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Value-added path of service-oriented manufacturingbased on structural equation model: the case ofelectric car rental for instanceRui Miaoa, Jintao Caoa, Kai Zhanga, Boxiao Chena, Zhibin Jianga & Liya Wanga

a Department of Industrial Engineering & Logistics Management, Shanghai Jiao TongUniversity, Shanghai, ChinaPublished online: 16 May 2014.

To cite this article: Rui Miao, Jintao Cao, Kai Zhang, Boxiao Chen, Zhibin Jiang & Liya Wang (2014) Value-added path ofservice-oriented manufacturing based on structural equation model: the case of electric car rental for instance, InternationalJournal of Production Research, 52:18, 5502-5513, DOI: 10.1080/00207543.2014.916824

To link to this article: http://dx.doi.org/10.1080/00207543.2014.916824

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Value-added path of service-oriented manufacturing based on structural equation model: thecase of electric car rental for instance

Rui Miao*, Jintao Cao, Kai Zhang, Boxiao Chen, Zhibin Jiang and Liya Wang

Department of Industrial Engineering & Logistics Management, Shanghai Jiao Tong University, Shanghai, China

(Received 27 February 2013; accepted 5 April 2014)

Electric cars foreshadow the future direction of the automotive industry owing to its great potential for environmentprotection. Electric car rental may be an effective mode to popularise the electric car, before the cost of the electric carcan be reduced substantially. To provide the electric car rental service well, the company must understand how consum-ers value renting an electric car and what they consider to be value-added, which is the main objective of the research.First, the concepts of customer participation (CP), service quality (SERVQUAL) and customer value (CV) are identified.Then, the value-added path structural equation is constructed by exploratory factor analysis and confirmatory factoranalysis. Use path coefficient and standard factor loading to identify the value-added paths and key factors in the electriccar rental. The results show that CP will improve customer satisfaction and the post-purchase intentions, through theimprovements of SERVQUAL and CV and thus realising the added value of the path.

Keywords: service-oriented manufacturing; value-added path; customer participation; electric car rental; structuralequation model

1. Introduction

Electric cars foreshadow the future direction of the automotive industry due to its great potential for environment protec-tion (Eberle and von Helmolt 2010). So many car manufacturers rank it as the business strategy (Hodson and Newman2009). However, because of high production cost, the lack of the infrastructure and other technical problems like shortcruising range, its pace of development has been limited. It is an urgency to explore a suitable way to introduce peopleto experience the advantage of electric car. Hence, it is important to figure out how consumers value renting an electriccar and what they consider to be value-added.

Train and Winston (2004) pointed that nearly all of the loss in market share for U.S. manufacturers can be explainedby changes in the basic attributes of a car: price, size, power, operating cost, and body type. Eggers and Eggers (2011)showed that purchase price, range, timing of the market entry, or environmental evolution are different critical factors ofthe adoption of all electric car. After a direct experience of driving a battery electric car, some consumers indicated thatthey might start to consider electric cars as second cars if they had a range of 100 miles, and as main cars if they had arange of 150 miles; they may be willing to pay modest premiums over conventional cars, equivalent to around threeyears’ running cost savings, and most would recharge at home overnight (Skippon and Garwood 2011). So, due to theprice, usage cost and other problems (like difficulty for charging), the risk for consumers to buy electric cars is largerthan the traditional cars. Therefore, before the cost of the electric car is reduced substantially, rental service can not onlymake consumers have the opportunity to drive electric cars at a relatively low cost and experience the advantage ofelectric cars directly, but also can attract consumers through reducing their risk. So this may be an effective mode topopularise the electric car. Furthermore, electric car manufacturers will obtain direct customers’ feedback to assistdecision-making. To provide the electric car rental service well, the company must know what factor (price, chargingnetwork, etc.) consumers concern in the rental process, which is the main objective of this research.

