EXPLORING FIRM STRATEGIC ORIENTATIONS: A COMPLEMENTARITY PERSPECTIVE By Victor Chernetsky A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Business Administration – Marketing – Doctor of Philosophy 2023 ABSTRACT A firm’s strategic orientation is a critical component of its organizational culture as it shapes its strategies, operations, and performance. Firms face meaningful trade-offs when they adopt traditional or emerging strategic approaches. Thus, a better understanding of various strategic orientations and their interplay is critical to guide scholars investigating organizational culture and senior executives looking to determine the appropriate strategic orientation(s) for their firms. This dissertation consists of two separate but interrelated essays, which investigate the concept of network centricity – an emerging firm strategic orientation reflecting the recent developments in information technology – as well as its interplay with other firm orientations and performance. Leveraging the resource-based view and the VRIO framework, the first essay examines the relative impacts of customer centricity and competitor centricity on innovativeness, market position, and financial position. I apply advanced meta-analytic structural equation modeling (MASEM) techniques to study both strategic orientations simultaneously and estimate the model with 583 effect sizes from 179 independent samples obtained from 171 studies. This study also explores why some firms benefit more or less from customer or competitor centricity under various environmental conditions. The second essay develops the concept of network centricity. I examine its individual performance outcomes as well as its interplay with customer centricity and competitor centricity to explore the complementarity among these strategic orientations. Increasingly, a firm’s ability to engage in strategic partnerships to compete with other firm networks determines the success and failure of firms in the marketplace. I identify this phenomenon as network centricity and define it as a set of company-wide values, norms, and beliefs that promote the development of firm’s long-term multilateral partnerships with other organizations. I offer conceptualization and operationalization of the firm’s network centricity and leverage data from multiple sources (Medtrack pharmaceutical business intelligence database, text analysis of the letters to shareholders in firm annual reports, Compustat) to explore the effects of complementarity of network centricity, customer centricity, and competitor centricity on firm performance. Copyright by VICTOR CHERNETSKY 2023 This dissertation is dedicated to Olga, Andrew, and Anna. v ACKNOWLEDGEMENTS I want to express my gratitude to everyone who has been a part of my remarkable Ph.D. experience. First, I would like to extend my appreciation to Dr. Ahmet Kirca, my dissertation chair, for the guidance and expert advice throughout this entire process. Your mentorship has been instrumental in shaping my research and academic pursuits. I am grateful to Dr. Suman Basuroy, Dr. Sung Ham, and Dr. Anita Pansari, my dissertation committee members, for your critical insights, constructive feedback, and thoughtful contributions that have truly elevated the quality of my work. I also would like to acknowledge that this research was partially funded by a CIBER grant from the Department of Education. Special thank you are also due to Dr. Douglas Hughes, my first advisor and co-author, for providing mentorship at the early stages of my Ph.D. journey. I would like to express my gratitude to Dr. Wyatt Schrock, my co-author in several papers, for the valuable collaboration and intellectual exchange that enriched my research. A thank you goes to Dr. Clay Voorhees, who believed in my potential and granted me the opportunity to join the Ph.D. program. I would like to acknowledge Brandon Holle, with whom we navigated the job market. I am also thankful to Erin Johnson who was so helpful in resolving various procedural matters. Lastly, I would like to extend a special thank you to my family who has been by my side throughout this journey: my wife Olga, whose unconditional support and understanding made this journey possible; my son Andrew and daughter Anna, whose enthusiasm and love have filled my life with joy and inspiration. Your support, encouragement, and belief in me are the true reasons behind my success. vi TABLE OF CONTENTS LIST OF ABBREVIATIONS .......................................................................................... viii ESSAY ONE: SHOULD COMPANIES FIGHT RIVALS OR INDULGE CUSTOMERS? A META-ANALYTIC ASSESSMENT OF THE IMPACTS OF CUSTOMER AND COMPETITOR CENTRICITY ...........................................................................................1 Abstract .......................................................................................................................2 Introduction .................................................................................................................3 Literature Review........................................................................................................8 Theoretical Development ..........................................................................................13 Research Method ......................................................................................................22 Results .......................................................................................................................29 Discussion .................................................................................................................34 REFERENCES .........................................................................................................42 APPENDIX A: TABLES ..........................................................................................50 ESSAY TWO: NETWORK CENTRICITY: AN EMERGING FIRM STRATEGIC ORIENTATION AND ITS FIRM PERFORMANCE OUTCOMES ...............................60 Abstract .....................................................................................................................61 Introduction ...............................................................................................................62 Conceptual Development ..........................................................................................66 Sample and Data .......................................................................................................79 Empirical Approach ..................................................................................................83 Results .......................................................................................................................85 Discussion .................................................................................................................88 REFERENCES .........................................................................................................94 APPENDIX A: TABLES ........................................................................................103 vii LIST OF ABBREVIATIONS FE fixed effects GLS generalized least squares HQ headquarters LIWC linguistic inquiry and word count MASEM meta-analytic structural equation modeling OLS ordinary least squares ROA return on assets ROI return on investment R&D research and development SAMD sample-adjusted meta-analytic deviancy SEM structural equation modeling SIC standard industrial classification SME small and medium enterprises TSSEM two-stage structural equation modeling VRIO value, rarity, inimitability, organization WLS weighted least squares viii ESSAY ONE: SHOULD COMPANIES FIGHT RIVALS OR INDULGE CUSTOMERS? A META-ANALYTIC ASSESSMENT OF THE IMPACTS OF CUSTOMER AND COMPETITOR CENTRICITY 1 Abstract The positive effects of customer centricity and competitor centricity are well documented in the marketing literature. However, the relative importance of these firm strategic orientations under various conditions for different firms still remains unexplored. Leveraging the resource- based view and the VRIO framework, this study explores the relative impacts of customer centricity and competitor centricity on innovativeness, market position, and financial position. I apply an advanced meta-analytic structural equation modeling (MASEM) technique to study both strategic orientations simultaneously and estimate the model with 583 effect sizes from 179 independent samples obtained from 171 studies. The results reveal the complex nature of customer centricity and competitor centricity. Competitor centricity is more strongly associated with innovativeness than customer centricity, while customer centricity has a stronger impact on firm performance. This study also examines why some firms benefit more or less from customer or competitor centricity under various environmental conditions, such as firm size, firm type, industry type, and country context. Keywords: customer centricity; competitor centricity; innovativeness; firm performance; meta- analysis, MASEM 2 Introduction “The essence of strategy formulation is coping with competition.” (Porter 1979) “If you’re competitor-focused, you have to wait until there is a competitor doing something. Being customer-focused allows you to be more pioneering.” (Jeff Bezos, founder of Amazon; LaGesse 2008) To what extent firms should focus on understanding and satisfying customer needs and/or on following and surpassing the activities of their competitors is still an ongoing debate among practitioners and researchers in the marketing literature. This issue has also attracted substantial attention in the business press as the dominant view supports that an emphasis on customers should come first (Bonchek and Cornfield 2016; Nelson 2017), while the alternative perspective stresses the importance of focusing on competitors (Hyken 2021). Similarly, academic literature discusses the potential benefits and drawbacks of a firm’s customer centricity (Christensen 1997; Christensen and Bower 1996; Lee et al. 2015) and competitor centricity (Day and Wensley 1983; Luo, Rindfleisch, and Tse 2007). Moreover, prior research also highlights the tension between customer and competitor centricity and related trade-offs (Armstrong and Collopy 1996; Armstrong and Green 2007). Nevertheless, Day and colleagues emphasize the merits of both approaches and the need to maintain a balance between the firm’s customer centricity and competitor centricity (Day and Nedungadi 1994; Day and Wensley 1988). Customer centricity and competitor centricity are fundamental concepts in marketing that represent the two fundamental facets of an organization that adopts the marketing concept (Shapiro 1988).1 A firm’s customer centricity refers to the strategic orientation of the firm that is 1 I use the terms “centricity” and “orientation” interchangeably when referring to specific firm strategic orientations throughout this paper, consistent with Fader (2020) and Lee et al. (2015). 3 centered on customer needs and interests (Deshpandé, Farley, and Webster 1993; Narver and Slater 1990). On the other hand, competitor centricity refers to a firm that prioritizes understanding and responding to the actions of competitors (Narver and Slater 1990). Oftentimes, firms pursue one of these strategic orientations or try to implement them both based on how senior managers view the relative importance of these strategic orientations in achieving firm objectives, existing organizational capabilities, or the current market situation (Day and Nedungadi 1994). Firms’ proclivity to focus on customers or competitors impacts various facets of the company’s operations. Specifically, it influences the type of information managers are exposed to, which ultimately affects their beliefs, mindsets, and decision-making (Armstrong and Collopy 1996; Day and Nedungadi 1994). Companies constantly make choices related to the allocation of limited organizational resources and establish ways to compete via identification and implementation of specific firm strategic orientations (Slater, Olson, and Hult 2006). Both customer centricity and competitor centricity are important for firm performance. However, the simultaneous implementation of these strategic orientations, and maintaining a balance between them, could be a hardly attainable target for many companies (Day and Nedungadi 1994). Respectively, the following questions still remain actively discussed but unanswered in both academic literature and business press: How should firms maintain the balance between customer centricity and competitor centricity? Is one of these strategic orientations more important than the other? In which situations should firms prioritize customer and competitor centricity? Customer and competitor centricity have been traditionally operationalized as two dimensions of a firm’s market orientation (Narver and Slater 1990). However, over time marketing literature drifted away from a broad concept of market orientation to a more focused 4 concept of customer centricity of the firm (Crecelius et al. 2019; Fader 2020; Jayachandran et al. 2005; Shah et al. 2006). In addition, customer centricity and competitor centricity are often examined separately providing a one-sided perspective. However, academic literature stresses the importance of joint examination of different strategic orientations (Grinstein 2008b; Hakala 2011; Noble, Sinha, and Kumar 2002; Schweiger et al. 2019). Thus, a joint investigation of customer centricity and competitor centricity can offer a more complete and nuanced perspective. A series of studies examined both customer centricity and competitor centricity simultaneously (Atuahene-Gima 2005; Gatignon and Xuereb 1997; Noble, Sinha, and Kumar 2002; Ozkaya et al. 2015; Saboo and Grewal 2013). The insights suggest that while these strategic orientations are usually beneficial for firms, there are certain conditions when one of them could be more advantageous. On the one hand, competitor-centric firms may be preoccupied with matching their rivals and as a result sacrifice company profits (Armstrong and Collopy 1996). On the other hand, firms that predominantly focus on customers may overlook the development of emerging market trends and fail to track competitors’ actions (Christensen and Bower 1996). Still, research on this issue remains scattered and this marketing phenomenon has not been sufficiently explored. Specifically, marketing literature lacks an understanding of the relative impact of these firm strategic orientations that could guide scholars and practitioners. A comprehensive meta-analytic assessment can help to fill this research gap and extend our understanding of the relative impacts of customer centricity and competitor centricity on firm performance and innovativeness. While there are several meta-analytic studies of the broader concept of market orientation as a standalone phenomenon (e.g., Cano, Carrillat, and Jaramillo 2004; Kirca, Jayachandran, and Bearden 2005) or jointly with other strategic orientations (e.g., Grinstein 2008b; Schweiger et al. 2019), I could trace only a few meta-analyses that provide 5 insights into customer centricity and competitor centricity. As summarized in Table 1.1, these studies examined the relationship between market orientation and its components on innovation outcomes (Grinstein 2008a), the antecedents and consequences of new product innovation (Calantone, Harmancioglu, and Droge 2010), and drivers of new product performance (Tsai, Huang, and Tsai 2013). ---- Insert Table 1.1 about here ---- Notwithstanding the benefits of the meta-analytic work described above, these studies are different from the current study in several ways. First, these meta-analyses were structured to answer related but different research topics regarding the impacts of various strategic orientations on innovation and new product performance, but they do not focus on customer and competitor centricity as their focal constructs. As such, these studies do not offer insights into the impact of customer and competitor centricity on firm performance. Second, some of these studies were conducted more than a decade ago, included a relatively small number of effect sizes related to customer centricity and competitor centricity, or had methodological limitations. Finally, the relative impact of customer centricity and competitor centricity on firm outcomes was not explored. My paper aims to address the debate regarding the relative importance of firms’ customer centricity versus competitor centricity via a meta-analytic assessment of existing academic literature. I leverage the meta-analytic structural equation modeling (MASEM) approach. This technique combines structural equation modeling (SEM) and meta-analysis (Cheung 2015). Specifically, I employ an advanced TSSEM (two-stage structural equation modeling) MASEM approach that has several advantages over other meta-analytic techniques (Cheung 2015; Cheung and Chan 2005). By using the weighted least squares (WLS) estimation method, this approach 6 corrects the limitations of conventional approaches, like generalized least squares (GLS) estimation. I estimate the model with 583 effect sizes from 179 independent samples obtained from 171 studies. This meta-analysis covers studies published until the beginning of 2020. This paper makes three contributions to the marketing literature. First, I integrate several decades of marketing literature on customer centricity and competitor centricity. In doing so, I provide a comprehensive analysis of prior research findings concerning these firm strategic orientations. This paper traces the historical development of the marketing concept, distinguishing between different approaches and conceptualizations, and highlights the importance of balancing between customer centricity and competitor centricity. While both strategic approaches are important and usually beneficial for firms, sometimes companies cannot afford to develop customer centricity and competitor centricity simultaneously. Moreover, academics and practitioners should be aware that various internal and external situations could make the adoption of one of these strategic orientations more important than the other. Second, leveraging the resource-based view (Barney 1991) and the VRIO framework (Barney and Hesterly 2015), I assess and compare how customer centricity and competitor centricity impact firm innovativeness, market position, and financial position. By applying an advanced meta-analytic technique (TSSEM MASEM; Cheung 2015; Cheung and Chan 2005), I distinguish the impact of each strategic orientation while controlling for the role of the other. Previous meta-analytic studies provide limited insights related to the relationship between these strategic orientations and firm innovation. I extend this knowledge via advanced methodology and a much larger sample size. In addition, I examine the impact of customer centricity and competitor centricity on different aspects of firm performance: market position and financial position (Rubera and Kirca 2012). 7 Third, my investigation of moderators helps delineate the effects of theoretically relevant contextual factors that affect the relationships involving the effects of customer centricity and competitor centricity. Specifically, I extend the understanding of contextual moderators related to the impact of customer centricity and competitor centricity on innovativeness, market position, and financial position: firm size, firm type, industry type, and state of the country’s economic development. In addition, I reconcile conflicting findings related to the moderating role of firm type (products vs. services; Calantone, Harmancioglu, and Droge 2010; Grinstein 2008a). Thus, my insights will help scholars and managers better understand the variation of business results across companies, industries, and countries. Overall, the findings of this paper should be beneficial to marketing academics investigating firm strategic orientations and industry practitioners who aim to maximize the efficiency and productivity of the firms. Literature Review Firm Strategic Orientations Firm strategic orientation is a pivotal aspect of the company’s operations. It has a major impact on multiple aspects of firm activities, organizational performance, and the development of the firm competitive advantage in the market. As a part of organizational culture (Deshpandé, Farley, and Webster 1993; Deshpandé and Webster 1989), a firm’s strategic orientation reflects organizational norms, values, and beliefs, and guides firm employees on appropriate behaviors and overall approach to doing business. Strategic orientations both guide and help explain the decision-making process in organizations (Deshpandé, Farley, and Webster 1993). Prior literature reveals the important role of firm strategic orientations in directing specific business actions of a company. Strategic orientations influence a company’s marketing strategy (Noble, Sinha, and Kumar 2002), help achieve superior business performance (Slater, 8 Olson, and Hult 2006), and reflect broader strategic priorities of the firm (Gatignon and Xuereb 1997). Scholars examined various strategic orientations, including competitor orientation, customer orientation, employee orientation, engagement orientation, entrepreneurial orientation, innovation orientation, interaction orientation, learning orientation, market orientation, production orientation, selling orientation, and technology orientation (Gatignon and Xuereb 1997; Grinstein 2008b; Hakala 2011; Kirca, Jayachandran, and Bearden 2005; Kumar and Pansari 2016; Narver and Slater 1990; Noble, Sinha, and Kumar 2002; Ramani and Kumar 2008). However, among the multiplicity of strategic orientations developed over past decades, market orientation is probably the one most frequently studied in the marketing literature and adopted by firms. Marketing Concept Evolution Drucker (1974) indicates that the creation of a customer is the ultimate business purpose of a firm, while a customer is created through two basic functions of a company: marketing and innovation. These firm functions are also the only ones that ultimately generate firm performance (Drucker 1974). Marketing research has also shown that a combination of customer centricity and innovativeness are the characteristics of the best-performing firms (Deshpandé, Farley, and Webster 1993). Kirca, Jayachandran, and Bearden (2005) demonstrated that market orientation is positively related to innovation and firm performance. I plan to extend these findings considering the latest theoretical and methodological developments as well as capturing studies conducted since 2005. A firm’s market orientation (Kirca, Jayachandran, and Bearden 2005) has long been among the most widespread organizational strategic orientations (Kumar and Pansari 2016). Although the marketing concept was introduced in the 1950s, market orientation was 9 systematically and formally theorized only at the beginning of the 1990s. Two leading approaches emerged in the marketing literature (Homburg and Pflesser 2000; Jaworski and Kohli 2017): one is focused on behavioral attributes (i.e., Kohli and Jaworski 1990), while the other follows a cultural perspective (i.e., Narver and Slater 1990). Kohli and Jaworski (1990) define market orientation as “the organizationwide generation of market intelligence pertaining to current and future customer needs, dissemination of the intelligence across departments, and organizationwide responsiveness to it” (Kohli and Jaworski 1990, p.6, emphasis in original). To note, while this approach represents market orientation, the conceptualization is focused on customer needs. Relatedly, the following studies often treated market orientation and customer orientation synonymously with ‘market’ being defined as a representation of potential customers of the firm (e.g., Deshpandé, Farley, and Webster 1993). Narver and Slater (1990) propose an alternative conceptualization with three components of market orientation: customer orientation, competitor orientation, and interfunctional coordination. The first two aspects are focused on a thorough understanding of the firm’s customers and competitors. The third component is closely connected to the first two and reflects the need for coordinated integration of firm resources targeted at the creation of superior customer value. While this approach is traditionally labeled as cultural, Narver and Slater (1990) note the importance of both cultural and behavioral aspects and define market orientation as the organizational culture that creates certain necessary behaviors. The beneficial outcomes of market orientation are widely accepted by scholars (Cano, Carrillat, and Jaramillo 2004; Grinstein 2008a; Kirca, Jayachandran, and Bearden 2005; Schweiger et al. 2019). For many years market orientation was considered a source of sustainable competitive advantage that can safeguard superior firm performance (Sett 2018). 