Dynamic Capability Building in Service Value ... - Semantic Scholar

Decision Sciences Volume 40 Number 3 August 2009

C 2009, The Author C 2009, Decision Sciences Institute Journal compilation

Dynamic Capability Building in Service Value Networks for Achieving Service Innovation Renu Agarwal† School of Management, Faculty of Business, University of Technology, Sydney NSW 2007, Australia, e-mail: [email protected]

Willem Selen Business Administration Department, College of Business & Economics, United Arab Emirates University, Al Ain P.O. Box 17555, United Arab Emirates, e-mail: [email protected]

ABSTRACT Service organizations increasingly create new service offerings that are the result of collaborative arrangements operating on a value network level. This leads to the notion of “elevated service offerings,” our definition of service innovation, implying new or enhanced service offerings that can only be eventuated as a result of partnering, and one that could not be delivered on individual organizational merits. Using empirical data from a large telecommunications company, we demonstrate through structural equation modeling (SEM) that higher-order dynamic capabilities in services are generated as a result of collaboration between stakeholders. Furthermore, it is through collaboration and education of the stakeholders that additional higher-order capabilities emerge (customer engagement [CuE], collaborative agility [CA], entrepreneurial alertness [EA], and collaborative innovative capacity), all of which influence the service innovation outcome. Our study also reveals empirical evidence for an ongoing process of continuous dynamic capability building in accordance with the changing dynamics of business. Managers of service organizations should recognize the potential embedded in these higher-order skill sets, starting from collaboration, learning, and management of creative ideas for both strategic and operational benefits. Moreover, the capabilities of CA, EA, and CuE are even more important in managing the flexibility, timely delivery, and reliability of service offerings. Managers should take measures to inculcate, promote, and manage these dynamic capability skill sets to foster innovation in services.

Subject Areas: Collaboration, Construct Development, Dynamic Capability Building, Innovation in Services, Service Value Networks, and Structural Equation Modeling. INTRODUCTION Many service organizations create new service offerings and service concepts through collaborative arrangements and partnerships (Maitland, Bauer, & Westerveld, 2002; Olla & Patel, 2002; Hamilton & Selen, 2004; Stuart & Tax, 2004). † Corresponding

author.

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According to Barney (1991), organizational creativity and performance is built through the deployment and use of idiosyncratic, valuable, and inimitable resources and capabilities that might be heterogeneously distributed across the organization. Further, Makadok (2001) highlights that organizations leverage two types of resources, namely resource picking and capability building. However, Sambamurthy, Bharadwaj, and Grover (2003) argue the need for capability-building leveraged resources over resource picking for the sake of supernormal performance. Capabilitybuilding leveraged resources have also been known as dynamic capabilities: “the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve and die” (Eisenhardt & Martin, 2000, p. 1107). Further, Teece, Pisano, and Shuen (1997) define capability-building leverage as “firms ability to integrate, build, and reconfigure internal and external resources in creating the higher-order capabilities that are embedded in their social, structural, and cultural context” (p. 516). In the context of very dynamic markets, the logic of leverage and its relevance regarding strategic conduct has been questioned, and hence the need for understanding how organizations can develop capabilities to explore, exploit, and capture market opportunities and relentless innovations with speed and surprise as an important imperative for organizational success (D’Aveni, 1994; Brown & Eisenhardt, 1997; Christensen, 1997b; Goldman, Nagel, & Preiss, 1999). The Porter Report (Porter & Ketels, 2003) furthermore highlights that networking translates into innovative outcomes and that interorganizational networking is prime for the development of innovative capability. According to the logic of dynamic capability leverage, this innovation capability is about both the generation and the exploitation of new products, services, processes, and business practices (Pittaway, Robertson, Munir, Denyer, & Neely, 2004). Furthermore, with ever-changing customer demands, organizations not only have to be innovative but agile and responsive to business needs as well. In this context, it is the expansion and development of these competencies and skills that deserve special attention. This research examines the impact of collaboration on innovation in services through dynamic capability-building processes, within the telecommunications industry using empirical data from one major Australian telecommunication service provider and its partnering organizations. While external, market and organizational factors, organizational capabilities, actions taken by management, and support from industry and government influence innovation at a firm level, this study focuses on innovation at the enterprise level. More specifically, our research focuses on the impact of dynamic capability building on service innovation through elevated service offerings (ESO), with ESO defined as a new or enhanced service offering that can only eventuate as a result of a collaborative arrangement, one that could not otherwise be delivered on individual organizational merits. Yet we realize that a better service offering is possible through improved capacity management, improved customer interaction, improved training, and many more reasons leading to service innovations that can be implemented using an organization’s existing resources and capabilities. However, in this research we focus particularly on innovation that can be implemented through partnering—arrangements that may create new competencies, capablities, and resources that result in ESO. As such, our research seeks to contribute to the knowledge of which dynamic

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capability-building processes occur in light of the different dynamic capability skill sets that are nascent and require fostering for the sake of innovation in services. If it is possible to identify the circumstances and manners in which partnering organizations learn and foster dynamic capabilities, then strategic benefits such as innovations and new service offerings and operational benefits such as performance and productivity measures may flow from incremental improvements through dynamic capability building. The remainder of the article is organized as follows: first, the theoretical background and justification of the hypotheses and construct development are discussed. This is followed by an overview of the research design and methodology and analyses and results. Subsequently, managerial implications are discussed. Finally, main conclusions are drawn, together with limitations of the study and suggestions for future research. The next section details the theoretical background of our research, construct development, and resulting research hypotheses.

THEORETICAL BACKGROUND AND RESEARCH HYPOTHESES Collaboration or relationship management, defined as “a firms set of relationships with other organizations” (Perez Perez & Sanchez, 2002, p. 261), is fundamental to the logic of leveraging resources, which includes the establishment and maintenance of relationships with partners such as suppliers, customers, and other key stakeholders. It is a managerial capability and a skill that largely reflects knowledge sharing, communication, and the learning ability of the firm (Slater, 1995; Dyer & Singh, 1998). This provides the ability to build, change, or mobilize resources and assets through the establishment of networks where organizations team in a plug-and-play mode. Agarwal and Selen (2007) classify organizational relationship capital (ORC) as a higher-order construct, which is made up of three subconstructs: relational capital, employee capital, and prior relationship. Relational capital refers to the wealth in the form of mutual trust, respect, friendship, and high reciprocity among individuals at the personal level between partner organizations (Kale, Singh, & Perlmutter, 2000). Employee capital refers to interorganizational product, service, and process knowledge present in their employees’ minds (Nonaka & Takeuchi, 1995; den Hertog, 2000) and the management-driven reward systems with recognition mechanisms prevailing across partnerships as a means for personal motivation (Wickham, 2006). The construct of prior relationship is based on a factor that produces trust and interaction as its proxy, which is believed to generate a high degree of learning and information or know-how exchange between partners (Ring & Van de Ven, 1992; Gulati, 1995). The literature shows evidence of the effects of relational orientation promoting organizational learning, which influences innovation, supply chain effectiveness, and performance (Lai, 2004; Panayides & So, 2004; Prahinski & Benton, 2004). Panayides and So (2004) in their empirical study demonstrate that relationship orientation has a positive impact on key organizational capabilities such as organizational learning, innovation, and improvement in supply chain effectiveness and performance. Therefore, we postulate that members of the service

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value networks (SVN) who possess relational capital, employee capital, and have a proven track record of previous relationships, will positively influence ESO, organizational learning, and collaborative innovative capacity (CIC) of all partners, including customers. We next discuss the notion of dynamic capabilities and their inherent processes that may influence ESO.

Dynamic Capability-Building Processes Teece (2000) acknowledges, and Galbreath (2005) empirically supports, the fact that capabilities are not only tacit in nature, but they are inextricably embedded within the organizational experience, learning, and practices adopted by the firm (e.g., in relationship management and collaborative communication). Organizations therefore need to pay greater and focused attention to the development and preservation of internal skills and abilities, tacit interaction, and relationship capital (Sivadas & Dwyer, 2000). This implies changing the knowledge base within an organization, the way the organizations use existing knowledge to compete, and the way individuals interact and how management cultivates these attributes. In the context of logistics service provider–client relationships, collaboration has been shown as a precursor to organizational learning, which in turn influences innovation capability, leading to supply chain effectiveness, and hence supply chain performance, with collaboration also having a direct effect on supply chain effectiveness (Panayides & So, 2004). Nambisan (2002), Kohli and Jaworski (1990), Vargo and Lusch (2004), and more recently Bitner and Brown (2008) all point out the importance of customer as a co-creator of value; hence development of relations with the customer through their engagement becomes pivotal in the development, design, and delivery of innovative products and services. Information communication technology (ICT) systems are capable of sustaining interfirm interactions that are necessary in a business relationship, despite the challenges of resource heterogeneity, process complexity, codification and diffusion of knowledge, and capturing of tacit knowledge and informal communication into standardized models (Baraldi & Nadin, 2006). Such knowledge-based dynamic capabilities make resources more entrepreneurial, innovative, and agile and in return are likely to provide the competitive advantage organizations are looking for. Dynamic capability (“how you change your operational routines”) results in creation of higher-order skills, which are beyond the zero-level strategic and operational functional skills (“how you earn a living”), which are essential during strategic goal setting and operational event management, especially when interacting with customers. Collis (1994) points out that a hierarchy of higher-order capabilities exists, whereas Winter (2003) defines a “zero-level” capability as “dynamic capabilities that operate to extend, modify or create ordinary capabilities” (p. 991). In particular, Winter (2003, p. 992) states that dynamic capability building enhances the “zero-level” operational skill sets required for standard new product development or new service development processes. Our research question can therefore be stated as follows: what are the dynamic capabilities that collaborative organizations need to manage and inculcate in order to create and deliver ESO in collaborative settings, with particular focus on customer interactions in service organizations?