In addition to seeking the new technology to improve the electric car performance, it is also very important to under-stand how consumers use and evaluate the electric car in order to explore a suitable way to the development of electriccar. Unfortunately, it has drawn so little attention. Graham-Rowe et al. (2012) Investigates the customer’s feedback ondriving electric vehicles compared with hybrid energy vehicle. Axsen et al. (2011) research how customer behaviourimpacts on the new energy automotive industry. As far as we know there is limited prior research on customer perceived

*Corresponding author. Email: miaorui@sjtu.edu.cn

© 2014 Taylor & Francis

International Journal of Production Research, 2014Vol. 52, No. 18, 5502–5513, http://dx.doi.org/10.1080/00207543.2014.916824

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value of electric cars. The present work is an attempt to bridge this gap. The value of electric car rental is integrated byservice value (rental service) and physical value (car value). And the perceived value usually is formed in a specificcontext, which need the customer participation (CP) in (Zhang et al. 2011). This can be considered as a typical form ofservice-oriented manufacturing. Service-oriented manufacturing is a new model of manufacturing, which means integrat-ing the manufacturing and service through manufacturing expansion to service and service penetration to manufacturing.Enterprises obtain the profit at the same time to create the greatest value for customers. For example, IBM integratedinternal and external resources to provide customers with trinity of hardware, software and service (Erkoyuncu et al.2011). The traditional manufacturing sells products, whose value is based on real objects. So it’s easy to describe andgrasp. While the value of service-oriented manufacturing which is integrated by service and manufacturing is combinedwith physical value and service value. The CP impacts on customer value (CV) (Nambisan and Baron 2009).Customer’s cognition of using products which produces costs and gains determines its impact on evaluation of compre-hensive value of service-oriented manufacturing system. Therefore, it’s an urgency to discuss the value-added path andtheory, and to explore a new method to grasp the CP, service quality (SERVQUAL) and CV in the service-orientedmanufacturing of electric cars.

Because of high premium of selling price and perceived disadvantages of recharging availability and performance, thequantity of electric car sale is very low although the governments provide preferential policies. Rental service or car sharingmake customers use the electric cars only to pay the cost of using stage instead of buying an electric car, Green et al.provides some case studies for car sharing: ‘car sharing organisations (CSO) eliminate the purchase price burden of Plug-inelectric vehicles (PEVs) for members, reduce auto mobile operating costs, and address range anxiety by allowing membersto choose a car “fit for the trip” (e.g. a PEV for shorter trips and a conventional or hybrid vehicle for longer trips)’ (Greenet al. 2014). This operation process of car sharing is similar to rental service, so consumers may also adopt electric carsthrough rental service easier than buying them. Therefore. the SAIC MOTOR introduces the electric car rental service toChinese market and cooperates with eHi car services beginning in Shanghai. For implementing electric car rental servicebetter, the companies need know how customers perceive the value of rental service they involve. To ensure theindependence of the survey, they entrust us complete this work. After the introduction, the remainder of the paper isstructured as follows: on the basis of previous literature, we construct a hypothetical model in Section 2. In Sections 3 and4, we collect the samples and use software SPSS 17.0 and AMOS 17.0 to analyse the sample data; then discuss the results.Finally, we summarise the paper in Section 5.

2. Literature review and hypothesis of constructing

The key factor for enterprises to establish competitive advantage and win the market share is to improve customers’post-purchase intentions through ensuring the SERVQUAL in order to improve CV and customer satisfaction. So it isvery important to study how consumers assess the value of products (Jayaraman, Singh, and Anandnarayan 2012).Customer participating in the service process is one of significant characteristics, which will have an uncertain impacton CV as well as the company performance. CP refers to customers’ behaviour reacting to the design, production anddelivery for specific goals, including customers’ unilateral efforts as well as the interactive behaviour between customerand enterprise (Bendapudi and Leone 2003). Claycomb and Lawrence (2001) designed a nine-amount-table to measureCP including three dimensions of the information sharing (IS), cooperative production (CB) and personal communica-tion (PC); meanwhile validated the reliability and validity empirically. This essay will divide CP into three dimensionswhich are IS, CB and PC.

Studies have shown that CP has a positive impact on the SERVQUAL (Franke, Schreier, and Kaiser 2010).SERVQUAL is the discrepancy between customer expectations and service performance that customer perceived.Gronroos (2000) recognises that the SERVQUAL contains three dimensions which are technical quality, functional qual-ity and company image. CV is the comparison result of customers` perception between the outcome for some goals andpayoff for obtaining this outcome (Zeithaml 1988). Sweeney and Soutar (2001) develop a measurement model PERVALto measure CV in retail sales node, which is practically operational. Because the dimension of PERVAL is closest to theproperties of electric car value, such as driving, environmental protection and aesthetic, this paper will divide the CV ofrenting an electric car into four basic dimensions on the basis of PERVAL, which contains Emotional Value (EV, theemotional feelings of product), Social Value (SV, utility produced by enhancing the consumer’s social self-concept),Price Effect (PE, utility due to the short-term and long-term cost reduction) and Product Function (PF, the characteristicsof products to achieve a certain function). It has also been found that the SERVQUAL can positively affect CV in theresearch of mobile services business in China and Canada, and most other empirical studies have also pointed out thatthe SERVQUAL has a positive impact on CV (Wang, Lo, and Yang 2004; Yang and Peterson 2008; Yee et al. 2010).

