Designing service quality to survive: Empirical evidence from Chinese new ventures

Journal of Business Research 66 (2013) 1098–1107

Contents lists available at SciVerse ScienceDirect

Journal of Business Research

Designing service quality to survive: Empirical evidence from Chinese new ventures Y. Lisa Zhao a,⁎, C. Anthony Di Benedetto b, c a b c

Department of Global Entrepreneurship and Innovation, Henry W. Bloch School of Management, University of Missouri-Kansas City, Kansas City, MO United States Department of Marketing and Supply Chain Management, Temple University, 523 Alter Hall, 1801 Liacouras Walk, Philadelphia, PA 19122, United States High-Tech Entrepreneurial Marketing, Technische Universiteit Eindhoven, The Netherlands

a r t i c l e

i n f o

Article history: Received November 2012 Accepted April 2013 Available online 1 April 2012 Keywords: New service venture Service quality New venture survival Service scalability

a b s t r a c t The sizeable literature on service success suggests that service quality is a major success factor in that it drives customer retention and market share; the service provider's ability to capitalize on scale economies is also an antecedent of success. This literature, however, generally studies established firms and does not consider the special challenges faced by new service startups. In addition, the potentially complex interactions between service quality dimensions and scalability have not been studied. This study proposes a model of survival of new service ventures based on the dimensions of service quality and examines the contingency role of scalability, develops research hypotheses, and empirically tests them using a sample of 479 new service ventures in China. The study provides a rich theoretical understanding of the antecedents to new service venture survival and insight to new service managers who can better allocate their scarce resources to build quality and scalability effectively. © 2012 Elsevier Inc. All rights reserved.

1. Introduction Services are the dominant economic sector in most industrialized nations. About 80% of the U.S. gross domestic product comprises services. GNP percentages of 70% or higher are seen throughout Western Europe as well as in Canada, Japan, Australia, and elsewhere; other countries that have lagged behind, such as China and India, are rapidly increasing their investments in service infrastructure (Bitner & Brown, 2008). Due to the significant contribution of service to the global economy, service innovation has become high-priority for many firms, and will continue to be important well into the future (Jana, 2007). The Organization for Economic Cooperation and Development recently identified the need for more service innovation in order to boost the productivity of the service sector (OECD, 2005). This unprecedented growth in the service sector has encouraged entrepreneurs to initiate new service ventures and to face the associated risks (Bitner & Brown, 2008). However, new ventures face significant obstacles relative to larger and well-established competitors: they lack experience and reputation, and are very likely to lack financial resources (Williamson, 1985). Due to these challenges, new ventures are known to have a low survival rate (Dunne, Roberts, & Samuelson, 1989; Headd, 2003; Shook, Priem, & McGee, 2003). In a recent study of over 11,000 new technology ventures established between 1991 and 2000 in the U.S., Song, Podoynitsyna, van der Bij, and Halman (2008) find ⁎ Corresponding author at: Marketing Department, University of Missouri—Kansas City, Bloch School, 5100 Rockhill Road, Kansas City, MO 64110-2499, United States. E-mail addresses: [email protected] (Y.L. Zhao), [email protected] (C.A. Di Benedetto). 0148-2963/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2012.03.006

that only about 4000 firms (36%) with more than five full-time employees had survived after 4 years. After the fifth year, the survival rate fell further to below 22%, leaving fewer than 2500 surviving firms in the sample. Although there is a large literature that investigates the factors leading to service success, academic study of service development and management continues to lag behind manufactured-goods research (Hauser, Tellis, & Griffin, 2006) and, according to Jim Spohrer, director of service research at IBM's Almaden Research Center, service management remains relatively poorly understood (Spohrer, Maglio, Bailey, & Gruhl, 2007; Metters & Marucheck, 2007). Service quality is an important differentiator in a competitive business environment, and a key driver to firm performance (Parasuraman, Zeithaml, & Berry, 1985, 1988; Parasuraman, Berry, & Zeithaml, 1991; Zeithaml, Berry, & Parasumaran, 1996). Providing excellent service quality is critical to customer satisfaction and retention, and in turn to market share growth and profitability (Fornell & Wernerfelt, 1987; Buzzell & Gale, 1987; Fornell, Johnson, Anderson, Cha, & Bryant, 1996; Anderson, Fornell, & Lehmann, 1994; Piccoli, Brohman, Watson, & Parasuraman, 2004; Escrig-Tena & Bou-Llusar, 2005). Generally accepted dimensions of service quality from the frequently cited SERVQUAL scale include tangibles, reliability, responsiveness, assurance, and empathy (Parasuraman et al., 1988). Numerous studies confirm that these five service quality dimensions affect the service provider's performance (e.g., Parasuraman et al., 1991; Anderson et al., 1994; Easton & Pullman, 2001). Another important driver of service performance is its level of service scalability, defined as the service provider's ability to expand its initial concept and scope such that it can reach a larger market-space and achieve scale economies (Bharadwaj,

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

Varadarajan, & Fahy, 1993; Berry, Shankar, Parish, Cadwallader, & Dotzel, 2006). However, most of these studies examine established firms. Although some literature specifically studies service offers by new ventures and finds that excellent service quality helps these ventures build corporate reputation (Fombrun, 1996; Walsh & Beatty, 2007), no study to date has attempted to link the new service venture's ability to deliver quality service to its likelihood of survival. None of these studies addresses the issue of how a small-sized, resource-poor new venture can compete or even survive in the marketplace. Specifically, a new venture is unlikely to have the necessary resources to invest in all five dimensions of quality, and it is unclear how these should be prioritized such that the scarce resources are allocated to increase performance and maximize the chances of survival. Additionally, while some new service ventures may seek to establish successful niche positions and are unconcerned about pursuing scale economies, the ability to achieve economies of scale may be quite important to many new ventures. Scalability may be one of the most important ways for a small, new service provider to increase efficiency, reduce costs, and ultimately provide added value for its customers. Nevertheless, the extent to which scalability affects the service quality dimensions is still not understood. Considering that some services are by definition more scalable than others, it is possible that some dimensions of quality are more important in services where scalability is easy to attain, while others are more important in cases where economies of scale are difficult to achieve. This issue has received no research interest so far, despite its importance to service providers seeking how best to allocate their financial resources across quality dimensions. Service quality involves outcomes (what the customer actually receives) and the process (how the service is received) (Parasuraman et al., 1985). Although service quality measures customer satisfaction with a service actually delivered, managers of ventures must understand customer expectations, design the materials and facilities and train employees such that customers' expectations are met. The research objectives of this study are to investigate how the service quality design of a new venture is linked to its survival, to assess the relative impacts of each dimension of service quality, and to assess the contingency role of scalability on the service quality dimensions. The study builds a conceptual model based on the SERVQUAL literature and adopts the five SERVQUAL dimensions of service quality (Parasuraman et al., 1988). The research assesses the contingency role of scalability by determining its interaction with each of the service quality dimensions. The study develops research hypotheses from the conceptual model, validates the SERVQUAL scale, and tests the hypotheses using a sample of 479 new service ventures based in China. The Chinese market is undergoing economic reform, and small business initiatives are increasing rapidly in China. The Chinese economy is currently the second largest in the world, and China is second after the U.S. in terms of value of services produced (The World Factbook, https://www.cia.gov/library/publications/the-world-factbook/geos/ch. html). Yet China is still in the process of transitioning from a planned to a market economy, and the government continues to maintain significant ownership positions in many key firms (Liu, Luo, & Shi, 2003). A better understanding of how service quality affects service venture performance can potentially be gained by generalizing and adapting these models, developed in Western economies, to the Chinese business environment. The theoretical contribution of the results is a greater conceptual understanding of how service quality affects the survival of small, new service ventures and how scalability can magnify the positive effects of service quality on new venture survival. The results also provide insight to new service managers who can allocate their scarce resources to build quality and scalability in the most effective manner, such that they remain viable despite their limited resources.