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Next, we discuss the theoretical foundations of these higher-order capabilities and describe them as five combined capabilities: entrepreneurial alertness (EA), collaborative agility (CA), customer engagement (CuE), CIC, and collaborative organizational learning (COL). These capabilities are enabled through the ORC of the SVN. Each of these and their interrelationship are discussed next.

EA According to Yu (2001), an opportunity exists only if it is perceived and will not be discovered if the alertness system is switched off. Yu (2001) presents two views, one defined as “ordinary discovery” events that depict a “backward” interpretation for entrepreneurs who endeavor to exploit opportunities by doing some things better and exploiting things that were so far unnoticed. The second view encompasses the notion of “extraordinary discovery,” referring to radical implications and events and reinterpreting those incoming events into new ideas. Underpinning the logic of opportunity and innovation, entrepreneurial skills are likely to help front-of-house staff maintain customer satisfaction and provide operations staff with a higher-order ability to explore and exploit options when subjected to varying customer needs, thus arming them with an ability to spontaneously deliver customized solutions to customers. Sambamurthy et al. (2003) define EA as the “dynamic capability of an organization to explore its marketplace, and detect areas of current and future market place threats and opportunities” (p. 250), which comprises two specific capabilities, namely strategic foresight and systemic insight, that are described in detail in Agarwal and Selen (2007). According to Sambamurthy et al. (2003), strategic foresight is the ability to anticipate discontinuities, threats, and opportunities of the future while making us more vigilant of market place dynamics. When delivering services to customers, foresight is critical to entrepreneurial action taken by the front-of-house staff in real time, as it reflects the ability to anticipate and visualize market imperfections, and at the same time to gauge opportunities for information technology–based competitive actions (Christensen, 1997a) and acts as a “probing and learning” mechanism, a means by which it only provides time to simply learn, and act at the speed at which the industry changes (Costanzo, 2004). Similarly, according to Sambamurthy et al. (2003), systemic insight is the ability to visualize and apply knowledge and experience in architecting competitive actions, that is, to be in a situation where one can contrast the views from the inside and the outside of the system (Rubinstein-Nabarro, 1992). CA Sambamurthy et al. (2003) suggest that “agility encompasses a firm’s capabilities which are related to interactions with customers, orchestration of internal operations, and utilization of its ecosystem of external business partners” (p. 245) and that “agility encompasses the exploration and exploitation of opportunities for market arbitrage” (p. 245). Thus, according to Sambamurthy et al. (2003), agility comprises of three interrelated capability subconstructs, namely, customer agility, partnering agility (PA), and operational agility (OA).

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The definition of agility requires amendment to include the adaptations made in the context of cultural, organizational orientations and focus on customers. Thus, we reclassify agility in accordance with Sambamurthy et al.’s (2003) definition and give it a wider connotation. Specifically, renaming agility in the context of collaborating organizations, CA entails the swiftness and immediate response by value network participants, which includes the time to leverage capability and time to deliver outcome (Ogulin & Selen, 2004). Thus, CA forms the basis of a dynamic and adaptive capability provided by SVN in response to customer needs and demands. Adapting to Sambamurthy et. al.’s (2003) and Dyer and Singh’s (1998) definition of PA to suit services in particular, we define PA as “an organizations ability to explore and exploit opportunities through sourcing and staging service delivery processes, or customer interfaces and customer support assets and resources, and provide organizations with an ability to adapt or modify their extended networks when it needs access to assets, competencies, or knowledge not currently resident in the networks.” The second agility, OA, is related to the operational processes that form the basis of organizations, and refers to the ability to rapidly change and redesign existing processes and create new processes for exploiting dynamic marketplaces (Sambamurthy et al., 2003). It refers to the organizational processes and the ability to accomplish change with speed, accuracy, and cost efficiency in the exploitation of opportunities and competitive actions. According to Amit and Zott (2001), ICT enhances OA by leveraging the information and knowledge access through ICT network interconnections among partners. This enables faster and more informed decision making by management. In addition, ICT reduces the asymmetry of information flow among partners, which acts as a mechanism for rapid and up-to-date supply of consolidated information through the use of electronic communication channels. As a consequence of this, and due to existing standardized interfaces among ICT applications across organizations, business processes are becoming modular and atomized, such that they can be easily juggled into end-to-end processes flowing transparently across organizational boundaries. In light of these developments, we modify the definition of OA to include structural adaptations and modular processes. Acting within the boundaries directed by new organizational networks, employees would be able to respond to a variety of customer demands with speed, accuracy, and organizational decorum. In face-to-face technologymediated customer–supplier service interactions and the OA required to manage these interactions, we define OA as the managerial capability to rapidly adapt and change network structures and organizational cultures, integrate modular processes to rapidly change and redesign existing processes, and create new processes for exploiting a dynamic marketplace.

CuE According to Nambisan (2002), customers present a triangular purpose in stimulating an organization’s competitive actions, namely as a co-creator in the development and design of innovative products and services, as a source of innovative ideas, and as a user for prototype testing or helping other users in learning about the product or service. It is in this context that customers play the most strategic

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role as co-creators of value, and hence this warrants a customer-orientated approach while managing the market dynamics in search of new service offerings. Not only that, according to Kohli and Jaworski (1990), customer agility describes the firm’s ability to use customers’ expectations in deciphering market intelligence and identifying competitive opportunities. Further, ICT was found to be an enabler for building and enhancing virtual customer communities, and hence customer agility (Kambil, Friesen, & Sundaram, 1984; Holmstrom, 2001; Nambisan, 2002). Customer agility, as defined by Sambamurthy et al. (2003), suggests the coopting of customers in the exploration and exploitation of opportunities for innovation and competitive actions. However, according to the services dominant logic theory, customers and their knowledge should be seen as a dynamic operant resource (Lusch, Vargo, & Malter, 2006; Vargo & Lusch, 2006, 2008; Vargo, 2007). We believe that this form of agility needs to be dual and bidirectional in nature, as such we rename this customer agility CuE. We define CuE as the ability of the SVN to encourage customers to participate and engage during the service encounter (face to face or technology mediated), and through the customer’s engaging and learning process, judge and respond to customer’s needs and expectations with agility and innovativeness.

CIC The scope of idea creation is wider than just customer requirements and has been extended to accommodate ideas from employees with cultivation of ideas from customers and suppliers (Oke, 2007). The Pentathlon framework (Goffin & Pfeiffer, 1999; Oke & Goffin, 2001) depicts a static view, but includes soft organizational, process issues, and organizational climate aspects. Ideas once identified, and if not executed, are likely to be lost. Further, collaborative organizations require greater alignment across partnering organizations in managing the innovation strategy and its implementation. In line with the Department of Trade and Industry report (Voss & Zomerdijk, 2007), we propose a feedback loop to accommodate the capturing of ideas for exploration at a later stage, irrespective of them being materialized in that innovation cycle. This ensures that all ideas put out at anytime should still be in the repository and eligible for future consideration when reviewing the next cycle of innovation. Both strategically and operationally, the SVN organization needs to possess the higher-order capability to capture ideas and execute them by bringing innovation not only from the perspective of new service or product offerings in response to competitor actions, but also to make changes to delivery methods or adapt marketing techniques. Therefore, the challenge lies in coming up with new organizational innovations or business models. Fuchs, Mifflin, Miller, and Whitney (2000) develop the concept of higherorder integration capabilities, and Lawson and Samson (2001) propose innovation as “a higher-order integration capability, that is the ability to mould and manage multiple capabilities” (p. 380). Organizations in search for newstream innovation also leverage of their knowledge base (Cohen & Levinthal, 1990). In this context, Lawson and Samson (2001) define innovation capability “as the ability to continuously transform knowledge and ideas into new products, processes and systems for the benefit of the firm and its stakeholders” (p. 384), and then they also highlight the importance of synthesizing the two operating paradigms of mainstream and newstream innovation into one.

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Therefore, we define CIC as a dynamic skill that is developed when collaborating with partners and consists of an ability that evolves within individuals or groups; it is an ability to come up with innovative ideas, which gives partnering organizations the capacity to introduce new services, new or modified processes, new or modified operating structures, new ways to market products or services, or ideas through the integration of capabilities and resources in an urge to incite innovation. Further, CIC may broaden horizons and equip partnering organizations with an ability to cross-fertilize ideas and allow the application of ideas within and across industry sectors. It is also a skill set that promotes lateral thinking.

COL Because dynamic capabilities are underpinned by organizational resources, activities, and routines (Mathews, 2006), it is the learning and knowledge management processes that guide their development, evolution, and use (Eisenhardt & Martin, 2000; Cepeda & Vera, 2007). According to Grant (1996), organizations get better at managing any given tasks, hence they develop higher-order capabilities to manage those tasks. This is possible because they accumulate and apply knowledge (Collis, 1996) pertinent to the task; and they learn and accumulate such knowledge by making deliberate associations between past actions, future situations, and their effectiveness each time they use it (Fiol & Lyles, 1985). Furthermore, repeated practice helps organizations better understand a routine (Argote, 1999); helps in searching for information regarding new product routines; allows the search for information to occur in depth and breadth (Katila & Ahuja, 2002); allows creation and amendments to routines through learning by doing and improvization methods (Crossan & Sorrenti, 1997); and makes experience and routines easier to apply through knowledge codification into procedures, and through use of technologies, which makes tasks and activities streamlined (Zander & Kogut, 1995). Recently, two empirical studies on knowledge-based dynamic capabilities provide evidence for learning processes as the basis for developing dynamic capabilities (Cepeda & Vera, 2007; Kale & Singh, 2007). Zollow and Winter (2002) have proposed that deliberate learning efforts articulate and codify collective knowledge, which translate into higher-order managerial skills and dynamic capabilities, through which the SVN is likely to modify its strategic and operating routines in pursuit of greater effectiveness and improved efficiency. This in turn assists development of new information and new knowledge about the routines performance, with executed changes becoming routines over time and knowledge gradually becoming increasingly embedded in human behavior. In our research, we believe that organizational learning and customer learning are focused on the development processes related to product, process, organizational, and marketing innovations. We classify COL as a higher-order construct, which is made up two subconstructs: COL–yours and COL–partners. There are benefits to each partnering organization in that each is seeking a particular set of skills and/or resources that it does not possess (Cravens & Shipp, 1993), and that both partners promote rapid diffusion of new technologies and mutual learning (Lorange & Roos, 1991). Within the context of the partnership “capabilities are based on developing, carrying, and exchanging information through the firm’s

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Figure 1: A dynamic capability-building framework for elevated service offerings.

human capital” (Amit & Schoemaker, 1993, cited in Andreu & Ciborra, 1996, p. 113), and in our case this extends to any type of partnership.