10 Consequently, market orientation remains among the most widespread organizational strategic orientations (Kumar and Pansari 2016) and is also among the top three most relevant topics for practitioners (Jedidi et al. 2021). However, driven by the rapidly changing business environment, the understanding of market orientation is still evolving (Sett 2018). Moreover, as more companies adopt this orientation, it no longer remains a source of competitive advantage, but rather becomes a cost of doing business (Hunt and Morgan 1995; Kumar et al. 2011). The ubiquitous firms’ adoption of the marketing concept propelled scholars to consider alternative approaches and conceptualizations of this phenomenon. In recent years, marketing literature drifted away from a broad concept of market orientation to a more focused concept of customer centricity of the firm (Crecelius et al. 2019; Fader 2020; Jayachandran et al. 2005; Shah et al. 2006). These developments warrant a more thorough analysis of customers’ and competitors’ perspectives of the marketing concept. Balancing the Focus on Customers and Competitors The meaningful distinction between customer centricity and competitor centricity was brought up several decades ago (Day and Wensley 1983). Scholars point out the importance of both addressing customer needs and managing the pressure from rivals and claim that these two strategic directions are the fundamental aspects of business organizations’ effective operations. Day and colleagues theorize about the benefits and challenges of pursuing customer centricity and competitor centricity and highlight the importance of both approaches (Day and Nedungadi 1994; Day and Wensley 1988). The choice of specific firm orientations may be driven by firm priorities, capabilities, and expenditures that the company is ready to carry out to handle related activities. 11 Nelson (2017) suggests that if competitors follow the actions of a certain company, while this firm keeps focusing on customers, the focal company will maintain an advantageous position and remain ahead of the competition. Similarly, Slater and Narver (1995) argue that firms should be able to change faster than their competitors to achieve sustainable competitive advantage. Such an approach could be necessary to survive in fast-paced and competitive markets. Companies need to have excellent customer-related intelligence as well as a permanent ability and willingness to change and innovate to remain ahead of their competitors. However, firms that develop customer centricity recognize that the process of obtaining customer-related information is among the most difficult and expensive (Day and Nedungadi 1994). Moreover, driven by the fast advances in the modern economy (Autio, Mudambi, and Yoo 2021), a company’s gains due to superior customer insights might be short-lived as competitors can often copy firm strategies undermining its competitive advantage (Christensen, Cook, and Hall 2005). Day and Wensley (1988) discuss the importance of balancing the focus of the company on customers and competitors. They also present the limitations brought by leveraging only one of the approaches. When firms are predominantly customer-centric, some of their customer- targeted objectives might be loosely related to everyday business activities. In addition, companies might be unaware of potential competitor actions and be unable to cope with rivals’ customer-focused strategic moves. Alternatively, firms that rely too heavily on competitor centricity might miss meaningful market opportunities related to changing market structure and customer preferences. Such an approach usually limits experimentation and the introduction of innovative strategies that might change the nature of competition and help establish a firm’s competitive advantage. In addition, companies might follow competitors which are inefficient and repeat their mistakes. Ultimately, neither approach alone provides a comprehensive 12 perspective that could successfully guide business leaders. Specifically, the current understanding of the field could benefit from further exploration of the comparative influence of customer centricity and competitor centricity on company outcomes. Theoretical Development The Relative Impact of Customer Centricity and Competitor Centricity According to the resource-based view firms achieve a sustainable competitive advantage over their rivals to establish a leading position in the market (Barney 1991; Kozlenkova, Samaha, and Palmatier 2014). This competitive advantage is achieved via the utilization of the firm strategic resources and capabilities. However, it is not sufficient to acquire unique and valuable resources. Companies should also possess capabilities to efficiently leverage these resources and transform them into internal competencies and specific actions. An in-depth and up-to-date understanding of customer needs, wants, and aspirations, as well as the knowledge of competitors’ latest moves and potential future actions, serve as an important basis for firm strategic actions. The resources and capabilities of a firm can be categorized into four broad groups: financial, physical, individual, and organizational (Barney and Hesterly 2015). Due to firm resource heterogeneity, some companies may possess larger amounts of certain resources and thus be more proficient in accomplishing certain activities than other firms. Barney and Hesterly (2015) propose to analyze firm resources and capabilities with the help of the VRIO framework,2 which captures the four characteristics of resources and capabilities that create sustainable competitive advantage: Value, Rarity, Imitability, and Organization. The value dimension 2 The VRIO framework was introduced instead of the previously used VRIN framework (i.e., valuable, rare, inimitable, and non-substitutable) to acknowledge that firms should not only possess but also effectively leverage resources (Kozlenkova, Samaha, and Palmatier 2014). 13 reflects the degree to which a resource enables a company to exploit existing opportunities or minimize threats. The rarity dimension considers the number of companies that control a resource. Imitability captures the extent to which challenges associated with obtaining or developing a given resource result in firms’ cost disadvantage. Finally, the organizational factor reflects the presence and efficiency of company’s policies and procedures that enable a firm to exploit valuable, rare, and inimitable resources. The firm’s focus on customers or competitors is likely to have meaningful implications for company resources and capabilities and related market competitive advantage. As it is more difficult to obtain information related to customers than intelligence related to competitors (Day and Nedungadi 1994), the former will be rarer and more valuable than the latter. While there is usually a certain number of companies competing in the market, there is a multiplicity of customer segments, needs, and insights. Moreover, some of this customer information is tacit, making it difficult for both firms and customers to express, process, and comprehend. It might be easier to determine firm(s) that have leading market positions and focus market intelligence on these companies. However, it is a more demanding task to uncover which customer segments to focus on and how to gain related consumer insights. Choosing the best ways to exploit these insights is another challenge for firms. If the firm has a strong customer centric organization that ensures both a constant pull of new valuable information and internal capabilities to process this information, it becomes both a valuable (i.e., information) and an inimitable (i.e., internal capability) resource providing a more sustainable competitive advantage in the market. Such a firm would enjoy an ongoing advantage over competitors. Even if rivals manage to copy the firm’s products or services over time, the focal firm will have more innovative solutions by that time, diminishing the efforts of competitor centric rivals and maintaining market competitive 14 advantage. Overall, firms that are focused on customers should enhance their performance more strongly than their competitor-centric rivals, despite the higher costs required to maintain customer centric organizational structures (Lee et. al 2015). Thus, customer centricity should enhance firm productivity by enabling them to focus on more profitable and high potential market segments faster and more effectively than their competitors (Sheth, Sisodia, and Sharma 2000). In addition, the capabilities of a company depend on the firm’s interaction with the market and how managers interpret and utilize external information based on various cues (Atuahene-Gima 2005). Driven by the firm’s internal and external environment, managers adopt different mental models that reflect their understanding of the firm’s competitive position and the potential to achieve competitive advantage (Day and Nedungadi 1994). The types of previously adopted firm strategic orientations may also impact the mental models of business leaders. However, what might seem to be the right action might turn out to be a suboptimal decision. For example, managers who focus on beating the competition are not always doing what is best for their firm (Armstrong and Collopy 1996). When a company prioritizes customer centricity over competitor centricity, managers focus on the generation and dissemination of specific information related to customers over competitors, respectively. The type of information that managers are exposed to (i.e., customer-focused versus competitor-focused) impacts their actions and priorities. For example, when employees are frequently exposed to the performance metrics of their competitors, managers might be willing to sacrifice company profits when they pursue the objective to outperform the rivals. Thus, excessive focus on competitor information and its utilization as a performance indicator could be detrimental to firm financial performance (Armstrong and Collopy 1996; Armstrong and Green 2007). 15 Furthermore, companies that adopt competitor centricity are often focused on establishing a competitive advantage in the market via direct comparison with their rivals (Day and Wensley 1983, 1988). Competitor centric firms sometimes treat the battle for customers as an ultimate prize they achieve by beating other companies. Such a target may become an obsession and hinder other firm performance indicators. While competitor understanding is important for the development of sound firm strategy and unique positioning, the hyper- competition blurs the distinction between the firms in the market. Porter (1996, p. 64) notes that “the more benchmarking companies do, the more they look alike.” Although competitor centricity helps firms produce novel products, such market offerings are often not meaningful as they result in imitation instead of innovation (Im and Workman 2004). Finally, the downsides of excessive competitor centricity also include the issue that firms may overestimate their ability to match the activities of other organizations. Companies may successfully copy selected activities of their rivals, though, it is unlikely that they will be able to match the whole ecosystem of their competitors (Porter 1996). Overall, following the resource-based view, I hypothesize the following: Hypothesis 1: Customer centricity is more positively associated with innovativeness (1a), market position (1b), and financial position (1c) than competitor centricity. Contextual Moderators In addition to examining the direct impact of customer centricity and competitor centricity on their outcomes, I also explore the role of contextual moderators. I expect that the magnitude of relationships will vary across different environments because the value of customer- and competitor-related resources depends on the context in which these resources are employed (Barney 1991; Barney and Hesterly 2015). Below, I provide the theoretical arguments leading to 16 my hypotheses related to the moderating role of firm size (large vs. small), firm type (services vs. products), industry type (high-tech vs. low-tech), and country context (emerging vs. developed). Based on the discussion in support of Hypothesis 1, I also predict that these moderating effects will be stronger for relationships involving customer centricity and its consequences than for relationships involving competitor centricity and its consequences. Firm size. I predict that the impact of customer centricity and competitor centricity on their consequences will be more positive for large firms than for small and medium enterprises (SMEs). The development of new products is a complex and costly process. Not all firms have the resources required for the development of new innovative solutions efficiently. However, large companies have substantial financial resources and technological capabilities that small firms cannot match (Chandy and Tellis 2000). They benefit from economies of scale and can spread the costs and risks related to the development of new products and services across a large innovation portfolio. Large firms can also afford to have multiple innovation projects and be more willing to experiment leading to radical innovations that are especially risky (Sorescu and Spanjol 2008). As most innovations fail (Sharma, Saboo, and Kumar 2018), smaller firms with limited resources might be more cautious about developing risky breakthrough innovations that have the highest failure rate. Thus, large firms will be more predisposed and willing to experiment with new ideas that result in the development of innovative products. The abundant financial resources of large firms enable them to employ and leverage appropriate organizational policies and procedures to efficiently manage various resources (VRIO framework; Barney and Hesterly 2015). Thus, for example, larger firms can afford the implementation of costly customer centric organizational structures, which nevertheless are beneficial for companies (Lee et. al 2015). Furthermore, as large firms often own multiple brands 17 (Morgan and Rego 2009), usually within the same product categories, customer and competitor information could be leveraged more efficiently, representing another beneficial aspect of the economy of scale for large companies. Based on the above, I state: Hypothesis 2: Firm size moderates the relationships between customer centricity and its consequences (H2a) and competitor centricity and its consequences (H2b) such that these relationships are stronger for large firms than for SMEs. Hypothesis 3: The moderating effects of firm size (large firms vs. SMEs) are stronger for the relationships between customer centricity and innovativeness (H3a), market position (H3b), and financial position (H3c) than for the relationships between competitor centricity and their consequences. Firm type. The type of company output – tangible goods or intangible services – also has meaningful implications for the impact of customer centricity and competitor centricity on their outcomes. The differences between service offerings and manufactured goods include intangibility, inseparability (i.e., they are produced and consumed simultaneously), heterogeneity of quality and service standards, and perishability (Zeithaml, Parasuraman, and Berry 1985). It is relatively easy to evaluate the quality of tangible products and compare them with other offerings in the market. However, the quality evaluation of intangible services is a more complex process (Parasuraman, Zeithaml, and Berry 1985). When customers are choosing and evaluating services, they have fewer tangible cues to compare them with other solutions available in the market. In addition, service quality is evaluated not only based on the outcome of service as customers also consider the process of service delivery. A more complex nature of service offerings provides more opportunities for innovative solutions that firms may implement empowered by customer and competitor insights. That also makes the whole complex service 18 proposition a more inimitable resource according to the VRIO framework (Barney and Hesterly 2015). Thus, I expect a more meaningful impact of customer centricity and competitor centricity on their outcomes in service industries and state the following hypothesis: Hypothesis 4: Firm type moderates the relationships between customer centricity and its consequences (H4a) and competitor centricity and its consequences (H4b) such that these relationships are stronger for service companies than for manufacturing firms. Hypothesis 5: The moderating effects of firm type (service vs. manufacturing) are stronger for the relationships between customer centricity and innovativeness (H5a), market position (H5b), and financial position (H5c) than for the relationships between competitor centricity and their consequences. Industry type. Industry affiliation presents another important dimension that might impact the role and importance of customer centricity and competitor centricity in the firm. Innovativeness is essential for high-technology companies to remain competitive (Dutta, Narasimhan, and Rajiv 1999; Rubera and Kirca 2012). High-technology markets are characterized by a high level of dynamism and a constant need for the introduction of innovative products and services. The ability to innovate fast and efficiently results in a pool of new customer offerings that often define a firm’s market position and brand reputation (Aaker and Jacobson 2001). Firms operating in high-technology markets are critically dependent on the ability to capture the latest market trends and relevant information about customer needs and actions of their competitors as previously accumulated knowledge may become obsolete soon. This constant pool of up-to-date market knowledge becomes the basis for the development and 19 introduction of new technological products in a high-paced competitive environment. Driven by limited organizational resources, it also shifts organizational emphasis toward value creation (i.e., investments related to innovativeness) (Mizik and Jacobson 2003). Furthermore, according to the resource-based view, the complexity of high-technology propositions is more difficult to replicate leading to the establishment of firms’ competitive advantage. Market information is a more valuable resource in high-technology markets (VRIO framework; Barney and Hesterly 2015). The importance of customer and competitor information increases in technologically intensive industries as companies must replenish market knowledge that is deteriorating fast (Saboo and Grewal 2013). Prior research suggests that firms providing effective customer solutions are more efficient in high-profit industries (Lee et al. 2015). Less profitable industries are closer to commodity markets with firms providing standardized offerings and customers looking for the lowest price. Customers in the high-profit market, such as the high-technology industries, are more willing to pay for product novelty and customization. That further strengthens the importance of permanent monitoring of customer needs and competitor activities for the development of high-technology offerings to reflect the latest market dynamics and stay ahead of the competition with innovative propositions. Respectively, I predict that firms that thoroughly understand customers and competitors will be more innovative and have a stronger firm performance than their competitors in high-technology markets. More formally, I state: Hypothesis 6: Industry type moderates the relationships between customer centricity and its consequences (H6a) and competitor centricity and its consequences (H6b) such that these relationships are stronger for high-tech companies than for low-tech firms. 20 Hypothesis 7: The moderating effects of industry type (high-tech vs. low-tech) are stronger for the relationships between customer centricity and innovativeness (H7a), market position (H7b), and financial position (H7c) than for the relationships between competitor centricity and their consequences. Country context. I also expect the impact of customer centricity and competitor centricity to differ across countries based on the level of their economic development. Firms are increasingly operating globally covering both developed and emerging markets in a modern interconnected world (Autio, Mudambi, and Yoo 2021). The reflection of business differences across markets, and specifically the understanding of emerging countries’ specifics and the ways to succeed there, is becoming the question of survival for many firms around the world (Burgess and Steenkamp 2013). This is especially important given the fact that emerging markets are expected to surpass developed markets and deliver the most economic growth in the coming years. Emerging economies are radically distinct from mature markets (Sheth 2011). The cultural, regulative, and socioeconomic environment of emerging countries differs significantly from those of developed economies. Specifically, emerging markets are often characterized by rapid economic, political, and social changes; young and growing populations; and substantially different cultural and political systems (Burgess and Steenkamp 2006). While developed economies usually have stable demand, the market situation in emerging marketing is usually more uncertain. A firm’s ability to generate innovative products in response to turbulent market conditions is critical for company success (Im and Workman 2004). Companies that can timely capture and leverage customer and competitor insights might gain a competitive edge in these 21 unstable markets (Jaworski and Kohli 1993). Following the VRIO framework (Barney and Hesterly 2015), an up-to-date understanding of customers and competition is a rarer resource in emerging economies. In addition, emerging markets allow firms to develop efficient business solutions at a lower cost (Burgess and Steenkamp 2006). Thus, companies may create and introduce innovative products and services faster and cheaper than similar offerings in developed markets. I therefore hypothesize: Hypothesis 8: Country type moderates the relationships between customer centricity and its consequences (H8a) and competitor centricity and its consequences (H8b) such that these relationships are stronger for emerging countries than for developed countries. Hypothesis 9: The moderating effects of country type (emerging vs. developed) are stronger for the relationships between customer centricity and innovativeness (H9a), market position (H9b), and financial position (H9c) than for the relationships between competitor centricity and their consequences. Research Method A meta-analysis is a dominant approach to synthesizing research findings and an essential research tool that can integrate and advance the knowledge base of the field (Aguinis et al. 2011; Grewal, Puccinelli, and Monroe 2018; Hulland and Houston 2020; Hunter and Schmidt 2004). Thus, meta-analytic assessment could be a valuable approach for such an important but scattered research domain as customer centricity and competitor centricity. 22 Literature Search I conducted a multi-step procedure to search literature and identify relevant published and unpublished studies. In doing so, I followed approaches recommended by the literature and the procedures applied in previous meta-analyses to ensure the representativeness of my sample (Cooper, Hedges, and Valentine 2009; Rubera and Kirca 2012). First, I thoroughly examined and reviewed relevant strategic orientation literature examining firm focus on the market, customers, and competitors (Kirca, Jayachandran, and Bearden 2005; Kohli and Jaworski 1990; Narver and Slater 1990; Sett 2018; Varadarajan 2017) to determine the meaningful concepts and valid boundaries for my research. In the second step, I conducted a search in academic research databases ABI/Inform (PROQUEST) and EBSCO Business Source Complete. I have searched for the following terms in publication abstracts: “market orientation”, “marketing orientation”, “customer orientation”, “customer centricity”, “customer centric”, “competitor orientation”, and “competitor centricity”. I have considered the publications since 1990 as this is the year when related constructs were operationalized, consistent with Kirca, Jayachandran, and Bearden (2005). The search was limited to studies written in English and included scholarly journal articles, conference proceedings, dissertations, or working papers. The initial search in ABI/Inform (PROQUEST One Business database) delivered 5,249 results. I examined the abstracts of these papers – and if needed the whole papers – to determine the relevance of the articles for my study. The third step of my literature search involved conducting manual issue-by-issue searches of the leading marketing and management journals. I examined abstracts of the articles published in the following journals: Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, 23 Journal of Business Research, Strategic Management Journal, and Academy of Management Journal. Fourth, I applied the snowballing approach to detect additional studies by examining the references of the articles identified during the previous stages. Fifth, using the lists of studies included in other meta-analyses on similar topics ensured that all the relevant studies are included in my sample (Calantone, Harmancioglu, and Droge 2010; Grinstein 2008b; Kirca, Jayachandran, and Bearden 2005). I completed the search process at the beginning of 2020. I applied several criteria to select primary studies for my data set. First, I checked that the articles conceptualized and measured strategic orientations at the firm level. Thus, for example, I excluded studies investigating the customer centricity of salespeople. Second, I retained studies that reported necessary statistics (i.e., sample size, correlation coefficients) suitable for further meta-analytic assessment. Thus, I removed conceptual papers and empirical studies that do not report correlations for the variables of interest. Third, I checked for the presence of articles with substantially overlapping samples and selected the ones that provided the most appropriate information. I removed the other publication(s) that used the same variables or very similar samples. My database included 212 studies that I coded to use in this meta-analysis. Coding Procedures To minimize coding error, I developed a coding protocol that specified the information to be extracted from each study, as well as a description of key variables of interest (Lipsey and Wilson 2001; Rubera and Kirca 2012). Table 1.2 summarizes the variables included in this study. I trained a graduate research assistant, who coded the entire sample of studies according to the coding protocol. Then, I independently coded 40 studies (19% of the sample) with concurrence on more than 95% of the coded data. The discrepancies were discussed and resolved through discussion. I extracted the following information for each study: the sample size, the 24 correlations for the variables of interest, the reliability of each variable, and study characteristics. The coding resulted in 597 effect sizes capturing the relationships between customer centricity and competitor centricity and their outcomes. ---- Insert Table 1.2 about here ---- Outliers I leveraged the sample-adjusted meta-analytic deviancy (SAMD) statistic to detect outlier coefficients in my dataset (Huffcutt and Arthur 1995). The SAMD technique calculates the difference between the mean sample-weighted effect size and the effect size of each primary study. It then corrects that difference for the sample size of the study. The elimination of extreme values by the SAMD statistic makes the meta-analytic data sets more representative (Huffcutt and Arthur 1995). Specifically, this procedure helps reduce “residual variabilities and a corresponding increase in the percentage of variance accounted for by statistical artifacts after removal of outlier study coefficients” (Huffcutt and Arthur 1995, p. 327). Following the SADM procedure, I identified and removed 14 outliers that constitute 2.3% of the original dataset of 597 effect sizes.3 Ultimately, the final dataset for bivariate analysis includes 583 effect sizes from 179 independent samples obtained from 171 studies. Customer centricity consisted of 352 effects that relate to innovativeness (n = 153) and firm performance (n = 199). Customer centricity consisted of 231 effects that relate to innovativeness (n = 88) and firm performance (n = 143). 