Collaborative capability and organizational performance: Assessing strategic choice and purity

Accepted Manuscript Exploring Strategy’s Influence on a Firm’s Cross-Functional Collaboration

Yao “Henry” Jin, Neil R. Anderson, T. Richard, Stanley Fawcett, Dee Fawcett, David Swanson PII:

S0925-5273(19)30129-X

DOI:

10.1016/j.ijpe.2019.04.006

Reference:

PROECO 7347

To appear in:

International Journal of Production Economics

Received Date:

10 September 2018

Accepted Date:

09 April 2019

Please cite this article as: Yao “Henry” Jin, Neil R. Anderson, T. Richard, Stanley Fawcett, Dee Fawcett, David Swanson, Exploring Strategy’s Influence on a Firm’s Cross-Functional Collaboration, International Journal of Production Economics (2019), doi: 10.1016/j.ijpe. 2019.04.006

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EXPLORING STRATEGY’S INFLUENCE ON A FIRM’S CROSS-FUNCTIONAL COLLABORATION

Yao “Henry” Jin, Ph.D. Neil R. Anderson Assistant Professor Department of Management Miami University Richard T. Farmer School of Business FSB 3022 Miami University Oxford, OH 45056 Stanley Fawcett, Ph.D. John B. Goddard Endowed Chair of Global Supply Chain Management Director of the Moyes Center for Supply Chain Excellence Department of Supply Chain Management John B. Goddard School of Business & Economics Weber State University 1337 Edvalson St, Dept 3801 Ogden, Utah 84408-3801 Dee Fawcett, Ph.D. Assistant Professor of Supply Chain Management Department of Supply Chain Management John B. Goddard School of Business & Economics Weber State University 1337 Edvalson St, Dept 3801 Ogden, Utah 84408-3801 David Swanson, Ph.D.*** Assistant Professor of Transportation and Logistics Department of Marketing and Logistics University of North Florida 1 UNF Drive, Building 42, Office 3232 Jacksonville, FL 32224 (904) 620-5228 [email protected] *** Corresponding Author

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COLLABORATIVE CAPABILITY AND ORGANIZATIONAL PERFORMANCE: ASSESSING STRATEGIC CHOICE AND PURITY Abstract A frequently asked research question is, “Does cross-functional collaboration improve firm performance?” The findings are mixed. Much research reports a positive relationship. Some research finds a negative relationship. Other research argues that the question is not so easy to answer. The fact that boundary conditions remain underexplored may provide the key to reconciling divergent findings. We assess the potential moderating role of strategic choice and strategic purity on the collaboration-performance linkage. Drawing on Constituency-Based and Organizational Conflict theories, we hypothesize various aspects of strategy (orientation and purity) as direct influences on a firm’s collaborative capability as well as moderating influence on the collaboration-performance linkage. Hypotheses are tested utilizing survey responses from European supply chain managers and a multilevel model. We find that while the differentiation strategy moderated the collaboration-performance linkage, counterintuitive results associated with strategic purity are explained by strategic intensity. This suggests that collaboration does not passively align divergent goals among multiple constituents of the firm. Rather, proactive alignment is required as a superordinate enabler. These results are found to be true regardless of strategic purity.

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If you ask me what I stay awake at night thinking about, its cross-functional processes. The challenge is to become more process focused while maintaining functional expertise. —Mike Wells, VP Logistics Hershey Corp

Introduction Since Toyota popularized JIT manufacturing in the 1980s, setting the stage for the lean revolution of the 1990s, supply chain scholars have sought to understand why some crossfunctional integration (CFI) initiatives succeed and others fail (Frolich and Westbrook, 2001; Fawcett et al., 2012). Regrettably, recent research reveals that managing value creation across functional boundaries remains a 21st-century challenge (e.g., Nielsen, 2015; Swink and Schoenherr, 2015). To grasp the persistence of the challenge, decision makers need to recognize that cross-functional processes are not the natural organizational state (Scott, 2003; Ellinger et al., 2006; Senge, 2006). Rather, as the epigraph intimates, companies organize functionally for a specific and important reason. They need deep functional skills to remain viable in an intensely competitive global marketplace (Anderson, 1982; Kylaheiko, et al., 2002; Teece, 2007; Handfield et al., 2009; Allred et al., 2011). This organizational choice evokes goal incongruence, communication barriers, functional rivalry, and process complexity, each of which inhibits process integration (Fawcett et al., 2015). To overcome the organizational inertia inherent to a functional orientation and move toward more effective process integration, decision makers need to learn to work together despite the organizational impediments that stand in their way (Hannan and Freeman, 1984; Hammer, 1990; Ellinger et al., 2006). Collaboration may be the key to rethinking how “departments conduct their work and relate to one another.” (Hammer, 2004: 88) A collaborative capability— the ability to align goals, resolve conflict, and leverage trust to improve working relationships—

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reduces cross-functional friction. This enables enhanced value creation across functions (Allred et al., 2011; Jin et al., 2013). Although the CFI and collaboration literatures are extensive, neither can be considered exhaustive (Autry et al., 2014). Much of the extant literature on both CFI and collaboration focuses on whether a positive performance linkage exists (e.g., Narasimhan and Kim, 2002; Rosenzweig et al., 2003; Nyaga et al., 2010; Schoenherr and Swink, 2012; Huo et al., 2016). Additional research focuses on whether they jointly impact organizational initiatives such as environmental sustainability and manufacturing performance (Vachon and Klassen, 2008). Moderating influences remain underexplored (Frankel and Mollenkopf, 2015; Chang et al., 2016). To influence practice, we need to more fully explore critical why, when, and how questions (Knemeyer and Fawcett, 2015). To bring clarity to the discussion of how to organize for value creation, research is needed that investigates the boundary conditions that govern when and how to invest in a collaborative capability to enable process integration. Because organizational structure and its operational performance are functions of firm strategy (Waggoner et al., 1999), strategy almost certainly influences the development of a collaborative capability and CFI (Porter, 1991; Jacobides, 2006; Fawcett and Waller, 2013). For instance, in production, strategy influences key processes like manufacturing flexibility, information technology adoption, and product and process innovation (Tseng, 2004; Chen and Wang, 2009; Prajogo, 2016). From this perspective, we posit the research question: How do strategic orientation and strategic purity influence the development of a collaborative capability that enhances firm performance? To do this, we elicited responses from 192 European managers engaged in diverse CFI initiatives. We evaluate diverse outcome variables and discover that strategy does influence both collaborative capability and performance, but in a more nuanced way than the literature suggests. 3