Research hypotheses The overall dynamic capability-building framework in SVNs for achieving service innovation (ESO) is illustrated in Figure 1. This framework includes the constructs of ORC, COL, CuE, CA, EA, and CIC, all of which influence the service innovation outcome—our notion of ESO. This model shows interrelationships between the constructs and how these constructs influence the service offering of an organization operating under a collaborative environment leading to an ESO, which implies new or enhanced service offerings that can only be eventuated as a result of partnering, one that could not be delivered on individual organizational merits. We now in turn present the research hypotheses to be investigated. Recall from our discussion of ORC, we postulate that members of the SVN who possess relational capital, employee capital, and have a proven track record of previous relationships, will positively influence ESO, organizational learning, and CIC of all partners, including customers. We also believe that organizational learning will in turn enhance the ability to come up with new ideas and innovations and hence will act as a mediator between organizational relationship and CIC.

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Hence we hypothesize: H1a: There will be a positive relationship between ORC and the outcome ESO. H1b: There will be a positive relationship between ORC and the COL of the SVN partners. H1c: There will be a positive relationship between ORC and the CIC of the SVN partners. H1d: There will be a positive relationship between ORC and CIC, which is mediated by the COL of the SVN partners. According to Harker (1999), relationship orientation refers to the proactive creation, development, and maintenance of relationships with customers. However, Panayides and So (2004) define relationship orientation as a philosophy of doing business, and a culture that puts the buyer–supplier relationship at the center of an organization’s strategic and operational thinking. Keeping this in mind, we hypothesize that having good relations with our customers encourages customers to participate, engage, and involve in tasks and activities. We also believe that organizational relationship enhances customers’ and other partnering organizations’ knowledge and learning, which in turn encourages customers to engage in tasks and activities that relate to service design, service delivery, and service interface encounters. Hence, we hypothesize: H1e: There will be a positive relationship between ORC and CuE of the SVN partners. H1f: There will be a positive relationship between ORC and CuE, which is mediated by the COL of the SVN partners. When partners team up, they have open communication, and hence appropriate and accurate sharing of information amidst partners occurs. This indeed would facilitate decision making and execution of tasks that span inter- and intraorganizations with greater speed and flexibility, increased awareness, and alertness through the exploration and exploitation of ideas. Hence, we hypothesize that organizational relationship influences the development of CA and EA capabilities of SVN members: H1g: There will be a positive relationship between ORC and CA of the SVN partners. H1h: There will be a positive relationship between ORC and EA of the SVN partners. In SVN, the partnering organizations’ routines, resources, and activities are integrated and coordinated, and information reach and richness allows management to make the right decisions at the right times. This will most likely influence CA in terms of examining the feasibility of resources, operations, and interand intraorganizational options and coming up with timely executable responses. Through the partner’s involvement and the customer’s engagement, and through their experiences and learnings, coupled with the access to the right information

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and a creative mind, SVN members are expected to be innovative. This process can be exacerbated and accelerated to some extent through CA, which in turn will influence the execution of the service solution in creative ways never seen before, in the form of an ESO. Hence we hypothesize: H3a: There will be a positive relationship between CIC and EA of the SVN partners. H3b: There will be a positive relationship between EA and CIC, which is mediated by the CA of the SVN partners. H3c: There will be a positive relationship between EA and CA of the SVN partners. Following our discussion of CA earlier, CA gives partnering organizations an ability to combine or recombine resources, tasks, and activities at short notice, leading to flexibility, customization, and delivery of services with speed. Not only that, the integration of organizational systems and processes provides greater reach and richness of information and knowledge sharing across partners, creating enabling infrastructures in terms of new or better technologies, procedures, and internal and external relationships, possible only through CA. This new insight gives the SVN greater ability to strategically and operationally exploit ideas, and the “knowledge evolution cycle” (Zollow & Winter, 2002) starts all over again, which is why the mediation role of collaborative innovative capability is deemed essential. Therefore, we hypothesize: H4a: There will be a positive relationship between CA and CIC of the SVN partners. H4b: There will be a positive relationship between CA and the outcome ESO. H4c: There will be a positive relationship between CA and ESO, which is mediated by the CIC of the SVN partners. It is expected that staff are empowered with an ability to comprehend customer expectations and deliver customized services spontaneously during the CuE process. Therefore, we hypothesize: H5a: There will be a positive relationship between CuE and CA of the SVN partners. H5b: There will be a positive relationship between CuE and EA of the SVN partners. H5c: There will be a positive relationship between CuE and CIC of the SVN partners. H5d: There will be a positive relationship between CuE and the outcome ESO. Based on extant literature, Fuchs et al. (2000) examine innovation in the context of dynamic capabilities and define the innovation capability as a higher-order integration capability (i.e., the ability to mould and manage multiple capabilities). Accordingly, organizations that possess this dynamic capability have the ability

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to integrate key capabilities and resources of their firms to successfully stimulate innovation. Organizations in search for newstream innovation also leverage their knowledge base (Cohen & Levinthal, 1990). Further, in the context of SVN, we hypothesize that CIC may broaden the horizons and equip partnering organizations with an ability to cross-fertilize ideas, allow the application of ideas within and across industry sectors, and also promote lateral thinking. Consequently, this capability may induce some form of “extraordinary discovery” or an “ordinary discovery” (Yu, 2001), resulting in ESO. Finally, following our discussion of CIC, we hypothesize: H6a: There will be a positive relationship between CIC and the outcome ESO. H6b: There will be a positive relationship between ORC and ESO, mediated by COL and CIC of the SVN partners. Referring to our earlier definition and discussion of COL, we believe that learning happens on both sides of the relationship, and in reality it is only possible through creating enabling infrastructures in terms of technologies, procedures, and internal and external relationships, which in turn is possible only through CA. We also hypothesize that, as customers learn, their implicit understanding of the service offering and knowledge increases, which heightens the probability of customers engaging in activities with their service providers. Not only that, it increases the alertness of the SVN partners. Further, the integration of organizational systems and processes with partnering organizations brings greater reach and richness of information and knowledge sharing across partners. The “knowledge evolution cycle” (Zollow & Winter, 2002) and its four phases—generating and exploiting ideas, evaluation of ideas and examining the viability and feasibility of options, selection of optimal solution, and the codification of the change and its diffusion— starts all over again. In this process the alertness system is turned on, resulting in “ordinary discovery” (deBano, 1977; Cheah, 1992; Yu, 2001) largely doing some things better and exploiting things that were largely unnoticed or relating to doing things completely differently, known as “extraordinary discovery” (Klein, 1999; Yu, 2001). It is in these contexts that we believe the direct and mediating effects of CA, EA, and CIC emerge, depending on the starting point of the evolution cycle. Therefore, we hypothesize: H2a: There will be a positive relationship between COL and EA of the SVN partners. H2b: There will be a positive relationship between COL and CuE of the SVN partners. H2c: There will be a positive relationship between COL and CA of the SVN partners. H2d: There will be a positive relationship between COL and CIC of the SVN partners. H2e: There will be a positive relationship between COL and CIC, which is mediated by the CA of the SVN partners.

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Figure 2: Proposed model.

CuE = customer engagement; CA = collaborative agility; EA = entrepreneurial alertness; CIC = collaborative innovative capacity; ORC = organizational relationship capital; COL = collaborative organizational learning; ESO = elevated service offerings.

H2f: There will be a positive relationship between CIC and CA, which is mediated through EA of the SVN partners. H2g: There will be a positive relationship between COL and the outcome ESO. H2h: There will be a positive relationship between COL and ESO, which is mediated by the CIC of the SVN partners. Hence, the described framework in Figure 1 now translates into the hypothesized relationships to be tested that are shown in Figure 2. Next, we discuss the research design and methodology of our study.

RESEARCH DESIGN AND METHODOLOGY The research methodology and research design are depicted in Figure 3.

