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British Journal of Management, Vol. 25, 285–304 (2014) DOI: 10.1111/1467-8551.12004

Top Management Team Members’ Decision Influence and Cooperative Behaviour: An Empirical Study in the Information Technology Industry Tine Buyl, Christophe Boone and Walter Hendriks1 University of Antwerp, Faculty of Applied Economics, Department of Management, Antwerp Centre of Evolutionary Demography, Prinsstraat 13, 2000 Antwerpen, Belgium, and 1Hasselt University, KIZOK Research Centre, Agoralaan, Building D, 3590 Diepenbeek, Belgium Corresponding author e-mail: [email protected] Organizational leadership is generally distributed between the chief executive officer (CEO) and the top management team (TMT) members. Building on this observation, we present an empirical investigation of the cues for CEOs to delegate decision-making influence to particular TMT members. In the literature, explanations both based on expertise and driven by similarity are described. In this study, we reconcile both explanations by examining the moderating role of the TMT’s level of ‘cooperative behaviour’ (collaboration and information exchange). We analyse when and in what circumstances TMT members’ expertise and similarity to the CEO regarding his/her functional background and/or locus-of-control predict their decision-making influence. We postulate that TMT cooperative behaviour will advance the effect of expertise on TMT members’ decision influence but impede the effect of similarity to the CEO. Our hypotheses are tested on a data set of 135 TMT members from 32 Dutch and Belgian information technology firms. Overall, we find that our proposed research model is confirmed for technology-oriented decisions. Furthermore, we draw exploratory conclusions about the effect of TMT cooperative behaviour on the systematic distribution of decision influence in TMTs.

Introduction Top management teams (TMTs) are often formed with the explicit goal of uniting individuals who differ in expertise and experience to improve decision quality and organizational performance We wish to thank the participants of the Strategic Decision Making session of the Academy of Management Meeting in Chicago (2009) and the members of the Antwerp Centre of Evolutionary Demography for their constructive comments. Furthermore, we gratefully acknowledge financial support from the Research Foundation – Flanders (FWO-Vlaanderen), both through the Odysseus programme and through a personal scholarship of the first author.

(Jehn, Northcraft and Neale, 1999). Nevertheless, research in the ‘upper echelons’ stream (Hambrick and Mason, 1984), which typically studies the effects of (diversity in) TMT members’ (demographic) characteristics and experiences on organizational outcomes, shows equivocal results (Certo et al., 2006; Patzelt, zu KnyphausenAufess and Nikol, 2008). Several scholars argue that these ambiguous findings might be due to the implicit assumption in upper echelons research that the TMT as a whole acts as an organization’s decision-making unit (Roberto, 2003) and that each TMT member’s unique expertise and knowledge will be used collectively in TMT decisionmaking (Bunderson, 2003a). However, this

© 2013 The Author(s) British Journal of Management © 2013 British Academy of Management. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.

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collective perspective on TMTs implies that important individual-level effects are overlooked (Carpenter, Geletkanycz and Sanders, 2004).1 In our study, we explicitly consider the fact that not all TMT members have equal impact on strategic decisions and we propose that TMT members’ decision influence depends on their own (demographic) backgrounds and the TMT’s processes. By treating the TMT as a monolithic unit, TMT scholars ignore that TMTs are essentially hierarchical decision-making bodies (Hollenbeck et al., 1995), underestimating the often dominant role of the chief executive officer (CEO). The CEO – the ‘formal leader’ of the TMT and the organization – plays a unique and decisive role in the distribution of decision influence by delegating it to the TMT members (Hakimi, van Knippenberg and Giessner, 2010). Former research (e.g. Arendt, Priem and Ndofor, 2005; Bunderson, 2003a) suggests that CEOs use different cues to delegate decision influence to certain TMT members. First, the ‘strategic contingencies’ view (Hickson et al., 1971) suggests that CEOs should be guided by TMT members’ expertise, as groups perform better (e.g. better quality decisions, fewer errors, higher efficiency) when expert members are allowed to influence group decisions (e.g. Libby, Trotman and Zimmer, 1987; Littlepage et al., 1995). Second, CEOs tend to attribute decision influence to those who are similar to them (Hoffman and Maier, 1966). In contrast to the strategic contingencies view, behavioural theories of decision-making de-emphasize the primacy of expertise and stress the importance of sociopsychological forces in shaping TMT decisionmaking (Eisenhardt and Zbaracki, 1992). The ‘homophily’ mechanism – implying that people consciously and unconsciously prefer working with others who are similar to them (for a review see McGuire, 1985) – has been introduced as a major potential source of decision influence. CEOs tend to delegate decision influence to similar subordinates because they expect them to perform in ways that reinforce their own personal interests (Jackson et al., 1991; Tsui and O’Reilly, 1989). Hence, we identify two separate cues that may induce CEOs to delegate decision influence to par-

1

See Menz (2012) for a recent review, synthesis and research agenda on individual-level TMT research.

ticular TMT members2 – their expertise and similarity – but it remains inconclusive which of these two will prevail. Little systematic research has been performed on the exact determinants of decision influence of individual TMT members (Menz, 2012; Smith et al., 2006). We attend to this gap by proposing that the salience of the two cues for CEOs’ delegation of decision influence will be moderated by the TMT’s internal processes – here, TMT cooperative behaviour, which includes collaboration and information exchange between TMT members (Milton and Westphal, 2005). TMT cooperative behaviour captures a team climate where members of the TMT act as a ‘real’ team as opposed to a fragmented, loosely coupled collection of executives3 (cf. Hambrick, 1994). Recent research underscores the central role of such cooperative climate for TMT functioning (Boone and Hendriks, 2009; Friedrich et al., 2009). For instance, Carmeli and Schaubroeck (2006, p. 442) found that TMTs that are ‘characterized by intense interaction that produces open information exchange and collaboratively based solutions’ display higher quality in their decisionmaking. We expect that high TMT cooperative behaviour increases the CEO’s reliance on the expertise of its TMT members to delegate decision influence and decreases the relevance of the sociopsychological forces of TMT member similarity. In cooperative teams, the CEO is aware of the areas of expertise of the TMT members (Rulke and Galaskiewicz, 2000) and is better able to draw from the executives’ knowledge (Lubatkin et al., 2006). Therefore, cooperative behaviour increases competence-based trust (Carmeli and Schaubroeck, 2006), which stimu2

Although both cues suggest different sources of TMT members’ decision influence – i.e. expertise versus similarity to the CEO – they are not fundamentally incompatible. A TMT member might simultaneously be both expert and similar to the CEO. 3 TMT cooperative behaviour is highly related to the construct ‘behavioural integration’ (Hambrick, 1994), which is generally defined as a meta-construct including three dimensions: collaborative behaviour, information exchange, and joint decision-making. In this study, we particularly focus on the two dimensions pertaining to the TMT’s cooperative climate – collaboration and information exchange – and study how this climate affects the dyadic relation between the CEO and individual TMT members concerning delegation of decision influence.

© 2013 The Author(s) British Journal of Management © 2013 British Academy of Management.