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Theoretical Development: The Centrality of an Organizing Mechanism Constituency-based theory (CBT) informs the process-integration challenge (see Figure 1). Specifically, CBT is a behavioral theory of the firm that attempts to specify the role of “functional areas in the goal setting and strategic planning process.” (Anderson, 1982: 15). Anderson (1982: 23-24) elaborates, “Strategic plans are seen as the outcome of a bargaining process among functional areas. Each area attempts to move the corporation toward what it views as the preferred position for long-run survival, subject to the constraints imposed by the positioning strategies of the other functional units.” Three key implications for CFI arise from CBT. 1. Specialization. Functional units specialize to cultivate deep skills (Coase, 1937), leverage constituency relationships that yield resource advantages (Pfeffer and Salancik, 1978), and increase the function’s influence or power. 2. Advocacy. Each function becomes a strong advocate for its bargaining and strategic position within the firm. Not surprisingly, “specialists” don’t just seek their own goals, they promote them (Davenport and Nohria, 2012). 3. Organizational Conflict. As each function seeks to promote its goals in the strategic planning process, organizational conflicts will arise. The conflict provoked by goal incongruence is magnified as each function competes for the resources needed to promote its long-term positions (Thomas, 1992). As a behavioral theory of the firm, CBT explains why strong functional orientations persist in an era when managers acknowledge the need for more innovative, integrated processes (Staw et al., 1981, Hannan, 1984, Hammer, 2004; Fawcett et al. 2012). 4

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Figure 1: Strategy as a Moderator of Collaboration Capability and Performance

The Functionally Organized Firm Engineering

Production

Human Resources

Marketing

Strategy Orientation

Purity

Organizing Mechanism

Firm Performance Workforce

Collaborative Capability

Customer Service Operating Financial

Purchasing

R&D

Finance

Logistics

CBT also implicitly calls for a mechanism to mitigate the counterproductive, conflictoriented behaviors that emerge in functionally organized firms. Importantly, most conceptualizations of CFI also recognize the need for a synchronizing capability (see Table 1). Collaboration, cooperation, and coordination are terms commonly used to denote a capability that reduces the friction and costs of working across functional boundaries (e.g., Flynn et al., 2010; Adams et al., 2014). These terms all describe a proactive process of working across organizational boundaries to more efficiently and effectively create value and improve organizational performance.

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Table 1: Collaboration’s Role in Cross-Functional Integration Year and Authors 1996 Kahn & Mentzer 1998 Kahn & Mentzer 2000 Ellinger, Daugherty & Keller 2004 Pagell

Definition CFI is a process of interdepartmental interaction and interdepartmental collaboration that brings departments together into a cohesive organization. CFI is a function of interdepartmental interaction and collaboration Focus on the behavioral dimensions of CFI: collaboration, consultation & information exchange

2007 Chen, Mattioda & Daugherty

Integration is defined as a process of interdepartmental interaction and collaboration that brings departments together into a cohesive organization. Integration is a process of interdepartmental interaction and interdepartmental collaboration that brings departments together into a cohesive organization.

2007 Whipple & Russell

CFI is defined as internal collaboration-collaboration across the internal functions of the firm

2008 Chen, Daugherty, and Roath

Internal collaboration is crossing within the firm boundaries and across firm functions.

2015 Swink & Schoenherr

CFI s defined as the mutual alignment of cross-functional interdependencies through interaction, information sharing, and collaboration.

Central Elements in Definition Internal integration, Interdepartmental collaboration Interdepartmental, Interaction, Collaboration Collaboration, Exchange, Information Collaboration, Interaction, Process Collaboration, Interaction Collaboration, Internal functions Internal collaboration, Crossing, Within the firm boundaries, Across firm functions Cross-functional interdependencies, Information sharing, Collaboration

Finally, CBT doesn’t just explain the nature and outcomes of organizational design. CBT was theorized as a way to link functional decision-making to goal formation and strategic planning. This intent calls out strategy’s role as a boundary condition that influences how well companies bridge the cross-functional conflicts that hinder value-creation. CBT thus supports the idea that a firm’s strategic choice directly influences the firm’s commitment and ability to develop a strong collaborative capability in order to reap the performance benefits of crossfunctional collaboration (see Figure 1). 6

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Successful collaboration across functions relies on establishing predominantly informal processes based on trust, mutual respect, and successful mitigation and remediation of potentially conflicting objectives (Ellinger et al., 2000). More importantly, firms with high levels of intrafirm collaboration typically have cross-functional processes that emphasize information sharing, joint decision-making, as well as collective responsibility for outcomes (Chen et al., 2008). Underlying superior cross-functional collaboration is the sense that cross-functional teams willingly cooperate to achieve desired outcomes at the firm level by breaking down silos (Swink & Schoenherr, 2015). The goal of high levels of cross-functional collaboration include superior customer service, improved product innovation, and greater financial performance (Ellinger et al., 2000).

Strategic Choice as a Boundary Condition of Collaboration Drawing on Industrial Organization theory (Porter, 1980), strategy performs three core roles as related to how a company structures and invests in operations for value creation and competitive success. Specifically, strategy delimits the firm’s value propositions, defines the requisite competencies, and determines how firms allocate resources for value creation (Porter, 1991). From a CBT perspective, efforts to derive positive functional outcomes throughout the strategic planning process incents functional advocacy, often leading to cross-functional conflict (Anderson, 1982; Fawcett et al. 2010). Yet, Core Competence theory notably posits that core competencies are “the collective learning in the organization, especially how to coordinate diverse production skills and integrate multiple streams of technologies.” (Prahalad and Hamel,1990). Stalk et al. (2000) argued that these critical competencies are “collective and cross-functional—a small part of many people's jobs, not a large part of a few.” A synchronizing mechanisms appears to be critical. Strategy

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should therefore support integration—we posit through a collaborative capability—in order to improve manufacturing performance (e.g., Swink et al., 2007). Vachon and Klassen (2008), for instance, found that collaboration on key environmental initiatives resulted in superior manufacturing performance. Ramanathan and Gunasekaran (2014) concluded that successful collaboration outcomes may incentivize firms to make strategic investment toward future collaborative initiatives on various operational processes. Porter (1980) identified two strategic decisions that are particularly relevant: Orientation and Purity. Strategic Orientation. Porter (1980) argued that companies formulate corporate strategies along a continuum anchored by either cost leadership or truly distinctive and differentiated products and services. Critically, strategic choice defines goals, resource allocation, and the value-added capabilities a company must develop (Anderson, 1982; Miles and Snow, 1978; Thornhill and White, 2007). More specifically, cost leadership and differentiation take divergent approaches to managing innovation (Lee and Yang, 2000). Becoming a cost leader requires that a company achieve unparalleled operational efficiency, minimizing costs across product development, production, and logistics (Brenes et al., 2014). By contrast, differentiation requires fast-cycle innovation as well as high-quality and responsive production and logistics (Gebauer et al., 2011; Zatnik et al., 2012). Strategic orientation thus influences resource investments influenced by and designed to engage various constituencies (i.e., functions) to build a distinctive competitive advantage. Both cost reduction and innovation orientations may incentivize firms to invest in a collaborative capability (e.g., Anderson, 1982; De Luca and Atuahene-Gima, 2007). However, implementing such processes can require costly investment in information technology, which is often used for time-intensive knowledge integration processes to create differentiated products