1099

2. Theoretical framework Customer satisfaction is theorized to be related to business performance. According to the extant literature, increased customer satisfaction leads to more positive customer word-of-mouth, higher levels of customer loyalty and repeat purchase intentions, and, ultimately, improved business performance (Anderson et al., 1994; Anderson, Fornell, & Rust, 1997; Fornell, 1992; Gremler & Gwinner, 2000). Empirical studies confirm that customer satisfaction, as measured by standard scales (e.g., Fornell et al., 1996; Ittner & Larcker, 2003), is significantly related to business performance metrics (Morgan & Rego, 2006). Due to differences between the marketing of services versus the marketing of tangible goods, in particular the importance of the customer's direct encounter and interaction with service personnel, a service is evaluated not only by what customers receive but also the manner in which the services are received (Parasuraman et al., 1985). Service quality measures how well the service delivered matches customer expectations, and it takes such things as materials, facilities and personnel into account. Service quality is an important driver of the level of customer satisfaction and also of the customer's expectation of the service encounter (Parasuraman et al., 1985). Several studies in the literature define and operationalize service quality (e.g., Cronin & Taylor, 1992, 1994; Wolfinbarger & Gilly, 2003). Parasuraman et al. (1985) represent service quality as a combination of discrete elements; while their original version included as many as ten elements, the most common conceptualization of service quality comprised five dimensions, all highly correlated with service performance (Zeithaml et al., 1996). The five components of service quality are: tangibles (the appearance of the new venture's physical facilities, equipment, employees, printed matter, and so forth), reliability (the new venture's capability to perform the service accurately and dependably), responsiveness (a willingness to work with customers and provide prompt service), assurance (knowledge and courtesy of front line employees; their ability to inspire trust and confidence), and empathy (the ability to provide caring, individualized attention to customers) (Parasuraman et al., 1988, 1991). Parasuraman et al. (1988) also develop and validate the SERVQUAL scale, which assesses customer perceptions of a service provider's performance on all of these dimensions. If low levels of customer satisfaction are identified, this scale can be used to diagnose the weak points and identify where service personnel training must be focused to boost customer satisfaction. There is controversy about the suitability and generalizability of the SERVQUAL scale to different contexts, in particular whether it generalizes across different kinds of services (Babakus & Margold, 1992; Van Dyke, Prybutok, & Kappelman, 1999; Dabholkar, Shepherd, & Thorpe, 2000) or across different countries (Donthu & Yoo, 1998; Mattila, 1999; Zhao, Bai, & Hui, 2002; Lai, Hutchinson, Li, & Bai, 2007; Carrillat, Jaramillo, & Mulki, 2007). Other studies raise similar generalizability issues or other objections related to the use of SERVQUAL (Carman, 1990; Babakus & Boller, 1992; Smith, 1995; Silvestro, Fitzgerald, & Johnston, 1992). In order to reflect different business or cultural settings, the SERVQUAL scale is often adapted when applied in international studies (Kettinger & Lee, 1994; Dabholkar et al., 2000) or in studies across multiple types of service industries (Dabholkar, Thorpe, & Rentz, 1996). Nevertheless, numerous studies (including some international ones) confirm that the five SERVQUAL dimensions affect the service provider's business performance (e.g., Parasuraman et al., 1991; Anderson et al., 1994; Easton & Pullman, 2001; Piccoli et al., 2004; Escrig-Tena & Bou-Llusar, 2005; Lai et al., 2007), suggesting that the SERVQUAL is a predictor of service performance with pragmatic validity. Performance measures include market growth, profitability, market share, and the ability to charge premium prices (Zeithaml et al., 1996 summarizes this literature). Although service quality measures customer satisfaction with a service, it is important for new venture managers to understand the impact of service quality on new venture survival, and to design

1100

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

materials and facilities and to properly train the employees so as to meet the customer expectations. Consistent with the Cox proportional hazard models (Cox, 1972) for survival analysis, the following sections develop research hypotheses linking the service quality dimensions to a new service venture's failure hazard, which is the odds that a new service venture would fail at a given time.

2.1. Direct effects of service quality dimensions on new service venture failure hazard

a service provider high on this dimension has the ability to inspire trust, confidence, and a sense of security in customers (Parasuraman et al., 1988; Zeithaml et al., 1996). A perception that the front line employees are not expending the required amount of effort is related to low customer satisfaction and switching behavior (Keaveney, 1995; Mohr & Bitner, 1995). The new service provider lacks the financial resources to offer training to its employees and may find it difficult to compete with larger, more established firms on this dimension. As seen above, lacking an established reputation, the new service venture may suffer more from customer switch out than larger competitors. Thus:

The first five hypotheses express the direct impacts of the dimensions of service quality on new service venture failure. Since services are intangible, the customer cannot easily evaluate them and may not be able to distinguish them easily from competitors (Lovelock & Wirtz, 2007, Ch. 1 and Ch. 15). Since the new venture, by definition, has little or no brand equity, loyalty, or built-up trust among customers, it cannot fall back on its reputation to differentiate its service; furthermore, its limited size and available financial resources may make it difficult or impossible to achieve high enough levels of promotion or advertising to overcome its lack of recognition. Consequently it must work especially hard to provide tangible cues of the service's superiority, such as physical facilities, quality equipment, visual attractiveness of printed material, or even appearance of personnel (Parasuraman et al., 1991). Tangible evidence of quality is important for services as they leave lasting impressions in the customer's mind (Bitner, Brown, & Meuter, 2000); they are considered one of the most important customer “touchpoints” in managing the customer experience (Berry, Seiders, & Grewal, 2002). Lacking tangible evidence of quality, it would be difficult even for a large firm to communicate quality; it may be fatal for a small, resource-poor new venture. Thus:

The empathy dimension refers to a new venture's capability to provide caring, individualized attention to its customers (Parasuraman et al., 1988, 1991; Zeithaml et al., 1996). The service provider stresses customer convenience (such as convenient operating hours of the business) and a keen understanding of customers' needs. Service encounters often entail personalized face-to-face interactions between front line employees and customers, much more so than is the case for manufactured goods, and the social content of these interactions has been shown to significantly affect customer perceptions of overall service quality (Mittal & Lassar, 1996). Indeed, the behavior of front line employees is also considered a “touchpoint” of the customer experience that must be carefully managed (Berry et al., 2002). Failing to establish an empathetic attitude among its front line employees, the new service venture will find it hard to survive in the face of competition. Thus:

Hypothesis 1a. An increase in the tangible quality dimension is related to a decrease in the failure hazard faced by the new service venture.

Hypothesis 1e. An increase in the empathy quality dimension is related to a decrease in the failure hazard faced by the new service venture.