Top Management Team Members’ Decision Influence

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Figure 1. Research framework

lates the CEO to delegate decision influence to ‘experts’ in the TMT (cf. Arendt, Priem and Ndofor, 2005; Hakimi, van Knippenberg and Giessner, 2010). On the other hand, in TMTs characterized by low levels of cooperative behaviour, CEOs generally lack trust in the TMT members’ competences and in collective team efforts (Magni et al., 2009) and tend to resile to more psychologically driven heuristics, such as similarity, to attribute decision influence to team members. To assess TMT members’ expertise and similarity as sources of decision influence, we build on upper echelons research (Bunderson, 2003b; Hambrick and Mason, 1984) and focus on TMT members’ (demographic) background characteristics. More particularly, we include TMT members’ ‘expert’ functional background and their similarity to the CEO in functional background and personality (locus-of-control). Functional background has repeatedly been described in team research as the most salient indicator of both TMT members’ expertise and differences in their experiences (Bunderson and Sutcliffe, 2002; Menz, 2012). Locus-of-control is a deep-level personality trait that refers to individual differences in a generalized belief in internal versus external control of reinforcement (Rotter, 1966) and is systematically correlated with personality dimensions such as self-esteem, self-efficacy and emotional stability (Declerck, Boone and De Brabander, 2006). Similarity in TMT members’ locus-of-control has been proved to be very relevant for understanding TMT functioning (Boone and Hendriks, 2009) because of the associated similarity in deep-level self-concepts, attitudes and expectations (Hiller and Hambrick, 2005). Figure 1 depicts the research model. The hypotheses are tested on a

detailed data set of 135 executives from 32 Belgian and Dutch information technology (IT) firms.

Theoretical framework ‘Expert’ functional background A first cue for CEOs to delegate decision influence to TMT members is drawn from the strategic contingencies perspective, which recommends that decisions are taken by ‘experts’, i.e. by whoever can cope best with the main uncertainties involved in the decision area (e.g. Finkelstein, 1992; Hambrick, 1981; Hickson et al., 1971). ‘Expertise’ can be defined as ‘high levels of skill or knowledge within a given domain’ (Salas, Rosen and DiazGranados, 2010, p. 946). CEOs identify TMT members with such expertise and give greater weight to the advice, suggestions and opinions of these expert members in making decisions. By aligning intragroup influence with members’ expertise, groups are better able to translate the expertise of their members into higher quality solutions and decisions (Bunderson, 2003a). Hence, the way in which decision influence is matched with expertise is very relevant for decision quality and, subsequently, organizational performance (Boone and Hendriks, 2009; Brodbeck et al., 2007). We follow Bunderson (2003a) and Finkelstein (1992) to identify a TMT member’s functional background (FB) as the most salient indicator for his/her expertise. In prior research individuals’ FB is generally seen as a proxy for their experience, knowledge and cognitive base (Boone and Hendriks, 2009; Menz, 2012; Williams and O’Reilly, 1998). FB – which covers a TMT member’s whole career – is a more appropriate


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indicator of a TMT member’s expertise in decision-making than, for instance, his/her job title, given the fact that many executives gain experience outside their dominant career track or different from their current job titles (Bunderson and Sutcliffe, 2002). Furthermore, FB exceeds educational background – another commonly used measure of expertise (e.g. Dahlin, Weingart and Hinds, 2005) – as a measure for TMT members’ expertise, because by the time managers reach higher echelons they will have gained so much workplace experience that their formal education will have become less critical in their decision-making (Barkema and Shvyrkov, 2007). We consider two different areas of decisions: marketing-related and technology-oriented decisions. This split of FBs corresponds to a split in firm knowledge that is rather common in research (e.g. Kogut and Zander, 1992; Shane, 2000): that between marketing and technological knowledge. A long tradition of scholars has demonstrated that both are essential (e.g. Crittenden, 1992; Miller, Hickson and Wilson, 2008; Shapiro, 1977), especially in highly dynamic and technology-intensive industries such as the IT industry, the setting of this study (Cooper and Kleinschmidt, 1994). Technological knowledge generally refers to rather specialized and complex knowledge on technologies and/or processes; marketing knowledge denotes applications and commercialization opportunities for technological knowledge (Teece, 2007; Van den Bosch, Volberda and de Boer, 1999). Therefore, TMT members with an FB in marketing and sales are considered to be experts in marketing decisions and TMT members with a technological FB in technology decisions.

Similarity to the CEO The second cue for CEOs to delegate decision influence is not normative but descriptive and based on socio-psychological forces: TMT members’ similarity to the CEO (Bunderson, 2003a; Hoffman and Maier, 1966). One of the most robust and reliable findings in sociopsychology research is that similarity between individuals instigates interpersonal attraction, liking and trust (e.g. Byrne, 1971; Westphal and Zajac, 1995), and that people consciously and unconsciously prefer interacting with others who

are similar to them (McPherson, Smith-Lovin and Cook, 2001; Ruef, Aldrich and Carter, 2003). Three plausible reasons underscore this desire to interact with and rely on similar others. First, ample evidence supports the similarity/attraction paradigm (Byrne, 1971), which argues that people are attracted to, like and trust others like themselves regarding experiences and/or demographic characteristics (Hoffman and Maier, 1966; Tenney, Turkheimer and Oltmanns, 2009). Second, self-categorization theory suggests that people derive self-esteem and self-identity from perceived group membership. Consequently, TMT members may prefer interacting with similar others in an attempt to increase the salience of their group membership and, hence, to strengthen their self-esteem and self-identity (Westphal and Zajac, 1995). Third, from a sociopolitical perspective (Eisenhardt and Zbaracki, 1992), people, especially in a TMT setting, might prefer working and interacting with others who are similar on, for instance, strategic preferences as this helps to reduce their uncertainty and consolidate their power (Boone et al., 2004). Klein et al. (2004) found that individuals tend to seek advice from similar others who they believe are likely to hold priorities and perspectives similar to their own. Because the CEO is the principal actor in the TMT, who has a central role in delegating decision influence (Arendt, Priem and Ndofor, 2005; Papadakis and Barwise, 2002), we posit that similarity to the CEO enhances TMT members’ decision influence. Hakimi, van Knippenberg and Giessner (2010) found that leaders’ trust in their followers, e.g. instigated by similarity, is of crucial importance in their decision to empower followers.4 We include two types of similarity that appear to be especially relevant for CEOs. First, in information-processing groups, such as TMTs, a team member’s FB is particularly salient and forms one of the principal axes of inter-individual 4

Nevertheless, resource dependence theory (e.g. Lynall, Golden and Hillman, 2003) and the entrepreneurship literature (e.g. Vissa, 2011) argue that CEOs would prefer allocating decision influence to those TMT members that are dissimilar to them, precisely because these dissimilar TMT members might bring nonredundant knowledge and perspectives to the TMT’s decisions (cf. Brodbeck et al., 2007). In the results and discussion sections we come back to this issue.