3 I eliminated seven effect sizes for customer centricity - innovativeness (4.4 %), two effect sizes for customer centricity - firm performance (1.0 %), two effect sizes for competitor centricity - innovativeness (2.2 %), three effect sizes for competitor centricity - firm performance (2.1 %). The proportion of eliminated effect sizes is below those reported in other meta-analyses that use this approach (Blume et al. 2010; Huffcutt and Arthur 1995; O’Boyle Jr. et al. 2012). 25 Data Analysis Data analysis was performed following the recommended guidelines for meta-analyses (Lipsey and Wilson 2001). These approaches were also applied in previous meta-analyses (Good et al. 2022; Rubera and Kirca 2012). After collecting and aggregating individual study results, I adjusted the effect sizes for measurement error. In doing so, I divided the correlation coefficient by the product of the square root of the reliabilities of the two constructs (Lipsey and Wilson 2001). When reliability scores were not available, I used average reliability for that variable across all studies. Then, the reliability-corrected correlations were transformed into Fisher’s z- coefficients. As a next step, z-coefficients were averaged and weighted by an estimate of the inverse of their variance (N – 3). This approach ensures that a greater weight is assigned to more precise estimates with larger sample sizes. After that, z-scores were transformed back to obtain the revised correlation coefficients (Lipsey and Wilson 2001). I also calculated 95% confidence intervals around the estimate to reflect the degree of precision of the effect size estimate. In the following steps of my analysis, I aimed to address the ‘file drawer problem’ which refers to the situation when studies are conducted but not reported (Rosenthal 1979). I calculated fail-safe N which estimates the number of non-reported studies with the null hypothesis that would make the estimated results non-significant (Lipsey and Wilson 2001). In other words, a fail-safe N of 1,000 would mean that the estimated result would be insignificant if there were 1,000 other unknown empirical studies. This static is also called availability bias. Meta-Analytic Structural Equation Modeling (MASEM) I examined the unique impact of customer centricity and competitor centricity on their consequences (innovativeness, market position, financial position) using the meta-analytic structural equation modeling (MASEM) approach (Cheung 2015; Cheung and Chan 2005). This 26 approach combines structural equation modeling (SEM) and meta-analysis and allows researchers to estimate each individual relationship after controlling for the impact of other variables in the model (Cheung 2015). General MASEM conceptualization, the advantages of this approach in testing complex models, and the key steps in its implementation were presented by Viswesvaran and Ones (1995). Cheung (2015) summarizes three MASEM methods that have emerged over time: the univariate approach (Hedges and Olkin 1985; Hunter and Schmidt 2004), the generalized least squares (GLS) approach (Becker 1992, 1995), and the two-stage structural equation modeling (TSSEM) approach (Cheung 2015; Cheung and Chan 2005). There is consensus that multivariate approaches (GLS, TSSEM) are preferable to univariate approaches as the latter do not take into account the dependence among the correlation matrices in MASEM (Cheung 2015). Among the multivariate approaches, the TSSEM approach is superior to the GLS approach. TSSEM leverages the weighted least squares (WLS) estimation method and addresses the limitations of the GLS technique. Specifically, Cheung (2015, p. 223, emphasis in original) describes the following advantages of the TSSEM technique over the GLS approach: “First, a single correct sample size is used.4 Second, the correlation matrix can be correctly analyzed. Third, it includes the sampling variations of the pooled correlation matrix in the stage 2 analysis.” Comparing the results of different MASEM approaches Cheung and Chan (2005, p. 53) summarize that “the TSSEM approach was found to be the best among all the methods.” Thus, the TSSEM technique is considered an advanced MASEM technique that offers several advantages over conventional approaches (Cheung 2015; Jak 2015). I leverage this advanced approach in my study. 4 Conventional MASEM approaches use the arithmetic mean, the harmonic mean, or the median for sample size calculation (Cheung 2015; Cheung and Chan 2005). 27 To construct the meta-analytic aggregate correlation matrix, I coded primary studies for 21 pairs of correlations among seven constructs of interest: customer centricity, competitor centricity, innovativeness, market position, financial position, firm size, and competitive intensity, as presented in Table 1.3. Consistent with meta-analyses published in prior research, I constructed a large (seven by seven) correlation matrix reflecting zero-order correlation coefficients between the variables of interest (Viswesvaran and Ones 1995). In my meta-analytic correlation matrix, the lowest number of effect sizes was acquired for the market position- competitive intensity relationship (n = 6), while I have obtained a large number of effect sizes for key relationships investigated in this study. Ultimately, I constructed the pooled average correlation matrix reported in Table 1.3 that was used as an input for MASEM analysis. The analysis was conducted using webMASEM, a web application for conducting MASEM analysis (Jak et al. 2021). ---- Insert Table 1.3 about here ---- Moderation Analysis I assessed moderating effects first by using sub-group analysis (Good et al. 2022; Kirca et al. 2011). First, I estimated the homogeneity of the population by leveraging Q-statistic (Hedges and Olkin 1985). Q- statistic is calculated as Q = ∑ (ni – 3) (zi – z̅)2 with chi-square distribution at (k-1) degrees of freedom (Hedges and Olkin 1985). A significant Q-value demonstrates statistical heterogeneity and indicates that the search for potential moderating effects is appropriate. I examined the moderating effects of firm size, firm type, industry type, and county type. 28 Results Table 1.4 presents the bivariate correlations between firm strategic orientations (customer centricity and competitor centricity) and their consequences (innovativeness and firm performance). I summarized the number of effect sizes, total sample size, mean-corrected correlations, standard errors, and 95% confidence intervals around the average-corrected correlations for each pairwise relationship. I also present the availability bias for each relationship and Q-statistics, which is a test of the homogeneity of population correlations. When a Q-value is significant, it indicates that the effect size estimates at the study level do not reflect the common population effect size. Therefore, further examination is required to identify the potential moderating effects. ---- Insert Table 1.4 about here ---- The bivariate results show that customer centricity (r = .480, 95% CI = 0.470 to 0.491) is less strongly associated with innovativeness than competitor centricity (r = .515, 95% CI = 0.500 to 0.530), as the confidence intervals around the mean effect size do not overlap. The fail-safe sample sizes (i.e., publication bias) for these relationships were 6,957 and 2,967, respectively. This indicates that the results of my meta-analysis are unlikely to be influenced by unpublished studies (Rosenthal 1979). Notwithstanding, I find the opposite impact of firm strategic orientations on performance. Customer centricity (r = .351, 95% CI = .342 to .360) was more strongly associated with firm performance than competitor centricity (r = .324, 95% CI = 0.312 to 0.336). Likewise, I did not identify a publication bias issue, as the fail-safe sample sizes for these relationships were 7,322 and 3,744, respectively. Thus, the bivariate analysis shows that customer centricity and competitor centricity impact firm outcomes differently. While the former 29 has a stronger impact on firm performance, the latter is more efficient at developing innovativeness. I found no significant difference between the impact of customer centricity and competitor centricity on different types of firm performance. The confidence interval of the relationship between the strategic orientations of interest on financial position (CI = .256 to .289; CI = .235 to .278) and market position (CI = .153 to .194; CI = .131 to .189) overlapped. Similarly, there was no statistically significant difference between the impact of customer centricity (CI = .493 to .538) and competitor centricity (CI = .448 to .510) on new product performance. Nevertheless, customer centricity (r = .530, 95% CI = .497 to .563) has a stronger impact on the quality of products and services than competitor centricity (r = .354, 95% CI = .303 to .406). Meta-Analytic Structural Equation Modeling (MASEM) I leverage the advanced TSSEM MASEM technique (Cheung 2015; Cheung and Chan 2005) that can efficiently estimate the relationships, minimize the methodological issues of alternative approaches, and provide consistent results across all models, as shown in Table 1.5. This approach also allows for the estimation of the individual impact of each variable, controlling for other factors in the model. Specifically, I aim to differentiate the individual impacts of customer centricity and competitor centricity, as these firm strategic orientations are highly correlated (.50) and are often conceptualized as parts of market orientation. First, I tested Model 1 capturing hypothesized relationships. The model did not have a good fit with SRMR > 10. Then, I estimated Model 2 with additional relationships between innovativeness and firm performance, following literature suggestions (Rubera and Kirca 2012). Finally, I estimated Model 3 with additional controls for firm size and competitive intensity. Model 3 fits the data 30 well (𝜒2 (6) = 38.834, RMSEA = 0.010, c = 0.043, CFI = 0.942) and I use it for my analysis presented below. ---- Insert Table 1.5 about here ---- My analysis further confirms the complex nature of the roles of customer centricity and competitor centricity in the firm. In particular, the results indicate that, after controlling for the effects of other variables in the model, the effects of both customer centricity (β = .214, p < .001) and competitor centricity (β = .264, p < .001) on innovativeness are statistically significant. Importantly, competitor centricity is more strongly associated with innovativeness than customer centricity, which is the opposite of what I predicted in Hypothesis 1a. However, in support of Hypotheses 1b and 1c, I found a statistically significant relationship between firm performance and customer centricity, but not competitor centricity. Specifically, customer centricity is positively associated with financial position (β = .126, p < .01), while the relationship between competitive centricity and financial position is positive but not significant (β = .042, n.s.). I found a similar pattern for the relationships between firm strategic orientation and marker position. Customer centricity is positively associated with market position (β = .120, p < .05), while the impact of competitive centricity on market position is positive but not significant (β = .062, n.s.). My analyses also showed that, controlling for the impact of customer centricity and competitor centricity, the innovativeness-firm performance relationship is significant for financial position (β = .208, p < .05), but not for market position (β = .026, n.s.). Moderation Analysis I examined the impact of contextual effects using subgroup analysis (Kirca et al. 2011). To analyze the moderating effects, I grouped the study samples into separate categories based on different study characteristics. The results are presented in Table 1.6 (firm size; Hypotheses 2-3), 31 Table 1.7 (firm type; Hypotheses 4-5), Table 1.8 (industry type; Hypotheses 6-7), and Table 1.9 (county type; Hypotheses 8-9). ---- Insert Table 1.6 about here ---- ---- Insert Table 1.7 about here ---- ---- Insert Table 1.8 about here ---- ---- Insert Table 1.9 about here ---- As presented in Table 1.6, customer centricity was more strongly associated with market position (r = .272 vs. r = .084, p < .01) and financial position (r = .252 vs. r = .202, p < .01) for large companies (vs. SME) but less strongly associated with innovativeness (r = .472 vs. r = .539, p < .01). As for competitor centricity for large (vs. small) firms, it was more strongly associated with innovativeness (r = .500 vs. r = .493, p < .01) but less strongly associated with financial position (r = .202 vs. r = .284, p < .01). I also identified that for large firms the impact of customer centricity (vs. competitor centricity) was stronger on financial position (r = .252 vs. r = .202, p < .01), but weaker on innovativeness (r = .472 vs. r = .500, p < .01). I had a different pattern for SMEs. Customer centricity (vs. competitor centricity) in small companies had a stronger impact on innovativeness (r = .539 vs. r = .493, p < .01) but a weaker impact on market position (r = .084 vs. r = .101, p < .01) and financial position (r = .202 vs. r = .284, p < .01). Thus, hypotheses 2 and 3 received partial support, except for H3b which I could not properly test due to data limitations. Table 1.7 presents results for the moderating effects of firm type. I supported H4b, H5a, H5c and received partial support for the other hypotheses. Specifically, customer centricity is more strongly associated with innovativeness (r = .653 vs. r = .444, p < .01) and market position (r = .188 vs. r = .137, p < .01) for service (vs. manufacturing) firms, but not with financial 32 position (r = .280 vs. r = .309, p < .01). Competitor centricity is more strongly associated with all three outcomes for service firms: innovativeness (r = .530 vs. r = .390, p < .01), market position (r = .391 vs. r = .101, p < .01), and financial position (r = .276 vs. r = .223, p < .01). For service firms, the impact of customer centricity (vs. competitor centricity) was stronger on innovativeness (r = .653 vs. r = .530, p < .01) and financial position (r = .280 vs. r = .276, p < .01), but weaker on market position (r = .188 vs. r = .391, p < .01). For manufacturing companies, customer (vs. competitor) centricity had a stronger impact on innovativeness (r = .444 vs. r = .390, p < .01), market position (r = .137 vs. r = .101, p < .01), and financial position (r = .309 vs. r = .223, p < .01). The moderating effects of industry type are presented in Table 1.8. Due to data limitations, I could conduct only some analyses related to market position and financial position. I reveal that both customer centricity (r = .498 vs. r = .401, p < .01) and competitor centricity (r = .486 vs. r = .385, p < .01) were more strongly associated with innovativeness for high-tech (vs. low-tech) companies. For high-tech firms, the impact of customer centricity on innovativeness was stronger than the impact of competitor centricity (r = .498 vs. r = .486, p < .01). For low-tech companies, the impact of customer (vs. competitor) centricity was stronger on innovativeness (r = .401 vs. r = .385, p < .01), but weaker on market position (r = .076 vs. r = .124, p < .01) and financial position (r = .142 vs. r = .155, p < .01). Thus, H7a is supported, H6a and H6b are partially supported, H7b and H7c are not supported. Table 1.9 presents results for the moderating effects of country context. As predicted in H8a, customer centricity is more strongly associated with innovativeness (r = .561 vs. r = .422, p < .01), market position (r = .253 vs. r = .158, p < .01), and financial position (r = .421 vs. r = .180, p < .01) for emerging (vs. developed) markets. I also found support for H8b. Customer 33 centricity is also more strongly associated with innovativeness (r = .670 vs. r = .386, p < .01), market position (r = .304 vs. r = .095, p < .01), and financial position (r = .334 vs. r = .227, p < .01) for emerging (vs. developed) markets. Results show that in emerging markets the impact of customer (vs. competitor) centricity was stronger on financial position (r = .421 vs. r = .334, p < .01), but weaker on innovativeness (r = .561 vs. r = .670, p < .01) and market position (r = .253 vs. r = .304, p < .01). The situation with developed markets was different. The impact of customer (vs. competitor) centricity was stronger on innovativeness (r = .422 vs. r = .386, p < .01) and market position (r = .158 vs. r = .095, p < .01), but weaker on financial position (r = .180 vs. r = .227, p < .01). Thus, H9a-c received partial support. Overall, five of my moderating hypotheses received full support, eleven hypotheses had partial support, and three hypotheses were not supported.5 The summary results of hypotheses 2- 9 are visualized in Table 1.10. ---- Insert Table 1.10 about here ---- Discussion This study strengthens my understanding of the role of customer centricity and competitor centricity. Building on aggregated results from 179 samples published in 171 studies, I meta-analytically summarize more than three decades of research on these firm strategic orientations. I scrutinize the complex nature of customer centricity and competitor centricity through the lens of a resource-based view (Barney 1991) and the VRIO framework (Barney and Hesterly 2015). Then, I empirically test their individual and relative role in driving innovativeness and firm performance via an advanced MASEM approach (Cheung and Chan 5 Hypotheses H2b, H3a, H3b, H6a, H6b, H7b, and H7c could not be fully tested due to data limitations (i.e., a limited number of observations for some of the sub-groups). 34 2005). I also study contextual moderators that strengthen the impact of these firm strategic orientations. Scholars have long discussed both the meaningful differences between customer centricity and competitor centricity and the importance of balancing both strategic orientations (Day and Nedungadi 1994; Day and Wensley 1983, 1988). Each of these strategic orientations is important for firm performance, however, the simultaneous implementation of customer centricity and competitor centricity, and maintaining a balance between them, could be a hardly attainable target for many companies (Day and Nedungadi 1994). Firms that aim to simultaneously deliver superior customer offerings and match other players in the market are likely to face growing organizational complexity and associated high operating costs. Moreover, the implementation of customer centricity may impede the development of competitor centricity (and vice versa) given the conflicting priorities of these strategic orientations. Thus, the understanding of the relative impact of customer centricity and competitor centricity on firm outcomes could enable a more efficient distribution of limited organizational resources. The awareness of various contextual situations that could strengthen the impact of firm strategic orientations could further improve firm efficiency and performance outcomes. Theoretical Contributions First, this research illustrates the complex role of customer centricity and competitor centricity. My results indicate that while the former is more important in driving market position and financial position, the latter is more impactful in strengthening firm innovativeness. Thus, firms should prioritize the focus on customers over competitors if they plan to develop their market and financial performance. In addition, my study suggests that controlling for customer centricity and competitor centricity, innovativeness positively impacts financial position but not 35 market position. This further strengthens the importance of customer centricity as I did not identify a statistically significant impact of either competitor centricity or innovativeness on market position. At the same time, the relative impact of firm strategic orientations on innovativeness was opposite of the hypothesized direction; the focus on competitors is more impactful. There could be several explanations for this outcome. Firm rivalry adds urgency to firm actions. Faced with immediate or upcoming competitive moves, companies have to adjust their internal processes and speed up innovation to remain competitive (Atuahene-Gima 2005). In addition, customer centric firms may address expressed customer needs but omit the latent ones (Slater and Narver 1998). Relatedly, customer insights might lead to limited innovation improvements as marketers empowered by this information focus on satisfying existing customer needs and overlook major market shifts driven by competitive moves (Christensen, Cook, and Hall 2005). Second, my study indicates that the effects of customer and competitor centricity on innovativeness, market position, and financial position are context dependent and that various internal and external factors could strengthen these relationships. In this regard, prior research presents a mixed picture. For example, Grinstein (2008a) demonstrates that the impact of market orientation on innovation outcomes varies across firm product types. However, analyzing customer and competitor centricity, Calantone, Harmancioglu, and Droge (2010) hypothesize the absence of moderation between products and services. I examine the moderating role of firm type and extend the learnings into new contexts. I also examine the moderation between customer centricity and competitor centricity and firm performance. The findings point to the importance of considering the role of firm strategic orientations across firm, industry, and country contexts. 