Reliability refers to the ability to perform the service right the first time when required by the customer, which also communicates quality (Parasuraman et al., 1988, 1991; Zeithaml et al., 1996). A new service venture startup may not have access to, or the ability to afford, sophisticated controls for operational inputs and outputs that minimize variability and increase service productivity; and may be less able to shield customers in the case of service failure and/or quickly implement a contingency plan to make up for poor initial service performance (Lovelock & Wirtz, 2007, Ch. 1 and Ch. 15). The small new venture then must work at building reliability into its service offering, as well as develop effective contingency planning, so that it has a chance to compete against larger, better-equipped firms. Thus, Hypothesis 1b. An increase in the reliability quality dimension is related to a decrease in the failure hazard faced by the new service venture. The responsiveness dimension of service quality is related to the service provider's willingness to assist customers and provide prompt, helpful service (Parasuraman et al., 1988, 1991; Zeithaml et al., 1996). Again, the larger firm is generally more able to afford front line employee training such that the customer receives quick, knowledgeable service at all times, and also is treated in a helpful manner by the employee. Lacking a positive reputation, familiarity, or brand loyalty, and hampered by smaller size, a perceived lack of responsiveness will likely hurt the new service venture provider more than the larger firm as the customer has little reason to return if dissatisfied. Thus: Hypothesis 1c. An increase in the responsiveness quality dimension is related to a decrease in the failure hazard faced by the new service venture. The assurance dimension implies that the service provider's front line employees are credible, trustworthy, competent, and courteous;

Hypothesis 1d. An increase in the assurance quality dimension is related to a decrease in the failure hazard faced by the new service venture.

2.2. Moderating effects of scalability Service research studies (e.g., Parasuraman et al., 1991; Bharadwaj et al., 1993) define service scalability as the service provider's ability to expand the initial scope of the business. Having a scalable business model is quite obviously advantageous for manufactured goods, perhaps less so for services. Services are often people-intensive, which makes it difficult to achieve scale advantages (Barber & Strack, 2005; Berry et al., 2006). Nevertheless, there are opportunities for service providers to achieve scalability. This can be done, for example, by becoming more capital-intensive (eBay can handle a huge growth of customers without having to hire too many new employees by investing in its computer technology), or by transferring some of the workload to the customer (Hertz #1 Gold card members can avoid the customer service counter and go right to their rental car) (Quinn & Gagnon, 1986; Berry et al., 2006). Other recommendations for achieving scalability include: scaling down and outsourcing (Quinn, Doorley, & Paquette, 1990); adopting new technology or automation (Quinn & Gagnon, 1986); or adopting quality-control and standardization practices (Upah, 1980). If a new service venture is able to overcome barriers to scalability, and achieve economies of scale, it will be able to increase production and market share without unduly increasing its costs. In fact, scale economies may be especially important to the survival of a new service venture, since its initial service output is by definition very small and it must ramp up production substantially (without escalating costs) to compete against larger competitors. If a new service venture can increase its scalability while improving service quality, it should further reduce the likelihood of failure. This effect would be reflected in a significant moderating effect, by which each service quality dimension's ability to reduce the

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

failure hazard will be magnified. Stated as moderating effect hypotheses, then: Hypothesis 2. An increase in scalability increases the negative effect of the (a) tangibility, (b) reliability, (c) responsiveness, (d) assurance, and (e) empathy service quality dimensions on the failure hazard.

3. Methodology 3.1. Study measures The SERVQUAL scale developed by Parasuraman et al. (1988) and validated extensively in the marketing and management literatures (e.g., Parasuraman et al., 1991) was adopted for this study. Also, case studies were conducted with executives from two firms (in engineering and software) located in Guangzhou, two firms (in computer and electronics parts) located in Shanghai, and four firms (in technical research and testing, programming, computer and computer peripheral equipment retailing, and computer maintenance and repair) located in Putian. The results confirm that the SERVQUAL measures are appropriate for measuring service quality in China and that the executives had no trouble in relating their experience to the measurements. For this study, rather than measuring customer perceptions of service quality, the level of service quality as designed by the venture was measured in terms of materials, facilities, and personnel. Respondents were asked to describe the extent to which their companies have the features described by the statements, using 1–7 Likert-type scales (1 = strongly disagree that the company has that feature, 7 = strongly agree that the company has that feature). (The questionnaire appears in Appendix A). The tangibles' scale (TANG) had four items, rating whether the new service venture used modern-looking equipment, had visually appealing facilities and materials associated with the services, and had neat-appearing service delivery people. The reliability scale (RELI) contained five scale items that measured if the service venture delivered services on time, and if it showed a sincere interest in solving the customer's problem, kept promises to customers, and performed the service right the first time and at the promised time. Four items of the responsiveness scale (RESP) assessed the new service venture's willingness to help customers, giving prompt services and responses to customers, and telling customers exactly when the service will be performed. Four items of the assurance scale (ASSU) rated the new service venture's ability to instill confidence in customers, its knowledge to answer customer questions, its courtesy, and its ability to make customers feel safe in their transactions. The empathy scale contained five items measuring the new service venture's ability to give customers their complete attention, to provide convenient operating hours, to understand customers' specific needs, to give customers individual attention, and to always have the best interest of the customers. Finally, the five items of the scalability scale (SCAL) rated the new service venture's opportunities for exploring and achieving scale economies, the extent to produce equipment-based and technology based rather than people-based services, and the ability to centralize service production facilities and critical equipment-intensive activities. 3.2. Data collection The initial sample consisted of lists of the new ventures in service industries provided by the Bureau of Industry and Business (official Chinese government business registration and management agency) in three Chinese cities: Guangzhou, Shanghai, and Putian. These lists consisted of 1754 new service ventures established in 2001 with legal registration in three service industries: 1) engineering, professional, scientific, technical, research and testing services; 2) computer and software related services (programming, computer processing,

1101

data preparation and processing, information-retrieval, computer facilities management, computer rental and leasing, computer maintenance and repair), and 3) wholesale trade and retail (electronic parts and equipment, home appliance, computer and computer peripheral equipment, and software). Data were collected from the founders of the new service ventures using a mail survey. Service quality data were collected from the founders of the ventures instead of consumers for two reasons. First, new ventures mostly likely do not have a customer base at the time of founding. It is, therefore, almost impossible to collect customers' perceived service quality of a new venture. It is nevertheless important for new venture founders to understand customer expectations, and to design the service to meet these expectations. Second, collecting data from founders conveys information about the founders' intended service quality, which is information that cannot be obtained from customers. To increase the response rate, a supporting letter from the Bureau of Industry and Business was obtained. The first mailing packet included a personalized letter to the founder of the company, the government endorsement letter, a business card, a copy of the survey, and a selfaddressed, individualized envelope with postage. After 2 weeks, a follow-up letter to each of the companies was sent. After 4 weeks, a second follow-up letter was sent with a complete package to each of the companies that had not responded. In some cases a third follow-up letter was sent. In the end, completed questionnaires from 479 firms were received, representing a response rate of 27%. The industries included in the final data are: 153 new ventures in industry 1 (engineering, professional, scientific, technical, research and testing services); 160 new ventures in industry 2 (computer and software related services); and 166 new ventures in industry 3 (wholesale trade and retail). In addition, characteristics about the founding team were also obtained, as previous studies show that new ventures with strong founding teams can generate strong early sales and can sustain these sales through time (Eisenhardt & Schoonhoven, 1990). Similarly, a “flawed” new venture team lacking key skills will find it difficult to sustain performance through time (Kamm, Shuman, Seeger, & Nurick, 1990). A review of the literature on founding teams (Ancona & Caldwell, 1992; McGee, Dowling, & Megginson, 1995; Marino & De Noble, 1997; Murray, 1989) suggested five potentially important founding team variables: team size (number of founding members), years of startup experience, years of industry experience, years of marketing experience, and years of service design experience. These were collected as additional control variables.

