service delivery innovation: antecedents and impact on firm performance

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2009 12: 36 originally published online 8 June 2009Journal of Service ResearchJa-Shen Chen, Hung Tai Tsou and Astrid Ya-Hui Huang

Service Delivery Innovation: Antecedents and Impact on Firm Performance

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36

Service Delivery Innovation

Antecedents and Impact on Firm Performance

Ja-Shen ChenHung Tai TsouYuan Ze University

Astrid Ya-Hui HuangAmtran Technology Corporation

Service innovation is one means of gaining an advantage in a highly competitive environment. In addition, service delivery has played a key role in interactions with customers in recent years. Yet, research on the link between innovation and service delivery is scant. This article theoretically and empirically examines innovation in service delivery and its antecedents and consequences. The authors identify innovation orientation, external partner collaboration, and information technology capa-bility as the antecedents of service delivery innovation and analyze the impact of service delivery innovation on firm per-formance. Respondents were managers in the marketing and information technology departments of financial firms in Taiwan. Overall, 298 responses were received (including 123 paired responses from both department managers). Findings indicated that service delivery innovation contributes to firm performance. These results support the crucial influences of innovation orientation and information technology capability on service delivery innovation.

Keywords: service innovation; service delivery innovation; innovation orientation; external partner collaboration; infor-mation technology capability

The emerging importance of service innovation for contemporary firms cannot be overlooked. It is clear

that when products or services become more homoge-neous or an original competitive advantage cannot be sustained, service innovation becomes an effective way for a company to accelerate its growth rate and profit-ability (Berry et al. 2006). Accordingly, researchers and practitioners are interested in explaining and predicting key antecedents of and outcomes associated with service innovation. Much of the research on service innovation in the last few decades has addressed many consider-ations, including decisions of service innovation adop-tion (Frambach et al. 1998; Kleijnen, de Ruyter, and Andreassen 2005), characteristics (e.g., Gallouj and Weinstein 1997; Nijssen et al. 2006) or typologies (e.g., Avlonitis, Papastathopoulou, and Gounaris 2001; Chan, Go, and Pine 1998; Drejer 2004; Gadrey, Gallouj, and Weinstein 1995; Preissl 1999) of service innovation, service innovation strategy and process (e.g., Alam 2006; Blazevic and Lievens 2004; Blazevic, Lievens, and Klein 2003; Lievens, Moenaert, and Jegers 1999), and drivers of service innovation (Berry et al. 2006; van Riel, Lemmink, and Ouwersloot 2004). Thus, subsequent

work attests to the importance of business service prac-tices within the conversation of innovation management and highlights the need to focus research in this area.

Nevertheless, in the modern economy, with rapidly changing consumer preferences and the emergence of multiple consumer segments with different tastes, val-ues, and shopping patterns, firms seek to deliver services and products in a cost-effective fashion, deliver greater value to customers, and improve service delivery meth-ods to increase profitability and decrease costs. In fact, it is often argued that most (or all) service providers at present deliver services and products to their respective customers in similar ways (i.e., using similar strategies; that is, service providers aren’t yet being individually creative). This is because the core of service delivery operations is transforming inputs into outputs, which is typically accomplished through a set of similar processes regardless of the type of service (Johnston and Clark 2001). Therefore, to achieve a competitive service posi-tion, service firms1 must deliver services and products through available distribution modes (e.g., customer interaction centers, online processing, telephone support; Wiertz et al. 2004) in new, creative ways that apply their

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Chen et al. / Service Delivery Innovation 37

specialized competences (i.e., knowledge and skills). Indeed, the creative use of delivery modes is increas-ingly becoming a new source of differentiation and inno-vation for firms. As suggested by Berry et al. (2006), service innovation aims to create new markets by inno-vating service delivery and thereby extending a firm’s service reach. However, while this topic has recently attracted increasing interest from academics and practi-tioners alike (e.g., Blind 2006; Dean 2004; Ho and Zheng 2004; Meuter et al. 2005; Pavlovski 2007; Verganti and Buganza 2005; Zomerdijk and de Vries 2007), there is little evidence of significant innovation in service delivery. We argue that there is no full and ade-quate understanding of the concept of service delivery innovation and its role in competition.

Recently, the field of marketing has evolved toward a service-dominant (S-D) logic (Vargo and Lusch 2004) through which we can reexamine the role of innovation in service delivery. Compared to traditional goods-dominant (G-D) logic, service in S-D logic is the application of spe-cialized competences (knowledge and skills, i.e., operant resources) to provide through goods (tools, distribution mechanisms, i.e., operand resources) that benefit an entity. In addition, it is perceived as the process of doing something beneficial for and in conjunction with consum-ers. As Vargo and Lusch (2004) noted, tangible goods serve as appliances for service provision. In this perspec-tive, the S-D logic is concerned with the vertical relation-ship between service and goods, rather than the horizontal difference between services and goods (Vargo and Lusch 2008) where the services are special types of products (i.e., intangible goods) or value-adding enhancements to tangible goods (Lusch, Vargo, and O’Brien 2007).

In the present article, we argue that service delivery is the process of applying specialized competences through goods (mechanisms), consistent with S-D logic. Innovation in this context is the process of applying new ideas or cur-rent thinking in fundamentally different ways, resulting in significant changes. According to S-D logic, innovation that significantly changes customer behaviors and the firm’s value creation is “discontinuous” (Michel, Brown, and Gallan 2008). As Vargo and Lusch (2004) noted, when firms deliver services, the customer must learn to employ the “appliance” (p. 11) to meet his or her needs, usage situation, and behaviors. Therefore, by implement-ing innovative practices in service delivery, firms could change their method of creating customer value and potentially positively impact their customers’ behaviors.

Accordingly, we argue that service delivery innovation involves an entire organization viewing and addressing both value creation and customer behavior within an S-D

logic framework. The purpose of this article is to contrib-ute to the literature on service delivery innovation by developing and empirically testing a model that attempts to explain what motivates service delivery innovation and, in turn, influences firm performance. We had three research objectives: (a) to understand the role of service delivery innovation, (b) to investigate the relationships between antecedents and service delivery innovation, and (c) to examine whether practicing service delivery inno-vation can result in better firm performance. Data came from Taiwanese financial firms. We chose the financial services industry because it includes more frequent cus-tomer contact and services in a highly competitive, com-plex, and technology-driven environment (Lievens and Moenaert 2000). Online banking or e-business combined with advanced software applications serve as service innovation practices in this industry. Furthermore, activi-ties such as mergers and acquisitions of financial firms create a rapidly changing environment that has led to an upsurge of innovation-related activities in the industry (Blazevic and Lievens 2004).

This article makes three contributions to the literature. First, we help to clarify the nature of service delivery innovation. By studying innovation within the framework of S-D logic, we view service delivery innovation as a type of discontinuous innovation that changes customer behavior and the ability of a firm to create value (Michel, Brown, and Gallan 2008). Second, based on resource-advantage (R-A) theory (Hunt and Morgan 1995), we test the links among organization (internal and external), technology, service delivery innovation, and perfor-mance through an empirical survey with paired samples from the marketing and information technology (IT) departments of 123 firms in the Taiwanese financial industry. Third, the results provide practical steps for man-agers interested in innovation practices supporting service delivery. The article is structured as follows. First, we review R-A theory to identify the key operant resources that facilitate service delivery innovation. Then, after describing the research framework (Figure 1), we report the results of a study conducted in the financial industry aimed at empirically testing the research model (Figure 2). We conclude with a discussion of theoretical and manage-rial implications and directions for future research.

