Pilot,Pivot and Advisory Boards: The Role of Governance Configurations in Innovation Commitment

Abstract

This study examines how governance configurations comprised of board capital, CEO power and the presence of large shareholders are associated with innovation commitment in organizations. We take a configurational perspective, proposing that organizational innovation commitment is contingent upon how interdependent governance attributes associated with monitoring and resource provisioning can either enhance or constrain management’s discretion to invest in research and development (R&D). Using fuzzy-set qualitative comparative analysis (fsQCA), we identify complementarities which lead to three board archetypes that foster firm innovation commitment. ‘Pilot boards’ have both board capital breadth and depth allowing for active and close participation in innovation decision-making. ‘Pivot boards’ possess the depth of industry-specific expertise and linkages required for providing resources and oversight of powerful CEOs. And ‘advisory boards’ have less power but have outside directors who have breadth of expertise and relational capital that complements the oversight provided by powerful family owners so as to effectively advise management on innovation decisions. Our findings underscore that governance mechanisms work in tandem, not in isolation, to explain significant organizational outcomes, specifically those associated with innovation commitment.

Keywords

board capital CEO power fuzzy-set qualitative comparative analysis (fsQCA)innovation commitment ownership structure

In research-intensive industries, innovation commitment is a critical input for developing technological capabilities and sustainable competitive advantage (Ndofor, Sirmon, & He, 2011; Wu, 2008), underscoring how important it is for organizational leadership teams to commit resources to the innovation process (Cottam, Ensor, & Band, 2001). Innovation commitment is defined as ‘managerial willingness to allocate resources and champion activities that lead to the development of new products, technologies, and processes consistent with marketplace opportunities’ (Hitt, Hoskisson, & Ireland, 1990: 29). Investment in research and development (R&D) is an effective way that organizations provide commitment to innovation (Heeley, Matusik, & Jain, 2007). As noted by Dalziel, Gentry and Bowerman (2011, p. 1219) ‘R&D spending is necessary for corporations that pursue innovation.’ Despite the key role of innovation commitment in building and maintaining a firm’s competitive advantage, previous research provides inconsistent findings about the effects of boards of directors, CEOs and powerful owners on innovation commitment (Kor, 2006; Wincent, Anokhin, & Ortqvist, 2010; Wu, 2008). These mixed findings may in part be due to the focus of the extant literature on the role of the board and powerful owners in monitoring CEOs, without also considering the other roles that these important governance actors perform (Dalziel et al., 2011).

Agency theorists argue that powerful CEOs may opportunistically reduce R&D spending to enhance short-term earnings potential (Aboody & Lev, 2000). From this perspective, the role of other powerful governance actors is to monitor the CEO so that he or she allocates resources in such a way that benefits the overall organization (Jensen & Murphy, 1990). However, boards of directors and powerful owners also perform resource provisioning roles by providing important human and relational capital (Hillman & Dalziel, 2003). This resource provisioning role is especially important in research-intensive industries, where outside directors can foster innovation by sharing specialized knowledge and facilitating access to innovation networks (Kor & Sundaramurthy, 2009; Yoo & Sung, 2015).

Haynes and Hillman (2010) propose that the extent to which a board will be capable of effectively performing the dual roles of monitoring and resource provisioning is a function of board capital breadth (BCB) and depth (BCD). BCB represents the heterogeneity of outside directors’ functional, occupational, professional experiences and extra-industry ties whereas BCD depicts how embedded outside directors are in an organization’s industry through their directorships and professional experience within the industry.

In this study, we explore how these two dimensions and other governance mechanisms (e.g. CEO power, family and institutional ownership) interact in complementary ways as part of unique governance configurations to facilitate innovation commitment. To do so, we use a theory of complementarity (Milgrom & Roberts, 1995) to integrate logic from resource dependence theory and agency theory to show how different types of expertise and relational capital (e.g. industry-specific or non-industry) mutually reinforce one another to provide effective monitoring and resource provisioning which facilitates firm-level innovation commitment.

In order to adequately explore the complementarities arising from such interdependent relationships, we utilize the Canadian pharmaceutical industry as our research setting and take a configurational approach by using fuzzy-set qualitative comparative analysis (fsQCA) (Fiss, Cambré, & Marx, 2013; Grandori & Furnari, 2008). Our findings show that there are three governance configurations that consistently lead to high innovation commitment. These configurations represent three governance archetypes: pilot, pivot and advisory boards which arise from complementarities that allow boards to enact their monitoring and resource provisioning roles with respect to other powerful actors (CEOs, family owners and institutional investors) in ways that lead to high innovation commitment.

Our findings contribute to organizational literature and in particular that associated with the relationship between corporate governance and innovation. Overall, our study underscores that monitoring and resource provisioning by governance actors do not work in isolation, but in complementary ways to explain important organizational outcomes. This is important as much of the extant research has examined governance mechanisms in isolation, overlooking linkages among them and essentially discounting their combined effect on organizational outcomes (García-Castro, Aguilera, & Ariño, 2013). Our identification and configurational development of board archetypes which facilitate commitment to innovation extends research advocating that, rather than universal relationships between specific governance arrangements and organizational outcomes, there may be a variety of combinations that represent effective governance (Aguilera, Desender, & de Castro, 2011).

Corporate Governance and Innovation Commitment

Innovation is considered essential to generate growth, provide shareholder returns, as well as to maintain a firm’s competitive advantage in a rapidly evolving environment (Padgett & Galan, 2010; Wu, 2008). Committing resources to innovation activities is a strategic decision made by organizations to leverage internal resources and capabilities to cope with external environmental conditions (Cottam et al., 2001). R&D spending is a ‘precursor to innovation’ (Dalziel et al., 2011, p. 1217) and ‘is the organizational process most directly involved with innovations’ (Greve, 2003, p. 687). Thus, R&D spending is an important means by which organizations signal their commitment to innovation (Hitt et al., 1990). Despite the key role of R&D as an input for innovation, organizations differ in their commitment of financial resources to such activities (Kor, 2006). This may in part be due to different preferences among key actors in the organization’s governance structure. Investment in R&D is generally a high risk/high return strategy intended to increase shareholders’ long-term profitability and the value of their investments (Le, Walters, & Kroll, 2006). However, since CEOs’ prominence and wealth are closely linked to organizational performance, they may be reluctant to invest in R&D since it often reduces short-term returns and cash flow (Chen & Chuang, 2009; Finkelstein, Hambrick, & Cannella, 2009).

