No. 002/2020/POM

A Staged Performance Model for Service Innovation

Qian Yang
School of Management, Northwestern Polytechnical University
Qiang Wang*
Department of Innovation, Entrepreneurship, and Strategy
The School of Management, Xi’an Jiaotong University (XJTU)

Xiande Zhao
Department of Economics and Decision Sciences
China Europe International Business School (CEIBS)January 2020*

Corresponding author: Qiang Wang (qiangwang@xjtu.edu.cn). Address: Department of Innovation, Entrepreneurship, andStrategy, School of Management, Xi’an Jiaotong University (XJTU), No. 28 Xianning West Road, Beilin District, Xi’an, China
710049.

A staged performance model for service innovation
Abstract
Purpose – Superior innovation performance is vital for enterprises to survive and maintaincompetitiveness, especially in Internet related industries which are rapidly growing andchanging. However, not all innovations can survive to later stages and bring benefits forenterprises. From a staged process perspective, we divide innovation performance into threestages: development performance (cost/time efficiency and experience/solution effectiveness),
deployment performance, and financial/non-financial performance, and aim to examine therelationship between modes of service innovation (business model, service process, and service
product) and their performance implications in different stages.
Design/methodology/approach – Based on survey data of 200 innovation projects from theInternet related industries in China, this study uses bootstrapping-based partial least squares(PLS) approach to structural equation modeling (SEM) to empirically test the hypothesized
relationships.
Findings – We found that business model innovation and service product innovation would
have better development (effectiveness) and deployment performance, and process innovationis positively associated with development performance (efficiency). Furthermore, efficiency
performance needs to be transformed into good deployment performance to get better final
financial performance. When final non-financial performance is considered, non-mediated(effectiveness) and mediated (efficiency) relationships are found.
Practical implications –The findings of this study suggest that managers should evaluate
service innovation performance in a staged process perspective. Managers should pay attention
to the modes of service innovation when assessing the intermediary performance indicators inthe staged model because different modes of service innovation impact these indicators
differently. This study also identifies the mediating effects of development and deployment
performance between modes of service innovation and final performance, which inspires
managers that for a better final performance (both financial and non-financial), resources andattentions should be made to achieve high-quality development and deployment activities.
Originality/value – This study empirically examines key staged performance indicators
derived from the new service development process, ties them to modes of innovation and final
performance (financial and non-financial), and explores the mediating effects of those staged
performance indicators between modes of service innovation and final performance. It extends
our understanding of service innovation performance from a staged process perspective.
Keywords: modes of service innovation; new service development process; new service
development performance; financial/non-financial performance
Paper type: Research paper
1

1.

INTRODUCTION

After decades of development, services have not only dominated our daily lives but also
become the most effective driving engine of the world economy, as evidenced by its 65.08%
share of the world’s GDP (The World Bank Data, 2017). Under this trend, numerous newservices emerge and manufacturing firms that historically focused on producing goods begin
to add accompanying services to their products for higher profit margins (Carlborg andKindström, 2014; Visnjic et al., 2016). Due to the unique characteristics such as intangibility,
heterogeneity, perishability, and inseparability of services (Zeithaml et al., 1985), service
demands are highly diversified, personalized, unstable, and unpredictable. As such, it isimperative for service firms to continuously innovate to attract customers and gain competitive
advantage. Service innovation has thus been viewed as the “next big thing” (Jana, 2007) andthe driving force for superior performance (Biemans et al., 2015). A large body of research has
been committed to the antecedents and mechanisms of service innovation, such as the modesof service innovation (e.g., Voss and Zomerdijk, 2007; Wang et al., 2015), service innovation
process (e.g., Alam and Perry, 2002; Song et al., 2009), service innovation capability (e.g.,
Menor and Roth, 2007; Menor and Roth, 2008; Agarwal and Selen, 2009), and factors for
success (e.g., Storey et al., 2016; Aspara et al., 2018). However, the success rate of service
innovations is not high (Page and Schirr, 2008), and firms’ performance is not necessarily
guaranteed by service innovation (Storey and Hughes, 2013). The reasons for this tension
between service innovation and its outcomes may lie behind the way how performance ofservice innovation is captured and measured.
Firstly, after three decades of development, the studies of service innovation have2

expanded from broad in scope to specific in detail to bring about several key antecedents ofservice innovation performance (Arnold et al., 2011; Storey and Perks, 2015). While those
antecedents already cover multifaceted factors related to organization structure, organization
strategy, innovation strategy, innovation efficiency, internal communication, and external
partnerships (Storey et al., 2016), systematic examination between innovation success andmodes of service innovation is still needed (Wang et al., 2015). Since different modes ofinnovation have different objectives, appear in different domains, exhibit diverse
innovativeness, require heterogeneous resources, involve distinct external partners, and arelaunched in different environments (Voss and Zomerdijk, 2007), the development patterns andperformance outcomes derived from different types of service innovation are likely to bedifferent (Avlonitis et al., 2001). For instance, while Kastalli et al. (2013)’s research reports a
non-linear relationship between service business model innovation and firm profitability,
studies demonstrate positive correlations between financial performance and service process
innovation (Chen and Tsou, 2012). In addition, an innovation can rarely be a single mode
innovation. A substantial proportion of innovations can be considered as multi-mode
innovations (Wang et al., 2015). Different combinations of diverse innovation modes anddegrees are likely to generate different performance implications.
More importantly, although researches have already recognized that service innovation
performance is a multidimensional concept (Cheng and Huizingh, 2014), historically, themajority of performance-related studies only focused on different dimensions of the final
performance (either commercial success or strategic competitive advantage), ignoring
important intermediate performance dimensions in the service innovation process and their3

associations with final performance (Storey et al., 2016). As indicated by previous studies, due
to the simultaneous production and consumption nature of services, they cannot be felt by
customers prior to purchase (Avlonitis et al., 2001). Without pre-purchase evaluation, the onsite perception of customers become critical to a service innovation’s success. Thus, theperformance of a service innovation may be influenced by both the service itself and the service
delivery system (Sasser et al., 1978). Besides, the intangibility and heterogeneity natures ofservices make service experience vary across providers and staffs, leading to different
perceptions of service quality and willingness to pay (Johne and Storey, 1998). That’s why
service firms constantly emphasize on staff training. Further, Carbonell et al. (2009) found that
operational outcomes (innovation speed and technical quality) were the intermediary between
customer involvement and final outcomes (sales performance and competitive superiority) in
new service development and suggest future endeavors to test other measures of operational
outcomes such as development cost or customer satisfaction. That is, intermediate stage
outcomes at the development and deployment stages in the new service development process(Melton and Hartline, 2010) are indeed vital to the final outcomes of a service innovation.
However, the literature is almost silent on the details of the staged performance model ofservice innovation.
Against this background, the purpose of this study is to build up a staged performancemodel for different modes of service innovation. That is, this research attempts to identify key
staged-performance indicators for different stages of the new service development process andassociate them with different modes of innovation and final performance for the first time.
More specifically, this study focuses on performances of two stages: the development stage and4deployment stage of the service innovation process. Since the development stage has two dual
objectives:

efficiency(time/cost)andeffectiveness(functionality/experience),thedevelopment performance has two dimensions (Sasser et al., 1978; Carbonell et al., 2009). In
doing this, this study (1) identifies the modes of 200 innovation projects from the IT service
industry; (2) explores the effects of each mode of innovation on three stage-performanceindicators; and (3) investigates the mediating effects of the development performance anddeployment performance between modes of innovation and the final innovation performance(financial and non-financial).
2.