Questionnaire design Based on relevant management literature described earlier, the theoretical framework was proposed and the preliminary survey questionnaire was designed. The

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Figure 3: Research methodology and research design.

survey instrument included questions on dynamic capabilities, including ORC, COL, CIC, EA, CA, and ESO, as well as a number of constructs that were part of a larger SVN framework and beyond the scope of our research framework. As shown earlier in Figure 3, the initial phase of this research primarily employed qualitative methods to explore and demonstrate the existence of collaborative structures across partnering organizations. As this area of research is new, the case-study approach was used to investigate a contemporary phenomenon within a dynamically changing, real-life context (Carson, Gilmore, Gronhaug, & Perry, 2001; Yin, 2003). This was studied in depth through four case studies spanning across mobile handset supply chain, collaborative learning and development, virtual critical care in an emergency situation, and provisioning of services for commonwealth games. Each of the four case studies was underpinned by convergent interviewing (Rao & Perry, 2003) to help address and identify issues in less-researched areas such as the emerging phenomenon of ESO in collaborative environments. This approach provided a more detailed insight into the workings of a collaborative project, with richer and deeper contextual data as it used a wide variety of data sources (Yin, 2003), including documents and face-to-face

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interviews with executives and key players across the telecommunications organization and contextual partnering organizations. The convergent interviewing involved interviewing executives, senior management, and middle managers in order to gain insight into their experiences, opinions, aspirations, and attitudes about collaborative arrangements within organizations, which converged on important issues (Dick, 1990). Furthermore, convergent interviews have been adopted in other comparable studies (Rao & Perry, 2003), where it was found to be most effective in developing a conceptual framework about relationship constructs in an internet environment. On average, eight to nine interviews per case study were conducted with executives across all partnering organizations. Convergent interviewing showed that the intention to achieve better quality, innovative outcomes faster was the prime objective behind the collaboration, and in this respect some respondents saw collaboration leading to success as being possible only through good management, governance, coordination, and integration of practices. Aside from existent constructs used in the literature, the need for development of new constructs emerged as a result of the insights and findings from the case studies and convergent interviews. The measurement items for each construct are presented in Table A1. ORC is measured by relationship capital adapted from Kale et al. (2000) to reflect interaction and relationships that develop at an individual level between alliance partners, employee capital adapted from Goldstein (2003) with two new items added, and prior relationship measurement taken from Kale et al. (2000). COL was partly adapted from Kale et al. (2000), with new items added to differentiate the learning developed by the partnering organization, indicating that learning takes place on both sides of the partnership. CIC was adapted from Ganesan, Malter, and Rindfleisch (2005) with new items added pertinent to application and diffusion of knowledge. CA and CuE scales were self-developed, based on definitions taken from Sambamurthy et al. (2003). The ESO construct was adapted from extant literature with some minor modifications (Heskett, Sasser, & Hart, 1990; Roth, 1993; Sharifi & Zhang, 1999; Goldman et al., 1999; Verma & Young, 2000; Van Hoek, Harrison, & Christopher, 2001; Prajogo, 2006; Swafford, Ghosh, & Murthy, 2006). All measurement items of the above constructs are measured using a fivepoint Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The measurement items confirmed through one-factor congeneric modeling using confirmatory factor analysis (CFA) are listed in Table A4. Table 1 below summarizes the constructs and measures of the dynamic capability framework.

Sampling and Data Collection The survey instrument was pilot tested on 79 employees belonging to a particular case study within a telecommunications service provider and its partnering organizations. The main round online survey was circulated to an additional 1,717 individuals across the telecommunications service provider and its partnering organizations, resulting in 380 valid responses, a response rate of 22.13%. Of these, approximately 31% responses were submitted by the partnering organization, 22% by the customer organizations, and the remaining 47% by the parent

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Table 1: Map of research model, constructs, and measures used. Variable Category

Theoretical Context

Endogenous variable

Dynamic capability

Construct Operationalization Organizational relationship capital (ORC) Collaborative organizational learning (COL) Entrepreneurial alertness (EA) Collaborative agility (CA)

Final endogenous variable

Elevated service offering

Collaborative innovative capacity (CIC) Customer engagement (CuE) Elevated service offering (ESO)

Survey Scale Partner relationship Employee relationship Previous relationship Organizational learning—yours Organizational learning—partners Strategic foresight Systemic insight Operational agility Partnering agility Collaborative innovative capacity Customer engagement ESO—Strategic ESO—Performance ESO—Productivity

telecommunications organization. The data of the pilot and main rounds were subsequently checked for missing observations, outliers, and normality. Missing value analysis using expectation maximization treatment (Little & Rubin, 1987; Graham, Hofer, & Mackinnon, 1996) of missing data was used, resulting in a fully populated combined data set (77 for pilot round, 372 for the main round) with 449 sample observations. The sample and tenure demographics are listed in Tables A2 and A3, respectively. The data were randomly split in equal proportion (data sets 1 and 2) to fulfill data requirements for subsequent exploratory factor analysis (EFA)-, CFA one-factor congeneric-, and structural equation modeling (SEM) exploratory model and validation phases.

Nonresponse and Common Method Bias Nonresponse bias is the difference between the answers given by nonrespondents and respondents (Lambert & Harrington, 1990). The method as adopted by Paulraj (2002) in the context of supply chain management was used, wherein randomly selected variables and demographic variables were chosen for nonresponse bias analysis. The final round sample was split into two groups, one set comprised responses received prior to sending the reminder, and the second set responses received after the reminder e-mail was sent. A t test showed no significant statistical difference across the two groups (at 95% confidence interval) for the survey items tested, indicating that nonresponse bias is not a major concern in our study. According to Spector (1987), common method variance is an artifact of measurement that biases results when relations are explored among constructs

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measured by the same method. We established discriminant validity and convergent validity as a safeguard against common method variance. Furthermore, the overarching empirical study validated the research model using a triangulation research methodology, initially with a qualitative case-study method, which was underpinned by convergent interviewing; followed by quantitative research involving EFA, one-factor congeneric modeling with item parceling for construct validation, followed by SEM model building and model validation. The demographic information requested in the interview was the number of years of overall work experience, years worked in this organization, and years in their current role. Additionally, the qualitative and quantitative data were collected across several departments spanning inter- and intrapartnering organizations. Due to the complexity in dimensions, we did not control for firm type (supplier, parent, and customer) and firm size. Data were collected across different departments belonging to the parent organization, customer groups (internal/external), and supplier/partner organizations.

Data Preparation As this research involves the development of several new constructs, along with the testing of existing constructs in a collaborative setting, the research methodology required an exploratory phase. Gerbing and Hamilton (1996) and Anderson and Gerbing (1988) recommended a two-stage process in the exploration and validation of the factorial structure of questionnaire items, which was used by Currie, Cunningham, and Findlay (2004) in the validation of instruments for developing the internalized homonegativity scale. To enable this two-stage process, two data sets (1:1 ratio) that were created randomly were used for different stages of the quantitative process, classified as data set 1 (DS1) and data set 2 (DS2), respectively. The data set numbers for the pilot stage as they stood were not sufficient for both EFA and CFA analysis, nor were they sufficient for the SEM model analysis, hence an additional 148 records were randomly selected from the main round data to increase the pilot data from 77 to 225 data records, designated as data set 1 (DS1). Consequently, the main round respondent data set was reduced from 372 to 224 data records, designated as data set 2 (DS2). According to Joreskog (1971), data can be pooled only if the underlying factor structures are similar, which is exhibited via a lack of significant differences between the covariance matrices for the two sample data sets. In our case, it was acceptable to pool responses from the pilot survey and the main round survey as the difference in chi-square test was found to be nonsignificant, hence the variance–covariance matrices were seen as equivalent across the trial and final data sets. Thus, samples could be pooled resulting in a total of 449 samples, which then allowed the data to be equally split randomly and by doing so fulfilled the requirements for both the EFA and CFA one-factor congeneric model and also the SEM exploratory model and validation phases. DS1 (n = 225) was used for construct extraction during the EFA stage, and DS2 (n = 224) was used for validation during the CFA stage using a one-factor congeneric model. In a similar manner, with respect to path analysis using SEM, DS1 was used for model building and path analysis, while DS2 was used for model validation.

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In an attempt to get clean data for the purpose of quantitative analysis, and to overcome and minimize any statistical discrepancies, tests were conducted to view outliers, level of skewness, and kurtosis existing for each item and scale. “BollenStine bootstrap p” (Bollen & Stine, 1992), a post hoc adjustment to account for nonnormality as advocated by Holmes-Smith, Coote, and Cunningham (2005) when using nonnormal data in SEM, was used to conduct a bootstrap modification of the model χ 2 . An adjusted p value is then computed and the model is rejected if p < .05. The number of bootstrap samples in this research study was set at 1,000. Further, the item-parceling technique was used to reduce the degree of nonnormality in the data (Bagozzi & Heatherton, 1994; Bagozzi & Edwards, 1998). Hence, the parceled solutions are expected to provide better models of fit, and data are more likely to meet the underlying assumptions of SEM (Little, Cunningham, Shahar, & Widaman, 2002).

Reliability and Validity A rigorous process was used to develop and validate the survey instrument, modeled on previous empirical studies (e.g., Kale et al., 2000; Goldstein, 2003; Ganesan et al., 2005). Prior to data collection, content validity was supported by previous literature, executive interviews, and pilot tests. After the data collection, a series of analyses was performed to test the reliability and validity of the constructs. Reliability is the dependency and consistency of the measure and is determined for all the constructs using Cronbach’s alpha (Cronbach, 1951; Cramer, 2003). We followed the two-step method used in Narasimhan and Jayaram (1998) to test construct reliability, employing EFA to ensure unidimensionality of the scales, followed by Cronbach’s alpha for assessing construct reliability. In the first stage, EFA using maximum-likelihood extraction with oblique rotation with Kaiser normalization was used to reduce the large set of items into a couple of bundled underlying variables. Subsequently, the responses from the second independent group of participants were used in a series of one-factor congeneric CFA analyses. Once the items were associated with their factors, the item-parceling technique was adopted to form composite constructs in preparation for the SEM model and hypotheses testing required for testing the dynamic capability-building framework. The EFA of the combined dynamic capabilities scales that fundamentally forms the core of dynamic capabilities resulted in six interpretable factors being extracted. The interconnectedness between these capabilities and the expected possibility of overlaps among these closely related dynamic capability constructs made us conduct the analysis in a combined way to help us assess the distinction between these scales. The first factor contains a five-item scale for EA. According to our theory, we had anticipated two subconstructs of EA, namely, strategic foresight and systemic insight. However, upon analysis, EA evolved as a singlefactor construct. The second factor was termed CIC, which is composed of three items. From a CA perspective, two subfactors were expected—OA and PA— however, a third factor, resource agility (ResA), was also distinctly identified. The OA scale was loaded with three items, the PA was loaded with six well-defined items, and lastly the ResA was loaded with three items. The CuE scale was very