3.3. New venture survival data collection After the initial survey data collection, each company's survival status (“health” check) was tracked every December through the Chinese Bureau of Industry and Business. If a firm was out of business at the time of the health check, the company was classified as “dead” in the calendar year and the life of the firm was recorded. If a firm was still operating at the time of the health check, the firm was classified as “alive” and life of the firm was not recorded. If a company had merged with or sold to another company at the time of the health check, the company was classified as “censored” and the life of the firm is recorded. The life of a firm is the number of years the firm operated as an independent business. For example, if a firm was founded in 2001 and was found “dead” or “censored” at the data collection in December 2002, the life of the company is 1 year. In 2007, the last year of health checking, 105 firms were still operating as independent businesses, 56 firms had merged or sold either that year or before that year, and 318 firms had died either that year or before that year. For a firm that was still operating at the conclusion of our data collection, the firm is classified as “censored” and life of the firm is recorded (as 6). Table 1 displays the details of the survival data.

1102

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

Table 1 Company survival data (sample size = 479).

Table 2b Confirmatory factor analysis results.

Year

Number of firm failed

Number of firms sold or merged

Number of firms operating

Factor

2001 (founding year) 2002 2003 2004 2005 2006 2007 At the end of the study At the end of the study percentage

X 110 72 44 21 29 42 318 66.39%

X 2 17 6 1 4 26 56 11.69%

479 367 278 228 206 173 105 105 21.92%

Scalability (SCALE)

4. Data analyses and results 4.1. Measurement validation First, an exploratory factor analysis, was performed, generating six dimensions with each item loaded to the right construct as shown in Table 2a. Using standard procedures as recommended by Anderson and Gerbing (1988), the SERVQUAL and scalability scale items were further validated using a confirmatory factory analyses (CFA). Table 2b reports the results from CFA. The unidimensionality and convergent validity were established by factor loadings, the overall fit of the measurement model, the standardized residuals, and Cronbach alpha reliabilities suggested by Gerbing and Anderson (1992) and Jöreskog and Sörbom (1999). All items have significant (at pb 0.01) loadings on their expected constructs. The overall fit indices for the measurement model are: χ2 = 803.5078; df= 300; GFI= 0.8925; CFI =0.9247; RMSEA=0.0593. 90% of the elements of standardized residual covariance matrix are less than 2.58. The smallest alpha is 0.72. Discriminant validity was examined using two methods: chi-square difference tests and comparing AVE with shared variance (Gerbing & Anderson, 1992; Fornell & Larcker, 1981). The chi-square difference Table 2a Exploratory factor analysis loadings.

SCALE4 SCALE1 SCALE2 SCALE5 SCALE3 RELI1 RELI3 RELI5 RELI2 RELI4 EMPA2 EMPA1 EMPA3 EMPA4 EMPA5 TANG4 TANG1 TANG3 TANG2 RESP4 RESP1 RESP2 RESP3 ASSU1 ASSU2 ASSU3 ASSU4

Scalability

Reliable

Empathy

Tangible

Responsive

Assurance

0.73 0.70 0.67 0.66 0.66 0.04 −0.04 0.01 −0.01 0.01 0.23 0.14 0.15 0.17 0.14 0.12 0.15 0.14 0.10 0.22 0.24 0.31 0.15 0.11 0.08 0.20 0.03

0.05 0.00 − 0.08 0.04 0.01 0.84 0.77 0.71 0.69 0.63 0.18 0.09 0.08 0.02 0.17 0.08 0.02 0.17 0.03 0.10 −0.02 −0.01 −0.04 0.03 −0.03 0.03 −0.14

0.10 0.32 0.11 0.23 0.25 0.05 0.02 0.11 0.11 0.13 0.76 0.67 0.65 0.61 0.54 0.12 0.14 0.05 0.11 0.17 0.19 0.16 0.05 0.17 0.25 0.03 0.16

0.12 0.12 0.16 0.13 0.10 0.03 0.04 − 0.05 0.15 0.13 0.07 0.10 0.08 0.09 0.14 0.83 0.80 0.71 0.70 0.15 0.16 0.16 0.08 0.11 0.09 0.00 0.04

0.17 0.22 0.21 0.20 0.22 0.00 0.12 −0.01 0.04 −0.14 0.12 0.25 0.04 0.08 0.13 0.04 0.21 0.09 0.16 0.86 0.78 0.76 0.42 0.05 0.13 −0.12 0.16

0.12 0.08 0.09 0.22 0.11 0.05 −0.02 −0.01 −0.03 −0.10 0.14 0.13 0.06 0.22 0.21 0.02 0.07 0.11 0.07 0.01 0.02 0.00 0.17 0.71 0.63 0.54 0.51

Note: orthogonal rotation was used; the bold numbers indicate factor loadings associated with the items load to the construct.

Items

Standardized factor loading

Factor

SCALE1 0.8401 Tangibles SCALE2 0.7537 (TANG) SCALE3 0.7627 SCALE4 0.6744 SCALE5 0.7289 Reliability RELI1 0.8691 Responsiveness (RELI) RELI2 0.7066 (RESP) RELI3 0.8126 RELI4 0.6049 RELI5 0.6555 Empathy EMPA1 0.7479 Assurance (EMPA) EMPA2 0.9075 (ASSU) EMPA3 0.5851 EMPA4 0.6390 EMPA5 0.6573 Measurement model fit summary χ2 = 803.5078; d.f. = 300; χ2/d.f. = 2.6784 GFI = 0.8925; CFI = 0.9247; RMSEA = 0.0593 Pairwise χ2 test: χ2 > 316; d.f. = 1; p b 0.0001