36 Finally, the advanced MASEM methodological approaches (i.e., TSSEM; Cheung and Chan 2005) should be considered for future meta-analytic studies investigating complex structural equation models. MASEM models are used to estimate the relationships of interest while controlling for other variables in the model. TSSEM technique provides several advantages over conventional approaches. Specifically, it applies the appropriate sample size, correctly analyzes the correlation matrix, and includes the sampling variations of the pooled correlation matrix. Scholars may choose from a variety of software applications ranging from R to user-friendly webMASEM (Jak et al. 2021). Managerial Implications My research provides several insights for business leaders considering the adoption and development of firm strategic directions. Company executives face the dilemma of choosing strategic priorities for their firms given limited organizational resources. Ultimately, companies do not adopt a single strategic orientation, but rather different strategic orientations gradually evolve and compete within the organizations (Gebhardt, Carpenter, and Sherry 2006; Noble, Sinha, and Kumar 2002). Day and Nedungadi (1994) discuss the importance of the balance between customer centricity and competitor centricity. However, such a balance is potentially neither attainable nor desirable for every firm, and the ideal situation may vary based on individual circumstances. Driven by internal and external constraints, firms may choose the optimal balance that would fit their objectives and organizational resources and ultimately lead to superior performance (Slater, Olson, and Hult 2006). Business leaders should understand the specifics of both approaches to develop the equilibrium of customer centricity and competitor centricity according to their organizational objectives. Managers should also consider specific 37 business circumstances when the impact of customer centricity and competitor centricity is more prominent (i.e., SMEs, services firms, high-technology companies, and emerging markets). Furthermore, firm strategic orientation impact managerial mindsets or mental models. As managers are exposed to different information, it shapes their mindsets and priorities (Armstrong and Collopy 1996; Day and Nedungadi 1994). Managerial mental models prompt specific organizational focuses, including customer-oriented, competitor-centered, or both. As customers and competitors are the two most prominent characteristics of a competitive market (Day and Nedungadi 1994), each of these groups becomes a dimension along which managers can assess the competitive advantage of their firms. When companies balance the focus on both dimensions, it allows them to better understand both rivals and clients and consider both perspectives in organizational decision-making. As an outcome, such firms have the most stable strategies that both help control costs and strengthen customer-related performance indicators. Limitations and Future Research Despite study contributions, it has several limitations that should be considered during the interpretation of the research findings. First, as with any meta-analysis, my study is constrained by the subject matter and parameters of the original studies that serve as its basis (Hunter and Schmidt 2004). Specifically, my research was limited to exploring the effects present in existing published studies. The lack of data or limited descriptions provided in the original research limited my ability to code or include specific studies. Second, most original studies in my sample are cross-sectional in nature, which limits the ability to make causal inferences regarding the relationships between customer and competitor centricity and their consequences. Still, by aggregating a substantial amount of data over several decades and founded on the theoretical support provided by this body of research summarized in the 38 theoretical development section, I can provide evidence regarding the causal directions of the focal relationships investigated in this study. Third, I only examined such firm outcomes as innovativeness and firm performance because I could obtain a sufficient number of effect sizes for these relations. This means that few studies considered other important customer and firm level outcomes. Specifically, future research should examine other customer outcomes such as brand equity, customer satisfaction, customer loyalty, customer acquisition and retention, word of mouth, and customer lifetime value using primary data (Katsikeas et al. 2016). Fourth, I could not fully test some of my moderation hypotheses due to data limitations (i.e., a limited number of observations for some of the sub-groups). Future scholars may also explore other potential moderators for the relationships between customer centricity and competitor centricity and their consequences. Despite the limitations, my study presents a novel perspective on the relative impact of firm strategic orientations and identifies future research opportunities in this domain. For example, marketing researchers may scrutinize the role of adaptive marketing capabilities (Day 2011). The accelerating complexity of markets brings forward the need to re-consider marketing capabilities as companies have limited ability to respond to fast-changing market demands. The exploration of the interplay and balance of customer centricity and competitor centricity presents another potential area for future investigations. Prior research suggests that at least a minimum level of customer centricity should be present for competitor centricity to have a positive impact on firm outcomes (Grinstein 2008a). Considering the competitors’ perspective firms may perform an additional ‘reality check’ and ultimately strengthen customer centricity (Christensen and Bower 1996). Thus, companies should develop both strategic orientations in parallel, despite associated organizational complexity and financial constraints. Moreover, the most successful 39 firms balance the focus on both dimensions (Day and Nedungadi 1994). That allows companies to better understand rivals and clients and consider both perspectives in organizational decision- making. As an outcome, such firms have the most stable strategies that both help control costs and strengthen customer-related performance indicators. Nevertheless, Day and Nedungadi (1994) note that only a limited number of firms can efficiently maintain this equilibrium due to multiple challenges associated with achieving and maintaining the balance between the focus on customers and competitors. Future researchers may also explore the interplay of customer centricity and competitor centricity with organizational inter-functional relationships (Chernetsky, Hughes, and Schrock 2022). Specifically, as marketing and sales departments are the two functions directly dealing with customers and competitors, scholars may explore how different firm strategic orientations impact inter-functional dynamics. Additionally, which organizational functions play the key role in promoting specific firm strategic orientations in the organization or maintaining their balance? Future meta-analytic research may also examine potential mediators of the impact of customer centricity and competitor centricity on firm outcomes. Finally, investigating firms’ focus on customers and competitors, future research may consider the role of coopetition, which is defined as a situation when rival firms simultaneously compete and collaborate (Devece, Ribeiro-Soriano, and Palacios-Marqués 2019; Gernsheimer, Kanbach, and Gast 2021). Marketing literature suggests that coping with competition is not a zero-sum game and examines the positive aspects of collaboration with rivals through cooperative alliances (Luo, Rindfleisch, and Tse 2007; Wu et al. 2015). Notwithstanding the potential benefits of coopetition, collaboration with competitors may backfire long term. Prior research has demonstrated that when companies are extensively involved in collaborative 40 alliances with rivals, they become less customer-oriented over time (Rindfleisch and Moorman 2003). 41 REFERENCES Aaker, David A. and Robert Jacobson (2001), “The Value Relevance of Brand Attitude in High- Technology Markets,” Journal of Marketing Research, 38 (4), 485–93. Aguinis, Herman, Charles A. Pierce, Frank A. Bosco, Dan R. Dalton, and Catherine M. Dalton (2011), “Debunking Myths and Urban Legends About Meta-Analysis,” Organizational Research Methods, 14 (2), 306–31. Armstrong, J. Scott and Fred Collopy (1996), “Competitor Orientation: Effects of Objectives and Information on Managerial Decisions and Profitability,” Journal of Marketing Research, 33 (2), 188–99. Armstrong, J. Scott and Kesten C. Green (2007), “Competitor-Oriented Objectives: Myth of Market Share,” International Journal of Business, 12, 117–36. Atuahene-Gima, Kwaku (2005), “Resolving the Capability: Rigidity Paradox in New Product Innovation,” Journal of Marketing, 69 (4), 61–83. Autio, Erkko, Ram Mudambi, and Youngjin Yoo (2021), “Digitalization and globalization in a turbulent world: Centrifugal and centripetal forces,” Global Strategy Journal, 11 (1), 3– 16. Barney, Jay B. (1991), “Firm Resources and Sustained Competitive Advantage,” Journal of Management, 17 (1), 99–120. Barney, Jay B. and William S. Hesterly (2015), Strategic Management and Competitive Advantage: Concepts and Cases (5th ed.), Essex: Pearson. Becker, Betsy Jane (1992), “Using Results From Replicated Studies to Estimate Linear Models,” Journal of Educational Statistics, 17 (4), 341–62. Becker, Betsy Jane (1995), “Corrections to ‘Using Results From Replicated Studies to Estimate Linear Models,’” Journal of Educational and Behavioral Statistics, 20 (1), 100–102. Blume, Brian D., J. Kevin Ford, Timothy T. Baldwin, and Jason L. Huang (2010), “Transfer of Training: A Meta-Analytic Review,” Journal of Management, 36 (4), 1065–1105. Bonchek, Mark and Gene Cornfield (2016), “Focus on Keeping Up with Your Customers, Not Your Competitors,” Harvard Business Review, April 28. Burgess, Steven M. and Jan-Benedict E.M. Steenkamp (2006), “Marketing renaissance: How research in emerging markets advances marketing science and practice,” International Journal of Research in Marketing, 23 (4), 337–56. Burgess, Steven M. and Jan-Benedict E.M. Steenkamp (2013), “Editorial: Introduction to the Special Issue on Marketing in Emerging Markets,” International Journal of Research in Marketing, 30 (1), 1–3. 42 Calantone, Roger J., Nukhet Harmancioglu, and Cornelia Droge (2010), “Inconclusive Innovation ‘Returns’: A Meta-Analysis of Research on Innovation in New Product Development,” Journal of Product Innovation Management, 27 (7), 1065–81. Cano, Cynthia Rodriguez, Francois A. Carrillat, and Fernando Jaramillo (2004), “A meta- analysis of the relationship between market orientation and business performance: evidence from five continents,” International Journal of Research in Marketing, 21 (2), 179–200. Chandy, Rajesh K. and Gerard J. Tellis (2000), “The Incumbent’s Curse? Incumbency, Size, and Radical Product Innovation,” Journal of Marketing, 64 (3), 1–17. Chernetsky, Victor V., Douglas E. Hughes, and Wyatt A. Schrock (2022), “A synthesis of research on the marketing-sales interface (1984–2020),” Industrial Marketing Management, 105, 159–81. Cheung, Mike W.-L. (2015), Meta-Analysis: A Structural Equation Modeling Approach, John Wiley & Sons. Cheung, Mike W.-L. and Wai Chan (2005), “Meta-analytic structural equation modeling: A two- stage approach,” Psychological Methods, 10 (1), 40–64. Christensen, Clayton M. (1997), The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, Boston, MA: Harvard Business School Press. Christensen, Clayton M. and Joseph L. Bower (1996), “Customer power, strategic investment, and the failure of leading firms,” Strategic Management Journal, 17 (3), 197–218. Christensen, Clayton M., Scott Cook, and Taddy Hall (2005), “Marketing Malpractice: The Cause and the Cure,” Harvard Business Review, 83 (December), 74–83. Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine (Eds.) (2009), The Handbook of Research Synthesis and Meta-Analysis, New York: Russell Sage Foundation. Crecelius, Andrew T., Justin M. Lawrence, Ju-Yeon Lee, Son K. Lam, and Lisa K. Scheer (2019), “Effects of channel members’ customer-centric structures on supplier performance,” Journal of the Academy of Marketing Science, 47 (1), 56–75. Day, George S. (2011), “Closing the Marketing Capabilities Gap,” Journal of Marketing, 75 (4), 183–95. Day, George S. and Prakash Nedungadi (1994), “Managerial Representations of Competitive Advantage,” Journal of Marketing, 58 (2), 31–44. Day, George S. and Robin Wensley (1983), “Marketing Theory with a Strategic Orientation,” Journal of Marketing, 47 (4), 79–89. 43 Day, George S. and Robin Wensley (1988), “Assessing Advantage: A Framework for Diagnosing Competitive Superiority,” Journal of Marketing, 52 (2), 1–20. Deshpandé, Rohit, John U. Farley, and Frederick E. Webster (1993), “Corporate Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad Analysis,” Journal of Marketing, 57 (1), 23–37. Deshpandé, Rohit and Frederick E. Webster (1989), “Organizational Culture and Marketing: Defining the Research Agenda,” Journal of Marketing, 53 (1), 3–15. Devece, Carlos, D. Enrique Ribeiro-Soriano, and Daniel Palacios-Marqués (2019), “Coopetition as the new trend in inter-firm alliances: literature review and research patterns,” Review of Managerial Science, 13 (2), 207–26. Drucker, Peter F. (1974), Management: Tasks, Responsibilities, Practices, New York: Harper & Row. Dutta, Shantanu, Om Narasimhan, and Surendra Rajiv (1999), “Success in High-Technology Markets: Is Marketing Capability Critical?” Marketing Science, 18 (4), 547–68. Fader, Peter (2020), Customer Centricity: Focus on the Right Customers for Strategic Advantage, 2nd ed., Philadelphia: Wharton Digital Press. Gatignon, Hubert and Jean-Marc Xuereb (1997), “Strategic Orientation of the Firm and New Product Performance,” Journal of Marketing Research, 34 (1), 77–90. Gebhardt, Gary F., Gregory S. Carpenter, and John F. Sherry (2006), “Creating a Market Orientation: A Longitudinal, Multifirm, Grounded Analysis of Cultural Transformation,” Journal of Marketing, 70 (4), 37–55. Gernsheimer, Oliver, Dominik K. Kanbach, and Johanna Gast (2021), “Coopetition research - A systematic literature review on recent accomplishments and trajectories,” Industrial Marketing Management, 96, 113–34. Good, Valerie, Douglas E. Hughes, Ahmet H. Kirca, and Sean McGrath (2022), “A self- determination theory-based meta-analysis on the differential effects of intrinsic and extrinsic motivation on salesperson performance,” Journal of the Academy of Marketing Science, 50 (3), 586–614. Grewal, Dhruv, Nancy Puccinelli, and Kent B. Monroe (2018), “Meta-analysis: integrating accumulated knowledge,” Journal of the Academy of Marketing Science, 46 (1), 9–30. Grinstein, Amir (2008a), “The effect of market orientation and its components on innovation consequences: a meta-analysis,” Journal of the Academy of Marketing Science, 36 (2), 166–73. Grinstein, Amir (2008b), “The relationships between market orientation and alternative strategic orientations: A meta‐analysis,” European Journal of Marketing, 42 (1/2), 115–34. 44 Hakala, Henri (2011), “Strategic Orientations in Management Literature: Three Approaches to Understanding the Interaction between Market, Technology, Entrepreneurial and Learning Orientations,” International Journal of Management Reviews, 13 (2), 199–217. Hedges, Larry V. and Ingram Olkin (1985), Statistical Methods for Meta-Analysis, Orlando, FL: Academic Press. Homburg, Christian and Christian Pflesser (2000), “A Multiple-Layer Model of Market-Oriented Organizational Culture: Measurement Issues and Performance Outcomes,” Journal of Marketing Research, 37 (4), 449–62. Huffcutt, Allen I. and Winfred Arthur (1995), “Development of a new outlier statistic for meta- analytic data,” Journal of Applied Psychology, 80 (2), 327–34. Hulland, John and Mark B. Houston (2020), “Why systematic review papers and meta-analyses matter: an introduction to the special issue on generalizations in marketing,” Journal of the Academy of Marketing Science, 48 (3), 351–59. Hunt, Shelby D. and Robert M. Morgan (1995), “The Comparative Advantage Theory of Competition,” Journal of Marketing, 59 (2), 1–15. Hunter, John E. and Frank L. Schmidt (2004), Methods of meta-analysis: Correcting error and bias in research findings, Thousand Oaks, CA: Sage. Hyken, Shep (2021), “Keep Your Customers Close And Your Competitors Closer,” Forbes, July 18. Im, Subin and John P. Workman (2004), “Market Orientation, Creativity, and New Product Performance in High-Technology Firms,” Journal of Marketing, 68 (2), 114–32. Jak, Suzanne (2015), “Introduction to Meta-Analysis and Structural Equation Modeling,” in Meta-Analytic Structural Equation Modelling, Springer, 1–14. Jak, Suzanne, Hongli Li, Laura Kolbe, Hannelies de Jonge, and Mike W.-L. Cheung (2021), “Meta‐analytic structural equation modeling made easy: A tutorial and web application for one‐stage MASEM,” Research Synthesis Methods, 12 (5), 590–606. Jaworski, Bernard J. and Ajay K. Kohli (2017), “Conducting field-based, discovery-oriented research: lessons from our market orientation research experience,” AMS Review, 7 (1), 4–12. Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and Consequences,” Journal of Marketing, 57 (3), 53–70. Jayachandran, Satish, Subhash Sharma, Peter Kaufman, and Pushkala Raman (2005), “The Role of Relational Information Processes and Technology Use in Customer Relationship Management,” Journal of Marketing, 69 (4), 177–92. 45 Jedidi, Kamel, Bernd H. Schmitt, Malek Ben Sliman, and Yanyan Li (2021), “R2M Index 1.0: Assessing the Practical Relevance of Academic Marketing Articles,” Journal of Marketing, 85 (5), 22–41. Katsikeas, Constantine S., Neil A. Morgan, Leonidas C. Leonidou, and G. Tomas M. Hult (2016), “Assessing Performance Outcomes in Marketing,” Journal of Marketing, 80 (2), 1–20. Kirca, Ahmet H., G. Tomas M. Hult, Kendall Roth, S. Tamer Cavusgil, Morys Z. Perryy, M. Billur Akdeniz, Seyda Z. Deligonul, Jeannette A. Mena, Wesley A. Pollitte, Jessica J. Hoppner, Joseph C. Miller, and Ryan C. White (2011), “Firm-Specific Assets, Multinationality, and Financial Performance: A Meta-analytic Review and Theoretical Integration,” Academy of Management Journal, 54 (1), 47–72. Kirca, Ahmet H., Satish Jayachandran, and William O. Bearden (2005), “Market Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on Performance,” Journal of Marketing, 69 (2), 24–41. Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Construct, Research Propositions, and Managerial Implications,” Journal of Marketing, 54 (2), 1–18. Kozlenkova, Irina V., Stephen A. Samaha, and Robert W. Palmatier (2014), “Resource-based theory in marketing,” Journal of the Academy of Marketing Science, 42 (1), 1–21. Kumar, V., Eli Jones, Rajkumar Venkatesan, and Robert P. Leone (2011), “Is Market Orientation a Source of Sustainable Competitive Advantage or Simply the Cost of Competing?” Journal of Marketing, 75 (1), 16–30. Kumar, V. and Anita Pansari (2016), “Competitive Advantage Through Engagement,” Journal of Marketing Research, 53 (4), 497–514. LaGesse, David (2008), “America’s Best Leaders: Jeff Bezos, Amazon.com CEO,” (accessed February 4, 2023), [available at https://www.usnews.com/news/best- leaders/articles/2008/11/19/americas-best-leaders-jeff-bezos-amazoncom-ceo]. Lee, Ju-Yeon, Shrihari Sridhar, Conor M. Henderson, and Robert W. Palmatier (2015), “Effect of Customer-Centric Structure on Long-Term Financial Performance,” Marketing Science, 34 (2), 250–68. Lipsey, Mark W. and David B. Wilson (2001), Practical Meta-Analysis, Thousand Oaks, CA: Sage Publications. Luo, Xueming, Aric Rindfleisch, and David K. Tse (2007), “Working with Rivals: The Impact of Competitor Alliances on Financial Performance,” Journal of Marketing Research, 44 (1), 73–83. 46 Mizik, Natalie and Robert Jacobson (2003), “Trading off between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis,” Journal of Marketing, 67 (1), 63–76. Morgan, Neil A. and Lopo L. Rego (2009), “Brand Portfolio Strategy and Firm Performance,” Journal of Marketing, 73 (1), 59–74. Narver, John C. and Stanley F. Slater (1990), “The Effect of a Market Orientation on Business Profitability,” Journal of Marketing, 54 (4), 20–35. Nelson, Tara-Nicholle (2017), “Obsess Over Your Customers, Not Your Rivals,” Harvard Business Review, May 11. Noble, Charles H., Rajiv K. Sinha, and Ajith Kumar (2002), “Market Orientation and Alternative Strategic Orientations: A Longitudinal Assessment of Performance Implications,” Journal of Marketing, 66 (4), 25–39. O’Boyle Jr., Ernest H., Donelson R. Forsyth, George C. Banks, and Michael A. McDaniel (2012), “A meta-analysis of the Dark Triad and work behavior: A social exchange perspective,” Journal of Applied Psychology, 97 (3), 557–79. Ozkaya, H. Erkan, Cornelia Droge, G. Tomas M. Hult, Roger Calantone, and Elif Ozkaya (2015), “Market orientation, knowledge competence, and innovation,” International Journal of Research in Marketing, 32 (3), 309–18. Parasuraman, A., Valarie A. Zeithaml, and Leonard L. Berry (1985), “A Conceptual Model of Service Quality and Its Implications for Future Research,” Journal of Marketing, 49 (4), 41–50. Porter, Michael E. (1979), “How Competitive Forces Shape Strategy,” Harvard Business Review, March-April, 137–45. Porter, Michael E. (1996), “What Is Strategy?” Harvard Business Review, November-December, 61–78. Ramani, Girish and V. Kumar (2008), “Interaction Orientation and Firm Performance,” Journal of Marketing, 72 (1), 27–45. Rindfleisch, Aric and Christine Moorman (2003), “Interfirm Cooperation and Customer Orientation,” Journal of Marketing Research, 40 (4), 421–36. Rosenthal, Robert (1979), “The file drawer problem and tolerance for null results,” Psychological Bulletin, 86, 638–41. Rubera, Gaia and Ahmet H. Kirca (2012), “Firm Innovativeness and Its Performance Outcomes: A Meta-Analytic Review and Theoretical Integration,” Journal of Marketing, 76 (3), 130–47. 47 Saboo, Alok R. and Rajdeep Grewal (2013), “Stock Market Reactions to Customer and Competitor Orientations: The Case of Initial Public Offerings,” Marketing Science, 32 (1), 70–88. Schweiger, Simone A., Tatiana R. Stettler, Artur Baldauf, and César Zamudio (2019), “The complementarity of strategic orientations: A meta-analytic synthesis and theory extension,” Strategic Management Journal, 40 (11), 1822–51. Sett, Rahul K. (2018), “Market orientation − firm performance link in a dynamic environment: looking inside the black box,” AMS Review, 8 (3), 163–79. Shah, Denish, Roland T. Rust, A. Parasuraman, Richard Staelin, and George S. Day (2006), “The Path to Customer Centricity,” Journal of Service Research, 9 (2), 113–24. Shapiro, Benson P. (1988), “What the Hell Is ‘Market Oriented’?” Harvard Business Review, 66 (November-December), 119–25. Sharma, Amalesh, Alok R. Saboo, and V. Kumar (2018), “Investigating the Influence of Characteristics of the New Product Introduction Process on Firm Value: The Case of the Pharmaceutical Industry,” Journal of Marketing, 82 (5), 66–85. Sheth, Jagdish N., Rajendra S. Sisodia, and Arun Sharma (2000), “The antecedents and consequences of customer-centric marketing,” Journal of the Academy of Marketing Science, 28 (1), 55–66. Sheth, Jagdish N. (2011), “Impact of Emerging Markets on Marketing: Rethinking Existing Perspectives and Practices,” Journal of Marketing, 75 (4), 166–82. Sheth, Jagdish N., Rajendra S. Sisodia, and Arun Sharma (2000), “The antecedents and consequences of customer-centric marketing,” Journal of the Academy of Marketing Science, 28 (1), 55–66 Slater, Stanley F. and John C. Narver (1998), “Customer-led and market-oriented: let’s not confuse the two,” Strategic Management Journal, 19 (10), 1001–6. Slater, Stanley F. and John C. Narver (1995), “Market Orientation and the Learning Organization,” Journal of Marketing, 59 (3), 63–74. Slater, Stanley F., Eric M. Olson, and G. Tomas M. Hult (2006), “The moderating influence of strategic orientation on the strategy formation capability–performance relationship,” Strategic Management Journal, 27 (12), 1221–31. Sorescu, Alina B. and Jelena Spanjol (2008), “Innovation’s Effect on Firm Value and Risk: Insights from Consumer Packaged Goods,” Journal of Marketing, 72 (2), 114–32 Tsai, Kuen-Hung, Chi-Tsun Huang, and Mu-Lin Tsai (2013), “Reviews of Market Drivers of New Product Performance: Effects and Relationships,” International Journal of Market Research, 55 (5), 719–38. 48 Varadarajan, Rajan (2017), “Research on market orientation: Some lessons shared and issues discussed in a doctoral seminar,” AMS Review, 7 (1), 26–35. Viswesvaran, Chockalingam and Deniz S. Ones (1995), “Theory testing: Combining psychometric meta‐analysis and structural equations modeling,” Personnel Psychology, 48 (4), 865–85. Wu, Qingsheng, Xueming Luo, Rebecca J. Slotegraaf, and Jaakko Aspara (2015), “Sleeping with competitors: the impact of NPD phases on stock market reactions to horizontal collaboration,” Journal of the Academy of Marketing Science, 43 (4), 490–511. Zeithaml, Valarie A., A. Parasuraman, and Leonard L. Berry (1985), “Problems and Strategies in Services Marketing,” Journal of Marketing, 49 (2), 33–46. 49 APPENDIX A: TABLES Table 1.1. Meta-Analytic Studies Investigating Customer Centricity and Competitor Centricity # of CuC/CoC CuC/CoC The relative Contextual MASEM Article Focus of the article Period effect impact on impact on impact of moderators * approach sizes * innovation performance CuC/CoC The relative impact of customer centricity and 1990 – This research competitor centricity on 583 ✓ ✓ ✓ ✓ WLS 2019 innovativeness and firm performance. The impact of market Grinstein 1994 – orientation and its components 41 ✓ -- -- -- -- (2008a) 2006 on innovation consequences. Calantone, Antecedents and consequences Harmancioglu, 1970 – of innovation in new product 132 ✓ -- -- ✓ GLS and Droge 2006 development. (2010) Tsai, Huang, Market drivers of new until 21 ✓ -- -- -- GLS and Tsai (2013) product performance. 2011 * related to customer/competitor centricity. Notes: CuC = customer centricity; CoC = competitor centricity; MASEM = meta-analytic structural equation modeling; GLS = generalized least squares; WLS = weighted least squares. 