According to agency theory, CEOs will be less tolerant of risky investments than shareholders, who are able to limit their exposure by holding a diverse portfolio of stocks (Eisenhardt, 1989). Agency theory also suggests that CEOs have greater knowledge about their organizations in comparison to their boards and outside shareholders, which provides opportunities for self-interested action by the CEO (Jensen & Meckling, 1976). Committing fewer resources to innovation by reducing R&D spending is one means by which powerful CEOs can enhance performance in the short term (Fong, 2010). Yet, research underpinned by psychology theories suggests that the experience of power may actually induce CEOs to take greater risks on behalf of their organizations (Lewellyn & Muller-Kahle, 2012). Further, CEOs may have pro-organizational motives and therefore use their power as a means of leveraging their firm- and industry-specific knowledge for the benefit of their organizations (Finkelstein et al., 2009).

The board of directors has a key responsibility to promote innovation by effectively monitoring CEO decisions and by providing strategic advice about the appropriate level of R&D spending that will lead to future organizational success (Kor, 2006). Large shareholders are also important actors in the governance structure through their ownership and voting control (Baysinger, Kosnik, & Turk, 1991). Similar to the board of directors, powerful owners are expected to provide monitoring of and strategic input to decisions the CEO makes on behalf of the organization. In particular, when their level of ownership makes them the largest owner, they will have greater interest and legitimacy in influencing organizational decisions (Desender, Aguilera, Crespi, & García-Cestona, 2013). Additionally, the owners’ identities are expected to affect their ability and willingness to monitor and provide strategic resources to the organization (Desender et al., 2013).

Commitment to innovation is therefore a strategic decision influenced by multiple governance actors whose impacts are likely to be interdependent with one another. In order to better understand the association between an organization’s commitment to innovation and its governance, we draw from prior literature and focus on the combined interdependent effects of governance attributes related to monitoring and resource provisioning: outside directors’ human and social capital (BCB and BCD), CEO power and ownership structure (family and large institutional shareholders). We suggest that while each of these attributes is associated with specific incentives and abilities that actors in an organization’s governance system have, it will be complementarities among them that will lead to high innovation commitment. In other words, the effectiveness of a particular governance mechanism will be enhanced when combined with one or more other governance mechanisms (Aguilera, Filatotchev, Gospel, & Jackson, 2008). Therefore, in this study we address the following overarching research question: How do board capital breadth (BCB), board capital depth (BCD), CEO power and the presence of powerful owners combine to promote firm innovation commitment?

Elements of Governance Configurations

Outside directors’ board capital

Resource dependence theory (Pfeffer & Salancik, 1978) highlights that board capital, defined as the sum of individual directors’ human and relational capital, is critical for the board to effectively fulfil its resource provisioning and monitoring roles (Hillman & Dalziel, 2003). Board members’ ‘expertise, experience, knowledge, reputation and skills’ (Haynes & Hillman, 2010, p. 1146) constitute board human capital. A board’s relational capital is defined as ‘the sum of the actual and potential resources embedded within, available through, and derived from, the network relationships possessed by an individual’ (Haynes & Hillman, 2010, p. 1146). Previous research supports the importance of board capital, indicating that it has a net effect on organizational strategies, structures and policies (Dalziel et al., 2011). To better assess the relationship between board capital and how effectively boards fulfil their roles, Haynes and Hillman (2010) decomposed the concept into BCB and BCD.

Board capital breadth

BCB is defined as the ‘portfolio of directors’ functional, occupational, social, professional experiences and extra-industry ties and captures the heterogeneity of directors’ human and social capital’ (Haynes & Hillman, 2010, p. 1147). The concept builds on research about group processes, which suggests that more heterogeneous groups exhibit better decision-making processes (Forbes & Milliken, 1999).

Haynes and Hillman (2010) predict that since boards with greater BCB have more varied experiences and types of knowledge, they will be more willing to consider different perspectives, which will lead to greater strategic change. Their argument is empirically supported. Although Haynes and Hillman (2010) are the only ones to specifically use the BCB construct, other studies have also shown that heterogeneous boards are positively related to investments in risky strategic ventures (e.g. Golden & Zajak, 2001; Kosnik, 1990). However, there are also arguments and empirical evidence that heterogeneity within the board may be negatively related to strategic investments as the varied interests and experiences of directors may lead to divergent cognitive biases, making consensus more difficult (Goodstein, Gautam, & Boeker, 1994).

Based on the above discussion, we take the perspective that BCB may both facilitate and hinder innovation commitment, depending upon how it combines with other governance elements. For instance, in the presence of a powerful CEO, who may be inclined to reduce R&D spending to enhance short-term earnings potential, he or she may be better able to act upon this when the board is more diverse and unable to work together effectively. Or perhaps because of the diversity of perspectives, the board is able to rein in a powerful CEO as they are able to foresee greater possibilities by supporting innovation commitment.

Board capital depth

BCD refers to the ‘embeddedness of directors in the firm’s primary industry through interlocking directorships, managerial positions, or occupational experience in the primary industry of the firm’ (Haynes & Hillman, 2010, p. 1148). The concept of BCD builds on cognitive research suggesting that groups with experience and networks concentrated in a related domain, rather than dispersed across different industries, have highly developed knowledge structures for that specific industry (Carpenter & Westphal, 2001). Directors’ intra-industry networks provide them with access to valuable resources, including industry-specific information about market conditions, opportunities, potential partners and emerging technological trends (George, Zahra, & Wood, 2002). The acquisition of industry-specific information via outside directors enables the organization to address environmental uncertainty by gaining superior knowledge of competitors and industry opportunities (Wincent et al., 2010; Wu, 2008). Since BCD captures the industry-based shared mental models of individual directors, it shapes how they provide resources to the organization as well as their monitoring of strategic decisions (Haynes & Hillman, 2010).

A board with high BCD may indicate that directors have biases grounded in their previous experiences, which may lead to beliefs that higher or lower R&D spending is needed for the organization to be successful (Dalziel et al., 2011). For instance, high BCD may lead to greater awareness by outside directors of the importance of committing resources to innovation and, thus, promote high investment in R&D. However, it may also make outside directors more cognizant of the inefficiencies and risks associated with innovation, making them more reluctant to invest in R&D. Again, we expect there to be interdependencies with other governance elements, such that they complement the knowledge associated with BCD. For example, due to their high level of tacit knowledge of industry opportunities and threats, BCD may provide greater legitimacy and capabilities in challenging a powerful CEO that wishes to decrease the organization’s R&D spending (Dosi, 1990). On the other hand, high BCD may provide the support a powerful CEO needs with other important governance actors to decrease or increase the organization’s commitment to innovation.

CEO power

Power is a relational construct; individuals and groups have power only in relation to other organizational actors (Golden & Zajac, 2001). The power of the CEO relative to the board can originate from structural sources, ownership and firm or industry expertise (Finkelstein et al., 2009). Boards of directors that are free from undue influence by powerful CEOs are assumed to engage in more discussion and debate, which allows more diverse viewpoints to surface. Autonomous boards also engage in more cognitive conflict and tend to make greater use of their knowledge and skills (Forbes & Milliken, 1999).