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(Tsai and Hsu, 2014). Returns on investment are particularly opaque when unproven information technologies serve as the foundation for knowledge acquisition, integration, and dissemination (Hedlund, 1994; Lee and Yang, 2000). As a result, cost-conscious firms tend to eschew investing in emerging technologies that enable, for instance, advanced information capabilities (Porter, 1980; Gebauer et al., 2011). For instance, the leading mass discount merchant, Walmart, avoided investing in a third-party ERP system for decades in favor of its in-house solutions until it became clear that SAP offered superior value (Doyle, 2010). Firms seeking to differentiate from the competition rely on innovative capabilities to serve customers with greater precision and capture higher premiums (Samson and Gloet, 2013). This process requires various functions within the firm to collaborate and incentivizes them to jointly determine optimal solutions that balance the demands placed on each function by their respective external coalitions (Anderson, 1982; Juttner et al., 2010). In comparison to the judicious nature of a cost-focused approach to resource investment, a differentiation strategy tend to less sensitive to either upfront costs or uncertainty in developing new products (Tsai and Hsu, 2014). For instance, Nielsen (2015) found that cross-functional collaboration’s effectiveness in generating, designing, and executing new product concepts increases substantially with larger teams, which also become more difficult to manage and require significantly more time and resources. Unencumbered by strict guidelines to control cost, differentiation-focused firms may thus develop greater collaborative capabilities across functions to facilitate new product development and process innovation (De Luca and Atuahene-Gima, 2007; Cennamo and Santalo, 2013). Therefore, we hypothesize: Hypothesis 1: Firms that adopt a differentiation strategy cultivate higher-level collaborative capabilities than firms that adopt a cost leadership strategy.

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Strategic Purity. Consistent with strategic orientation, the notion of strategic purity—i.e., setting strategic priorities, targeting investments, and remaining focused—posits that firms compete best when they direct resources toward a single, superordinate goal (e.g., Thornhill and White, 2007). Simply stated, to compete successfully, firms must select what they are going to do best (Jin et al., 2017). Under CBT, internal coalitions (e.g., operations and other functional areas) consistently strive to influence organizational goals and acquire resources necessary to position themselves for long-term success (Anderson, 1982; Noble and Mokwa, 1999). The need for cost-focused firms to exploit every possible source of economies of scale is in direct conflict against the need for differentiated firms to develop unique products that tend to be difficult to scale (Gebauer et al., 2011; Zatnik et al., 2012). The simultaneous pursuit of both strategies (i.e., hybrid strategy) leads to incongruous goals and misaligned incentive systems (Anderson, 1982; Mentzer et al., 2004; Fawcett et al., 2012). The resultant divergence creates conflict among functional specialists that is deleterious to collaboration and costly to overcome (Chang et al., 2016; Noble and Mokwa, 1999; Crul and Zinkhan, 2008; Piercy and Ellinger, 2015). As a single unified strategy provides a clear roadmap for all constituents within a firm, we argue that strategically impure (i.e., hybrid) firms will exhibit lower levels of collaborative capabilities. Hypothesis 2: Strategically pure firms have higher collaborative capabilities than strategically impure (hybrid) firms. Performance Implications Strategic orientation and strategic purity should influence both how—and how well—firms collaborate, enhancing or impeding firm performance. Specifically, a collaborative capability helps align cross-functional goals and reduce cross-functional conflicts, by promoting greater

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information and process visibility through enhanced technical linkages (Rishel et. al., 2003; Schoenherr and Swink, 2012; Li et al., 2017). Consequentially, performance as measured by operational, customer-oriented, workforce performance, and financial measures should improve (e.g., Troy et al., 2008; Flynn et al., 2010; Wong et al., 2011; Schoenherr and Swink, 2012; Srivastava et al., 2015). •

Workforce Performance. Enhanced collaborative capabilities remove internal distrust and create channels of communication for unhindered transmission of knowledge across functions (Tsai and Hsu, 2014). Improved social linkages minimize workflow disruptions and improve joint-planning processes (Oliva and Watson, 2011), resulting in both worker productivity gains and innovative products and services (Lee and Yang, 2000; Brenes et al., 2014).

•

Customer Service. As enhanced collaboration across functions mitigate internal strife and confusion, firms may unambiguously signal their strategic intent and the value proposition of their products and services to clearly establish performance expectations with their customers (Read et al., 2014). Additionally, by performing to their promise, firms would enjoy greater customer satisfaction and inspire increased customer loyalty (e.g., Stank et al., 2012; Alftan, Kaipia and Spens, 2015).

• Operating Performance. Through its operations, a firm utilizes its resources to serve different customer needs (Ghoshal and Moran, 1996). Improved operating performance may be reflected by either improved operational efficiency for cost savings to customers (Flynn et al, 2010; Zatnik et al., 2012) or improved product/service quality (Gebauer et al., 2011). The extent to which a firm chooses to emphasize either operational efficiency or product/service quality is generally determined by its overall strategy based on market positioning (Thornhill and White, 2007). •

Financial Performance. Collaboration’s linkage to financial performance is commonly supported (e.g., Allred et al., 2011). Whereas cost leaders parlay operational savings due to collaborative planning, such as improved inventory management, to financial gains (e.g., Jin et al., 2013), differentiators generate supernormal returns by devising unique products and services through cross-functional collaboration (Gebauer et al., 2011). Although a collaborative capability should benefit both firms that emphasize low cost and

differentiation, the extent to which they benefit each performance dimension may differ. For instance, cost leaders may be reticent to commit extensive time and resource investment toward the collaboration initiatives necessary for developing innovative products that carry uncertain 11

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returns (Porter, 1980; Zatnik et al., 2013; Nielsen, 2015). Such an approach may encourage internal coalitions to espouse a resource distribution approach that overly emphasize efficiency at the expense of riskier but potentially more lucrative pursuits to ultimately hinder the full performance benefits of collaboration processes (Anderson, 1982; Juttner et al., 2010). Differentiator, by contrast, seek to utilize their collaborative capability with the goal of “creative utilization of every participant’s potential” (Jassawalla and Sashittal, 1998, p. 245). As a result, cost leaders may selectively pursue collaboration initiatives that only yield efficiency benefits while differentiators seek to develop innovative capabilities as well (Lee and Yang, 2000; Samson and Gloet, 2013). In doing so, differentiators’ workforce-maximizing and knowledge-integrative collaboration capabilities fully leverage each internal coalition’s strengths while balancing the needs of their respective external coalitions (Anderson, 1982; Juttner et al., 2010). Consequentially, differentiators are more adept at identifying market opportunities (Gebauer et al., 2011), developing and commercialize new products (De Luca and AtuaheneGima, 2007; Tsai and Hsu, 2014; Lin et al., 2015). Differentiators are thus better strategically positioned to leverage their aligned constituents to create a distinctive, inimitable, and more profitable advantage (e.g., Daugherty et al., 2008). More succinctly stated, Hypothesis 3 (a-d): Collaborative capability’s positive impact on (a) workforce performance, (b) customer service, (c) operating performance, and (d) financial performance is greater for firms that compete through product or service differentiation than for those who compete as cost leaders. A fundamental tenant of CBT is that different functions operate with disparate goals that they aggressively promote because they believe “in the importance of their own constituencies.” (Anderson, 1982: 22). Importantly, Anderson (1982: 24) argues that reconciliation requires that each function must “understand the unique orientations and decision methodologies employed by