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well identified and was composed of four items that loaded well on the scale. Hence, in the first stage of EFA analysis, the factor-pattern coefficients for the six factors of the dynamic capability-building scale—EA, CuE, CIC, PA, ResA, and OA, derived from oblique rotations—are shown in Table A4. Further, Table A4 depicts all the items that loaded greater than the cutoff value of 0.3 (Hair, Anderson, Tatham, & Black, 1998; Cunningham, Holmes-Smith, & Coote, 2006). Further, all items had loadings of greater than 0.4 except for the item, “working with partners brings about new ways of managing organizational structures and partnerships,” which loaded at 0.325 and is shown in bold. In all cases, communalities were well above 0.4 without significant cross-loadings, and several items loading on each construct was also evident (Widaman, 1993). The final results of EFA for ORC, COL, EA, CA, CIC, and ESO indicated that the measurement items had strong loadings on the construct they were supposed to measure and lower loadings on the constructs they were not supposed to measure, thereby demonstrating unidimensionality. Typically, a 0.70 reliability coefficient is considered adequate for reliability (Cronbach, 1951; Sellitz, Wrightman, & Cook, 1976; Nunnally, 1978), but according to Nunnally (1978), the permissible alpha values ≥ 0.6 may be considered acceptable provided they are grounded in literature and based on modified scales. For newer scales, permissible alpha values extended to 0.6 are also acceptable (Cramer, 2003). Further, Bagozzi and Li (1988) highlight a new threshold for the average variance extracted for a construct. They believe it should exceed 0.50 and that a slightly lower alpha value for a scale with fewer items is considered permissible. Cronbach’s alpha values for all constructs in Table 2 indicate that all constructs are reliable for this research (Nunnally, 1978). Next, discriminant validity and convergent validity were tested. Discriminant validity is the degree to which measures of different latent variables are unique, whereas convergent validity relates to the degree to which multiple methods of

Table 2: Reliability analysis. Construct Organizational relationship capital Collaborative organizational learning a. Your organizational learning b. Your partner organizational learning Entrepreneurial alertness Collaborative innovative capacity Customer engagement Collaborative agility a. Resource agility b. Operational agility c. Partner agility Elevated service offering a. Strategic b. Performance c. Productivity

Number of Items

Cronbach’s Alpha

5

0.870

4 3 4 3 4

0.813 0.897 0.844 0.715 0.817

3 3 6

0.798 0.844 0.823

5 4 3

0.828 0.876 0.879

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measuring a variable provide the same results (O’Leary-Kelly & Vokurka, 1998). O’Leary-Kelly and Vokurka (1998) also suggested that CFA for assessing convergent and discriminant validity is a more powerful tool and requires fewer assumptions than the traditional Multitrait-Multimethod Matrix Method. As such, CFA is used in our study to ascertain convergent and discriminant validity. In the model, each item is linked to its corresponding construct and the covariances among those constructs are freely estimated. A construct with either loadings of indicators of at least 0.5, a significant t value (t > 2.0), or both, is considered to be convergent valid (Fornell & Larcker, 1981). DS2 (n = 224) was used to examine and validate the factor structure prior to its use in SEM. Recall that EFA resulted in six factors. As such, a measurement model for each of the six factors was developed. This step was done to examine the extent to which the observed variables were assessing the latent variables in terms of reliability and validity, wherein the relationships between the observed variables and the latent variables were described by factor loadings, and convergent validity is reflected in the magnitude of the factor loadings. Our analysis showed all factor loadings listed in Table A1 to be greater than 0.50, except for one item, “working with partners gives us an ability to innovate our service offerings technologically,” and all t values to be greater than 2.0. This item was retained due to its importance and dependence in today’s technological world of service offerings. Therefore, convergent validity is achieved. All the dynamic capability constructs extracted during EFA and CFA came as proposed during the theory-building stages, except for EA that stood out as one single construct instead of two components—strategic foresight and systemic insight. In addition, the CA construct was shown to be made up of three elements— resource, operational, and partnering, instead of the two theoretical components, namely, partnering and operational.

Item Parceling According to Kishton and Widaman (1994), item parceling is a technique whereby parcels are constructed from summing or averaging a number of item responses from a construct that is assumed to be unidimensional. In these instances, these parcels can then be used as indicator variables of latent constructs for further SEM analysis provided they meet Cronbach’s alpha reliability standard of values equal to or greater than 0.5 (Pedazur & Schmelkin, 1991) and are unidimensional as indicated by scree plots (Cattell, 1966). In this study, the item-parceling technique is very effective as it helps manage the sample size ratio. In models where the sample size to variable ratio is small, use of the item-parceling technique is deemed appropriate (Bandalos & Finney, 2001; Bandalos, 2002). Recently, Sass and Smith’s (2006) empirical findings suggest that, when a single unidimensional scale is used to represent a latent construct, the use of individual items, item parcels, or an appropriate representation of measurement error through a single observed variable will all result in identical disattenuated structural coefficient estimates as long as the underlying model assumption of unidimensionality is met. As such, after completing EFA and one-factor congeneric model analysis checking for unidimenionality via scree plots, we carried out the task of item parceling. Item parceling reduces the number of parameters estimated, resulting

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in more stable parameter estimates and proper solutions of model fit (Bandalos & Finney, 2001; Little et al., 2002). In this research study, we had 47 measured items, and even assuming a 5:1 sample size ratio, the statistical stability obtained from AMOS could not have been achieved, for example, with more than 45 parameters (Kline, 2005). As such, through the use of item parceling we reduced the number of measured items, thus obtaining 21 measured parameters and 12 item-parceled latent variables. Based on item parceling, ORC, EA, CIC, and CuE constructs were single-factor latent constructs and were designed using Munck’s (1979) approach, whereas COL, CA, and ESO represented higher-order constructs and contained two to three parceled indicator variables each. In the literature, several opinions prevail regarding the appropriate ratio of sample size to number of variables used, ranging from five cases per measured variable (Bentler & Chou, 1987) to 10 cases per measured variable (Kline, 2005) and even extending up to 15 (Stevens, 1996). In our case, the sample size ratio happens to be 10:1. In addition, the intercorrelations between the item-parceled scales were computed and also confirmed discriminant validity between all the latent variables for both data sets.

SEM ANALYSIS AND RESULTS SEM estimates were generated using AMOS 7.0 (Arbuckle, 2006) and the maximum-likelihood estimation method, applied to DS1. The nonnormal distribution for DS1 (n = 225) was evident by Mardia’s coefficient of 45.110. The binary space partitioning (BSP) of 0.331 indicates that the model can be accepted. In SEM, there is no single test of significance that can absolutely identify a correct model based on the sample data (Schumacker & Lomax, 1996; Holmes-Smith et al., 2005; Shah & Goldstein, 2006). Many goodness-of-fit criteria have been established to assess acceptable model fit. We report on the recommended fit indices as suggested by Garson (1998) and Kline (2005). The goodness-of-fit indices for our model are: χ 2 (47) = 70.746, n = 225, chi-square statistics minimum sample discrepancy/degree of freedom (CMIN/df) = 1.505, p = .014, BSP = 0.475, goodness-of-fit indices (GFI) = 0.950, adjusted goodness-of-fit Index (AGFI) = 0.917, Tucker–Lewis Index (TLI) = 0.968, comparative fit index (CFI) = 0.978, root mean residual (RMR) = 0.0360, root mean square error of approximation (RMSEA) = 0.047, and consistent Akaike’s information criterion (CAIC) = 269.645. These indices are better than the threshold values suggested by Hair et al. (1998), Garson (1998), and Kline (2005). A validation study was conducted, using DS2, deleting nonsignificant paths and adding theoretically and statistically appropriate paths. The standardized residual covariances for the best fit initial model and matching validation model were examined to assess if any highly correlated variables were a cause of any problem, but no values greater than 1.866 and 1.667, respectively, were found. Further, Mardia’s coefficient for both the best-fit initial model and the matching validation model was found to be 39.982. The goodness-of-fit indices for the validation model are χ 2 (49) = 86.637, n = 224, CMIN/df = 1.768, p = .001, BSP = 0.165, GFI = 0.942, AGFI = 0.908, TLI = 0.960, CFI = 0.971, RMR = 0.0417, RMSEA = 0.059, and CAIC = 272.575. The path loadings of the best-fit initial model and validation study are illustrated in Figure 4. The Bollen-Stine p values of .131 and .165 for the matching

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Figure 4: Estimated structural equation model showing explanatory power and path loadings for both initial and validation study, respectively.

CuE = customer engagement; CA = collaborative agility; EA = entrepreneurial alertness; CIC = collaborative innovative capacity; ORC = organizational relationship capital; COL = collaborative organizational learning; ESO = elevated service offerings.

and the best-fit validation model, respectively, was indicative of a good model fit. The results shown above indicate that the model can be accepted for future discussion. Because the Bollen-Stine p value was invoked to test the overall model fit for the data, it is important that the corrected standard errors of the parameter estimates are also used in determining path coefficients that are statistically significant (Nevitt & Hancock, 2001). Referring to Figure 4, the two hypothesized relationship paths—CA to CIC and CuE to CA—indicated mixed results, wherein both the paths were significant in the initial study and found to be not significant in the validation study. As such, the confidence interval for the standard regression weights of these paths was examined. The confidence intervals for the respective regression paths across the two studies were found to be overlapping for both paths, but in the case of CA to CIC the overlap is marginally greater than half the average margin of error (Cumming & Finch, 2005); as such, it can be concluded that both studies agree. Furthermore, R2 values for the ESO construct were .81 for the initial study and .67 for the validation study, respectively, indicating that 81% and 67%, respectively, of the variation in ESO outcome is explained by the combined effect of ORC, COL, CuE, EA, CIC, and CA for the initial and validated model analyses. It is evident from the above analyses that dynamic capabilities contribute significantly to the creation and delivery of ESO. Table 3 lists the hypotheses that were/were not supported.

