Service innovation: A comparative study of U.S. and Indian service firms

Journal of Business Research 66 (2013) 1108–1123

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Journal of Business Research

Service innovation: A comparative study of U.S. and Indian service firms Ramendra Thakur a,⁎, Dena Hale b, 1 a b

The Lafayette Coca-Cola/BORSF Professor of Marketing, Department of Marketing, B.I. Moody III College of Business Administration, University of Louisiana at Lafayette, LA 70504, USA Assistant Professor of Marketing, H. Wayne Huizenga School of Business & Entrepreneurship, Huizenga Sales Institute, Nova Southeastern University, 3301 College Avenue, Davie, Florida 33314, USA

a r t i c l e

i n f o

Article history: Received November 2012 Accepted April 2013 Available online 2 April 2012 Keywords: Service innovation Structure-conduct-performance paradigm Customer demand Competition Knowledge-based network Strategic innovation paradigm Performance

a b s t r a c t Although major contributions are being made by the service sector in creating wealth, the sector's substantive role in generating and use of innovation is lacking meticulous examination. Academics, managers, and policy makers lack insights into outcomes of service innovation and identifying factors influencing why some innovations succeed and others fail. This study contributes to service management by: (1) developing a comparative theoretical model based on the Strategic Innovation Paradigm, Bain's Social-ConductPerformance (S-C-P) Paradigm and Social Capital Theory of Innovation; (2) testing the model with Partial Least Squares (PLS) using managerial data from various service industries; and (3) comparing results across a developed (U.S.) and an emerging (India) economy. Results indicate similar managerial perceptions of service innovation success and impeding factors. U.S. managers indicate that factors beyond their control have a negative impact on service innovation, while these factors are not a significant predictor of innovation in the Indian sample. Findings further indicate that service innovation positively relates to the firms' nonfinancial and financial performance. Related implications for firms, consumers, public policy and future research are discussed. © 2012 Elsevier Inc. All rights reserved.

1. Introduction In economies based on service and innovation, leading companies, innovation consultants, and academic researchers are shifting focus from the product to the service (Djellal & Gallouj, 2001). In order to remain competitive in today's market, firms need to continually innovate their service offerings and service processes. The service sector, making up over 70% of the world's advanced economies' gross domestic product (GDP), plays a substantial role in the generation and use of innovation (Ostrom et al., 2010). Service innovation shapes value creation (Moller, Rajala, & Westerlund, 2008) and is a means to increasing market performance, efficiency, and customer value (Chapman, Soosay, & Kandampully, 2003). As this special issue of Journal of Business Research highlights, the notion of service innovation is of central importance in service marketing and the concept is deserving of greater attention from academics (Berry, Shankar, Parish, Cadwallader, & Dotzel, 2006). While increasing, research on service innovation is still an under-researched, under-developed phenomenon and in need of more empirical research (Grawe, Chen, & Daugherty, 2009). Arizona State University's Center for Services Leadership lists stimulating service innovation research

⁎ Corresponding author. Tel.: + 1 337 482 6659. E-mail addresses: [email protected] (R. Thakur), [email protected] (D. Hale). 1 Tel.: + 1 954 262 5042. 0148-2963/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2012.03.007

and understanding, especially from an international perspective, as one of the ten research priorities for the science of service (Ostrom et al., 2010). An analysis of current studies in the area of service innovation demonstrates a primary focus on single country analysis with the focus on developed economies, including North America (e.g., Scheuing & Johnson, 1989), Europe (e.g., Edvardsson, Haglund, & Mattson, 1995), and Australia (e.g., Alam & Perry, 2002). Where authors take a cross cultural perspective, primary foci for research centers on new service development and strategies, still among developed economies (see Table 1 for a sampling of studies). Aside from two studies (Alam, 2007; Song et al., 2000), comparative research of service innovation concepts between developed and emerging economies is lacking. Furthermore, existing studies focus on new service development and new service strategy within a specific industry, most notably financial and telecom/ IT services (see Schilling & Werr, 2009). Thus, research comparing service innovation across service industries and/or between developed and emerging economies seems limited. Developed and emerging economies differ both culturally and in overall business systems; these differences are represented by two nations considered in this study: United States (U.S.) and India (Alam, 2007; Sureshchandar, Chandrasekharan, Anantharaman, & Kamalanabhan, 2002). The U.S. represents a Western, developed economy whereas India is a rapidly developing economy in the Asian market. Approximately 70 to 80% of the developed U.S. and the emerging Indian economies depend on the service industry and the success of that industry relies on innovation (Ostrom et al., 2010).

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Table 1 Sampling of previous service innovations studies. Author(s)

Journal

Examined

Industry examined

County analysis

Methods

Andrea Ordanini and A. Parasuraman (2011)

Journal of Service Research

Service innovation within SDL perspective; customer collaboration; orientation Journal NSD Process and definition NSD Process and Stages

Hotel industry

Italy

Phone survey interviews

Real estate, Construction, KIBS

Finland

Case research; Interview

Financial services

U.S. vs. India Survey research; ANOVA Netherlands

Marja Toivonen and Tiina Tuominen (2009) Ian Alam (2007)

The Service Industries Journal of Global Marketing Edwin Nijssen, Bas International Hillebrand, Pat rick A.M. Vermeulen, Journal of and Ron G.M. Kemp (2006) Research in Marketing Ian Alam(2006a) Industrial Marketing Management Ian Alam (2006b) International Marketing Review Christiane Hipp and Hariolf Grupp Research (2005) Policy Vera Blazevic and Annouk Lievensb Journal of (2004) Business Research Peter R. Magnusson, Jonas Mat thing, and Per Kristensson (2003) Allard van Riel, Jos Lemmink, and Hans Owersloot (2004)

Ian Alam (2002)

Sally Wyatt (2000)

X. Michael Song, C. Anthony Di Benedetto, and Lisa Song (2000)

Andrew Chan, Frank M. Go and Ray Pine (1998) Ulrike de Brentani (1995)

Journal of Service Research Journal of Product Innovation Management Journal of the Academy of Marketing science International Journal of Innovation Management

NSD vs. NPD

Semi-government agency panel of small and medium (SME's) -sized service (hotel, transport , financial) and product (construction, building, metal) enterprises

NSD “Fuzzy frontend”

Financial service

U.S.

Field interviews

NSD Process and Strategy

Financial service

U.S. vs. Australia

NSD Process

Service Type: Knowledge-Intensive, Network-based, Scale, Supplier Dominated Banking industry

Germany Belgium

Survey research; ANOVA Survey; univariate Survey research; Hierarchical Regression

End user involvement in NSD

Telecommunication

Sweden

Internal innovation success factors

High-technology services

U.S., U.K., and Japan

Electronic survey; ANOVA

Financial services

Australia

Case research

Government; technology services

U.S. and U.K.

Interviews

Financial, Retail, Transportation, Hotel and Food, Insurance, Communication, Real Estate, and Consulting

U.S., Germany, Japan, China, Hong Kong, South Korea, and Singapore Hong Kong

Survey research: questionnaire; One-tailed test Questionnaire (Survey method) Exploratory interviews and survey

NSD, effect of Program Learning

NSD at program level; Service Provider Involvement Development of information networks by governments for innovation Journal of Managerial Product perceptions of Innovation pioneering Management innovation

The Service Industries Journal Journal of Business Research

Innovation behaviors Retail, Financial services, Hotels, and Tourism of managers Issues of NSD success/failure

Industrial services Canada (e.g., financial, transportation, and management services)

India is emerging as a country of immense industrial power by actively pursuing the policies of economic liberalization, deregulation and privatization of the service sector (Alam, 2007). Western firms increasingly outsource knowledge-intensive services (i.e. technology services, financial services, and call centers) to India (Hira & Hira, 2005), setting the pace for economic growth and making India an attractive alternative for foreign investors (Sheshabalaya, 2004). Research on service innovation is regarded as being of significant importance by marketing practitioners and scholars due to the large proportion of wealth created by the service sector in both developed (e.g., U.S., Canada, UK, among others) and emerging (India, Brazil, among others) economies (Chapman et al., 2003; Tidd & Hull, 2003). Surprisingly, service innovation in Asian markets does not attracted much attention from scholars, despite the fact that some of the emerging markets (e.g., India and China) are growing rapidly (Ozer, 2006) and may soon be among the world's top economies (Prahalad, 2005). The importance of services and the predominance

Phone Survey; SEM

of the U.S. and Indian economies make for premier research areas, yet very little is known about the reasons for the success or failure of service innovative firms within these two countries. As a result, this study seeks to understand and compare managerial perceptions about the enablers and barriers of service innovation within and between the U.S. and Indian economies, focusing on managerial perceptions across a variety of service industries. Service managers typically have some level of control in the process with which new service innovations are developed and delivered to the market (customer) (Alam, 2007). Therefore, this study considers new service innovations whereby the service manager has direct influence, knowledge, and interaction during the service innovation process. The present study adds value to the current stream of research by providing empirical evidence using data from U.S. and Indian service managers to examine perceptions of the enablers, barriers, and output of service innovation. Two key research objectives of this study are to understand: (1) the perceived enablers and barriers of service