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differences between members (Bunderson, 2003a). A manager’s FB is likely to be represented in the specific content of his/her belief structures (Dearborn and Simon, 1958), which guide information acquisition and retrieval (Cantor and Mischel, 1977; Ross and Sicoly, 1979). For instance, an executive with an FB in operations is more sensitive to changes in the efficiency of operations and more prone to initiate actions in this area, such as increasing the level of resources in operations (Waller, Huber and Glick, 1995). Therefore, CEOs might assume that similar TMT members will approach business issues in the same way as themselves (Klein et al., 2004), which increases their willingness to delegate decision influence to similar thinking team members. With regard to this, Homburg, Workman and Krohmer (1999) found that similarity to the CEO’s FB was a source of influence over key decisions. Second, similarity might also refer to more deep-level features.5 Studies in other areas have observed that characteristics that are less obvious to observers, such as personality, are still prominent in decision-makers’ assessments of individual effectiveness and ‘fit’ (Ferris and Judge, 1991). Personality differences mostly become manifest when ensuing differences in behavioural styles and attitudes create difficulties in interpersonal interactions. We select the personality trait locus-of-control to assess attitudinal similarity. Locus-of-control is a well-documented personality trait that refers to differences in a generalized belief in internal versus external control of reinforcement (Rotter, 1966) – i.e. the influence of ability and effort (internal locus-of-control) versus luck and context (external locus-ofcontrol) – that can be used to judge deep-level individual differences between executives. The reason for selecting locus-of-control as a measure of similarity to the CEO is twofold. First, locus-of-control is a broad overarching trait (Judge et al., 2002) that is systematically correlated with more specific personality dimensions

such as self-esteem, self-efficacy and conscientiousness (Declerck, Boone and De Brabander, 2006). Therefore, it is preferred over the use of, for example, the widely used Big 5 personality dimensions (openness, conscientiousness, extraversion, agreeableness and neuroticism), which include traits that are less obviously related to executives’ decision-making behaviour. Locus-of-control relates to individuals’ universal beliefs about who or what controls the occurrence of events (Hiller and Hambrick, 2005) and is repeatedly found to predict variables associated with executives’ decision-making, such as risk-taking and strategic choices (Miller, 1983). Second, the fundamental differences between managers with an internal versus external locusof-control make them react differently to contingencies in their environment. Whereas internals are proactive, action-oriented and more inclined to take risks, externals are more reactive, passive and risk-averse (Lefcourt, 1982). These attitudinal differences are likely to result in different ways to analyse, interpret and act upon the same decision situation (Boone, van Olffen and van Witteloostuijn, 2005). For instance, internal CEOs are found to be more inclined to pursue innovative and risky strategies compared with external CEOs (Miller, 1983), irrespective of the market environment in which they operate (Boone, De Brabander and van Witteloostuijn, 1996). Boone and Hendriks (2009) found that dissimilarities in TMT members’ locus-of-control hampered TMT functioning. As the CEO assumes that similar TMT members approach business issues in the same way as him/ herself (Klein et al., 2004), locus-of-control similarity will increase the CEO’s willingness to delegate decision influence to these similar thinking team members. Relatedly, in the literature on personal relationships perceived locus-of-control similarity has consistently been found to drive friendships and romantic relations (Morry, 2003).

5

Both cues for CEOs to delegate decision influence are supported by theory but could lead to different conclusions regarding TMT members’ decision influence. We add to this stalemate by introducing the TMT’s level of cooperative behaviour as a moderator. We argue that TMT cooperative behaviour – defined here as the degree

We prefer studying a deep-level personality trait instead of surface-level or ‘high visibility’ demographic characteristics such as age and gender. Research on group processes and team decision-making (Harrison, Price and Bell, 1998; Harrison et al., 2002) has shown that in teams that work together for a longer period, such as TMTs, the effects of deep-level characteristics quickly transcend those of surface-level characteristics.

TMT cooperative behaviour as a moderator


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of collaboration and information exchange between TMT members (Milton and Westphal, 2005) – will have diverging effects for the CEO’s delegation of decision influence based on expertise on the one hand and driven by similarity on the other hand. Hence, we propose that the nature of the dyadic relations between the CEO and the individual TMT members is affected by TMTlevel processes. For the delegation of decision influence based on expertise, we expect a positive moderating effect of TMT cooperative behaviour. Highly cooperative TMTs are characterized by high within-team collaboration and information exchange (Milton and Westphal, 2005). Hence, the CEO and TMT members have the chance to develop knowledge about each other’s areas of expertise (Rulke and Galaskiewicz, 2000). Because of this enhanced knowledge of each other, competence-based trust within the team is built (Carmeli and Schaubroeck, 2006) which is required for the optimal deployment of expertise (Olson, Parayitam and Bao, 2007). Given the high familiarity of TMT members’ knowledge and experience resulting from cooperative behaviour, the CEO is more likely to delegate decision influence to ‘experts’ in the TMT (cf. Arendt, Priem and Ndofor, 2005; Hakimi, van Knippenberg and Giessner, 2010). From the TMT members’ point of view, TMT cooperative behaviour increases the team members’ psychological safety and familiarity with the other TMT members, which boosts their willingness to express their opinions and expert knowledge (Gruenfeld et al., 1996). The feeling of collective responsibility for the organization’s performance in cooperative TMTs (Simsek et al., 2005) also represents a reinforcing process for TMT members to exert their expertise. Put differently, team members in highly cooperative TMTs will be motivated and enthused to be involved in decision-making themselves. For these reasons, we expect that TMT members’ expertise will predict CEOs’ delegation of decision influence more strongly when TMT cooperative behaviour is high. Extant research provides support for this argument, since the capacity to integrate TMT members’ opinions into balanced and high-quality strategic decisions is found to be higher in TMTs with high levels of information exchange and collaboration (Carmeli and Schaubroeck, 2006).

H1a. A TMT members’ marketing FB will positively affect his/her decision influence in marketing decisions if the TMT’s level of cooperative behaviour is high. H1b. A TMT members’ technological FB will positively affect his/her decision influence in technology decisions if the TMT’s level of cooperative behaviour is high. Conversely, the opposite effect occurs for CEOs’ delegation of decision influence based on similarity. TMTs with low levels of information exchange and collaboration fail to act as ‘real teams’ (Hambrick, 1994). TMT members often experience difficulties in achieving a collective understanding (Lubatkin et al., 2006) and lose confidence in and support of collective team efforts (Magni et al., 2009). Such fragmented ‘loosely coupled’ TMTs lack collective ‘multiway’ interaction (Hambrick, 1994). The level of knowledge of each other’s unique expertise and competences is thus probably lower (Carmeli and Schaubroeck, 2006). Therefore, CEOs might rely on TMT members that are similar to them for decision-making. In this way, CEOs can still reduce uncertainty in decision-making (Boone et al., 2004; Westphal and Zajac, 1995) despite the lack of confidence in team efforts and in the TMT members’ competences, as they assume that these similar TMT members will act and react in a similar way in decision-making compared to themselves. Further adding to this argument, fragmented TMTs will most probably be highly political, characterized by the salience of informal alliances and coalitions to exert pressure (Eisenhardt and Zbaracki, 1992). In such teams, whom you know is more important than what you know. TMT members who are able to forge alliances with the CEO by virtue of their similarity might be better positioned to have influence over key decisions (Bunderson, 2003a). Hence, we propose that (FB and/or locus-ofcontrol) similarity to the CEO increases a TMT member’s decision influence in case of low cooperative behaviour. H2a. A TMT member’s FB similarity to the CEO will positively affect his/her decision influence in marketing decisions if the TMT’s level of cooperative behaviour is low. H2b. A TMT member’s FB similarity to the CEO will positively affect his/her decision influ-


Top Management Team Members’ Decision Influence ence in technology decisions if the TMT’s level of cooperative behaviour is low. H3a. A TMT member’s locus-of-control similarity to the CEO will positively affect his/her decision influence in marketing decisions if the TMT’s level of cooperative behaviour is low. H3b. A TMT member’s locus-of-control similarity to the CEO will positively affect his/her decision influence in technology decisions if the TMT’s level of cooperative behaviour is low.