Theoretical Background and Hypothesis Development

Most prior innovation literature has treated “service innovation” as product (i.e., goods) innovation. However,



38 Journal of Service Research

due to the IHIP2 characteristics of services, service innovation is unique to a certain degree. For example, it includes the interaction (i.e., co-production with cus-tomers) between new service development and service delivery (Tatikonda and Zeithaml 2001). Although stra-tegic innovation theory (Markides, 1997) addresses a new way of delivering new products or services to exist-ing or new customer segments and most adequately explains service innovation (Sundbo, 1997), it focuses mainly on goods (i.e., operand resources) but not on operant resources.

Important for our research, R-A theory is compatible with the S-D logic’s emphasis on competences, value propositions, and operant resources. In this study, we view service delivery innovation from the R-A theory to better understand the intricate relationships among stra-tegic and organizational issues. R-A theory is a process

theory of competition, which asserts that firms achieve superior financial performance by occupying market-place positions of competitive advantage. Such market-place positions rely on those resources that provide the firm a comparative advantage over its competitors. These comparative resources, in the S-D logic perspec-tive, are mainly operant resources. In addition, innova-tion plays a key role in R-A theory. As R-A theory has proposed, once competitors achieve superior perfor-mance by obtaining positions of competitive advantage, firms attempt to neutralize and/or leapfrog their com-petitors via major practice innovations. Drawing on R-A theory and S-D logic, we focus on operant resources (i.e., all competences/capabilities), innovation practices, and firm performance (competitive advantage and finan-cial performance). Figure 1 presents our research frame-work. Operant resources that can be leveraged to develop

Figure 1Research Framework

OperantResources

InnovationPractices

FinancialPerformance

CompetitiveAdvantage

Firm Performance

Operant Resources

Informational

IT Capability• IT Infrastructure• Human IT Resources• IT-Enabled Intangibles

H1

H2

H 3

H 4

H 5

Organizational

Innovation Orientation

Relational

External PartnerCollaboration

Innovation Practices

Service DeliveryInnovation

Firm Performance


Non-FinancialPerformance

H6

Figure 2Research Model




innovation practices, a source of sustained competitive advantage, produce superior financial performance gains for firms. Thus, we are also suggesting a direct effect between innovation practices and financial performance.

In our discussion, we refer to the concept of applying R-A theory to support service delivery innovation research. To determine which operant resources facilitate service delivery innovation, however, one needs a model describing the resources/capabilities of a firm and how these enable service delivery innovation. Our mindset was sparked by a model by Verganti and Buganza (2005) that describes service delivery as being facilitated by organization (internal and external) and technology. Accordingly, we propose that IT-related knowledge and skills have made it possible for financial firms to expand services and provide new delivery mechanisms, such as mobile and online banking and investment (Kumar and van Hillegersberg 2004). To respond to these situations, organizational structures within financial firms need to redesign or rebuild. Internally, firms may need to become more flexible, open, and interconnected; externally, they may need to be more agile to connect as needed to exter-nal parties. We therefore propose a research model and suggest that innovation practices in service delivery are mainly influenced by organizational, relational, and informational resources (Hunt 2004). In R-A theory, organizational (e.g., cultures), relational (e.g., relation-ships with partners), and informational (e.g., technology) assets are operant resources. We further identify organi-zational resources as innovation orientation, relational resources as external partner collaboration, and informa-tional resources as IT capabilities. The emphasis in the literature is the discussion of organization, relationships, and technology and their influence on service delivery innovation and firm performance. Figure 2 presents our research model. It shows the relationships that we hypothesized exist among innovation orientation, exter-nal partner collaboration, IT capability, service delivery innovation, and firm performance. In addition, we assume that these antecedents are independent from each other, because we attempt to examine what types of operant resources have the greatest impacts on service delivery innovation. What follows is a detailed descrip-tion of each element of the research model as it was intended for use in this research.

Service Delivery Innovation

Service delivery refers to the actual delivery of a ser-vice (Zeithaml, Berry, and Parasuraman 1988) and the

delivery of services and products (i.e., a firm’s goods) to the customer (Lovelock and Wright 2002; Moorman and Rust 1999). It concerns where, when, and how a service product is delivered to the customer and whether this is with high, medium, or low contact. Using S-D logic, we argue that service delivery is the process of applying specialized competences (knowledge and skills) to pro-vide customer service (comprising the service itself and the service channel; see Zeithaml and Bitner 2003). We follow the traditional innovation definition of Thompson (1965), defining it as the generation, acceptance, and implementation of new processes, products, or services for the first time within an organization setting. It is often described in terms of changes in what a firm offers the world and the ways in which it creates and delivers those offerings (Francis and Bessant 2005). Accordingly, inno-vations in service delivery may be regarded as novel mechanisms of delivery that offer customers greater con-venience (Lovelock and Wright 2002) and improve a firm’s competitive position.

However, there has been relatively little academic research on service delivery innovation. We employ Bolton’s (2003) marketing activities channel matrix to investigate service delivery innovation in terms of the customer service and services delivered and how they are delivered. We propose that there are two main types of service delivery innovation in an S-D logic frame-work: the introduction of new service channels for exist-ing customer service and services (hereafter, new/existing [NE] innovation) and the introduction of new service channels for new customer service and/or ser-vices (hereafter, new/new [NN] innovation). NE innova-tion refers to the use of new service channels to provide customer service and services that are already provided. For example, a company that provides mobile banking, remittance processing, merchant transactions, e-mail, and stock alert services via the Internet may start offer-ing these same services via cellular phone. NN innova-tion refers to the introduction of new service channels to provide new customer service and/or new services. For example, 7-11, the largest convenience store chain in Taiwan, has introduced an in-store kiosk with a new “ibon” service (www.ibon.com.tw). The kiosk interface (new channel) provides new customer service such as payment processing for insurance fees and traffic fines as well as new services never before offered in Taiwan convenience store chains, including downloadable cou-pons, movie tickets, and fortune teller predictions. Another example of this type of innovation is the use of global positioning system technology to introduce a new automobile telecommunication channel that enables new




services in car navigation and digital map applications. Both types of innovation depend on a firm’s suite of competences/capabilities, which can be renewed, cre-ated, integrated, and/or transformed. We further propose that there are three primary types of driving forces that lead to service delivery innovation: innovation orienta-tion, external partner collaboration, and IT capability.

Innovation Orientation

Innovation orientation refers to an organization’s openness to new ideas (as an aspect of a firm’s culture; see Hurley and Hult 1998) and propensity to change through adopting new technologies, resources, skills, and administrative systems (Zhou et al. 2005). Innovation orientation consists of both openness to innovation (Zaltman, Duncan, and Holbek 1973) and capacity to innovate (Burns and Stalker 1977). Openness to innova-tion is the critical part of the innovation process and is determined by the degree of willingness of members in an organization to consider the adoption of new ideas (Berthon, Hulbert, and Pitt 1999; Zaltman, Duncan, and Holbek 1973). Van de Ven (1986) referred to openness to innovation as the management of a firm’s recognition of the need for new ideas and action in the organization. Capacity to innovate refers to the ability to “introduce . . . some new process, product, or idea in the organization” (Hult, Hurley, and Knight 2004, p. 430). It then follows in S-D logic that knowledge development and deploy-ment (i.e., competences) is an inherent part of innovation orientation (Siguaw, Simpson, and Enz 2006). Because innovation orientation involves understandings and beliefs about innovation (i.e., continuous and radical change, adoption of new methods), firms with innova-tion orientation encourage employees to keep pace with market trends and contribute new ideas. Therefore, inno-vation orientation represents the extent to which (a) an organization is open to new ideas (i.e., culture) through the adoption of new technologies and integrated resources and (b) members are encouraged to consider the adop-tion of innovation.

