UNDERSTANDING THE ROLE OF TECHNOLOGY IN SERVICE INNOVATION: A COMPARISION OF THREE THEORETICAL PERSPECTIVES

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1 UNDERSTANDING THE ROLE OF TECHNOLOGY IN SERVICE INNOVATION: A COMPARISION OF THREE THEORETICAL PERSPECTIVES Hyun-Sun Ryu, Korea University Business School, Seoul, South Korea, hamkkai@korea.ac.kr Jae-Nam Lee, Korea University Business School, Seoul, South Korea, isjnlee@korea.ac.kr Juyeon Ham, Korea University Business School, Seoul, South Korea, parangdol@gmail.com Abstract Service innovation has been considered as a new way to gain competitive advantage and enhance firm performance in a highly competitive market. In addition, technology is believed to encourage and facilitate service innovation. Although several service innovation studies have stressed the importance of technology in service innovation, the roles of technology in service innovation are at the center of debates in service innovation literature. One major research stream in service innovation regards technology as a service innovation dimension (e.g., Gallouj and Weinstein and den Hertog s service innovation frameworks), whereas another major stream considers it an operant resource (e.g., service-dominant logic, resource-advantage theory). The contradictory situation induces the necessity to better understand the role of technology in service innovation. Therefore, this study aims to identify the role of technology in service innovation, i.e., whether it is one of service innovation dimensions or of operant resources. From a resource-based approach, this study proposes a model which considers technology as an operant resource and investigates the impact of technology on firm performance through three service innovation strategies (i.e., service creation-focused, service delivery-focused, and customer interaction-focused strategies). From a dimension-based approach, we also propose two competing models that view technology as a service innovation dimension. In the competing models, technology can influence firm performance either as a mediator between operant resources and performance, or as moderator between service innovation strategies and performance. Based on empirical data from 224 service firms in Korea, we explain the role of technology in service innovation. Our findings indicate that each approach can be used to create different theoretical arguments that explain how to leverage technology in service innovation. Our findings also show the most acceptable role of technology within this study. Keyword: Service innovation, Service innovation strategy, Technology, Operant resource.

2 1. INTRODUCTION The impact of service innovation on competitive advantage and business performance has increased noticeably during the last decade. Service innovation is an effective way to enhance firm performance and ensure long-term survival in a highly competitive market (Chen et al., 2009). However, service innovation does not always guarantee the desired outcome. Therefore, careful attention needs to be paid to what makes service innovation successful. In service innovation literature (Den Hertog, 2000;Sundbo, 1997), technology is considered as an enabling and facilitating factor that creates favorable conditions for developing better services and improving more business performance. An increasing number of studies have focused on building service innovation success using technology (Gago and Rubalcaba, 2007). Although several studies have stressed the importance of IT capability in service innovation (Bygstad and Lanestedt, 2009), only few of these have identified the role of technology in service innovation. Furthermore, previous service innovation studies have reported differing, sometimes conflicting, roles of technology from different theoretical perspectives (e.g., dimension-based and resource-based approaches). This deficiency in the service innovation literature needs to be addressed, and a more detailed understanding of technology in service innovation must be obtained before more significant progress can be made. Therefore, this study attempts to provide the underlying guidelines in the role of technology that allow firms to achieve more successful service innovation, thereby enhancing firm performance. Owing to the fact that two apparent approaches of technology coexist in service innovation literature, we need to identify the role of technology in service innovation. Based on the service innovation framework proposed by Den Hertog (2000) and on resource advantage (R-A) theory, the objective of the current study is to investigate the structural relationships among operant resources, service innovation strategies (SIS) and firm performance, as well as to identify the role of technology in the relationships by recognizing a more appropriate theoretical basis in explaining the degree of firm performance. To do so, three different models are proposed: 1) the first model is based on resource-based approach, which considers technology as an operant resource and investigates the impact of technology on firm performance through three SIS (i.e., service creation-focused, service delivery-focused, and customer interaction-focused strategies); and 2) two competing models are based on dimension-based approach, which views technology as a service innovation dimension wherein technology can influence firm performance either as a mediator between operant resources and performance, or as a moderator between SIS and performance. Each model can be used to identify the different roles of technology in achieving service innovation success. Based on empirical data collected from 224 service firms in Korea, these three models are tested and compared to determine the approach that best explains the role of technology in service innovation. 2. THEORETICAL BACKGROUND 2.1 Two Approaches of Technology in Service Innovation In innovation literature, technology has two primary components, physical and informational. A physical component comprises technological items such as product, tool, equipment, and blueprint. An informational component consists of technological knowledge and skills in management, marketing, production, quality control, skilled labor, and functional area (Kumar et al., 1999). Given that technology may influence a firm s ability to create value that can transform the ways by which customers interact with an offering (Chen et al., 2009), technology has mainly played enabling and facilitating roles in service innovation, which create favorable conditions for developing better services and achieving more profitable business deals. Although an increasing number of studies have stressed the importance of technology, the roles of technology are still at the center of much debate in service innovation. The