Methods Sample We contacted the CEOs of 206 Dutch and Belgian IT firms with 20–500 employees; 54 firms agreed to participate. We collected data through structured interviews with the CEOs and questionnaires (which were thoroughly validated by two IT experts and one IT firm) filled out by every TMT member. To optimize accuracy and reliability of the data, we only included firms if every TMT member completed a questionnaire. Complete information was received from the executives of 35 firms, equalling an overall firm-level response rate of 17% (35/206). Given that we asked top executives to reveal detailed personal information and that we requested participation of every TMT member, this response rate is rather high. We excluded three firms that had a TMT of only two members (as such dyads cannot be considered as real teams). Hence, our working sample contained information on 167 executives from 32 (14 Belgian and 18 Dutch) fully covered TMTs with three or more team members. Analogous to Bunderson (2003a), we excluded the 32 CEOs from our data set since we focus on the decision influence of non-CEO TMT members. Hence, our final working sample comprises 135 observations. On average, the organizations in our sample have 63.55 employees (with a minimum of 20 and a maximum of 207), while the mean TMT size is 5.72 (with a minimum of 3 and a maximum of 9). Table 1 reports some characteristics and major tendency statistics of the CEOs and TMT members. We do not believe that sample selection bias is an issue. Not only does our response rate exceed 10%, which is considered to be the lower limit for selection bias (Pedzahur and Schmelkin, 1991),

291 Table 1. Sample characteristics and major tendency statistics

Organizational tenure (years) Mean SD TMT tenure (months) Mean SD Age (years) Mean SD Functional background Software development and project management Software operations and maintenance Financial and administration services Marketing and sales Human resources Software product management % male

CEOs

TMT members

9.38 5.64

5.41 4.54

87.44 66.83

31.83 47.98

45.75 6.52

38.84 6.60

16/32

65/135

5/32 5/32 22/32 3/32 2/32 97%

30/135 29/135 62/135 12/135 9/135 82%

but we also investigate complex interaction relationships, in which case selection bias is unlikely to pose a threat (Simons, Pelled and Smith, 1999). Measures Dependent variable. Our dependent variable concerns the TMT members’ amount of influence on strategic decisions. In the questionnaire, we listed 17 strategic decisions (depicted in Table 2), which were identified with the help of IT industry experts and which represent the main decisions TMTs are faced with in the IT industry. The CEO and TMT members were asked to indicate which one of the TMT members (including the CEO) had the principal decision-making role in each of the 17 decisions. Based on the questionnaire responses, we performed a cluster analysis to categorize the 17 decisions into different decision domains. We used the standard between group’s linkage as the cluster method, and the Jaccard similarity measure for classifying binary data into relatively homogeneous groups. The cluster analysis resulted in a breakdown of the 17 decisions into four decision domains (see Table 2) – general management (decisions 6, 7, 11, 14 and 17), marketing and sales (decisions 3, 12 and 13), technology (1, 2, 8, 16) and human resources (5, 9, 15). Decisions 4 and 10 were not classified into any of the clusters. Note that in the IT industry research and development (R&D) generally involves the development of new ver-


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Table 2. Decision domains Decision 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Development of new products/services (R&D strategy) Choice of tools, platforms and standards Pricing strategy and price fixing Cooperation/collaboration with external experts Training policy (training, determining of training budgets) Defining general targets Growth strategy (mergers and acquisitions etc.) Choice of hardware and software components suppliers Personnel policy (selection methods, recruitment) Quality standards and observance Distribution of the firm’s total budget Market strategy Sales strategy Firm financing strategy Personnel payment policy Budgeting of the R&D strategy Organization of the firm’s activities

Decision domain (cluster analysis) Technology Technology Marketing and sales / Human resources General management General management Technology Human resources / General management Marketing and sales Marketing and sales General management Human resources Technology General management

sions and refinement of software packages. R&D is thus mainly technologically oriented in this industry. Therefore the clustering of R&D decisions (decisions 1 and 16) with software-related decisions (decisions 2 and 8) as one cluster representing ‘technology’ decisions is not surprising. In this study, we focus on the decision influence of non-CEO TMT members. Therefore, we have taken a closer look at the CEOs’ responses to examine, for each decision, whether the CEOs indicated that they themselves were the main decision-maker or, conversely, that one of the other TMT members had the principal decision influence. For decisions concerning general management and human resources, the proportion of non-CEO TMT members who had most decision influence according to the CEO was very small, while for decisions concerning marketing and sales and technology, non-CEO TMT members did have substantial decision influence. More than half of the CEOs in our sample indicated that the main decision influence in these decisions was in the hands of non-CEO TMT members. Based on these observations, we decided to focus on the two decision domains in which non-CEO

TMT members have a substantial impact: marketing decisions and technology decisions. Prior research (e.g. Benson, 1977) indicates that both marketing and sales decisions and technology decisions are important domains in organizations’ strategic decision-making, especially in the highly dynamic and technology-intensive IT industry. The two dependent variables in our study, ‘decision influence on marketing issues’ and ‘decision influence on technology issues’, were operationalized using the formula Sni=1pi/n where pi is the proportion of TMT members (including the CEO) who indicated that the focal TMT member had an impact on decision i and n is the number of decisions included (three for marketing decisions and four for technology decisions). Two issues arise with regard to this operationalization of TMT members’ decision influence in marketing and technology issues. First, it might be hard for managers to assess TMT members’ decision influence in retrospect. We acknowledge this issue. However, we used an approach that is similar to the one used and validated by Bunderson (2003a). In addition, in the questionnaire we asked managers to indicate their overall impression of who of the TMT members had dominant influence in the various decision domains in general, instead of referring to one particular decision in the past. This reduces the potential retrospective bias in the TMT members’ responses. Furthermore, we used the responses of all TMT members to construct our measure of a particular TMT member’s decision influence, further reducing the potential existence of such bias. As a robustness check, we calculated TMT member decision influence based on the CEO’s answers alone (instead of the answers of the whole TMT). Although it was probably not easy for managers to assess TMT members’ decision influence in 17 different decision domains, we still found that the CEOs’ and TMT members’ responses were very much in line – illustrated by the high correlation coefficients of 0.88 for ‘decision influence on marketing issues’ and 0.82 for ‘decision influence on technology issues’ – underscoring the robustness of the variables. The second issue concerning our operationalization of TMT members’ decision influence is that the responses might be biased because of TMT members’ tendency to answer questions regarding decision influence in an obvious and predictable


Top Management Team Members’ Decision Influence way, e.g. by ascribing influence in decisions regarding marketing automatically to TMT members with marketing FBs. However, this tendency to – inaccurately – attribute decision influence to experts would primarily interfere with the main effect of TMT members’ expert FB, while we are mostly focused on the interaction effects of the sources of TMT members’ decision influence with TMT behavioural integration. Independent variables. Two of our independent variables concern TMT members’ FB. With the help of the IT industry experts, seven functional categories relevant to the IT industry were identified in the questionnaire: (i) software development and project management; (ii) software operations and maintenance; (iii) financial and administrative services; (iv) marketing and sales; (v) human resources; (vi) software product management; and (vii) a negligible rest category. TMT members were asked to indicate at least one and a maximum of three of these functional categories in which they had gained the most experience during their professional career. To indicate TMT members’ functional expertise in decisions on marketing and technology issues, respectively, we included two dummy variables: ‘FB marketing and sales’ equals one if the TMT member has indicated experience in marketing and sales and ‘FB technology’ equals one if the TMT member has experience in one or more of the technology-oriented functional categories, i.e. software development and project management, software operations and maintenance, and software product management. To operationalize ‘FB similarity to the CEO’, we have counted the number of functions that were indicated by both the CEO and the focal TMT member and divided this number by the total number of functions that were indicated by the TMT member. Our third independent variable is locus-ofcontrol similarity to the CEO. The TMT members’ locus-of-control is measured with a Dutch translation of the Rotter scale (Rotter, 1966), which consists of 23 forced-choice items where the respondent has to choose between an internal and an external control alternative. Example statements are ‘Many times I feel that I have little influence over the things that happen to me’ (external control alternative) and ‘It is impossible for me to believe that chance or luck plays an