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innovation in U.S and Indian firms and (2) the consequences of service innovation on the performance in both U.S. and Indian firms. This study supplements the compendium of knowledge in the area of service innovation by providing an empirical examination of an element of service marketing that is growing in importance but severely under-researched (Clayton, 2003; O'Cass, Song, & Yuan, 2010) and comparing manager perceptions about enablers and barriers of service innovation. Scholars provide several characteristics of service innovation: service new-to-the world, service new-to-the company, improvement of existing service, new delivery process, and/or service modification (e.g., Avlontis, Papastathopoulou, & Gounaris, 2001). Literature defines service newness as the difference between new and existing services (Zinger & Maidique, 1990), whereas service newness to the market (customers) refers to those services that require a certain degree of effort by customers to adopt a new service (Atuahene-Gima, 1996). In alignment with these foundations, service innovation is defined as those service offerings and processes that are new-to-the company and/or new-to-the market (customer), where the intention is meant to create value for any of the service stakeholders (Hipp, Tether, & Miles, 2000). This paper begins with an in-depth literature review supporting the presented research questions, then proceeds to a description of the conceptual framework and research design, research findings, managerial implications, limitations, and ending with conclusions/ suggestions for future research on service innovation. 2. Theoretical background and research framework Service innovation is considered from a strategic and managerial perspective. Therefore, it is necessary to base the proposed framework on theories grounded in strategy. The strategic innovation paradigm is the most adequate to explain the success factors, impeding factors, and consequences of service innovation in this study (see Sundbo, 1997). Social capital theory of innovation (Gulati, 1999; Sherif, Hoffman, & Thomas, 2006) and Bain's (1951) structureconduct-performance paradigm (S-C-P) further support aspects of the theoretical framework considered in the present study.

processes. An interaction of the firm's social network resource, including expansion of the network itself, creates social capital (Gulati, 1999). Social capital theory of innovation suggests that the relationships between actors add value by increasing the speed and efficiency of information transfer and new knowledge development (i.e. innovation) (Lee, Lee, & Pennings, 2001; Leenders & Gabbey, 1988; Sherif et al., 2006); thereby reducing transaction costs. Social capital is a source for both sustainability and competitive advantage and should play an important part in the success of service firms. Social capital provides opportunity if adequately managed. Specifically, firm investment in employee social capital allows for successful access to, and usage of, information, knowledge, and resources that exist within the firm's knowledge-based networks (Maurer & Ebers, 2006). More importantly, firm investment in employee social capital enhances a culture built on mutual trust and cooperation.

2.3. Structure-conduct-performance (S-C-P) paradigm The S-C-P paradigm, from the industrial economics literature (Panagiotou, 2006; Scherer, 1996), postulates that the firm's performance (P) is determined by its conduct (C), such as innovation. The paradigm further postulates that the conduct (C) of the firm is influenced by the market structure (S) (Lusch & Laczniak, 1989), where S relates to the notion of a firm's operating environment (de Jong & Brouwer, 1999). The firm's operating conditions and/or climate are those conditions that are not within the firm's control, such as industry concentration, market regulations and competition, and entry and exit conditions (Davis & Haltiwanger, 1999). One may contend that the success of a firm is influenced by the firm's conduct (behavior), such as service innovation. Both exogenous (i.e., outside the firm's discretion) and endogenous (i.e., within the firm's control) factors influence innovation. This influence of factors is a probing issue for both top business executives (e.g. CEO, CIO) and managers. The S-CP Model is appropriate as a framework for this study due to the general acceptance that market structure (S) affects innovation levels conducted (C) by the firm (Markham, 1965).

2.1. Strategic innovation paradigm 2.4. Proposed model of service innovation The strategic innovation paradigm originates from marketing and is further developed within the strategic innovation literature (Rumelt, Schendel, & Teece, 1994). The paradigm states that innovations are largely market-driven and are a strategically determined process for service firms (Sundbo, 1997). The paradigm further suggests that strategy works as an inspiration for innovation, particularly in service firms, because strategy informs employees of customer desires and internal resources available within the firm (Sundbo, 1997; Teece, 1992). This theoretical framework is one of the more appropriate and important foundations for service management theory (Sundbo, 1997). According to the strategic innovation paradigm, a service firm is market driven (see Sundbo, 1997). The market-driven aspect of the strategic innovation paradigm denotes that the market situation (i.e. customer demand, competition and market possibilities) is usually an important success factor for innovation activities (de Brentani, 1989; Laing, 1993; Sundbo, 1997). Furthermore, ideas for innovation, whether for goods or services, “come from all parts of the organization and from the external network of the firm” (Sundbo, 1997, p. 436); innovations from the firm's networks are explained by the social capital theory of innovation. 2.2. Social capital theory of innovation Knowledge resources, such as past experience, competitor actions and customer feedback, provide information for innovation of service

The intention of the present study is to examine some of the perceived enabling and impeding factors to service innovation and the final impact of these factors on firm performance within the structure of the service firm. Strategic innovation paradigm, Bain's S-C-P model and social capital theory of innovation support the proposed theoretical framework in this study. In Fig. 1 the relationships between the constructs (customer demand, competition, service innovation and performance) within the dotted box obtain support from the strategic innovation and S-C-P paradigms; however, other relationships between the constructs (knowledge-based network, economic factors, internal factors, external factors and service innovation), which are outside the dotted box, obtain support from the strategic innovation paradigm, social capital theory of innovation, and other innovation research in marketing, management and innovation literatures. Market-pull theory, with the relationship to social capital theory of innovation, provides support for service firms creating successful service innovations based on customer demand and competitor actions. Additionally, the intangible, unreplicable advantages created by knowledge-based networks (cooperative networks) should enable successful implementation of service innovations. In this study, financial costs, lack of human capital, rigid corporate culture, risk of imitation, and government regulations are constraints to successful service innovation.

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Structure

Conduct

1111

Performance

Theoretical roots in structure-conduct-performance (S-C-P) paradigm (Bain 1951)

Success factors Customer demand Competition

H1 ( H2 (

21) 22)

H7(a) (

Nonfinancial outcome

11)

Service Innovation (SI)

Financial outcome H7(b) (

12)

H3 ( 23)

Success factor Knowledgebased network

Theoretical support: Chapman et al (2003); Kandampully (2002); Clark and Staunton Theoretical support for the path from (1989)

H4 ( H5 ( H6 (

Impeding factors Economic factors Internal factors Other (External) factors

24) 25) 26)

impeding factors to service innovation: Sundbo and Gallouj (1998); Evangelista (1998); Tether and Howells (2007); Hipp, Tether and Miles (2000); Hauknes (1998); Salter and Tether (2006); Vermeulen and Van der Aa (2003); Miles (2000); Easingwood (1986).

Fig. 1. Theoretical model of service innovation: Antecedents and consequences. Note: Relationships within the dotted box is supported by SCP paradigm.Other relationships are supported theoretically by the Strategic Innovation Paradigm, Social Capital Theory and the literature.

3. Hypotheses development 3.1. Service innovation success factors 3.1.1. Customer demand and service innovation S-C-P paradigm, in conjunction with market pull theory of innovation, theorizes that market conditions, such as customer demand and competition, drive the behavior of the firm. Service firms compete by recognizing the need to develop new services to satisfy customer demands in a timely and responsive manner (Alam, 2002; Kelly & Storey, 2000; Lovelock, Patterson, & Walker, 2001); an essence of market pull theory of innovation. According to the market pull model, stimulus for innovation comes from the need of society or a particular section of the market (Godin, 2006). In actual fact, to be successful firms should listen to customers' demands while anticipating and developing innovative, value-added services that drive the marketplace (offer superior value to the customer) (Chapman et al., 2003; Kandampully, 2002; Magnusson et al., 2003), which is at the core of service management theory (Sundbo, 1997). The importance of customer contact and experience that enable the understanding of customer demand and the implications on service innovation design is well noted in service literature (Lievens & Moenaert, 2000; Ostrom et al., 2010; Spohrer & Maglio, 2008). Consistent with past research, Berry et al. (2006) argue that in all markets the firm's understanding of customer demand helps in delivering a comprehensive customer experience that is relevant for creating service innovation. Thus it is hypothesized that: Hypothesis 1. For U.S. and Indian firms, customer demand is positively related to service innovation.