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other departments.” For strategically pure firms, understanding cross-functional orientations and methodologies is easier because the firm pursues a single strategic direction, a fact that provides a consistent goal across all functions (Thornhill and White, 2007; Cennamo and Santalo, 2013). Cross-functional conflicts are thus mitigated, especially when a strong collaborative capability is established. The improved communications across functions should improve various aspects of firm performance (Allred et al., 2011; Oliva and Watson, 2011). Simply put, strategic purity promotes improved visibility and aligned goals, enabling a diversity of constituents to more effectively utilize existing company resources to achieve firm directives (Anderson, 1982). In contrast, hybrid strategies distract decision makers (Thornhill and White, 2007; Jin et al., 2017), potentially leading to undisciplined behavior as various functions pursue conflicting objectives (Treacy and Wiersema, 1985; Brenes et al., 2014). Consequentially, internal coalitions would seek to preserve and maximize their own access to firm resources at the expense of other functions to pursue objectives set forth by their respective external coalitions (Anderson, 1982; O’Leary-Kelly and Flores, 2002; Juttner et al., 2010), thereby compromising optimal outcomes at the firm level. For instance, Jin et al. (2017) found that for motor carriers, strategically pure truckload carriers may thrive under hyper competition while those pursuing a hybrid strategy languish among their peers. As each function seeks to gain greater control of limited resources, turf manifests to disrupt information flow across functions. Employee productivity erodes and organizational confusion emerges, impeding the firm’s ability to efficiently and effectively serve customers (Miles and Snow, 1978; Treacy and Wiersema, 1985; Thornhill and White, 2007). Therefore, we posit: Hypothesis 4 (a-d): Collaborative capability’s positive impact on (a) workforce performance, (b) customer service, (c) operating performance, and (d) financial performance is lower for firms that compete through hybrid strategies than those who compete with strategic purity. 13

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To summarize, we expect a firm’s strategic boundaries to influence both its ability to develop an effective collaborative capability and its overall performance. Strategic orientation directs resource flows toward either a narrow scope of collaboration focused on efficiency or a broad range of activities designed to drive product innovation and commercialization. Similarly, strategic purity determines the ease at which collaborative capability is developed and translated into performance gains (see Figure 2). Figure 2: Empirical Framework

METHODOLOGY Data Collection To test our hypotheses, we surveyed European supply chain managers. Based on extant literatures, the questionnaire was originally developed in English. It was first translated into 14

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German by a team of German speakers who are knowledgeable in the topic areas of supply chain management and organizational research. Following established protocols to ensure that the translation process did not distort intended meanings of the original instrument, the survey was retranslated back into English (Douglas and Craig, 1983). The survey was first given to a group of supply chain executives and academic researchers to review readability and to detect any ambiguity (Dillman, 2000). An initial pilot test (n = 20) further evaluated the survey’s face validity. This feedback process led to several minor modifications in phrasing and structure of the questions. To ensure the salience of survey responses, the final instrument was deployed to supply chain and purchasing managers who had engaged in supply chain professional development program offered by a leading European business school. These managers were all members of a supply chain and purchasing consortium. We chose supply chain and purchasing managers because extant literature indicated that supply chain and purchasing managers play a central role in firm processes such as sales and operations planning (Oliva and Watson, 2010). Additionally, purchasing performance is also dependent on the degree of cross-functional collaboration and coordination (Foerstl et al., 2013). In all, we targeted a random sample of 1,000 managers to avoid systematic biases. The survey process followed the Dillman (2000) Total Design Method. A mail survey was first sent to all prospective respondents, followed by an email announcement to inform respondents of the survey’s impending arrival. To stimulate a higher response rate, two follow-up mailings were sent at two week intervals. Altogether, a total of 192 usable surveys were returned over an eight-week time period. No statistically significant differences were found for survey responses from the initial 4 weeks

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as compared to those from the latter 4 weeks for any of the key variables, thereby alleviating concerns for potential nonresponse bias (Lambert and Harrington, 1990). Table 2: Overview of Survey Demographic Characteristics Company Size < 50 2.90% <250 18.10% <500 15.70% <1000 17.20% <2500 9.80% <15000 22.50% >15000 15.70%

Industry Sector Manufacturing 27.00% Construction 10.20% Retail 14.80% Health 9.70% Chemical 4.10% Financial 4.10% Medical 4.60% Public 4.10% Other

23.50%

An overview of the demographic characteristics can be seen in Table 2. The number of respondents was fairly equal among all firm sizes with the largest number of responses (22.5%) coming from firms with employees between 2,501 and 15,000. A large portion of the respondents came from the manufacturing industry (25%). Managers from retailers represented 13%, and managers from both construction and healthcare industries represented an additional 9% each. Managers from chemicals and public companies each represented 6 percent of respondents. Finally, 32% of the respondents did not designate their industry affiliation. We compared the demographic information of respondents with non-respondents among our target sample and did not find any statistically significant differences. For the few occasions of missing responses, data were imputed using the ‘median of nearby points’ method in SPSS (Hair et al., 2010). The Shapiro-Wilk test for data normality indicates that data non-normality is not a significant concern. Finally, the Mahalanobis distance evaluation indicated the existence of 5 potential outliers, which we removed. Our final dataset is composed of a total of 187 responses, representing an effective response rate of 18.7 percent. 16

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Construct Evaluation Convergent and Discriminant Validities. Because our survey items were developed through an iterative translation process, we used exploratory factor analysis to evaluate construct validity (see Table 3). With the exception of one item, all factor loadings exceeded the 0.60 threshold (Chin, 1998). While a factor loading of 0.51 for item C3 is below the recommended level, we opted to keep it for two reasons. First, the item has substantial content validity as supported by both executive and academic feedback during the initial phase of survey design. Second, it uniquely loads to its construct as specified a priori and does not present cross-loading concerns. With its inclusion, the average variance extracted remains satisfactory at 0.50 (Fornell and Larcker, 1981), and its composite reliability exceeds the 0.60 threshold for exploratory research (Nunnally and Berstein, 1994). Finally, the inclusion of item C3 helps to ensure that a multifaceted construct is fully represented.