Powerful CEOs may be better able to influence the role a board’s capital has on strategic decisions, such that CEOs’ preferences for the status quo or increased risk-taking associated with innovation commitment may prevail over the board’s guidance (Haynes & Hillman, 2010). The power distribution is likely to influence the nature and level of information that flows from the CEO to directors (and vice versa), affecting the way the board’s human and relational capital are mobilized for decision-making with respect to innovation. Similarly, powerful CEOs may have an easier time managing the flow of information to large shareholders about the need for committing resources (Finkelstein et al., 2009). In other words, a CEO’s power will affect how monitoring and resource provisioning roles of both the board and large owners are likely to be carried out and capitalized upon (Golden & Zajac, 2001; Yoshikawa, Zhu, & Wang, 2014).

Ownership structure

Ownership structure is another important governance element that affects decision-making within organizations (Desender et al., 2013). In particular, when a shareholder is the largest investor, their power vis-a-vis other shareholders, the board and the CEO is enhanced. They will be in an advantageous position to monitor CEOs directly or indirectly by exerting their will on board members to do so. Thus, large ownership stakes serve as a power base from which to monitor and influence organizational innovation investments (Baysinger et al., 1991; Cumming & Macintosh, 2000).

The identity of large shareholders can also influence strategic decision-making (Hoskisson, Hitt, Johnson, & Grossman, 2002). Institutional investors are assumed to be effective monitors because not only are they intrinsically motivated but they have oversight skills of professional investors (Le et al., 2006). In addition, large institutional investors often play an active role in firms in which they invest, and are therefore more in touch with respect to the long-term value creation process, thus favouring the commitment of resources to innovation (Baysinger et al., 1991).Yet, some have argued that due to institutional investors’ intense focus on current earnings and stock price performance, they will exert pressure on organizational leaders to do likewise, resulting in minimizing commitment to innovation (O’Conor & Rafferty, 2012).

Similarly, large founding family shareholders may have conflicting preferences with regard to innovation commitment. One perspective is that founding family owners will suffer less managerial myopia (Desender et al., 2013; He, 2008) and promote innovation to ensure that the firm develops long-term capabilities (Yoo & Sung, 2015). Yet, in research-intensive and/or high-tech industries, their goal may be to cash in on their investment by making the company an attractive acquisition target, whereby they too may prefer enhancing short-term financial performance by reducing R&D spending (Munoz-Bullon & Sanchez-Bueno, 2011).

Given their involvement in founding and managing the firm, family shareholders are likely to have knowledge across various functional areas, and therefore have significant capacity to absorb, understand and assess the potential outcomes of R&D efforts, very often beyond the outcomes reported to the board by the CEO (Dalziel et al., 2011; Meuer, 2014; Wincent et al., 2010). Thus, along with financial motivation, large founding family owners are expected not only to engage in monitoring of innovation investments, but also to exercise an advisory role due to their firm- and industry-specific knowledge. In this way, founding families may complement the board’s resource provisioning efforts by either reinforcing or compensating for board capital. However, similar to organizations with high BCD, having an influential founding family owner may lead to tunnel vision or reluctance to consider other beneficial perspectives.

Consistent with the above discussion, we expect BCB, BCD, CEO power and ownership structure to be indicative of how strategic decisions are monitored and how resources are effectively provided to an organization, both of which are important drivers of innovation commitment. However, the knowledge and monitoring capabilities associated with each of these governance elements do not occur in isolation but rather concurrently. Therefore, we propose assessing their effectiveness as configurations of interdependent mechanisms having complementarities that are important for innovation commitment.

A Configurational Approach to Governance

A configurational approach provides a means for exploring simultaneous interactions and interdependencies among multiple actors involved in organizational governance (Fiss et al., 2013). With a configurational approach, we can assess joint effects to improve our understanding of how organizational attributes associated with these two important governance functions – monitoring and resource provisioning – can combine in complementary ways to affect innovation commitment (Aguilera et al., 2011).

Complementarity implies that governance mechanisms mutually reinforce the effectiveness of one another (Aguilera et al., 2011; Aversa, Furnari, & Haefliger, 2015). As noted by Grandori and Furnari (2008), complementarity is developed via the heterogeneity of elements within a configuration and occurs not only through ‘similar in kind’ elements but also ‘different in kind’. Thus, in the context of our study, complementarities underpinning governance configurations leading to high innovation commitment may arise from attributes focused on enhancing one function of the board (e.g. monitoring) representing similar in kind, or from those that reflect the different functions of monitoring and resource provisioning. A configurational approach is able to account for causally complex relationships as it allows for conjunctural causation, where each causal condition is considered ‘in conjunction’ with others, as opposed to ‘in addition’ to others (Befani, 2013). This is important to our theoretical argument that the effectiveness of how boards carry out their roles of monitoring and resource provisioning in promoting innovation commitment rely on how they work together. Moreover, a configurational approach is compatible with equifinality, so that we can explore how innovation commitment may be achieved with different complementarities arising within multiple governance configurations. In sum, a configurational approach provides the means for adequately exploring our overarching research question.

Methods and Data

Research setting

Our analysis focuses on the pharmaceutical industry in Canada. The use of a single industry in a single country allows us to control for technological opportunities, economic conditions and regulatory environments (Tylecote, 2007). Relatedly, external monitoring functions such as the market for corporate control, the managerial labour market and the product market are also industry-related mechanisms that may be driven by a given country-level institutional environment (Aguilera et al., 2008).

We selected the Canadian pharmaceutical industry as our research setting for a number of specific reasons. First, Canada’s innovation commitment lags behind those of other countries. In 2008, innovation commitment (as measured by R&D/sales) in Canada was only 8.1% compared to 18% in the United States and 18.5% in Europe (Mergent, 2008a, 2008b). Thus, it is important to explain why some firms in Canada have high innovation commitment while others do not. Second, the Canadian pharmaceutical industry operates under a very similar regulatory environment as the US, the world’s largest pharmaceutical market (Mergent, 2008a). Third, this research setting provides a context where both founding families and institutional investors are important shareholders (Munoz-Bullon & Sanchez-Bueno, 2011). Lastly, Canada operates within the Anglo-American governance environment which increases the generalizability of our findings to firms operating within this specific governance environment.

In the pharmaceutical industry, commitment to innovation is essential for firm success and even survival (Schoenecker & Swanson, 2002). Investors who perceive the product development cycle as an incubation period pay particular attention to the level of commitment to innovation (George et al., 2002). It is therefore a strategic imperative for pharmaceutical firms to invest in R&D as an input to generating continuous and successful innovation projects (Schilling & Hill, 1998). Moreover, access to specialized knowledge and innovation networks through board members is a priority in this industry (Cumming & Macintosh, 2000).