Hurley and Hult (1998) investigated the conse-quences of innovation orientation and concluded that innovation orientation is a determinant of organizational innovation. They also noted that innovation orientation is a key driver for overcoming hurdles and enhancing a firm’s ability to successfully adopt or implement new systems, processes, or products. When a firm is innova-tion oriented, creates an open atmosphere, and empha-sizes creativity, it usually considers novel ideas and methods more acceptable. However, we further extend

Hurley and Hult’s (1998) work based on R-A theory and argue that organizational innovation practices can be affected by the behaviors of consumers or competitors. Firms must attempt to better tailor their offerings to cus-tomers’ needs and wants and neutralize and/or leapfrog the advantages of competitors. Such firms consider how to develop and deploy knowledge and skills (i.e., compe-tences) to deliver new and existing customer service effectively, whether there are any new ideas or thinking to enhance new and existing service delivery methods or approaches, or how to provide more customer services to fulfill customer needs through innovative culture. Hence, we propose that a firm’s ability to adapt to changing (or introducing) or existing (or new) means of customer service and changing competitor actions depends on the firm’s innovation orientation. We formulate the follow-ing hypothesis:

Hypothesis 1: Innovation orientation has a positive impact on service delivery innovation.

External Partner Collaboration

We adopt the concept of external partner collabora-tion provided by Faems, Looy, and Debackere (2005), defining this as an interaction process whereby com-plementary assets are exchanged with external part-ners. As Harrigan (1997) suggested, firms are inclined to cooperate when their resources and objectives complement one another, and it may include sharing both tangible and intangible resources and capabili-ties. According to S-D logic, collaborative processes with customers, partners, and employees are essential to innovation. Firms may then exchange with other organizations to form networks that can provide other innovations. Very often, firms are compelled to cooper-ate for innovation because they often do not have all the necessary resources internally (Tether 2002). This results from Teece (1986), who addressed how integra-tion of specialized complementary assets could allow companies to better profit from innovation and could subsequently result in broad discussions of external constituents in increasingly open innovation processes. Recently, open attitudes toward innovation from inter-nal capability to external collaboration have been the trend among organizations. Interorganizational collab-oration is important in supplementing the internal inno-vative activities of organizations (Deeds and Rothaermel 2003; Hagedoorn 2002). Therefore, firms need to col-laborate to build greater innovation practices and lock-in partners for the long term.




Due to a cooperative relationship aimed at innovation (Sarin and Mahajan 2001), firms may improve their abil-ity to engage in process innovation by managing their relationships with suppliers and customers (Kaufman, Wood, and Theyel 2000). The “models of interorganiza-tional collaboration” (e.g., Faems, Looy, and Debackere 2005) reflect the degree to which resources and capa-bilities are shared as well as the variety of external part-ners from public, private, or nonprofit sectors (e.g., private-public, private-private, private-nonprofit, etc.). Here, the type of collaboration for innovation refers to two or more distinct sectors, that is, private-public, pri-vate-private, and private-nonprofit collaboration, provid-ing each other resources and capabilities to deliver an innovative service. Therefore, we propose that innova-tive service delivery that a firm creates and provides is based on internal anticipation of customers’ current and future needs, as perceived from external partners’ col-laboration (e.g., customers, suppliers, research institu-tions, and universities). In addition, Simmie (1997) discussed the importance of collaboration between peo-ple with varied knowledge to create new ideas. Professional skill, the tacit knowledge of external part-ners, and communication are important sources of inno-vative ideas. We propose that firms having stronger collaborations with external partners will be better at developing new methods (approaches) of service deliv-ery for suppliers or customers. Therefore, we hypothe-size the following:

Hypothesis 2: External partner collaboration has a positive impact on service delivery innovation.

IT Capability

A number of recent studies have examined IT capabil-ity from a resource-based perspective (e.g., Bharadwaj 2000; Bhatt and Grover 2005; Tippins and Sohi 2003). Some have also provided opinions on the definition and taxonomy of IT capability (e.g., Bharadwaj 2000; Ross, Beath, and Goodhue 1996; Sabherwal and Kirs 1994; Tippins and Sohi 2003). To identify the key dimensions that drive the degree of IT capability, this study includes three dimensions based on Bharadwaj’s (2000) categori-zation, namely, (a) IT infrastructure, (b) human IT resources, and (c) IT-enabled intangibles, because this categorization is commonly used when measuring IT capability in the IT literature. S-D logic recognizes tech-nology as bundled, operant resources. Building on R-A theory’s notion of basic resources (typically human, orga-nizational, informational, and relational) and higher order

resources, Madhavaram and Hunt (2008) proposed a hier-archy of basic, composite (e.g., resource A + resource B + resource C = composite operant resource D), and inter-connected (e.g., A × B, A × C, B × C, and/or A × B × C on each other and on desired outcomes) operant resources. Because capabilities and/or competences are operant resources that bundle basic resources (Hunt 2000), we propose that IT capability is a hierarchy of composite operant resources (COR) that includes IT infrastructure, human IT resources, and IT-enabled intangibles. COR can be formatively measured, and the lower order resources that combine to become the COR can be either tangible or intangible (Madhavaram and Hunt 2008).

Technology may influence a firm’s ability to create value that will transform the way customers interact with an offering. For example, IT enhances a company’s response to customer demands with shorter delivery times (Jackson 1990) and enables customers to monitor their deliveries (Tinnilä and Vepsäläinen 1995). In addi-tion, IT enables firms to deal with customer information rapidly and effectively and infuses employees’ distinct knowledge into delivery innovation processes through the integration of systems and tacit skills of human IT resources. Furthermore, companies take advantage of IT when designing or modifying new processes for service delivery (Avlonitis, Papastathopoulou, and Gounaris 2001). To create a new channel or method of service delivery, firms need to possess IT infrastructure, human IT, and IT-enabled intangible resources. Firms with stronger IT capability will better facilitate service deliv-ery and delivery process innovation. Thus, IT capability is the operant resource for a new service that offers an opportunity to provide new and innovative services. We hypothesize the following:

Hypothesis 3: IT capability has a positive impact on service delivery innovation.

Firm Performance

Prior research has studied business performance from different perspectives, such as financial performance, business unit performance, or organizational perfor-mance (Venkatraman and Ramanujam 1986). To mea-sure innovation performance, one must consider the financial and non-financial performance of a firm (Avlonitis, Papastathopoulou, and Gounaris 2001; Gounaris, Papastathopoulou, and Avlonitis 2003).

Financial performance refers to a measure of how well a firm uses assets from its primary mode of business to generate revenues. The service delivery process should




have service quality to produce benefits regarding profit, cost savings, and market share (Thompson, DeSouza, and Gale 1985). As Avlonitis, Papastathopoulou, and Gounaris (2001) revealed, new delivery processes relate positively to financial performance, particularly in terms of profit-ability and sales. In other words, innovation in service delivery could create better profits and sales performance. Based on R-A theory, once competitors achieve superior financial performance through obtaining marketplace positions of competitive advantage, firms attempt to neu-tralize and/or leapfrog the advantages of competitors through major innovation practices. Accordingly, when competitors’ use of different methods or approaches to deliver products and services to customers enables them to achieve superior performance, firms attempt to neutral-ize and/or leapfrog the advantages of competitors through major innovation practices in service delivery. We there-fore propose that if firms are able to innovate in more varied ways to deliver new (existing) customer service, they will achieve superior financial performance objec-tives (e.g., higher gross and operations margins, increased revenue and profit, lower cost of sales and compliance, etc.). We postulate the following:

Hypothesis 4: Service delivery innovation has a posi-tive impact on firm financial performance.