50 Table 1.2. List of Variables Variable Definition Firm’s strategic orientation focused on the understanding of Customer centricity current and future customers (Narver and Slater 1990). Firm’s strategic orientation focused on the understanding of Competitor centricity current and potential competitors (Narver and Slater 1990). Firm’s adoption of new ideas which impacts the development and Innovativeness introduction of new products (Rubera and Kirca 2012). New product performance Market performance of firm’s new products. Service/product quality Quality of firm’s products and services. Firm performance measures that are focused on revenues (i.e., Market position sales, sales growth, market share) (Rubera and Kirca 2012). Firm performance measures that are focused on the cost of Financial position operations (i.e., profit, ROA, ROI) (Rubera and Kirca 2012). Large firms Firms with 500 and more employees. SMEs Firms with less than 500 employees. Companies that have primarily intangible output (e.g., banks, Services firms hospitality, consulting). Companies that produce primarily physical products (e.g., Manufacturing firms electronics, industrial equipment, furniture). Industries with a high level of technological intensity (e.g., High-tech industry biotechnology, semiconductors, aircraft). Industries with a low level of technological intensity (e.g., food Low-tech industry products, wholesale trade, sporting goods). Developing economies and economies in transition (2020 UN Emerging country country classification; e.g., China, Russia, Pakistan, Ghana). Developed economies (2020 UN country classification; e.g., Developed country USA, Germany, Japan, Australia). 51 Table 1.3. Meta-Analytic Correlation Matrix 1 2 3 4 5 6 7 111 53 25 73 33 60 1. Customer centricity 1.0 (24,084) (13,133) (10,546) (24,002) (15,157) (19,234) 42 17 44 26 28 2. Competitor centricity .504 1.0 (8,591) (3,691) (11,205) (5,967) (6,571) 15 16 7 17 3. Financial position .221 .184 1.0 (4,012) (3,104) (1,420) (3,743) 9 6 12 4. Market position .164 .142 .395 1.0 (5,952) (5,380) (7,189) 12 27 5. Innovativeness .347 .373 .270 .093 1.0 (6,485) (10,891) 18 6. Competitive intensity .108 .135 -.134 .062 .072 1.0 (8,225) 7. Firm size .043 .033 .078 .351 .041 .031 1.0 Notes: Average sample-size-weighted correlation (r) values are reflected below the diagonal. The number of studies per correlation coefficient (k) and total sample size per correlation coefficient (N, in parentheses) are reflected above the diagonal. 52 Table 1.4. Overview of Consequences of Customer Centricity and Competitor Centricity 95% No. of Total Corrected Availability Q- Relationships SE Confidence Effects Sample Size Meana r Biasb Statisticc Interval Innovativeness CuC 153 36,416 .480 ** .005 .470 to .491 6,957 2,895 CoC 88 17,718 .515 ** .008 .500 to .530 2,967 1,698 New product performance CuC 37 7,536 .515 ** .012 .493 to .538 801 543 CoC 22 4,070 .479 ** .016 .448 to .510 318 360 Service/product quality CuC 18 3,606 .530 ** .017 .497 to .563 272 450 CoC 9 1,480 .354 ** .026 .303 to .406 53 100 Performance CuC 199 45,152 .351 ** .005 .342 to .360 7,322 3,707 CoC 143 27,407 .324 ** .006 .312 to .336 3,744 2,440 Financial Position CuC 62 14,302 .272 ** .008 .256 to .289 960 999 CoC 47 8,476 .256 ** .011 .235 to .278 514 470 Market Position CuC 36 9,172 .174 ** .011 .153 to .194 268 393 CoC 25 4,638 .151 ** .015 .131 to .189 113 283 ** p < .01 a The corrected mean correlation coefficients (r) are the sample size weighted, reliability-corrected estimates of the population correlation coefficients. b Availability bias indicates the number of unpublished studies reporting null results needed to reduce the aggregate effect size across studies to the point of non-significance (Lipsey and Wilson 2001). c Q-statistic provides a test of the homogeneity of the population correlations. The significant Q-value suggests that study-level effect size estimates do not estimate a common population effect size, and the subsequent search for the moderating effects is warranted. Notes: CuC = customer centricity; CoC = competitor centricity 53 Table 1.5. Results of Meta-Analytic Structural Equation Modeling Model 1 Model 2 Model 3 Customer Centricity → Innovativeness .217 *** .214 *** .214 *** Competitor Centricity → Innovativeness .265 *** .264 *** .264 *** Customer Centricity → Financial Position .173 *** .127 ** .126 ** Competitor Centricity → Financial Position .104 * .042 .042 Customer Centricity → Market Position .131 * .120 * .120 * Competitor Centricity → Market Position .083 .064 .062 Customer Centricity ↔ Competitor Centricity .503 *** .504 *** .503 *** Innovativeness → Financial Position .213 * .208 * Innovativeness → Market Position .052 .026 Firm Size → Financial Position 1.156 Firm Size → Market Position 5.121 Competitive Intensity → Financial Position -.752 Competitive Intensity → Market Position .337 χ2 17.504 12.173 38.834 df 3 1 6 RMSEA 0.010 0.014 0.010 SRMR 0.123 0.108 0.043 CFI 0.972 0.978 0.942 * p < .05 ** p < .01 *** p < .001 Notes: df = degree of freedom; RMSEA = root mean square error of approximation; SRMR = standardized root mean squared residual; CFI = comparative fit index 54 Table 1.6. Moderating Effects of Firm Size Dependent No. of 95% Confidence Corrected Summary of Levels Variable Effect Sizes Interval Meana r Results Innovativeness a. customer centricity x large firms 27 .446 to .499 .472 ** a < c ** b. competitor centricity x large firms 12 .464 to .537 .500 ** b > d ** c. customer centricity x SMEs 42 .518 to .560 .539 ** a < b ** d. competitor centricity x SMEs 30 .468 to .517 .493 ** c > d ** Market Position a. customer centricity x large firms 5 .227 to .317 .272 ** b. competitor centricity x large firms b — — — a > c ** c. customer centricity x SMEs 12 .052 to .116 .084 ** c < d ** d. competitor centricity x SMEs 8 .049 to .154 .101 ** Financial Position a. customer centricity x large firms 12 .210 to .294 .252 ** a > c ** b. competitor centricity x large firms 7 .148 to .256 .202 ** b < d ** c. customer centricity x SMEs 16 .171 to .233 .202 ** a > b ** d. competitor centricity x SMEs 13 .244 to .324 .284 ** c < d ** ** p < .01 a The corrected mean correlation coefficients (r) are the sample size weighted, reliability-corrected estimates of the population correlation coefficients. b Groups with less than three observations were excluded from the analyses to avoid misrepresentation. 55 Table 1.7. Moderating Effects of Firm Type Dependent No. of 95% Confidence Corrected Summary of Levels Variable Effect Sizes Interval Meana r Results Innovativeness a. customer centricity x services 20 .618 to .687 .653 ** a > c ** b. competitor centricity x services 14 .487 to .573 .530 ** b > d ** c. customer centricity x manufacturing 50 .426 to .462 .444 ** a > b ** d. competitor centricity x manufacturing 25 .363 to .418 .390 ** c > d ** Market Position a. customer centricity x services 9 .148 to .227 .188 ** a > c ** b. competitor centricity x services 6 .311 to .470 .391 ** b > d ** c. customer centricity x manufacturing 5 .087 to .188 .137 ** a < b ** d. competitor centricity x manufacturing 4 .047 to .154 .101 ** c > d ** Financial Position a. customer centricity x services 17 .246 to .315 .280 ** a < c ** b. competitor centricity x services 12 .220 to .331 .276 ** b > d ** c. customer centricity x manufacturing 15 .281 to .337 .309 ** a > b ** d. competitor centricity x manufacturing 11 .185 to .262 .223 ** c > d ** ** p < .01 a The corrected mean correlation coefficients (r) are the sample size weighted, reliability-corrected estimates of the population correlation coefficients. 56 Table 1.8. Moderating Effects of Industry Type Dependent No. of 95% Confidence Corrected Summary of Levels Variable Effect Sizes Interval Meana r Results Innovativeness a. customer centricity x high-tech 31 .472 to .523 .498 ** a > c ** b. competitor centricity x high-tech 17 .449 to .523 .486 ** b > d ** c. customer centricity x low-tech 18 .368 to .434 .401 ** a > b ** d. competitor centricity x low-tech 10 .337 to .434 .385 ** c > d ** Market Position a. customer centricity x high-tech b — — — b. competitor centricity x high-tech b — — — c < d ** c. customer centricity x low-tech 11 .037 to .115 .076 ** d. competitor centricity x low-tech 8 .051 to .198 .124 ** Financial Position a. customer centricity x high-tech b — — — b. competitor centricity x high-tech b — — — c < d ** c. customer centricity x low-tech 18 .103 to .180 .142 ** d. competitor centricity x low-tech 15 .105 to .204 .155 ** ** p < .01 a The corrected mean correlation coefficients (r) are the sample size weighted, reliability-corrected estimates of the population correlation coefficients. b Groups with less than three observations were excluded from the analyses to avoid misrepresentation. 57 Table 1.9. Moderating Effects of Country Context Dependent No. of 95% Confidence Corrected Summary of Levels Variable Effect Sizes Interval Meana r Results Innovativeness a. customer centricity x emerging 68 .545 to .577 .561 ** a > c ** b. competitor centricity x emerging 42 .648 to .692 .670 ** b > d ** c. customer centricity x developed 69 .407 to .438 .422 ** a < b ** d. competitor centricity x developed 40 .365 to .407 .386 ** c > d ** Market Position a. customer centricity x emerging 12 .207 to .299 .253 ** a > c ** b. competitor centricity x emerging 9 .250 to .358 .304 ** b > d ** c. customer centricity x developed 21 .134 to .181 .158 ** a < b ** d. competitor centricity x developed 14 .059 to .131 .095 ** c > d ** Financial Position a. customer centricity x emerging 21 .395 to .447 .421 ** a > c ** b. competitor centricity x emerging 17 .298 to .370 .334 ** b > d ** c. customer centricity x developed 38 .158 to .203 .180 ** a > b ** d. competitor centricity x developed 27 .198 to .255 .227 ** c < d ** ** p < .01 a The corrected mean correlation coefficients (r) are the sample size weighted, reliability-corrected estimates of the population correlation coefficients. 58 Table 1.10. Summary Results of Hypotheses 2-9 Firm size Firm type H2a H2b * H3a H3b * H3c H4a H4b H5a H5b H5c Supported ✓ ✓ ✓ Partially supported ✓ ✓ ✓ ✓ ✓ ✓ Not supported ✓ Industry type Country context H6a * H6b * H7a H7b * H7c * H8a H8b H9a H9b H9c Supported ✓ ✓ ✓ Partially supported ✓ ✓ ✓ ✓ ✓ Not supported ✓ ✓ * The hypothesis could not be fully tested due to data limitations (i.e., a limited number of observations for some of the sub-groups). 59 ESSAY TWO: NETWORK CENTRICITY: AN EMERGING FIRM STRATEGIC ORIENTATION AND ITS FIRM PERFORMANCE OUTCOMES 60 Abstract The development of digital technologies and connected consumers in this digital, global business environment lead to the evolution of new business models. Increasingly, a firm’s ability to engage in strategic partnerships to compete with other firm networks determines the success and failure of firms in the marketplace. I identify this phenomenon as network centricity and define it as a set of company-wide values, norms, and beliefs that foster firm’s long-term multilateral partnerships with other organizations. I offer conceptualization and operationalization of the firm’s network centricity and explore the theoretical foundations of this emerging strategic orientation. To gain a better understanding into the phenomenon of interest and its firm performance consequences, I leverage data from multiple sources. Specifically, I utilize firm partnership data from Medtrack, a leading pharmaceutical industry business intelligence database, text analysis of the letters to shareholders in firm annual reports, and firm financial data from Compustat. Covering 126 firms during the 2005-2017 period, I examine the individual performance outcomes of network centricity as well as the role of its interplay with customer centricity and competitor centricity to explore the complementarity among these strategic orientations. Keywords: network centricity; customer centricity; competitor centricity; firm strategic orientation; firm performance; conversion ability 61 Introduction After a century of technological rivalry between Mercedes and BMW (Grobalt 2016), two leading automakers announced the development of the joint mobility company. Daimler, the producer of Mercedes-Benz, and BMW signed a $1.1 billion partnership and announced the development of five different divisions to compete with technology companies such as Uber (Reid 2019). Recently, BP, a leading global energy firm, entered into a joint venture to create innovative charging infrastructures for customers (BMW Group 2021). Relatedly, the COVID-19 pandemic reaffirmed the importance of inter-firm partnerships in delivering innovative solutions quickly (Druedahl, Minssen, and Price 2021; Nature 2021). Top quality vaccines were developed in record time due to collaboration between biotechnology companies (i.e., Pfizer-BioNTech vaccine), as well as collaboration between industry and academia (i.e., Oxford-AstraZeneca vaccine) (Bourla 2021; Nature 2021). The global economic order created in the middle of the twentieth century is undergoing major structural changes (Petricevic and Teece 2019). Digitalization and globalization of markets change business landscape and transform the way companies create, deliver, and capture value (Autio, Mudambi, and Yoo 2021; Borah et al. 2022) and give rise to new business models, processes, and structures (Alcácer, Cantwell, and Piscitello 2016). Digitalized interactions, consumer co-creation, and new interactive platforms shift our understanding of the value creation process from a traditional transactional, exchange-based perspective to value creation through complex interactions (Ramaswamy and Ozcan 2018). Firm interactions with other companies are no longer limited by the constraints of collocation and transportation (Autio, Mudambi, and Yoo 2021). Companies can now create and capture value “beyond the boundaries of a given firm” (Alcácer, Cantwell, and Piscitello 2016, p. 506) and develop knowledge 62 networks instead (Cano-Kollmann et al. 2016). Increasingly, a firm’s ability to engage in strategic partnerships to compete with other firm networks determines the success and failure of firms in the marketplace. As these intangible, relational assets play a growing role in the world economy, firms need to develop appropriate mindsets to manage these assets (Mudambi 2008; Srivastava, Fahey, and Christensen 2001), which is especially important for knowledge-intensive industries (Powell, Koput, and Smith-Doerr 1996). Technological innovation can no longer be considered a sustained source of competitive advantage, as usually it can be imitated at a lower cost (Alvarez and Barney 2001). In addition to technological developments and digitalization, rapid global changes present additional challenges for firms: how to properly frame the boundaries of their businesses (cf. Alcácer, Cantwell, and Piscitello 2016)? New digital technologies and business models, like that of Uber, Tesla, or Netflix, shift firm boundaries and blur the borders between industries and competitors (Cano-Kollmann et al. 2016; Christensen, Raynor, and McDonald 2015). Firms that used to be major competitors in one industry are forming partnerships to jointly face the emerging challenges of new market entrants and enter new business segments. The most innovative companies are extensively leveraging technology platforms and ecosystems as a foundation for the development of new business offerings domestically and across borders (BCG 2019). Increasingly, firms have to look for new ways to compete efficiently in the changing world and adjust business approaches to the fast-evolving environment (MSI 2022). Driven by the changing economic order, firms increasingly collaborate with other organizations and operate as networks of firms.6 6 Following Provan, Fish, and Sydow (2007), I differentiate firm networks from firm alliances. The former represents complex multilateral long-term partnerships, while the latter involves simpler and short-term dyadic relationships. 63 Inter-organizational networks emerge from dyadic interfirm ties (Novoselova 2022) and their impact is larger than the sum of their components. As such, they affect multiple outcomes, such as firm strategies, resources, innovativeness, and customers. Ultimately, these networks impact “organizational competitive advantage, performance, and survival” (Novoselova 2022, p. 874). Some firms adjust their strategic priorities and the way they operate focusing on building networks with other companies as a way to establish their market advantage in competing with the other networks. I call this phenomenon network centricity. More formally, I define network centricity as the extent to which a company values/emphasizes the development of firm’s long- term multilateral partnerships with other organizations and considers these partnerships as a source of competitive advantage. Network centric firms create value in collaboration with their network partners through their joint or relational assets in efforts to compete with other networks. As such, network centricity reflects socio-economic changes when firms establish partnerships in a networked economy or “quasi-corporations” (Achrol 1991). Given a continuing shortage of research helping define and explain this phenomenon (Borah et al. 2022), our understanding of a network organization can be improved in three ways. First, what are the key characteristics of network organizations and how can they be measured? Answering this question can extend prior work dedicated to the evolution of marketing organization (Achrol 1991), value creation in the networks (Frels, Shervani, and Srivastava 2003; Swaminathan and Moorman 2009), and the emergence of network type of organization (Achrol 1997; Achrol and Kotler 1999; Powell 1990). Specifically, we need a better understanding of the organizational mindset or a set of specific values, norms, and beliefs of a network organization. I present network centricity as firm strategic orientation, or a reflection of the firm’s philosophy guiding key business decisions 64 (Zhou, Yim, and Tse 2005).7 This study explores this emerging phenomenon and offers the conceptualization and operationalization of the firm’s network centricity. Second, I leverage complementarity approach (Hakala 2011) to investigate the interplay between network centricity, customer centricity, and competitor centricity. As detailed subsequently, network centric firms incorporate a focus on customers (i.e., customer centricity) and multi-level interactions with competitors (i.e., competitor centricity) – collaboration with competitors within the network and competition with other networks. These two strategic orientations are critical to better understand the concept of network centricity because network centricity inherently incorporates a dual focus on both customers and competitors. Third, I explore the consequences of network centricity. The results suggest that firms adopting network centricity should still maintain the focus on customers (i.e., customer centricity) and the focus on competitors (i.e., customer centricity). Moreover, without proper reflection of customer and competitor perspectives, network centricity may be necessary but not sufficient condition for successful firm operations. In addition, network centricity plays an important role in a firm’s ability to successfully convert research ideas into commercialized products, a critical factor that can determine the success of innovative companies (Chandy et al. 2006). I examine these relationships in the context of the biotechnology industry leveraging data from Medtrack, a leading business intelligence database that tracks the drug development process in pharmaceutical industry. This database provides access to information on more than 44,000 company profiles, 143,000 deals, and 181,000 drugs, along with patents, indications, and 7 Firm strategic orientation is a part of a more fundamental corporate culture, which captures “the pattern of shared values and beliefs that help individuals understand organizational functioning and thus provide them norms for behavior in the organization” (Deshpandé and Webster 1989, p. 4). 65 drug delivery technologies. I supplement this data with the analysis of letters to shareholders in firm annual reports (Noble, Sinha, and Kumar 2002) and firm financial data from Compustat. Overall, my panel dataset covers 126 companies over the 2005-2017 period. Given the novelty of network centricity phenomenon – and in-line with recent research (Kirca et al. 2020; Talay, Pauwels, and Seggie 2023) – I adopt an “empirics first” approach (Golder et al. 2023; Graebner et al. 2023) for this study to identify the complementarities involving network centricity, customer centricity, and competitor centricity. Conceptual Development Networked Economy and Quasi-Corporations The development of digital technologies and connected consumers in this digital, global business environment lead to the evolution of new business models, or specific ways by which a firm generates and delivers value to customers (Teece 2010). Digitalization and globalization of markets simplify technology duplication at a fast pace, while the decrease in coordination costs enables the creation of ever-complex business configurations. However, innovative business models do not provide a firm’s competitive advantage by themselves, but rather require the selection and implementation of appropriate business strategies. Corporations must develop long-term strategies reflecting major geopolitical changes in the world and collaborate with other firms in a new way (Teece 2022). Interfirm networks are predicted to redefine how business operates in the information-rich environment of the 4th Industrial Revolution (Achrol and Kotler 2022). Firms’ move to partnerships in a networked economy was predicted long ago as an evolutional step of the exchange relationships (Achrol 1991). Kotler (1992, p. 52) states that partnership is “the ultimate form of relationship marketing.” Day (2000, p. 24) views 66 collaborative exchanges as the final stage of the relationship spectrum and defines them as such that “feature very close information, social, and process linkages, and mutual commitments made in expectation of long-run benefits.” These researchers consider a firm’s ability to create and maintain relationships with other actors to be a strong basis for competitive advantage. This approach is founded on the principles of organizational culture and thus substantially differs from a “transaction mentality” that dominates the bilateral relationships involving firms and their customers/competitors (Day 2000). However, Day (2000) limited what he called “relationship orientation” to the firm’s relationship with its most important customers. Nevertheless, Kotler (1991, in Day 2000) and Achrol (1991) were among the first marketing researchers who viewed close relationships between firms as the basis for a new emerging paradigm of networks. Scholars have long envisioned the “organization of the future” and predicted that firms evolve from hierarchical structures into “quasi-firms” (Eccles 1981) or “quasi-corporations” (Achrol 1991) that will manage complex exchange relationships between partner companies via the distribution of valuable skills and resources. Drucker (1988) posited that a future company will be “knowledge-based” and will probably resemble a hospital or a research university. He called it an “information-based organization.” Miles and Snow (1984) coined the term “dynamic network organization” as the evolving organizational firm of the future. They envisioned that, driven by highly complex and constantly changing environmental factors outside of the firm’s control, organizations of the future will be temporary and “vertically disintegrated.” Such companies will have the ability to quickly reshape whenever needed, adjusting to changing external circumstances. There was a wave of firm restructurings in the 1980s reflecting organizational revolution (Miles and Snow 1992). Companies downsized, focused on their core 67 competencies, implemented flexible structures, and moved from vertical integration toward alliances with other organizations. The network perspective is receiving close attention across “virtually all of the sciences from anthropology to physics” (Borgatti and Li 2009, p. 5). Houston et al. (2004, p. 247) define a network as “a group of actors or social entities connected by a set of linkages through which they exchange information or resources, or both.” Achrol (1991, 1997) analyzed the role of marketing in the network organization. He posits that the environment of the future is likely to have high levels of diversity, knowledge richness, and turbulence, so large vertically integrated organizations are no longer efficient (Achrol and Kotler 1999). Achrol (1997, p. 58) suggests that future marketing firms to be “integrators… rather than inventors or producers of technologies”. This notion goes in-line with Thorelli (1986), who states that the role of marketing is central in network management. An early analysis of the network organization was developed by Powell (1990). He contrasted network organization to market and hierarchy forms of organization and presented a network form of organization characterized by interdependent actors operating in a mutually beneficial climate. Powell (1990) indicates that this organizational setup is preferred to the hierarchical organizational design, which makes employees less enthusiastic and motivated as the work tasks are usually driven by their superiors. At the same time, employees of network- oriented companies have more flexibility and operate under the shared social norms of trust, reciprocity, and mutual dependency. He argues that market transactions are more suitable for the exchange of resources, while networks are better suited for knowledge-intensive activities and