Sample

Our sample includes all firms that were listed on the Toronto Stock Exchange (TSE), included in the S&P/TSX index and operated in the pharmaceutical industry¹ in the period 2007 to 2008. Innovation commitment data is from 2008, while the causal conditions are from 2007. Since the analysis covers years associated with the global financial crisis, we expect certain pressures that divert attention from a long-term focus to be particularly salient in our sample. Information on innovation commitment was collected from the firms’ annual reports for the year 2008. Data on individual board members regarding outside status, educational background, professional experience, other directorships, and firm-level governance conditions associated with CEO power and ownership structure were hand collected from firms’ 2007 proxy statements. Firm proxy statements and annual reports were retrieved from the SEDAR² database. At the end of 2008, there were 61 pharmaceutical firms being publicly traded on the TSE. Data for the outcome condition of innovation commitment was not available for 7 firms; thus, our final sample consists of 54 firms.

FsQCA methodology

FsQCA allows a configurational examination of the causal relationships between a group of antecedent conditions and a related outcome (Ragin, 2000). With this methodology, cases (i.e. organizations) are described as combinations of causal conditions whereby each case is assigned a group membership score in every causal condition, hence the term ‘fuzzy set’. In addition, unlike classic regression analysis, fsQCA does not require a normal probability distribution and is less sensitive to small sample size (Fiss, 2011). This methodology is particularly ‘good at unraveling’ causal complexity associated with configurational research (Schneider & Wagemann, 2012, p. 78), making it an appropriate analytical approach for our study.

Measurement of outcomes and causal conditions

Innovation commitment

To measure the outcome of innovation commitment, we use R&D spending. We measure R&D spending as the ratio of R&D investments to total assets at the end of the financial year, so as to take into account organizational size and financial capacity (Kor, 2006). Compared to US financial reporting requirements, Canadian rules allow R&D spending to be capitalized. Therefore, following Callimaci and Landry (2004), the capitalized portion of R&D expenditures is included in total annual spending.

Board capital breadth

BCB is the sum of the outside directors’ functional, occupational and relational heterogeneity. Functional background was coded based on the taxonomy used by Haynes and Hillman (2010). This taxonomy categorizes outside directors into one of three board functional roles: business experts (BE), support specialists (SS) or community influentials (CI). Similar to Haynes and Hillman (2010), the BE category captures outside directors’ knowledge and expertise in general management. SS includes legal experts, finance/accounting specialists, venture capitalists and investment bankers, and sales/marketing professionals, while CI includes politicians, academics and other community members who command respect, prestige and power in nonprofit organizations.

Similar to Haynes and Hillman (2010), occupational background is captured by coding outside directors’ backgrounds as one of the nine following categories: general management; finance/accounting; sales/marketing; legal, information systems; operations, engineering; human resource; and others (this last category includes military/government and real estate).³ Again following a similar approach to Haynes and Hillman (2010), we measure relational heterogeneity by the SIC codes of outside directors’ interlocked industries. Interlocks refer to the number of directorships held at other for-profit corporations. However, given our single industry sample, we classified directorships into only two categories, whether in the same (pharmaceutical) or different industry of the focal firm.⁴ Each of the three heterogeneity measures is calculated using Blau’s (1977) heterogeneity index, which is a version of the Herfindal index and is calculated using the following formula:

B o a r d f u n c t i o n a l, o c c u p a t i o n a l a n d r e l a t i o n a l h e t e r o g e n e i t y_{i t} = 1 - \sum p_{i} 2

where p_i is the proportion of directors in each category. The three heterogeneity measures are summed together; thus, higher values indicate higher BCB. The Cronbach’s (1951) alpha for BCB was 0.81, which is comparable to Haynes and Hillman (2010), and at a level that indicates acceptable reliability (Hair, Sarstedt, Ringle, & Mena, 2012).

Board capital depth

Following Haynes and Hillman (2010), two indicators are used to measure BCD: the ratio of outside directors’ interlocks in the pharmaceutical industry to the total number of outside directors’ interlocks (depth of linkages), and the proportion of outside directors with professional experience in the pharmaceutical industry (depth of industry expertise). Total interlocks were computed by summing the numbers of directorships held by outside directors at other for-profit organizations, whereas pharmaceutical industry interlocks were computed by comparing the SIC codes of board directorship with the SIC codes included in our sample (2833 to 2836). Professional experience in the pharmaceutical industry was computed as the ratio of outside directors holding current or past positions in pharmaceutical firms (based on focal firm’s four-digit SIC codes) to the total number of outside directors. The two indicators were summed to create a single index where higher values indicate a higher degree BCD. The Cronbach’s (1951) alpha for BCD was 0.75 and is comparable with Haynes and Hillman (2010).

CEO power

Similar to the measure used by Haynes and Hillman (2010), we created a composite index of CEO power capturing CEO structural and ownership power. Structural power is measured with three indicators: CEO duality, the ratio of unrelated (independent) directors to board size (reverse coded), and the ratio of directors who were appointed after the CEO to total board size. CEO ownership power was measured by the ratio of the CEO’s equity holdings to the total equity holdings of the board members in the focal firm. These four indicators were standardized and summed to create a composite index of CEO power. The Cronbach’s (1951) alpha for the CEO power measure is 0.71 and is higher than the construct in Haynes and Hillman (2010).

Ownership structure

To measure ownership structure, we first distinguish between organizations with diffuse (14 firms) and concentrated ownership structure (40 firms). Ownership concentration is measured as the proportion of outstanding shares directly or indirectly controlled by individuals or groups of large shareholders⁵ as disclosed in proxy statements (Schiehll & Bellavance, 2009). If the largest shareholder was the founder or member of the founding family of the focal organization, which is the case in 13 firms, the condition Founding family as the largest shareholder takes a value of 1, and 0 otherwise. Likewise, if the largest shareholder was an institutional investor, which is the case in 27 firms, the condition Institutional investor as the largest shareholder is coded 1 and 0 otherwise. We used similar criteria to Schiehll and Bellavance (2009) to classify large shareholders as institutional investors.

Calibration of conditions

FsQCA requires that data be calibrated into set memberships, whereby each case (i.e. firm) is assigned a group membership score in each causal condition (Ragin, 2008). Cases may be full members or non-members but may also have varying degrees of membership in a given causal condition. The fsQCA 2.0 software produces continuous calibrated measures from 0 to 1 based on three designated anchors. Full membership and full non-membership correspond to 1 and 0, respectively, with 0.5 representing the crossover point for a case membership to become ‘more in’ or ‘more out’ of a given set.