Non-financial performance is a long-term operational objective that emphasizes the importance of increasing customer loyalty, attracting new customers, and enhanc-ing the image and reputation of a firm (Blazevic and Lievens 2004). The most innovative new services make the strongest contribution to non-financial performance (Avlonitis, Papastathopoulou, and Gounaris 2001). We propose that service delivery innovation enhances a firm’s ability to lock-in customer loyalty through an easier buying process, clearer communication of deliver-ables and outcomes, and an increased ability to meet customer needs that results in obtaining a position of competitive advantage in a market segment or segments. The result is that customers will prefer to stay with exist-ing service providers. That is, we expect that firms that implement service delivery innovation will have better non-financial performance. At the same time, the more service delivery innovation, the more extra values such as convenience for customers. That could be helpful to obtain a better image and reputation among customers. Hence, we postulate the following:

Hypothesis 5: Service delivery innovation has a posi-tive impact on firm non-financial performance.

The relationship between non-financial performance and financial performance is conceptually specified in the literature (e.g., Chakravarthy 1986). As previously dis-cussed, R-A theory suggests that organizations that plan strategically to attain superior financial performance are likely to also reap non-financial performance benefits, such as occupying marketplace positions of competitive advantage. These organizations are likely to have high customer loyalty and a positive image and reputation and be better able to attract new customers than organizations that do not. Hence, a direct effect of non-financial perfor-mance on financial performance is specified in the theo-retical model. Hence, we postulate the following:

Hypothesis 6: Non-financial performance has a posi-tive impact on financial performance.

Research Methodology

Operationalization of Constructs

Innovation orientation is measured by using six items drawn from Zhou et al. (2005) and Hurley and Hult (1998). These items measured the extent to which an organization is open to new ideas and actively empha-sizes an innovative atmosphere.

External partner collaboration is modified by the scales of Faems, Looy, and Debackere (2005) to obtain five items that measured the degree of a firm’s participa-tion in innovative projects in exchanging resources and capabilities with external partners such as universities, research institutions, customers, and suppliers.

IT capability is modeled as a second-order construct formed by three first-order factors: IT infrastructure, human IT resources, and IT-enabled intangibles. We measured IT infrastructure using four items that mea-sured whether a firm invests in tangible IT resources including computer and communication technologies as well as shareable technical platforms and databases (Bharadwaj 2000; Ray, Muhanna, and Barney 2005; Sircar, Turnbow, and Bordoloi 2000). We measured human IT resources with three items that referred to whether a firm owns technical and managerial IT skills involving the training, experience, and investment of its employees (Bharadwaj 2000; Scott Morton 1995). IT-enabled intangibles were illustrated by three elements based on Bharadwaj (2000). We separately used cus-tomer orientation, knowledge assets, and synergy with a total of 10 items to measure whether a firm invests in and uses IT systems to improve customer service; whether it




has the ability to integrate, transfer, and leverage embed-ded knowledge across the organization; and whether it shares resources across divisions to achieve communica-tion and provide the right response (Bharadwaj 2000; Kohli, Jaworski, and Kumar 1993; Ray, Muhanna, and Barney 2005).

Service delivery innovation is measured by using 10 items adapted and modified from research on the S-D logic perspective by Vargo and Lusch (2004), marketing activities channel matrix by Bolton (2003), service deliv-ery performance by Tidd and Hull (2003), and service delivery capacity by Blazevic and Lievens (2004).

Firm performance includes two categories, namely, financial performance and non-financial performance. We adopted and modified these two categories from Avlonitis, Papastathopoulou, and Gounaris (2001) and Blazevic and Lievens (2004). We measured financial performance using five items that reflected whether a firm could enhance sales and profitability and exceed market share objectives through innovative service delivery processes. In addition, we used five items to measure non-financial performance (e.g., whether a firm could increase its image and reputation and attract new customers by providing new service delivery methods).

Control variables. We controlled for firm age, capital, and size, as these variables reflect a firm’s resources and market power to exploit existing competences, build new ones, and develop innovations (Chandy and Tellis 1998). We do not propose hypotheses related to these variables because, in this article, we are not attempting to develop theory related to their effects. However, we included them in the research model to assess the effects of ser-vice delivery innovation on firm performance.

Firm age may influence performance (Baum, Calabrese, and Silverman 2000). Established firms may have a first-mover advantage in obtaining sustained superior performance (Barney 1991) or, alternatively, startups could enhance their initial performance by forming alliances with established rivals that provide access to diverse information and capabilities with minimum operational costs and provide more opportu-nities for learning and less risk of intra-alliance rivalry (Baum, Calabrese, and Silverman 2000). Therefore, we included firm age as a control variable and measured it by number of years the firm had been established. Firm capital (i.e., financial capital) has a positive effect on sales growth (Florin, Lubatkin, and Schulze 2003). We propose that firms that are able to accumulate more funds and resources can better afford to grow fast

(Chandler and Hanks 1994), which, in turn, leads to superior performance. Therefore, we included firm capital as a control variable. Firm size may influence performance by offering innovation synergy creation (Tanriverdi 2006). The larger the firm, the more innova-tive practices may be created. In addition, large firms have more resources and thus enjoy economies of scale. To account for such relationships, this study controlled for firm size as measured by number of employees. Table 1 lists operationalized constructs and sources.

Instrument Design

We generated a structured questionnaire based on academic- and practitioner-oriented literature. Data were secured by means of a 4-page self-administered ques-tionnaire as part of a larger examination of the anteced-ents of service delivery innovation, service delivery innovation, and firm performance in the financial indus-try. Following the suggestions of Churchill (1979), we adopted, modified, and extended existing scales. Since this study was conducted in Taiwan, our survey instru-ment was in Chinese. By using the parallel-translation method, question items were first translated into Chinese by one person and then retranslated into English by a second person to make sure that the meanings of the question items were correctly transformed from English to Chinese. The two translators then jointly reconciled all differences. The suitability of the Chinese version of the questionnaires was verified based on interviews with seven managers from the financial industry (marketing manager, new product manager, product manager, ser-vice manager, vice president, and market research man-ager), then the questionnaires were distributed and collected by the authors. Questions used 5-point Likert-type scales ranging from strongly disagree to strongly agree (see the appendix).

Sample and Data Collection

We collected data from 420 Taiwanese financial firms drawn from a list published by the Taiwan Joint Credit Information Center. We also referred to sources from major financial associations, including the Financial Supervisory Commission Executive Yuan, Taiwan Securities Association, Securities Investment Trust and Consulting Association, Life Insurance Association, and Non-life Insurance Association. Combining these sources resulted in a pool of 819 financial firms. We narrowed down the sample by excluding farmers’ and fishermen’s associations whose main roles were not in financial ser-vices, and also firms that had recently merged or closed




down or whose rights had been suspended. This pro-duced a final population of 491 firms of various types.

To enhance accuracy, we collected data from two major functional areas, namely, the IT and marketing departments. To ensure that our respondents were quali-fied to participate in the study, we asked both department managers as the key informants to complete the survey. We believe that both marketing managers and IT manag-ers must have a certain level of awareness about their organization’s service practices and performance. In addition to marketing managers, the choice of IT manag-ers as respondents seems reasonable because there is concern about technological resources in this study, par-ticularly with regard to IT capability. Thus, we mailed 982 questionnaires to the managers of the two depart-ments of the 491 firms; along with the questionnaire, each department received a cover letter outlining the objectives of the research. We assured respondents of the

confidentiality of their answers and asked them to reply to all questions as honestly as possible. We agreed to give each respondent a gift certificate and an electronic copy of the executive summary of our research results.