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Customer satisfaction is composed of the cognition of consuming experience and customers’ emotional response, whichis affected by the emotions of customer in the process of consumption and can’t be ignored. Post-purchase intention isthat customers tend to repurchase services or products in the same store or company and advocate the consumingexperience to their friends and relatives. In the study of traditional retailing, sites and online shopping, scholars foundthat CV has a positive influence on customer satisfaction (Bauer et al. 2006). Tung (2004) found that customer satisfac-tion has a positive influence on the post-purchase intentions in the study of multimedia telecommunications services inGermany and Singapore. Gupta and Kim (2008) also support this view.

Based on the analyses above, the existing research only defined the generic connotation and investigated therelationships among CP, SERVQUAL, CV, customer satisfaction and post-purchase intentions empirically in either theservice industry or the manufacturing industry. Little attention has been paid to the relationships among the factorsabove in a context of the integration of manufacturing and service, for instance, the electric car rental, whose valueincludes tangible product index (electric car) and intangible service index (rental process). The relationships among themhave been validated in many conditions, but to our knowledge, there is no literature to validate the relationships in theelectric car rental service environment. In such a rental system, which can be seen as a service-oriented manufacturingsystem, are the empirical conclusions indicating the relationships among the factors above suitable for use directly? Anddoes CP have an influence on value-added path of service-oriented manufacturing? In this study, based on existing stud-ies, we establish assumptions of relationship between CP and the value of the service-oriented manufacturing asfollows.

H1: in electric car rental service environment, CP has a positive impact on the SERVQUAL;H2: in electric car rental service environment, the SERVQUAL has a positive impact on CV;H3: in electric car rental service environment, CV has a positive influence on customer satisfaction andH4: in electric car rental service environment, customer satisfaction has a positive influence on post-purchase intentions.

If these hypotheses are also established in electric car rental service environment, the existing experience can beused for daily operations of the electric car rental service to avoid unnecessary losses and ensure service efficiency.

One concept can be characterised by a number of factors, like CP containing three factors (IS, CB, PC). The magni-tude of coefficient between two factors implies that how different factors reflect the influence on value-added paths. Theinteractions between the two factors are shown in Figure 1. λ denotes the external variables’ coefficient of influence path

Figure 1. Theoretical model.Notes: CP means customer participation; IS means information sharing; CB means cooperative production; PC means personal com-munication; SQ means service quality; TQ means technical quality; CI means company image; FQ means functional quality; CVmeans customer value; EV means emotional value; SV means social value; PE means price effect; PF means product function; CSmeans customer satisfaction; PI means post-purchase intentions.

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on internal variable, β represents the internal variables’ path coefficients on other internal variables. Each single arrowmeans that arrowhead has a positive impact on the end, such that λ11 means assuming IS has a positive influence ontechnical quality in electric car leasing.

3. Research methods and sample collection

Service-oriented manufacturing is a new manufacturing mode that aims to eliminate the boundaries of manufacturingand services and provide a whole solution for consumer’s individual expectation. The purpose of service-oriented manu-facturing is to provide ‘product service system (PSS)’ (Gao et al. 2011; Wang et al. 2013). There are three types: prod-uct-oriented PSS, use-oriented PSS, result oriented PSS; rental service is a representative of use-oriented PSS (Mont2002; Williams 2007). With the corresponding of PSS, service-oriented manufacturing also can be divided to threetypes. So we consider the electric car rental service can be used to represent service-oriented manufacturing mode. Thisarticle selected the electric car rental industry, which only includes passenger car leasing that serve the individual con-sumers, as the representative to test and verify the theoretical assumptions and analyse the value-added path of service-oriented manufacturing. First we design the questionnaire based on the literature review and expert interviews. Then usethe statistical analysis to test the hypotheses model. After a sufficient analysis of the literature, we list the items of eachdimension; then we choose some experts including two professors of marketing, two leasing dealers, and one electriccar manufacturer, for semi-structured interviews to revise the items. The whole process lasted for several rounds untilwe obtain a unified version of the items through the standard Delphi Method (Okoli and Pawlowsi 2004). Secondly,after affirming all items, we finish the standardised questionnaire using the classification scale for respondents to fill outbasic personal information section. In addition, 7 points LIKERT scale (Olhager and Selldin 2004) is used in the mainpart of the questionnaire, where 1–7 points represent strongly disagree to strongly agree.

