knowledge than with colleagues having different levels of knowledge? Unlike reverse causality, the risk of spurious correlation is not diminished by our choice of network question. If marketers truly favored similarly knowledgeable colleagues as interaction partners, then this would have resulted in a positive bias in the network autoregression coefficient. This could explain the positive SNE effects for customers and competitors, but not the negative one for technology. Neither could this account for any of the differential absorption effects, either positive or negative. Finally, if being highly knowledgeable really affected the odds of having highly knowledgeable direct contacts and so increased one’s SNE, then one would expect stronger correlations between knowledge and SNE than the low values observed in Table 1. In short, the specifics of our measurement approach and of the pattern in our results indicate that that reverse causality and spurious regression—though possible in principle—are unlikely threats to validity. Given our focus on how social network might benefit marketers’ knowledge outcomes, we have focused on the recipients’ access to knowledge and the possibly non- linear effects of their

30

extant knowledge on absorption. Though we controlled for Coleman’s (1988) argument that network closure might facilitate cooperation from those who hold resources, we did not actually focus on colleagues’ willingness to share their knowledge. With the exception of Frenzen and Nakamoto (1993), this “other side of the medal” has not attracted much attention in prior research (e.g., Hansen 1999) and our study does little to redress the balance. The present study focused purely on network-structural effects. It did not measure the quality of ties, and controlled for unobserved firm- level variance using dummy variables. As a result, our study does not shed light on possible cross- level interactions. One such issue of particular interest is the role of norms and trust in knowledge sharing, as these can be instilled and enforced at the dyadic level via strong ties (e.g., Moorman et al. 1992), at the triadic and network level via third-party sanctioning and closure (e.g., Wuyts et al. 2002), and at the broader organization level via culture (e.g., Moorman 1995). Investigating to what extent norm-creating and normenforcing mechanisms at one level serve as substitutes or complements for those at another level is an issue tha t would bring together several strands of research in marketing. Finally, some may find it more convincing to use some objective measure of knowledge rather than self- reports in future research. The benefits of doing so, however, must not be overestimated. A first possible drawback of using self- reports is an upward bias in the responses. To the extent that respondents inflated their reported knowledge level, this should have inflated the intercept but without biasing the coefficients of interest. A second possible drawback of using self-reports is that these may be imprecise. Random measurement error would have increased the amount of error variance in the regression equations’ error terms. This would not have biased the coefficients but would have increased their standard error, making it harder—not easier—to find support for the hypotheses. A third possible drawback of using self-reports is

31

mono- method bias. This would occur when both the dependent variable and the regressors are measured using the same instrument such that personal response idiosyncrasies affect all answers and artificially boost all correlations and regression coefficients. The present study, however, is not subject to this threat since (i) the dependent variable is measured on a Likert scale while the variables of theoretical interest are based on measures of weekly social interaction and (ii) we use instrumental variables estimation to avoid correlation between knowledge-based regressors and the regression error term. Everything considered, the use of self-reported knowledge is unlikely to have biased our results in favor of the hypothesized effects. On the contrary, if measurement error was present, it is more likely to have rendered our hypothesis test overly restrictive rather than overly liberal.

Conclusion The role of social networks in marketers’ market learning is an issue about which still little is known. The role of ability, motivation, and opportunity to learn about markets is another area warranting investigation. Our study raises new questions pointing to the need to further investigate and bound the impact of social networks on market knowledge, and provides several concepts from social network and organization theory, methodological tools already familiar to marketing scientists, and new findings to advance in this endeavor.

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Appendix A: Scales for market knowledge (items and Cronbach α )

Customers (Cronbach α = .92) I am well informed ... ... about changes in customer needs. ... on new user requirements. ... about changes in the potential market. ... on the buyer behavior of the potential customer. Competitors (Cronbach α = .86) I am well informed ... ... on the technological strategy of the competition. ... on the marketing strategy of the competition. ... about the activities of our competitors. ... about new product strategies of competitors. Technologies (e.g. EDP & financial technologies) (Cronbach α = .88) I am well informed ... ... about the quality of the applied technologies (e.g., information technologies). ... on the user- friendliness of the technologies. ... on the cost-efficiency of the technologies. ... concerning the performance of the involved information technologies.

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Appendix B: A cross-sectional model of spillovers under equilibrium The dynamic process of knowledge acquisition can be represented by the following equation: =

λyi(t-1)

+ (1-λ) [ α xi + ρ Σ j wij yj(t-1) ] + ηi(t),

(B1)

yi(t)

where

yi(t)

=

knowledge of i at time t ;

λ

=

knowledge decay parameter (0 ≤ λ < 1);

xi

=

vector of human capital, time- invariant social capital, and control variables;

wij

=

time- invariant social weight;

α, ρ

=

vector of parameters and parameter, respectively, to be estimated;

ηi(t)

=

error term, independently and normally distributed.