Ideally, the anchor values should be based on previous research findings or be theoretically informed (Ragin, 2000, 2008). When the literature lacks such precedence, researchers often use empirical calibration based on case-specific knowledge, using percentile splits (e.g. Fiss, 2011; Ganter & Hecker, 2013), which we did in this study. As noted previously, at the end of 2008 there were 61 pharmaceutical firms being publicly traded on the TSE and, since we were able to find board and CEO data for all of these, we use this data from the 61 pharmaceutical firms as the basis for our percentile calibration, as these essentially represent the population of Canadian pharmaceutical firms operating in 2008. Greckhamer (2011) used a similar strategy whereby he based calibrations on the total available data, not just on his sample. We calibrated the outcome of innovation commitment, as well as the causal conditions of BCB, BCD and CEO power, such that cases in the 75th percentile were considered to be ‘fully in’ the set of high values. Those in the 25th percentile were calibrated as ‘fully out’ and the 50th percentile value is used as the crossover point, which follows previous fsQCA studies (e.g. Fiss, 2011). A summary of the values used for calibrating set membership are presented in Table 1.

Table 1.

Calibration of conditions.

Condition	Membership criteria	Calibration
Innovation commitment	R&D investment ⩾ 0.53	Full
	R&D investment 0.52–0.29	More in than out
	R&D investment 0.28–0.07	More out than in
	R&D investment ⩽ 0.06	Out
Board capital breadth (BCB)	BCB ⩾ 1.64	Full
	BCB 1.63–1.51	More in than out
	BCB 1.50–1.23	More out than in
	BCB ⩽ 1.22	Out
Board capital depth (BCD)	BCD ⩾ 1.35	Full
	BCD 1.34–1.17	More in than out
	BCD 1.16–0.84	More out than in
	BCD ⩽ 0.83	Out
CEO power	CEO Power ⩾ 2.47	Full
	CEO Power 2.46–1.85	More in than out
	CEO Power 1.84–1.05	More out than in
	CEO Power ⩽ 1.04	Out
Founding family as largest shareholder	Founding family is the largest shareholder	Full
Founding family as largest shareholder	Founding family is not the largest shareholder	Out
Institutional investor as largest shareholder	An institutional investor is the largest shareholder	Full
Institutional investor as largest shareholder	An institutional investor is not the largest shareholder	Out

Full: fully in the set of high values.

More in than out: above the crossover point to the full value.

More out than in: below the crossover point up to the out value.

Out: full non-membership in the set of high values (i.e. fully out).

To assess that these calibration values represent logical and meaningful values for BCB and BCD, we calculated the ‘fully in’ theoretical maximum values and compared them with reported external governance criteria related to the Canadian context. More precisely, the 75^th percentile value for BCB is 1.64, representing 79% of the theoretical maximum (2.08),⁶ which aligns with guidelines from the Deposit Insurance Corporation of Ontario (DICO) (2012), an agency involved with corporate board training in Canada.⁷ They recommend that 75% of board members possess competencies associated with board effectiveness (DICO, 2012). These competencies include skills that align with the various functional and occupational categories in our BCB measure.

For BCD, the theoretical maximum value is 2.00, where each of the two dimensions, the proportion of interlocks in the pharmaceutical industry and proportion of directors with professional experience in the pharmaceutical industry, could have a maximum of 1.00. The 75^th percentile value for this condition is 1.35, representing 68% of the theoretical maximum, which aligns with data reported by the Spencer Stuart Board Index (2009). They found that 64% of the directors of 100 large publicly traded Canadian firms came from within the industry of the firms they represented. Despite using empirical calibration, the use of external data to validate our calibration approach embeds our study in the research context.

Since the conditions founding family as largest shareholder and institutional investor as largest shareholder are dichotomous, both were calibrated as crisp sets, such that firms with either of these conditions are deemed full members in the set (coded 1) and those without are considered non-members of the set (coded 0).

Analysis procedure

Using the fsQCA 2.0 software, we first checked whether any causal conditions were necessary for the outcome, since necessary conditions should be excluded from the subsequent fsQCA sufficiency analysis (Ragin, 2008). Conditions with consistency values greater than or equal to 0.90 are considered necessary or ‘almost’ necessary as per recommendations in the literature (e.g. Schneider & Wagemann, 2012). Consistency values represent the degree to which firms with a given condition exhibit high or low innovation commitment. Table 2 indicates that none of the conditions are necessary for high or low innovation commitment, providing support for using a configurational approach to explore our research question.

Table 2.

Analysis of necessary conditions for high and low innovation commitment.

Causal Conditions	High Innovation Commitment		Low Innovation Commitment
	Consistency	Coverage	Consistency	Coverage
Board capital breadth (BCB)	0.79	0.55	0.72	0.52
~ Board capital breadth	0.30	0.51	0.37	0.65
Board capital depth (BCD)	0.57	0.56	0.51	0.53
~ Board capital depth	0.52	0.51	0.58	0.58
CEO power	0.70	0.57	0.56	0.47
~CEO power	0.35	0.44	0.48	0.63
Founding family as largest shareholder	0.21	0.43	0.27	0.57
~ Founding family as largest shareholder	0.79	0.51	0.73	0.49
Institutional investor as largest shareholder	0.44	0.43	0.56	0.57
~ Institutional investor as largest shareholder	0.56	0.55	0.44	0.45

Notes: ~ Indicates negation of the condition.

The first step in the sufficiency analysis is to generate a truth table showing all the possible configurations (2⁵), resulting from the five governance attributes included in our analysis. Following García-Castro et al. (2013), we provide the ‘nested’ truth table (Table 3).

Table 3.

Nested truth table for high and low innovation commitment.

Institutional investor as largest shareholder	Founding family as largest shareholder	CEO power	(BCD)	(BCB)	Number of firms	Consistencyfor High innovation commitment	ConsistencyFor Low innovation commitment	Governance configuration
0	0	0	0	0	0
			0	1	3	0.60	0.72
			1	0	2	0.82	0.57
			1	1	1	0.99	0.39	C1
		1	0	0	1	0.57	0.65
			0	1	2	0.76	0.41
			1	0	1	0.92	0.32	C2
			1	1	4	0.78	0.31
	1	0	0	0	0
			0	1	4	0.49	0.83	C6
			1	0	0
			1	1	2	0.50	0.84	C6
		1	0	0	1	0.41	0.77
			0	1	5	0.85	0.28	C3
			1	0	1	0.39	0.91	C5
			1	1	0
1	0	0	0	0	1	0.31	0.94	C4
			0	1	2	0.49	0.70
			1	0	4	0.42	0.75
			1	1	7	0.52	0.59
		1	0	0	0
			0	1	3	0.43	0.73
			1	0	5	0.50	0.59
			1	1	5	0.65	0.49
	1	0	0	0	0
			0	1	0
			1	0	0
			1	1	0
		1	0	0	0
			0	1	0
			1	0	0
			1	1	0

In order to identify configurations that are consistently linked to the outcome, we need to specify both frequency and consistency thresholds. We used a frequency of 1 as is the convention in small sample fsQCA studies (Greckhamer, Misangyi, & Fiss, 2013). With respect to the consistency threshold, we first excluded any configurations with consistencies below the recommended minimum value of 0.75 (Ragin, 2008) and then used a value where natural gaps in consistency scores occurred (e.g., Fiss, 2011; Misangyi & Acharya, 2014), which for our data occurs at 0.85. The threshold value indicates ‘which truth table rows can be interpreted as sufficient conditions and can thus be included in the logical minimization process’ as part of the fsQCA sufficiency analysis (Schneider & Wagemann, 2012, p. 129).