Some market researches in different countries (US, Germany, china) show that early adopters have similar character-istics like highly educated and environmentally sensitive (Zhang, Yu, and Zou 2011; Carley et al. 2013; Hackbarth andMadlener 2013). So we consider that this group of respondents with these characteristics will provide more accuratefeedbacks for our questionnaire, and choose the samples to comply with the above characteristics. Furthermore, consid-ering the effectiveness of samples, they must have rented electric cars before (at least once per week).

We choose the students, who are PhD students or postgraduates in universities in Shanghai. These samples can sat-isfy above conditions. Then the students are not in full-time ones, but on-job ones, who will finish the study using theirrest time. And these students are major in vehicle engineering; this makes them distinguish the difference between elec-tric cars and fuel cars. Furthermore, Shanghai is one of the major electric car rental markets in China. Big cities are themain market of electric cars, because the air pollution and traffic condition have an impact on big cities more seriously(Rienstra and Nijkamp 1998). Secondly, the respondents received a good higher education in university, and have envi-ronmental awareness. This kind of group is the main potential consumer of electric cars (Hidrue et al. 2011). Thirdly,they have an experience of renting an electric car for different aims, and can be regarded as the representative of mainconsumers in China (the people who have a driving licence, but with very low income); and they often rent electric cars(at least once per week). Finally, this research is in cooperation with SAIC MOTOR, the company hope us completethis survey in Shanghai. Previous research affirms that there is no significant difference across main cities in China, likePeking, Shanghai, and Hangzhou (Yan 2013). In a small-scale test, we collect 48 questionnaires in order to check theavailability of our questionnaire. We use the SPSS 17.0 statistical analysis software to test the reliability and validity aswell as the exploratory factor analysis (EFA). Then based on the test results, further modifications have been done tothe questionnaire. Using principal component analysis and orthogonal rotation, according to the standard that the cumu-late item-total correlation is greater than 0.5 and factor loading greater than 0.4, combined with internal consistency testthat the Cronbach’s α > 0.7, finally appropriate items for large scale sample test were selected (we list all items inAppendix 1).

In large-scale tests, 243 questionnaires have been collected from the four key universities in Shanghai, but 36 ques-tionnaires did not meet the screening requirements leading to 207 valid samples, and the pass rate is 85.2%. The statisti-cal analysis software SPSS 17.0 and AMOS 17.0 were used to analyse the sample data. Firstly, we conduct theconfirmatory factor analysis (CFA) to verify the reliability, validity, and the goodness of fit of the corresponding mea-surement model based on above assumptions. Then use structural equation model (SEM), a widely used approach insocial science (Roberts et al. 2010; Vinodh and Joy 2012), to establish the value-added path among constructs. Struc-tural equation modelling can handle the relationship between multiple reasons and multiple results at the same time andthe variables unobserved directly (latent variables). So it has been considered as a very good method in the study ofsocial science since 1980s (Gupta and Kim 2008). This method is very suitable for our research. After testing thereliability, the validity, and the goodness of fit, we finally figure out the value-added path.

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4. Data analysis and discussion

4.1 Confirmatory Factor Analysis

4.1.1 Pre-test

The Bartlett sphericity test can be used to determine whether the data is suitable for factor analysis. By testing the datawith Bartlett spherical test, Kaiser–Meyer–Olkin (KMO) value is calculated to be 0.927, and significance level is 0.01,indicating a strong correlation between the variables, so factor analysis is feasible.

4.1.2 Reliability analysis

In this study, through analysing the indicators of the measurement entries under each construct: Cronbach’s α, thesquared multiple correlation coefficient (R2) and composite reliability (CR), the reliability of the measurement scale canbe tested. From Table 1, we know that Cronbach’s α of the various constructs ranges from 0.745 to 0.881, andCronbach’s α of the total measurement model is 0.956, which are greater than the minimum threshold value 0.70; R2 ofeach measurement entry is within a range of 0.603–0.806, which are higher than the generally accepted minimum value0.50; the CR of the latent variables is 0.838, which is higher than the recommended minimum critical level 0.70,indicating that all measures have good reliability. So it can be concluded that the measurement scale used in this studyhas a good consistency, stability and high reliability.