3.1.2. Competition and service innovation Service management theory generally supports customer involvement as an important factor for service innovation (Normann, 2001); however, studies (see de Brentani, 1993, 1995) indicate that service firms (e.g., financial services) weakly engage customers in innovation activities due to the problem of imitation by competitors. Globalization of the market economy increases the intensity of competition in the service industry. This intensity impels service firms to seek innovation for survival in both the developed (e.g., U.S.) and the emerging (e.g., India) economies. In the global economy, firm innovations are largely market-driven (e.g., based on competition) and are formulated within the firm's strategic framework (i.e. Calantone, Schmidt, & Di Benedetto, 1997; Sundbo, 1997). According to the S-C-P paradigm, the conduct (C), such as service innovation, is influenced by the market structure (S), such as competition and market regulations (Davis & Haltiwanger, 1999; Lusch & Laczniak, 1989). Firms that consider the competitive nature of the industry are more aware of the existing competitive threats, which may be the stimulus to service innovation development. Previously Indian firms had little need or incentive for innovation before the period of economic liberalization, particularly in the service industry, because India was a protected and inward-looking economy lacking intense competition (Krishnan, 2003). Dutz (2007) posited that stronger competition among enterprises unleashes innovation in emerging economies like India. Dutz's study also indicates that businesses in India should focus on increasing competition that will drive innovation. Deregulation and heightened competition in the U.S. and in India force companies to innovate (Alam, 2007). As a result, innovation becomes a key activity in the service industry in both economies.

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A review of the literature reveals that internationalization is a strong driver of innovation in services (Love, Ashcroft, & Dunlop, 1996) due to the transmission of ideas across national borders. Internationalization results in intensification of external competition due to spurred globalization. Sustaining growth in such a competitive economy then becomes a challenge for both U.S. and Indian firms. To overcome such challenges, firms have to unleash innovativeness (Dutz, 2007). Thus, one can say that high levels of competition increase service innovation (Lee, Ginn, & Naylor, 2009) for both U.S. and Indian firms. This research provides the basis for the second hypothesis: Hypothesis 2. For U.S. and Indian firms, competition is positively related to service innovation. 3.1.3. Knowledge-based networks and service innovation In the present study, the term knowledge-based networks is defined as creating, acquiring, managing, and exchanging information within/between departments and exchange partners that facilitates knowledge development. For U.S. and Indian firms, innovation is generally a collective, boundary spanning process in which the employees, managers, and exchange partners participate at both formal and informal levels (Sundbo & Gallouj, 2000; Tuli, Kohli, & Bharadwaj, 2007). This boundary spanning facilitates the spread of information about innovation, enabling the adoption of innovative ideas (Frambach, Barkema, Nooteboom, and Wedel,1998). Such market information exchange within/between departments and partners (also known as actors in the relationship network) facilitates processing of information and organization learning (Gulati, 1999; Sinkula, 1994); thereby enabling innovation. Thus, one can say that service innovation in U.S and Indian firms is the result of knowledge gained through integrated relationships with the firm's network resources including such as key partners, suppliers and employees. Such networks of collaboration and information exchange within/between networks are a source of innovation, the essence of the social capital theory of innovation (Leenders & Gabbey, 1988). The dependence on knowledge gained via relationships is likely to be more significant for service-providing firms than for those in manufacturing (Eisingerich, Rubera, & Seifert, 2009). Dutz (2007) showcases that “India's innovative strategies should be built on the complementarities between knowledge creation, knowledge diffusion and absorption (greater acquisition and use of existing knowledge)” (p. 4). Clark and Staunton (1989) indicate that an organization facilitates innovation through the acquisition of information and by stimulating information exchange between departments, partners and suppliers. In fact, relationships developed with exchange partners may affect the degree to which service firms focus on service innovation (Eisingerich et al., 2009). These factors lead to the development of hypothesis three, which states: Hypothesis 3. For U.S. and Indian firms, a firm's knowledge-based network resources are positively related to service innovation. 3.2. Impeding factors of service innovation Service innovation, new services and/or new ways of delivering services encounter certain barriers. Greis, Dibner, and Bean (1995) note that U.S. firms report regulations, lack of management expertise, and lack of skilled personnel among the top five barriers to innovation. Cost, personnel skills, corporate culture, and government regulations may impede service innovation. Those impediments are clustered into three key factors: (1) Economic, (2) Internal and (3) External (Other). Though economic factors could be considered as a portion of the firm's internal factors, in this study economic factors are considered as separate from the internal factors of the firm. Factors not solely within the control of the firm at the tactical level often strategically affect financial

resources. For example, strategic and tactical shifts determined by a board of directors, loss of revenues from poor sales due to natural disaster, or fluctuations in the stock market may affect a business unit's available resources. Therefore, economic factors represent a firm's financial resources that have a stronger impact on the firm's service innovation than other internal resources of the firm. Internal factors are intrinsic traits of the firm over which the firm has control; factors related to the firm's own abilities that limit success to innovate. Discussion pertaining to the degree to which these factors may inhibit or limit innovation will be described in subsequent sections. 3.2.1. Economic factors and service innovation In this study economic factors include a firm's financial well-being or financial resources that influence the organization's capacity to successfully innovate. Economic factors can also be defined as financial constraints that hinder firm innovation. Lack of funding for R&D, as well as other means of uncovering market demands, impedes a firm's efforts for innovation and investment in social capital resources. Few studies indicate the importance of economic factors on the innovation process. Of the few empirical studies in existence, results are contradictory (e.g. Ostrom et al., 2010; Sirilli & Evangelista, 1998; Sundbo & Gallouj, 1998). Grawe et al. (2009) find no support for the firm's cost orientation on service innovation capability. In contrast, Sundbo and Gallouj (1998) indicate that financial constraints are an important obstacle in the service development process. According to Kim Gravell of Cardinal Health, cost containment is both a challenge to, and an opportunity for, service innovation (Ostrom et al., 2010). Furthermore, Sirilli and Evangelista (1998) suggest that the major obstacles for innovation, in both U.S. and Indian firms, are of an economic nature (i.e., lack of financing, cost of innovation, long pay-back period, etc.). The Sirilli and Evangelista (1998) study also indicates that, for approximately 23% of service firms in a developed economy, lack of appropriate financial resources is the main obstacle in introducing innovation. This argument leads to the following hypothesis: Hypothesis 4. For U.S. and Indian firms, lack of adequate economic resources is negatively related to service innovation. 3.2.2. Internal factors and service innovation Internal factors are those factors over which the firm has control or related to the firm's own abilities; internal impediments to innovation. The firm's lack of skilled personnel, lack of management training in innovation management and cultural rigidness to change are good examples of internal factors. Lack of investment in social capital resources is one source of such an impediment. Firms often do not invest in the organizational members' social capital; thereby failing to maximize a strategic resource (Hitt & Ireland, 2002). The lack of investment in such capital may limit the managerial and personnel skills necessary to effectively implement service innovations. Innovation and overall success in service require a variety of management and workforce skills (Hipp, Tether, and Miles, 2000). Members of the firm must have the necessary training to support successful service innovation (Shipton, West, Dawson, Birdi, & Patterson, 2006; Wycoff, 2003). Professional skill is an essential building block of innovation (Nogueira & Marques, 2008). Few people in organizations are provided any formal training in innovation management (den Hertog & de Jong, 2007; Tether & Howells, 2007), especially in services and service-related innovation work. Consistent with these findings, Hauknes (1998) indicates “lack of sufficient management capabilities and/or skills to be a major impediment to service innovation” in U.S. firms (p. viii). Dutz (2007) indicates that limited skills and training are a major bottleneck for service innovation in Indian firms. For example, the author found that Indian firms that provide in-service training to employees and managers are 23 to 28% more productive than those that do not. The skills bottleneck can be corrected if firms invest in