THEORETICAL FRAMEWORK

2.1 Modes of Service Innovation
It is vital to distinguish between innovation types because due to the huge differences in processof generation and characteristics of different types of innovation, their adoption at the firm or
project level is also quite different from each other (Tornatzky and Fleischer, 1990; Damanpour
et al., 2009). Existing literature has introduced several conceptual typologies for service
innovation. A general and most commonly used way of categorizing is based on theinnovativeness of the service innovation. For instance, Wheelwright and Clark’s (1992) three
types of innovation, namely, breakthrough projects, platform projects (new product lines), andderivative projects, distinguished fundamental and incremental changes in different areas.
Johnson et al. (2000) grouped service innovation into radical innovations (e.g., majorinnovation, start-up businesses, and new services to existing market) and incremental
innovations (e.g., service line extensions, service improvements, and style changes). Moreover,combined with the focus and outcome of innovation, Avlonitis et al. (2001) offered an
5expanded innovation typology: new to market services, new to company services, new delivery
processes, service modification, service line extensions, and service repositioning. Similar tothis, considering the sources of idea generation, a classification of push innovation (enterprisedriven), pull innovation (market driven), refining services from products, information andtechnology driven service innovation, and innovation on societal trends were given by
Fitzsimmons and Fitzsimmons (2011). Further, Berry et al. (2006) differentiated service
innovation into flexible solutions, controllable convenience, comfortable gains, and respectfulaccess. Voss and Zomerdijk (2007) used the level and domain of service to classify service
innovation into service product innovation, service process innovation, and service businessmodel innovation. Bettencourt (2010) classified service innovation into new service innovation,
core service innovation, service delivery innovation, and supplementary service innovation. Insummary, the existing literature offers several typologies on service innovation. In this study,
we employ Voss and Zomerdijk’s (2007) typology because it clearly distinguishes service
innovations occurred in different levels and domains of an organization which is mostapplicable to our research design.
Since service offered by service firms are conceptualized to be similar to products
provided by manufacturing firms, service product innovation is the most analogous totraditional manufacturing-based product innovation and sometimes the term “productinnovation” is used interchangeably with “service product innovation” in the service sector(Oke, 2007; Wang et al., 2015). Service product innovation involves the introduction of newservice products, which could be one of the following: new to the market, new to the providing
firm, new to customers, service product line extensions, bundling or unbundling of existing
6

service products, modifications or repositioning of an existing service product (Voss andZomerdijk, 2007; Wang et al., 2015). Thus, service product innovation is related to thedevelopments in the core offerings of a service firm and bring revenues to firms by providing
new service or solutions, increasing existing service quality, price, and sales volume (Boone,
2000; Oke, 2007). A good example of a timely service product innovation is the return
insurance in the finance industry. Online shopping causes an expenditure in mailing whencustomers return goods. Insurance companies cooperate with online sellers to provide a return
insurance service at the time of purchase. Once the customer does return after purchase, theinsurance company will compensate for the costs due to the return. To increase customer
confidence and improve sales, many sellers will buy this service for their customers. In thisstudy, we further divide service product innovation into incremental service product innovation
and radical service product innovation.
Process innovation focuses on changes in the internal organizational processes to increase
firms’ efficiency and effectiveness to produce and deliver products or services (Piening andSalge, 2015). For manufacturing firms, the internal nature makes process innovation separate
from production and marketing activities and thus can hardly be felt by customers (Wang et al.,2015). Whereas, in the service sector, simultaneous production and consumption of services
makes process innovation a proactive way for service firms to better meet customer needs andcreate new service experience (Boone, 2000). Service process innovation primarily takes placein the operational areas of the service system, including significant changes in the way of
information exchange or interface between the service provider and its customers, and the
back-office processes or internal organizational structure that can increase the efficiency to7

develop and deliver innovative services (Zomerdijk and Voss, 2011). Aimed at improving firms’
efficiency and effectiveness in technological and administrative processes, several subdimensions of process innovation were proposed by previous studies, such as technologicalprocess innovation (modification of organization’s operating processes and systems by
information technology), administration process innovation (new approaches and practices tomotivate organizational members), and operational process innovations (new techniques incustomer services and procurement) (Hamel, 2006; Damanpour et al., 2009; Piening and Salge,2015). Process innovation benefits firms through product/service quality enhancement,
production and delivery efficiency improvement, turnover growth, and cost and time saving incommunication and administration (He and Wong, 2004). For instance, due to the dramaticdevelopment of the mobile communication technology, fast food restaurants like McDonald's
and Burger King use automatic-order machine and cellphone application to replace manual
ordering. This largely increases ordering efficiency and reduces labor cost.
A business model outlines the technologies and features in a firm’s product/service, thearchitecture of revenues, costs and profits associated with the firm developing it, and themechanism of how the firm delivers the value of the product/service to customers (Teece, 2010).
Compared with product and process, the design of business model is more fundamental to afirm’s competitiveness since it not only defines a firm’s product and operation process, but also
how to allocate a firm’s asset or resource to fulfill customer needs and convert payments
received into profits (Gambardella and McGahan, 2010; Teece, 2010). Thus, the innovation ofbusiness model involves both a significant or even complete change in the way firms earn theirrevenues and the corresponding product and organizational arrangements made to accompany
8with the change (Voss and Zomerdijk, 2007). The success of a business model is typicallygrounded in a firm’s intangible scientific knowledge and intellectual property to use the asset
and resource base of the firm to develop activities and accumulate resources to create a novel
consumption system or service experience for customers (Gambardella and McGahan, 2010).
Imitation is thus difficult for competitors due to the need to copy the entire system andcustomers’ incentives to stay and interact with the system (Amit and Zott, 2012). Apple’sbusiness model successfully combines mobile phone sales, telecom operation service, andentertainment contents download together. High difficult of competitor replication and highcustomer loyalty due to the ease of use the integrated service facilitate Apple to have higher
price and sales volume in the market and accordingly bring great success to it all over the world.
2.2 A Process Perspective of Service Innovation Performance
A review of the service innovation process literature is necessary for the design of a staged
performance model of service innovation. Compared with the numerous studies concerning
tangible product, literature about new service development stages are relatively scarce. Early
exploratory researches based on the product innovation process came out with many stages ofservice innovation, and examples include the eight linear stages in Bowers (1989), 15 stages in
Scheuing and Johnson (1989), and 10 stages in Alam and Perry (2002). Recent studies tend tosimplify and merge stages due to the need to speed up innovation and great development oftechnology. Johnson et al. (2000) capture the basic shared steps in previous service innovation
process literature and propose four general stages of service innovation: design, analysis,
development and full launch. Since studies suggest that service innovations must be effectively
deployed so as to exert a positive outcome, we extended the label of full launch into launch
9and deployment to cover both innovation deployment and market launch activities (Fichman
and Kemerer, 1999; Wang et al., 2018). Every firm begins its new service development processfrom the design stage and puts a great effort to it. Typically, it is deeply integrated in theorganizational process and involves market research on customers and competitors, discussion
by employees and senior managers, and evaluation by current experience (Zomerdijk and Voss,2011). The aim of this stage is to generate an idea with the greatest potential and design
corresponding strategies and objectives. Then, in the analysis stage, “managers assess thepotential profitability of the project and obtain company authorization to proceed” (Melton andHartline, 2010). Any idea that can eventually be commercialized into a service product isperceived to have potential in profitability in these two stages according to the specificevaluation system of an organization. Therefore, we did not put the performance at these twostages into our conceptual model.
The tasks at the development stage are to allocate resources to develop and test thefunctionality of the core service, associated delivery system, and marketing program; train
operation and delivery personnel, and get internal and external feedbacks to refine the offering(Melton and Hartline, 2010). Naturally, the more resources and efforts put into the development
process, the better the functionality or customer experience of the service. Thus, firms need toachieve a tradeoff between efficiency (cost/time) and effectiveness (functionality/experience)at this stage based on their own capability and market competitiveness. Accordingly, we divide
thedevelopment