facilitate sharing of specific competencies, such as skills or knowledge. Unlike traditional exchange relationships, partnerships may facilitate the transfer of tacit knowledge, which is 68 difficult to codify and pass on. In addition, networks provide an opportunity for “learning by doing”. Achrol and Kotler (1999) define network organization as a coalition of specialized economic entities that operates without hierarchical control. The actors are connected by shared values of reciprocity and mutuality. The idea of network organization is centered on “creating new knowledge”, rather than knowledge utilization (Achrol and Kotler 2012). Network organization is expected to create new value via “value conversion” (Allee 2009). The network form of the organization might foster a shift to a “market-driving model of marketing as an alternative to the market-driven” (Achrol and Kotler 1999, p. 153). Salancik (1995) considers networks beyond the interaction of companies and proposes that networks are formed by the interaction of individuals, either “organizations or humans.” Among other actors, networks may also include competitors (Thorelli 1986). Cooperation between firms within networks could be more beneficial than firms’ competition. Indeed, there is a trend of forming cooperative partnerships focused on the development of new product and service solutions. Increasingly, firms are engaged in such partnerships as one company can no longer have necessary capabilities or control the necessary resources. Importantly, such partnerships feature both collaboration and competition simultaneously (Dean, Griffith, and Yalcinkaya 2023). Moreover, Humphreys and Carpenter (2018, p. 157) argue that “firms can actually enhance their competitive advantage by sharing unique resources with rivals” (emphasis in original). Specifically, the firm’s market advantage could be enhanced via the reinforcement of its leadership position and status. Cooperation, as compared to competition, fosters organizational flexibility, knowledge transfer, and higher productivity (Deutsch 1949). The development of partnerships, alliances, and other types of cooperative agreements between the 69 firms gives rise to “strategic network competition” (Hunt and Morgan 1994). Firms enter a new format of competition: from competition between individual companies to “competition between networks” (Thorelli 1986). The network perspective spreads across different research fields and multiple facets of firm operations (Borgatti and Li 2009) as a “salient determinant of firm performance” (Kumar, Liu, and Zaheer 2022, p. 1435). Literature presents numerous benefits of firm networks, such as availability of novel, reliable, and timely information; opportunity to substitute the lack of technological expertise; ability to accumulate additional financial resources (Borah et al. 2022; Kumar, Liu, and Zaheer 2022), and “other important outcomes reflected in organizational competitive advantage, performance, and survival” (Novoselova 2022, p. 874). Still, this domain suffers from a continued shortage of empirical research (Borah et al. 2022). Furthermore, while prior research examined the societal changes that lead to the development of network organizations and their structural characteristics (Achrol 1991, 1997; Achrol and Kotler 1999, 2012, 2022), the organizational mindset or a set of specific values, norms, and beliefs of a networked organization remains a gap in the literature. To the best of my knowledge, this study is the first attempt to investigate network paradigm from a new viewpoint: network centricity as firm strategic orientation. Firm Strategic Orientations and Their Prevalence A firm’s strategic orientation represents its organizational culture (Deshpandé, Farley, and Webster 1993; Deshpandé and Webster 1989) and directs specific business actions of the company (Slater, Olson, and Hult 2006). Firms identify and implement specific firm strategic orientations and accordingly make choices related to the allocation of limited organizational resources (Slater, Olson, and Hult 2006). The development of digital technologies and the 70 evolution of traditional marketing processes bring new realities to the way firms compete in the third millennium. For example, market orientation (Kirca, Jayachandran, and Bearden 2005) gradually transformed from being the firm’s competitive advantage to a necessary failure prevention condition (Kumar et al. 2011). Recent research also indicates the progression of traditional conceptualization as there is a shift from a broad market orientation concept to a more targeted concept of firm’s customer centricity (Crecelius et al. 2019; Fader 2020; Jayachandran et al. 2005; Shah et al. 2006).8 Over the past 15 years marketing scholars proposed several alternative strategic orientations to reflect the organizational cultures firms adopt in response to environmental changes and the evolution of marketing science and society at large. For example, the interaction orientation approach (Lee and Griffith 2019; Ramani and Kumar 2008) considers value cocreation during the process of firm-customer interactions. This strategic orientation is focused on the establishment of effective firm-customer relationships via interactions with individual customers. As customers are increasingly interested in customization of products and services, firms strengthen customer relationships and obtain valuable information via customer-firm and customer-customer interactions. As such, interaction orientation captures the dyadic nature of the firm-customer relationship and it represents four unique dimensions: customer concept, customer empowerment, interaction response capacity, and customer value management. Engagement orientation (Kumar and Pansari 2016) reflects organizational efforts targeted at the engagement of firm customers and employees. The authors consider both internal (i.e., employees) and external (i.e., customers) stakeholders of the firm. Similarly, Hillebrand, Driessen, and Koll (2015) argue that marketing scholars should move away from focus on 8 The terms “centricity” and “orientation” are used interchangeably throughout this paper when referring to specific firm strategic orientations. 71 customers toward multiple stakeholder perspective. Thus, stakeholder marketing (Hult et al. 2011) considers multiple stakeholders and their role in activities and processes that facilitate exchange relationships. The authors identify six primary stakeholders – such as customers, employees, shareholders, suppliers, regulators, and the local community – and a set of secondary stakeholders. However, the business world constantly evolves bringing new realities, challenges, and opportunities. In spite of availability of approaches above mentioned, firms still keep looking for new strategic orientations that could help them gain competitive advantage in a fast changing world (Kumar and Pansari 2016). In this study, I present network centricity as an organizational culture that helps firms to achieve competitive advantage in this global network economy. Table 2.1 compares and contrasts network centricity with different firm strategic orientations investigated in prior marketing literature with their definitions, the type of relationships, and how firms adopting these strategic orientations create value. Specifically, we observe that firms are increasingly involved in more complex relationships. The 1990s and early 2000s (i.e., market orientation, customer centricity, competitor centricity) were characterized by unilateral relationships. Companies were focused on generation and utilization of market information as well as understanding of their customers and competitors. However, the development of marketing paradigm changed the nature of relationships and prior unilateral approach was not sufficient (Ramani and Kumar 2008). Starting from the end of the 2000s, organizations move to bilateral relationships and start interacting with different stakeholders, including employees and customers (i.e., interaction orientation, stakeholder marketing, engagement orientation). Network centricity reflects the realities of the 2020s and examines multilateral relationships between different organizations. I define this phenomenon as the extent to which a company 72 values/emphasizes the development of firm’s long-term multilateral partnerships with other organizations and considers these partnerships as a source of competitive advantage. ---- Insert Table 2.1 about here ---- Interplay of Firm Strategic Orientations The socio-economic changes brought by globalization and digitalization (Autio, Mudambi, and Yoo 2021; Borah et al. 2022) present both new business challenges and opportunities. Network centricity shifts market rivalry from the competition between individual firms to the competition between firm networks. Firms that adopt network centricity develop long-term multilateral partnerships with other companies. These partnerships serve as a source of value creation and a joint asset in the competition with other networks. Networks of firms aggregate idiosyncratic firm assets, resources, and knowledge which enables the development of unique capabilities and customer solutions. The resource-based view (Barney 1991; Barney, Ketchen, and Wright 2021) considers the utilization of firm strategic resources as a way to achieve a company’s sustained competitive advantage. Organizations can achieve a sustained competitive advantage when other firms cannot replicate the outcomes of their activities. Firm networks have a higher level of strategic resource heterogeneity due to the complexity of relationships and value creation processes within the network. An individual company can hardly match the resource capabilities of the network of firms long-term. Respectively, companies that adopt network centricity will be more competitive in the market and outperform other firms. Yet, these firm networks still serve customers and are still involved in competitive activities. Moreover, they are often formed with competitors. The nature of these relationships gets more complex, and companies need to carefully select partners with which they create firm networks. On the one hand, these new partnerships are formed to strengthen joint capabilities and 73 better serve customers. On the other hand, these partnerships often include competitors, and organizations need to ensure that new partnerships do not undermine the individual competition between these companies at the firm level. Ultimately, as firms adopt network centricity, the focus on both customers and competitors remains relevant and important. In other words, network centricity is likely to co-exist with customer centricity and competitor centricity. As companies gradually evolve from vertically integrated organizations to highly specialized economic entities (Achrol and Kotler 1999; Miles and Snow 1992), the increased firm specialization expands the capabilities of individual companies to serve their customers. At the same time, company specialization narrows the subset of customers the firm may serve and its respective target market. Such firms would be interested in joining firm networks that open new markets and expand their prospective customer base. However, firms should possess a high level of customer understanding to be attractive partners for firm networks. Companies set up networks of firms to combine individual capabilities and jointly generate customer value. Networks of highly specialized firms aggregate the individual capabilities of companies and their in-depth understanding of target customers. Relatedly, competitor centricity has important implications for firms pursuing network centricity. Firms within a network share knowledge, assets, and capabilities. Competitor centric organizations have a thorough understanding of their competitors, including competitors’ strengths and weaknesses, both from short-term and long-term perspectives (Narver and Slater 1990). This comprehensive understanding of the competitive landscape, along with long-term predictions of market development, allows for a meticulous selection of network partners. Firms create networks with other companies – including competitors – when new partnerships supplement focal firm assets and capabilities. At the same time, these partnerships with 74 competitors should not jeopardize market positions of individual firms and competition at the firm level. Thus, network centricity should be considered jointly with customer centricity and competitor centricity, given the interconnectedness and complementary of these firm strategic orientations. Indeed, scholars emphasize that while different firm strategic orientations are often conceptually distinct, they are also interrelated. Respectively, they suggest a complementarity approach to study them jointly (Hakala 2011; Schweiger et al. 2019). The complementary approach considers that companies may have several strategic orientations simultaneously and allows researchers to obtain a more definite understanding of the phenomena. As a result, “organizations may not need to choose between orientations, but may stretch their resources and thus, adopt a bespoke pattern of orientations” (Hakala 2011, p. 206). Hakala (2011, p. 210) further explains the specifics of the complementary approach: “The starting point for the view of a complementary combination of orientations is that different orientations support each other in some way. It may be that one precedes another in the sequence of development (sequential) or that one is required to transmit the effects of the other (mediation) or to change those effects (moderation). The orientations may also be complementary in the sense that they provide a toolbox of adaptation mechanisms (alternatives) for the firm to use, depending on its goals or situation. The core of the complementary approach is that the relationship between orientations is also considered, and that the pattern, rather than the orientations separately, create the desired effects in the dependent variable.” 75 Thus, firms adopting network centricity should still maintain the focus on customers (i.e., customer centricity) and the focus on competitors (i.e., customer centricity). Moreover, without proper reflection of customer and competitor perspectives, network centricity may be necessary but not sufficient condition for successful firm operations. Converting Ideas into Products Powell, Koput, and Smith-Doerr (1996) predicted that market complexity and dispersion of expertise will shift the locus of innovation from individual firms to networks of organizations. As firms create, deliver, and capture value in new ways due to digitalization and globalization (Autio, Mudambi, and Yoo 2021; Borah et al. 2022), new business processes appear. Combined with the increased specialization of individual companies, these processes alter the value creation processes and make them more complex (Ramaswamy and Ozcan 2018). Increasingly, firms are engaged in new product development partnerships as one company can no longer have the necessary capabilities or control the necessary resources (Griffith, Dean, and Yalcinkaya 2021). However, new product development partnerships face adversity as more than half of such partnerships fail to achieve their objectives (Chakravarty, Zhou, and Sharma 2020). Specifically, firms struggle to convert numerous research ideas into commercialized products (Chandy et al. 2006). Product conversion ability often becomes a determinant of success or failure. While there is a significant variance in firms’ ability to convert initial ideas into final products, two factors hurt companies the most: the generation of too many ideas and the focus on bringing products to market too fast (Chandy et al. 2006). The abundance of firm resources, and specifically resources focused on research and development (R&D), may be particularly salient in this context. The more resources companies have, the more ideas they are likely to be able to generate and attempt to implement. Firm network centricity may serve as a balancing factor here. 76 R&D intensity, or the ratio of R&D expenditure to firm’s sales, is an important characteristic of network firms. Increased R&D expenditures strengthen firm’s technological capabilities and shift firm’s strategic emphasis toward value creation (Mizik and Jacobson 2003). Importantly, the R&D capabilities and R&D intensity of partner firms reinforce each other. Firms with high R&D intensity benefit from interaction with other R&D-active companies as external and internal R&D efforts are complementary (Teece 1998). On top of that, network centric firms might better manage the whole process of new product development. The presence of multiple partners in the network might prevent excessive focus on some aspects of the process (i.e., focusing on idea generation only and generating too many ideas). At the same time, the need to collect input and consider perspectives of different partners within the network is likely to prevent too fast product development. Thus, network centricity, and specifically the combination of network centricity and R&D intensity, could be a meaningful aspect of firm’s product conversion ability. Study Approach Given the novelty of the investigational phenomenon (i.e., network centricity as firm strategic orientation) and the diversity of theoretical perspectives that could be considered in analyzing the interactions of network centricity with other firm strategic orientations (cf. Hakala 2011; Schweiger et al. 2019), I employ an “empirics-first” approach (Golder et al. 2023) or “empirical inquiry without hypotheses” (Graebner et al. 2023, p. 3). Marketing and management literature suggests this approach to investigate novel real-world phenomena and/or causally complex relationships. Specifically, this approach “refers to research that (1) is grounded in (originates from) a real-world marketing phenomenon, problem, or observation, (2) involves obtaining and analyzing data, and (3) produces valid marketing-relevant insights without 77 necessarily developing or testing theory” (Golder et al. 2023, p. 319). One of the principles of this approach is to explore the research domain without preconceptions. The domain in question presents an appropriate context for the application of empirics- first approach due to the following reasons. First, this is a real-world phenomenon that we observe: increasingly companies create networks of firms to compete with other networks. Second, this is an underexplored phenomenon, given that this study represents the first attempt to investigate network centricity as firm strategic orientation. Third, the investigation of this phenomenon can be considered from multiple theoretical perspectives. Network centricity could be studied as a standalone firm strategic orientation. However, multiple strategic orientations usually co-exist (Hakala 2011; Schweiger et al. 2019). Moreover, the interplay of these strategic orientations can be complex with a questionable determination of the causality in the relationships between orientations (Hakala 2011). Moreover, multiple theoretical perspectives could be considered for the analysis of network centricity: resource-based view (Barney 1991) to consider the utilization of firm strategic resources within the network; resource-advantage theory (Hunt and Morgan 1995) to reflect how comparative advantage is achieved in firm networks as a bundle of resources; organizational learning and information processing (Huber 1991); institutional theory (DiMaggio and Powell 1983); or strategic choice theory (Child 1972), among others. While an empirics-first concept was formulated recently, this type of research “occurs across all parts of the field” (Golder et al. 2023, p. 321) and was leveraged over the past few decades (e.g., Bolton, Warlop, and Alba 2003; Gebhardt, Carpenter, and Sherry 2006; Golder and Tellis 1993; Stahl et al. 2012). It has also been implemented in recent marketing studies (e.g., Kirca et al. 2020; Talay, Pauwels, and Seggie 2023; Rust et al. 2021). Thus, in-line with an 78 empirics-first approach, I do not state formal hypotheses, but instead adopt an exploratory investigation of performance outcomes of network centricity as well as its various interaction with customer centricity and competitor centricity. In the following sections, I provide information related to the sample and data used in this research. Sample and Data Sample I study the firms’ network centricity in the context of the biotechnology industry. Partnerships play an important role in this industry as biotechnological firms often cooperate with large pharmaceutical companies during the development of new products (Stremersch and Van Dyck 2009). Powell, Koput, and Smith-Doerr (1996) were among the first to study the “networks of learning” among biotechnological companies. The authors stress the important role of various ties in the industry, specifically, ties with the scientific community, venture capitalists, and large pharmaceutical companies. The biotechnological industry was also previously studied by marketing scholars investigating various aspects of innovation in the firm (e.g., Fang, Lee, and Yang 2015; Rao, Chandy, and Prabhu 2008; Saboo et al. 2017; Wuyts, Dutta, and Stremersch 2004). Innovation is among the key business drivers of the pharmaceutical industry. However, the development of new drugs is research-intense and extremely expensive, with development expenses exceeding $100 million per product (Powell, Koput, and Smith-Doerr 1996). Moreover, it is a lengthy process with a low success rate. While the drug development process can exceed 10 years (Chandy et al. 2006; Powell, Koput, and Smith-Doerr 1996), only a fraction of all inventions makes it to the market. Less than one out of 5,000 new drug developments reaches the stage of market introduction (Stremersch and Van Dyck 2009) and only one out of 79 60,000 new inventions is considered highly successful (Sharma, Saboo, and Kumar 2018). Thus, I consider the biotechnology industry to be a suitable context for the investigation of the firm’s network centricity and its firm performance and innovation outcomes. In addition, the study of a single industry allows for the elimination of cross-industry factors that might impact the investigated outcomes (Sharma, Saboo, and Kumar 2018). Data Collection and Measures Table 2.2 presents the description of variables and measures used in this study. I leverage data from multiple sources – Medtrack pharmaceutical business intelligence database, text analysis of the letters to shareholders in firm annual reports, and Compustat financial database – as detailed below. After combining data from these sources and dropping firm-years with missing data on the focal variables, my final panel data sample consists of 126 firms over 13 years (2005-2017), for a total of 640 firm-year observations. ---- Insert Table 2.2 about here ---- Medtrack Database I use Medtrack, a leading business intelligence database that tracks the drug development process in the biopharmaceutical and pharmaceutical industry, as a data source for evaluation of the firms’ network centricity and product portfolio as well as product portfolio conversion ability. This database provides access to information about more than 44,000 company profiles, 143,000 deals, and 181,000 drugs, along with patents, indications, and drug delivery technologies. Medtrack database is managed by Pharma Intelligence, the same company that provides Pharmaprojects, a database well accepted in the previous marketing studies of the pharmaceutical industry (e.g., Chandy et al. 2006; Grewal et al. 2008; Prabhu, Chandy, and Ellis 2005; Rao, Chandy, and Prabhu 2008). 