293 important role in my life’ (internal control alternative). We obtained a measure of internality by counting the number of internal control alternatives chosen (minimum of 0 and maximum of 23). Cronbach’s alpha equals 0.61, which reaches the lower limits of acceptability (Nunnally, 1978) and concurs with values reported in other studies (Boone, van Olffen and van Witteloostuijn, 2005; Rotter, 1966). To operationalize ‘locus-of-control similarity to the CEO’, we calculated the absolute difference between the CEO’s and TMT member’s scores, and subtracted this difference from the maximum difference found in the data set. TMT cooperative behaviour. TMT cooperative behaviour is measured using six questionnaire items. We were inspired by Milton and Westphal’s (2005, p. 192) description of cooperation as an interactive and relational behaviour that occurs between members of a work group and that is directed at task achievement in the group through information exchange and collaboration. Our measure of ‘cooperative behaviour’ comprises six items that are closely related to this description: three items to measure ‘information exchange and integration’ and three items for ‘collaborative behaviour’. Both three-item scales have been used in previous research (Boone and Hendriks, 2009; Buyl et al., 2011). The three items measuring ‘information exchange and integration’ were adapted from O’Reilly and Roberts’s (1976) scale of ‘perceived communication openness’: (1) the communication in this team normally goes without hidden agendas; (2) in general, differences of opinion with respect to task execution are discussed openly and thoroughly; (3) in decision-making, usually every team members’ input is used. The three items to measure ‘collaborative behaviour’ were inspired by Hambrick’s (1994) notion of ‘teamness’: (1) there is a fruitful, rewarding cooperation within this team; (2) it is easy to ask advice from any member of this group; and (3) the management team of this firm operates as a ‘real’ team. All TMT members (including the CEO) evaluated the six items on a five-point Likert scale. A factor analysis revealed that the six items load on a single factor. The reliability for the six items (Cronbach’s alpha) equalled 0.829. The median within-team interrater agreement coefficient (rwg; James, Demaree and Wolf, 1984) equalled 0.85 (minimum rwg = 0.54). A one-way analysis of vari-


294


ance indicated that team member ratings differed significantly between teams (p < 0.000), with an intraclass correlation coefficient (ICC[1]) (Bliese, 2000) of 0.41. The reliability of the group means, measured by ICC[2], was 0.58. These analyses guarantee that the team’s mean could be used as a team-level variable of cooperative behaviour. Control variables. Controls on the TMT member, CEO, TMT and firm level are incorporated. First, ‘individual locus-of-control’ and ‘individual team tenure’ (in months) of the TMT member are included, since the TMT member’s locus-of-control score and seniority might affect decision influence. We also include several control variables at the CEO level, such as ‘CEO team tenure’, ‘CEO founder’, ‘CEO age’ and the CEO’s own FB – i.e. ‘CEO marketing and sales FB’ and ‘CEO technology FB’ – since these variables might have an impact on the degree of strategic decision influence that the CEO will be willing to delegate to TMT members.6 At the TMT level, ‘team size’ is included as a control variable. Finally, we also incorporate two firm-level control variables: ‘firm size’ and ‘firm growth’ (both measured in terms of the number of the organization’s employees).

Results Table 3 shows the means, standard deviations and correlations of the variables. Since the observations are clustered into firms, we use the Huber/ White/sandwich estimator to calculate robust standard errors in the regression models (ordinary least squares estimates).7 6

We prefer to include (individual and CEO) team tenure over organizational tenure as team tenure is believed to be more relevant for team processes. Furthermore, we prefer to incorporate the individual’s locus-of-control over the CEO’s locus-of-control as we expect a larger effect of the former variable (we could not include both as ‘locus-of-control similarity to the CEO’ is also included in the model). However, we have tested models in which we substitute team tenure with organizational tenure and individual locus-of-control with CEO locusof-control (available from the authors). These models indicate no change in direction or significance of the variables under study, which underscores the robustness of our findings. 7 The variables involved in estimating the interaction effects were mean-centred to facilitate interpretation.

Table 4 represents the model estimations for marketing decisions. Model 1 (Table 4) includes control variables only. Apparently, individual team tenure has a positive effect on TMT members’ marketing decision influence. Further, TMT members’ marketing-related decision influence increases when the CEO does not have an FB in marketing and sales him/herself, and the TMT’s level of cooperative behaviour has a negative effect on TMT members’ marketing decision influence. In model 2 (Table 4), the direct effects of ‘FB marketing and sales’ and ‘FB technology’ are added. Although not formally hypothesized, we found a positive and significant direct effect of a TMT member’s FB in marketing and sales (B = 0.24, p < 0.001). Hypothesis 1a, on the interaction effect of ‘FB marketing and sales’ and ‘cooperative behaviour’, is not supported, as the interaction coefficient is not significant (Table 4, model 3). Models 4–6 (Table 4) depict our results for the (main and interaction) effects of ‘FB similarity’ and ‘locus-of-control similarity’ on marketing decision influence. Overall, it appears that similarity is not a good indicator for TMT members’ decision influence concerning marketing issues. Both the direct effects (model 4) and the interaction effects with ‘cooperative behaviour’ (models 5 and 6) depict insignificant coefficients. As a consequence, for marketing decisions Hypotheses 2a and 3a could not be confirmed. Table 5 reports the model estimations for technology decisions. Again, model 1 (Table 5) displays the effects of the control variables only. We find that TMT members’ technology decision influence is higher in the case of an older CEO and a smaller TMT. Similar to our findings for marketing decisions, our results indicate that expertise – here ‘technology FB’ – has a large positive and significant effect (B = 0.09; p < 0.001) on technology decision influence (Table 5, model 2). Hypothesis 1b is confirmed, as demonstrated by the positive and significant interaction effect of ‘technology FB’ with TMT ‘cooperative behaviour’ (B = 0.10; p < 0.05) in model 3 (Table 5). Our findings for (FB and locus-of-control) similarity are shown in models 4–6 (Table 5). Hypothesis 2b on the interaction effects of ‘FB similarity’ and ‘cooperative behaviour’ on technology decision influence is not supported, as the corresponding interaction coefficient is not



N = 135. *p < 0.05 (two-tailed).