performance

into

two

dimensions:cost/time

efficiency

andfunctionality/experience effectiveness. Lastly, the deployment stage includes activities tosuccessfully transfer innovations across all appropriate departments in the firm, lead to proper
10internal use and delivery to external customers in the entire market, and further evaluate andmodify the initiatives as needed (Wang et al., 2018). Greater proficiency in these two stages
will be related to improved financial and non-financial performance of the service innovation.
Specifically, if firms can get accurate perception of customer needs for the service based on
certain modes of innovation, and allocate superior organizational resources and state-of-the-art
technologies into service design in the development stage, the service will correctly address
the targeted market segments’ requirements so that the service will be easily delivered andlaunched on its promised value proposition and accepted by the target segments in the launchand deployment stage (Crawford and Di Benedetto, 2006; Song et al., 2009). Thus,
development performance may mediate the relationships between different modes ofinnovation and deployment performance.
Similarly, proficiency in the deployment stage increases the efficiency of adoption anddelivery of the innovation both within the firm and outside to customers which are conductive
to assess internal and external reactions (Wang et al., 2018). Besides, firms doing good in thisstage generally have a well-designed commercialization system including pricing strategy,
marketing and promotion programs, distribution channels, and after-sale services which
contribute to a better chance of both financial benefits and non-financial related outcomes(Song and Parry, 1997; Song et al., 2009). In this manner, deployment performance may also
serve as a mediator between development performance and final performance. Taken together,
the relationships between modes of service innovation and final performance (financial andnon-financial) are likely to be mediated by development performance and deployment
performance in a sequential way. Based on the above argument, we propose our conceptual11model as depicted in Figure 1.
-------------------------------Insert Figure 1 about Here-------------------------------3.

HYPOTHESE DEVELOPMENT

Service product innovation is the development of the core offerings has the ability to influence
service firms’ operation patterns and performance outcomes (Avlonitis et al., 2001; Oke, 2007).
For incremental service product innovations in the case of improvements, modifications,
repositioning, or line extensions, firms are familiar with the original service, its technology,
design, pricing and marketing programs (de Brentani, 2001). This can significantly reduce thedifficult, effort, and cycle time needed for the project in the development stage and facilitate
firms to develop comparatively advantageous services in a timely manner (Song and MontoyaWeiss, 1998). By contrast, when undertaking service product innovations with dramatic
departure from current services, more trial, time, resources, learning, breakthrough technology,
employee training, internal synergy, and external partner collaborations are needed to solve
design and develop problems (Wang et al., 2015). Moreover, for radical service productinnovations which are totally new to the customer, market, or even the firm itself, firms also
face the challenge to get customers accept the new concept and appreciate its superiority ascompared to existing services (Berry, 1995). Due to the intangible nature of services, more
efforts are also needed for service firms to involve customers into the development stage andincorporate customers’ feedbacks into the unfamiliar service to create a better customer
experience (Zeithaml et al., 1993). Taken together, firms may not able to develop the radical
service product innovation in an efficient (time/cost) way.
Process innovation is increasingly viewed as an important source of “competitiveness and12organizational renewal” (Keupp et al., 2012), especially in dynamic environment whereorganizational processes need to be reconfigured from time to time in response to rapidlychanging technology, customer preference, market trend, and legal framework (Damanpour etal., 2009). By significantly changing the back-office processes and organizational structures
supporting the front-end processes, process innovation increases the efficiency of interfunctional communication and coordination between different departments within the firm
(Wang et al., 2015; Piening and Salge, 2015). By changing the way that firms interact withcustomers, process innovation facilitates firms’ acquiring information on customer demands
and feedbacks so that they design, test, and modify services in a timely manner. Process
innovation may also change the interfaces firms cooperate with external partners, providing
firms more external supports to improve the efficiency of knowledge acquisition, resource
integration, and market expansion (Westphal et al., 1997). Improved efficiency inside andoutside the firm together save time and reduce costs in the development stage of the new service
innovation.
Business model innovation is the most disruptive innovation in organizations and involves
substantial or even complete changes in the way firms convert revenues into profits (Markides,
2006; Voss and Zomerdijk, 2007). Thus, business model innovation typically leads to a better
performance on the functionality of a firm’s service, but may take more cost and time. First ofall, aimed at providing a completely different service experience for customers, business model
innovation not only involves changes on products, processes, delivery channels, marketing
programs, but also contains transformations in organization structure, organization culture,
governance mechanism, and transaction architectures (Amit and Zott, 2001). Accordingly,
13more resources and time are needed to conceptualize, design, experiment, fail and reexperiment to generate a new and better business model for the firm (Chesbrough, 2010). At
the same time, due to its fundamental nature, business model innovation is supposed to leads
to the revolution of an industry, the creation of a new market, or the transformation of acompany (Kindström and Kowalkowski, 2015). Time is needed for the new one to completely
take over the old one no matter at industry, market, or the firm level (Chesbrough, 2010).
Furthermore, as demonstrated by previous in-depth case studies, the execution of businessmodel innovation is a network collaboration rather than a single-firm behavior, involving value
co-creation with external suppliers, complementors, and customers in the form of strategic
alliances and joint ventures (Dyer and Singh, 1998; Amit and Zott, 2001; Wang et al., 2015).
The efficiency of the development of business model innovation is thus lowered due to multiparty decision making and coordination of collaborative efforts. Here, we propose:
H1a: Incremental service product innovation is positively associated with the efficiency
dimension (cost/time) of development performance.
H1b: Radical service product innovation is negatively associated with the efficiency
dimension (cost/time) of development performance.
H1c: Service process innovation is positively associated with the efficiency dimension(cost/time) of development performance.
H1d: Service business model innovation is negatively associated with the efficiency
dimension (cost/time) of development performance.
Service product innovation also improves the effectiveness dimension of development
performance. An important basis for incremental service product innovation is to differentiate
a service offering from homogenous offerings by providing a more satisfying experience forcustomers (Zeithaml et al., 1993). Thus, incremental service product innovations are typicallydeveloped in a service design or marketing department (Wang et al., 2015), aiming to provide
a more efficient solution, more professional service personnel, or an enhanced responsiveness14

relationship to customers (de Brentani, 2001). These improvements or modifications gradually
remedy service insufficiency, recover customer complaints, and repair technology bugs andultimately contribute to a continuous slight improvement of the functionality/experience of theservice product. On the contrary, radical service product innovations come out with newservices that differ greatly from current service and technology category. Those services are
typically supposed to either meet customers’ specific requirements that can’t be met previously
due to technology or resource constraints or create a totally new type of service due totechnology breakthroughs (Lovelock and Yip, 1996). Thus, the functionality of the services atthe development stage will be greatly increased.
Process innovation enhances the functionality of development performance as well.
Improvements of the communication efficiency with customers help firms to timely capture
information concerning customers’ preferences and unmet requirements so that firms candevelop new services to accurately address customers’ expectations. Due to the intangible andheterogeneous nature of services, after-sale service and service remedy are both importantfeatures to distinguish a firm’s services from competitive offerings (Fitzsimmons andFitzsimmons, 2011). Through efficient customer communication enabled by process
innovation, firms can also get valuable feedbacks about their defects in the after-sale service
and service remedy policies of existing services and use these to increase customer service infuture service develop (Chen and Tsou, 2012). Moreover, the adoption of technological andadministrative process innovations enables firms to reconfigure their operational processes, getaccess to more resources and funding, and upgrade their capabilities through resource
combination (Zahra et al., 2006; Piening and Salge, 2015). Strong innovation ability and15