Despite these incentives, within 1 month of the initial mailing, we received only 104 responses (only 12 firms were represented with responses from both depart-ments). We followed up by telephone, fax, and e-mail for 2 months in 2007. This boosted the final response rate to 298 usable questionnaires, with 123 paired responses, corresponding to a valid response rate of 25%. To examine non-response bias, we compared early and late responders following Armstrong and Overton (1977). We classified responders to the initial mailing as early (n = 104) and those to the follow-up contacts as late (n = 194). Independent samples t tests revealed no statistically significant differences between the two groups in terms of any research construct. We used paired

Table 1Operational Definitions of Observed Variables

Variable

Innovation orientation

External partner collaboration

IT capability

Service delivery innovation

Firm performance

Operational Definition

the extent to which (a) an organization is open to new ideas through adopting new technologies and integrated resources and (b) the members of the organization are encouraged to consider the adoption of innovation

an interactive process of the exchange of complementary assets with external partners such as schools, research institutions, customers, and suppliers

• IT infrastructure• tangible and physical resources including computer and communication

technologies as well as shareable technical platforms and databases• human IT resources• technical and managerial IT skills

• IT-enabled intangibles• illustrated by three elements: customer orientation, knowledge assets,

and synergy

• new service that delivers new or existing customer service with new services (new goods; new distribution mechanisms)

• new service that delivers new customer service with existing services (goods; distribution mechanisms)

• financial performance• enhancing sales and profitability of firms• profitable• profit and sales objectives•market share

• non-financial performance• customer loyalty• competitive advantage• attracting new customers• perceived image• reputation

Source

Hurley and Hult (1998); Zhou et al. (2005)

Faems, Looy, and Debackere (2005)

Ray, Muhanna, and Barney (2005); Sircar, Turnbow, and Bordoloi (2000)

Bharadwaj (2000)

Bharadwaj (2000); Kohli, Jaworski, and Kumar (1993); Ray, Muhanna, and Barney (2005)

Blazevic and Lievens (2004); Bolton (2003); Tidd and Hull (2003); Vargo and Lusch (2004)

Avlonitis, Papastathopoulou, and Gounaris (2001); Blazevic et al. (2004)

Note: IT = information technology.




samples t tests to examine whether there were significant differences between the two departments. The results appear in Table 2 (all ps > .05). Thus, there were no sig-nificant differences in any construct between the IT and marketing departments. In other words, innovation opin-ions from the two departments in the same company were consistent. Notably, data were self-reported and retro-spectively reported. Because the dependent and indepen-dent variables were both measured through responses from the same respondents, we checked for potential common method bias using the Harman one-factor test (Podsakoff et al. 2003; Podsakoff and Organ 1986). Because a single factor did not emerge and Factor 1 did not explain most of the variance, common method bias is unlikely to be a concern in our data.

Data Analysis and Results

We used partial least squares (PLS) for the data analy-sis. While several methods can be used to analyze the data, we chose PLS for three reasons. First, we had a relatively small sample size (n = 123 paired responses; Chin, Marcolin, and Newsted 1996). Second, our model has formative constructs (i.e., IT capability) and PLS uses components-based algorithms and can estimate formative constructs (Chin 1998). Third, PLS is more appropriate when the research model is in an early stage of develop-ment and has not been tested extensively (Teo, Wei, and Benbasat 2003). Because a review of the literature indi-cated that empirical tests of service delivery innovation remain sparse, our focus was on theory development. Hence, PLS is the appropriate technique for our research

purpose. We used PLS Graph 3.0 (Chin 2001) to perform structural equation modeling and to evaluate both the quality of the measurement model and the interrelation-ships of the constructs of the structural model.

Sample Demographics

Table 3 shows the demographic information for the firms. According to the data, 79.7% of firms were local companies and 20.3% were foreign. The majority of firms (44.7%) had been established more than 20 years; 30.9% had firm capital over US$310 million; and 37.0% had between 101 and 500 employees. Moreover, 35.0% were banking firms and cooperatives, and 30.1% were security trust and consulting firms.

Measurement Properties

We tested measurement invariance of IT and market-ing groups using AMOS 5.0. Our results showed that there were no significant differences between the IT and marketing departments for factor loadings, ∆χ2(28) = 39.46, p = .074; intercepts invariance, ∆χ2(34) = 36.78, p = .341; and testing simultaneously for factor loadings and intercepts invariance, ∆χ2(62) = 76.24, p = .105. Because the factor loadings and intercepts showed invari-ance across two groups, we could pool the data from the IT and marketing samples. Thus, for each question, we combined and averaged the paired data into one sample data value for each firm. Cronbach’s alpha values ranged from .83 to .95 for the eight constructs. The values were all above .70, indicating a high internal consistency of measure reliability (Nunnally 1978). Composite reliability

Table 2Results of Paired Samples t Test

Mean Correlations Paired Test

Variable M T Mean Difference Correlation Sig. t p Value

IO 3.87 3.75 .12 .225 .012 1.452 .149EPC 3.31 3.20 .11 .091 .317 1.330 .186INF 3.72 3.76 -.04 .151 .096 -0.456 .649HIR 3.90 3.91 -.01 .223 .013 -0.034 .973IEI 3.65 3.57 .08 .214 .018 1.043 .299SDI 3.70 3.61 .09 .312 .000 1.203 .231FP 3.69 3.52 .17 .124 .17 1.908 .059NFP 3.89 3.77 .12 .208 .021 1.506 .135

Note: Column “M” contains the means of variables from the marketing department; column “T” contains the means of variables from the information technology (IT) department. p values are two-tailed. IO = innovation orientation; EPC = external partner collaboration; INF = IT infrastructure; HIR = human IT resources; IEI = IT-enabled intangibles; SDI = service delivery innovation; FP = financial performance; NFP = non-financial performance.




was stated by examining ρc for constructs, and all were above the suggested threshold of .70, indicating that the measurement was reliable. We assessed convergent validity using average variance extracted (AVE), the ratio of construct variance to total variance among indi-cators. Table 4 shows the values of AVE for the eight constructs; all exceeded .50 (Barclay, Thompson, and Higgins 1995), confirming that all measures demon-strated satisfactory convergent validity. Furthermore, we examined discriminant validity with a correlation matrix. The values of the square root of AVE for the measures on the diagonal were all greater than the correlations among the measures off the diagonal (Fornell and Larcker 1981). Hence, discriminant validity was satis-factory. Consistent with our earlier conceptualization, IT capability is modeled as a second-order construct. We

specified all first-order constructs of IT capability as formative constructs.3 As evident from the path loadings of IT infrastructure (see Figure 3), human IT resources, and IT-enabled intangibles, each of these three dimen-sions of IT capability is significant (p < .001) and of high magnitude, thereby providing evidence for the plausibil-ity of the thesis that IT capability is a multifaceted con-struct that includes IT infrastructure, human IT resources, and IT-enabled intangibles.