Control variables 8 Individual locus-of-control 9 Individual team tenure 10 CEO team tenure 11 CEO founder 12 CEO age 13 CEO FB marketing and sales 14 CEO FB technology 15 Team size 16 Firm size 17 Firm growth

Moderator 7 Cooperative behaviour

Sources of decision influence 3 FB marketing and sales 4 FB technology 5 FB similarity to the CEO 6 Locus-of-control similarity to the CEO

Dependent variables 1 Decision influence – marketing 2 Decision influence – technology

Measures – 0.04

1

–

2

3

0.46 –0.09 0.08 0.14 0.10 –0.02 0.18* –0.02 –0.10 –0.24* 0.05 –0.04

0.12 –0.01 –0.09 –0.04 –0.06 –0.02 –0.09 0.09 –0.01 –0.01

0.17* –0.15

0.50 0.49* –0.03 – 0.49 –0.19* 0.29* –0.23* 0.41 0.00 –0.10 0.32* 2.79 –0.06 –0.10 –0.03

0.25 0.16

SD

14.96 2.97 0.10 32.27 48.07 0.09 82.19 65.63 0.01 0.67 0.47 –0.01 45.39 6.99 0.05 0.73 0.44 –0.17* 0.66 0.48 0.05 5.72 1.49 –0.09 63.55 42.67 –0.03 0.18 0.17 0.06

4.07

0.46 0.59 0.45 9.45

0.14 0.13

Mean

Table 3. Means, standard deviations and correlations

– 0.02

5

0.14 0.07 –0.05 –0.05 0.03 0.11 0.02 0.12 0.10 –0.19* 0.05 0.26* –0.06 0.25* –0.07 0.29* 0.01 –0.10 0.01 0.06

–0.05 –0.09

– 0.10 0.01

4

0.32* –0.01 –0.20* –0.01 0.03 0.13 0.13 0.10 –0.11 –0.12

–0.11

–

6

0.05 0.22* 0.34* –0.17 0.03 0.08 –0.04 –0.13 0.20* –0.03

–

7

– –0.01 –0.01 0.00 0.16 –0.00 –0.01 0.05 0.11 –0.08

8

10

11

12

13

14

15

16

– 0.19* – 0.03 0.34* – 0.22* 0.15 0.22* – 0.04 0.12 –0.21* –0.11 – 0.02 0.16 0.25* –0.09 –0.22* – 0.10 –0.25* 0.04 0.08 0.34* 0.27* – 0.00 –0.16 –0.43* 0.27* 0.20* –0.39* 0.10 – 0.02 0.23* 0.18* 0.12 –0.27* 0.41* 0.08 –0.15

9

Top Management Team Members’ Decision Influence 295

296


Table 4. TMT member characteristics and decision influence on marketing decisions 1 Constant

0.07 (0.13) 0.01 (0.01) 0.00* (0.00) 0.00 (0.00) –0.05 (0.04) 0.00 (0.00) –0.09* (0.04) 0.02 (0.04) –0.01 (0.01) –0.09* (0.04) 0.00 (0.00) 0.03 (0.08) –

2

3 –0.02 (0.12) 0.00 (0.01) 0.00** (0.00) 0.00 (0.00) –0.02 (0.04) 0.00 (0.00) –0.04 (0.04) 0.07* (0.04) –0.03** (0.01) –0.07* (0.03) 0.00 (0.00) 0.02 (0.08) 0.24*** (0.04) –0.04 (0.04) – –

4

FB technology

–

FB similarity to the CEO

–

–0.02 (0.12) 0.00 (0.01) 0.00* (0.00) 0.00 (0.00) –0.03 (0.04) 0.00 (0.00) –0.04 (0.04) 0.07* (0.04) –0.03** (0.01) –0.05 (0.03) 0.00 (0.00) 0.02 (0.08) 0.24*** (0.04) –0.05 (0.04) –

Locus-of-control similarity to the CEO

–

–

FB marketing * cooperative behaviour

–

–

FB similarity * cooperative behaviour

–

–

Locus-of-control similarity * cooperative behavioural

–

–

–

–

4.69*** 0.07

13.12*** 0.32

11.81*** 0.32

4.61*** 0.08

Individual locus-of-control Individual team tenure CEO team tenure CEO founder CEO age CEO FB marketing and sales CEO FB technology Team size Cooperative behaviour Firm size Firm growth FB marketing and sales

F-value R2

0.06 (0.09) –

0.03 (0.14) 0.01 (0.01) 0.00* (0.00) 0.00 (0.00) –0.06 (0.04) 0.00 (0.00) –0.08 (0.06) 0.03 (0.04) –0.02 (0.01) –0.09* (0.04) –0.00 (0.00) 0.02 (0.08) – – 0.03 (0.05) –0.01 (0.01) – –

5 0.03 (0.14) 0.01 (0.01) 0.00* (0.00) 0.00 (0.00) –0.06 (0.04) 0.00 (0.00) –0.08 (0.06) 0.03 (0.04) –0.02 (0.01) –0.09* (0.04) –0.00 (0.00) 0.01 (0.08) – – 0.03 (0.06) –0.01 (0.01) – 0.02 (0.12) – 4.47*** 0.08

6 0.04 (0.13) 0.01 (0.01) 0.00* (0.00) 0.00 (0.00) –0.05 (0.04) 0.00 (0.00) –0.08 (0.05) 0.03 (0.04) –0.02 (0.01) –0.08* (0.05) –0.00 (0.00) 0.02 (0.09) – – 0.03 (0.05) –0.01 (0.01) – – –0.00 (0.02) 4.55*** 0.08

N = 135. Unstandardized coefficients are shown; standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001 (one-tailed).

significant (Table 5, model 5). However, model 6 (Table 5) indicates that for ‘locus-of-control similarity’ the interaction coefficient with ‘cooperative behaviour’ (B = –0.01; p < 0.05) is significant and in the expected direction, supporting Hypothesis 3b. We plot the supported interaction effects in Figures 2 and 3. The figures show that in general our results are consistent with the theoretical expectations. First, the positive relationship

between technology FB and technology decision influence is stronger when the level of cooperative behaviour is high. Second, locus-of-control similarity to the CEO is positively associated with technology decision influence in the case of a low level of cooperative behaviour but negatively related to decision influence in highly cooperative TMTs (inflection point 3.59). The negative relation between locus-of-control similarity and decision influence with high TMT



297

Table 5. TMT member characteristics and decision influence on technology decisions

Constant

1

2

3

4

5

6

0.03 (0.10) 0.00 (0.00) 0.00 (0.00) –0.00 (0.00) –0.00 (0.02) 0.00** (0.00) 0.04 (0.04) 0.01 (0.03) –0.03** (0.01) 0.04 (0.03) –0.00 (0.00) 0.01 (0.08) –

0.00 (0.09) 0.00 (0.00) 0.00 (0.00) –0.00 (0.00) –0.00 (0.02) 0.00** (0.00) 0.03 (0.04) 0.02 (0.03) –0.03** (0.01) –0.01 (0.04) –0.00 (0.00) 0.01 (0.07) 0.03 (0.03) 0.09*** (0.02) –

–0.02 (0.09) 0.01 (0.00) 0.00 (0.00) –0.00 (0.00) –0.00 (0.02) 0.01** (0.00) 0.06 (0.03) 0.03 (0.03) –0.04*** (0.01) 0.04 (0.03) –0.00 (0.00) –0.00 (0.08) –

–0.02 (0.10) 0.01 (0.00) 0.00 (0.00) –0.00 (0.00) –0.00 (0.02) 0.01** (0.00) 0.07* (0.04) 0.03 (0.03) –0.04*** (0.01) 0.04 (0.03) –0.00 (0.00) –0.00 (0.08) –

0.01 (0.10) 0.01 (0.00) 0.00 (0.00) –0.00 (0.00) 0.00 (0.02) 0.00** (0.00) 0.06 (0.03) 0.02 (0.03) –0.04** (0.01) 0.05 (0.04) –0.00 (0.00) 0.00 (0.09) –

–

–

–

–

FB technology

–

FB similarity to the CEO

–

0.02 (0.09) 0.00 (0.00) 0.00 (0.00) –0.00 (0.00) 0.00 (0.02) 0.00** (0.00) 0.04 (0.04) 0.02 (0.03) –0.03** (0.01) 0.05 (0.03) –0.00 (0.00) –0.01 (0.07) 0.03 (0.03) 0.09*** (0.03) –