abundant resources naturally lead to better functionality and experience of the service
development.
Regarding business model innovation, since it can thoroughly change both the service andthe way customers consume the service, it will bring customers a novel service experience thatthey have never experienced before (Amit and Zott, 2001). In addition, the value embedded in
the service provided by business model innovation is derived from the bundle of resources andcapabilities from both the firm itself and multiple externally related sources. In light with theresource-based view, such services are more comprehensive, more integrated, and moreproductive in use (Barney, 1991; Amit and Zott, 2001). These services are hard to replicate andsubstitute by competitors in a short period of time. Customers are firmly locked in the service
system of the new business model of a certain firm exert high loyalty to the firm. As a result,
we postulate:H2a: Incremental service product innovation is positively associated with theeffectiveness dimension (functionality/experience) of development performance.
H2b: Radical service product innovation is positively associated with the effectivenessdimension (functionality/experience) of development performance.
H2c: Service process innovation is positively associated with the effectiveness dimension(functionality/experience) of development performance.
H2d: Service business model innovation is positively associated with the effectivenessdimension (functionality/experience) of development performance.
As the world economy becomes more service oriented, firms compete fiercely on newservice offerings (Bitnet et al., 2000). If a firm can develop a new service faster and with lesscosts than competitors, it will have enough time and resources to transfer the service across all
appropriate intrafirm departments and external lead customers in the deployment stage (Wanget al., 2018). In a similar vein, when a new service has preeminent functionality, it must be
designed and developed properly based on in-depth market research, superior organization16

resources, and state-of-the-art technologies (Song et al., 2009). Careful development of thevalue proposition ensures that the service can deliver value correctly to both internal andexternal target users (Crawford and Di Benedetto, 2006). It is easily for the service innovation
to be deployed and diffused inside the firm to synergize supports from different departments
and get feedbacks from external users to refine the new offering. Taken together, we posit:
H3a: The efficiency dimension (cost/time) of development performance is positively
associated with deployment performance.
H3b: The effectiveness dimension (functionality/experience) of development performanceis positively associated with deployment performance.
Better intrafirm deployment enables different departments within the firm to have anadequate understanding of the service innovation, reducing the difficulties in justifying thenecessity to allocate managerial resources and exert synergetic efforts to support the launch
and after-launch activities of the new service (Wang et al., 2018). Externally, early delivery tolead and other potential customers provides the firm an opportunity to collect initial market
reactions to the proposed service and make sufficient adjustments to ensure the service
addresses customer needs correctly at full launch (Song et al., 2009). Early market launch andsufficient managerial support facilitate the firm to grab market share and achieve superior
benefits ahead of competitors.
Moreover, the more proficiently the intrafirm deployment and refining is undertaken, thebetter the value proposition of the new service will be (Wang et al., 2018). Subsequently,
superior value proposition of the new service increases the target market’s acceptance to theservice and gradually locks customers in the service. Due to the high searching costs andswitching costs for customers to switch to another service provider, the firm can charge a
relatively high price for premier benefits which ultimately lead to a better financial
17performance. Moreover, service experience derived from the superior functionality that is notprovided by other service providers increases customers’ satisfaction and commitment as well.
Therefore, we hypothesize:
H4a: Deployment performance is positively associated with financial performance.
H4b: Deployment performance is positively associated with non-financial performance.
4.

METHODS

4.1 Sampling and data collection
To test our hypotheses, 1000 companies were randomly selected from the four first-tier cities
in China, namely Beijing, Shanghai, Shenzhen and Guangzhou. The sampling pool consistedof the service firms listed in the database of National Bureau of Statistics and headquartered inany of the four cities. According the national standard for the classification of industries in
China (GB/T 4754-2011), we selected the “Category I: information communication, software,and IT services”, as these industries are the most prosperous ones in recent years in terms ofservice innovation.
The unit of analysis is the NSD project. Based on the feedback gathered during the pilot
tests of the questionnaire, it was established that the most appropriate way was to get the project
leaders as the key informants who are knowledgeable and familiar with NSD activities. The
survey was conducted from 2015 to 2016, and one of the largest professional survey companies
in China was employed to collect the data. First, the survey company trained its data managers
about the data collection criteria and process. Then, randomly selected firms were contacted
by the data managers via telephone to identify the contact information of the most suitable
informants, and each firm could provide contact information of no more than two service
innovation projects in the charge of different leaders. Finally, an appointment with each18

informant was made by a data manager, who would then take a printed copy of thequestionnaire to the informant and conduct an on-site visit to collect the data. In total, the datacollection efforts resulted in usable responses of 200 service innovation project from 141
companies for a response rate of 14.1%.
4.2 Measures
To ensure that the measurement items used in this study were appropriate, we reviewed
previous literature and interviewed both academics and practitioners in service industries. To
design the measurement items, we invited three operations management professors and one
marketing professor, all of whom were actively involved in teaching and research in Chineseand Western universities. 60 managers of service innovation projects were invited to pilot-test
the questionnaire, and face-to-face interviews were conducted with them to examine whether
the measurement items were appropriate and relevant to their practices and whether any
important aspect might be missing. To ensure the reliability of the questionnaire, it wasdeveloped in both Chinese and English, with two-way translations double-checked by Chineseand Western professors on the research team. Based on these results, the measurement items
were developed (as listed in Table 1).
In this study, incremental and radical service product innovativeness were measured bythree items respectively capturing the magnitude of newness, adapting from Avlonitis et al.
(2001) and Wang et al. (2015). In a similar manner, business model innovativeness and service
process innovativeness were measured by four items capturing the changes regarding businessmodel and service process respectively. We used a 7-point Likert scale with ‘1’ anchored as‘strongly disagree’ and ‘7’ as ‘strongly agree’.

The question was framed as “before the actual19

development of this innovation, we thought the concept of this innovation has the following
characteristics”, and respondents were asked to indicate their degree of agreement.
Development performance was measured by four items regarding efficiency (i.e., time andcost) and three items of effectiveness (i.e., functionality and experience), and deployment
performance was measured using three items on efficiency in terms of speed, cost and otherobjectives, adapted from previous studies including Carbonell et al. (2009) and Melton andHartline (2010). Financial performance was measured by five items from Song and Parry (1997)
and non-financial performance was measured by six items adapted from Avlonitis et al. (2001).
4.3 Respondent profile
A wide variety of service innovation projects were included, and respondents were mainly fromtop management or general managers. The average number of project team members was 11.86;
the average project development investment was 2.22 million RMB; the average project
deployment investment was 0.86 million RMB.
5. Analysis and Results
5.1 Non-response bias and common method bias
As in all survey-based empirical studies, non-response bias is a concern. To address thisproblem, the early and late (after several rounds of calls) responses for number of employees,
number of project team members and the other variables used in this study were compared(Armstrong and Overton, 1977; Stank et al., 2001); t-tests showed no significant differences,indicating that non-response bias does not appear to be a major concern in this study.
As we used one informant from each firm to answer the self-reported questionnaire in thisstudy, the potential for common method bias in the results was assessed. First, as appropriate
20arrangements of the items in a questionnaire can somewhat reduce respondents’ consistent
motivation and thus decrease the common method bias in self-reporting (Podsakoff et al., 2003),
we adopted different instructions for different scales, and the adjacent variables in theconceptual model were put in distinct sections. Second, to confirm this conclusion, we
conducted a test following the recommendation of Podsakoff et al. (2003). Accordingly, twomeasurement models were compared following the analytical procedure in PLS proposed by
Liang et al. (2007), with one measurement model including all of the traits and the other model
adding in a method factor. The results showed that the path coefficients were very subtle andinsignificant. Third, we checked the correlation matrix to see if there were any high correlations,as Pavlou et al. (2007) suggested that common method bias is unlikely if there are noexcessively high correlations (> 0.9). The results of these tests suggested that the common
method bias is unlikely to exist in this study.
5.2 Reliability and validity
A rigorous process was used to develop and validate the survey instruments. Prior to the datacollection, content validity was supported by previous studies, executive interviews, and pilot
tests. After the data collection, a series of analyses were performed to test the reliability andvalidity of the constructs.
We followed a two-step method to test construct reliability. First, we conducted exploratory
factor analyses (EFA) using both orthogonal and oblique rotations to ensure high loadings on
the hypothesized factors and low loadings on cross-loadings in the datasets. All of the items
loaded onto the expected factors without significant cross-loadings. Then, the reliability of eachconstruct was tested using Cronbach’s alpha. The Cronbach’s alpha values, shown in Table 1,21