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training the work force in innovation management; that is, invest in employee social capital. Service firm management may not be able to effectively manage change facilitated by innovations, nor will managers be able to manage the contradiction between a strong, flexible corporate culture and service innovation (Lyons, Chatman, & Joyce, 2007). Tether and Howells (2007) find an organization's rigidity (e.g., weak culture of innovation) to be another impediment to service innovation. Organizations are confronted with institutional pressure when attempting to make a change. This pressure creates organizational inertia and stifles innovation (Nijssen, Hillebrand, Vermeulen, & Kemp, 2006). Nijssen et al. (2006) propose that the “stronger the inertia, the less innovative a firm will be, and thus the fewer innovative new products and services it will develop and bring to market” (p. 243). In light of this information, the following is hypothesized: Hypothesis 5. For U.S. and Indian firms, lack of adequate internal resources (factors) is negatively related to service innovation. 3.2.3. Factors external to the firm and service innovation External factors are defined as those factors that are outside the firm's control, such as risk of imitation and regulatory constraints that impede service innovation. Risk of imitation is the firm's inability to protect innovation against imitation by competitors after launch. Risk of imitation is very crucial for service firms because service innovations are simple and incremental, making for easy imitation. Regulatory constraints include government regulations on service firms that prevent them from innovation. Firms in a developed economy (Canada) report that forces external to the firm, such as government regulations and the patent process (to enable protection from imitators), are the largest barrier to innovation (Hall & Bagchi-Sen, 2002). Studies by Vermeulen and Van der Aa (2003), Cowell (1988), and Easingwood (1986) each suggest that greater ease of service imitation means greater competitive risk faced by service firms. This suggestion leads to the argument that “if service firms cannot easily protect their innovation against imitation, they cannot appropriate the returns to innovations, and therefore have little incentive to innovate” (Salter & Tether, 2006, p. 26). Innovations in services are not tangible and therefore not patentable. As a result, service innovations are easy to imitate at a lower cost (Avlontis et al., 2001; Tufano, 1989) in both the U.S and India. In addition to imitation, regulatory factors in some service sectors (e.g., financial services, medical services) also prevent organizations from being innovative (Edquist, 2005; Nelson, 1993; Sundbo & Gallouj, 1998). Preissl (1998) demonstrates how regulations may prevent service firms from entering into new markets or offering certain services. For example, in many countries (e.g., U.S., Canada, England, Brazil, Russia, China, India, among others) the government has specific rules and regulations for doing business in health care, banking, insurance, and telecommunication markets that prevent firms from offering certain services. Research by Krishnan (2003) indicates that the Indian government encourages the process of innovation in the pharmaceutical industry. However in many other industries, changes in innovation profiles still are limited due to government constraints (Krishnan, 2003). Lee et al. (2009) find that regulatory forces do not lead to the creation of service innovativeness. A study by the World Bank (Dutz, 2007) indicates that government regulatory constraints (e.g., bureaucratic and rigid nature of the government) in countries like India limit the effectiveness of innovative ideas. Often government and industry regulations impede upon the self-regulating norms, development and use of knowledge-based networks – important social capital resources. In alignment with such service innovation studies, Preissl (1998) finds that government regulations may hinder service firms from innovation. Hence, forces external to the firm, such as greater ease of service imitation and regulatory factors (government regulations) in service areas (e.g., financial and medical services) disincentivize innovation in these

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firms (Hall & Bagchi-Sen, 2002; Preissl, 1998; Salter & Tether, 2006). Therefore, it is hypothesized: Hypothesis 6. For U.S. and Indian firms, lack of control of external factors, specifically imitation and government regulations, are negatively related to service innovation. 3.3. Service innovation consequences: non-financial & financial performance Several studies show that service innovation positively affects a firms' performance (i.e., Avlontis et al., 2001; Grawe et al., 2009; Nijssen et al., 2006). According to Slater and Narver (1995), global market firms compete on the basis of service rather than on the basis of products because innovation in services is a value-creating activity that drives market orientation and firm performance in the marketplace. Alam (2007) suggests that most U.S. service firms, compared to Indian service firms, emphasize value creating activities that drives market orientation and performance. The industrial economics (Bain's S-C-P paradigm) and marketing literature note that performance, measured as output or productivity, is driven by behaviors (or conduct, such as innovation) of the firm. As Kandampully (2002) claims: “customers today expect firms to delight them with creativity. Hence, continuous and creative innovation is … the only strategy that can sustain the long-term success of the firm” (p. 25) in highly competitive economies, such as U.S and India. Lievens and Moenaert's (2000) study confirms the link between innovation and its impact on firm success in the context of financial services. Policymakers also recognize the importance of service innovation as a driving force in improving firm productivity: financial, non-financial or both (Avlontis et al., 2001; Cainelli, Evangelista, & Savanom, 2004; Chen, Tsou, & Huang, 2009; Clayton, 2003; Storey & Kelley, 2001). Examples of financial outcomes include company profitability, sales, and market share (Avlontis et al., 2001; Menor, Tatikonda, & Sampson, 2002; O'Sullivan & Abela, 2007; Zou & Cavusgil, 2002). Examples of non-financial outcomes include new customers, perceived image, loyalty, and competitive position (Avlontis et al., 2001; Lievens & Moenaert, 2000). Evidence from the above studies supports innovation as a key source of improved output for service firms. Based on the review of literature: Hypothesis 7. For U.S. and Indian firms, service innovation will have a significant positive effect on a firm's (a) non-financial performance and (b) financial performance. 4. Research design Four service industries were originally chosen for this study: i) financial; ii) medical; iii) food and hospitality; and iv) communication services. These industries were selected because they were seen to be active in service innovation (Bowers, 1989) and operating in both developed and emerging economies (Alam, 2007). The instrument used for data collection was developed using existing scales collected from an extensive literature review. Before collecting the data, the research instrument was tested for face validity and content validity (Dillman, 1978; Hunt, Sparkman, & Wilcox, 1982). The instrument was pilot tested with four university professors with experience in academic research, and eleven managers involved with innovation for their firm. The 15 pilot test participants were asked to assess each survey item for clarity, specificity, and representativeness. After establishing face and content validities, pretesting of the instrument was conducted. After the pre-testing, an online research firm with corporate panel members from the United States and India was hired for data collection purposes. Data was collected from U.S. and Indian managers using an online questionnaire method. The survey was sent to the respondents via email along

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with a cover letter explaining the purpose and importance of the study. Respondents were promised a monetary reward of $15 (USD) for participation. The researchers also promised to share the findings of their result with those who participated in the survey. A majority of questions used in the instrument were six-point Likert-type scales, eliminating the neutral response category (Gordon, Kaminski, Calantone, & Di Benedetto, 1993). The instrument was designed to assess managerial perceptions about: (1) the enablers and barriers of service innovation and (2) outcomes of service innovation. Prior to the actual survey, two screening questions were used to eliminate those managers who were not correct subjects for this study. The first screening question was related to the manager's involvement in service innovation for the firm. Participants in this study indicated his/her level of involvement in service innovation decisions for the firm, on a five-point scale. Possible responses ranged from No Involvement (1) to Very High Involvement (5). Only those managers who said they were involved in service innovation (answering two through five) for their firms were the subjects for this survey. Results indicated that a majority of the respondents in U.S. and Indian samples (75.7% and 83.6%, respectively) answered that they had quite a bit of involvement to very high involvement with service innovation decision for their firms. The remaining (24.3% and 16.4%) of respondents in both samples had answered some involvement. As recommended by Armstrong and Overton (1977), we compared those respondents who answered some level of involvement (two and three) and those who said high involvement (four and five) in service decisions for their firm. The results indicated that there was no statistically significant difference between the respondents based upon the level of involvement in service decision for their firm, indicating that differences in the level of involvement of the key informant is not related to differences in respondent characteristics. The second screening question identified whether or not the firm had introduced a new service in the market and/or a new method of delivering services to their customers within the past three years. If the respondent answered “yes” to both screening questions, s/he was able to proceed to the survey; otherwise, the respondent was screened out. Thus, all the subjects who completed this study had knowledge of, and active involvement in, service innovation for their firms within the past three years. A total of 791 managers in the U.S. and 440 managers in India were invited to participate. Of the 791 U.S. managers, 275 did not meet qualifying criteria set forth in this study and 347 chose not to participate; resulting in 169 completed U.S. manager surveys. Of the 440 Indian managers invited to participate, 56 did not meet qualifying criteria and 235 chose not to participate; resulting in 146 completed Indian manager surveys. Thus, a completed response rate of 21.4% and 33.2% for U.S. and Indian samples was received. There was no missing data in the U.S. and Indian samples because subjects who were qualified and participated for this study had to complete each and every question before they were allowed to go to the next question in the survey. Therefore, missing data points in the sample were not a problem. 4.1. Sample characteristics All respondents used in this study had two key characteristics. First, they were experienced practicing managers (senior and middle level executives) with experience in the service industry; second, they were involved in the service innovation decisions for their firm. For U.S. firms, approximately 26.6% of the respondents were CEOs/CIOs for their firms. For Indian firms, only 17.8% of the respondents were CEOs /CIOs for their firms. More Indian than U.S. managers (64.9% vs. 51.7%, respectively) categorized themselves as marketing, service, financial or production managers. The sample was representative of a number of service industries, including financial, medical, food and hospitality services, and communication sectors. Due to the availability for the service providers to self-identify the firm's service, some service

managers did not classify the firm as one of the four aforementioned service industries; therefore, a fifth category of “other” was added to the study. Various firm sizes were also represented in the two samples. For a complete summary of sample characteristics, please see Table 2.