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Table 3: Construct Validity (N=187) Mean

Standard Deviation

Workforce Performance

4.89

1.02

1 Employees contribute creative ideas that lead to success. 2 Employees' willingness to provide high levels of customer service. 3 Employee productivity

5.17

1.07

0.80

4.66

1.32

0.82

5.01

1.27

0.83

4 Trust within the company

4.98

1.42

0.79

Collaboration 1 We have great working relationships--we resolve conflicts collaboratively. 2 We trust each other because day-to-day promises are entirely met. 3 We work collaboratively--we know how to deal with power differences. 4 We work toward a mutual goal because we know that we need each other. Customer Service

4.74

1.15

4.89

1.29

0.83

4.63

1.34

0.85

4.90

1.38

0.89

4.57

1.30

0.90

4.77

1.36

1 … is clearly understood by our customers.

4.43

1.61

0.86

2 … helped us achieve very high levels of customer loyalty

4.62

1.58

0.82

3 … enabled us to increase overall customer satisfaction

4.96

1.41

0.79

Operating Performance

4.79

0.94

1 Order fulfilment lead times

5.02

1.07

0.85

2 Overall product cost

4.55

1.08

0.78

3 Overall product quality

4.90

1.13

0.74

Financial Performance

5.04

1.12

1 Growth in return on assets

5.32

1.17

0.86

2 Market share growth

4.81

1.45

0.81

3 Sales growth

4.75

1.57

0.75

Differentiation 1 Our company expands through our own developed products and services 2 Our core processes include market research and product development 3 Our customers appreciate our unique products and services 4 Our company culture enables product innovation and leadership 5 Our core processes include service for partner companies and customers Cost Leadership 1 Our company expands through low-cost standard products, services, and concepts 2 Our customers appreciate our everyday low prices

5.17

1.00

5.08

1.82

0.82

4.65

1.73

0.77

5.49

1.29

0.73

5.11

1.44

0.70

5.36

1.29

0.66

4.12

1.19

3.82

1.83

0.83

3.02

1.74

0.79

3 Our company culture enables us to drive down costs

5.45

1.48

0.51

Construct / Item

Factor Loading

Composite Reliability

Variance Extracted

0.884

0.656

0.924

0.753

0.864

0.679

0.834

0.626

0.849

0.653

0.856

0.545

0.761

0.524

Our Strategy…

Collaboration has improved our…

Compared to your leading rivals in the last three years…

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Table 4: Discriminant Validity

(1) Workforce Productivity (2) Collaboration (3) Customer Service (4) Operating Performance (5) Financial Performance (6) Differentiation (7) Cost Leadership

(1) 0.656 0.540 0.524 0.494 0.286 0.424 0.166

(2)

(3)

(4)

(5)

(6)

(7)

0.753 0.397 0.395 0.320 0.379 0.156

0.679 0.623 0.577 0.536 0.174

0.626 0.432 0.483 0.215

0.653 0.519 0.096

0.545 -0.012

0.524

Average variance extracted in bold along the diagonal

Common Method Biases. As shown in Table 4, shared variances among constructs are all well below each construct’s AVE to demonstrate discriminant validity (Fornell and Larcker, 1981). Finally, we conducted Harman’s single-factor test for common method bias (Podsakoff, MacKenzie, Lee, and Podsakoff, 2003), which yielded a clear multi-factor solution that attributed less than 50 percent variance-explained to the most influential common factor, which accounted for 29% of the overall variance (Podsakoff and Organ, 1986). EMPIRICAL ANALYSIS AND RESULTS Our sample for analysis comprises 187 firms of varying sizes across 7 industries. Because competitive forces unique to each industry exert influence on strategy and its influence on resource allocation (Wong et al., 2011), we specify multilevel models to directly test our hypotheses which are specified at the firm level (Raudenbush and Bryk, 2002). For hypotheses 1 and 2, our dependent variable is firm-level collaborative capability: 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 = 𝜃0 + 𝐼𝑛𝑑0𝑖 + 𝑒𝑖𝑓 + 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,1𝑆𝑖𝑧𝑒𝑖𝑓 + 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,2𝐷𝑖𝑓𝑓𝑖𝑓 + 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,3𝐶𝐿𝑖𝑓 + 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,4𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓

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Where 𝜃0 is the fixed intercept parameter, while the random effect parameter of industry i is 𝐼𝑛𝑑0𝑖 and the random effect parameter of firm f is 𝑒𝑖𝑓. The number of employees for each firm is included to control for firm size (𝑆𝑖𝑧𝑒𝑖𝑓). 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,2 captures the statistical influence of differentiation strategy on 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 to test hypothesis 1. 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,4 captures the effect of pursuing both differentiation and cost leadership strategies (i.e., hybrid) on 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 to test hypothesis 2.. To test hypotheses 3a-d and 4a-d, our multilevel model is specified for each outcome1: 𝑃𝑒𝑟𝑓𝑑,𝑖𝑓

= 𝜃𝑑,0 + 𝐼𝑛𝑑𝑑,0𝑖 + 𝑒𝑑,𝑖𝑓 + 𝜋𝑑,1𝑆𝑖𝑧𝑒𝑖𝑓 + 𝜋𝑑,2𝐷𝑖𝑓𝑓𝑖𝑓 + 𝜋𝑑,3𝐶𝐿𝑖𝑓 + 𝜋𝑑,4𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 + 𝜋𝑑,5 𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓 + 𝜋𝑑,6𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 + 𝜋𝑑,7𝐶𝐿𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 + 𝜋𝑑,8𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓

where 𝑃𝑒𝑟𝑓𝑑,𝑖𝑓 comprises four dimensions of performance, which are measured as workforce performance, customer service, operating performance, and financial performance. All parameters are estimated for each performance dimension d. For all performance models, 𝜋𝑑,6 assesses the moderating influence of 𝐷𝑖𝑓𝑓𝑑,𝑖𝑓 on 𝐶𝑜𝑙𝑙𝑎𝑏𝑑,𝑖𝑓’s influence on 𝑃𝑒𝑟𝑓𝑑,𝑖𝑓 (Hypotheses 3a-d). 𝜋𝑑,8 is the parameter estimate for the threeway interaction term, 𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓, to assess the moderating effect of a hybrid differentiation/cost leadership strategy on 𝐶𝑜𝑙𝑙𝑎𝑏𝑑,𝑖𝑓’s influence on 𝑃𝑒𝑟𝑓𝑑,𝑖𝑓 (Hypotheses 4a-d). All models are estimated using maximum likelihood estimation (ML), following established procedures (e.g., DeHoratius and Raman, 2008).

To verify that alternate model structure does not alter our results, we also specified a multi-level, fixed effects model as opposed to the random-effects HLM. The alternate model specification did not qualitative alter our findings. 1

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Table 5: Multilevel Model Results for Hypotheses 1 and 2

Intercept Direct Effects Size H1 Diff CL Interaction H2 Diff*CL Model Fit -2 Log Likelihood

Collaboration (N = 187) Null Direct Interaction 4.741*** 2.542** 2.284** -

-0.039 0.451*** -

-0.054 0.384** 0.065

-

-

0.019

598.921

548.273 50.648***

547.056 1.217

χ2

*p<0.10; **p<0.05; ***p<0.01 Strategic Boundaries of Collaborative Capabilities. HLM results for testing Hypotheses 1 and 2 are presented in Table 5, with their corresponding coefficient estimates in bold. In the direct effects model, 𝐷𝑖𝑓𝑓𝑖𝑓 is shown to positively influence collaboration (𝜋𝑐𝑜𝑙𝑙𝑎𝑏,2 = 0.451;𝑝 < 0.01) in support of Hypothesis 1. In the interaction model, 𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓 is not statistically significant ( 𝜋𝑐𝑜𝑙𝑙𝑎𝑏,4 = 0.019;𝑝 > 0.10), which fails to support the notion that a hybrid strategy is associated with inferior collaborative capabilities (Hypothesis 2). The lack of incremental explanatory power associated with the interaction term is further illustrated by the negligible decrease in -2 Log Likelihood.