were over 0.7 for all of the constructs, indicating that all of the constructs were reliable.
Next, convergent validity and discriminant validity (O’Leary-Kelly and Vokurka, 1998)
were tested using the service and manufacturing datasets. Following Bagozzi and Yi (1988),
we computed composite reliability (CR) scores to assess construct reliability. As reported inTable 1, all of the factors had CRs greater than 0.70, and the average variance extracted (AVE)suggested by Fornell and Larcker (1981) for all of the constructs satisfactorily exceeded 0.50.
For our model, all of the factor loadings were greater than 0.50, and all of the t-values weregreater than 2.0, thus convergent validity was achieved. Further, the squared correlation
between each pair of constructs was less than the AVE reported in Table 1 for each individual
construct. These results provided strong evidence of discriminant validity.
--- Insert Table 1 about Here --5.3 Structural equation modeling and results
In this study, the bootstrapping-based partial least squares (PLS) approach to structural
equation modeling (SEM) was used. PLS is a second-generation modelling technique thatsimultaneously assesses the quality of research constructs and the proposed relationships
between these constructs, and has been widely adopted in business research fields such as
information systems, marketing, and operations management (Peng and Lai, 2012). PLS also
has a better capacity in handling complex model with relatively small sample size. In this study,
SmartPLS software (3.2.7 version) was used to assess the measurement and structural models.
We used the bootstrapping-based partial least squares (PLS) approach to structural
equation modeling (SEM) to test the overall model and relevant hypotheses. PLS
simultaneously assesses the quality of research constructs and the proposed relationships
22between constructs, and has been widely adopted in business research fields such as operations
management, information systems, and marketing (Peng and Lai, 2012).
Results show that incremental service product innovation exerted a significant positiveeffect on development efficiency (β = 0.196, p < 0.01). Radical service product innovation,
service process innovation and service business model innovation exerted a nonsignificant
positive effect on development efficiency respectively (β = 0.088, p = 0.381; β = 0.111, p =0.261; β = 0.139, p = 0.238). As such, H1a was supported while H1b, H1c, H1d were notsupported. Incremental service product innovation and service process innovation exerted anonsignificant effect on development effectiveness respectively (β = 0.075, p =0.345; β = 0.098,
p =0.267). Radical service product innovation and service business model innovation exerted
a significant positive effect on development effectiveness respectively (β = 0.163, p < 0.10; β= 0.173, p < 0.05). As such, H2b and H2d were supported while H2a and H2c were notsupported.
Further, both development efficiency and development effectiveness exerted a significant
positive effect on deployment performance (β = 0.251, p < 0.001; β = 0.356, p < 0.001
respectively). H3a and H3b were thus supported. Finally, deployment performance exerted
significant positive effects on both financial performance and non-financial performance (β =0.569, p < 0.001; β = 0.441, p < 0.001 respectively), supporting H4a and H4b.
6. CONCLUSION AND IMPLICATIONSIn recent years, the service sector has become dominant in economy and service firms compete
fiercely in new offerings (Bitnet et al., 2000). Given the growing popularity of service
innovation activities and the complex outcomes of service innovation, this study attempts to23

build a staged-performance model for service innovation. The main objectives of this study
were to identify key staged performance indicators derived from the new service development
process, tie them to modes of innovation and final performance (financial and non-financial),and explore the mediating effects of those staged performance indicators between modes ofservice innovation and final performance. Three basic findings emerged from the data analysis
of 200 service innovation projects: modes of service innovation do exert effects on final
performance in a staged way; different modes of innovation have different effects on eachstaged performance indicator; the staged performance indicators have some mediation effects
between modes of service innovation and final performance. Each of these makes important
contribution to extant literature.
To start with, this study highlights the importance to see service innovation performancein a staged model. There is a concern from both academy and industry that firms’ performancecannot be necessarily guaranteed by service innovation even a huge investment has been madeinto innovation (Page and Schirr, 2008; Storey and Hughes, 2013). This may be attribute to theoverly emphasis on direct economic dimensions of performance in evaluating innovation
performance in previous studies (Avlonitis et al., 2001). Our study, by providing a staged modelwith multiple performance indicators, points out the need to assess innovation performancefrom a staged process perspective since service innovation performance not only contains
different dimensions, but also presents in a sequential way. The empirical results of our results
suggest that different modes of service innovation first impact development performance, thendeployment performance, and ultimately financial and non-financial performance. In light with
this, although some types of innovation cannot directly save costs and time for a firm (e.g.,24radical service product innovation, business model innovation), longitudinally they may
improve the functionality of the service, simplify launch and deployment efforts, and ultimately
earn customers’ satisfaction and bring benefits to the firm. Thus, it is more rationale andaccurate to look at innovation performance from a staged process perspective.
Further, we enrich the extant research on service innovation by showing that different
modes of innovation have different staged performance models. Although previous research
has already pointed out that different types of innovation may be associated with different
development patterns and performance outcomes, relevant framework concerning such
associations is still lacking (e.g., Avlonitis et al., 2001). We find that incremental service
product innovation is significantly related to efficiency (cost/time) but not to effectiveness(functionality) of development performance. For incremental service product innovation,
improvement, modification, updating, and bug repair to the existing service are the main
purposes. Speed and costs are naturally the primary considerations rather than adding new
functions to the service. In contrast, radical service product innovation is significantly related
to effectiveness (functionality) but not to efficiency (cost/time) of development performance.
Radical innovation typically comes up with brand new service which can bring a new service
concept into existing market, start up a new business, or cause the perish of old services(Johnson et al., 2000). Breakthroughs in functionalities of these services are the most important.
Time and costs spent in the innovation are not big concerns.
In addition, the effects of service process innovation on efficiency (cost/time) andeffectiveness (functionality) of development performance were both non-significant. Thesefindings appear to be inconsistent with what has been suggested in literature. More specifically,25

it is argued that process innovation has the potential to reduce costs and time, improve quality,
productivity, and turnover (e.g., Baer and Frese, 2003; He and Wong, 2004; Piening and Salge,2015). However, we should notice that in previous studies those positive effects of process
innovation are embedded in its ability to sense market and technology opportunities (Teece,2007), induce various activities to acquire, integrate, and disseminate knowledge and resources(Pavlou and Sawy, 2011), and train employees (Edmondson et al., 2001) for the success of thecore offerings of a firm. Our study, by providing non-significant empirical evidence, showsthat process innovation cannot improve development performance directly. Lastly, service
business model innovation was found to be significantly associated with effectiveness(functionality) but not with efficiency (cost/time) of development performance. This has
something in common with the results of radical service product innovation in that businessmodel innovations are usually radical innovations that fundamentally change both the service
experience and the way customers consume the service.
Third, this study also explored the mediation effects of development performance anddeployment performance between modes of service innovation and final performance.
Regarding development performance, its efficiency (cost/time) dimension fully mediates therelationship between incremental service product innovation and deployment performance.
And its effectiveness (functionality/experience) dimension fully mediates the relationshipbetween service business model innovation and deployment performance. These results
provide favorable evidence for the argument in existing literature about the importance of the
proficiency in development stage of service innovation (e.g., Carbonell et al., 2009; Song etal., 2009), especially for incremental service product innovation and service business model
26