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Table 3: Summary of hypotheses results. Hypothesis Tested H1a H1b H2d H6a H2g H1d H2h H1c H6b H1e H1f H1g H1h H2a H2b H2c H2e H2f H3a H3b H3c H4a H4b H4c H5a H5b H5c H5d

Hypothesis Relationship ORC directly related to ESO ORC directly related to COL COL directly related to CIC CIC directly related to ESO COL directly related to ESO ORC related to CIC via COL COL related to ESO via CIC ORC directly related to CIC ORC related to ESO via COL & CIC ORC directly related to CuE ORC related to CuE via COL ORC directly related to CA ORC directly related to EA COL directly related to EA COL directly related to CuE COL directly related to CA COL related to CIC via CA CIC related to CA via EA CIC directly related to EA EA related to CIC via CA EA directly related to CA CA directly related to CIC CA directly related to ESO CA related to ESO via CIC CuE directly related to CA CuE directly related to EA CuE directly related to CIC CuE directly related to ESO

Final Comment on Hypothesis Based on p-Value and Confidence Interval Check Not supported Supported Supported Supported Not supported Supported Supported Not supported Supported Not supported Supported Supported Not supported Not supported Supported Not supported Not supported Supported Supported Supported, future research tc Supported Supported, future research tc Supported Supported, future research tc Supported, future research tc Supported Not supported Not supported

ORC = Organizational relationship capital; ESO = Elevated service offering; COL = Collaborative organizational learning; CIC = Collaborative innovative capacity; CuE = Customer engagement; CA = Collaborative agility; EA = Entrepreneurial alertness. Note: “Supported, future research tc” implies that the hypothesis is supported in this research based on p value and confidence interval check but with bigger sample size these paths can possibly be further validated.

We now turn to a detailed discussion of some mediating effects among dynamic capabilities in leveraging service innovation (ESO).

Impact of CuE While the direct path between CuE→CA is not supported, it seems logical that the customer’s willingness to engage and participate through learning enhances the employee’s skills to question, improve their ability to anticipate discontinuities, and become more reactive and proactive (Lumpkin & Dess, 1996; Ganesan et al., 2005; Jambulingam, Kathuria, & Doucette, 2005). As employees are fed with meaningful input from the customer, and through the empowerment and foresight

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that they possess, they indirectly influence the SVN CA (Sambamurthy et al., 2003), which is an essential ingredient for competitiveness (Yusuf, Sarhadi, & Gunasekaran, 1999). Agility is all about customer responsiveness (Dyer & Singh, 1998; Van Hoek et al., 2001). As such, these indirect effects of CuE on CA help the SVN act and deliver services with greater speed, customization, flexibility, and reliability (Narasimhan & Das, 1999). This is supported by the path CuE→EA→CA→ESO. At the same time, SVN’s ability to innovate is also enhanced through higher-order capabilities pivoted around learning (Lawson & Samson, 2001), which helps deliver ESO, as shown by the hypothesized relationship path CuE→EA→CA→CIC→ESO. Thus, an organization that can assimilate new ideas and can convert them into action faster than their competitors ought to be successful and more competitive (Ulrich, Von Glinow, & Jick, 1993). The customers’ role here is of a dynamic operant resource that contributes to the exploration and exploitation of opportunities for innovation and competitive actions (Lusch et al., 2006; Vargo & Lusch, 2006; Vargo, 2007). The development of these higher-order skills starts from resource building and are fundamental to the logic of leveraging resources. Because services are inherently bidirectional in nature, employees interact and engage with customers in developing an exchange relationship of trust and communication (Chen & Paulraj, 2004; Harris & Goode, 2004; Lopez, Montes Peon, & Vazquez Ordas, 2004). This is depicted by the ORC→COL→CuE pathway, which in turn triggers the positively influencing effects of dynamic capability building (den Hertog, 2000; Kale Dyer, & Singh, 2002), resulting in a win–win situation for all parties of the SVN (Dyer & Singh, 1998; Perez Perez & Sanchez, 2002). In summary, partnership with customers is pivotal to the value creation of the SVN and is central to the dynamic capability-building competencies and skill sets.

Impact of CA COL of both the suppliers and customers, with customer as an operant resource (Vargo & Lusch, 2004, 2006; Vargo, 2007), gives the SVN an ability to maneuver internal resources, routines, and tasks at short notice (Mathews, 2006), supporting the hypothesized relationship path ORC→CA→ESO (Swafford et al., 2006). As the interaction with the customer deepens, more so in the mode of a customer as an operant resource, the employees of the SVN become more entrepreneurial and innovative in delivering services. The instantaneous feedback received from the customer helps decipher market intelligence and identify competitive opportunities. This encounter gives the organization an ability to act, respond, and deliver service offerings with an immediate and customized response, one which is more satisfying to the customers and provides a memorable experience (Pine & Gilmore, 1998; Verma, Thompson, & Louviere, 1999; Pullman & Gross, 2004; Stuart & Tax, 2004; Vargo & Lusch, 2004; Voss & Zomerdijk, 2007). Further, CA mediates between ORC and CIC, thus providing the SVN team with strength to innovate, to think strategically, and to apply business operations methods in unique ways. Coming up with strategic changes such as new service offerings, new ways of business operations, new delivery mechanisms, or even

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customer encounter interface requires significant investment in terms of money, resources, and time on the part of any organization. However, possessing this CA capability through integration of processes and systems and access to online information will intensify the operational process of action in a most effective manner (Yusuf et al., 1999; Amit & Zott, 2001). This gives the SVN an ability to arrange and realign resources, business processes, and structures at short notice (Swafford et al., 2006). In the context of services in particular, this agility enhances the operational capability to manage seasonality of demands and resource capacity planning and do better services facilities management (Lee, Padmanabham, & Whang, 1997; Akkermanns & Vos, 2003). Through EA, the notion of CA brings about the fast execution to changes through increased flexibility, speed, and reliability, in real time, while accommodating for variability in customer inputs (Shingo, 1986). These competencies are required to meet and exceed heterogeneous customer expectations spontaneously.

Impact of EA The hypothesized relationship path ORC→COL→CuE→EA→CA→ESO provides evidence that flexibility, customization, and delivery of services with speed is attained through customer participation and understanding of customer needs (CuE), employees’ EA and organizational agility (CA) capabilities, and the ability of the SVN to incrementally combine and recombine processes and routines at short notice. On the other hand, the hypothesized relationship path ORC→COL→CIC→ EA→CA→ESO provides evidence for extraordinary discovery, wherein an idea (during the CIC stage) initially makes no sense but, through alertness (EA) and simultaneous knowledge of customer expectations through engagement (CuE), later proves to make logical sense upon execution (CA) (Yu, 2001). Similarly, the partners learning and absorptive capacity (COL) gives employees an ability to explore and exploit opportunities and options (EA) via innovative idea creations (CIC), which when executed (CA) deliver ESO. This brings about major change to the current methods of operations, routines, and tasks. Hence, it promotes change of an existing situation, leading to the creation and delivery of ESO. MANAGERIAL IMPLICATIONS Today’s organizations operate in a value network on a global basis wherein organizations partner with suppliers, customers, and other stakeholders in pursuit of a sustainable competitive advantage. In addition to testing theory, the research findings are significant for management practice and policies directed at building a collaborative network through development and nurturing of dynamic capabilities for the achievement of innovation in services—our notion of ESO. In order to highlight and illustrate the management implications of our study, we refer to a virtual critical-care (ViCCU) tele-health case study (Li et al., 2006) that helped ease emergency specialist shortages in regional Australia, and which operated in an SVN setting. This business setting involved Commonwealth Scientific and Industrial Research Organisation (CSIRO), CentiE, and Sydney West Area Health

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Services as SVN partners, and deals with the support of critical-care services between a referral hospital and a rural hospital by transmitting very high-quality, real-time multimedia information, including images, audio, and real-time video, over an IP-based network. In the ViCCU case, the radical approach of treating emergency patients located remotely, especially in areas where the reach of appropriate emergency services was difficult to deliver in time, was a far-reaching discovery and can be denoted as an ESO. Through the use of relationship management (patient, nurses, doctors, telecommunications company, CSIRO), knowledge management (the use of tacit and explicit knowledge of partners in coming up with the practical solution of this need), technology management (appropriate use of technology—Internet protocol (IP)–based network with excellent video quality), and process management (design of new resources, routines, and tasks and the integration with old practices through ICT systems and processes integration across partners), a new system was developed to deliver customized emergency hospital care. This case illustrates the use and building of dynamic capabilities, demonstrating the innovative, agile, CuE, and entrepreneurial action processes leading to the emergence of ESO during the real-time critical interactions between the patient (customer) and the service providers (doctors and nurses). A first management implication of our study relates to the multidimensional notion of innovation in services. The notion of ESO was found to consist of three dimensions, namely ESO_Strat, ESO_Perf, and ESO_Prod. ESO_Strat is comprised of a new service offering, a new customer encounter interface, a new operating structure, a new service delivery process, and/or an increase in the attributes of an existing service offering. The other two dimensions of ESO_Prod and ESO_Perf are comprised of items that relate to productivity efficiencies and performance improvements, respectively. As such, managers of service organizations need to understand that innovation in services is not just about process or product innovation, or even performance and productivity improvements, but in fact a much wider concept that includes managerial or organizational innovations. As evident from the ViCCU example, the service—the provision of timely and expert health service during an emergency situation—was enabled through a new service encounter interface. This novel service delivering method brought along with it a new operating structure between the near and remote hospitals in terms of resource management and design of new processes, routines, and tasks, such as special training and operation protocols, resulting from the adoption of a new technology platform. From a patient’s point of view, significant performance improvements were achieved (a difference between life and death), with the hospitals gaining a more efficient and productive use of resources. As such, managers of service organizations need to visualize innovation in services differently from traditional new product development and new service development processes. In particular, the concept of innovation should be extended to include organizational forms of innovation, and collaboration recognized as a powerful tool for achieving service innovation. Our health services example illustrates that service innovation is about process, product, and/or organizational innovation and/or even performance and productivity improvements culminating from proactive creation, development, and maintenance of relationships with partners—customers, suppliers, or other stakeholders—resulting in a multidimensional service innovation capability (ESO).