Table 6: Multilevel Model Results for Hypotheses 3a-d and 4a-d

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Strategic Boundaries of the Collaboration-Performance Linkage. HLM results for hypotheses 3a-d and 4a-d are presented in Table 6. As with before, coefficients of interest are in bold. For strategic orientation, Hypothesis 3 states that the differentiation strategy offers superior enhancement to the collaboration-performance linkage than the cost leadership strategy. Figure 3 illustrates the significant interaction terms found for 𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓, for Workforce Performance (𝜋𝑊𝑃,7 = 0.071;𝑝 < 0.05), Operating Performance (𝜋𝑉𝐶,7 = 0.077;𝑝 < 0.05), and Financial Performance (𝜋𝐹𝑃,7 = 0.114;𝑝 < 0.01). While these coefficients and their interaction plots suggest that differentiation’s enhancing effect on collaboration’s performance impact is significant and positive for the three measures, only those 95% confidence intervals for 𝜋𝑊𝑃,7 and 𝜋𝐹𝑃,7 are higher than their respective coefficients for the 𝐶𝐿𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓 (𝜋𝑊𝑃,8 and 𝜋𝐹𝑃,8). For Operating Performance, although 𝜋𝑉𝐶,7 is in the direction as hypothesized, 𝜋𝑉𝐶,8 is also 22

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positive (0.091; p<0.05) with overlapping 95% confidence intervals to suggest that differentiation’s positive influence on collaboration’s operational improvement effect is not statistically superior to that of cost leadership’s. Therefore, only Hypotheses 3a and 3d are considered statistically supported. Figure 3: Interaction Effect of Differentiation and Collaboration on Performance

Hypotheses 4a-d are tested using the three-way interaction term of 𝐷𝑖𝑓𝑓𝑖𝑓 ∗ 𝐶𝐿𝑖𝑓 ∗ 𝐶𝑜𝑙𝑙𝑎𝑏𝑖𝑓. When a firm reports high degrees of emphasis on both differentiation and cost leadership strategies (i.e., hybrid strategy), their multiplicative effect further moderates collaboration’s influence on performance outcomes beyond each strategy’s respective two-way interaction with collaboration. Interpretation of its coefficient is straightforward: if 𝜋𝑑,8 is negative, then a hybrid strategy is expected to lower the collaboration-performance linkage. Results show that not only

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are Hypotheses 4 not supported for all performance dimensions, the statistical effects are actually reversed for 4a and 4d (𝜋𝑊𝑃,8 = 0.095;𝑝 < 0.05;𝜋𝐹𝑃,8 = 0.104;𝑝 < 0.05). That is, moving toward a hybrid strategy positively impacts both workforce and financial performance (Figure 4). Figure 4: Interaction of Pure/Hybrid Strategy and Collaboration on Performance

Summary of Results. We present in Table 7 a summary of our hypothesis testing results. Our empirical tests both support and refute conventional wisdom. That is, collaboration helps firms to leverage the wide-range of competencies required to succeed in a differentiation strategy and improve performance outcomes, as illustrated in Figure 6. Further, collaboration enables firms to employ both strategies effectively, helping them get the best of both cost and differentiation

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worlds. This finding stands in stark contrast to emerging views on the need for strategic purity. More specifically, results indicate the following: 1. Statistical support for Hypothesis 1 but not for Hypothesis 2 shows that firms recognize the importance of and invest in collaboration as necessary for a successful differentiation strategy. Further, the need for collaborative capability does not diminish even as firms adopt a hybrid cost/differentiation strategy. 2. Support for Hypotheses 3a and 3d indicates that firms that adopt a differentiation strategy in particular are able to enhance collaborative capability’s positive impact both financially and productivity-wise. This boundary condition explains persistent discrepancies as observed in extant literature between the rhetoric and reality of supply chain integration and collaboration (Fawcett and Magnan, 2002; Jin et al., 2013). Distinct strategic boundaries exist to determine whether collaboration translates to performance gains in various dimensions. 3. The universal lack of support for Hypotheses 4a-d, of which 4a and 4d are reversed, indicates that collaboration’s primary function in the firm is not to passively enhance a unidirectional strategy by aligning divergent objectives among multiple constituencies. Rather, the statistical evidence in support of the contrary hints that collaboration enables firms to successfully pursue hybrid strategies to yield superior workforce performance and financial performance. Table 7: Hypothesis Testing Summary Hypotheses Hypothesis 1 Differentiation enhances collaborative capability Hypothesis 2 A hybrid strategy lowers collaborative capability Hypotheses 3 Differentiation amplifies collaborationperformance relationship for… a) Workforce Performance b) Customer Service c) Operating Performance d) Financial Performance Hypotheses 4 A hybrid strategy attenuates collaborationperformance relationship for… a) Workforce Performance b) Customer Service c) Operating Performance d) Financial Performance

Hypothesized Direction (+) (-)

Statistical Results (+) n.s.

(+) (+) (+) (+)

(+) n.s. n.s. (+)

(-) (-) (-) (-)

(+) n.s. n.s. (+)

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It’s important to note that Strategic Purity is conceptualized as a measure of relative preference for one strategy over another, which is more closely aligned with strategic choice (Shinkle et al., 2013). As reflected by its empirical measurement, Strategic Purity only examines the distal relationship between a firm’s demonstrated commitment to cost leadership and differentiation (e.g., Thornhill and White, 2007). Strategic Purity does not differentiate between firms that saliently committed to both strategies (Rosenzweig et al., 2003; Shinkle et al., 2013) and those simply doing so opportunistically. Therefore, we pursued an exploratory analysis of the overall intensity at which a firm pursues either or both strategies would allow us to gain a more salient view on their relationship with collaboration as uncovered by our empirical model. Exploratory Analysis: Delineating Purity and Intensity Strategic Intensity indicates the degree to which strategy is salient to a firm’s overall business model (Inkpen and Choudhury, 1995). Not only can firms demonstrate significant Strategic Intensity by committing to both cost leadership and differentiation (Shinkle et al., 2013), but their combinative benefits may be enhanced by integrated supply chains (e.g., Rosenzweig et al., 2003). Opposite of a two-pronged approach to strategic commitment is a complete absence of commitment to either, in which case a firm simply operates purely by exploiting contemporary market opportunities without a salient direction (Inkpen and Choudhury, 1995). As posited by Wernerfelt and Karnani (1987), key to strategic success is to determine a particular direction to pursue by allocating resources toward technologies supporting a set of competitive objectives that may encompass both cost and differentiation. Thus, simply examining the distal relationship between cost leadership and differentiation fails to consider intensity and focus. More importantly, our empirical model suggests that collaboration does not merely function as a