innovation. Referring to deployment performance, it fully mediates the relationship between
efficiency (cost/time) of development performance and both financial and non-financial
performance. While only a partially mediation effect is found between effectiveness(functionality/experience) of development performance and non-financial performance, it canalso fully mediate the relationship between effectiveness (functionality/experience) ofdevelopment performance and financial performance. While studies have emphasized on thecritical role of development performance in the final performance of service innovation,
empirical evidence concerning the importance of deployment performance is still rare (Wanget al., 2018). The facts of the three full mediation and one partial mediation effect between two
dimensions of development performance and two final performance strongly enrich theexisting literature about the importance of development performance and financial
performance in service innovation.
6.1 Managerial Implications
Although subject to future refinement and testing, the results of this study have implications
for managers operating in project innovation in service firms. First, managers frequently
confront a question: whether a new service idea can bring benefits to a firm after launched tothe market? The findings of this study suggest that they should evaluate service innovation
performance in a staged way. For instance, a poor final financial performance or non-financial
performance do not necessarily mean that the service idea does not meet market demands. It is
likely due to the poor execution and performance in the development and deployment of theservice innovation.
Second, managers should pay attention to the modes of service innovation when assessing
27

the intermediary performance indicators in the staged model because different modes of service
innovation impact these indicators differently. Concerning incremental service product,
efficiency (cost/time) of development performance is an important mid-range indicator. Whilefor radical service product innovation and business model innovation, effectiveness(functionality/experience) of development performance is the critical index. The mostchallenging job for managers is the performance evaluation of service process innovation due
to its insignificant direct effects on both efficiency and effectiveness of development
performance. A possible solution is to indirectly examine process innovation’s ability inhelping firms to acquire, integrate, and disseminate knowledge and resources.
Finally, this study also identifies the mediating effects of development performance anddeployment performance between modes of service innovation and final performance. This
inspires managers that for a better final performance (both financial and non-financial),
resources and attentions should be made to achieve high-quality development activities anddeployment activities in service innovation since only through well-executed development
activities can service innovation ideas be developed and launched to customers, and only
through well execution of deployment can efforts made in development activities be transferred
into final financial and non-financial performance.
6.2 Limitations and Recommendations for Future Research
The findings of this research have several limitations that also indicate opportunities for future
research. First, this study used data from IT-related service industries only. Therefore, theresults found in this research are tentative and subjective to characteristics of IT services.
Further replications efforts are needed to test the conceptual model and relevant hypotheses in28other service settings, such as personal services, professional services, or tangible products
related services to advance the literature. Likewise, the cross-sectional nature of the data may
cause biased results. Longitudinal data, multiple informant data, or data from multiple sources
are therefore required in future studies to verify the results of this study. Third, our sample isconstrained to the Chinese firms. Thus, the generalization of the results in other areas beyond
China should be made with caution.
Lastly, in the empirical analysis of this study, we captured modes of service innovationfrom 200 innovation projects from IT-related service industries and then explored theindividual effect of each mode on different staged performance indicators. However, assuggested by existing literature, different modes of innovation can coexist in a same innovation
project (Wang et al., 2015) and exert synergetic effects on innovation performance (Visnjic etal., 2016). Considering the sample size and the complexity of the conceptual model, we neither
proposed nor tested the interaction effects of different modes of innovation in this study. Thus,
further empirical research is needed to enhance our understanding of the coexistence ofdifferent modes of service innovation and their interactive effects on staged innovation
performance indicators.

29

Reference
Agarwal, R. and Selen, W. (2009). “Dynamic capability building in service value networks forachieving service innovation”, Decision Sciences, Vol. 40, No. 3, pp. 431-475.
Alam, I. and Perry, C. (2002). “A customer-oriented new service development process”,
Journal of Services Marketing, Vol. 16 No. 6, pp. 515-534.
Amit, R. and Zott, C. (2012). “Creating value through business model innovation”, MIT Sloan
Management Review, Vol. 53 No. 3, pp. 41-49.
Armstrong, J.S. and Overton, T.S. (1977). “Estimating nonresponse bias in mail surveys”,
Journal of Marketing Research, Vol. 14 No. 3, pp. 396-402.
Arnold, T. J., Fang, E.E. and Palmatier, R.W. (2011). “The effects of customer acquisition andretention orientations on a firm’s radical and incremental innovation performance”,
Journal of the Academy of Marketing Science, Vol. 39 No. 2, pp. 234–51.
Aspara, J., Klein, J. F., Luo, X. and Tikkanen, H. (2018). “The dilemma of service productivity
and service innovation”, Journal of Service Research, Vol. 21, No. 2, pp. 249-262.
Avlonitis, G.J., Papastathopoulou, P.G. and Gounaris, S.P. (2001), “An empirically-based
typology of product innovativeness for new financial services: success and failure
scenarios”, Journal of Product Innovation Management, Vol. 18 No. 5, pp. 324-342.
Barney, J. (2009). “Firm resources and sustained competitive advantage”, Journal ofManagement, Vol. 17 No.1, pp. 3-10.
Baer, M. and Frese, M. (2003). “Innovation is not enough: climates for initiative andpsychological safety, process innovations, and firm performance”, Journal of
Organizational Behavior, Vol. 24 No.1, pp. 45-68.
Berry, LL. (1995). “Relationship Marketing of services: Growing interest, emerging
perspective”, Journal of the Academy of Marketing Science, Vol. 23 No. 4, pp. 236-45.
Berry, L.L., Shankar, V., Parish, J.T., Cadwallader, S. and Dotzel, T. (2006). “Creating newmarkets through service innovation”, MIT Sloan Management Review, Vol. 47 No. 2, pp.
56–63.
Bettencourt, L. (2010). Service Innovation: How to go From Customer Needs to BreakthroughServices, McGraw-Hill, New York, NY.
Biemans, W.G., Griffin, A. and Moenaert, R.K. (2015). “New service development: How thefield developed, its current status and recommendations for moving the field forward”,
Journal of Product Innovation Management, Vol. 33 No. 4, pp. 382-397.
Bitner, M.J., Brown, S.W. and Meuter, M. L. (2000). “Technology infusion in service
encounters”, Journal of the Academy of Marketing Science, Vol. 28 No.1, pp. 138–49.
Boone, T. (2000). “Exploring the link between product and process innovation in services”, inFitzsimmons, J.A. and Fitzsimmons, M.J. (Eds.), New Service Development: Creating
Memorable Experiences, Sage, Thousand Oaks, CA, pp. 92-107.
Bowers, M.R. (1989). “Developing new services: Improving the process makes it better”,
Journal of Services Marketing, Vol. 3 No. 1, pp. 15-20.
Carbonell, P., Rodrı´guez-Escudero, A.I. and Pujari, D. (2009). “Customer involvement in newservice development: An examination of antecedents and outcomes”, Journal of ProductInnovation Management, Vol. 26 No. 5, pp. 536-550.
30