Locus-of-control similarity to the CEO FB technology * cooperative behaviour FB similarity * cooperative behaviour Locus-of-control similarity * cooperative behaviour

–

–

–

–

–

–

–

–

–

–

5.47*** 0.14

8.80*** 0.21

11.95*** 0.23

5.37*** 0.16

Individual locus-of-control Individual team tenure CEO team tenure CEO founder CEO age CEO FB marketing and sales CEO FB technology Team size Cooperative behaviour Firm size Firm growth FB marketing and sales

F-value R2

0.10* (0.06) –

0.00 (0.04) –0.01 (0.00) – –

0.00 (0.04) –0.01 (0.00) – 0.06 (0.08) – 5.07*** 0.16

0.00 (0.04) –0.01* (0.00) – – –0.01* (0.01) 5.09*** 0.16

N = 135. Unstandardized coefficients are shown; standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001 (one-tailed).

cooperative behaviour (Figure 3) is notable. We expected that the relation would be less positive or become irrelevant, but we did not anticipate that it would turn negative. To understand this negative relation more thoroughly, we analysed the interaction effects of the CEO’s and the TMT member’s locus-of-control (analyses available from the authors upon request). It appears that the effect is asymmetrical: while external CEOs tend to delegate decision power to internal TMT

members, this reliance on non-similar TMT members does not appear to occur for internal CEOs. Furthermore, a three-way interaction between locus-of-control of the CEO, locus-ofcontrol of the TMT member and cooperative behaviour reveals that this asymmetrical delegation effect was only present in highly cooperative TMTs. Hence, the negative relation between locus-of-control similarity and decision influence in cooperative TMTs (Figure 3) needs to be quali-


298


Figure 2. Interaction of technology FB and cooperative behaviour on decision influence in technology decisions

Figure 3. Interaction of locus-of-control similarity to the CEO and cooperative behaviour on decision influence in technology decisions

fied: in cooperative TMTs, external CEOs appear to delegate decision influence to internal TMT members. This finding is in agreement with the resource dependence approach that argues that CEOs prefer allocating decision influence to those TMT members who are dissimilar to them (see footnote 4), i.e. external CEOs might consider internal TMT members valuable because they bring more proactive and action-oriented behaviour to the TMT (Lefcourt, 1982).

Discussion Recently, scholars (e.g. Hambrick, 2007; Smith et al., 2006) have appealed for studies on the individual TMT member level to determine how deci-

sion influence is distributed within the team, given its crucial impact on decision quality and firm performance (Boone and Hendriks, 2009; Brodbeck et al., 2007; Menz, 2012). We build on prior research (e.g. Bunderson, 2003a; Withers, Hillman and Cannella, 2012) to propose that CEOs’ delegation of decision influence to TMT members is driven both by the TMT member’s unique expertise and by similarity. We empirically investigate the moderating role of the TMT’s degree of cooperative behaviour – collaboration and information exchange – to integrate the expertise and similarity explanations of TMT members’ decision influence in IT firms. We argue that expertise prevails in cooperative TMTs, while similarity processes gain importance in fragmented TMTs. Hence, we use TMT cooperative


Top Management Team Members’ Decision Influence behaviour as a switch between the expertise and similarity explanations of TMT members’ decision influence. We executed two series of analysis, one for marketing-related decisions and one for technology decisions. For technology decisions, the pattern of findings generally supports the proposed research model. As hypothesized, the impact of expertise (technology FB) on TMT members’ decision influence increases with TMT cooperative behaviour, while the effect of locusof-control similarity to the CEO decreases with cooperative behaviour. Conversely, for marketing decisions, our proposed research model did not hold true, as none of the hypotheses could be confirmed. Although we did find a positive main effect of expertise (marketing FB), this effect was not moderated by TMT cooperative behaviour. Additionally, we did not find any evidence of the relevance of (FB and locus-of-control) similarity to the CEO as a source of marketing-related decision influence. These differences in findings for marketing versus technology decisions might be explained by the differences intrinsic to the two decision domains. Social psychology literature suggests that the nature of decision-making varies with the degree of uncertainty – i.e. the lack of consensus about purposes and the means of achieving them – that surrounds the decision domain (Thompson and Tuden, 1959). When uncertainty is high, there are fewer or no ‘universalistic’ (consensually agreed-upon, well-defined) criteria available to evaluate decision outcomes (Pfeffer, Salancik and Leblebici, 1976). The use of ‘particularistic criteria’, such as social relationships and similarity, then provides a means for resolving the uncertainty. Perrow (1972, p. 11) writes: ‘Competence is hard to judge, so we rely upon familiarity’. It appears that, in our sample, technology decisions are more uncertain than marketing decisions. For IT firms, marketing decisions relate to the selection of markets and the identification of new market opportunities. Generally, they involve the exploitation of the available technological knowledge to markets (Teece, 2007), such as redesigning existing software applications to fit new markets. For marketing decisions objective performance criteria (e.g. market share, client growth etc.) are available. Technological decisions are more exploratory in nature as they involve recognizing and acquiring external knowledge (e.g. the selec-

299 tion of new IT platforms) and the assimilation of new technological knowledge (e.g. the development of IT applications). The combination of the exploratory nature of technology decisions and the rapid development of IT technologies make technology decisions more uncertain and illstructured, rendering them more difficult to judge based on objective and well-defined criteria. The effects of some of the control variables are indicative for these differences in uncertainty. In models 1 (Tables 4 and 5) we find that the CEO’s own expertise (marketing or technology FB, respectively) has a negative impact on TMT members’ marketing decision influence (B = –0.09; p < 0.05) but a positive, non-significant impact on TMT members’ technology decision influence. Hence, while CEOs tend to take marketing decisions themselves if they have an expert FB, they appear to outsource technology decisions even if they are technical experts. Moreover, the impact of the CEO’s age on TMT members’ technology decisions is positive (B = 0.00; p < 0.01). These findings suggest that technology is a decision area that evolves at such a high speed that CEOs are not able to keep up the pace, even when they have technological expertise, and especially not when they grow older. This implies that, for technology decisions, CEOs are highly dependent on the TMT members. The uncertain nature of technology decisions limits the use of objective criteria. To avoid reverting to particularistic criteria, such as similarity, the CEO needs a mechanism that increases trust in the expertise of TMT members. TMT cooperative behaviour provides such a mechanism. In cooperative TMTs, CEOs are aware of TMT members’ specific expertise (Rulke and Galaskiewicz, 2000), allowing them to trust ‘expert’ TMT members. In fragmented TMTs, the CEOs’ competence-based trust in TMT members is lower. Social psychology literature (Thompson and Tuden, 1959) predicts that in this case CEOs will rely on particularistic criteria, such as similarity, as they provide a means to reduce uncertainty because CEOs expect similar TMT members to analyse, interpret and act similarly upon the same decision situation. For marketing decisions, the CEO’s dependence on TMT members appears not to be that strong. This corresponds to the nature of marketing decisions, being less complex and ill-defined, evolving less quickly compared with technology decisions