Items

Standardized factor loading

TANG1 TANG2 TANG3 TANG4

0.9401 0.7569 0.6272 0.7553

RESP1 RESP2 RESP3 RESP4

0.9120 0.8957 0.4160 0.8966

ASSU1 ASSU2 ASSU3 ASSU4

0.7709 0.7283 0.5188 0.5480

test compares two models. The first model allows the two factors to covary. The second model collapses the two factors. The discriminant validity is demonstrated if the chi-square value is significantly lower for the first model than for the second model. Pair-wise tests for each pair of constructs were carried out. All of the tests show significant chi-square differences (smallest χ2 difference=316) at p b 0.001. Discriminant validity was further investigated by comparing the AVE with shared variance between different constructs. Comparing the square root of AVE (displayed in Table 3) with correlation coefficients (displayed in Table 3) reveals that the smallest square root of AVE (0.65) is bigger than the largest correlation coefficient (0.48). Therefore, the discriminant validity is further confirmed (Fornell & Larcker, 1981). After establishing unidimensionality, convergent validity, and discriminant validity, each construct was measured by the average of the items that are loaded to the construct. Table 3 presents the basic statistics of the constructs and founding team variables. 4.2. Survival analysis This study applies the Cox proportional hazard model to the survival analysis of new ventures. The explanatory variables are the five dimensions of a new venture's service quality and service scalability. The study also includes industry dummies and five founding team variables as control variables. Since all firms that fail during a particular year are recorded as failing at the same time (even though in reality they will have failed at different times during the year), ties may result due to imprecise recording. The study used the “exact” method to handle ties (Kalbfleisch and Prentice, 1980). The exact method includes all possible orders of failures in the semi-likelihood function. For example, if three firms (A, B, and C) failed during a particular year, the semi-likelihood function would include orders A-B-C, A-C-B, B-A-C, B-C-A, C-A-B, and C-B-A (see Allison, 1995 for details for Cox regression and the Kalbfleisch and Prentice method). 4.3. Models, results and discussion Table 4 presents the estimates from four Cox proportional regression models. Service quality variables and scalability are centered around the means. Model 1 includes only industry dummies as the covariates; Model 2 adds the five founding team variables; Model 3 adds five service quality dimensions; and Model 4 adds scalability and two interaction terms.

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

1103

Table 3 Means, standard deviation, construct reliability, and correlations.

Note: 1. All correlation coefficients are significantly different from zero at p = 0.05 except those that are denoted with ns. 2. The bold diagonals in the selected boxes are square roots of average variance extracted (AVE) from the confirmatory factory analysis.

The study tested the interactions of scalability with all five service quality dimensions, and for the simplicity of presentation, Model 4 only includes the two interaction terms that are significant: interaction between scalability and tangible and interaction between scalability and responsiveness. Chi-square (χ2) tests on the likelihood ratios suggest that Model 2 fits the data significantly better than Model 1; Model 3 fits the data significantly better than Model 1 and Model 2; Model 4 (the full model presented in Table 4) fits the data significantly better than other models. Therefore, Model 4 is retained and all remaining discussion is drawn from the Model 4 results. The regression coefficients shown in Table 4 are Cox model coefficients that estimate the hazard ratio (or conditional odds of failure): a negative coefficient indicates a decrease in failure rate while a positive coefficient indicates an increase. Also shown in Table 4 are hazard ratios. The value (1 — hazard ratio) can be interpreted directly as the change in the odds of survival related to a one-unit increase in the covariate (the direction of the change can be inferred from the sign of the regression coefficient).

The Model 4 results in Table 4 show no significant industry effects (none of the industry regression coefficients is significant at the 0.05 level). Only one of the founding team variables, founding team industry experience, significantly affected new service venture survival (coeff. = −0.03, significant at p b 0.01, hazard ratio = 0.97). That is, for each additional year of industry experience, there was a (1–0.97) = 3 percent decrease in the odds of failure. Of the five service quality dimensions, four were significantly related to new service venture survival when the scalability is at the mean level (4.23). As Table 4 indicates, the coefficients for reliability, responsiveness, assurance, and empathy are −0.12, −0.24, −0.22, and −0.18 respectively, all significant at p b 0.01 (the coefficient for tangibles is not significant). The corresponding hazard ratios are 0.89, 0.79, 0.81 and 0.84 respectively. Using the (1 — hazard ratio) calculation as above, one can interpret these findings to mean that a one-unit increase in reliability, responsiveness, assurance, and empathy is related to an 11%, 21%, 19%, and 16% decrease in the odds of failure (or increase in odds of survival) respectively. These findings support Hypothesis 1b, Hypothesis 1c, Hypothesis 1d, and Hypothesis 1e; Hypothesis 1a is not

Table 4 Cox proportional hazard rate regression results. Model 1 Coef. Industry 1 Industry 2 Industry 3 Team size Startup experience Industry experience Marketing experience Service design experience Scalability (SCAL) Tangibles (TANG) Reliability (RELI) Responsiveness (RESP) Assurance (ASSU) Empathy (EMPA) SCAL × TANG SCAL × RESP − 2 log likelihoodd

−0.30 − 0.16 0.00

Model 2 Std

b

1584.35

0.14 0.13 –

Hazard ratio 0.74 0.86 –

a

Coef. b

− 0.30 − 0.07 0.00 0.01 0.06 −0.06c − 0.02c − 0.06c

1482.74c

Model 3 Std

Hazard ratio

0.14 0.13

0.74 0.93

0.06 0.10 0.01 0.01 0.02

1.01 1.07 0.94 0.98 0.94

a

Model 4

Coef.

Std

Hazard ratio

−0.12 − 0.02 0.00 −0.09 − 0.19 −0.04c −0.01 0.02 −0.20c −0.06 −0.11b −0.16c −0.20c −0.19c

0.14 0.13 – 0.07 0.11 0.01 0.01 0.02 0.06 0.08 0.04 0.05 0.05 0.06

0.89 0.98 – 0.92 0.83 0.96 0.99 1.02 0.82 0.94 0.90 0.85 0.82 0.83

1397.21c

a

Coef.

Std

Hazard ratioa

−0.17 − 0.02 0.00 − 0.05 −0.18 − 0.03c −0.01 0.01 − 0.20c − 0.03 − 0.12c − 0.24c − 0.22c − 0.18c 0.09b −0.16c 1376.33c

0.14 0.13 – 0.07 0.11 0.01 0.01 0.02 0.05 0.08 0.04 0.05 0.05 0.06 0.04 0.04

0.85 0.99 – 0.95 0.84 0.97 0.99 1.01 0.82 0.97 0.89 0.79 0.81 0.84 1.10 0.85

Note: −2 log likelihood of the model without covariates = 1589.03. a 1-hazard ratio is the reduction in the odds of failure by one unit increase in the covariate. For example, hazard ratio associated with scalability in model 3 is 0.82, which implies that one unit increase in scalability leads to (1–0.82) or 18% reduction in the odds that the venture would fail. If the hazard ratio is smaller than 1, an increase in the covariate leads to decrease in the odds of failure, and if it is bigger than 1, an increase in the covariate leads to increase in the odds of failure. b p b 0.05. c p b 0.01. d Chi square test was used to compare the models where chi-square for model k = (− 2log likelihood of model k) − (− 2log likelihood of model k-1). The model without covariates was used to test the model significance for Model 1.