Carlborg, P. and Kindström, D. (2014). “Service process modularization and modular
strategies”, Journal of Business & Industrial Marketing, Vol. 29 No. 4, pp. 313-323.
Chen, J.S. and Tsou, H.T. (2012). “Performance effects of it capability, service process
innovation, and the mediating role of customer service”, Journal of Engineering andTechnology Management, Vol. 29 No.1, pp. 71-94.
Cheng, C.C.J. and Huizingh, E.K.R.E. (2014). “When is open innovation beneficial? The roleof strategic orientation”, Journal of Product Innovation Management, Vol. 31 No.6, pp.
1235–53.
Chesbrough, H. (2010). “Business model innovation: opportunities and barriers”, Long Range
Planning, Vol. 43 No.2, pp. 354-363.
Crawford, M. and Di Benedetto, C.A. (2006). New products management (9th ed.), Burr Ridge,
IL: Irwin/McGraw-Hill.
Damanpour, F., Walker, R. and Avellaneda, C. (2009). “Combinative effects of innovation types
and organizational performance: A longitudinal study of service organizations”, Journalof Management Studies, Vol. 46 No. 4, pp. 650-675.
De Brentani, U. (2001). “Innovative versus incremental new business services: Different keys
for achieving success”, Journal of Product Innovation Management, Vol.18 No. 3, pp.
169–187.
Dyer, J. H. and Singh, H. (1998). “The relational view: cooperative strategy and sources ofinterorganizational competitive advantage”, Academy of Management Review, Vol. 23 No.
4, pp. 660-679.
Edmondson, A.C., Bohmer, R.M.J. and Pisano, G. P. (2001). “Disrupted routines: Teamlearning and new technology implementation in hospitals”, Administrative Science
Quarterly, Vol. 46 No. 4, pp. 685–716.
Fichman, R.G. and Kemerer, C.F. (1999). “The illusory diffusion of innovation: An
examination of assimilation gaps”, Information Systems Research, Vol. 10 No. 3, pp. 255–
275.
Fitzsimmons, J.A. and Fitzsimmons, M.J. (2011). Service Management: Operations, Strategy,
Information Technology, 7th Edition, The McGraw-Hill Companies, Inc.
Gambardella, A. and Mcgahan, A.M. (2010). “Business-model innovation: general purpose
technologies and their implications for industry structure”, Long Range Planning, Vol. 43
No. 2, pp. 262-271.
Hamel, G. (2006). “The why, what, and how of management innovation”, Harvard Business
Review, Vol. 84 No. 2, pp. 72.
He, Z.L., and Wong, P.K. (2004). “Exploration vs. exploitation: An empirical test of theambidexterity hypothesis”, Organization Science, Vol. 15 No.4, pp. 481–94.
Jana, R. (2007). “Service innovation: The next big thing”, Business Week, March 29.
Johne, A. and Storey, C. (1998). “New service development: a review of the literature andannotated bibliography”, European Journal of Marketing, Vol. 32 No. 3/4, pp. 184-184.
Johnson, S.P., Menor, L.J., Roth, A.V. and Chase, R.B. (2000). “A critical evaluation of the newservice development process”, in Fitzsimmons, J.A. and Fitzsimmons, M.J. (Eds.), New
Service Development, Sage Publications, Thousand Oaks, CA, pp. 1-32.
Keupp, M.M., Palmié, M. and Gassmann, O. (2012). “The strategic management of innovation:a systematic review and paths for future research”, International Journal of Management31Reviews, Vol. 14 No.4, pp. 367-390.
Kindström, D. and Kowalkowski, C. (2015). “Service-driven business model innovation:
Organizing the shift from a product-based to a service-centric business model”, Business
Model Innovation: The Organizational Dimension.
Liang, H., Saraf, N., Hu, Q., and Xue, Y. (2007). “Assimilation of enterprise systems: the effectof institutional pressures and the mediation role of top management”, MIS Quarterly, Vol.
31 No. 1, pp. 59–89.
Lovelock, C.H. and Yip, G.S. (1996). “Developing global strategies for service businesses”,
California Management Review, Vol. 38 No.2, pp. 64-86.
Markides, C. (2006). “Disruptive innovation: in need of better theory”, Journal of ProductInnovation Management, Vol. 23 No. 1, pp. 19-25.
Melton, H.L. and Hartline, M.D. (2010). “Customer and frontline employee influence on newservice development performance”, Journal of Service Research, Vol. 13 No. 4, pp. 411425.
Menor, L. J. and Roth, A.V. (2007). “New service development competence in retail banking:Construct development and measurement validation”, Journal of Operations
Management, Vol. 25 No.4, pp. 825-846.
Menor, L. J. and Roth, A.V. (2008). “New service development competence and performance:
An empirical investigation in retail banking”, Production and Operations Management,
Vol. 17, No. 3, pp. 267-284.
Oke, A. (2007). “Innovation types and innovation management practices in service companies”,
International Journal of Operations & Production Management, Vol. 27 No. 6, pp. 564587.
O’Leary-Kelly, S. W. and Vokurka, R.J. (1998). “The empirical assessment of constructvalidity”, Journal of Operations Management, Vol. 16 No.4, pp. 387-405.
Page, A.L. and Schirr, G.R. (2008). “Growth and development of a body of knowledge: 16
years of new product development research, 1989–2004”, Journal of Product Innovation
Management, Vol. 25 No. 3, pp. 233–48.
Pavlou, P. A. and Sawy, O.A.E. (2011). “Understanding the elusive black box of dynamic
capabilities”, Decision Sciences, Vol. 42 No.1, pp. 239–273.
Pavlou, P.A., Liang, H. and Xue, Y. (2007). Understanding and mitigating uncertainty in online
exchange relationships: a principal-agent perspective. MIS Quarterly, Vol. 31 No. 1, pp.
105–136.
Peng, D.X. and Lai, F. (2012). “Using partial least squares in operations management research:
A practical guideline and summary of past research”, Journal of Operations Management,
Vol. 30 No. 6, pp. 467-480.
Piening, E.P. and Salge, T.O. (2015). “Understanding the antecedents, contingencies, andperformance implications of process innovation: a dynamic capabilities perspective”,
Journal of Product Innovation Management, Vol. 32 No. 1, pp. 80–97.
Podsakoff, P. M., Mackenzie, S.B., Podsakoff, N.P. and Lee, J.Y. (2003). “Common methodbiases in behavioral research: A critical review of the literature and recommended
remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Sasser, E., Olsen, P. and Wyckoff, D. (1987). Management of Service Operations, Boston, MA:
Allyn and Bacon.
32

Scheuing, E.E. and Johnson, E.M. (1989). “A proposed model for new service development”,
Journal of Services Marketing, Vol. 3 No. 2, pp. 25-35.
Song, L.Z., Song, M. and Benedetto, C.A. (2009). “A staged service innovation model”,
Decision Sciences, Vol. 40 No. 3, pp. 571-599.
Song, X. M. and Parry, M. E. (1997). “A cross-national comparative study of new productdevelopment processes: Japan and the United States”, Journal of Marketing, Vol.61 No.
April, pp. 1–18.
Song, X.M. and Montoya-Weiss, M.M. (2001). “The effect of perceived technologicaluncertainty on Japanese new product development”, Academy of Management Journal,
Vol. 44 No. 1, pp. 61–80.
Stank, T.P., Keller, S.B. and Closs, D.J. (2001). “Performance benefits of supply chain
logistical integration”, Transportation Journal, Vol. 41 No. 2-3, pp. 32–46.
Storey, C. and M. Hughes. (2013). “The relative impact of culture, strategic orientation andcapability on new service development performance”, European Journal of Marketing,Vol. 47 No. 5/6, pp. 833–56.
Storey, C. and Perks, H. (2015). “Mixing rich and asynchronous communication for new
service development performance”, R&D Management, Vol. 45 No. 2, pp. 107–25.
Storey, C., Cankurtaran, P., Papastathopoulou, P. and Hultink, E.J. (2016). “Success factors forservice innovation: A meta-analysis”, Journal of Product Innovation Management, Vol.
33, No. 5, pp. 527-548.
Teece, D.J. (2009). “Business models, business strategy and innovation”, Long Range Planning,
Vol. 43 No. 2, pp. 172-194.
The World Bank Data. (2017), “Services, value added (% of GDP), 2017 estimates”, available
at:https://data.worldbank.org/indicator/NV.SRV.TOTL.ZS?end=2017&start=1960&view=chart.
Tornatzky, L.G. and Fleischer, M. (1990). The Processes of Technological Innovation,
Lexington, MA: Lexington Books.
Visnjic, I., Wiengarten, F. and Neely, A. (2016). “Only the brave: product innovation, service
business model innovation, and their impact on performance”, Journal of ProductInnovation Management, Vol. 33 No. 1, pp.36-52.
Visnjic, K.I. and Looy, V.B. (2013). “Servitization: disentangling the impact of service businessmodel innovation on manufacturing firm performance”, Journal of Operations
Management, Vol. 31 No.4, pp. 169–180.
Voss, C.A. and Zomerdijk, L.G. (2007). “Innovation in experiential services – an empiricalview”, in DTI (Ed.), Innovation in Services, DTI, London, pp. 97-134.
Wang, Q., Voss, C., Zhao, X. and Wang, Z. (2015). "Modes of service innovation: A typology",
Industrial Management & Data Systems, Vol. 115 No. 7, pp. 1358-1382.
Qiang, W., Chris, V., & Xiande, Z. . (2018). Deployment strategies for service innovation. IEEE
Transactions on Engineering Management, pp. 1-15.
Westphal, J.D., Gulati, R. and Shortell, S.M. (1997). “Customization or conformity? aninstitutional and network perspective on the content and consequences of tqm adoption”,
Administrative Science Quarterly, Vol.42 No.2, pp. 366-394.
Wheelwright, S.C. and Clark, K.B. (1992). “Creating project plans to focus productdevelopment”, Harvard Business Review, Vol.70 No.2, pp. 70-82.
33