Results

Table 4 shows the results of the fsQCA sufficiency analysis,⁸ namely, the three governance configurations sufficient for the occurrence of high innovation commitment. The overall solution consistency of 0.90 indicates that the configurations lead to high innovation commitment 90 percent of the time. The coverage value provides a measure of how much of the outcome is explained by the configurations; thus, 27 percent of the high innovation commitment outcome is explained by the configurations. These results indicate that the configurations make a significant contribution in explaining high innovation commitment and, although they do not explain all of the cases, the coverages are higher or in line with recent organizational-level governance studies.⁹

Table 4.

Configurations sufficient for high innovation commitment.

	High innovation commitment
Consistency thresholdFrequency threshold	0.85 1.0
Configurations	C1 Pilot	C2 Pivot	C3 Advisory
Board capital breadth (BCB)	●	⊗	●
Board capital depth (BCD)	●	●	⊗
CEO power	⊗	●	●
Founding family as the largest shareholder	⊗	⊗	●
Institutional investor as the largest shareholder	⊗	⊗	⊗
Consistency	0.99	0.92	0.85
Raw coverage	0.07	0.04	0.15
Unique coverage	0.06	0.05	0.15
Solution consistency	0.90
Solution coverage	0.27

Note: ● indicate the presence of a condition, ⊗ indicate its absence, and blank spaces indicate that the causal condition may be either present or absent.

The raw and unique coverage values are also reported for each configuration. Raw coverage values indicate the cases (organizations) that have both the outcome and that specific configuration (Ragin, 2008). The unique coverage refers to how much of the outcome is only covered by a given configuration (Schneider & Wagemann, 2012). For C3, the raw and unique coverage values are identical, indicating that none of the cases (organizations) are part of more than one configuration. Configurations C1 and C2 have raw and unique coverages that are very close together, but not identical, indicating there may be instances of overlap between these configurations (Greckhamer, 2011).

Configuration C1 has high BCB and BCD, low CEO power and lacks powerful owners, indicating that cases in this configuration have a diffuse ownership structure. This configuration represents organizations with ‘pilot boards’, where outside directors have the autonomy as well as BCB and BCD required for active participation in innovation decision-making by bringing specific expertise and knowledge to bear (Jonsson, 2005). Vasogen Inc., a large organization in terms of market capitalization (in 2007), founded in 1980 (IPO in 1991) and with a diffuse ownership structure since 1994 illustrates this configuration. According to press releases, the board has directors who ‘have considerable experience directing the research, development, and business initiatives of companies commercializing products for the healthcare industry’ as well as those who serve on many other boards external to the pharmaceutical industry (Vasogen, 2006).

In configuration C2, which we label ‘pivot boards’, outside directors lack BCB but possess high levels of BCD, allowing them to provide valuable industry-specific expertise and connections, which may improve their ability to be effective monitors of powerful CEOs. Similar to C1, this configuration lacks the monitoring exerted by powerful owners. Hence, in this configuration, boards pivot between their resource provisioning and monitoring roles. PreMD Inc., a medium to large organization in terms of market capitalization (in 2007), with a diffuse ownership structure since its IPO in 1992, represents this configuration. PreMD has a small board size,¹⁰ only four directors all of whom have industry experience, and a long-serving CEO (since 1993), who is a physician, holds an MBA, and was a former executive of Johnson & Johnson.

Configuration C3 represents organizations with ‘advisory boards’ where outside directors possess high levels of BCB and where the industry-specific knowledge needed to foster innovation is provided by founding owners. These boards have directors with less autonomy, given the presence of founding family owners and powerful CEOs, but their out-of-industry expertise, experience and relational capital enables them to advise management. Nymox Pharma represents this configuration. Nymox is a small organization in terms of market capitalization (in 2007), founded in 1995 and focuses on developing therapeutic and diagnostic products that are subsequently sold to larger pharmaceutical companies. Its founder is a former physician and university researcher, who (in 2007) held the CEO and board Chair positions and whose family was the largest shareholder. In the discussion section, we elaborate further on these board archetypes and the nature of the complementarities underpinning the high innovation governance configurations.

Configurations leading to low innovation commitment

An important feature of fsQCA is causal asymmetry, whereby the configurations of conditions leading to low innovation commitment may not be the negation of those associated with high innovation commitment (Fiss, 2011). Similar to above, having verified that there are no necessary conditions (Table 2), we retain all the conditions for the sufficiency analysis. We again use a frequency of one, and base the consistency threshold on where a gap occurs in the raw consistencies (0.83). As shown in Table 5, there are three configurations leading to low innovation commitment, none of which contain the same patterns of complementarities we have with the pilot, pivot and advisory board configurations.

Table 5.

Configurations sufficient for low innovation commitment.

	Low innovation commitment
Consistency thresholdFrequency threshold	0.83 1.0
Configurations	C4	C5	C6
Board capital breadth (BCB)	⊗	⊗	●
Board capital depth (BCD)	⊗	●
CEO power	⊗	●	⊗
Founding family as the largest shareholder	⊗	●	●
Institutional investor as the largest shareholder	●	⊗	⊗
Consistency	0.94	0.91	0.80
Raw coverage	0.07	0.04	0.15
Unique coverage	0.05	0.03	0.13
Solution consistency	0.84
Solution coverage	0.24

Note: ● indicate the presence of a condition, ⊗ indicate its absence, and blank spaces indicate that the causal condition may be either present or absent.

Configuration C4 is the only configuration that includes the presence of an institutional investor as the largest shareholder. DiagnoCure, a large organization in terms of market capitalization with a US-based private investment bank as the largest shareholder (12% in 2007), represents this configuration. DiagnoCure has a relatively large board (nine directors) where four outside directors are financial experts, which may cause the organization to be overly focused on short-term earnings.

Configuration C5 has high BCD and the presence of a powerful CEO and founding family owner. Microbix Biosystem Inc., a medium-sized organization where the powerful CEO is the founder and the largest shareholder is represented by the founder and his family, illustrates this configuration. Board members have extensive industry connections and experience.