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A second managerial implication relates to the customer as the co-creator of value. Organizations can only make value propositions through the people, resources, and technology they use, however, the true value creation comes from the customers engagement, participation, interpretation, and co-creation of value (Michel, Brown, & Gallan, 2008; Moller, Rajala, & Westerlund, 2008). As is evident from the ViCCU example, the real-time patient’s engagement and input was found to be different in each event, and his co-operation, even in these critical stages, irrespective of the use of the new technology, became very important in providing the best timely care and customized medical solution by doctors and nurses on duty, resulting in a responsive, unique, and satisfying experience. Time-criticality in an emergency situation often deters people from trialling new ways of clinical practice, with no room for human error. Yet, by involving the patient in a collaborative way through making the patient (customer) understand the emergency support system and the value-added service he/she is getting, customer learning and customer experience are enhanced. From the medical team’s perspective as well, communication and interaction with the customer, their participation and engagement, and the patient’s endurance and understanding assist medical staff to give the patient the best possible treatment. As such, a real-time collaborative effort led to a smart solution of treating patients located remotely, with a novel approach of patient involvement without which this ESO would have been unsuccessful. This is also echoed in our research, which illustrated a pathway wherein organizational relationship enhances customers’ knowledge and learning, which in turn encourages customers to engage in tasks and activities that relate to service design, service delivery, and service interface encounters. As the customer starts engaging, our hypothesized relationship path triggered the positively influencing effects of dynamic capability building, hence making the customer a co-creator of value. A third managerial takeaway from our study is dynamic capability building through collaboration. In our research, we have demonstrated that, through collaboration and learning of the stakeholders, higher-order capabilities emerge (CuE, CA, EA, and CIC), all of which influence the service innovation (ESO) outcome. This is also well illustrated in the ViCCU health care example where the specialist intensivist located at one hospital could supervise a resuscitation team located at a peripheral hospital. This implied real-time input from all stakeholders, in particular the cooperation of the patient in his critical stages, allowed the intensivist to make judgments on patient treatment in real time, as if he or she were present at the peripheral hospital. Such outcome was only possible through higher-order capabilities that evolved over time—agility, innovativeness, and entrepreneurial actions on part of the team members and in particular the patient, in spite of being in a critical health condition. Our empirical study also revealed a feedback loop amidst CIC, EA, and CA, which demonstrates an ongoing process of continuous dynamic capability building in accordance with the changing dynamics of business. It is therefore important that managers of service organizations recognize the potential embedded in these higher-order skill sets, starting from collaboration, learning, and management of creative ideas for both strategic and operational benefits. Through such skill sets, organizations are able to realign, position, and reconfigure resources and routines for better resource-capacity planning, service

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facilities management, and management of seasonality of demands. In short, managers should take measures to inculcate, promote, and manage these skill sets for service innovation. Our research has clearly demonstrated the process and causal relationships between different dynamic capabilities and their impact on ESO. In our empirical study, 81% (initial study) and 67% (validation study) of the variation in ESO outcome is attributed to these dynamic capability skill sets. Our results therefore provide a deeper understanding of how the ability to innovate and to create knowledge is substantially enhanced through structured collaboration and dynamic capability building.

CONCLUSIONS AND FUTURE RESEARCH We demonstrated that dynamic capabilities, an approach to building higher-order competencies and skills to address rapidly changing environments, are paramount in contemporary service organizations, as they provide a systematic and proactive way to explore new opportunities and at the same time help anticipate threats from competitive innovations. This is consistent with the recent views regarding the notion of higher-order dynamic capabilities through learning and alliance formation (Zollow & Winter, 2002; Cepeda & Vera, 2007; Harreld, O’Reilly, & Tushman, 2007; Kale & Singh, 2007; Michel et al., 2008; Moller et al., 2008). Further, this is also in agreement with the views by Kale and Singh (2007) wherein dynamic capabilities enable an organization to modify, extend, and improve its strategic and operational capabilities to manage any given task. In our context, EA, CA, CuE, and CIC all culminate in the creation and delivery of ESO in a collaborative environment. This study also has its limitations. The first limitation is related to the research context. The qualitative and empirical data analysis was undertaken with data collected from a single large telecommunications service provider organization and its partnering organizations. To further foster the multidisciplinary debate, while maintaining an engagement with practice, future research may seek to collect data from the entire telecommunications industry sector and their partnering organizations, across other service sectors or cross-service industry collaborations, or even any other organization where collaboration is pivotal to their success. This may also include additional data collection from the clients’ side, with particular focus on consumers. Further, a longitudinal study involving data collection from end-consumer/customer–supplier relationships may provide greater insights into how relationships develop, as well as their effect on ESO as they unfold over time. Given the potential impact of the SVN model and the domain investigated, several areas of research not covered in our study are the impact of collaborative architecture management (a governance construct made up of coordination, integration, and alignment), the impact of different organizational cultures, and the impact of ICT. Issues pertinent to cross-cultural interpretations across countries of the domain in question are not addressed either. Prior research has indicated that the culture of a host country might impact workplace relationships to the point where the results of this study may not be replicable in other countries (Kickul, Lester, & Belgio, 2004). Further studies may address these dimensions.

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Although the validity and reliability assessments showed strong support for the constructs developed using item parceling, future studies may test additional items for operationalizing the constructs. By the same token, three distinct and separate ESO construct dimensions could also be examined including strategic, tactical, and operational measures (Gunasekaran, Patel, & Tirtiroglu, 2001), as well as the inclusion of new performance measurements as recently suggested by Pitzer (2007). Another area of future research may address the segregation of the multidimensional construct ESO, currently represented as one higher-order construct divided into three discrete constructs: ESO_Strat, ESO_Perf, and ESO_Prod. Such analyses may determine whether the three dimensions of ESO—strategic, productivity, and performance—are mutually exclusive and, if they are, whether they manifest themselves in the same causal direction or in different directions (tradeoff among ESO dimensions). Alternative models in dynamic capabilities building could also be considered in future studies. [Received: January 2008. Accepted: April 2009.]

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APPENDIX Table A1: Construct measurement and CFA results. Dynamic Capabilities

Factor Loading

Organizational Relationship Capital (ORC) There is close, personal interaction between the partners at multiple levels The collaboration is characterized by mutual respect between the partners at multiple levels The collaboration is characterized by mutual trust between the partners at multiple levels The collaboration is characterized by high reciprocity among partners Once we establish collaborative arrangements, we develop long-term relationships

0.67 0.82 0.87 0.81 0.50

Fit measures: χ 2 = 13.933, n = 224, df = 5, CMIN/df = 2.386, p = .016, BSP = 0.121, GFI = 0.976, AGFI = 0.928, TLI = 0.965, CFI = 0.982, RMR = 0.0326, and RMSEA = 0.090 Note: ORC was a single factor construct in final configuration

Collaborative Organizational Learning (COL) Collaborative Organizational Learning—Yours Your organization has learned or acquired new or important information/knowledge from the partner including weakness, strength, gaps, and discontinuities Your organization has learned or acquired new critical capability or skill from the partner

0.63

0.85 Continued

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Table A1: (Continued) Dynamic Capabilities Your organization has enhanced its existing capabilities/skills as a result of the partnership Working with partners increases our contextual capability and knowledge

Factor Loading 0.82 0.58

Fit Measures: χ 2 = 6.224, n = 224, df = 2, CMIN/df = 3.112, p = .045, BSP = 0.101, GFI = 0.986, AGFI = 0.932, TLI = 0.959, CFI = 0.986, RMR = 0.0296, and RMSEA = 0.097

Collaborative Organizational Learning—Partners The collaborative arrangement has helped the partner learn or acquire new critical capability or skill The collaborative arrangement with the partners has helped the partner acquire new or important information/knowledge including weakness, strength, gaps, and discontinuities The collaborative arrangement has helped the partner enhance their existing capabilities/skills

0.82 0.89

0.84

Fit Measures: χ 2 = 2.009, n = 224, df = 2, CMIN/df = 1.004, p = .366, GFI = 0.994, AGFI = 0.982, TLI = 1.000, CFI = 1.000, RMR = 0.0115, and RMSEA = 0.004

Collaborative Innovative Capacity (CIC) There is always a continuous and plentiful supply of good ideas from partners and customers We collaboratively come up with novel and interesting ideas when solving problems Working in collaboration breaks perceptual and cognitive sets of information promoting lateral and fresh thinking

0.56 0.78 0.72


Entrepreneurial Alertness (EA) Working in partnership gives us an ability to anticipate discontinuities arising in the future Working in partnership makes us more entrepreneurial, both individually and collaboratively Working in partnership gives us greater reactive and proactive strength Partnering gives us the ability to question existing business models and industry structures

0.62 0.84 0.81 0.76

Fit Measures: χ 2 = 9.936, n = 224, df = 2, CMIN/df = 4.968, p = .007, BSP = 0.053, GFI = 0.978, AGFI = 0.892, TLI = 0.936, CFI = 0.979, RMR = 0.0307, RMSEA = 0.133 Note: EA came out as a single factor construct

Customer Engagement (CuE) Co-opting with the customer gives us greater contextual ability to explore opportunities and options Engaging customers helps us evaluate and align our service offering attributes to customer needs Use of virtual customer communities helps to detect opportunities and service solution options

0.70 0.84 0.70 Continued

470


Table A1: (Continued) Dynamic Capabilities Partnering with the customer makes the customer service experience more memorable

Factor Loading 0.68

Fit Measures: χ 2 = 1.1321, n = 224, df = 2, CMIN/df = 0.660, p = .517, BSP = 0.053, GFI = 0.997, AGFI = 0.986, TLI = 1.007, CFI = 1.000, RMR = 0.0117, RMSEA = 0.000

Collaborative Agility (CA) Operational Agility When we partner we accomplish greater speed in delivering service offerings When we partner we are dynamic and accomplish greater flexibility in customizing services Through partnership our reliability of service offerings has increased Fit Measures:

0.72 0.87 0.68

= 0.020, n = 224, df = 1, CMIN/df = 0.020, p = .887, GFI = 1.000, AGFI = 1.000, TLI = 1.014, CFI = 1.000, RMR = 0.0018, RMSEA = 0.000