Zahra, S.A., Sapienza, H.J. and Davidson. P. (2006). “Entrepreneurship and dynamic
capabilities: A review, model, and research agenda”, Journal of Management Studies, Vol.
43 No.4, pp. 917–55.
Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1993). “The nature and determinants ofcustomer expectations of service”, Journal of the Academy of Marketing Science, Vol. 21
No. 1, pp. 1.
Zeithaml, V.A., Parasuraman, A. and Berry, L.L. (1985). “Problems and strategies in services
marketing”, Journal of Marketing, Vol. 49 No. 2, pp. 33-46.
Zomerdijk, L.G. and Voss, C.A. (2011). “NSD processes and practices in experiential services”,
Journal of Product Innovation Management, Vol. 28 No. 1, pp. 63-80.
Bagozzi, R. P. and Yi, Y. (1988). “On the evaluation of structural equation models”, Journal of
the Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94.
Fornell, C. and Larcker, D.F. (1981). “Structural equation models with unobservable variables
and measurement error: algebra and statistics”, Journal of Marketing Research, Vol. 18
No. 1, pp. 39-50.

34

Figure 1. Conceptual Model

Modes of Service
Innovation
Radical Service
Product Innovation
Incremental Service
Product Innovation
Service Process
Innovation

Ultimate Performance

Development
Performance
Cost/Time

Deployment and Launch
Performance

Functionality/
Experience

Service Business
Model Innovation

35

Financial
Performance
Non-Financial
PerformanceTable 1. Construct reliability and validity
FactorConstructs and items

α

CR

AVE
loading

Business model innovativeness (BMI)

0.708

0.816

0.525

BMI1. would change the revenue model of our company

0.765

BMI2. would create a new market for our company

0.708

BMI3. would enable our company to enter into a totally different market, compared with the market currently served

0.719

BMI4. would dramatically change the cost structure for the delivery of the service

0.711

Radical service product innovation (RSI)

0.776

0.865

0.683

RSI1. would develop new service product(s) to the market

0.909

RSI2. would develop new service product(s) of our firm

0.839

RSI3. would develop new service product(s) to our existing customers

0.723

Incremental service product innovation (ISI)

0.762

0.860

0.672

ISI1. would be mainly about bundling or unbundling of existing service products

0.771

ISI2. would be mainly about modifications of an existing service product

0.859

ISI3. would be a repositioning of an existing service product

0.827

Service process innovativeness (SPI)

0.733

0.847

0.649

SPI1. require changes in the service delivery process

0.836

SPI2. require changes in the internal organizational processes

0.777

SPI3. require changes in the IT systems
Service development efficiency (SDEa)

0.800
0.812

0.887

0.642

SDE1. This innovation was developed faster than major competitors

0.758

SDE2. This innovation was completed in less time than what was considered normal for industry

0.849

SDE3. This innovation was launched ahead of the original schedule

0.865

SDE4. This innovation had less than planned new service development project costs

0.724

Service development effectiveness (SDEb)

0.755

0.861

0.676

SDE5. This innovation had fewer technical problems than our nearest competitors

0.715

SDE6. This innovation had better service experience than our competitors

0.887

SDE7. This innovation had provided customers a better solution than our competitors

0.853

Service deployment performance (SDP)

0.827

0.885

0.659

SDP1. The deployment was faster than originally expected

0.823

SDP2. The deployment had less than planned project deployment costs

0.739

SDP3. The deployment had well achieved the performance objectives for deployment

0.872

SDP4. The resources needed for deployment were well allocated0.808

Financial performance (FP)

0.912

0.935

0.741

FP1. Relative to your firm’s objectives for this innovation, how successful was it in terms of impact on profits?

0.847

FP2. impact on revenues

0.889

FP3. impact on sales

0.873

FP4. impact on market share

0.798

Table 2. Results of the hypothesis testing
Path in the structural model

Path coefficientβ

ISI → SDEa (H1a)0.196
RSI → SDEa (H1b)
0.088
SPI → SDEa (H1c)
0.111
BMI → SDEa (H1d)
0.139
ISI → SDEb (H2a)0.075
RSI → SDEb (H2b)0.163
SPI → SDEb (H2c)
0.098
BMI → SDEb (H2d)0.173
SDEa → SDP (H3a)0.251
SDEb → SDP (H3b)0.356
SDP → FP
(H4a)
0.569
SDP → NFP (H4b)0.441*
p < .1, **p < .05, ***p < .01, ****p < .001

p
***

0.381
0.261
0.238
0.345
*

0.267
**
****
****
****
****

Outcome
Supported
Not supported
Not supported
Not supported
Not supported
Supported
Not supported
Supported
Supported
Supported
Supported
SupportedTable 3. Post hoc analyses of mediation effects
Path coefficient (simple/full model)
Path in the structural modelpβ
ISI → SDP
0.144/0.085*(0.083)/0.234
RSI → SDP
-0.057/-0.166
0.640/**SPI → SDP
-0.039/0.085
0.684/0.118
BMI → SDP
0.316/0.237****/***
ISI → SDEa → SDP
0.191, 0.239***, ****
ISI → SDEb→ SDP
0.063, 0.357
0.359, ****
BMI → SDEa → SDP
0.121, 0.239
0.248, ****
BMI → SDEb→ SDP
0.158, 0.357*(0.061)/, ****
SDEa → FP
0.184/0.071**/0.387
SDEa→ NFP
0.167/0.114**/0.119
SDEb → FP
0.310/0.147****/*(0.059)
SDEb→ NFP
0.443/0.362****/****
SDEa → SDP→ FP
0.247, 0.467****, ****
SDEa → SDP→ NFP
0.247, 0.212****, ***
SDEb → SDP→ FP
0.354, 0.467****, ****
SDEb → SDP→ NFP
0.354, 0.212****, ***
*
**
***
****
p < .1, p < .05, p < .01, p < .001

OutcomeSDEa fully
mediates
ISI→SDP; SDEb
partially mediates
BMI→SDPSDP fully
mediates SDEa
→ FP and SDEa
→ NFP; SDP
partially mediates
SDEb → FP

FP5. impact on revenue growth rate
Non-Financial performance (NFP)

0.893
0.830

0.876

0.542

NFP1. This innovation had a positive impact on the company’s perceived image

0.632

NFP2. This innovation improved the loyalty of the company’s existing customers

0.695

NFP3. This innovation enhanced sales and profitability of other services offered by our organization

0.705

NFP4. This innovation attracted a significant number of new customers to the company

0.760

NFP5. This innovation gave our company an important competitive advantage

0.842

NFP6. The innovation was highly recognized by our customer community.

0.758