3 differing and conflicting roles of technology have coexisted in previous studies, and such coexistence confused researchers and practitioners as to the precise roles and effects of technology on service innovation and firm performance. Two apparent approaches to understanding technology in service innovation are found in previous service innovation research, namely, resource-based approach and dimension-based approach. Resourcebased approach regards technology as an operant resource (e.g., Vargo and Lusch 2004). Servicedominant(S-D) logic focuses on the intangible and dynamic resources (i.e., skills/knowledge, competences/capabilities) defined as operant resources, which form the heart of competitive advantage and superior performance (Vargo and Lusch, 2004). Thus, S-D logic recognizes technology as one of the bundled, informational operant resources that benefits service innovation. Additionally, R-A theory is compatible with S-D logic s emphasis on competences, value proposition, and operant resources (Hunt and Morgan, 1995). Chen et al. (2009) reported that technology is an operant resource, which can be leveraged to develop innovation practices to produce superior firm performance. Thus, technology induces better firm performance indirectly through service innovation practices. However, the other approach considers technology as a service innovation dimension (e.g., Gallouj and Weinstein (1997) s and Den Hertog (2000) s service innovation framework). Service innovation dimension refers to the area where service innovation occurs (Droege et al., 2009). Previous researchers defined service innovation dimension as a specific service innovation practice (Den Hertog, 2000) or specific competency in service (Gallouj and Weinstein, 1997). Thus, service innovation is defined as any change affecting one or more service innovation dimensions. Dimension-based approach considers technology as a specific service innovation dimension (Den Hertog, 2000) or specific vector of characteristics and competences in service (Gallouj and Weinstein, 1997). Therefore, the change of technology influences innovation success and firm performance independently or through interacting with other different service innovation dimensions. 2.2 Service Innovation Strategy Service innovation can be related to changes in various characteristics of the service product (Den Hertog, 2000). The current study defines service innovation as a change of the specific innovation dimensions related to various characteristics of service (Den Hertog, 2000). In addition, Cooper et al. (1999) found that successful service innovation requires tangible and visible top management support, especially in terms of consistent strategies. Given that innovation strategy in service development processes plays a more critical role compared with those in product development processes (Griffin, 1997), service innovation strategy enables managers to plan and engage adequate resources for specific innovation practices (Menor and Roth, 2007). Therefore, in this study, a strategic decision or guideline concerning the specific service innovation practices is referred to as SIS. In this sense, we define SIS as strategic decision of firms on service innovation practices to gain a sustainable competitive advantage. SIS is not only a single decision that is consciously made, but also a manifestation of multiple decisions. Next, we need to identify the salient service innovation decisions that constitute or reflect SIS. Den Hertog (2000) argued that service innovation indicated several changes within the service innovation dimensions related to various practices of services in an organization, rather than just changes in several elements or characteristics of the final service offering, as earlier stressed by Gallouj and Weinstein (1997). Den Hertog (2000) defined different service innovation dimensions and proposed a descriptive model, which dealt with the four defined dimensions, namely, service concept, service delivery, client interface, and technology and three capabilities, namely, human resource management (HRM) capability, collaboration capability, and marketing capability. Three capabilities build effective relationships between service innovation dimensions. Service concept refers to the service prototype that offers a new value proposition in a particular market (Edvardsson, 1997). This dimension is crucial in establishing new services with changed service characteristics Den Hertog (2000). He describes two key operant resources of this dimension, namely, organizational capability and marketing capability. Service delivery is defined as the service innovation process comprising the sequential activities and internal organizational

4 arrangement for delivering a new or existing service; it includes the two operant resources of organizational capability and HRM capability. Client interface is defined as the design of the interface between the service provider and its clients; its critical elements are HRM capability and marketing capability. Finally, technology is optional though it plays an important role through interacting with other dimensions as a specific service innovation dimension.. Although Den Hertog et al. (2010) conceptually extended their original service innovation model by adding two more dimensions (i.e., new business partner and new revenue model) and introduced a sixdimensional service innovation model, we believe that their original concept of service innovation dimension is more appropriate for developing SIS. Using and validating their extended framework is premature because their original framework has not been empirically validated. Therefore, this study was designed to develop SIS by using Den Hertog (2000) s original service innovation model as a starting point to validate not only the concept of SIS but also its relationship with