80 I use a composite variable to measure firm’s network centricity as an average of two variables: number of partnerships and diversity of partnerships. I estimate nine separate categories of firm deals (i.e., R&D, manufacturing, marketing) and calculate the diversity of partnerships following the approach proposed by Powell, Koput, and Smith-Doerr (1996). The range of deals that a company is engaged in at a given time reflects a portfolio of firm’s partnerships. Partnerships diversity is computed for each company in each year as follows. For firm i in year t, I denote the number of deals of type j as nit,j and the total number of deals aggregated over all types (j = 1… J; J = 9) as nit. The proportion of firm i’s deals of type j, out of the total number of deals, is denoted pit,j and given by pit,j = nit,j / nit. Each pit,j is squared and then the sum is taken over all j’s and subtracted from 1, resulting in the index of diversity, yit, so that: yit = 1 - ∑Jj=1 p2it, j This is equivalent to Blau’s index of heterogeneity (Blau 1977). Diversity can be treated as a continuous random variable, though bounded in the interval [0, 8/9]. In addition, I measure the number of partnerships, which captures the total number of firm’s deals for firm i in year t. This measure was then rescaled to zero to one range to be comparable to the scale of diversity of partnerships. Ultimately, an average of two measures was calculated to form the composite variable that allows to reduce the number of parameters to be estimated in the model. I also leverage the Medtrack database to measure firm’s conversion ability in product development (Chandy et al. 2006) as the proportion of investigational drugs beyond stage three. Letters to Shareholders I measure customer centricity, competitor centricity, and international scope via text analysis of the letters to shareholders in corporate annual reports (Noble, Sinha, and Kumar 81 2002; Saboo and Grewal 2013). I developed a dictionary of phrases that captures constructs of interest (Saboo and Grewal 2013) and use LIWC (Linguistic Inquiry and Word Count) text analysis software (Nath et al. 2019; Pennebaker et al. 2022). Table 2.3 presents dictionary word lists to capture customer centricity, competitor centricity, and international scope. For customer centricity and competitor centricity, I relied on the dictionary by (Homburg, Theel, and Hohenberg 2020), which was based on the work of other scholars investigating these strategic orientations (Narver and Slater 1990; Saboo and Grewal 2013; Zachary et al. 2011). Given the specifics of the pharmaceutical industry, I implemented some dictionary modifications as selected words are used with different connotations in this context. Table 2.4 presents the modifications implemented for specific words and sample phrases to illustrate how these words are used in the letters to shareholders in biotech industry. ---- Insert Table 2.3 about here ---- ---- Insert Table 2.4 about here ---- I also developed a new dictionary to capture firm’s international scope following the procedures applied in prior research (Zachary et al. 2011). International scope captures the geographical dispersion of firm activities (Feng, Patel, and Sivakumar 2020). I started by developing a deductively generated list of keywords based on extant international business and marketing literature (Kirca et al. 2011). Then, I also searched for potential synonyms of the words identified during the first stage. As a next step, I extended the list of keywords by an inductive approach. I analyzed the list of words extracted from my data and identified relevant words that were not captured during the deductive word search. I also randomly pulled sample letters to shareholders and verified if coded words reflected the constructs of interest. The final list of the dictionary words was discussed with scholars knowledgeable in this research domain. 82 Compustat I use S&P Compustat annual database to capture firm profit, firm revenue, number of employees, and R&D intensity (i.e., the ratio of R&D expenditures to sales). I also capture the cost of coordination as the location of the firm headquarter (Lu et al. 2022), and firm industry (SIC code number). I focus on profit as a measure of firm performance as “maximizing the amount of profit… is a firm’s superordinate performance objective” (Bhattacharya, Morgan, and Rego 2022, p. 78). Empirical Approach I developed two models to estimate the impact of network centricity on firm performance and product portfolio conversion ability. Firm Performance In my panel data setting, I observe the performance of 126 firms over 13 years. I seek a robust assessment of the effects of network centricity and its interaction with customer centricity and competitor centricity on firm performance. I implemented the following measure to address potential concerns regarding endogeneity, serial correlation, heteroskedasticity, and unobserved heterogeneity that may bias my results (Wooldridge 2016). First, to address potential firm- specific omitted variable endogeneity, I included a set of relevant control variables, such as firm size, international scope, R&D intensity, and a distinguishment between developed and early- stage companies (i.e., presence of positive revenues). Second, to account for time-invariant sources of unobserved heterogeneity, that might cause an endogeneity bias, I used fixed effects (FE) model estimation, which is appropriate for my data as suggested by the Hausman test. Third, to account for unobservable time effects, I included year fixed effects in all my estimations. Fourth, I use year dummies to help address potential concerns regarding 83 heteroskedasticity and unobserved heterogeneity. Fifth, I included a one-period lagged dependent variable (i.e., revenue) to alleviate serial correlation concerns. It also helps address the potential omitted variable bias as well persistence and dependence on observations from prior periods. Finally, I used robust standard errors to allow for possible heteroskedasticity. I specify my model as follows: (1) FP it = β0 + β1 NC it + β2 CUC it + β3 COC it + β4 NC it * CUC it + β5 NC it * COC it + β6 CUC it * COC it + β7 NC it * CUC it * COC it + β8 INTS it + β9 RDI it + β10 FSD it + β11 REVD it + β12 GP it-1 + ∑2017 k=2005 β13k TIME kit + α i + ε it, where i represents the individual firm, and t represents time (the year of observation); β0 captures the intercept term; FP is firm performance (measured in terms of gross profit); NC is network centricity; CUC = customer centricity; COC = competitor centricity, INTS is international scope; RDI is R&D intensity; FSD is firm size (dummy, SME = 1); REVD is revenue (dummy, firms with positive revenue = 1); TIME is a set of mutually exclusive dummies for 2006-2017, with 2005 as the base year; α are firm-specific errors or firm fixed effects (fixed unknown constants); ε represents the error term capturing the unexplained variation in FP. Table 2.5 presents the correlation matrix and descriptive statistics of this study. All of the correlations are below 0.5, except for the correlation between revenue and revenue (t-1) (r = 0.982), which indicates a low multicollinearity threat. ---- Insert Table 2.5 about here ---- 84 Conversion Ability While the key variables in the panel dataset are available for 2005-2017, some variables are cross-sectional. Specifically, the firm’s product portfolio data that is used for estimation of conversion ability is available for 2019 only. Respectively, I apply ordinary least squares (OLS) regression estimate to measure the impact of network centricity on conversion ability and use the future period (t+2) for the latter. This should adequately capture firm’s conversion ability given that drug development is a multi-year process (Chandy et al. 2006). I specify the model as follows: (2) CA it+2 = β0 + β1 NC i + β2 RDI i + β3 NC i * RDI i + β4 FSD i + β5 REV i + β ∑ CCRD + β ∑ IND + ε i, where i represents the individual firm, and t represents time (the year of observation); β0 captures the intercept term; CA is conversion ability; NC is network centricity; RDI is R&D intensity; FSD is firm size (dummy, SME = 1); REV is revenue; CCRD is cost of coordination (the dummy for HQ location); IND is industry (the dummy for SIC); ε represents the error term capturing the unexplained variation in CA. Results Firm Performance My first research question concerns the impact of network centricity on firm performance, both individually and in interaction with customer centricity and competitor centricity. I tested several models to estimate main effects and the interaction among the variables of interest (see Table 2.6). I first ran a model with control variables only (Model 1). Then, I tested Model 2 which presents the main effect of network centricity on firm performance. 85 Model 3 included two-way interaction (i.e., NC * CUC, NC * COC). The results discussed below are based on final Model 4, which adds a three-way interaction (i.e., NC * CUC * COC). The results demonstrate that the individual impact of network centricity is not statistically significant (β = -215.857, n.s.). As for two-way interactions, the relationship between NC * CUC and performance is positive and not significant (β = 626.139, n.s.), while the relationship between NC * COC and performance is negative and marginally significant (β = -1989.225, p > .10). However, the firm performance impact of the three-way interaction between network centricity, customer centricity, and competitor centricity is positive and significant (β = 1396.339, p > .05). This suggests that network centricity is a necessary but not sufficient condition for stronger firm performance. ---- Insert Table 2.6 about here ---- Conversion Ability The results concerning my second research question – the impact of network centricity on conversion ability – are presented in Table 2.7. I tested several models to estimate main effects and the interaction among the variables of interest. I first ran a model with control variables only (Model 1). Then, I tested Model 2 which estimates main effects. Model 3, which results are discussed below, included a two-way interaction between network centricity and R&D intensity. The results demonstrate that network centricity is not associated with firm’s conversion ability (β = -.058, n.s.). However, R&D intensity diminishes the speed with which companies convert ideas into products (β = -.001, p > .001). At the same time, the interaction between network centricity and R&D intensity has a positive and significant impact (β = .003, p > .01). Thus, these results suggest that companies that develop network centricity may not only diminish the 86 negative impact of R&D intensity on conversion ability, but also speed up their pace of idea- product conversion. ---- Insert Table 2.7 about here ---- Robustness Checks To establish the robustness of my findings, I conducted a series of additional analyses. Variable operationalization. I measure network centricity as a composite of two highly correlated variables (r = .68): number of partnerships and diversity of partnerships. To ensure that my results were not an artifact of measurement specification, I have conducted a robustness check to estimate the potential excess impact of sub-elements that are used to measure network centricity. I have created three dummies based on the median splits to reflect high-high, high- low, and low-high levels of these variables, with low-low as the base level. Then, I re-run my models with this set of dummies. The impact of dummies is not significant; the addition of dummies does not alter the substantive results regarding other variables. Observation period. I also conducted a panel model estimate using a different observation period (i.e., 2006–2015; cf. Kirca et al. 2020). The results of these additional analyses are presented in Table 2.8 and support my empirical findings presented in Table 2.6. Consistent with my findings, the impact of the three-way interaction of NC x CUC x COO on firm performance is positive and significant (β = 2739.624, p > .01). ---- Insert Table 2.8 about here ---- Number of R&D projects. Firms with just a few product development projects may have extreme levels of product conversation. I have conducted a robustness check to eliminate the potential impact of firms with a small number of R&D projects on conversion ability. In doing so, I have identified three outliers. These firms had conversion ability equal to one and a very 87 small number of R&D projects (ranging between two and four). I eliminated these firms and re- run my analyses. The remaining companies with conversion ability > .5 had at least 15 R&D projects. The elimination of outliers does not alter the substantive results regarding other variables. Discussion This paper presents network centricity as a new firm strategic orientation that is critical to developing a competitive advantage in today’s digitally connected, global business environment given the expansion of the marketing paradigm (cf. Achrol and Kotler 2012). Digitalized interactions and new interactive platforms shift the fundamentals of the value creation process from a traditional transactional, exchange-based perspective to value creation through complex interactions (Ramaswamy and Ozcan 2018). Respectively, marketing literature gradually shifts from the dominant dyadic perspective of interfirm relationships to a network perspective (Borah et al. 2022). However, while there is theoretical support for the network organization (Powell 1990) and the role of marketing within the network organization (Achrol 1991, 1997; Achrol and Kotler 1999), there is a continued shortage of research helping define and explain this phenomenon (Borah et al. 2022). This study extends this work by considering specific norms, values, and beliefs of a network firm. The conception of network centricity as the emerging strategic orientation presents novel insights that improve the understanding of how firms compete in the XXI century (cf. Achrol and Kotler 2022). This study provides conceptualization and operationalization of network centricity. Driven by globalization and digitalization, firms transform the way they operate, create value, and compete (Autio, Mudambi, and Yoo 2021; Borah et al. 2022). New business models and processes emerge (Alcácer, Cantwell, and Piscitello 2016). Firms that adopt a network centricity 88 approach can strengthen their competitive advantage and performance (Novoselova 2022). It may even be a question of organizational survival. As the locus of innovation shifts from individual companies to networks of firms (Powell, Koput, and Smith-Doerr 1996), the locus of competition also gradually moves from individual companies to firm networks (Thorelli 1986). I introduce the concept of network centricity, an emerging firm strategic orientation that reflects recent changes in the global business environment. This study examines the key characteristics of network organization and proposes a way to measure network centricity based on the analysis of firm partnerships. In addition, this research extends our understanding of the complementarity of firm strategic orientations (Hakala 2011; Schweiger et al. 2019). I investigate the interplay of network centricity with two other established firm strategic orientations: customer centricity and competitor centricity. Prior research emphasizes the importance of having both customer centricity and competitor centricity (Day and Nedungadi 1994), a characteristic of best performing companies. At the same, the authors note that the “emphasis on the customer and competitor dimensions is not intended to preclude the possibility that other dimensions also underlie the representational structure” (Day and Nedungadi 1994, p. 42). This research brings a network perspective of firm relationships into strategic orientation literature. To account for the interrelatedness of various strategic orientations (Schweiger et al. 2019), I follow the complementarity approach (Hakala 2011), which considers situations when companies have several strategic orientations simultaneously. Firm capabilities to align different strategic orientations is a key to superior firm performance via creation of synergies that “surpass the effects of individual strategic orientations” (Schweiger et al. 2019, p. 1822). As network 89 centricity inherently incorporates a dual focus on both customers and competitors, companies that incorporate all three strategic orientations may strengthen their competitive positions. Finally, this research examines the consequences of network centricity to illustrate why and how this strategic orientation matters from a managerial perspective. Results indicate that network centricity is beneficial to financial performance when firms develop it simultaneously with customer centricity and competitor centricity. In addition, I explore the role of network centricity in improving the conversion of research ideas into marketable products. Conversion ability is a major challenge and critical factor that determines success of innovative companies (Chandy et al. 2006). My results indicate that while excess R&D resources may hamper conversion ability, the interaction of network centricity and R&D intensity can reverse this impact. This could be driven by a more balanced approach toward new product development across different stages of the R&D process in network firms due to shared competencies and involvement of multiple partners preventing the excessive shift of resources toward initial stages. Combined, the learnings of this study should help scholars better understand the network centricity phenomenon, its performance outcomes, and specifics of its interactions with other firm strategic orientations. Managerial Implications Firms that adopt advanced strategic orientations outperform companies that follow preceding strategic orientations (Ramani and Kumar 2008). Over time, companies gradually transitioned from one dominant strategic orientation to another: from product orientation to sales orientation, from sales orientation to market orientation, and so on. Firms are gradually involved in more complex relationships. Specifically, after the unilateral relationships (i.e., market orientation, customer centricity, competitor centricity), companies shifted to bilateral 90 relationships (i.e., interaction orientation, stakeholder marketing, engagement orientation). Mirroring recent trends in globalization and digitalization, network centricity reflects the multilateral relationships between companies. Companies that pioneer the adoption of network centricity are likely to outperform firms focusing on preceding – and sometimes outdated – strategic orientations, which became a cost of doing business and can no longer provide a source of sustained competitive advantage. Still, senior executives considering or developing network centricity in their organizations should be conscious that this strategic orientation is necessary but not sufficient. Network centricity reflects the shifts in global business approach. However, to fully reveal its potential, companies need to acknowledge the complementarity of different business approaches and develop network centricity in addition to other critical strategic orientations. The focus on customers (i.e., customer centricity) and the focus on competitors (i.e., competitor centricity) are still relevant and important. Moreover, without proper reflection of customer and competitor perspectives, network centricity alone may not help achieve and maintain sustainable competitive advantage. The understanding of the network centricity’s interaction with other firm strategic orientations will have meaningful managerial implications and can guide senior executives in the selection of appropriate firm strategic orientations for their companies. Finally, company leaders should acknowledge the changing nature of competition. Digitalization and globalization of markets not only change the way companies create, deliver, and capture value (Autio, Mudambi, and Yoo 2021; Borah et al. 2022), but also transform the global economic order which existed over the past several decades (Petricevic and Teece 2019). As companies are no longer limited by the constraints of collocation and transportation (Autio, Mudambi, and Yoo 2021), new business models and processes emerge (Alcácer, Cantwell, and 91 Piscitello 2016). Top executives should go beyond traditional perceptions of businesses, markets, and competitors. New technologies and business models blur the boundaries between firms and industries (Cano-Kollmann et al. 2016; Christensen, Raynor, and McDonald 2015). Companies are forming partnerships with their traditional rivals to jointly enter new business segments and compete with new market entrants. Firm rivalry shifts from competition of individual companies to competition of networks. Limitations and Future Research As the first attempt to investigate network centricity as a firm strategic orientation, this research has some limitations which should be considered when interpreting the findings. At the same time, this study suggests multiple potential avenues for future research in this domain. First, I study the impact of network centricity on two outcomes: firm performance and product conversion ability. Thus, future researchers may investigate other potential consequences of network centricity. Second, I present the consequences of network centricity but do not explore its antecedents. The examination of the drivers of network centricity may bring valuable insights to academia and practice. Third, I explore the interplay of network centricity with two strategic orientations: customer centricity and competitor centricity. Scholars may also explore the interaction of network centricity with other firm strategic orientations, such as engagement orientation (Kumar and Pansari 2016), entrepreneurial orientation (Atuahene-Gima and Ko 2001), interaction orientation (Ramani and Kumar 2008), learning orientation (Hurley and Hult 1998), or technological orientation (Gatignon and Xuereb 1997). Fourth, I utilize firm partnership data from the Medtrack pharmaceutical database to measure network centricity. It might be relevant to investigate other potential data sources that could help capture and examine network centricity. Finally, I study network centricity in the context of biotechnology industry. I 92 believe that future research may consider other industries as well as study network centricity across several industries. That should provide valuable insights for researchers and managers. 93 REFERENCES Achrol, Ravi S. (1991), “Evolution of the Marketing Organization: New Forms for Turbulent Environments,” Journal of Marketing, 55 (4), 77–93. Achrol, Ravi S. (1997), “Changes in the Theory of Interorganizational Relations in Marketing: Toward a Network Paradigm,” Journal of the Academy of Marketing Science, 25 (1), 56– 71. Achrol, Ravi S. and Philip Kotler (1999), “Marketing in the Network Economy,” Journal of Marketing, 63, 146–63. Achrol, Ravi S. and Philip Kotler (2012), “Frontiers of the marketing paradigm in the third millennium,” Journal of the Academy of Marketing Science, 40 (1), 35–52. Achrol, Ravi S. and Philip Kotler (2022), “Distributed marketing networks: The fourth industrial revolution,” Journal of Business Research, 150, 515–27. Alcácer, Juan, John Cantwell, and Lucia Piscitello (2016), “Internationalization in the information age: A new era for places, firms, and international business networks?” Journal of International Business Studies, 47 (5), 499–512. Allee, Verna (2009), “Value‐creating networks: organizational issues and challenges,” The Learning Organization, 16 (6), 427–42. Alvarez, Sharon A. and Jay B. Barney (2001), “How entrepreneurial firms can benefit from alliances with large partners,” Academy of Management Perspectives, 15 (1), 139–48. Atuahene-Gima, Kwaku and Anthony Ko (2001), “An Empirical Investigation of the Effect of Market Orientation and Entrepreneurship Orientation Alignment on Product Innovation,” Organization Science, 12 (1), 54–74. Autio, Erkko, Ram Mudambi, and Youngjin Yoo (2021), “Digitalization and globalization in a turbulent world: Centrifugal and centripetal forces,” Global Strategy Journal, 11 (1), 3– 16. Barney, Jay B. (1991), “Firm Resources and Sustained Competitive Advantage,” Journal of Management, 17 (1), 99–120. Barney, Jay B., David J. Ketchen, and Mike Wright (2021), “Resource-Based Theory and the Value Creation Framework,” Journal of Management, 47 (7), 1936–55. BCG (2019), “Most Innovative Companies 2019: Rise of AI, Platforms, and Ecosystems,” Boston Consulting Group, Retrieved from https://www.bcg.com/publications/collections/most-innovative-companies-2019- artificial-intelligence-platforms-ecosystems.aspx. 94 Bhattacharya, Abhi, Neil A. Morgan, and Lopo L. Rego (2022), “Examining Why and When Market Share Drives Firm Profit,” Journal of Marketing, 86 (4), 73–94. Blau, Peter M. (1977), Inequality and Heterogeneity: A Primitive Theory of Social Structure, New York, NY: Free Press. BMW Group (2021), “BMW Group and Daimler Mobility join forces with bp as a partner for Digital Charging Solutions GmbH, to further accelerate the growth of electrification,” Retrieved from https://www.press.bmwgroup.com/global/article/detail/T0328813EN/bmw-group-and- daimler-mobility-join-forces-with-bp-as-a-partner-for-digital-charging-solutions-gmbh- to-further-accelerate-the-growth-of-electrification?language=en. Bolton, Lisa E., Luk Warlop, and Joseph W. Alba (2003), “Consumer Perceptions of Price (Un)Fairness,” Journal of Consumer Research, 29 (4), 474–91. Borah, Sourav Bikash, Girish Mallapragada, Raghu Bommaraju, Rajkumar Venkatesan, and Narongsak Thongpapanl (2022), “Interfirm collaboration and exchange relationships: An agenda for future research,” International Journal of Research in Marketing, 39 (2), 603– 18. Borgatti, Stephen P. and Xun Li (2009), “On Social Network Analysis In A Supply Chain Context,” Journal of Supply Chain Management, 45 (2), 5–22. Bourla, Albert (2021), “The CEO of Pfizer on Developing a Vaccine in Record Time,” Harvard Business Review, May-June. Cano-Kollmann, Marcelo, John Cantwell, Thomas J Hannigan, Ram Mudambi, and Jaeyong Song (2016), “Knowledge connectivity: An agenda for innovation research in international business,” Journal of International Business Studies, 47 (3), 255–62. Chakravarty, Anindita, Chen Zhou, and Ashish Sharma (2020), “Effect of Alliance Network Asymmetry on Firm Performance and Risk,” Journal of Marketing, 84 (6), 74–94. Chandy, Rajesh, Brigitte Hopstaken, Om Narasimhan, and Jaideep Prabhu (2006), “From Invention to Innovation: Conversion Ability in Product Development,” Journal of Marketing Research, 43 (3), 494–508. Child, John (1972), “Organizational Structure, Environment and Performance: The Role of Strategic Choice,” Sociology, 6 (1), 1–22. Christensen, Clayton M., Michael E. Raynor, and Rory McDonald (2015), “What Is Disruptive Innovation?” Harvard Business Review, 93 (12), 44–53. Crecelius, Andrew T., Justin M. Lawrence, Ju-Yeon Lee, Son K. Lam, and Lisa K. Scheer (2019), “Effects of channel members’ customer-centric structures on supplier performance,” Journal of the Academy of Marketing Science, 47 (1), 56–75. 