4.1.3 Discrimmination validity analysis

The validity of the distinction between the various constructs was tested by EFA. The results of analysis showed thatafter the orthogonal rotation, 12 factors whose characteristic values are greater than 1 were extracted, corresponding tothe theoretical model constructs exactly. Each measurement entry loads to one corresponding factor and no measuremententry is in two constructs at the same time. And the value of factor loading of each measurement entry on a single con-struct is from 0.714 to 0.916, which is far higher than the minimum standard 0.50. At the same time, factor loading ofeach entry on the irrelevant factor is rather low.

On the basis of EFA, this study uses CFA to test the discrimination validity of the measurement scale. As shown inTable 1, the square root of the average variance extracted (AVE) of each construct varies from 0.6081 to 0.7686, whilethe absolute value of the correlation coefficient between any two constructs varies from 0.015 to 0.640, and each latentvariable’s AVE is larger than the squared correlation between each pair of latent variables. Hence the discriminationvalidity is adequate.

4.1.4 Convergent validity analysis

The values of standardised factor loading of the 42 measurement entries on the corresponding construct vary from0.714 to 0.916, which are higher than the minimum requirement 0.50 and show strong statistical significance atp < 0.001; the AVE value of each construct varies from 0.6081 to 0.7686, which is higher than the recommended thresh-old value 0.50. In addition, the absolute value of the correlation coefficient between any two constructs is lower than0.80, indicating the level of correlation between two variables is acceptable. The results show that the measurementscales of the 12 constructs have high convergent validity in the theoretical model in this study.

4.1.5 Explanatory power analysis

In this study, the squared multiple correlation coefficient (R2) is used to validate the explanatory power of the modelbuilding. As shown in Table 1, the value of R2 of each construct is higher than the recommended value (0.30),indicating that the constructed model has better explanatory power.

4.2 Analysis of the value-added path

4.2.1 Results of parameter estimation

Based on the statistical results, we can get many variables to explain the relationships between main factors (like CP).Because of the complexity of service process, it is very difficult to control all variables. Through assigning these vari-ables a high priority to ensure their quality, we can provide the service level to satisfy customers to the maximumextent. To get the priority, we establish the SEM based on these variables. In order to clearly indicate the different levels

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of the degree of influence that each variable effect on the final customer perceived value, we use the coefficientsbetween variables in the SEM to show these differences. If the coefficient of one variable is larger, it shows the higherthe degree of influence.

Assuming that the causality between two latent variables is unidirectional and the observed variables coexist withthe latent variables, the model has the nature of the measurement model and the structural model, so the model of pathanalysis is the path analysis of latent variables recursive model. In Figure 1, the three dimensions of CP known as IS,

Table 1. Test results of standardised factor loadings, reliability and validity for formal scale.

Construct Measurement entry SFLCronbach’s

αα with the entry

deleted FN KMO R2 CR AVE

Informationsharing

IS1 inform supply 0.801 0.833 0.812 1 0.755 0.806 0.893 0.736IS2 disclose plans 0.916 0.794 0.799IS3 display products 0.853 0.781 0.802

Cooperativeproduction

CB1 check the condition 0.801 0.754 0.725 1 0.847 0.779 0.846 0.647CB2 recommended outlets andmodels set

0.860 0.737 0.790

CB3 feedback 0.748 0.619 0.774Personal

communicationPC1 live discussion 0.785 0.824 0.781 1 0.775 0.793 0.838 0.632PC2 process interconnection 0.822 0.792 0.740PC3 sale management 0.778 0.777 0.792

Technical quality TQ1 cruising range 0.754 0.802 0.866 1 0.846 0.769 0.885 0.608TQ2 max speed 0.803 0.863 0.796TQ3 charging time 0.868 0.867 0.603TQ4 charging network 0.751 0.874 0.665TQ5 energy consumption per onehundred kilometres

0.714 0.867 0.663

Functionalquality

FQ1 guarantee deposit used 0.854 0.847 0.793 1 0.865 0.613 0.915 0.682FQ2 unit costs of maintenance 0.840 0.783 0.770FQ3 warm service 0.851 0.774 0.777FQ4 clear lease contract 0.776 0.818 0.784FQ5 functional quality 0.805 0.797 0.725