technology. However, several dimensions must be revised to effectively develop SIS. First, we disregarded technology dimension in developing SIS. This step is taken because technology is a specific dimension interacting with other dimension, which is quite distinct from the three other dimensions mentioned above. Second, we changed the term client interface into customer interaction. Given that Den Hertog (2000) redefined a client interface dimension as customer interaction dimension in his new framework, the current study defines customer interaction to include both customer co-production and customer contact (Mills 1986). Consequently, we use three major service innovation dimensions (i.e., new service concept, new service delivery, and new customer interaction) to develop three SIS. SIS is not only a consciously made single decision, but also a manifestation of multiple decisions. In this study, SIS is investigated initially as a single decision for a basic understanding of the effect of SIS. For these reasons, we develop three different SIS along the aforementioned dimensions, where are 1) service creation-focused strategy (SCFS), 2) service delivery-focused strategy (SDFS), and 3) customer interaction-focused strategy (CIFS). Table 1 lists the characteristics of the three SIS. Service creation-focused strategy (SCFS) Service delivery-focused strategy (SDFS) Customer interaction-focused strategy (CIFS) Type of innovation Service concept (creation) Service delivery Customer-driven innovation innovation innovation Innovation emphasis Innovativeness and flexibility of services Efficiency of service delivery Effectiveness of communication between provider and customer Market emphasis New services, Unique value Low price, Customer convenience Quality of service, Customer satisfaction Type of offering service New services (including the Existing services Existing services change of bundle, package) Design flexibility High (Innovativeness) Low (Standard ) Middle Price policy Premium price Low price Middle price Service-market volume Average (Middle) Very broad (High) Very narrow (Low) Table 1. The characteristics of three service innovation strategies 3. THREE ALTERNATIVE MODELS OF TECHNOLOGY 3.1 A Model from the Resource-Based Approach The first model is based on Den Hertog (2000) s service innovation framework, S-D logic and R-A theory, and proposes better conceptual understanding of service innovation by adding the three SIS as intervening variables between the operant resources and firm performance. R-A theory is a process theory of competition, which asserts that firms achieve superior financial performance by conducting an innovation practice that relies on comparative resources (Hunt and Morgan, 1995). These resources

5 ensure a firm s comparative advantage over its competitors and mainly refer to operant resources in the S- D logic (Hunt and Morgan, 1995). In this study, we extend Den Hertog s framework into a causal model based on S-D logic and R-A theory, where operant resources are the antecedents of SIS and then SIS influences firm performance. We specifically focus on the role of technology as an operant resource, as well as its effect on firm performance through SIS in this model. A structural equation model representing such a causal model is shown in Figure 1-1. Based on Den Hertog (2000) s framework, the first model has four key operant resources namely, technology, HRM capability, collaboration capability and marketing capability, to develop SIS (i.e., SCFS, SDFS, and CIFS) that can achieve better firm performance. Figure 1-1. First model Figure 1-2. Second model Figure 1-3. Third model Figure 1. Three different models from resource-based and dimension-based approaches 1) Firm performance as a Dependent Variable: Non-financial performance of innovation has received a great deal of attention because it helps decision makers predict future financial performance (Low and Siesfeld, 1998). Therefore, we propose non-financial performance as a dependent variable. We also develop three perceptual measures (i.e., responsiveness to customer needs, customer satisfaction, and service customization) to assess the degree of firm performance in terms of service quality improvement (Jaworski and Kohli, 1993). 2) Service innovation strategies as Intervening Variables: We develop the three SIS (i.e., SCFS, SDFS, and CIFS) based on Den Hertog (2000) s framework. SCFS aims to give customers a unique value from new services through service creation innovation; thus, it continuously seeks new opportunities (customer needs) from new innovative services (service offerings). SCFS attempts to maintain innovative but relatively unstable services while selectively moving in carefully selected new areas. Although SCFS takes several risks in developing new services, SCFS improves firm competitiveness and increases firm performance. Meanwhile, SDFS tends to alter where, when, and how a service product is delivered to the customer to offer differentiated services. This strategy provides customers with greater convenience, which comes from an effective delivery process resulting from service delivery innovation and often offers services to customer at low cost. In turn, an effective delivery process strengthens the firm s positive image and reputation (Chen et al., 2009). Hence, firms adopting SDFS can easily improve the efficiency of their service delivery process to achieve better firm performance. Moreover, the process by which the service provider interacts with their customers could be a source of innovation (Den Hertog et al., 2010). In this case, CIFS concentrates on changes in the depth (customer co-production) and degree (customer contact) by which customers are involved in the service innovation processes. CIFS is likely to create a closer and more open relationship between customers and service providers. This leads to a better understanding of customer needs, increased customer satisfaction, and provision of higher quality services, resulting in better firm performance. Consequently, we present the hypotheses below. H1-1: Service creation-focused strategy has a positive impact on firm performance. H1-2: Service delivery-focused strategy has a positive impact on firm performance. H1-3: Customer interaction-focused strategy has a positive impact on firm performance.