Configuration C6 also has the presence of a powerful family owner along with high BCB. Valeant Pharma illustrates this configuration. The firm’s large board (10 directors) has eight outside directors who have a high degree of functional diversity and experience in several different industries. The founding family has a 12% ownership stake and a member of the family is the board chairman. The CEO is relatively new having a tenure of two years. In the discussion section, we elaborate on how the patterns seen in these configurations provide additional insights about our board archetypes.

Robustness analyses

To check the robustness of the results, we follow recommendations of Schneider and Wagemann (2012) and change consistency thresholds and calibration strategies. We summarize the results in Table 6.¹¹ With a lower consistency threshold we see the expected corresponding decrease in overall solution consistency and increase in coverage (Ragin, 2008). C3 remains the same, while C1 and C2 combine into one configuration, which is functionally equivalent to the results reported in the main analysis (Table 4). Specifically, BCB and CEO power become either present or absent. Although this solution does result in configurations with higher coverage values, we felt it prudent to favour consistency over coverage and retain the three configurations in our main analysis, since they provide important insights for our subsequent theory building. For the low innovation configurations, with higher consistency thresholds, C3 and C6 are eliminated and as expected the overall coverage values decrease.

Table 6.

Summary of robustness analyses for innovation commitment.

		Type of change made to the analysis
	Original analysis reported in results	Lower consistency threshold^a	Higher consistency threshold^a	Calibration based on theoretical maximum values for BCB, BCD and CEO power
High innovation commitment
Frequency/consistency thresholds	1 / 0.85	1 / 0.78	1 / 0.92	1 / 0.85
Number of configurations	3	2	2	3
Solution consistency	0.90	0.84	0.96	0.90
Solution coverage	0.27	0.40	0.12	0.28
Configuration differences		C1 and C2 become one functionally equivalent configuration, as BCB and CEO power become either present or absent	C3 eliminated	None
Low innovation commitment
Frequency/consistency thresholds	1 / 0.83	1 / 0.78	1 / 0.90	1 / 0.83
Number of configurations	3	3	2	3
Solution consistency	0.84	0.84	0.92	0.84
Solution coverage	0.24	0.24	0.11	0.24
Configuration differences		None	C6 eliminated	C6: BCD present

Determined by where a gap occurred in the raw consistency values (Ragin, 2008).

To asses if our results are robust with changes to the calibration of the BCB, BCD and CEO power conditions we used as our starting point the three theoretical maximum values for the measures, 2.08, 2.0 and 3.00. We then used 75% of these values as the fully in value, 25% as the fully out, and 50% as the crossover point. For the high innovation commitment, we find virtually the same results. For the low innovation commitment, C4 and C5 are the same, and C6 instead of having BCB present or absent has the condition as present. In sum, none of our reported configurations differ substantially with these changes, thus providing support for the robustness of our reported findings.

Discussion

In this study, we explored how various governance configurations of resource provisioning and monitoring mechanisms lead to different levels of innovation commitment. Our findings show that no single governance attribute is necessary or sufficient for high innovation commitment. Further, there are multiple ways the governance attributes combine to promote innovation commitment. Consistent with boards’ multifaceted roles, we identify three board archetypes that arise from different complementarities within the governance configurations that foster firm innovation commitment.

The ‘pilot board’ configuration (C1) allows outside directors to provide valuable knowledge and linkages to the organization, which also improves their ability to be effective monitors and active drivers of organizational decisions. Hence, this archetype arises from high BCB and BCD (directors’ diversified and in-depth industry knowledge) being complemented by a lack of constraint from powerful owners and CEOs, such that they are able to use these knowledge-based resources to monitor and to promote commitment to innovation.

The ‘pivot board’ configuration (C2) also lacks the influence of powerful owners but has powerful CEOs and outside directors with high levels of BCD. Given the absence of direct monitoring and industry expertise from powerful owners, outside directors are central actors in the governance structure by having the ability to provide valuable industry-specific expertise and linkages to the organization, while also using this knowledge to effectively monitor powerful CEOs. Thus, the board must pivot between effectively providing advice and relational resources to a CEO who has the power to enact his or her preferences, while also effectively providing oversight of such preferences. Similar to the complementarities in the ‘pilot board’ archetype, we find that outside directors’ knowledge, centred on industry-specific sources, is complemented by a lack of constraint from powerful owners, which provides motivation and the ability to pivot between their board roles.

In the ‘advisory board’ configuration (C3), outside directors have less power relative to other governance actors, but their breadth of expertise, experience and relational capital (BCB) enables them to advise management, and through their networks or connections complement the knowledge resources provided by powerful founding families. CEOs are able to use their high level of power to leverage these important resources provided by the advisory board to enhance firm innovation commitment.

Across the three board archetype configurations we see the importance of knowledge resources arising from different sources (BCB, BCD and founding family owners). Yet the ways in which this knowledge facilitates innovation commitment (e.g. providing expertise and/or monitoring capabilities) depends upon the presence of complementary mechanisms as we suggest in the following propositions:

P1: High innovation commitment can be facilitated by a pilot board governance configuration where both heterogeneous and industry-specific expertise, experience and connections of outside directors (BCB and BCD) are combined with the autonomy afforded by the lack of powerful CEOs and owners.

P2: High innovation commitment can be facilitated by a pivot board governance configuration where industry-specific expertise and connections provided by high BCD combined with the autonomy afforded by the lack of powerful owners allows informed outside directors to advise and monitor powerful CEOs.

P3: High innovation commitment can be facilitated by an advisory board governance configuration where the heterogeneous experiences and connections of outside directors (BCB) combined with the industry-specific knowledge of powerful founding family owners allows outside directors to serve as advisors to powerful CEOs who are monitored by founding family owners.

The governance configurations that lead to low innovation commitment (C4 to C6) also support our archetypes. In C4, there is a lack of knowledge provided by BCB and BCD as well as the absence of industry-specific expertise from the founding family owner. This substantiates the logic in our propositions that high innovation commitment requires governance configurations that provide in-depth industry knowledge and relationships from either high BCD or a powerful founding family. As noted previously, C4 is the only configuration with the presence of an institutional investor as the largest shareholder. Consistent with previous research, this configuration suggests that institutional investors with large ownership stakes may be overly focused on current earnings, thus exerting pressure to minimize commitment to innovation (O’Conor & Rafferty, 2012). Yet, our findings provide an explanation of how these types of owners are able to do so. The discretion institutional owners possess to exert their preferences is likely reinforced by the absence of outside directors having both high BCB and BCD as well as the absence of a powerful CEO.