95 Day, George S. (2000), “Managing market relationships,” Journal of the Academy of Marketing Science, 28 (1), 24–30. Day, George S. and Prakash Nedungadi (1994), “Managerial Representations of Competitive Advantage,” Journal of Marketing, 58 (2), 31–44. Dean, Tereza, David A. Griffith, and Goksel Yalcinkaya (2023), “The roles of shadow of the past and future in driving new product novelty and meaningfulness within coopetitive collaborations,” Industrial Marketing Management, 109, 174–87. Deshpandé, Rohit, John U. Farley, and Frederick E. Webster (1993), “Corporate Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad Analysis,” Journal of Marketing, 57 (1), 23–37. Deshpandé, Rohit and Frederick E. Webster (1989), “Organizational Culture and Marketing: Defining the Research Agenda,” Journal of Marketing, 53 (1), 3–15. Deutsch, Morton (1949), “A Theory of Co-operation and Competition,” Human Relations, 2 (2), 129–52. DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields,” American Sociological Review, 48 (2), 147–60. Drucker, Peter F. (1988), “The Coming of the New Organization,” Harvard Business Review, 66 (January-February), 45–53. Druedahl, Louise C., Timo Minssen, and W. Nicholson Price (2021), “Collaboration in times of crisis: A study on COVID-19 vaccine R&D partnerships,” Vaccine, 39 (42), 6291–95. Eccles, Robert G. (1981), “The quasifirm in the construction industry,” Journal of Economic Behavior & Organization, 2 (4), 335–57. Fader, Peter (2020), Customer Centricity: Focus on the Right Customers for Strategic Advantage, 2nd ed., Philadelphia: Wharton Digital Press. Fang, Eric (Er), Jongkuk Lee, and Zhi Yang (2015), “The Timing of Codevelopment Alliances in New Product Development Processes: Returns for Upstream and Downstream Partners,” Journal of Marketing, 79 (1), 64–82. Feng, Cong, Pankaj C. Patel, and K. Sivakumar (2020), “Chief global officers, geographical sales dispersion, and firm performance,” Journal of Business Research, 121, 58–72. Frels, Judy K., Tasadduq Shervani, and Rajendra K. Srivastava (2003), “The Integrated Networks Model: Explaining Resource Allocations in Network Markets,” Journal of Marketing, 67 (1), 29–45. 96 Gatignon, Hubert and Jean-Marc Xuereb (1997), “Strategic Orientation of the Firm and New Product Performance,” Journal of Marketing Research, 34 (1), 77–90. Gebhardt, Gary F., Gregory S. Carpenter, and John F. Sherry (2006), “Creating a Market Orientation: A Longitudinal, Multifirm, Grounded Analysis of Cultural Transformation,” Journal of Marketing, 70 (4), 37–55. Golder, Peter N., Marnik G. Dekimpe, Jake T. An, Harald J. van Heerde, Darren S.U. Kim, and Joseph W. Alba (2023), “Learning from Data: An Empirics-First Approach to Relevant Knowledge Generation,” Journal of Marketing, 87 (3), 319–36. Golder, Peter N. and Gerard J. Tellis (1993), “Pioneer Advantage: Marketing Logic or Marketing Legend?” Journal of Marketing Research, 30 (2), 158–70. Graebner, Melissa E., Anne Marie Knott, Marvin B. Lieberman, and Will Mitchell (2023), “Empirical inquiry without hypotheses: A question-driven, phenomenon-based approach to strategic management research,” Strategic Management Journal, 44 (1), 3–10. Grewal, Rajdeep, Anindita Chakravarty, Min Ding, and John Liechty (2008), “Counting chickens before the eggs hatch: Associating new product development portfolios with shareholder expectations in the pharmaceutical sector,” International Journal of Research in Marketing, 25 (4), 261–72. Griffith, David A., Tereza Dean, and Goksel Yalcinkaya (2021), “Building and leveraging competence exploitation and exploration for firm new product success,” Industrial Marketing Management, 97, 233–44. Grobalt, Sam (2016), “Benz vs. BMW: A Century of Out-Inventing Each Other,” Bloomberg Businessweek, June 9 (Retrieved from https://www.bloomberg.com/news/articles/2016- 06-09/benz-vs-bmw-a-century-of-out-inventing-each-other). Hakala, Henri (2011), “Strategic Orientations in Management Literature: Three Approaches to Understanding the Interaction between Market, Technology, Entrepreneurial and Learning Orientations,” International Journal of Management Reviews, 13 (2), 199–217. Hillebrand, Bas, Paul H. Driessen, and Oliver Koll (2015), “Stakeholder marketing: theoretical foundations and required capabilities,” Journal of the Academy of Marketing Science, 43 (4), 411–28. Homburg, Christian, Marcus Theel, and Sebastian Hohenberg (2020), “Marketing Excellence: Nature, Measurement, and Investor Valuations,” Journal of Marketing, 84 (4), 1–22. Houston, Mark B., Michael Hutt, Christine Moorman, Peter H. Reingen, Aric Rindfleisch, Vanitha Swaminathan, and Beth Walker (2004), “A Network Perspective on Marketing Strategy Performance,” in Assessing Marketing Strategy Performance, C. Moorman and D. R. Lehmann, eds., Boston, Mass.: Marketing Science Institute, 247–68. 97 Huber, George P. (1991), “Organizational Learning: The Contributing Processes and the Literatures,” Organization Science, 2 (1), 88–115. Hult, G. Tomas M., Jeannette A. Mena, O. C. Ferrell, and Linda Ferrell (2011), “Stakeholder marketing: a definition and conceptual framework,” AMS Review, 1 (1), 44–65. Humphreys, Ashlee and Gregory S. Carpenter (2018), “Status Games: Market Driving through Social Influence in the U. S. Wine Industry,” Journal of Marketing. Hunt, Shelby D. and Robert M. Morgan (1994), “Relationship marketing in the era of network competition,” Marketing Management, 3 (1), 18. Hunt, Shelby D. and Robert M. Morgan (1995), “The Comparative Advantage Theory of Competition,” Journal of Marketing, 59 (2), 1–15. Hurley, Robert F. and G. Tomas M. Hult (1998), “Innovation, Market Orientation, and Organizational Learning: An Integration and Empirical Examination,” Journal of Marketing, 62 (3), 42–54. Jayachandran, Satish, Subhash Sharma, Peter Kaufman, and Pushkala Raman (2005), “The Role of Relational Information Processes and Technology Use in Customer Relationship Management,” Journal of Marketing, 69 (4), 177–92. Kirca, Ahmet H., G. Tomas M. Hult, Kendall Roth, S. Tamer Cavusgil, Morys Z. Perryy, M. Billur Akdeniz, Seyda Z. Deligonul, Jeannette A. Mena, Wesley A. Pollitte, Jessica J. Hoppner, Joseph C. Miller, and Ryan C. White (2011), “Firm-Specific Assets, Multinationality, and Financial Performance: A Meta-analytic Review and Theoretical Integration,” Academy of Management Journal, 54 (1), 47–72. Kirca, Ahmet H., Satish Jayachandran, and William O. Bearden (2005), “Market Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on Performance,” Journal of Marketing, 69 (2), 24–41. Kirca, Ahmet H., Praneet Randhawa, M. Berk Talay, and M. Billur Akdeniz (2020), “The interactive effects of product and brand portfolio strategies on brand performance: Longitudinal evidence from the U.S. automotive industry,” International Journal of Research in Marketing, 37 (2), 421–39. Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Construct, Research Propositions, and Managerial Implications,” Journal of Marketing, 54 (2), 1–18. Kotler, Philip (1992), “Marketing’s New Paradigm: What’s Really Happening Out There,” Planning Review, 20 (5), 50–52. Kumar, Pankaj, Xiaojin Liu, and Akbar Zaheer (2022), “How much does the firm’s alliance network matter?” Strategic Management Journal, 43 (8), 1433–68. 98 Kumar, V., Eli Jones, Rajkumar Venkatesan, and Robert P. Leone (2011), “Is Market Orientation a Source of Sustainable Competitive Advantage or Simply the Cost of Competing?” Journal of Marketing, 75 (1), 16–30. Kumar, V. and Anita Pansari (2016), “Competitive Advantage Through Engagement,” Journal of Marketing Research, 53 (4), 497–514. Lee, Hannah Soobin and David A. Griffith (2019), “The Balancing of Country-Based Interaction Orientation and Marketing Strategy Implementation Adaptation/Standardization for Profit Growth in Multinational Corporations,” Journal of International Marketing, 27 (2), 22–37. Lu, Ruichang, Qiaowei Shen, Tenghui Wang, and Xiaojun Zhang (2022), “Frenemies: Corporate Advertising Under Common Ownership,” Management Science, 68 (6), 4645–69. Miles, Raymond E. and Charles C. Snow (1984), “Fit, Failure, and the Hall of Fame,” California Management Review, 26 (3), 10. Miles, Raymond E. and Charles C. Snow (1992), “Causes of Failure in Network Organizations,” California Management Review, 34 (4), 53–72. Mizik, Natalie and Robert Jacobson (2003), “Trading off between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis,” Journal of Marketing, 67 (1), 63–76. MSI (2022), Research Priorities 2022-2024, Cambridge, MA: Marketing Science Institute. Mudambi, Ram (2008), “Location, control and innovation in knowledge-intensive industries,” Journal of Economic Geography, 8 (5), 699–725. Narver, John C. and Stanley F. Slater (1990), “The Effect of a Market Orientation on Business Profitability,” Journal of Marketing, 54 (4), 20–35. Nath, Pravin, Ahmet H. Kirca, Saejoon Kim, and Trina Larsen Andras (2019), “The Effects of Retail Banner Standardization on the Performance of Global Retailers,” Journal of Retailing. Nature (2021), “COVID has shown the power of science–industry collaboration,” Nature, 594 (7863), 302–302. Noble, Charles H., Rajiv K. Sinha, and Ajith Kumar (2002), “Market Orientation and Alternative Strategic Orientations: A Longitudinal Assessment of Performance Implications,” Journal of Marketing, 66 (4), 25–39. Novoselova, Olga A. (2022), “What matters for interorganizational connectedness? Locating the drivers of multiplex corporate networks,” Strategic Management Journal, 43 (4), 872–99. 99 Pennebaker, James W., Roger J. Boyd, Roger J. Booth, Ashwini Ashokkumar, and Martha E. Francis (2022), “Linguistic Inquiry and Word Count: LIWC-22,” Pennebaker Conglomerates. Petricevic, Olga and David J. Teece (2019), “The structural reshaping of globalization: Implications for strategic sectors, profiting from innovation, and the multinational enterprise,” Journal of International Business Studies, 50 (9), 1487–1512. Powell, Walter W. (1990), “Neither market nor hierarchy: Network forms of organization,” Research in Organizational Behavior, 12, 295–336. Powell, Walter W., Kenneth W. Koput, and Laurel Smith-Doerr (1996), “Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology,” Administrative Science Quarterly, 41 (1), 116–45. Prabhu, Jaideep C., Rajesh K. Chandy, and Mark E. Ellis (2005), “The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic?” Journal of Marketing, 69 (1), 114–30. Provan, Keith G., Amy Fish, and Joerg Sydow (2007), “Interorganizational Networks at the Network Level: A Review of the Empirical Literature on Whole Networks,” Journal of Management, 33 (3), 479–516. Ramani, Girish and V. Kumar (2008), “Interaction Orientation and Firm Performance,” Journal of Marketing, 72 (1), 27–45. Ramaswamy, Venkat and Kerimcan Ozcan (2018), “Offerings as Digitalized Interactive Platforms: A Conceptual Framework and Implications,” Journal of Marketing, 82 (4), 19–31. Rao, Raghunath Singh, Rajesh K. Chandy, and Jaideep C. Prabhu (2008), “The Fruits of Legitimacy: Why Some New Ventures Gain More from Innovation Than Others,” Journal of Marketing, 72 (4), 58–75. Reid, David (2019), “Daimler and BMW announce $1.1 billion partnership in a bid to take on Uber,” CNBC, February 22 (Retrieved from https://www.cnbc.com/2019/02/22/daimler- and-bmw-announce-1point1-billion-partnership-to-rival-uber.html). Rust, Roland T., William Rand, Ming-Hui Huang, Andrew T. Stephen, Gillian Brooks, and Timur Chabuk (2021), “Real-Time Brand Reputation Tracking Using Social Media,” Journal of Marketing, 85 (4), 21–43. Saboo, Alok R. and Rajdeep Grewal (2013), “Stock Market Reactions to Customer and Competitor Orientations: The Case of Initial Public Offerings,” Marketing Science, 32 (1), 70–88. Saboo, Alok R., Amalesh Sharma, Anindita Chakravarty, and V. Kumar (2017), “Influencing Acquisition Performance in High-Technology Industries: The Role of Innovation and Relational Overlap,” Journal of Marketing Research, 54 (2), 219–38. 100 Salancik, Gerald R. (1995), “WANTED: A Good Network Theory of Organization,” Administrative Science Quarterly, (R. S. Burt, ed.), 40 (2), 345–49. Schweiger, Simone A., Tatiana R. Stettler, Artur Baldauf, and César Zamudio (2019), “The complementarity of strategic orientations: A meta-analytic synthesis and theory extension,” Strategic Management Journal, 40 (11), 1822–51. Shah, Denish, Roland T. Rust, A. Parasuraman, Richard Staelin, and George S. Day (2006), “The Path to Customer Centricity,” Journal of Service Research, 9 (2), 113–24. Sharma, Amalesh, Alok R. Saboo, and V. Kumar (2018), “Investigating the Influence of Characteristics of the New Product Introduction Process on Firm Value: The Case of the Pharmaceutical Industry,” Journal of Marketing, 82 (5), 66–85. Slater, Stanley F., Eric M. Olson, and G. Tomas M. Hult (2006), “The moderating influence of strategic orientation on the strategy formation capability–performance relationship,” Strategic Management Journal, 27 (12), 1221–31. Srivastava, Rajendra K., Liam Fahey, and H. Kurt Christensen (2001), “The resource-based view and marketing: The role of market-based assets in gaining competitive advantage,” Journal of Management, 27 (6), 777–802. Stahl, Florian, Mark Heitmann, Donald R. Lehmann, and Scott A. Neslin (2012), “The Impact of Brand Equity on Customer Acquisition, Retention, and Profit Margin,” Journal of Marketing, 76 (4), 44–63. Stremersch, Stefan and Walter Van Dyck (2009), “Marketing of the Life Sciences: A New Framework and Research Agenda for a Nascent Field,” Journal of Marketing, 73 (4), 4– 30. Swaminathan, Vanitha and Christine Moorman (2009), “Marketing Alliances, Firm Networks, and Firm Value Creation,” Journal of Marketing, 73 (5), 52–69. Talay, M. Berk, Koen Pauwels, and Steven H. Seggie (2023), “Why and when to launch new products during a recession: An empirical investigation of the U.K. FMCG industry and the U.S. automobile industry,” Journal of the Academy of Marketing Science. Teece, David J. (1998), “Capturing value from knowledge assets: The new economy, markets for know-how, and intangible assets,” California Management Review, 40 (3), 55–79. Teece, David J. (2010), “Business Models, Business Strategy and Innovation,” Business Models, 43 (2), 172–94. Teece, David J. (2022), “In a World Undergoing Seismic Change, Corporations Need to Think Long-Term,” ThinkSet, [available at https://thinksetmag.com/insights/teece-corporations- long-term]. 101 Thorelli, Hans B. (1986), “Networks: Between markets and hierarchies,” Strategic Management Journal, 7 (1), 37–51. Wooldridge, Jeffrey M. (2016), Introductory econometrics: A modern approach, Boston, MA: Cengage Learning. Wuyts, Stefan, Shantanu Dutta, and Stefan Stremersch (2004), “Portfolios of Interfirm Agreements in Technology-Intensive Markets: Consequences for Innovation and Profitability,” Journal of Marketing, 68 (2), 88–100. Zachary, Miles A., Aaron McKenny, Jeremy Collin Short, and G. Tyge Payne (2011), “Family Business and Market Orientation: Construct Validation and Comparative Analysis,” Family Business Review, 24 (3), 233–51. Zhou, Kevin Zheng, Chi Kin (Bennett) Yim, and David K. Tse (2005), “The Effects of Strategic Orientations on Technology- and Market-Based Breakthrough Innovations,” Journal of Marketing, 69 (2), 42–60. 102 APPENDIX A: TABLES Table 2.1. Comparison of Firm Strategic Orientations Orientation Definition Source of value creation Relationships “the organizationwide generation of market intelligence pertaining to Market current and future customer needs, dissemination of the intelligence generation and utilization orientation across departments, and organizationwide responsiveness to it” (Kohli of market information and Jaworski 1990, p. 6). organizationwide “understanding of one’s target buyers to be able to Customer create superior value for them continuously” (Narver and Slater 1990, p. customer understanding unilateral centricity 21). organizationwide understanding of “the short-term strengths and Competitor weaknesses and long-term capabilities and strategies of both the key competitor understanding centricity current and the key potential competitors” (Narver and Slater 1990, p. 21-22). “a firm’s ability to interact with its individual customers and to take Interaction advantage of information obtained from them through successive firm-customer orientation interactions to achieve profitable customer relationships” (Ramani and interactions Kumar 2008, p. 27). “activities and processes within a system of social institutions that exchange relationships Stakeholder facilitate and maintain value through exchange relationships with with multiple bilateral marketing multiple stakeholders” (Hult et al. 2011, p. 44). stakeholders “the process of embedding engagement in the organization as a policy customer and employee Engagement decision and ensuring that all strategies of the organization focus on engagement embedded in orientation engaging the customers and the employees, along with value the organization maximization for all stakeholders” (Kumar and Pansari 2016, p. 511). the extent to which a company promotes the development of firm’s long-term multilateral Network long-term multilateral partnerships with other organizations and partnerships with other multilateral centricity considers these partnerships as a source of value creation and a joint or organizations relational asset in the contest with other networks. 103 Table 2.2. List of Variables Variable Description Data source (i) number of partnerships: total number of Medtrack firm’s deals Network centricity (ii) diversity of partnerships: the range of (composite) different partnership types (Powell, Koput, Medtrack and Smith-Doerr 1996) letters to shareholders Customer centricity customer centricity of the firm in firm annual reports letters to shareholders Competitor centricity competitor centricity of the firm in firm annual reports geographical dispersion of firm activities letters to shareholders International scope (Feng, Patel, and Sivakumar 2020) in firm annual reports Firm performance Firm profit Compustat R&D intensity R&D expenditures / sales Compustat Revenue Firm revenue Compustat Firm size Number of employees Compustat Cost of coordination Firm HQ location of as proxy (Lu et al. 2022) Compustat Firm industry SIC code number Compustat the proportion of investigational drugs beyond Conversion ability Medtrack stage three (adapted from Chandy et al. 2006) 104 Table 2.3. Dictionaries Employed for LIWC Text Classification Construct Dictionary Word List attendee*, buyer*, buying, client*, clientele, consume*, customer*, Customer centricity emptor*, habitue, patron*, shopper*, spectator*, subscribe*, user*, vend, vended, visitor*, outpatient*, patient* advantage*, adversary, adverse*, aggression, aggressor, ambition*, ambitious, antagonist*, antagonize*, aspirant, assail, barricade, beat*, bid*, challenge*, clash*, collide*, compete*, competing, competit*, conflict*, confront*, conquer*, contend*, contentious, contest*, Competitor centricity counteract*, cutthroat, disputant*, enemies, entrant*, fight, foe*, formidable, fought, grapple*, imitator*, jockey, jockied, match*, opponent*, oppose*, opposition*, outclass*, outmatch*, outrank*, outrate*, resist, rival*, spar, sparing, strive*, struggle*, superior*, surpass*, vied, vying, war abroad, export, foreign, global*, import, intercontinental*, International scope international*, multinational*, offshore, overseas, transcontinental*, transnational*, worldwide 105 Table 2.4. Dictionary Modifications Dictionary Comments Words Examples of phrases from Letter to Shareholders Added words that are used to - “transformed the lives of an increasing number of patients” patient reflect customers/clients - “first and foremost, focused on patients” - “enter into… stock purchase agreement” Customer centricity purchase Removed words that are used - “that includes conditional purchase order” to reflect irrelevant contexts - “including purchasing the Shire vaccine business” purchasing - “after purchasing all of … remaining options” - “people who are fighting a battle against cancer” battle - “they battle together with their child” - “block harmful immune responses” block - “antibody to block infection” - “androgen receptor blockade” blockade - “blockade of cancer networks” Removed words that are used Competitor centricity - “combat the complexities of diseases” to reflect irrelevant contexts combat - “combat some of the most aggressive forms of cancer” - “engage me in a consulting contract” engage - “company broadly engaged in discovery research” - “medical records from 500,000 volunteer participants” participant - “neither the participant nor the investigator will know which treatment each individual is assigned” Note: modification of dictionaries by Homburg, Theel, and Hohenberg (2020) to measure customer centricity and competitor centricities 106 Table 2.5. Descriptive Statistics and Correlations Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) Network centricity (2) Customer centricity -.109** (3) Competitor centricity .083* -.127*** (4) Gross profit .289*** .074* .116*** (5) International scope .234*** .138*** -.056 .218*** (6) R&D intensity -.022 -.015 .066 -.018 -.048 (7) Gross profit (t-1) .296*** .048 .109** .982*** .217*** -.017 (8) Revenue (dummy) .173*** -.050 .085* .068*** .117*** .007 .067*** (9) Firm size (dummy) -.331*** -.073* -.084* -.401*** -.408*** -.029 -.386*** -.175*** Mean .116 .766 .161 208.091 .168 52.523 198.713 .796 .887 Standard deviation .167 .582 .195 1581.691 .194 575.735 1546.191 .403 .315 *** p < 0.001, ** p < 0.01, * p < 0.05. Notes: Correlations between measures pooled across 640 firm years (unbalanced panel of 126 firms observed over 13 years). 107 Table 2.6. The Impact of Network Centricity on Gross Profit Model 1 Model 2 Model 3 Model 4 Network centricity 138.194 (118.527) -478.710 (465.12) -215.857 (429.729) Network centricity x Customer centricity x 1396.339 (557.615)* Competitor centricity Network centricity x 913.627 (641.247) 626.139 (603.465) Customer centricity Network centricity x -720.813 (832.511) -1989.225 (1093.525)† Competitor centricity Customer centricity x -51.858 (125.094) Competitor centricity Customer centricity 117.618 (99.394) 116.603 (100.278) -24.311 (32.506) -11.711 (33.163) Competitor centricity -11.662 (51.989) -16.759 (50.614) 76.000 (123.747) 126.558 (129.329) International scope -478.892 (306.018) -481.665 (304.904) -465.843 (286.118) -452.960 (283.532) R&D intensity .009 (.004)* .009 (.004)* .006 (.003)† .005 (.003)† Gross profit (t-1) .951 (.035)*** .949 (.034)*** .951 (.031)*** .952 (.032)*** Firm size -66.304 (34.647)† -60.334 (33.195)† -51.849 (37.730) -54.860 (38.095) (SME dummy) Revenue (positive -130.564 (84.300) -133.848 (86.610) -37.562 (67.094) -22.384 (62.600) revenue dummy) Constant 246.233 (78.564)** 218.395 (80.530)** 224.796 (87.585)* 192.919 (78.035)* Firm fixed effects yes yes yes yes Year fixed effects yes yes yes yes Lagged DV yes yes yes yes #Observations 646 640 640 640 (firm-years) #Firms 131 126 126 126 R2 .957 .958 .958 .958 Note: fixed effects panel model, robust standard errors *** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1, standard errors in parentheses 108 Table 2.7. The Impact of Network Centricity on Conversion Ability Conversion Ability (t+2) Model 1 Model 2 Model 3 Controls Only Main Effects Interaction Effects Network centricity .005 (.125) -.058 (.127) R&D intensity -.000 (.000) † -.001 (.000)*** Network centricity x .003 (.001)** R&D intensity Firm size (SME dummy) -.102 (.050) * -. 085 (.058) -.101 (.058)† Revenue -.000 (.000) -.000 (.000) .000 (.000) Constant .213 (.056)*** .060 (.160) .099 (.157) Control for cost of yes yes yes coordination (HQ location) Control for industry yes yes yes (SIC code number) #Observations 107 107 107 (firm-years) #Firms 107 107 107 R2 .218 .308 .331 Note: OLS regression, robust standard errors *** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1, standard errors in parentheses 109 Table 2.8. The Impact of Network Centricity on Gross Profit Using 2006-2015 as the Observation Period Model 1 Model 2 Model 3 Model 4 Network centricity -82.546 (109.470) -588.870 (344.118)† -57.659 (192.561) Network centricity x Customer centricity x 2739.624 (998.046)** Competitor centricity Network centricity x 400.560 (280.699) -161.262 (124.694) Customer centricity Network centricity x 1071.266 (703.495) -1435.647 (820.585)† Competitor centricity Customer centricity x -192.219 (185.989) Competitor centricity Customer centricity 105.449 (65.467) 107.112 (66.143) 35.848 (41.122) 71.571 (36.324)† Competitor centricity 124.817 (103.289) 126.357 (103.226) -104.781 (94.945) 69.681 (117.432) International scope -37.275 (166.485) -34.108 (165.375) -19.616 (162.453) .607 (165.252) R&D intensity .014 (.003)*** .014 (.003)*** .015 (.003)*** .013 (.003)*** Gross profit (t-1) 1.323 (.063)*** 1.324 (.063)*** 1.327 (.061)*** 1.335 (.058)*** Firm size -30.199 (37.349) -34.025 (36.849) -26.442 (35.433) -43.146 (30.388) (SME dummy) Revenue (positive -14.282 (36.329) -12.182 (35.776) -44.436 (55.656) 10.521 (38.794) revenue dummy) Constant -72.094 (88.952) -58.755 (78.024) 63.449 (127.467)* -52.879 (60.025) Firm fixed effects yes yes yes Yes Year fixed effects yes yes yes Yes Lagged DV yes yes yes Yes #Observations 522 518 518 518 (firm-years) #Firms 121 118 118 118 R2 .955 .955 .955 .954 Note: fixed effects panel model, robust standard errors *** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1, standard errors in parentheses 110