Structural Model

Figure 3 illustrates the results of PLS estimation. In all, 45 of 48 factor loadings were above 0.80 (see the appen-dix), revealing that each measure accounted for 50% or more of the variance in the underlying latent variables. The path coefficients for the research constructs are expressed in a standardized form. Except for the link from external partner collaboration to service delivery innovation, which was not significant, five of the six path coefficients were greater than 0.3 (with the lowest being 0.305), indicating that they were meaningful and signifi-cant (Chin 1998). PLS implemented a bootstrap tech-nique to determine the significance of the structural paths. Figure 3 shows that all links in the model were significant (Hypotheses 1, 3, 4, 5, and 6 supported), except for external partner collaboration to service deliv-ery innovation (β = .163, t = 1.95, p > .05; Hypothesis 2 not supported). With regard to R2, innovation orientation, external partner collaboration, and IT capability explained 54% of the variance in service delivery innovation; ser-vice delivery innovation and non-financial performance explained 68% of the variance in financial performance; and service delivery innovation explained 50% of the variance in non-financial performance. These values were all significant (p < .001).

Discussion and Conclusion

With the rapid pace of structural change in the service industry, service innovation and its relationship with firm performance have increasingly attracted the atten-tion of both researchers and practitioners. In this study, we investigated the role of service delivery innovation as a mediator in a causal framework concerning the link with service delivery antecedents and the impact of inno-vation on performance. The primary findings suggest that (a) innovation orientation and IT capability are the key drivers that lead to service delivery innovation, (b) service delivery innovation leads to improved financial

Table 3Profile of Survey Firms

Descriptive Statistics

Number Rate

BelongingLocal company 98 79.7%Foreign company 25 20.3%

Years firm established in Taiwan 3 years and fewer 2 1.6%Over 3 years to 5 years 3 2.4%Over 5 years to 10 years 10 8.1%Over 10 years to 15 years 28 22.8%Over 15 years to 20 years 25 20.3%Over 20 years 55 44.7%

Firm capital (1 US dollar = 32 New Taiwan dollars)

Less than US$3.1 million 4 3.3%US$3.1 million to 31 million 36 29.3%US$31 million to 93 million 15 12.2%US$93 million to 155 million 17 13.8%US$155 million to 310 million 13 10.6 Over US$310 million 38 30.9%

Number of employees100 and fewer 27 22%101 to 500 37 37%501 to 1000 15 12.2%1001 to 2000 15 12.2%2001 to 3000 7 5.7%Over 3000 22 17.9%

Financial industry sectorsBanking and cooperative 43 35%Bill finance 10 8.1%Security trust and consulting 37 30.1%Trust and investment 1 0.8%Insurance company 28 22.8%Others 4 3.3%




and non-financial performance, (c) non-financial perfor-mance leads to improved financial performance, and (d) managers of the IT and marketing departments within a single firm generally have a consensus on matters of service delivery innovation.

Innovation orientation and IT capability for service delivery innovation. Innovation orientation and IT capa-bility have positive effects on service delivery innovation; these results are consistent with our own observations and those of other researchers. An innovation orientation leads to breakthrough innovation because of its focus on

creativity (Berthon, Hulbert, and Pitt 1999; Hurley and Hult 1998), and so it becomes an essential factor in creat-ing more extra (new) and convenient (renew) service delivery channels for customers. As for IT capability, Avlonitis, Papastathopoulou, and Gounaris (2001) stressed that new delivery processes are a type of new financial service that takes advantage of modern technology in the delivery of services. In addition, IT infrastructure, human IT resources, and IT-enabled intangibles, as implied in IT capability, could help companies estimate customers’ needs, share integrated resources across units, and carry out technology-driven service delivery innovation.

Table 4Means, Standard Deviations, Correlations, and Average Variance Extracted

Construct M SD AVE (a) (b) (c) (d) (e) (f) (g) (h)

IO (a) 3.81 .57 .79 .89EPC (b) 3.26 .52 .59 .71 .77INF (c) 3.74 .55 .74. .58 .50 .86HIR (d) 3.91 .56 .77 .58 .40 .72 .88IEI (e) 3.61 .52 .67 .67 .54 .79 .75 .82SDI (f) 3.66 .58 .74 .70 .58 .62 .57 .67 .86FP (g) 3.61 .55 .83 .61 .50 .43 .44 .56 .77 .91NFP (h) 3.84 .54 .79 .57 .46 .47 .44 .54 .73 .79 .89

Note: n = 123. Values in the shaded diagonal are the square root of the average variance extracted (AVE). IO = innovation orientation; EPC = external partner collaboration; INF = IT infrastructure; HIR = human IT resources; IEI = IT-enabled intangibles; SDI = service delivery innova-tion; FP = financial performance; NFP = non-financial performance.

Figure 3Results of the Structural Model

External PartnerCollaboration

0.305***(t=4.39)

0.163(t=1.95)

0.365***(t=4.02)

0.472***(t=5.00)

0.743***(t=12.12)

R2 = 54%


R2 = 68%

Non-FinancialPerformance

R2 = 50%

CV1

CV2

CV3

0.063(t=1.47)

0.023(t=0.49)

0.033(t=0.22)

–0.031(t=0.36)

0.020(t=0.26)

–0.101(t=1.27)IT

Infrastructure

Human ITResources

IT-EnableIntangibles

IT Capability

InnovationOrientation

Service DeliveryInnovation

0.404***(t=4.66)

0.970***(t=35.79)

0.798***(t=12.39)

0.913***(t=20.31)




External partner collaboration for service delivery innovation. Our findings revealed that the effect of exter-nal partner collaboration on service delivery innovation was not significant (β = .163, t = 1.95, p > .05), although it was very close to significant (i.e., t = 1.96, p = .05). There are two possible explanations for this. First, in prior studies, there has been a positive relationship between interorganizational collaboration and innova-tion performance (Faems, Looy, and Debackere 2005). Such issues are generally examined in the context of manufacturing firms, which focus heavily on research and development and thus may have more interaction with external partners than do service firms. Second, the correlation matrix (see Table 4) showed a strong rela-tionship between external partner collaboration and ser-vice delivery innovation (r = .58, p < .01); using single regression analysis showed that external partner collabo-ration had a significant positive effect on service deliv-ery innovation (β = .607, t = 7.50, p < .001). Thus, we suspected that the effects of innovation orientation and IT capability on service delivery innovation may weaken the effect of external partner collaboration.

To further analyze the effects of external partner col-laboration on service delivery innovation, we investi-gated the relationship from an industry-level view. Our results indicated that firms represented three major areas of the financial industry: banking and cooperatives (35.0%), security trust and consulting (30.1%), and insurance (22.8%) (see Table 3). The regression analysis results for banking and cooperative companies showed that external partner collaboration had a positive effect on service delivery innovation (β = .36, t = 3.00, p < .01). However, for security trust and consulting and insurance companies, external partner collaboration did not have a positive effect on service delivery innovation (β = .15, t = 1.89, p > .05 for security trust and consulting; β = .008, t = 0.04, p > .05 for insurance companies). These results indicate that the effects of external partner collaboration on service delivery innovation differ according to sector in the financial industry. The importance of external partner collaboration cannot be ignored, and firms can capture and accumulate advice, knowledge, customer information, and new competences from collaborating with external partners to achieve service delivery innova-tion. In fact, financial firms have consistently stressed the spirit of innovation and customer service in recent years and have initiated more and more cooperative projects with schools, research institutes, suppliers, and customers.

Service delivery innovation for firm performance (financial and non-financial performance). R2 values

indicated that service delivery innovation and non-finan-cial performance have a higher predictive ability for financial performance (68%); service delivery innova-tion also has a higher predictive ability for non-financial performance (50%) and interprets effects on firm perfor-mance. Managers must pay special attention to how service delivery innovation, in conjunction with suitable service channels, can enable all aspects of innovation interactions to obtain superior financial and non-finan-cial firm performance.