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

1.5

1

Log Hazard

supported. Similarly, the coefficient for scalability is significant and negative (−0.20, significant at p b 0.01), thus a one-unit increase in scalability is related to an 18% decrease in the odds of failure. (The coefficients in Table 4 are correct assuming all other dimensions are held at their mean levels). The results only partially support Hypothesis 2. The model initially included all five interactions between scalability and service quality dimensions. However, three of them were not statistically significant, and they were excluded from the final model. Table 4 shows that two of the moderation effects are significant. One significant moderation is in an unexpected direction: the interaction between scalability and tangibles is positive (0.09, significant at p b 0.05), indicating that achieving scalability decreases the impact of tangibility on survival. This finding contradicts Hypothesis 2(a). The other significant interaction between scalability and responsiveness is negative (− 0.16, significant at p b 0.01), which indicates that achieving scalability increases the impact of responsiveness on survival (that is, in reducing the odds of failure). This finding supports Hypothesis 2(c). No support is found for the other three moderation hypotheses. To understand the implications of the significant moderations, consider the results shown in Table 5. The Model 4 results showed that scalability negatively moderating the negative effects of the responsive dimension of failure hazard (i.e., increases the impact of responsiveness on survival), but positively moderates the negative effect of tangibles on failure hazard (Also recall that a significant main effect of tangibility was not found.). These effects can be better understood by examining the moderations shown in Table 5. This table shows that there is a significant interaction between tangibility and scalability, so correct assessment of the main effect of tangibility on failure rate must be done within the context of scalability. At very low levels of scalability (SCAL = 1 or 2), an increase in tangibles increases the survival odds, but this effect is not found for higher levels of scalability. To be precise, at SCAL = 1 and 2, a one-unit increase in tangibility decreases the odds of failure by 28% and 21% respectively. Thus, it would be more correct to say that the main effect hypothesis for tangibility, Hypothesis 1a is supported, but only in the case of low scalability. One possible reason for this finding is that the scalability scale includes many items that measure whether the service is technology or equipment based, or if the service facilities or activities can be centralized. When scalability (as measured by these items) is high, it is possible that there is lower need for customer interaction, and thus the impact of tangibles is reduced. Table 5 also provides additional insight into the interaction between scalability and responsiveness. At very low levels of scalability (SCAL = 1), an increase in responsiveness actually increases failure odds. In fact, at SCAL = 1, a one-unit increase in responsiveness increases the odds of failure by 32%. At higher levels of scalability (SCAL = 4 or higher), a significant positive effect of responsiveness occurs. At maximum scalability (SCAL = 7), the effect of a one-unit increase in responsiveness is a 50% increase in survival odds. This interaction is in the direction hypothesized in Hypothesis 2(c) (greater scalability increases the positive impact of responsiveness on survival). Figs. 1 and 2 present the impacts of tangibles and responsiveness at different levels of scalability and graphically illustrate these main and interaction effects discussed above. Adding the significant interaction effects to the model provides a richer understanding of the

Scale=1 Scale=2

0.5

Scale=3 Scale=4

0

Scale=5 Scale=6

-0.5

Scale=7

-1 1

2

3

4

5

6

7

Degree of Tangibles Note: Dashed lines indicate that the slopes are not significant Fig. 1. Impact of tangibles on failure hazard at different levels of scalability.

2.5 2 1.5 Scale=1

1

Log Hazard

1104

Scale=2

0.5

Scale=3

0

Scale=4

-0.5

Scale=5 Scale=6

-1

Scale=7

-1.5 -2 -2.5 1

2

3

4

5

6

7

Degree of Responsiveness Note: dashed lines indicate that the slopes are not significant Fig. 2. Impact of responsiveness on failure hazard at different levels of scalability.

effectiveness of each of the service quality dimensions on the odds of new venture survival rate: Tangibles if scalability is low, investing in tangibles has a positive effect on new venture survival. At moderate or high levels of scalability, it has no effect. Responsiveness if scalability is low, investing in responsiveness can have a positive effect on new venture failure. The benefits of investing in responsiveness are only present if scalability is moderate or high. Assurance, Empathy, and Reliability each of these has a positive effect on new venture survival (the magnitudes of these effects are in the order shown), unaffected by scalability.

5. Discussion and conclusion Table 5 Decrease in the odds of failure from one unit increase in tangibles or responsiveness at different level of scalability. SCAL = 1 SCAL = 2 SCAL = 3 SCAL= 4 SCAL= 5 SCAL= 6 SCAL = 7 TANG 0.28 RESP −0.32

0.21 n.s

n.s n.s.

n.s. 0.17

n.s. 0.31

n.s. 0.41

n.s 0.50

This study uses the service quality literature and the literature that supports the cost-saving benefits of scalability to develop a model of how the small new service venture can best allocate its scarce resources. The established service quality literature indicates that higher performance on all five service quality dimensions is related to more satisfied customers, more customer retention, fewer customer acquisition costs, and improved performance by the service provider (typically measured

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

by profit or market share) (Parasuraman et al., 1988; Zeithaml et al., 1996). In the case of the small, cash-starved and vulnerable new venture, a salient performance measure, quite simply, is survival. The research model of this study specifically investigates the main effects of each service quality dimension on survival independently, and also separately estimates the moderating effect of scalability on each of these impacts. This study collects and analyzes new venture survival data from three industries over a six-year period. Our results make a conceptual and theoretical contribution to the service quality literature and also to the new venture literature. Our theoretical contribution is not so much about survival or performance per se; rather, it is about the link between service quality and new venture survival. The results provide a deeper and more detailed understanding of how service quality affects new service venture survival. The study also provides managerial insight about how the new venture should prioritize its limited resources in order to improve service quality most efficiently and to take advantage of scalability. The study achieves this insight by obtaining our service quality data not from customers, but from the managers of the firms themselves, who can then use this information to link the firm's strategy in service quality delivery to new venture performance. As would be expected from the SERVQUAL literature, service quality is important for new ventures to survive the early, perilous years. However, the individual effects of each service quality dimension are different; second, their interaction patterns with scalability are different. Generally, the results suggest that a monolithic “investment in service quality” is not appropriate. Certain dimensions of service quality have a greater impact on new venture survival than others. A new venture should strategically locate its scarce resources to the dimensions that have the biggest impact on its survival. According to our results, assurance, empathy, and reliability are important for all new ventures regardless of the ventures' scalability, with assurance being the most important dimension. However, investing resources in tangibles and responsiveness does not necessarily increase service venture survival and can possibly even have adverse consequences. Further, the analysis of the moderating effects shows that the investment decision is difficult, and that the potential for service scalability needs to be fully considered to make the optimum investment. For example, the findings show that if a new service venture can achieve scalability with its service offering, it is wise to invest in strengthening responsiveness, since that dimension is closely related to new service venture success. Another reason to increase spending on responsiveness is that this dimension often requires fewer financial resources than tangibles. Scalability also has other added benefits, such as reducing costs of providing the service and increasing revenues per customer. However, a large investment in responsiveness does not help the service firm, if it is not capable of exploiting this responsiveness over a large scale of operations. In the case where scaling up is not feasible, the new service venture should invest in tangibles rather than responsiveness. That is, a new service provider unable to capitalize on scalability must find other ways to increase likelihood of success, since responsiveness seems to be most beneficial to service providers who can exploit it on a large scale. The results show that the best investment in this case is to build up the tangibility dimension, as this is the most effective way to increase the perception of quality among its customers. While previous studies have demonstrated the importance of scalability, this is not always a reachable goal, due to human or financial resource constraints. In some people-intensive service businesses, service usage may be very difficult to separate from production, and front line employees are the primary cost center as well as the creator of customer value (Barber & Strack, 2005). This might be the case in a medical practice, for example, where the immediate contact with the physician is a critical part of the service encounter and would be difficult to duplicate on a large scale (although consultations by phone or internet, or expert systems built on physicians' expertise and knowledge, allow a certain