Company image CI1 store image 0.769 0.867 0.864 1 0.807 0.760 0.846 0.647CI2 status of the company’sreputation

0.835 0.875 0.729

CI3 social concept of rental 0.807 0.848 0.699Emotional value EV1 convenience 0.895 0.878 0.85 1 0.699 0.855 0.664

EV2 fashion 0.799 0.844 0.684EV3 comfortable 0.743 0.855 0.709

Social value SV1 environment friendly 0.820 0.849 0.816 1 0.856 0.791 0.912 0.722SV2 improve traffic 0.836 0.791 0.767SV3 social status 0.871 0.778 0.761SV4 image building 0.870 0.841 0.780

Price effect PE1 reduce the maintenance fees 0.874 0.802 0.801 1 0.829 0.775 0.892 0.734PE2 save money for buying a car 0.841 0.89 0.737PE3 avoid cost of disposal 0.854 0.875 0.767

Product function PF1 suitable for situations thatrequire

0.794 0.833 0.799 1 0.849 0.666 0.896 0.684

PF2 adjustable using time 0.873 0.809 0.705PF3 product performance 0.857 0.799 0.680PF4 convenient to facilitate 0.781 0.817 0.651

Customersatisfaction

CS1 product and service satisfaction 0.882 0.881 0.861 1 0.815 0.640 0.909 0.769CS2 no complaint 0.866 0.867 0.688CS3 more than-expected 0.882 0.86 0.678

Post-purchaseintentions

PI1 continue to use 0.835 0.745 0.662 1 0.827 0.694 0.887 0.723PI2 purchase again 0.874 0.913 0.685PI3 active recommendation 0.841 0.766 0.673

Note: SFL: standardised factor loadings; FN: number of factors; KMO ¼PP

i¼jr2ijPP

i 6¼jr2ijþ

PPi6¼j

a2ij, AVE =

�Pk2

�P

k2þP

h, CR =

�Pk

�2

�Pk

�2

þP

h

, λis standardised factor loading while θ is measurement error.

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CB and PC are exogenous constructs, while the remaining ones are internal constructs. In this model, there are 42assumptions to be inspected and 110 free parameters to be estimated. Using AMOS 17.0 to calculate the estimatedvalue, the model can converge to identify causal model diagram of standard estimated value in Figure 2.

If the path coefficient is positive, then the starting point of a path has a positive influence on the end point; if thepath coefficient is negative, then such influence is negative. The SEM has 793° of freedom and 110 free parameters tobe estimated. The χ2 value of overall fit is 0.36, and probability value of significance p = 0.54, lower than the signifi-cance level of 0.05, so we accept the null hypothesis that the causal model of CP affecting the CV of service-orientedmanufacturing mentioned in this article can fit the actual survey data well, and assumed model of the path analysis isverified. Absolute value of path coefficient being less than 0.5 indicates no significant effect; the absolute value of pathcoefficient being larger than 0.5 means it has a significant effect; if the absolute value of path coefficient is larger than0.7, it would be said that the effect is very significant. The bold arrows in Figure 2 show very significant value-addedpaths. The standardised factor loadings show how important the measurement entries are to the corresponding constructand the entries in the bold rectangular box are relatively more important compared to others.

4.2.2 Model fit assessment

Quality of SEM can be judged by goodness of fit between data and model with the ratio of chi-square to degrees offreedom (v2=df ), Goodness of fit index (GFI), Adjusted goodness of fit index (AGFI), Normed fit index (NFI), Compar-ative fit index (CFI), Incremental fit index (IFI), Relative fit index (RFI), Root mean square error of approximation(RMSEA).

As can be seen from Table 2, all the model-fit indices exceed the respective common acceptance levels, indicatingthat the measurement model exhibit a good fit with the data collected.

4.3 Discussion

According to the analysis of value-added path above, the following conclusions can be done.Firstly, CP helps to improve the SERVQUAL. Customers have more communication with vendors on electric car

rental service provider and manufacturers through the IS. Manufacturers can better understand customers` needs on

Figure 2. Structural equation model of CP’s impact on value-added path of service-oriented manufacturing.