χ2

Partnering Agility When we partner employees accomplish greater soft skills required to manage customer encounters When we partner we are able to quickly implement new governance structures When we partner we are able to combine, recombine, and create new business processes at short notice Through online, rapid, and up-to-date communication across the partnership we are able to reduce information discrepancies Working with partners gives us an ability to innovate our service offerings technologically Working with partners brings about new ways of managing organizational structures and partnerships

0.68 0.73 0.76 0.75 0.44 0.58

Fit Measures: χ 2 = 20.592, n = 224, df = 9, CMIN/df = 0.204, p = .015, BSP = 0.179, GFI = 0.970, AGFI = 0.931, TLI = 0.954, CFI = 0.972, RMR = 0.0450, RMSEA = 0.076

Resource Agility Through partnerships we are better-off in managing seasonality of demands When we partner we can do better resource capacity planning—job and staff scheduling Through partnership we can do better service facilities management Fit Measures:

0.75 0.89 0.77

= 0.430, n = 224, df = 1, CMIN/df = 0.430, p = .512, GFI = 0.990, AGFI = 0.992, TLI = 1.006, CFI = 1.000, RMR = 0.0064, RMSEA = 0.000

χ2

Note: Resource agility stood out as a new subconstruct in final configuration, with items originally belonging to partnering agility. From extant literature, resource agility was found as a recently defined construct made up of four elements—material technologies, finance, social, and knowledge. It has been defined as “the ability which is dependent on the resources the enterprise possesses or which are at its disposal” (Trzcielinski, 2006). However, in our context of categorization, we would like to define resource agility: “as the flexibility with which a value network maneuvers and realigns the operational resources it possesses conjointly or which are at its disposal as part of the SVN operations in an urge to meet customer needs at short notice.” This flexibility and adaptability to reshuffle the pool of resources gives the SVN an ability to rearrange its end-to-end service operations in accordance with the customer needs at such short notice.

Continued

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Table A1: (Continued) Dynamic Capabilities

Factor Loading

Elevated Service Offering (ESO) Strategic ESO The elevated service offering through partnership results in • a new service offering • a new customer encounter interface • a new operating structure • a new service delivery process • an increase in the service attributes of an existing service offering

0.55 0.77 0.79 0.74 0.66

Fit Measures: χ 2 = 16.987, n = 224, df = 5, CMIN/df = 3.394, p = .005, BSP = 0.121, GFI = 0.970, AGFI = 0.911, TLI = 0.938, CFI = 0.969, RMR = 0.0366, and RMSEA = 0.104

Operational ESO—Performance The elevated service offering through partnership results in • an increase in the level of service customization • an improvement in level of customer satisfaction • an increase in level of customer retention • an increase in memorable service experience of customers

0.66 0.84 0.89 0.81


Operational ESO—Productivity • a reduction in service delivery lead times • an increase in on-time delivery of services • a reduction in customer waiting time

0.83 0.86 0.83


Note: Scales comprising of final items only are reported here. Scales were initially developed using EFA, and confirmed using one-factor congeneric modeling using CFA. Items dropped during EFA and CFA are not reported here. df = degree of freedom; CMIN/df = chi-square statistics minimum sample discrepancy/degree of freedom; BSP = binary space partitioning; GFI = goodness-of-fit indices; AGFI = adjusted goodness-of-fit Index; TLI = Tucker–Lewis Index; CFI = comparative fit index; RMR = root mean residual; RMSEA = root mean square error of approximation.

472


Table A2: Sample demographics. Data Set 1 (n = 225) Count Percentage (%)

Characteristics Employee organization Parent Parent partner Parent supplier Parent customer Intermediary Other Rank in organization Staff member Supervisor/team leader Manager General manager, managing director Group managing director, COO, CEO Other

Data Set 2 (n = 224) Count Percentage (%)

101 55 21 45 0 3

44.88 24.44 9.33 20.0 0.00 1.33

110 42 13 54 0 6

49.1 18.75 5.5 24.1 0.0 2.6

64 14 95 38 4 10

28.44 6.22 42.2 16.8 1.77 4.44

74 12 80 49 3 6

33.03 5.35 35.71 21.87 1.33 2.66

COO = chief operating officer; CEO = chief executive officer.

Table A3: Tenure demographics of survey respondents. Number and Percentage of Survey Respondents in Data Set (DS) 5 Years DS1 % DS1 DS2 % DS2

42 18.7 37 16.5

21 9.3 28 12.5

30 13.3 28 12.5

15 6.7 20 8.9

12 5.3 12 5.4

105 46.7 99 44.2

Table A4: Factor pattern coefficients for the six factors of the dynamic capability scale. Itemsa Through online, rapid, and up-to-date communication across the partnership we are able to reduce information discrepancies When we partner we are able to combine, recombine, and create new business processes at short notice When we partner we are able to quickly implement new governance structures

PA (1)

CuE (2)

0.759 −0.056

0.688

ResA (3) 0.050

EA (4)

CIC (6)

0.041 −0.033

0.054 −0.097 −0.019

0.581 −0.043 −0.216

OA (5)

0.018

0.057

0.133 −0.069

0.054 −0.004

Continued

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Table A4: (Continued) Itemsa When we partner employees accomplish greater soft skills required to manage customer encounters Working with partners gives us an ability to innovate our service offerings technologically Working with partners brings about new ways of managing organizational structures and partnerships Engaging customers helps us evaluate and align our service offering attributes to customer needs Co-opting with the customer gives us greater contextual ability to explore opportunities and options Partnering with the customer makes the customer service experience more memorable Use of virtual customer communities helps to detect opportunities and service solution options When we partner we can do better resource capacity planning—job and staff scheduling Through partnerships we are better-off in managing seasonality of demands Through partnership we can do better service facilities management Working in partnership makes us more entrepreneurial, both individually and collaboratively Partnering gives us the ability to question existing business models and industry structures Working in partnership gives us greater reactive and proactive strength Working in partnership gives us an ability to anticipate discontinuities arising in the future

PA (1)

CuE (2)

ResA (3)

EA (4)

OA (5)

CIC (6)

0.531 −0.103

0.008 −0.043

0.181

0.069

0.422 −0.071

0.076 −0.145

0.058

0.219

0.325 −0.065 −0.250 −0.252 −0.156

0.118

−0.015 −0.907

0.007 −0.171 −0.100 −0.086

0.012 −0.736 −0.085

0.116

0.007

0.012

0.102 −0.619

0.029

0.178

0.021

−0.009 −0.404 −0.062 −0.155

0.015

0.115

0.032

0.050 −0.074 −0.900

−0.059

0.066 −0.026 −0.058

0.041 −0.638 −0.075

0.132

0.122

0.085 −0.146 −0.591 −0.027

0.027

0.078

0.137 −0.011 −0.034 −0.748

0.130 −0.072

−0.117 −0.164

0.006 −0.619

−0.086 −0.092 −0.192 −0.613 0.110

0.000

0.120 −0.021 0.190

0.050

0.021 −0.600 −0.107

0.134

Continued

474


Table A4: (Continued) Itemsa Working with partners gives us an ability to manage forecasts and trends on a real time basis When we partner we accomplish greater speed in delivering service offerings When we partner we are dynamic and accomplish greater flexibility in customizing services Through partnership our reliability of service offerings has increased There is always a continuous and plentiful supply of good ideas from partners and customers We collaboratively come up with novel and interesting ideas when solving problems Working in collaboration breaks perceptual and cognitive sets of information promoting lateral and fresh thinking Factor intercorrelations Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Eigenvalue Total variance explained

PA (1)

CuE (2)

ResA (3)

EA (4)

0.301

0.047 −0.253 −0.369

OA (5)

CIC (6)

0.151 −0.109

0.076 −0.063 −0.092 −0.062

0.637

0.044

0.150 −0.149

0.077 −0.129

0.596

0.134

0.026 −0.184 −0.037

0.570

0.118

0.087

−0.049 −0.046 −0.028

0.056

0.036

0.697

0.063 −0.023

0.047

0.077

0.632

0.034 −0.051 −0.037 −0.301 −0.039

0.538

0.090

−0.302 −0.456 0.363 −0.383 0.438 0.366 0.432 −0.328 −0.421 −0.334 0.401 −0.282 −0.273 −0.339 9.125 1.885 1.498 1.352 67.284%

0.333 1.218

1.070

Note: a These item were measured using a five-point Likert scale, ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). PA = partnering agility; CuE = customer engagement; ResA = resource agility; EA = entrepreneurial alertness; OA = operational agility; CIC = collaborative innovative capacity.

Renu Agarwal is a senior lecturer at the School of Management in the University of Technology Sydney. She received her PhD from the Macquarie Graduate School of Management in 2008. Renu joined academia fairly recently after 23 years of industry experience where she held management positions at the State Rail Authority of NSW, Telstra Corporation and its joint venture global company REACH. She was awarded the ANZAM Best Doctoral Dissertation Award 2008 for her doctoral research and received earlier award recognition for her research work in services by IBM and the POMS College of Service Operations. She has teaching and research interests in operations management and management practices, with specific focus

Agarwal and Selen

475

on services, capability building, service value networks, and innovation in services and is a doctoral supervisor in the area of education and finance service sectors. Willem Selen is a professor of supply chain management and logistics at the United Arab Emirates University and previously held positions as professor and Coordinator of Business Programs at the Middle East Technical University–Northern Cyprus Campus and professor and chair of Operations Management at the Macquarie Graduate School of Management in Australia. He obtained a commercial engineering degree from Limburg University in Belgium and a PhD in business administration from the University of South Carolina (1982). His broad research interests span the (service) operations management, supply chain, and e-business areas, and he has published in leading journals, including the International Journal of Production and Operations Management, International Journal of Logistics Management, International Journal of Physical Distribution & Logistics Management, and the Journal of Operations Management (guest editor). He has also been active in short course delivery in operations and supply chain management to leading companies worldwide.

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