Paired tests of IT and marketing departments for ser-vice delivery innovation. Generally, there are differences of opinion between the IT and marketing departments in a firm. However, our empirical study showed no signifi-cant differences in views between these two depart-ments. There are two possible explanations for this. First, many IT development projects are outsourced to or co-executed with outside consulting firms so that, com-pared to the past, IT managers are now able to pay more attention to strategic-level issues and focus more on how IT functions can better align with business strategies and operations. Hence, when viewing service innovation practices, managers from both IT and marketing depart-ments are likely to form the same opinions. Second, in this study, the highest proportion of responses (35%) came from banks and cooperatives, and about 30.9% of respondents had firm capital greater than US$310 mil-lion. The bigger a company, the more mature its depart-ments. Firms emphasize agreement of cross-units on any business strategy. For these reasons, consistency in the attitudes of IT and marketing departments toward service delivery innovation strategies can be expected.

Implications for Research

Our results have three significant implications for research. First, our study highlights the S-D logic per-spective to link service delivery innovation, stimulated drivers of service delivery innovation, and firm perfor-mance. We discussed service innovation with a specific focus on service delivery and investigated its relation-ship with other variables in the R-A theory perspective through increased research in resources and capability. Reflecting this issue, research on developing service delivery innovation should focus on operant resources. There is evidence that the hierarchy of operant resources (e.g., COR) introduced in this study can help marketing research in the conceptualization of IT capability. In addi-tion, this study empirically demonstrates that financial firms are well advised to engage in developing service delivery innovation through innovation orientation. These




conceptualizations have significant implications for how researchers think about the valuation of organiza-tional (i.e., innovation orientation) and informational (i.e., IT capability) resources for service delivery inno-vation.

Second, regarding S-D logic, we built on the results of Bolton (2003) by showing that there are two types of ser-vice delivery innovation: the introduction of new service channels for existing customer service and services and the introduction of new service channels for new customer service and/or services. This study provides encouraging evidence for the value of service delivery innovation modes that integrate service providers’ competences, services, service channels, customers, and discontinuous innovations. Delivery innovation reshapes customers’ behaviors (the role of the customer) and helps firms inno-vate service value with customers (co-creation value with the customer). Therefore, we supplement the marketing research within S-D logic that service delivery innovation can be a new way to deliver new (existing) services to customers. This leads to a more comprehensive view of the service innovation behavior of firms. Researchers should investigate the issues with regard to service delivery inno-vation in the business/marketing literature.

Third, this study develops robust insights into the effects of innovation orientation and IT capability on firm performance. More importantly, we propose that this research model is a more suitable framework of ser-vice delivery innovation than others in the literature because it includes not only intraorganizational compo-nents (i.e., innovation orientation and IT capability) but also interorganizational ones (i.e., external partner col-laboration). Although external partner collaboration has been studied with regard to the manufacturing industry, our research extends this concept to the service industry and discusses its important influence on service delivery innovation. Most managers in this study recognized that external partner collaboration is necessary for service delivery innovation. In addition to an innovative internal culture and IT capability, external partner collaboration is an important source of service delivery innovation.

Implications for Practice

This study has several significant implications for practice. If a firm can create an advantage in operant resources, it can gain competitive advantage in the marketplace. First, with regard to innovation orienta-tion, managers need to foster innovation capability (e.g., creativity), train employees to accept or adopt any radical new ideas from the competitive market,

and develop an innovation environment or culture of openness within the organization (e.g., 3M’s innova-tive culture). With regard to IT capability, IT plays a critical role in the implementation of service delivery innovation practices, especially in financial firms. It supports flexible service delivery and continual ser-vice innovation. Most importantly, with stronger tech-nological resources, a firm can sharpen its focus on formulating relevant IT capability. For example, it is imperative for top management to carefully consider the role of IT managers in innovation initiatives. Before beginning major service delivery innovation programs, managers may need to think about imple-menting, acquiring, and developing lower order IT resources (such as IT infrastructure, human IT resources, and IT-enabled intangibles) that can take them up the hierarchy (i.e., IT capability) and facilitate service delivery innovation. As for external partner collaboration, interorganizational collaboration is important for supplementing the internal innovative activities of organizations (Deeds and Rothaermel 2003; Kalaignanam, Shankar, and Varadarajan 2007). This means that firms still need to collaborate with partners that offer different operant resources and with customers who offer their own operant resources to facilitate service delivery innovation.

Second, considering the two different types of service delivery innovation, we recommend that firms evaluate the risks/benefits of offering new service channels for both existing and new customer service and/or services. Such innovations would provide diverse methods and approaches for using their services and thus make them more convenient for customers. Firms can further iden-tify which kinds of new service delivery methods for serving customer segments are most effective so that they can attract more customers. In addition, service delivery innovation enables firms to differentiate themselves through new service delivery channels (e.g., new online services) that better satisfy customers’ needs and make it easier for customers to use new customer services.

Third, service delivery innovation plays a critical role in facilitating superior firm performance. Firms should implement innovation practices in service delivery pro-cesses that introduce profitable services and improve their ability to develop different kinds of customer service that lead to competitive advantage and superior financial performance. With regard to financial performance, it seems reasonable to reflect on good sales performance and the achievement of profit and market share objectives. With regard to non-financial performance, companies with diverse service channels have more opportunities




to interact with their customers. The more service chan-nels that companies use to deal with customer complaints and provide better pre-sale, in-sale, and after-sale ser-vices, the more information about customers companies can receive. In addition, customers notice firms’ efforts toward customer service, thereby strengthening compa-nies’ positive images and reputations. Accordingly, the convenience of multiple service channels contributes to customer satisfaction and loyalty and stimulates them to establish a long-term business–customer relationship. More importantly, it is a competitive advantage for the firm that, in turn, leads to superior financial perfor-mance.

Limitations and Future Research

Even though this study offers valuable insights into service delivery innovation, it still has some limitations. First, the study was conducted with Taiwanese financial firms, so the generalization of the results to other indus-tries may be limited. Second, the research model mea-sured one point in time and is thus essentially a static perspective. It may be worthwhile to study the relation-ship between service delivery innovation and firm perfor-mance (especially financial performance) over time to explain the effects of innovation on performance. This consideration is especially important because of the cen-tral role of innovation in this study. The effects of service delivery innovation on financial performance may not be immediately apparent. Third, we did not address the properties of the partner collaboration (e.g., governance structure, power, trust, etc.). Based on Faems, Looy, and Debackere’s (2005) definition, our construct of external partner collaboration focuses on collaboration with dif-ferent types of partners that relate to the innovative output of the firm. Different types of collaborative relationships may be better or worse for developing innovation prac-tices. In addition, assessing, transferring, and recombin-ing the necessary resources of different types of relationships could be difficult and thus affect the effi-ciency of collaboration for innovation practices. Further, we do not examine how complementary resources of firms are exchanged between partners. We also do not identify a specific type of collaboration that is relevant to service innovation. These aspects of collaborative rela-tionships will likely be important for fully understanding the relationship between interorganizational collaboration and service delivery innovation but are beyond the scope of our article. Fourth, we conceptualized the constructs of innovation orientation, external partner collaboration, and IT capabilities to represent organizational resources,

relational resources, and informational resources, respectively. However, the representations may not be sufficient to cover the entire scope of all three operant resources. Also, although our study focused on three operant resources as components of organization and technology, we did not include other operant resources. Developing other appropriate indicators of antecedents of service delivery innovation remains a task for future work.