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passive alignment tool for diverse constituents but instead as a proactive enabler of synchronized effort among them. Thus, we conduct an exploratory analysis to probe this observation. Developing a Strategic Purity-Intensity Typology Whereas we use our full sample to test our hypotheses under the tenets of Strategic Orientation and Purity, extant literature on Strategic Purity and Intensity does not offer guidance as to how their interplay influences collaboration and performance outcomes. To conduct a preliminary exploration and to develop a typology on firms exhibiting varying levels of strategic purity and intensity, we utilize extreme group analysis (Preacher et al., 2005). This allows us to identify those firms that exemplify the extremes of each strategic dimension: proactive and passive strategic commitment (i.e., high and low intensity), and clear pursuit of a single strategy (i.e., high and low purity). To do this, we first obtain a continuous measure of the degree to which firms pursue both differentiation and cost leadership strategies by summing the two ratings (i.e., 𝐷𝑖𝑓𝑓𝑓 + 𝐶𝐿𝑓) for all firms. Next, we used a tertile split to identify the leaders and laggards in strategic intensity. To obtain a measure of relative purity, we take the absolute value of the two strategies’ differences (i.e., 𝐴𝑏𝑠[𝐷𝑖𝑓𝑓𝑓 – 𝐶𝐿𝑓]) and used a tertile split to identify firms the leaders and laggards in strategic purity. Utilizing the two measures, we constructed a two-by-two Matrix as the basis of a typology for the firms (Figure 5): I.

Explorer: The first quadrant contains 18 firms that reported high Purity but low Intensity. That is, these firms have clearly shown a preference for either differentiation or cost leadership but have yet to fully commit to it and may continue to explore alternatives.

II.

Sharpshooter: The second quadrant contains 11 firms that reported high levels of Purity and Intensity. These firms have not only shown significant interest in one particular strategic orientation but are also among leaders in overall levels of strategic commitment. Simply put, these firms have a targeted strategic focus and have intensively dedicated resources in pursuit of their chosen strategy.

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III.

Roamer: The third quadrant contains 21 firms that reported both low levels of Purity and Intensity. In other words, these firms roam across a spectrum of strategies with little direction or resource commitment. Their strategic orientation is functionally hybrid but relatively little strategic focus overall.

IV.

Hybrid: The fourth quadrant contains 36 firms that have embraced a true hybrid strategy, in which they demonstrate significant resource commitment toward both cost leadership and differentiation. They want the best of both worlds and believe a collaboration capability can enable simultaneously competing on both strategy dimensions. Figure 5: Strategic Purity and Intensity

To assess how collaboration and performance outcomes differ among the four quadrants in our Strategic Purity-Intensity typology, we present in Table 8 their averages as organized in the same two-by-two matrix. The nominal differences once again lend support to our observations from the empirical model. Firms that reported high strategic intensity also report higher levels of collaboration as well as performance outcomes than those firms with lower strategic intensity. Due to small sample size for quadrants I (N = 18) and II (N = 11), we apply the nonparametric

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Kruskal-Wallis test to measure the statistical significance of their rankings for each variable of interest (Conover and Iman 1981).

Table 8: Kruskal-Wallis Statistical Rankings for the Strategic Purity-Effort Matrix

Collaboration* Explorer (N = 18) 4.79 3rd Roamer (N = 21) 4.33 4th

Sharpshooter (N = 11) 4.93 2nd Hybrid (N = 36) 5.35 1st

*Kruskal-Wallis Test p<0.05

Performance**

Workforce Productivity Customer Service Operating Performance Financial Workforce Productivity Customer Service Operating Performance Financial

Explorer (N = 18) 5.02 3rd 4.56 3rd 4.01 4th 4.30 3rd Roamer (N = 21) 4.08 4th 4.24 4th 4.21 3rd 3.43 4th

Sharpshooter (N = 11) 5.70 1st 5.39 1st 4.98 2nd 5.67 1st Hybrid (N = 36) 5.36 2nd 5.29 2nd 5.35 1st 5.38 2nd

**Kruskal-Wallis Test p<0.01

Emerging from our results is that strategic intensity plays an important role in differentiating both collaboration and various dimensions of performance. For collaboration, the group of firms reporting hybrid strategies and high level of strategic intensity is ranked first among all four groups. Coming second is the group composed of strategically pure firms that also report high intensity. High purity and low intensity is ranked third in terms of collaboration; 29

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and finally, hybrid and low intensity is ranked last. This theme persists for all four dimensions of performance, as well. These results suggest that our initial interpretation of the counterintuitive empirical results associated with Hypotheses 2 and 4a-d can be potentially explained by Strategic Intensity. Collaboration enables firms with hybrid strategies to cultivate performance benefits through proactive coordination among divergent constituents. We discuss the implications below. IMPLICATIONS In this study, we contribute to extant literature by delineating the strategic boundaries that serve to either dampen or amplify both collaboration and its relationship with various dimensions of firm performance. Despite abundant empirical support for its role as an enabler of crossfunctional integration (Allred et al., 2011) and its positive relationship with various performance outcomes (Chang et al., 2016), collaboration remains underinvested among firms and its impact occasionally questioned (Koufteros et al., 2010; Nielsen, 2015). Our results both support certain views in extant literature and identify counterintuitive performance outcomes. Our finding that firms pursuing differentiation tend to have greater cross-functional collaboration is congruent to the prevalent view that enhanced information flow across functions allows firms to better identify and serve their customers (e.g., Ellinger, 2000; Lin et al., 2015). On the other hand, a hybrid strategy’s amplifying effect on collaboration’s performance outcomes is counterintuitive under both CBT (e.g., Anderson, 1982; Juttner et al., 2010) and strategic purity perspectives (e.g., Thornhill and White, 2007; Jin et al., 2017). Regarding the Influence of Strategic Orientation: We posited that firms with a differentiation strategy to possess greater collaborative capabilities, which was supported by our empirical model. When compared to extant literature, this finding is adherent to the notion that firms with 30