4.2. Construct measurement The measurement items for the constructs used in this study are shown in Appendix A. Enablers of service innovation included three underlying constructs: i) consumer demand; ii) competition; and iii) knowledge-based network. Similarly, another three constructs were used as barriers of service innovation: i) economic factors; ii) internal factors; and iii) other (external) factors. The firm's performance was measured in terms of two dimensions, financial and non-financial. The nine underlying constructs examined [consumer demand (CD), competition (COMP), knowledge-based network (KRN), internal factors (IF), economic factors (EF), other (external) factors (OF), service innovation (SI), non-financial outcome (NFO), and financial outcome (FO)] in this study were measured using a total of thirty-six items. The items measuring the underlying constructs were extracted from published marketing, innovation and service management literatures. Three items were used to measure the constructs CD, IF, and OF, whereas constructs COMP, KRN, EF and NFO were measured using four items. Five and six items, respectively, were used to measure constructs, FO and SI. The scale used by Kandampully (2002) and Edwards and Croker (2001) was modified to measure the construct called customer demand (CD). Insights from managerial journals and scholarly literature (e.g., Edwards & Croker, 2001; van Riel et al., 2004) were used to create multi-item scales to measure COMP and KRN. Constructs EF, IF and OF were measured by items that were adopted from Gault and Pattinson (1994), Sirilli and Evangelista (1998) and Lonmo (2005). The items used to operationalize SI were phrased around the items adopted from Booz, Allen, and Hamilton (1982), Cooper and Kleinschmidt (1993), Olson, Walker, and Ruekert (1995), Atuahene-Gima (1995), Avlontis et al. (2001), Gounaris, Papastathopoulou, and Avlonitis (2003), and Chen and Tsou (2007). The service performance measures (outputs of SI in this study) were operationalized as a multidimensional construct (Cooper, Easingwood, Edgett, Kleinschmidt and Storey,2003; de Brentani, 1989, 1991; Storey & Easingwood, 1999), namely financial and non-financial performance. The items used to measure the underlying performance outcome Table 2 Summary of sample characteristics.

Level CEO/CIO Manager (marketing, service, financial, production) Manager (other) Service industry Financial/banking Medical Food/hospitality Communication Other service Annual sales >$900 million $401 to $900 million $201 to $400 million $51 to $200 million b$51 million Number of employees >1000 200 to 1000 b200

Percentage of firms

Number of firms

Percentage of firms

Number of firms

26.6 51.7

45 87

17.8 64.9

26 95

21.7 42.4

37 72

17.3 35.6

25 52

28.3 15.9 5.4 8.0

48 28 9 13

26.5 11.0 20.5 6.4

39 16 30 9

8.3 3.6 5.9 13.0 69.2

14 6 10 22 117

11.0 4.8 12.3 15.8 56.2

16 7 18 23 82

15.4 26.6 58.0

26 45 98

19.2 41.1 39.7

28 60 58

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[financial and non-financial (FO and NFO)], were derived from O'Sullivan and Abela (2007), Zou and Cavusgil (2002), Avlontis et al., 2001, Voss and Voss (2000), and Cooper, Easingwood, Edgett, Kleinschmidt, and Storey (2003). The items measuring the nine constructs are shown in the Appendix A.1. The reliabilities of all the constructs used in the U.S. and Indian surveys ranged between 0.780 and 0.962 as shown in Tables 5 and 6. Thus, all constructs showed acceptable to high reliability estimates (Cronbach & Meehl, 1967; Nunnally, 1967). 4.3. Model specifications and analysis Theory and hypotheses suggest the following model of service innovation (see Fig. 1): Non-financial outcome = β11 Service innovation + ε1 Financial outcome = β12 Service innovation + ε2 Service innovation = γ21 Customer demand + γ22 Competition + γ23 Knowledge-based network + γ24 Economic factor + γ25 Internal factor + γ26 Other (external) factors + ε3 This model was tested using partial least square (PLS) approach, specifically PLS-Graph (version 3.00, build 1126) software. The PLS approach was chosen due to the small sample size (Jöreskog & Wold, 1982), power analysis (MacCallum, Browne, & Sugawara, 1996) and reputation as a well-substantiated method for estimating cause-effectrelationship models in business research (Gudergan, Ringle, Wende, & Will, 2008). According to MacCallum et al. (1996), a minimum of 93 cases are required to obtain a standard 0.80 power level at the 0.05 alpha level and 127 cases are required for 0.01 alpha level. Additionally, the PLS approach is not constrained to sample size and is able to incorporate a small sample, depending upon the given model (Chin & Newstead, 1999; Sundaram, Schwarz, Jones, & Chin, 2007). Given the small sample size (169 U.S. managers and 146 Indian managers), the PLS approach was chosen for analysis over a two-step covariance based approach, as suggested by Anderson, Gerbing, and Bulletin (1988) and Gerbing and Anderson (1985). 5. Results Once the data were examined for assumptions of multivariate analysis (Hair, Black, Babbin, Anderson, & Tatham, 2006), the PLS approach was used for hypotheses testing. The path diagram (Fig. 1) tested in this study forms a recursive system; that is, all the hypotheses tested are directional in nature [one-way causal flow in the system (Chandy & Tellis, 1998)]. A one-tailed significance test for hypotheses testing was used. As suggested by Chin (1998), PLS approach for structural equation modeling was used to test the proposed model, in which the measurement model was first estimated and then the structural model was tested. The next section discusses the measurement and structural results of the enablers and barriers of service innovation for the U.S. and Indian sample. 5.1. Measurement model results (U.S. vs. India) In the PLS analysis, the adequacy of the construct was first examined by identifying whether all the items measured the appropriate underlying constructs. The measurement model results indicate that all the items measuring the underlying constructs ranged from 0.52 to 0.93 in the U.S. sample and 0.69 to 0.97 in the Indian sample, meeting the unidimensionality thresholds of 0.50 or above (Bollen, 1990; Chin, 1998). As suggested by Sundaram et al. (2007), the cross-loadings were computed (see Table 3 for U.S. sample and Table 4 for Indian sample) to determine if the items loaded only on their theorized construct or on the other constructs as well. Results of the cross-loadings in both

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the U.S. and the Indian samples indicate that none of the items loaded higher on any other construct than on their underlying construct. After assessing the unidimensionality and cross-loadings, the alpha coefficients and composite reliabilities were calculated for all constructs. Results indicated that the alpha coefficient and composite reliabilities ranged from 0.78 to 0.90 and 0.87 to 0.92 for the U.S. sample (refer to Table 5) and from 0.78 to 0.96 and 0.87 to 0.98 for the Indian sample (refer to Table 6). Thus, both alpha coefficients and composite reliabilities were greater than the recommended value (alpha > 0.70: Nunnally, 1978; Robinson, Shaver, & Wrightsman, 1991; composite reliability > 0.80: Hair et al., 2006; Sundaram et al., 2007). To test the construct validity, convergent validity and discriminant validity were performed. Convergent validity is supported if the loadings and average variance extracted (AVE) estimates for each underlying construct exceed 0.70 and 0.50 (Bentler, 1990a, 1990b; Chin, 1998). Discriminant validity is shown when the shared variance (squared correlation) between any two constructs is less than the square root of the AVE by the items measuring the construct (Fornell & Lacker, 1981a, 1981b). Table 5 and Table 6 showcase the evidence of convergent and discriminant validity among the constructs used in these two studies. 5.2. Structural model results (U.S. vs. India) Once the measurement model was estimated, the structural model was tested for significance. Figs. 2 and 3 showcase the structural model results for U.S and Indian samples using PLS Graph. Both figures provide the path coefficients along with corresponding significance levels and R-square value. The figures also showcase all the significant and non-significant hypotheses that were tested in the proposed model. From the results of Fig. 2 (U.S sample) all eight hypotheses tested for significance were supported at the 0.05 level. A direct path from customer demand (H1) (b = 0.15), competition (H2) (b = 0.16) and knowledge-based network (H3) (b = 0.20) to service innovation are positive and significant. Results support the concept of the strategic innovation paradigm (Sundbo, 1997) and the S-C-P paradigm (Bain, 1951). Barriers to innovation, such as economic factors (H4) (b= −0.11), internal factors (H5) (b=−0.20), and other (external) factors (b= −0.18), have an inverse relationship with (and are significant predictors of) service innovation. Results suggest that increased barriers reduced the firm's involvement in service innovation. These results align with the theoretical support of previous literature (Hauknes, 1998; Miles, 2000; Salter & Tether, 2006; Sundbo & Gallouj, 1998; Tether & Howells, 2007). The results also suggest that service innovation is positively related to the firm's non-financial (b = 0.51) and financial (b = 0.57) performance; which is consistent with the S-C-P paradigm, which postulates that the firm's performance (P) is determined by its conduct (C), such as innovation. The paradigm further postulates that the conduct (C) of the firm is influenced by the market structure (S) (Lusch & Laczniak, 1989). Therefore, Hypothesis 7a (nonfinancial performance) and Hypothesis 7b (financial performance) were supported. Results from the Indian sample (Fig. 3) indicate that, with the exception of hypothesis six, all hypotheses were significant and in the expected direction. From Fig. 3 it may be said that of the eight hypotheses tested for significance: • Hypothesis 1 (b = 0.11) was significant at 0.05 level; • three hypotheses, H3 (b= 0.21), H4 (b= −0.12) and H5 (b= −0.18), were significant at the 0.01 level; and the remaining three hypotheses, H2 (b= 0.44), H7a (b= 0.50) and H7b (b= 0.53) were significant at the 0.001 level. 6. Discussion of results from a managerial perspective The goal of this research is to identify a few of the perceived enablers and barriers of service innovation in developed and emerging

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Table 3 Loadings and cross-loadings (U.S. managers).