6 3) Operant resources as Antecedent Variables: To determine which operant resource facilitates each SIS, we use technology and three capabilities as operant resources from resource-based approach. Technology: Given that SCFS emphasizes on different positions mostly achieved by introduction of new services, technology allows firms to encourage and facilitate the development and application of new services (Henderson and Venkatraman, 1994) by improving the initial base of knowledge through the integration of system and tacit skills of human resources (Dewett and Jones, 2001), which effectively helping the firm achieve better performance (Frambach et al., 2003). Competitive positioning to pursue SDFS mainly depends on technology which can effectively deliver services to customers. Chen et al. (2009) indicated that technology, such as IT capability, is the key driver that leads to the success of service delivery innovation. Specifically, technology is more critical in SDFS, because firms pursuing SDFS depend on their ability to increase organizational efficiency. Likewise, technology helps organizations deal with customer needs rapidly and efficiently (Morton, 1988). Such efficiency in meeting to changes of customer demands improves customer satisfaction (Argyres, 1999). Technology can also offer a new channel, which facilitates direct access to customers and customer coproduction for new services (Quinn, 2000). Given the importance of fully understanding specified customer needs and facilitating the effective linkage between service provider and customers in CIFS, technology can help firms effectively implement CIFS. Therefore, we hypothesize the followings: H1-4: Technology has a positive impact on service creation-focused strategy. H1-5: Technology has a positive impact on service delivery-focused strategy. H1-6: Technology has a positive impact on customer interaction-focused strategy. HRM capability: Human resource is an important resource in a professional service firm (De Jong and Vermeulen, 2003). HRM capability refers to the ability to effectively attract, develop, and retain a firm's human resources (Bernardin and Russell, 1993). HRM capability is a source of competitive advantage as collective knowledge and skills embedded in employees (inimitable), which is developed over a period of time (rare), and valuable as a firm s routines for managing employees to meet objectives and create value (Lado and Wilson, 1994;Wright et al., 1997). Strengthened HRM capability enables firms to facilitate service delivery innovation by introducing new ideas and new methods, as well as by increasing the efficiency of delivery process through knowledgeable and skilled employees. In turn, when a new service delivery system is developed, HRM capability can help employees change the way they work or relate to customers by training and managing employees. In addition, employees distinct knowledge in customer interaction processes may encourage to design or modify new process of customer interaction. HRM capability also enhances the response of firms to customer demands by the changes in the way they interact with customers through firm-internal training. Accordingly, firms with strong HRM capability can better facilitate SDFS and CIFS, which leads to better firm performance. Thus, we propose below: H1-7: HRM capability has a positive impact on service delivery-focused strategy. H1-8: HRM capability has a positive impact on customer interaction-focused strategy. Collaboration capability: Den Hertog (2000) proposed an organizational capability between new service creation innovation and new service delivery innovation. Organizational capability has a broader meaning, but this meaning remains unclear in this framework. Thus, in this study, we narrow organizational capability to collaboration capability to effectively explain its effects on SIS. Collaboration capability refers to the collective ability of a firm s employees to work formally and informally together, share ideas, information, and resources, and to operate as a team (Ellinger et al., 2000). This ability is likely to foster both trust and dependency among separate functional units (Damanpour, 1991) that, in turn, provides a more receptive environment in which to develop new services (Argyris and Vecchio, 1983;Han et al., 1998). Firms with collaboration capability can better tailor organizational structure to their novel ideas or new methods, which lead to effective development of new services and new delivery

7 methods. Additionally, collaboration capability can enhance existing service delivery methods because the new combinations of knowledge and competencies with other teams can increase operational efficiency of the delivery process. Therefore, we hypothesize the followings: H1-9: Collaboration capability has a positive impact on service creation-focused strategy. H1-10: Collaboration capability has a positive impact on service delivery-focused strategy. Marketing capability: Marketing capability is defined as the ability to differentiate products and services from competitors by adding value and building successful brands (Kotabe et al., 2002). Firms with marketing capability use their tangible and intangible resources to interact with customers; these interactions help firms gain better understanding of complex consumer specific needs and market demands, as well as achieve product differentiation and superior brand equity (Song et al., 2008). Marketing capability is important in successfully developing new products and services, because specific customer needs and market demand for development are the results of marketing activity (Song et al., 1997). In addition, product differentiation by marketing capability helps firm in successfully developing and launching new services, thereby leading to innovation success. Furthermore, firms with marketing capability can maintain strong customer relationship, which is due to the marketing capability to serve the specific customer needs effectively compared with other firms (Jayachandran et al., 2005). Hence, firms with strong marketing capability can be better at developing new concepts of services, strong connections, and interactions with customers. From these, we propose the following hypotheses: H1-11: Marketing capability has a positive impact on service creation-focused strategy. H1-12: Marketing capability has a positive impact on customer interaction-focused strategy. 3.2 Two Competing Models from the Dimension-Based Approach Two competing models are developed based on dimension-based approach proposed by Den Hertog (2000) and Gallouj and Weinstein (1997). The assumption of these models is that technology should be considered as a strategic role with potential not only to support chosen SIS, but also to shape new SIS. The first competing model specifically suggests that technology moderately influences firm performance by interacting with other SIS. While the first model considers technology as an operant resource, the first competing model argues that technology is understood as a specific service innovation dimension that interacts with other SIS. Thus, the idea that three operant resources act as determinants of three SIS (H2-4 to H2-9) and technology moderately influences firm performance interacting with other SIS (H2-1 to H2-3) (Figure 1-2) is conceivable. We therefore propose the followings: H2-1: The impact of service creation-focused strategy on firm performance is moderated by technology. H2-2: The impact of service delivery-focused strategy on firm performance is moderated by technology. H2-3: The impact of customer interaction-focused strategy on firm performance is moderated by technology. According to the second competing model, technology influences firm performance as a mediator between operant resources and firm performance, whereas the first competing model regards technology as a moderator between SIS and firm performance. Technology itself is a source of innovation in service firms; it provides the initial base of knowledge, increases operational efficiency, and promotes communication efficiency between customers and service providers. Given that technology is an independent service innovation dimension, similar to other dimensions, it is considered as an intervening variable between operant resources and firm performance. Thus, a decision on adopting and using technology leads to achieve service innovation success and superior firm performance. Hence, we hypothesize the followings: H3-4: The impact of technology has a positive impact on firm performance.