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technical qualities such as mileage range of electric cars, battery charging time; then they can obtain a sort of technol-ogy development that the consumers are more concerned about. They can take this list as a reference of decision-mak-ing, because CP may has a negative impact on the innovation of new product (Gruner and Homburg 2000). The rentalservice provider can obtain the customers’ feedback about charging network, the guarantee deposit used, the lease con-tract and so on, thus they may have a chance to create a better SERVQUAL through eliminating the gap between thecurrent status of company and the customers’ exception got from the feedback. Due to customers’ lack of expertise, CBmainly contributes to the improvement of SERVQUAL and it influencing the service provider is more significant thanthe manufacturer.

SERVQUAL has a positive impact on CV and CP improves CV by affecting the quality of service. The assumptionthat the technical quality has a positive influence on the emotional value (β11 = 0.65), indicates that the cruising range,battery charging time and energy consumption per 100 km can necessarily to make customers feel more comfortableand stylish; The assumption that technical quality has a positive effect on the customer social values (β12 = 0.57), indi-cates that better technical performance of electric cars will help the customer feel more contributions on the community(like the electric car more environmentally friendly), thus enjoying a higher status. The influence of technical quality onproduct function is also very significant (β14 = 0.57). The assumption that functional quality has a positive influence onthe price effect (β33 = 0.82), indicates that initial freezing funds and single-day rental fee are very sensitive to the cus-tomer in electric car rental, so companies need to focus on this problem; besides, functional quality has a positive influ-ence on the product function (β34 = 0.68), shows that a clear lease contract and mature service delivery programme canhelp the customer have a better experience. Functional quality has a positive influence on the emotional value of thecustomer (β31 = 0.76), indicating that enthusiastic and proactive attitude of the rental service staff help customers feelcomfortable and convenient. Company image has a strong positive impact on the social value (β22 = 0.72), indicatingthat the higher the status of the company’s reputation is, the more customers feel they have done to protect theenvironment and improve the traffic.

CV has a positive impact on customer satisfaction. CP improves customer satisfaction by affecting SERVQUAL andCV. The assumption that the four dimensions of CV (emotional value, social value, price effect and product function)have a positive impact on customer satisfaction, indicates that CP indirectly improves customer satisfaction. Among thefour dimensions, the influence of price effect on customer satisfaction is the most significant.

The higher the customer satisfaction is, the stronger the customers’ post-purchase intention is. The assumption thatcustomer satisfaction has a positive impact on the post-purchase intention, indicates that customers enhance their satis-faction of the electric car rental service by their own participation, thus they are more willing to continue to lease andrecommend to their friends.

Secondly, we can get some information from the factor loadings of all measurement entries: IS2-disclose plans,CB2-recommended outlets and models set and PC2-process interconnection, they are the most important links toimprove CP. Enterprises should pay special attention to them. In the SERVQUAL, factor loadings of TQ3-chargingtime, FQ1-guarantee deposit used, FQ3-warm service and CI2-status of the company`s reputation are larger, indicatingthat these entries have a greater impact on the SERVQUAL. In CV, factor loadings of EV1-convenience, SV3-social sta-tus, SV4-image building, PE1-reduce the maintenance fees and PF2-adjustable using time are larger, indicating that theyare the critical points that customers concern about.

Thirdly, from the analysis above we can conclude several significant value-added paths:

(1) CB→TQ→PF→CS→PI: cooperative production improves technology quality, thereby affecting productfunction, thereby improving customer satisfaction, finally improving customer’ post-purchase intention;

(2) CB→FQ→EV→CS→PI: cooperative production improves functional quality, thereby affecting emotionalvalue, thereby improving customer satisfaction, finally improving customer’ post-purchase intention;

(3) PC→CI→SV→CS→PI: personal communication improves company image, thereby affecting social value,thereby improving customer satisfaction, finally improving customer’ post-purchase intention;

(4) PC→FQ→PF→CS→PI: personal communication improves functional quality, thereby affecting productfunction, thereby improving customer satisfaction, finally improving customer’ post-purchase intention.

Table 2. Test result of structural equation fitness.

v2=df RMSEA CFI NFI NNFI IFI GFI AGFI SRMR

Model 2.68 0.063 0.98 0.95 0.97 0.97 0.92 0.9 0.062Recommended standard <3.0 <0.08 >0.90 >0.90 >0.90 >0.90 >0.90 >0.80 <0.08

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So, at present the electric car manufacturers and rental operators should focus on strengthening CB and PC. Forexample, let customers participate in the discussion to develop new service programmes and encourage them to recom-mend outlets and models, and electric car manufacturers and rental operators should keep in touch with customers dur-ing the lease term to timely response to customer’ emergent demands. Then according to the customers’ advice, therental service provider should seek a reasonable way to reduce the charging time, compress the guarantee deposit, andimprove the company’s image, making customers feel comfortable and convenient to lease and save the money.