The first term on the right hand side indicates that current knowledge is a function of past knowledge, subject to decay (Argote 1999). The next set of terms captures learning, i.e., the increase in knowledge realized between t-1 and t. Human capital, components of social capital that are not a function of one’s contacts’ knowledge, and control variables are captured in vector xi . Social network exposure to others’ knowledge, finally, is operationalized as Σ j wij yj(t-1). In many studies of influence and spillover processes in social networks, only cross-sectional data are available. Researchers must then assume that the process is in equilibrium to obtain valid inferences (Cliff and Ord 1981). Often, this assumption is not made explicit. Starting with a dynamic model, in contrast, clearly identifies the type of assumptions one needs to make to go from a dynamic process in equation (B1) to a static representation. This only requires that the knowledge system observed is in equilibrium, such that yi(t) = yi(t-1). The dynamic model then reduces to a static specification in which the parameters have the same interpretation as in a dynamic model (Coleman 1981; Friedkin 1998): (B2)

yi

=

αxi + ρ Σ j wij yj + εi

34

(where εi = ηi /(1-λ)).

Table 1. Descriptive statistics (Mean, standard deviation and correlation matrix) ____________________________________________________________________________________________________________________________ 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

N Mean SD 1. Customer knowledge 94 3.19 0.85 2. Competitor knowledge 93 3.11 0.86 .44 3. Technology knowledge 92 2.84 0.78 .26 .36 4. Bank B 94 0.39 0.49 .06 .19 .19 5. Bank C 94 0.17 0.38 .10 -.04 .00 -.36 6. Market research 94 0.16 0.37 .14 .09 .07 .18 .03 7. Administrative support 94 0.10 0.30 -.15 -.04 .17 .03 .14 -.14 8. Marketing communications 94 0.38 0.49 -.23 -.25 -.25 -.05 -.07 -.34 -.26 9. Experience 94 5.51 6.92 .08 .05 -.01 .12 .13 .14 -.17 .29 10. Communication w/ other depts. 94 7.74 2.76 .08 .01 .17 -.22 .04 -.40 .06 .03 -.12 11. Communication w/ branches 94 0.74 0.44 -.15 -.10 .12 -.03 .07 -.41 .02 .06 .01 .50 12. Density 94 0.00 0.99 -.06 -.15 .02 .00 .00 .00 .06 -.03 -.08 -.11 -.19 13. Information centrality 94 0.00 0.99 .18 .17 -.20 .00 .00 .00 -.38 .00 -.17 .17 .09 -.48 14. Density x Information centrality 94 -0.47 1.98 .08 .11 .21 -.08 -.07 .05 .09 -.14 -.01 -.06 -.05 -.56 .13 15. SNE (Customers) 94 3.21 0.19 -.02 .14 -.02 .21 .26 .30 .01 -.44 -.21 -.13 -.07 -.13 .41 .07 16. SNE (Competitors) 94 3.16 0.25 .11 .21 .19 .53 -.30 .20 .02 -.33 .01 -.05 -.05 -.08 .15 .02 .54 17. SNE (Technology) 94 2.78 0.26 .08 .25 -.01 .66 -.06 .20 .11 -.24 .06 -.13 -.03 -.05 .20 -.15 .57 .79 _____________________________________________________________________________________________________________________________

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Table 2. Model estimates __________________________________________________________________________ Customers Coeff.

Competitors

t-stat

Coeff.

t-stat

Technology Coeff.

t-stat

__________________________________________________________________________ Intercept Bank B Bank C Market research Administrative support Marketing communications Experience Communication w/ other depts. Communication w/ branches Density Information centrality Density x Information centrality

2.012 0.570 0.540 0.044 0.018 -0.115 0.012 -0.013 0.271 0.077 -0.118 0.091

3.47 2.50 2.16 0.15 0.05 -0.55 1.03 -0.32 1.14 0.70 -1.08 2.21

2.254

5.21

SNE (Competitors)

-0.461 2.380

-0.55 2.43

ˆF x SNE (Competitors)

-8.689

-3.49

ˆF (Customers) ˆf (Customers) SNE (Customers)

ˆF x SNE (Customers)

3.024 0.289 0.748 -0.019 -0.411 -0.319 0.002 0.054 -0.482 0.146 0.037 0.071

6.91 1.11 1.90 -0.06 -1.45 -0.99 0.16 1.75 -2.41 1.45 0.28 1.94

1.029

2.50

-1.209

-1.85

2.835

2.11

-9.877

-2.07

ˆF (Competitors) ˆf (Competitors)

ˆF (Technology) ˆf (Technology) SNE (Technology)

ˆF x SNE (Technology)

1.483 0.243 0.570 0.388 -0.553 -0.148 -0.008 0.051 0.273 0.063 -0.138 0.089

2.99 0.88 1.53 1.72 -1.05 -0.82 -0.56 1.33 1.47 0.50 -0.83 1.26

1.019

1.93

0.378 -2.837

0.40 -1.55

6.870

2.14

__________________________________________________________________________ N = 91. To improve conditioning, the SNE variables were mean centered before estimation. The GMM t-statistics are robust to heteroscedasticity and take into account the possible error correlation across the three equations.

36

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