CD1 CD2 CD3 COMP1 COMP2 COMP4 COMP3 KRN3 KRN4 KRN5 KRN6 EF2 EF4 EF5 EF1 IF4 IF5 IF6 OF2 OF3 OF4 SNCOMP1SI SNCOMP2SI SNCOMP4SI SNCUST1SI SNCUST2SI SNCUST3SI NFO1 NFO2 NFO3 NFO4 FO9 FO10 FO11 FO12 FO13

Customer demand

Competition Knowledge & relationship network

Economic factor

Internal factor

Other factor

Service innovation

Non-financial outcome

Financial outcome

0.88 0.91 0.85 0.25 0.37 0.21 0.31 0.45 0.48 0.48 0.50 − 0.06 − 0.16 − 0.14 − 0.14 − 0.31 −0.28 − 0.36 − 0.25 − 0.14 −0.25 0.33 0.41 0.26 0.23 0.44 0.43 0.27 0.30 0.32 0.36 0.34 0.41 0.28 0.41 0.31

0.34 0.33 0.24 0.82 0.88 0.87 0.81 0.45 0.27 0.29 0.29 − 0.10 −0.20 − 0.15 − 0.25 − 0.15 −0.17 − 0.25 − 0.31 −0.15 − 0.14 0.36 0.32 0.31 0.27 0.31 0.28 0.24 0.27 0.30 0.35 0.34 0.31 0.35 0.34 0.23

− 0.08 −0.11 − 0.22 −0.18 − 0.15 − 0.22 − 0.18 − 0.09 − 0.08 − 0.10 − 0.12 0.52 0.93 0.93 0.88 0.14 0.18 0.06 0.16 0.19 0.24 − 0.12 −0.28 − 0.23 −0.28 − 0.16 −0.29 − 0.12 −0.02 −0.15 − 0.06 − 0.08 − 0.13 − 0.23 − 0.16 − 0.16

− 0.34 − 0.35 −0.27 − 0.16 − 0.27 − 0.12 −0.17 −0.37 −0.34 −0.33 − 0.31 0.13 0.12 0.16 0.09 0.89 0.92 0.81 0.32 0.35 0.37 − 0.36 −0.27 − 0.31 −0.35 − 0.48 − 0.34 − 0.29 − 0.33 − 0.25 − 0.39 − 0.50 − 0.39 − 0.39 −0.40 − 0.38

− 0.22 − 0.25 −0.23 − 0.21 − 0.23 − 0.23 − 0.13 − 0.23 − 0.24 − 0.29 −0.28 0.12 0.22 0.24 0.21 0.39 0.30 0.40 0.70 0.90 0.87 − 0.39 −0.34 − 0.37 − 0.35 − 0.33 − 0.20 − 0.27 − 0.33 − 0.36 −0.38 −0.28 − 0.17 − 0.25 − 0.32 − 0.29

0.42 0.39 0.40 0.30 0.39 0.31 0.34 0.43 0.41 0.45 0.39 − 0.05 − 0.24 −0.28 − 0.22 − 0.42 −0.38 − 0.41 − 0.31 − 0.30 − 0.43 0.82 0.73 0.78 0.81 0.82 0.68 0.38 0.40 0.44 0.46 0.44 0.38 0.47 0.51 0.54

0.34 0.34 0.31 0.29 0.37 0.28 0.23 0.40 0.35 0.31 0.40 − 0.09 − 0.11 − 0.08 − 0.09 − 0.35 − 0.30 − 0.34 − 0.36 − 0.29 − 0.33 0.47 0.43 0.34 0.36 0.44 0.26 0.85 0.86 0.83 0.84 0.41 0.38 0.44 0.45 0.43

0.37 0.35 0.38 0.35 0.30 0.32 0.29 0.40 0.40 0.41 0.44 − 0.06 − 0.15 − 0.17 − 0.19 − 0.37 − 0.40 − 0.50 − 0.36 − 0.18 − 0.25 0.43 0.40 0.34 0.43 0.56 0.43 0.34 0.38 0.40 0.57 0.80 0.79 0.85 0.87 0.87

0.55 0.50 0.44 0.28 0.39 0.35 0.27 0.84 0.87 0.85 0.80 −0.08 − 0.15 −0.10 − 0.07 −0.33 − 0.36 − 0.36 − 0.28 − 0.22 − 0.26 0.37 0.46 0.35 0.27 0.50 0.33 0.34 0.36 0.33 0.43 0.47 0.37 0.46 0.40 0.36

Note: Numbers in bold are item loadings on their underlying constructs. Other numbers are the cross-loadings. To calculate cross-loadings first we calculated the latent variable scores (provided by PLS-Graph) and standardized indicator scores for each construct. Then we correlated latent variable scores and standardized indicator scores to calculate cross-loadings. Numbers in the bold should be greater than cross-loadings (Sundaram et al., 2007).

economies. The results indicate that enablers and barriers of service innovation for the U.S. sample closely parallel those of the Indian sample. Table 7 illustrates the rank ordering of the enablers, barriers and outcomes of service innovation for both samples based on the standardized path loading beta coefficients (standardized parameter estimates). 6.1. Which enablers are more important in service innovation? In service firms, customer demand and competition are important enablers of innovation. However, the impact of competition on service innovation is stronger for Indian firms (b = 0.44) than U.S. firms (b = 0.16). This difference may be due to intense competition in the major emerging market of India (Johnson & Tellis, 2008), and less demanding consumers (due to low purchasing power) as compared to the developed U.S. economy. This finding suggests that firms in India may have a higher competitive orientation (Grawe et al., 2009). In the U.S., firms pay more attention to innovation to satisfy demanding consumers in additional to overcoming the competition. Managers of Indian service firms, compared to U.S. counterparts, focus more on what direct competitors are doing in the marketplace to satisfy customers and innovate accordingly. From a practical perspective, competition appears to be far more important than consumer demand in an Indian firm; however, U.S. service firms give almost equal importance to both competition and customer demand when developing an innovative service. Reasons for this distinction may be attributed to the fact that Indian firms develop less innovative services (i.e., copy of the

services that have been successful in other overseas market); hence, Indian firms tend to believe in collecting more analytical information about competitors in order to be successful (Alam, 2007). Conversely, U.S. firms (being more proficient and competitive) appear to believe in providing key service features that potentially provide desired benefits and attributes by understanding customer demand in addition to competitive activities in the marketplace. Both U.S. and Indian managers perceive that the firm's knowledgebased networks play a vital role in service innovation, consistent with previous studies. A knowledge-based network is defined as creating, acquiring, managing, and exchanging information within/between departments and exchange partners that facilitates knowledge development. These networks are built around integrated relationships with key partners, including employees, managers, and exchange partners who participate at both formal and informal levels (Sundbo & Gallouj, 2000). Such exchange of information across departmental and organizational boundaries reduces tangible capital of business while adding intangible value, or social capital, to the service that act as resources of innovation. Thus, managers should consider knowledge-based networks as a unique set of factors that can fuel innovation in service organizations. 6.2. What are the key barriers to service innovation? Managers in both countries perceive that service firms face obstacles that are often of an economic nature – cost, long pay-back period, and excessive risk – when introducing innovation. However, U.S. managers perceive that the impact of these economic barriers on service innovation

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Table 4 Loadings and cross-loadings (Indian managers).