1105

degree of scalability). A good example of a firm that has been able to build scalability despite its people-intensiveness is the tax accountancy H&R Block. Not long ago, growth into different geographic regions required hiring and training new staff, but more recently it has complemented traditional growth with TaxCut, a tax preparation software that allows users to do their own taxes (Barber & Strack, 2005). By contrast, other services such as fast food franchises or online services can more easily achieve scale economies. The research findings presented here provide guidance to the service provider on how best to invest in building specific dimensions of service quality, based on how scalable their service is. The study has several limitations. The research examines new service providers in only three service industries, and only one country (indeed, only a limited number of cities and regions within a very large and diverse country). It is unknown how well the findings generalize to other services or geographic locations. Previous studies also have found that environmental condition also affect new venture performance. It is not clear how the service quality dimensions affect new service venture survival under different environmental conditions. Still, by constraining the study to one country, the analysis is not confounded by the presence of cross-national or cross-cultural differences which may limit generalizability of the SERVQUAL scale (Babakus & Margold, 1992; Donthu & Yoo, 1998; Mattila, 1999; Van Dyke et al., 1999; Dabholkar et al., 2000). Previous studies have been equivocal as to how well SERVQUAL generalizes to the Chinese context (Zhao et al., 2002; Lai et al., 2007); the results of this study support the findings of Lai et al. (2007) who found that SERVQUAL was useful in the Chinese cultural context. Regarding generalizability to other service contexts, in several studies of retail services, fast food services, and others, the original fivefactor SERVQUAL scale did not replicate (see discussion in Dabholkar et al., 1996). This may be because the SERVQUAL scale was primarily derived for “pure services” as opposed to services with an important product component such as retail. As Dabholkar et al. (1996) notes, dimensions such as quality and availability of the merchandise may be important drivers of retail service quality, though these drivers are perhaps not well captured by the SERVQUAL dimensions. For these reasons, several researchers have made adaptations to SERVQUAL before administering this scale (see Carman, 1990; Dabholkar et al., 2000). In the three specific service industries we studied, this study did not find evidence that such adaptation was necessary, as the original five dimensions replicated. Nevertheless, it is possible that the findings would not replicate to other service industries, especially if major adaptations would need to be made to the SERVQUAL scale. There may also be many other direct or moderating factors that may influence the survival rate of new ventures (Dabholkar et al., 1996) and that the model can only explain a certain percentage of new venture survival rates. Different kinds of data and different modeling techniques may provide complementary insights. For example, one possible research direction would be to include archival macro-economic data for each time period, and examine the potential moderating roles they might play in new venture survival. These are all promising areas for future research. Acknowledgment The authors thank Michael Song for contributing to the theoretical model, research design, and data collection for this research project. Appendix A. Research variables and study measures Notes: The statements were in random order in the survey. The construct names and variable labels were not part of the survey. Service quality: adopted from Parasuraman et al. (1991). Listed below are some statements which may be related to your company's service quality during the first year of the operation. For each

1106

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107

statement, please indicate the extent to which you believe your company has the feature described by the statement. Please indicate your degree of agreement or disagreement by circling a number from one (1) to seven (7) on the scale to the right of each statement. Here: 1 = strongly disagree that your company has that feature, 7 = strongly agree that your company has that feature. You may circle any of the numbers in the middle that show your degree of agreement or disagreement. There are no right or wrong answers. All we are interested in is a number that best shows your perceptions. From the beginning of our new venture, our founders insist that,

Tangibles (TANG): Cronbach alpha = 0.87. TANG1 We have modern-looking equipment in all service offering facilities. TANG2 The service offering facilities are visually appealing. TANG3 Materials associated with the service (such as pamphlets or statements) are visually appearing TANG4 Our service delivery people are neat-appearing. Reliability (RELI): Cronbach alpha = 0.85. RELI1 When we promise to do something by a certain time, we do so. RELI2 When our customers have a problem, we show a sincere interest in solving it. RELI3 We perform the service right the first time RELI4 We provide the service at the time we promise to do so. RELI5 We insist on error-free records. Responsiveness (RESP): Cronbach alpha = 0.84 RESP1 We tell customers exactly when the service will be performed. RESP2 We are always willing to help customers. RESP3 We are never too busy to respond to customer requests. RESP4 We give prompt service to customers. Assurance (ASSU): Cronbach alpha = 0.73 ASSU1 We do everything possible to instill confidence in customers. ASSU2 Our employees have the knowledge to answer customer questions. ASSU3 We are consistently courteous with customers. ASSU4 We do everything possible to ensure that our customers feel safe in their transactions with us. Empathy (EMPA): Cronbach alpha = 0.83 EMPA1 We have employees who give customers personal attention. EMPA2 We give customers individual attention. EMPA3 We have operating hours convenient to our customers. EMPA4 We always have the customers' best interests at heart. EMPA5 We understand the specific needs of our customers. Scalability, new items developed from Bharadwaj et al. (1993), Cronbach alpha = 0.87 SCAL1 Related to other services in our industry, opportunities for exploring scale economies of our services are great. SCAL2 Related to other services in our industry, our services are more equipment-based service than people-based service. SCAL3 We can easily achieve economies of scale by centralizing our service production facilities. SCAL4 We can easily achieve economies of scale by centralizing certain critical (e.g., equipment-intensive) activities. SCAL5 Related to other services in our industry, our services are more technology-based service than people-based service.

References Allison, P. D. (1995). Survival analysis using SAS: A practical guide. : SAS Publishing. Ancona, D. G., & Caldwell, D. F. (1992). Demography and design: Predictors of new product team performance. Organization Science, 3(3), 321–341. Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58(7), 53–86. Anderson, E. W., Fornell, C., & Rust, R. T. (1997). Customer satisfaction, productivity, and profitability: Differences between goods and services. Marketing Science, 16(2), 129–145. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Babakus, E., & Boller, G. W. (1992). An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24, 253–268. Babakus, E., & Margold, W. G. (1992). Adapting the SERVQUAL scale to hospital services: An empirical investigation. Health Services Research, 26(6), 767–786. Barber, F., & Strack, R. (2005, June). The surprising economics of ‘people business’. Harvard Business Review, 80–90. Berry, L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1–17.