8 Technological knowledge and skills typically evolve over long periods of time through the accumulation of experience (Katz, 1974) and are often tacit and embedded in human resources. Additionally, collaboration capability enables a firm to effectively build and utilize new technological knowledge and skills through the sharing of ideas, information, and resources among employees and teams. Strong marketing capability provides the codified knowledge pool by which to respond to customer needs and market demand. Accordingly, innovations from technology rely on this knowledge pool by marketing capability. We thus present the hypotheses below. H3-7: HRM capability has a positive impact on technology. H3-10: Collaboration capability has a positive impact on technology. H3-13: Marketing capability has a positive impact on technology. Figure 1-3 describes that the three key operant resources acting as determinants of the four SIS (H3-5 to H3-13) and technology directly influences firm performance (H3-4), such as other SIS (H3-1 to H3-3). 4. RESEARCH METHODOLOGY 4.1 Measurement We developed our measure items based on an intensive literature review to obtain content validity. First, we developed multiple-item measures of the three different strategies (i.e., SCFS, SDFS, and CIFS) and technology. However, due to the scarcity of guidelines on measuring SIS and technology in service innovation activities, we developed completely new items in accordance with the steps suggested by Moore and Benbasat (1991). Second, comprehensive multiple-item measures were developed based on prior innovation and marketing literature for measuring the three operant resources (i.e., HRM, collaboration, and marketing capabilities). Third, we developed three perceptual measures to assess the degree of firm performance. Finally, we incorporated industry type and firm age as control variables. Prior to the main survey administration, a pilot test was conducted to examine the reliability and validity of the newly developed measures, using a focus group from 30 service firms operating in the market. The results of the pilot test led to a significant refinement and restructuring of the questionnaire, and helped on established the initial face and internal validity of the measures. All measures were based a five-point Likert-type scale, ranging from extremely low (1) to extremely high (5). The structure of all measures used in this study is presented in the Appendix (available upon request from the authors). 4.2 Sample and Data collection For the main survey, we focused on the Korean service firms three SIS, technology, three operant resources and firm performance developed within the past three years. Hence, respondents who implemented at least one service innovation activity in their firms within the reference period of 2008 to 2010 were asked to respond to the full questionnaire. Our survey samples were randomly selected from the entire population of service firms based on the 2010 Korean Innovation Survey ( We then distributed the questionnaires to 856 firms through , fax, telephone, and personal interview. We received 230 responses; of which 224 responses were determined as useful for this study. To avoid biased measurements, the operations managers were asked to answer questions related to firm performance, whereas the service innovation managers were asked to answer questions related to SIS, operant resources and technology. The responses had a usable response rate of 26.1%. Table 2 summarizes the respondents characteristics.