300


and involving less uncertainty (Teece, 2007). They can be evaluated more easily using objective criteria. As a result, marketing expertise per se might be sufficient for a CEO to entrust a TMT member with marketing decisions, irrespective of the TMT’s degree of cooperative behaviour. The combination of findings for marketing and technology decisions allows us to draw some more – speculative – conclusions. In particular, the direct effect of ‘cooperative behaviour’ on decision influence is negative and significant (B = –0.09; p < 0.05) for marketing-related issues (Table 4, model 1) but positive and marginally significant (B = 0.04; p < 0.10) for technology decisions (Table 5, model 1). Apparently, in highly cooperative TMTs, CEOs focus on the salient but less uncertain marketing decisions, especially when these CEOs have the congruent expertise, whereas the other TMT members focus on the more illdefined and uncertain technology decisions, especially when these TMT members have the congruent expertise. This finding draws attention to another way in which decision influence is distributed in the TMT: between the CEO and the TMT members. It suggests that the effects of TMT cooperative behaviour on the distribution of decision influence in the TMT are more complex than we expected a priori and that cooperative behaviour instigates a systematic and structural division of decision influence between the CEO and TMT members. This finding demands a more finegrained analysis of the distribution of roles and influence in TMTs, and how such distribution is established (Beckman and Burton, 2011; Hambrick, 2007; Menz, 2012), e.g. through processes of executive selection (Withers, Hillman and Cannella, 2012). Another finding that we did not hypothesize is the apparent negative effect of locus-of-control similarity on TMT members’ technology decision influence in cooperative TMTs (Figure 3). Post hoc analyses revealed that in integrated TMTs external CEOs delegate decision influence to internal TMT members. This finding contradicts prior literature in which deep-level differences, such as those in personality, are commonly demonstrated to have unfavourable effects on interpersonal processes (Schaubroeck and Lam, 2002). It suggests that in cooperative TMTs CEOs have enhanced degrees of freedom to distribute decision influence to TMT members that complement them, even in terms of personality. Further

research is needed to explore why the adverse effects of deep-level differences appear to be reduced in cooperative TMTs. Contribution The contribution that we make with this study is twofold. First, we add to the extant literature in the upper echelons research tradition by explicitly considering the fact that not all TMT members have equal impact on decisions and that TMT members’ decision influence depends on both their own (demographic) backgrounds and the TMT’s internal processes. Hambrick (1994) notes that many TMTs are not acting as ‘teams’ at all but as groups of fragmented individuals. He therefore challenges the ‘TMT label’, arguing that ‘top management group’ may be a more apt label given the high potential for intra-team fragmentation (Carpenter, Geletkanycz and Sanders, 2004). Our study clearly shows that the aggregation of TMT member characteristics is not universally advisable. Hence, our findings entail that future ‘upper echelons’ scholars should proceed very cautiously when identifying the organization’s ‘dominant coalition’ or ‘decision-making unit’ as this might vary depending on the issue under research (cf. Roberto, 2003). This observation is consistent with earlier calls to tailor the unit of analysis to specific research questions (Jackson, 1992; Pettigrew, 1992). Second, we contribute to the TMT literature by demonstrating that TMT-level processes also affect lower-level relations. To be specific, we find that TMT cooperative behaviour – which comprises the TMT’s climate – affects the dyadic relation between the CEO and individual TMT members by co-determining how the CEO delegates decision influence among TMT members. Our findings suggest that, especially in decision areas with high uncertainty such as technology, the ‘teamness’ (Hambrick, 1994) of cooperative TMTs is translated into augmented decision influence for ‘expert’ TMT members. In decisions areas with less uncertainty such as marketing, decision influence is always based on expertise and not affected by the TMT’s level of cooperative behaviour. Limitations Evidently, our study has to be seen in the light of its limitations. First, although our data set com-


Top Management Team Members’ Decision Influence prises information on 32 different firms, these firms all belong to the same industry, limiting the generalizability of the findings. Specifically, our choice of decision areas (marketing and technology) might not apply in other industries. It is also possible that our findings concerning the difference in results for marketing versus technology decisions are industry-related. In addition, the nature of the firms in our sample – i.e. rather small and young high-tech firms – may also have had an impact on the results, given that in such firms decision-making might be more efficiency and performance driven and less politically based than in large established firms. However, as the organizations we study are representative cases of the growing number of technology-based, small- and medium-sized firms operating in dynamic environments, we speculate that our results apply to this broader class of firms. Obviously, future research in other settings is necessary to better understand the contingencies that affect decision influence in TMTs. Second, the operationalization and measurement of the dependent variables, TMT members’ decision influence in marketing and technology issues, can be considered a limitation of this study, as it might suffer from biases such as retrospective sensemaking and respondents’ tendency to answer – inaccurately – in an obvious way. We undertook several measures to ensure the validity and reliability of the operationalization of the dependent variables in this study. For instance, in the questionnaire we focused on respondents’ general impression of decision influence instead of on a particular situation and we collected and used the responses of all TMT members to construct the dependent variables. However, future scholars are invited to complement our findings with studies using other measures of TMT members’ decision influence. Observational data, for instance, may provide an alternative approach. Finally, we focus on a limited number of variables to model the TMT members’ decision influence. FB, locus-of-control and TMT cooperative behaviour were selected based on their proven importance in prior research on TMT behaviour and outcomes (Boone and Hendriks, 2009; Bunderson, 2003a). Future research could extend our work by including more moderators at different levels of analysis, such as variables referring to the CEO’s power and/or dominance in the TMT

301 (Buyl et al., 2011). Similarly, additional characteristics of TMT members could be included. For instance, deep-level value differences between the CEO and the other team members might be particularly important in affecting the distribution of decision influence in TMTs (Harrison and Klein, 2007). Given the importance of interpersonal ties for team functioning (Balkundi and Harrison, 2006), future research may also try to map the relationship between TMT member background characteristics and network ties to unravel how both interact to affect decision influence (Menz, 2012; Reagans, Zuckerman and McEvily, 2004). Doing this would help to open the black box of TMT decision-making processes, which is essential for understanding how dominant coalitions influence organizational outcomes (Hambrick, 2007).

Conclusion Overall, this study indicates that TMT members’ background characteristics matter when it comes to determining their impact on strategic decisionmaking. It appears that in the case of technology decisions the TMT’s level of cooperative behaviour co-determines the CEOs’ delegation of decision influence based on both FB expertise and locus-of-control similarity. We hope that our study inspires other scholars to execute more finegrained analyses of TMT decision-making processes, for instance by investigating how role and influence distributions are established (cf. Menz, 2012). One particularly fascinating research area would be to link our findings with subsequent organizational performance. Will organizations in which decisions are delegated to experts achieve higher organizational performance? In this way, scholars might be able to unravel some of the essential ingredients of effective and successful strategic decision-making (Olson, Parayitam and Bao, 2007).

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Tine Buyl ([email protected]) is a post-doctoral researcher at the Antwerp Centre of Evolutionary Demography (ACED) at the University of Antwerp. She received her PhD at the same university. Her research focuses on the composition and dynamics of top management teams and their effects on organizational processes and outcomes. Her work has been published in international peer-reviewed journals such as the Journal of Management Studies and Strategic Organization. Christophe Boone ([email protected]) is a professor of organization theory and behavior at the Department of Management (University of Antwerp) and founding member of the Antwerp Centre of Evolutionary Demography (ACED). Current research topics include the dynamics of top management team composition, organizational ecology, and the neuroeconomics of cooperative behaviour. Recent work was published in journals such as Academy of Management Review, Academy of Management Journal, Hormones and Behavior, Journal of Economic Psychology and Strategic Organization. Walter Hendriks ([email protected]) is a post-doctoral researcher at Hasselt University. He received his PhD at Maastricht University (The Netherlands). He teaches and researches topics such as top management team composition, corporate governance, family firms and strategic management. His work has been published in international scholarly journals such as Management Science and Journal of Management Studies.