CD1 CD2 CD3 COMP1 COMP2 COMP3 COMP4 KRN3 KRN4 KRN5 KRN6 EF1 EF2 EF4 EF5 IF4 IF5 IF6 OF3 OF2 OF4 SNCOMP1SI SNCOMP2SI SNCOMP4SI SNCUST1SI SNCUST2SI SNCUST3SI NFO1 NFO2 NFO3 NFO4 FO9 FO10 FO11 FO12 FO13

Customer demand

Competition Knowledge & relationship network

Economic factor

Internal factor

Other factor

Service innovation

Non-financial outcome

Financial outcome

0.96 0.96 0.95 0.27 0.22 0.16 0.30 0.21 0.11 0.10 0.08 −0.17 − 0.15 − 0.09 − 0.06 − 0.36 − 0.38 − 0.36 0.16 0.21 0.05 0.21 0.28 0.28 0.33 0.37 0.23 0.12 0.18 0.17 0.14 0.12 0.05 0.11 − 0.03 0.03

0.31 0.26 0.22 0.87 0.88 0.83 0.88 0.45 0.50 0.41 0.39 − 0.21 − 0.27 − 0.21 − 0.21 − 0.39 − 0.35 − 0.31 0.33 0.33 0.27 0.58 0.63 0.59 0.40 0.46 0.51 0.53 0.45 0.47 0.44 0.45 0.44 0.45 0.38 0.43

− 0.12 − 0.17 − 0.09 − 0.18 − 0.22 − 0.25 − 0.24 − 0.23 − 0.27 − 0.25 − 0.29 0.91 0.92 0.92 0.83 0.21 0.20 0.22 − 0.51 − 0.49 − 0.32 − 0.23 − 0.28 − 0.29 − 0.24 − 0.25 − 0.29 − 0.12 − 0.15 − 0.28 − 0.25 − 0.19 − 0.15 − 0.24 − 0.27 − 0.21

− 0.39 − 0.37 − 0.36 − 0.37 − 0.37 − 0.28 −0.28 −0.33 −0.28 −0.24 −0.21 0.19 0.17 0.23 0.23 0.92 0.94 0.93 − 0.36 −0.40 − 0.10 − 0.38 − 0.39 − 0.42 − 0.38 − 0.39 − 0.33 − 0.31 − 0.33 − 0.34 − 0.27 − 0.26 − 0.23 − 0.25 −0.20 − 0.22

0.18 0.19 0.14 0.30 0.35 0.32 0.31 0.29 0.36 0.29 0.28 −0.46 −0.51 − 0.45 −0.49 − 0.38 −0.33 − 0.31 0.89 0.90 0.69 0.34 0.35 0.34 0.17 0.22 0.21 0.25 0.32 0.37 0.34 0.26 0.25 0.28 0.25 0.23

0.38 0.31 0.27 0.59 0.55 0.55 0.57 0.50 0.46 0.41 0.43 −0.25 − 0.35 −0.28 − 0.25 − 0.48 − 0.40 − 0.41 0.32 0.31 0.22 0.84 0.86 0.85 0.75 0.80 0.81 0.36 0.37 0.47 0.48 0.51 0.46 0.45 0.41 0.41

0.20 0.17 0.14 0.42 0.52 0.44 0.50 0.42 0.41 0.45 0.44 − 0.18 − 0.19 − 0.23 − 0.28 − 0.35 − 0.31 −0.34 0.35 0.34 0.22 0.41 0.53 0.39 0.34 0.39 0.34 0.81 0.88 0.92 0.85 0.49 0.49 0.43 0.31 0.50

0.13 0.04 0.03 0.42 0.56 0.41 0.38 0.58 0.64 0.54 0.55 − 0.20 − 0.24 − 0.21 − 0.24 − 0.33 − 0.19 − 0.23 0.27 0.24 0.24 0.47 0.44 0.50 0.37 0.44 0.39 0.40 0.42 0.51 0.48 0.88 0.84 0.83 0.84 0.84

0.18 0.13 0.10 0.45 0.46 0.45 0.39 0.87 0.86 0.86 0.89 −0.20 −0.26 − 0.32 − 0.28 − 0.32 − 0.25 − 0.28 0.37 0.34 0.14 0.43 0.50 0.47 0.34 0.41 0.37 0.40 0.43 0.43 0.46 0.62 0.58 0.56 0.51 0.52

Note: Numbers in bold are item loadings on their underlying constructs. Other numbers are the cross-loadings. To calculate cross-loadings first we calculated the latent variable scores (provided by PLS-Graph) and standardized indicator scores for each construct. Then we correlated latent variable scores and standardized indicator scores to calculate cross-loadings. Numbers in the bold should be greater than cross-loadings (Sundaram et al., 2007).

is weaker or less significant than do their Indian counterparts (see Figs. 2 and 3). This discrepancy may be an artifact of the differing economies: the U.S. being a stronger economy compared to that of India. However, internal factors of the firm (those factors under firm's control) have a negative impact on service innovation in both represented economies. Thus, a managerial goal should be to reduce barriers of service innovation by

providing training on innovation management to all personnel (management and other key staff members). Other (external) factors including regulatory constraints by the government and risk of imitation of innovative ideas have a negative impact on innovation in U.S. firms. However, these factors are not a significant predictor of innovation in the Indian sample. One possible

Table 5 Interconstruct correlations (U.S. managers).

Customer demand (CD) Competition (COMP) Knowledge-based Network (KRN) Economic factor (EF) Internal factor (IF) Other (External) factors (OF) Service innovation (SI) Non-financial outcome (NFO) Financial outcome (FO)

Cronbach alpha

Composite reliability

Average variance extracted

CD

COMP

KRN

EF

IF

OF

SI

NFO

0.86

0.91

0.78

0.88

0.87

0.91

0.73

0.35

0.85

0.87

0.91

0.72

0.57

0.39

0.85

0.86

0.90

0.70

− 0.16

− 0.21

− 0.12

0.84

0.85

0.91

0.77

− 0.37

− 0.22

− 0.41

0.15

0.88

0.78

0.87

0.69

−0.27

− 0.24

−0.31

0.25

0.42

0.83

0.87

0.90

0.61

0.46

0.40

0.50

− 0.27

− 0.47

− 0.44

0.78

0.87

0.91

0.73

0.38

0.35

0.44

− 0.11

− 0.38

− 0.40

0.51

0.85

0.90

0.92

0.71

0.42

0.37

0.49

− 0.19

− 0.49

− 0.32

0.57

0.51

FO

0.84

Note: Diagonal elements represent the square root of the average variance extracted (AVE) between the constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements.

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Table 6 Interconstruct correlations (Indian managers).

Customer demand (CD) Competition (COMP) Knowledge-based Network (KRN) Economic factor (EF) Internal factor (IF) Other (External) factors (OF) Service innovation (SI) Non-financial outcome (NFO) Financial outcome (FO)

Cronbach alpha Composite reliability Average variance extracted CD

COMP

KRN

EF

IF

OF

0.96 0.89 0.90 0.92 0.93 0.78 0.90 0.89 0.90

0.87 0.51 − 0.26 − 0.38 − 0.37 0.66 0.55 0.51

0.88 −0.30 − 0.31 − 0.36 0.52 0.50 0.67

0.90 0.30 0.54 − 0.33 −0.25 − 0.26

0.94 0.37 − 0.47 − 0.36 − 0.28

0.84 0.34 0.82 0.38 0.51 0.87 0.31 0.53 0.53 0.85

0.98 0.92 0.93 0.94 0.96 0.87 0.93 0.93 0.93

0.93 0.75 0.77 0.81 0.88 0.70 0.68 0.76 0.73

0.96 0.28 0.15 − 0.14 − 0.40 − 0.18 0.35 0.18 0.08

SI

NFO

FO

Note: Diagonal elements represent the square root of the average variance extracted (AVE) between the constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements.

explanation may be the fact that the government of India does not presently have regulatory policies which are as restrictive those found in the as the U.S. government for the industries examined in this study. If a firm has too many regulations, and therefore a rigid culture, networks may not be free to develop, limiting the access, exchange, and use of knowledge resources – thus limiting the firm's social capital resource. Therefore, innovation of service processes will be negatively affected.

6.3. Does service innovation impact performance? In both tested economies, service innovation explained more variance for financial performance than for non-financial performance. The variance explained (R 2) by service innovation for financial performance is 32.2% for the U.S. sample and 28.4% for the India sample. Less variance is explained for non-financial performance, with only 25.5% of the variance for the U.S. sample and 24.8% of the variance

ENABLERS

Customer demand

Competition

PERFORMANCE 0.15*

R2 = 0.26 Knowledgebased network

0.16**

0.51***

Nonfinancial outcome

0.20* Service Innovation

BARRIERS -0.11*

0. 57*** Economic factors

-0.20**

R2 = 0.43

R2 = 0.32

-0.18** Internal factors

Other (External) factors

Financial outcome

* p < 0.05 ** p < 0.01 *** p < 0.001

Fig. 2. Enablers and barriers of service innovation (U.S. managers).

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ENABLERS Customer demand

Competition

PERFORMANCE

0.11* Knowledgebased network

R2 = 0.25

0.44***

0. 50***

Nonfinancial outcome

0.21** Service Innovation

BARRIERS -0.12**

Financial outcome Economic factors

0. 53***

R2 = 0.54 -0.18**

R2 = 0.28

NS Internal factors

* p < 0.05 ** p < 0.01 *** p < 0.001 Dotted line indicates path is not significant

Other (External) factors

Fig. 3. Enabler and barriers of service innovation (Indian managers).

for the India sample being explained by service innovation. Both findings, while differing in magnitude, suggest that managers perceive service innovation to have a positive impact on firm performance (financial and non-financial in nature) in their respective economies. Table 7 Rank 0rdering of service innovation enablers, barriers and outcomes (U.S. vs. Indian firms). Service innovation enablers, barriers and outcome

U.S. firms Standardized parameter estimate

Enablers of service innovation ➤ Customer 0.15 demand ➤ Competition 0.16 ➤ Knowledge0.20 based network Barriers of service innovation ➤ Economic factor −0.11 ➤ Internal factor −0.20 ➤ Other (External) −0.18 factor Outcomes of service innovation ➤ Non-financial 0.51 ➤ Financial 0.57

In conclusion, managerial perceptions about the factors that enhance and hinder service innovation are remarkably consistent for both the U.S and Indian groups. While the rank order of the factors changes, the list of significant enablers and barriers shows considerable parallelism for the two samples.