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greater cross-functional collaboration are able to better synthesize a wider range of information and leverage deep skills from constituents across the firm. For instance, enhanced customer service depends heavily upon cross-functional information sharing (Ellinger, 2000), which also benefit new product and service development processes as documented in the marketing literature (De Luca and Atuahene-Gima, 2007; Lin et al., 2015). Vitally, our results support that pursuing a differentiation strategy requires functions to communicate, negotiate, and establish joint target outcomes under a broader firm strategy (O’Leary-Kelly and Flores, 2002; Sleep et al., 2016). Additionally, differentiation is found to enhance collaboration’s impact on the overall productivity of a firm’s employees, adhering to extant literature’s proposition that collaboration initiatives for developing unique products and services seek to actualize every constituent’s potential (Jassawalla and Sashittal, 1998). Further, despite extant literature’s position that both a differentiation strategy and high collaborative capabilities lead to enhanced service outcomes (Jin et al., 2017; Ramanathan and Gunasekaran, 2014), they do not jointly impact customer service. Instead, a cost-oriented strategy harms collaboration’s impact on service outcomes. Taken together, these results imply that enhancing customer service is one of collaboration’s basic firm objectives and can be harmed by overzealous efforts at cutting costs, which creates conflicts among constituents due to strategy-induced resource scarcity (Anderson, 1982; Juttner et al., 2010). Despite the above finding, cost-oriented strategies may still positively influence collaboration’s performance outcome if judiciously implemented with a focus on operating performance. For instance, cross-functional processes such as sales and operations planning (S&OP) have documented benefits for operating outcomes (Oliva and Watson, 2010), such as

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product cost and quality (Chae, 2009; Chou et al., 2007). Differentiation’s positive moderating influence on collaboration’s operating performance impact likely reflects that these metrics may also serve as the basis upon which firms can inspire customer loyalty (Davis-Sramek et al., 2008). However, strategic orientation’s interaction effects with collaboration on operating performance do not perfectly carryover to financial gains, as differentiation’s influence on the collaboration-financial performance linkage is stronger than that of cost leadership. Indeed, extant strategic management literature indicates that there tends to be only one cost leader that enjoys most of the financial benefits and those firms competing as low cost providers may instead risk being impoverished (Bowman and Ambrosini, 1997; Redoli et al., 2008). In contrast, differentiation allows firms to reap greater financial rewards from new products developed through collaboration among diverse constituents. Regarding the Purity-Intensity Typology. Grounded in CBT, we originally posited that the unidirectionality of pure strategies simplifies cross-functional coordination to create an environment that is conducive toward achieving the highest levels of collaboration and translating it to performance gains. Not only are the hypotheses unsupported by our empirical model, a dualfocus on both cost leadership and differentiation is found to positively moderate collaboration’s impact on workforce performance and firm performance without harming either customer service or operating performance. CBT elucidates that firms must minimize functional misalignments in order to ameliorate competition for access to and control over firm resources based on the demands of each function’s external coalitions (Anderson, 1982; Juttner et al., 2010). Having a single strategic focus (i.e., high strategic purity) may allow firms to have greater clarity concerning resource

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mobilization (Miles and Snow, 1983; Thornhill and White, 2007). Rather than being harmful, a dual-focus on both cost and differentiation instead enhances collaboration’s performance benefits. In comparison to past studies on strategic purity, the sole exception to its central tenet that hybrid strategies tend to underperform is when an economy is in transition and characterized by rapidly changing customer demands (Shinkle et al., 2013). Our results reflect that collaboration is the key to unlocking how a firm may successfully compete through a hybrid strategy, particularly as rapid technological advances also shift customer demand. As extant literature on collaboration finds, superior cross-functional collaboration enhances information flow across firms to bring both operational and product/service development benefits (Chae, 2009; Oliva and Watson, 2010; De Luca and Atuahene-Gima, 2007). Consequentially, accelerated product development cycles would better allow firms to serve an increasingly heterogeneous customer base (Gebauer et al., 2011). However, firm strategies require significant resource support. A lack of clear strategic orientation may not necessarily indicate commitment to hybrid strategies. To gain greater insight into our counterintuitive findings, our subsequent inquiry into the Purity-Intensity typology indicates that Intensity, rather than Purity, determines the degree to which collaboration translates into performance success. Among the four groups of firms, we find that firms in different stages of strategic purity and intensity invest achieve different levels of collaboration and performance. Their nonparametric ranking suggests that the intensity level at which firms pursue strategy matters more in the collaboration-performance linkage than strategic purity, which is in line with the tenets of strategic focus (Wernerfelt and Karnani, 1987). Results suggest that while firms need to determine a strategic direction in order to

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improve performance, the intensity of their strategic pursuit ultimately differentiates performance. Further, the clear differentiation in collaboration and performance along strategic intensity rather than purity suggests resource investment toward collaborative capability enables firms to pursue multiple disparate strategies due to its role in getting a firm’s constituents to work together. That is, collaboration makes it possible for firms to pursue combinative benefits from multiple dimensions of competitive outcomes (e.g., Rosenzweig et al., 2003; Kristal et al., 2010). In sum, collaboration’s role in a firm is not to passively align divergent goals among multiple constituents of the firm and requires proactive coordination as a superordinate enabler of strategic benefits—regardless of Strategic Purity. CONCLUSION Taken together, our results suggest that while a collaborative capability is a common enabler of performance benefits, how this vital capability is translated into enhanced firm performance is distinctly influenced by strategic boundaries. While some of these relationships may be intuitive, as posited by CBT, others are unexpected. Whereas firms can fully unlock the benefits of aligned constituencies through pursuing a differentiation-focused strategy, collaboration instead acts as an enabler of hybrid strategies. A common downfall of hybrid strategies as described by strategy theorists is that a lack of focus results in firms becoming vulnerable to competitors who may simply be “better” through specialization (Thornhill and White, 2007). Part of this downfall is due to conflicting constituents in a firm, resulting in internal paralysis and an inability to formulate coherent strategic response to threats from strategically purer competitors. With the recently increased emphasis on CFI and collaborative capabilities, the above view has begun to evolve. As collaboration mitigates conflicts among various constituents in a firm, their unique

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capabilities can be leveraged to yield a synergistic effect to pursue multiple segments of customers and translate the hybrid focus to performance-enhancing combinative benefits. LIMITATIONS AND FUTURE RESEARCH In this study, we employed a combination of CBT and strategic purity theoretical frameworks to identify cross-functional collaboration’s boundary conditions. Although our results shed light to why firms continue to observe uneven benefits of resource investment toward collaborative capabilities, neither CBT nor strategic purity consider how to sustain cross-functional collaboration’s performance benefits. Alternate theoretical lenses, particularly the resource-based view (RBV) and its dynamic capabilities extension (Barney, 1991; Barreto, 2010) can be used to examine the degree to which cross-functional collaboration’s impact on competitive outcomes of cost and differentiation strategies may be sustained. In particular, CBT and strategic purity theoretical frameworks do not explicitly recognize that resources are heterogeneously endowed among firms. Thus, our results may be further differentiated based on resource endowment and permeability of a firm’s boundary with its external coalitions (i.e., supply chain partners). Additionally, our study uses perceptual measures. A significant limitation of perceptual measures is that they do not reflect objective firm initiatives and performance outcomes. Further, survey items do not possess the richness of qualitative data to explain the why and how in significant depth. Thus, future studies may use the insights from this study, particularly the strategic purityintensity typology, to better understand how firm strategies influence cross-functional collaboration and its performance outcomes.

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