In addition to addressing these limitations, there is much work yet to be done in the area of service delivery innovation. The current findings are only a catalyst for future research in the S-D logic growing in theoretical and practical importance. First, potential differences in industry characteristics might influence research results. Scholars should investigate this issue in a wider variety of service industries or in different countries to over-come this limitation. Future research should also repli-cate the present study to guarantee the generalizability of the results. Second, it would be extremely interesting to explore other driving forces of service delivery innova-tion and investigate the relationships between these forces and firm performance in detail. In addition, within the boundary conditions of R-A theory, we rely solely on the relationships among operant resources, innova-tion practices, and firm performance. The present study did not consider other variables that might play an important role (e.g., moderating role) in influencing the relationships among these factors. To extend the research framework, future work should investigate other aspects of the moderating variables between driving forces and service delivery innovation and service delivery innova-tion and firm performance, such as technology integra-tion mechanisms, knowledge integration mechanisms, or additional control variables such as firm belonging (local or foreign). Third, as this empirical study included only self-reported data, future research should capture the points of view of external partners. Fourth, focusing on interactions among the antecedents would also be useful. For example, IT capability may precipitate innovation orientation, which in turn may impact external partner collaboration, as these interactions may require firms to rely more heavily on external partners. Finally, a particu-larly interesting result of our study is that IT and market-ing managers at the same firms shared consistent perceptions of service delivery innovation. This suggests that in-depth case studies might add to our knowledge of service delivery innovation, especially in empirical con-texts such as the financial industry, where the locus of service innovation is changing. Future research is needed to examine this finding.




AppendixSurvey Measurement Scales

Strongly Disagree = 1 Disagree = 2 Neither Agree nor Disagree = 3 Agree = 4 Strongly Agree = 5Please indicate, on a scale of 1 to 5, the degree to which you agree or disagree with the following statements.Innovation Antecedents Component

A. Innovation Orientation (IO) (Alpha = 0.947; ρc = 0.958; Range of factor loadings from CFA = .82-.92)IO1. Our company pays close attention to innovation.IO2. Our company emphasizes the need for innovation for development.IO3. Our company promotes the need for development and utilization of new resources.IO4. The extent to which a firm embraces, accepts, and measures innovation.IO5. Management actively seeks innovative ideas.IO6. People are encouraged for new ideas that don’t work.

B. External Partner Collaboration (EPC) (Alpha = 0.830; ρc = 0.880; Range of factor loadings from CFA = .68-.85)EPC1. Our company and universities have innovation-related collaboration that focuses on novelty.EPC2. Our company has innovation-related collaboration with research institutes to create new competences.EPC3. Our company has innovation-related collaboration with customers to discover the needs of existing market segments.EPC4. Our customers participate with us in the innovation process.EPC5. Suppliers work closely with us in the service development.

C. IT Capability (ITC)IT Infrastructure (INF) (Alpha = 0.875; ρc = 0.917; Range of factor loadings from CFA = .76-.93)INF1. Our company has been well developed in sophisticated Internet applications.INF2. Our company has established corporate rules and standards for hardware and operating systems to ensure platform compatibility.INF3. Our company has identified and standardized data to be shared across systems and business units.INF4. Our corporate data are currently shareable across business units through systems.

Human IT Resources (HIR) (Alpha = 0.841; ρc = 0.910; Range of factor loadings from CFA = .79-.93)HIR1. There are technical IT skills (programming, systems analysis and design, and competences in emerging technologies).HIR2. There are managerial IT skills (abilities of effective management of information system functions, coordination and interaction with user community, project management, and leadership skills).HIR3. Our company has kept the emphases on IT staffing and training.

IT-Enabled Intangibles (IEI) (Alpha = 0.944; ρc = 0.953; Range of factor loadings from CFA = .72-.88)IEI1. Our company invested in an IT system designed to improve its knowledge of customers across all business units (e.g., CRM system, call tracking).IEI2. Our company uses IT to track and predict changing customer preferences.IEI3. Our company employees understand how to use information technology to improve customer service.IEI4. The extent to which a firm’s knowledge is embedded in its databases and decision support systems is determining its ability to respond to environment changes.IEI5. IT systems enable knowledge formalization and consolidation of previous knowledge gains and their leverage across the organization.IEI6. Firm’s knowledge embedded in systems enables its rapid transfer to novices and other new members.IEI7. IT databases permit interorganizational teams to share and aid in the delivery of needed resources.IEI8. Representatives from different departments within our firm meet regularly to discuss our customers’ needs.IEI9. When one department obtains important information about our customers, it is circulated to other departments.IEI10. Most employees can give a right response while getting outside information.

Service Delivery Innovation (SDI) (Alpha = 0.959; ρc = 0.965; Range of factor loadings from CFA = .77-.90) Our company emphasizes . . .SDI1. offering new service channels for customers to order new services.SDI2. offering new service channels to adjust customer complaints.SDI3. offering innovative approaches to deliver new services.SDI4. offering new service channels to provide after-sales service.

(continued)




Notes

1. “Service firms” in this study refers to firms that provide ser-vices that require special competences (i.e., knowledge and skills).

2. IHIP refers to intangibility, heterogeneity, inseparability, and perishability (Lovelock and Gummesson 2004).

3. At the suggestion of one of the reviewers, we also re-ran the analysis treating the three indicators as reflective (as done by Tippins and Sohi 2003). The results showed that innovation orientation (β = .324, t = 4.47, p < .001) and IT capability (β = .336, t = 3.34, p < .001) both had significant positive effects on service delivery innovation; thus, this analysis also supported Hypotheses 1 and 3. External partner collaboration remained insignificant (β = .164, t = 1.95, p > .05). Further, the values of R2 (54%) remained the same. Thus, our overall findings were the same whether using indicators as formative or reflective for IT capability.

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Appendix (continued)

SDI5. the conformance of new service channels with existing service channels.SDI6. offering existing customer service and consultation via new service channels.SDI7. offering new service channels to deliver existing services.SDI8. offering new service platforms to easily introduce new services for customers.SDI9. offering new service platforms to easily develop and implement new services.SDI10. offering new service platforms to enhance service delivery capability of the firm.

Performance ComponentA. Financial Performance (FP) (Alpha = 0.946; ρc = 0.960; Range of factor loadings from CFA = .85-.94) Generally speaking, for the past few years,FP1. we have enhanced sales and profitability of the firm.FP2. we have been profitable.FP3. we have achieved profit objectives.FP4. we have achieved sales objectives.FP5. we have achieved market share objectives.

B. Non-Financial Performance (NFP) (Alpha = 0.932; ρc = 0.949; Range of factor loadings from CFA = .83-.92) Generally speaking, for the past few years,NFP1. we have improved the loyalty of existing customers.NFP2. we have attracted a significant number of new customers.NFP3. we have had an important competitive advantage.NFP4. we have had a well perceived image.NFP5. we have had a good reputation.

Note: CFA = confirmatory factor analysis; IT = information technology.




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Ja-Shen Chen is currently a professor and dean of the College of Management, Yuan Ze University, Taiwan. He holds MS and PhD

degrees from Rensselaer Polytechnic Institute, New York. He has published numerous papers in the areas of service innovation, cus-tomer relationship management, e-business management, and tech-nology management.

Hung Tai Tsou is currently a PhD candidate of the Graduate School of Management, Yuan Ze University. His primary research focus is on the investigation of service and collaboration issues such as service innovation and interfirm co-development, co-production compe-tences. His publications can be found in Information Research and Service Industries Journal, among other journals and conference proceedings.

Astrid Ya-Hui Huang is currently an international purchasing spe-cialist of Amtran Technology Corporation. She received her MBA degree from Yuan Ze University.

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