9 (a) Industry (b) Firm age Industry type Frequency Percent Year Frequency Percent Transport % Less than % Communication % 6~ % Computer and software % 11~ % Engineering and architecture % 21~ % Business consulting % 30 and above % Design services % Total % Total % (c) Number of employees (d) Total sales ($: US mil. dollar) Range Frequency Percent Range Frequency Percent 10~ % Less than $ % 30~ % $1~$ % 50~ % $10~$ % 100~ % $50~$ % 300 and above % $100 above % Total % Total % Table 2. Sample characteristics 4.3 Reliability and Validity of the Measuring Instrument This study adopted a two-stage analysis for structural equation modeling. The measurement model was first estimated, followed by the structural model, which was estimated in the second stage (Anderson and Gerbing, 1988). To validate the measurement model, the content, convergent, and discriminant validities of the items were assessed (Hair et al., 1998). Content validity was first established by interviewing practitioners and pilot-testing the measures. Convergent validity was assessed by looking at the composite reliability (CR) and average variance extracted (AVE) from the measures (Hair et al., 1998). The obtained CR values ranged from to 0.869, which exceeded the threshold value of 0.7 (Table 3). The AVE ranged from to 0.693, which was above the acceptable value of 0.5 (Fornell and Larcker, 1981). Finally, the discriminant validity of the instrument was verified by looking at the square root of the average variance extracted, as recommended by Fornell and Larcker (1981). The results revealed that the square root of the average variance extracted for each construct was greater than the correlations between it and all other constructs (Table 4). These results explain that measurement models were strongly supported by the data gathered, thus requiring further analysis. Measure Item CR AVE Service innovation strategy Service creation-focused strategy(scfs) Service delivery-focused strategy(sdfs) Customer interaction-focused strategy(cifs) Technology(TEC) Operant resource HRM capability(hrmc) Collaboration capability(clc) Marketing capability(mkc) Firm performance (FP) Table 3. Results of confirmatory factor analysis for all items

10 Constructs SCFS SDFS 0.414*** CIFS 0.23*** TEC 0.29*** 0.322*** 0.197** HRMC 0.339*** ** CLC 0.365*** 0.171** 0.266*** 0.304*** 0.396*** MKC 0.528*** 0.38*** 0.259*** 0.201** 0.151** 0.435*** FP 0.409*** 0.202** *** 0.216** *p < 0.10; **p < 0.05; ***p< 0.01 Table 4. Correlation between constructs Note: SCFS: Service creation-focused strategy, SDFS: Service delivery-focused strategy, CIFS: Customer interaction-focused strategy, TEC: Technology, HRMC: Human resource management capability, CLC: Collaboration capability, MKC: Marketing capability, FP: Firm performance. 5. ANALYSIS AND FINDINGS 5.1 Analytical Approach This study conducted a two-stage analysis for structural equation modeling to investigate the role of technology in service innovation. We used AMOS 18.0 to perform structural equation modeling, as well as to evaluate the interrelationship of the constructs of the structural model for the three proposed models (Byrne, 2010). The best approach to model assessment is to compare the proposed model with a series of alternative models, which serve as alternative explanations to the proposed model. This comparison is particularly relevant in structural equation modeling, because even though a model is shown to have an acceptable fit, this alone does not guarantee that a better model does not exist (Hoyle, 1995). Therefore, in this study, the first model was compared with two alternative models in terms of the following criteria: 1) overall fit measures of the model-implied correlation matrix to the sample correlation matrix (i.e., Absolute fit measures: Chi-square, GFI, RMSR, NCP; Incremental fit measures: AGFI, NNFI/TLI, NFI, CFI; and Parisimonious fit measures: Normed chi-square, PGFI, PNFI); 2) statistically significant percentage of the model s hypothesized paths; 3) ability to explain variances in the outcomes as measured by the squared multiple correlation of the firm performance variable; and 4) model power, which is the ability to detect and reject a poor model. After performing AMOS analysis on the three models, we then showed the results of comparing the first model using the resource-based approach with two competing models from dimension-based approach. Finally, we proposed a comprehensive interpretation of these results, which can provide valuable implications to decision makers in the area of service innovation. 5.2 Testing the First Model from Resource-Based Approach The first model was tested based on the three basic measures of absolute fit: the likelihood-ratio chisquare, the goodness-of-it index (GFI), and the root mean square residual (RMSR). The chi-square result (χ 2 (399)=578.8, p= 0.00) was significant, and GFI (0.857), was slightly below the desired threshold of 0.9. The input correlation matrix, the RMSR value (0.076) of the first model was close to zero and acceptable. With overall measures of fit, a model is then estimated in comparison to a null model, which is a singlefactor model with no measurement error. The Tucker-Lewis index (TLI) value of was acceptable. However the normed fit index (0.852) and the adjusted goodness-of-fit index (0.823) fell slightly below the desired threshold of 0.9. The comparative fit index (CIF) of first model was 0.935, which indicated a good fit. For parsimonious fit measures, one applicable measure for evaluating a single model is the normed chi-square measures. The value (1.450) for the first model was found to be within acceptable threshold limits ( or 3.0).