7. Limitations & directions for future research

Indian firms Ranking Standardized parameter estimate

Ranking

3

0.11

3

2 1

0.44 0.21

1 2

3 1 2

−0.12 −0.18 Non-significant

2 1 N/A

2 1

0.50 0.53

2 1

This study sheds some light into service innovation but suffers from a number of limitations that create some opportunities for future research. Due to limited economic resources, only selected items, industries, and countries are examined. Not all of the possible key success factors and impediments of service innovation are tested, many other key enablers and barriers of service innovation could be included. However, this study's main objective was not to develop a comprehensive model of all the drivers and impediments of service innovation, but to take an initial step toward a model that can be tested in both developed and emerging economies. Therefore, future research should be conducted to determine other important drivers and barriers of service innovation which may exist. Second, resource constraints forced the collection of data from two specific countries. Additionally, resource limitations necessitated a focus on understanding the managerial perceptions about the potential drivers and impediments of service innovation and their outcomes. Hence, in the future, managerial data should be collected

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from other developed countries [e.g., Sweden and United Kingdom because expenditure in service innovation is highest in these two countries among OECD member countries (Pilat, 2001)] and emerging countries (e.g., Brazil, Russia, China, etc) because they are fast becoming an important hub of the global economy. Additional comparative studies should be carried out to understand additional success and impediment factors of service innovation in these economies. Third, this study is based on four primary service industries, with the inclusion of an “other” non-specific service industry. The sample collected is skewed toward the medical and financial service industries. Research should determine if the results of this study hold equally well for other service industries. Fourth, results reported in this study should be interpreted with caution due to the reliance on managerial perceptions rather than behavioral data. Although this method is prevalent in the literature (Alam, 2002), the problem of bias effect is always present. Finally, the scale for some of the variables be improved. For example, the customer demand construct used in this study is measured in terms of three dimensions: newer service, superior value service, and quality service. Further research should identify more dimensions to measure customer demand. Marketing academics and practitioners have indicated the importance of service innovation in the information age. However, to date there is a lack of empirical research that deals with the enablers and impediments of innovation in various service industries. Systematic analysis of the effect of innovation on a business's performances across various industries is scant. This paper provides a comparative analysis of the drivers of service innovation in a developed and an emerging economy, and the impact on business performance, with particular interest in four service industries. Partial least square (PLS) methodology is used for analysis. Results indicate that managerial perceptions about the service innovation drivers in both economies are somewhat similar, except for one notable difference. Indian managers do not perceive that other factors beyond their control impact innovation. The PLS results also indicate that businesses focusing on customer demand, competition, and that rely on their knowledge base (attained through internal cooperation and with external partners) are likely to be innovative. Findings also indicate that service innovation is positively related to a firm's nonfinancial and financial performances in both U.S and Indian service industries.

Acknowledgments The authors thank Utah Valley University for the financial support to reach online panel and thank two anonymous JBR reviewers for their constructive comments and support. The authors also thank Dr. Charlie Pettijohn (Nova Southeastern University) for the editing assistance and guidance in completing this article.

Appendix A On a five point scale, please describe your level of involvement in service innovation decisions for your firm? No A little Some Quite a bit of Very high Involvement Involvement Involvement Involvement Involvement 1 2 3 4 5 Has your firm, in the last three years, introduced a new service to the market? Yes No 1 2 In the last three years has your firm introduced a new way of delivering services (to customers)? Yes No 1 2

Customer demand (CD) [Adopted from: Kandampully, 2002, Edwards & Croker, 2001] (Cronbach alpha= 0.96, Composite reliability= 0.98, Average variance extracted= 0.93) How important (1 = very unimportant, 2 = moderately unimportant, 3 = slightly unimportant, 4 = slightly important, 5 = moderately important, 6 = very important) is each of these statements for firms' service innovation: a) Customer demand for newer services (CD 1) b) Customer demand for services of superior value (CD 2) c) Customer demand for quality services (CD 3) Competition (COMP) [Adopted from: Edwards & Croker, 2001] (Cronbach alpha = 0.89, Composite reliability = 0.92, Average variance extracted = 0.75) Service firms owe their existence to: (1 = strongly disagree, 2 = moderately disagree, 3 = slightly disagree, 4 = slightly agree, 5 = moderately agree, 6 = strongly agree) a) b) c) d)

Globalization of the market economy (COMP 1) Intensified competition (COMP 2) Threat of foreign competition (COMP 3) Low barriers to entry (COMP 4)

Knowledge-based network (KRN) [Adopted from: van Riel et al., 2004] (Cronbach alpha = 0.90, Composite reliability = 0.93, Average variance extracted = 0.77) Service firms' draw innovative service ideas more extensively from: (1 = strongly disagree, 2 = moderately disagree, 3 = slightly disagree, 4 = slightly agree, 5 = moderately agree, 6 = strongly agree) a) Acquisition of knowledge through collaboration (KRN 6) b) Using their ability in creating, acquiring and managing knowledge (KRN 3) c) Stimulating information exchange between departments (KRN 4) d) Stimulating information exchange with partners or suppliers (KRN 5) Economic factors (EF); Internal factors (IF) and Other (External) factors (OF) (For EF: Cronbach alpha = 0.92, Composite reliability = 0.944, Average variance extracted = 0.81) (For IF: Cronbach alpha = 0.93, Composite reliability = 0.955, Average variance extracted = 0.88) (For OF: Cronbach alpha = 0.78, Composite reliability = 0.873, Average variance extracted = 0.70) What impact do each of these have on your firm's service innovation activities? (1 = crucial, 2 = very significant, 3 = moderately significant, 4 = slightly significant, 5 = insignificant) [Adopted from: Gault & Pattinson, 1994, Sirilli & Evangelista, 1998 and Lonmo, 2005] Lack of financing (money) (EF 1) Innovation costs too high (EF 2) Pay-back period of innovation too long (EF 4) Excessive perceived risk (EF 5) Lack of skilled personnel (IF 4) Lack of management training in innovation management (IF 5) Organizational rigidness to change (IF 6) Regulatory constraints (government standard and regulations) by local government (OF 2) i) Regulatory constraints (government standard and regulations) by foreign government (OF 3) j) Risk of imitation by competitors (OF 4)

a) b) c) d) e) f) g) h)

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Service Innovation (SI) [Adopted from: Booz et al., 1982, Cooper & Kleinschmidt, 1993, Gounaris et al., 2003, and Chen & Tsou, 2007] (Cronbach alpha = 0.90, Composite reliability = 0.93, Average variance extracted = 0.68) For past few years, our company has often produced…….. (1 = strongly disagree, 2 = moderately disagree, 3 = slightly disagree, 4 = slightly agree, 5 = moderately agree, 6 = strongly agree) a) a service/product that was totally new to the company (SNCOMP1 SI) b) a service/product that allowed the company to enter new market (SNCOMP2 SI) c) a service/product that created a new product line for the company (SNCOMP4 SI) d) a service/product that was totally new to the market (customer) (SNCUST1 SI) e) a service/product that offered new features vs. competitive products (SNCUST2 SI) f) a service/product that required change in the customer's buying behavior (SNCUST3 SI) Appendix A.1. Performance Non-financial outcome (NFO) [Adopted from: Avlontis et al., 2001] (Cronbach alpha = 0.89, Composite reliability = 0.93, Average variance extracted = 0.76) (1 = strongly disagree, 2 = moderately disagree, 3 = slightly disagree, 4 = slightly agree, 5 = moderately agree, 6 = strongly agree) a) The new service improved the loyalty of company's existing customers (NFO 1). b) The new service had a positive impact on the company's perceived image (NFO 2). c) The new service enhanced the profitability of other products (NFO 3) d) The new service attracted significant number of new customers to the company (NFO 4). Financial outcome (FO) [Adopted from: O'Sullivan & Abela, 2007, Zou & Cavusgil, 2002, Avlontis et al., 2001] (Cronbach alpha = 0.90, Composite reliability = 0.93, Average variance extracted = 0.73) (1 = strongly disagree, 2 = moderately disagree, 3 = slightly disagree, 4 = slightly agree, 5 = moderately agree, 6 = strongly agree) a) b) c) d) e)

The new service exceeded its market share objectives (FO 9) The new service exceeded its sales objectives (FO 10) The profitability of new service exceeded its objectives (FO 11) The new service was profitable (FO 12) Total sales of new service were high (FO 13)

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