The governance configurations C5 and C6 indicate that having the founding family as the largest owner may not always be beneficial for firm innovation commitment, which is in line with the findings of Munoz-Bullon and Sanchez-Bueno (2011) who investigated the impact of family ownership on Canadian firms’ R&D spending. More importantly, C5 and C6, in comparison to the high innovation commitment configurations, highlight the nuances associated with the ways the governance attributes complement one another. Specifically, C5 confirms that industry-specific human and relational capital coming from high levels of BCD does not complement that coming from powerful founding family owners, as this overabundance of industry-specific knowledge results in low innovation commitment. This underscores that it is not always ‘similar in kind’ governance elements that lead to complementarity (Grandori & Furnari, 2008). It may be that the group processes associated with such a governance configuration leads to organizational dynamics that promote tunnel vision, or reluctance to consider other beneficial perspectives, essentially leading to an ‘over-piloted board’.

Comparing C6 (low outcome) with C3 (high outcome) offers validation for the effectiveness of an advisory board in promoting innovation commitment. High BCB and the presence of a powerful founding family owner need to be complemented by the presence of a powerful CEO if commitment to innovation is to be high rather than low. Arguably, even though the breadth of expertise provided by high BCB may equip outside directors to serve as advisors and the founding family may be able to provide valuable industry expertise, innovation commitment may suffer from the absence of a CEO who has the power to act upon the different types of knowledge. Also, the lack of CEO power suggests that boards in C6 are more focused on their monitoring role, and that due to a lack of industry-specific knowledge, strategic decisions are being driven by founding family owners. Thus, unlike an advisory board, the diversity of expertise provided by high BCB is not complemented in ways that promote innovation commitment.

Theoretical implications

Our study adds to the growing stream of research that advocates for understanding the effectiveness of firm governance in terms of configurations (e.g. García-Castro et al., 2013; Misangyi & Acharya, 2014). Indeed, our results support the view that the influence of governance mechanisms on organizational outcomes is best understood by considering their interdependencies as part of governance configurations (Aguilera et al., 2008). Yet our objective with this study is not to merely support previous research, but to develop deeper understanding of how the resource provisioning and monitoring attributes of board capital (BCB and BCD), CEO power and powerful owners complement one another to influence innovation commitment. In doing so, we identify three board archetypes arising from complementarities occurring among governance mechanisms, which lead to high innovation commitment.

Importantly, our findings show that the complementarities associated with the board archetypes do not have to be ‘similar in kind’ to lead to effective organizational outcomes, but rather there are also beneficial ‘different in kind’ complementarities. Additional monitoring from powerful owners or an overabundance of knowledge resources does not reinforce the effectiveness of board capital with regard to innovation outcomes. These findings thus provide unique insights into this important phenomenon first noted by Grandori and Furnari (2008).

Similar to Haynes and Hillman (2010), we demonstrate that BCB and BCD affect outside directors’ role in contributing to organizational strategy. However, our results indicate that the influence of these board capital constructs on innovation commitment depends on how they combine with other firm-level governance attributes associated with the balance of power between outside directors, CEOs and large shareholders. The notion that firm innovation may be driven by corporate governance attributes is not new, but previous studies have largely adopted ‘net effects’ research approaches, such that the impacts of individual antecedent conditions are assessed while holding all others constant (Fiss, 2011). Our configurational study, while building on this previous governance research, assesses the simultaneous and interdependent effects of these conditions, thus contributing a new way to evaluate how corporate governance matters to innovation-related outcomes. Our study also supports certain aspects of the logic of both agency and resource dependence theory in explaining how various elements of a corporate governance system can complement one another to affect firm innovation. This underscores the saliency of Dalziel et al.’s (2011) argument for considering the effectiveness of corporate governance as an elaborate interplay between resource provisioning and monitoring, and not as competing functions.

Our findings have important implications for practice. Key organizational decision-makers (CEOs, large founding family owners and board members) might find our results helpful in assessing their own effectiveness in fulfilling their responsibility to promote innovation. Furthermore, attention should be devoted to board composition so that boards possess appropriate BCB and BCD in order to facilitate high innovation commitment. Our results also suggest that CEO power can be beneficial for innovation commitment, as long as it is counterbalanced by board capital and the presence of a dominant founding family owner.

Despite the significant findings, this study includes some limitations, which nevertheless offer opportunities for future research. First, whereas our sample size of only 54 firms is consistent with previous fsQCA studies in single industries (e.g. Meuer, 2014; Pajunen & Airo, 2013), it may limit the generalizability of our findings. Furthermore, governance researchers are increasingly recognizing that ‘firm-level governance outcomes should be analyzed in conjunction with institutional factors’ such as national governance systems and industry-specific regulations (Bell, Filatotchev, & Aguilera, 2014, p. 304). Although our study addresses this issue by focusing on firms operating in the same industry and country, this limits the generalizability of our findings. Exploring our research question in multiple countries and industries would offer the possibility of gaining further insights into what drives firm innovation.

There are also certain limitations associated with our fsQCA methodology. In order to ensure interpretable results, the number of conditions is limited. We acknowledge that other governance attributes may be important for innovation commitment and recommend that these be explored in future studies. In addition, the technique ‘does not explicitly integrate the time dimension and therefore does not allow for analysis of temporal processes’ (De Meur, Rihoux, & Yamasaki, 2009, p. 161), therefore we suggest that future research explore whether the influence of governance configurations on innovation commitment changes over time by using mixed method research designs.

In conclusion, we believe our configurational approach to examining the relationship between governance configurations and innovation commitment in Canadian pharmaceutical firms provides a deeper and more holistic understanding of this important organizational dynamic. We hope our study will motivate future configurational research into how corporate governance elements combine in complex ways to both facilitate and hinder key organizational outcomes.

Footnotes

Acknowledgements

The authors acknowledge the helpful comments by Robin Holt (Editor), Guido Möllering (Senior Editor) and two anonymous reviewers for their helpful comments on earlier versions of this article.

Funding

We gratefully acknowledge the Social science and humanities research council (SSHRC) of Canada funding for this research. Research grant n. 16231.

Notes

Author biographies

Eduardo Schiehll, MSc, PhD, FCPA, FCMA, is Professor of Accounting at HEC Montréal, University of Montréal (Canada), and Visiting Professor in Corporate Governance at Aalto University School of Business (Finland). His research and teaching activities are focused in the areas of corporate governance, management control systems design and performance management.

Krista B. Lewellyn is an Assistant Professor of Management at University of Wyoming (US). Her research interests include corporate governance, institutions, and how organizations handle conflicting contextual demands with respect to strategic-type decisions.

Maureen I. Muller-Kahle is an Associate Professor of Business at the Pennsylvania State University-York (US). Her research interests are in the areas of corporate governance and strategic leadership with a focus on gender dynamics.

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