11 The squared multiple correlation (SMC) for the structural equations for firm performance was low (firm performance = 0.101). Two-thirds of the variance in firm performance was explained by the direct effects of SCFS, SDFS, and CIFS, as well as the indirect effects of HRM, collaboration, and marketing capabilities. Eight of the 12 hypothesized paths in the proposed model were supported, as shown in Figure 2-1. The standardized estimates for the eight significant paths ranged from to (mean = 0.231). In addition, only one of three SIS variables was found to have significant effects on firm performance in the first model. The SMC of the first model exhibited firm performance at a value slightly over 10% (0.101). The power of the first model was unacceptable (0.596), because falls slightly below the desired level of 0.8. Figure 2-1. First model Figure 2-2. Second model Figure 2-3. Third model Figure 2. Result of the AMOS analysis of the three different models *p < 0.10; **p < 0.05; ***p< Testing the Two Competing Models from Dimension-Based Approach 1) Testing the second model: The chi-square of the second model was significant (χ 2 (412)=898.7, p=0.00). All absolute fit (GFI. RMSR, and NCP) and incremental fit measures (AGFI, TLI, NFI, and CFI) favored the first model over the second model. While eight (66.7%) of the 12 hypothesized paths in the first model were supported, six (66.7%) of the 9 hypothesized paths was supported in the second model, as shown in Figure 2-2. The standardized estimates for the six significant paths ranged from to (mean = 0.233). An insignificant path between collaboration capability and SDFS in the first model showed significant indirect effects in the second model. Among the three moderating variables, only the interaction between technology and CIFS significantly affected firm performance in the second model while three significant indirect paths between technology and three SIS were shown in the first model. The SMC for firm performance in the second model indicates that over 15% of the variance (firm performance = 0.162) resulted from the interaction effects between three SIS and technology. The increment to the SMC contributed by firm performance was In comparing the two models, differences of 0.06 to 0.09 were taken to indicate substantial model differences (Hair et al., 1998). As the SMC of firm performance in the second model was lower than that of the first model, the first model was superior in terms of firm performance. Figure 2-1 and 2-2 show a difference in parsimony between the second model and the first model (9 versus 12 paths). The two models were compared using the PNFI and PGFI because the CFI does not account for parsimony. GIF indices > 0.90 typically translate to parsimonious fit indices of less than 0.60 (Gebauer et al., 2010) because the PNFI and PGFI used for model comparison exhibit differing degrees of freedom are determined by both the GIF of the models and their parsimony. Table 5 shows significant differences in PNFI and PGFI values between the first model and the second model. The first model showed higher PNFI and PGFI values than the second model. The power of the second model was unacceptable (0.585). Thus, the first model exhibited higher power than the second model.

12 2) Testing the third model: The chi-square of the third model was also significant (χ 2 (322)=482.2, p=0.00). All absolute fit (GFI, RMSR, NCP) and incremental fit measures (AGFI, TLI, NFI, CFI) favored the third model over the first model. While the parsimonious goodness-of-fit index (PGFI=0.745) of the third model was slightly higher than that of the first model (0.741), the percentage of significant paths (61.5%) in the third model was lower than that of the first model (66.7%). The discrepancy between models is shown in Figure 2-3. Only one SIS variable (SCFS) had a significant effect on firm performance. The SMC for firm performance in the third model indicated that over 17% of the variance (firm performance = 0.171) resulted from direct and indirect effects. The SMC of firm performance in the third model was slightly higher than that of the first model. Thus, the third model was superior to the first model in terms of firm performance. Figures 2-1 and 2-3 show no difference in parsimony between the first and third models (8 versus 8 paths). When comparing the first and third models using PNFI and PGFI, the values (PNFI=0.739, PGFI=0.745) achieved by the third model were higher than those (PNFI=0.737, PGFI=0.741) achieved by the first model. Considering that differences of 0.06 to 0.09 are taken as indicative of substantial model difference (Hair et al., 1998), we can conclude that no important difference in PNFI and PGFI values exist between the first model and the third model. The power (0.665) of the third model was unacceptable because being below the desired level of 0.8. However, the power value of the third model was the highest among the three models. Comparison Measures Recommended Level First Model Second Model Third Model 1. Goodness-of-fit Measures Absolute Fit Measures. Chi-square; df (p-value) p> ;399 (p=0.00) 898.7;412 (p=0.00) 482.2;322 (p=0.00).goodness-of fit index(gfi) > Root mean square residual(rmsr) Close to Noncentrality parameter(ncp) Minimun Value Incremental Fit Measures.Adjusted goodness-of-fit index(agfi) > Tucker-Lewis index(tli)or (NNFI) > Normed fit index(nfi) > Comparative fit index(cfi) Maximun Value Parsimonious Fit Measures.Normed chi-square 1.0~2.0/ Parsimonious goodness-of-fit index(pgfi) Maximun Value Parsimonious normed fit index(pnfi) Maximun Value Percentage of significant paths.direct Effect % 33.3% 33.3% 25.0% (Sig./All) (1/3) (1/3) (1/4).Indirect Effect % 77.8% 83.3% 77.8% (Sig./All) (7/9) (5/6) (7/9) 3. Ability to explain the variance (SMC: Square Multiple Correlation). Firm performance Close to Power of model. Degree of model power > Table 5. Comparison among three different models 6. DISCUSSION AND IMPLICATIONS The three causal models were developed based on two approaches: the resource-based approach and the dimension-based approach. The models were tested in the context of the firm performance relationship. The hypothesized antecedents (HRM, collaboration, and marketing capabilities) explained over two-thirds of the variance in the SIS variables (SCFS, SDFS and CIFS), Meanwhile, the SIS variables explained over one-thirds of the variance in firm performance. Although the customary GIF