Berry, L. L., Shankar, V., Parish, J. T., Cadwallader, S., & Dotzel, T. (2006, Winter). Creating new markets through service innovation. MIT Sloan Management Review, 56–63. Bharadwaj, S. G., Varadarajan, P. R., & Fahy, J. (1993). Sustainable competitive advantage in service industries: A conceptual model and research propositions. Journal of Marketing, 57(4), 83–99. Bitner, M. J., & Brown, S. W. (2008). The service imperative. Business Horizons, 51, 39–46. Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology infusion in service encounters. Journal of the Academy of Marketing Science, 28(1), 138–149. Buzzell, R. D., & Gale, B. T. (1987). The PIMS principles: Linking strategy to performance. New York: Free Press. Carman, J. M. (1990). Consumer perceptions of service quality: An assessment of the SERVQUAL dimensions. Journal of Retailing, 66(1), 33–55. Carrillat, F. A., Jaramillo, F., & Mulki, J. P. (2007). The validity of the SERVQUAL and SERVPERF scales: A meta-analytic view of 17 years of research across five continents. International Journal of Service Industry Management, 18(5), 472–490. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society Series B — Statistical Methodology, 34(2), 187–220. Cronin, J. J., & Taylor, S. A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68. Cronin, J. J., & Taylor, S. A. (1994). SERVPERF vs. SERVQUAL: Reconciling performancebased and perceptions-minus-expectations measurement of service quality. Journal of Marketing, 58(1), 125–131. Dabholkar, P. A., Shepherd, C. D., & Thorpe, D. I. (2000). A comprehensive framework for service quality: An investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139–173. Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A measure of service quality for retail stores: Scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3–16. Donthu, N., & Yoo, B. (1998). Cultural influences on service quality expectations. Journal of Service Research, 1, 178–186. Dunne, T., Roberts, M. J., & Samuelson, L. (1989). The growth and failure of U.S. manufacturing plants. Quarterly Journal of Economics, 104, 671–698. Easton, F. F., & Pullman, M. E. (2001). Optimizing service attributes: The seller's utility problem. Decision Sciences, 32(2), 251–275. Eisenhardt, K. M., & Schoonhoven, C. B. (1990). Organizational growth: Linking founding team, strategy, environment, and growth among U.S. semiconductor ventures, 1978–1988. Administrative Science Quarterly, 35, 504–529. Escrig-Tena, A. B., & Bou-Llusar, J. C. (2005). A model for evaluating organizational competencies: An application in the context of a quality management initiative. Decision Sciences, 36(2), 221–257. Fombrun, C. J. (1996). Reputation: Realizing value from the corporate image. Boston: Harvard Business School Press. Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56(1), 6–21. Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7–18. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39–50. Fornell, C., & Wernerfelt, B. (1987). Defensive marketing strategy by customer complaint management: A theoretical analysis. Journal of Marketing Research, 24(4), 337–346. Gerbing, D. W., & Anderson, J. C. (1992). Monte Carlo evaluations of goodness of fit indices for structural equations models. Sociological Methods and Research, 21, 132–160. Gremler, D. D., & Gwinner, K. P. (2000). Customer–employee rapport in service relationships. Journal of Service Research, 3(1), 82–104. Hauser, J., Tellis, G. J., & Griffin, A. (2006). Research on innovation: A review and agenda for marketing science. Marketing Science, 25(6), 687–717. Headd, B. (2003). Redefining business success: Distinguishing between closure and failure. Small Business Economics, 21(1), 56–61. Ittner, C., & Larcker, D. (2003, November–December). Coming up short on nonfinancial performance measurement. Harvard Business Review, 88–95. Jana, R. (2007, March 29). Service innovation: The next big thing. Business Week. Jöreskog, K., & Sörbom, D. (1999). LISREL 8: User's reference guide (2nd Ed.). Chicago: Scientific Software International. Kalbfleisch, J. D., & Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley. Kamm, J. B., Shuman, J. C., Seeger, J. A., & Nurick, A. J. (1990, Summer). Entrepreneurial teams in new venture creation: A research agenda. Entrepreneurship Theory and Practice, 14, 7–15. Keaveney, S. M. (1995, April). Customer switching behavior in service industries: An exploratory study. Journal of Marketing, 59, 71–82. Kettinger, W. J., & Lee, C. (1994). Perceived service quality and user satisfaction with the information services function. Decision Sciences, 25(5–6), 737–766. Lai, F., Hutchinson, J., Li, D., & Bai, C. (2007). An empirical assessment and application of SERVQUAL in mainland China's mobile telecommunications industry. International Journal of Quality and Reliability Management, 24(3), 244–262. Liu, S. S., Luo, X., & Shi, Y. Z. (2003). Market-oriented organizations in an emerging economy: A study of missing links. Journal of Business Research, 56(6), 481–491. Lovelock, C., & Wirtz, J. (2007). Services marketing: People, technology, strategy (sixth edition). Upper Saddle River, NJ: Prentice-Hall. Marino, K. E., & De Noble, A. F. (1997). Growth and early returns in technology-based manufacturing ventures. Journal of High Technology Management Research, 8(2), 225–242.

Y.L. Zhao, C.A. Di Benedetto / Journal of Business Research 66 (2013) 1098–1107 Mattila, A. S. (1999). The role of culture in the service evaluation process. Journal of Service Research, 1, 250–261. McGee, J. E., Dowling, M. J., & Megginson, W. L. (1995). Cooperative strategy and new venture performance: The role of business strategy and management experience. Strategic Management Journal, 16(7), 565–580. Metters, R., & Marucheck, A. (2007). Service management: Academic issues and scholarly reflections from operations management researchers. Decision Sciences, 38(2), 195–214. Mittal, B., & Lassar, W. M. (1996). The role of personalization in service encounters. Journal of Retailing, 72(1), 95–109. Mohr, J. A., & Bitner, M. J. (1995). The role of employee effort in satisfaction with service transactions. Journal of Business Research, 32(3), 239–252. Morgan, N. A., & Rego, L. L. (2006). The value of different customer satisfaction and loyalty metrics in predicting business performance. Marketing Science, 25(5), 426–439. Murray, A. I. (1989). Top management group heterogeneity and firm performance. Strategic Management Journal, 10, 125–141. Organization for Economic Cooperation and Development (2005). Promoting innovation in services. Paris: OECD. Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420–450. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985, Fall). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49, 41–50. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–39. Piccoli, G., Brohman, M. K., Watson, R. T., & Parasuraman, A. (2004). Net-based customer service systems: evolution and revolution in web site functionalities. Decision Sciences, 35(3), 423–455. Quinn, J. B., Doorley, T. L., & Paquette, P. C. (1990, March–April). Beyond products: Services-based strategy. Harvard Business Review, 58–67.

1107

Quinn, J. B., & Gagnon, C. E. (1986, November–December). Will services follow manufacturing into decline? Harvard Business Review, 95–103. Shook, C. L., Priem, R. L., & McGee, J. E. (2003). Venture creation and enterprising individual: A review and synthesis. Journal of Management, 29(3), 379–399. Silvestro, R., Fitzgerald, L., & Johnston, R. (1992). Towards a classification of services processes. International Journal of Services Industry Management, 3(2), 62–75. Smith, A. M. (1995). Measuring service quality: Is SERVQUAL now redundant? Journal of Marketing Management, 11(1), 257–276. Song, M., Podoynitsyna, K., van der Bij, H., & Halman, J. (2008). Success factors in new ventures: A meta-analysis. Journal of Product Innovation Management, 25(1), 7–27. Spohrer, J., Maglio, P. P., Bailey, J., & Gruhl, D. (2007, January). Steps toward a science of service systems. Computer, 71–76. Upah, G. D. (1980, Fall). Mass marketing in service retailing: A review and synthesis of major methods. Journal of Retailing, 56(3), 59–76. Van Dyke, T. P., Prybutok, V. R., & Kappelman, L. A. (1999). Cautions on the use of the SERVQUAL measure to assess the quality of information systems services. Decision Sciences, 30(3), 877–891. Walsh, G., & Beatty, S. E. (2007). Customer-based corporate reputation of a service firm: Scale development and validation. Journal of the Academy of Marketing Science, 35, 127–143. Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press. Wolfinbarger, M., & Gilly, M. C. (2003). ETailQ: Dimensionalizing, measuring and predicting Etail quality. Journal of Retailing, 79, 183–198. Zeithaml, V. A., Berry, L. L., & Parasumaran, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. Zhao, X., Bai, C., & Hui, Y. V. (2002). An empirical assessment and application of SERVQUAL in a mainland Chinese department store. Total Quality Management, 13(2), 241–254.