13 measures showed an acceptable fit for all three formulated models, overall fit measures favored the third causal model in this study. Although the role of technology in service innovation is at the center of debates in service innovation literature, the third model shows that the antecedents (operant resource variables) influence firm performance through the intervening variables of SCFS, SDFS, CIFS and technology. Moreover, technology as a mediator between operant resources and firm performance is more effective in service innovation than that as moderator and as operant resource, although the direct effect of technology does not influence firm performance. Our study theoretically emphasizes two points. The first implication of this study contributes to increase our understanding of the role of technology in service innovation. Although several studies have stressed the importance of technology in service innovation (Bygstad and Lanestedt, 2009), few studies have identified and empirically validated the effect of technology in service innovation in this field. A more detailed understanding of technology in the field of service innovation must be achieved before more significant progress can be made. Therefore, this study adopts both resource and dimension roles of technology based on service innovation literature. The findings highlight that technology in service innovation should be viewed from the dimension-based approach rather than the resource-based approach; this preference indicates that technology, as a service innovation dimension, positively affects service innovation (Den Hertog, 2000;Gallouj and Weinstein, 1997). The theoretical and empirical demonstration of the effect of technology in service innovations is an important research contribution of this study. The second implication stems from the fact that technology mainly plays enabling and facilitating roles in service innovation, leading to competitive advantage and superior firm performance. The results of this study indicate that the effective involvement of technology in service innovation enhances firm performance. Firms that perform service innovation activities should consider their technology improvement as an assurance of substantial leverage. Technology is a crucial factor that contributes to service innovation success, which consequently enhances firm performance. Hence, this study suggests the great necessity of combining technology investment with service innovation toward the goal of firm performance enhancement. The results of this study also have several practical implications. The first implication to practical managers is associated with the role of technology in service innovation. The real value of technology lies in leveraging service innovation. Thus, firms should make a consensus of decisions regarding service innovation among the managers of business, service innovation and technology departments. For example, IT managers should pay greater attention to strategic-level issues and the use of IT in firm performance improvement through service innovation, which can explain difference in the firm performance across competing firms. Another interesting implication is to provide valuable practical guidance to manager for service innovation success. This study provides directions on how to utilize technology effectively to achieve success in service innovation. Improved understanding of the effect of technology on firm performance can help managers appropriately utilize technology to ensure better firm performance and provide organizations with a benchmark against which they can compare their own process in performing service innovation with technology. Despite the aforementioned contributions, this study has certain limitations. First, many service firms are not actually innovative, although they perceive themselves to be so. For this reason, this study has a gap between perception and representation that may result in the overestimation of the service innovation effect on firm performance. Future studies that can reduce this gap may provide stronger results. Second, compared with the established measures of the three operant resources, the measures of the three SIS developed in this study are not comprehensively tested. Although we have developed the measurements of the three SIS drawn from theoretical literature and validated them by following the steps suggested by Moore and Benbasat (1991), we urge future research to develop more valid measures to assess SIS in managing service innovation. Third, this study used customer satisfaction as non-financial performance, which was measured as comprehensive meanings, to represent firm performance. However, non-financial performance has been measured in various forms, such as operational efficiency, employee performance,

14 market extension, and innovativeness (Ittner and Larcker, 1998;Low and Siesfeld, 1998). Therefore, future research should utilize various non-financial measures of firm performance, in addition to financial performance. Fourth, sample characteristics show that around 80% of the respondents whose number of employees is less than 100. Thus, our results may not be generalizable but restrained to the SME companies. Future research should extend the boundary of work for the sake of rigor. Finally, we limited the study to only six service sectors: transport, communication, computer and software, engineering and architecture, business consulting, and design services. Therefore, our findings may not be generalizable to other service sectors. Future research should extend the scope of service sectors to explore more relevant alignment patterns. 7. CONCLUSIONS This study is one of the early attempts to conceptualize and investigate the role of technology in service innovation by developing and comparing three different models from different theoretical perspectives. The findings of this study facilitate substantial progress in future research toward exploring the role of technology in service innovation. This study is conducted with the understanding that each model can be used to enrich theoretical arguments that explain the different roles of technology in service innovation. Furthermore, our findings reveal that business managers should focus more on strategic-level issues, particularly on how technology can help improve firm performance and which approach best explains the role of technology in service innovation. Additionally, implications regarding the relationships among technology, SIS, and firm performance are used as a benchmark against which other practitioners can compare their current approach on technology, SIS, and firm performance for achieving service innovation success. Consequently, this study provides practical steps for managers interested in using technology to achieve service innovation success and improve business performance. References Anderson, J. C. and Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), Argyres, N. S. (1999). The impact of information technology on coordination: Evidence from the B-2 Stealth bomber. Organization Science, 10(2), Argyris, C. and Vecchio, R. P. (1983). Reasoning, learning, and action. Academy of Management Review, 8(4), Bernardin, H. J. and Russell, J. E. (1993). Human resource management: An experiential approach, McGraw-Hill New York. Bygstad, B. and Lanestedt, G. (2009). ICT based service innovation : A challenge for project management. International Journal of Project Management, 27(2009), Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming, Psychology Press. Chen, J. S., Tsou, H. T. and Huang, A. Y. H. (2009). Service delivery innovation. Journal of Service Research, 12(1), Cooper, R. G., Edgett, S. J. and Kleinschmidt, E. J. (1999). New product portfolio management: Practices and performance. Journal of Product Innovation Management, 16(4), Damanpour, F. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34(3),