Finally, we have noticed that the path coefficient of social value to the customer satisfaction is 0.58, indicating theinfluence is not significant; but the path coefficient of price effect to the customer satisfaction is 0.79, indicating that theinfluence is very significant. This result shows that the consumers are price-sensitive for electric car rental in China, andthe main group of consumers temporally can afford to buy a car and lease a car instead of buying one. And due to shortlease term, the customers cannot feel they make great contribution to the environmental protection. So in the course ofthe promotion of electric cars, the government and enterprises should actively give publicity to benefits of using electriccars to the environment and society, set up the fashion and glorious image of driving the electric car and build a goodsocial atmosphere for the promotion of electric cars.

5. Summary

Choosing the electric car rental industry as the background, from the views of the consumers, this article explores theinfluence of CP on SERVQUAL, CV, customer satisfaction, and post-purchase intentions in a service-oriented manufac-turing mode. The results show that CP will improve customer satisfaction and the post-purchase intentions through theSERVQUAL and CV, thus realising the added value. And CB and PC are the main constructs of CP. This researchshows that CP is essential for service-oriented manufacturing enterprises, because it will not only affect theSERVQUAL, but also affect the perceived value of the product. SERVQUAL and CV will ultimately affect consumersatisfaction and post-purchase intentions.

On the one hand, this research expands the scope of application of the theory between the CP and SERVQUAL,CV, customer satisfaction and post-purchase intentions, and its research method has a good reference outside of the elec-tric car rental industry. On the other hand, it proves that service-oriented companies must focus on CP and SERVQUAL,and this is the only way to get a higher customer satisfaction. The electric car rental is a typical form of service-orientedmanufacturing, which is oriented to use, we will continue to study the value-added path of the product-oriented andresult-oriented mode of service-oriented manufacturing, and the specific style of CP between the different types of enter-prises and customers and the difference of the value in the delivery process in the future. In addition, we will continuethis survey in the future, in order to observe the influence of sample size to the model.

Our research shows that it is feasible to improve customer satisfaction through improving electric car rental service,furthermore encourage consumers to adopt electric cars through the rental way. So they can achieve the purpose ofpromoting electric cars, and the electric car manufacturer can obtain consumers’ feedback about their electric cars tocontinue to improve the technical quality of the electric cars, starting from the one which the consumers most concernedabout. For example, charging time (TQ3) is the factor that consumers most care about, so the manufacturing shouldresolve it firstly, namely developing new technique to reduce the charging time.

Funding

The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China [grantnumber 70932004].

References

Axsen, Jonn, Kenneth S. Kurani, Ryan McCarthy, and Christopher Yang. 2011. “Plug-in Hybrid Vehicle GHG Impacts in California:Integrating Consumer-informed Recharge Profiles with an Electricity-dispatch Model.” Energy Policy 39 (3): 1617–1629.

Bauer, H. H., T. Falk, and M. Hammerschmidt. 2006. “eTransQual: A Transaction Process-based Approach for Capturing ServiceQuality in Online Shopping.” Journal of Business Research 59 (7): 866–875.

Bendapudi, N., and R. P. Leone. 2003. “Psychological Implications of Customer Participation in Co-production.” Journal ofMarketing 67 (1): 14–28.

Carley, Sanya, Rachel M. Krause, Bradley W. Lane, and John D. Graham. 2013. “Intent to Purchase a Plug-in Electric Vehicle: ASurvey of Early Impressions in Large US Cites.” Transportation Research Part D: Transport and Environment 18 (1): 39–45.

5510 R. Miao et al.

Dow

nloa

ded

by [

Shan

ghai

Jia

oton

g U

nive

rsity

] at

21:

16 1

3 D

ecem

ber

2014

Claycomb C., and W. I. Lawrence. 2001. “the Customer as a Productive Resource: A Pilot Study and Strategic Implications.” Journalof Business Strategies 18 (1). Accessed July 31. http://www.freepatentsonline.com/article/Journal-Business-Strategies/75372920.html.

Gronroos, Christian. 2000. Service Management and Marketing: A Customer Relationship Management Approach. West Sussex:Wiley.

Eberle, Ulrich, and Rittmar von Helmolt. 2010. “Sustainable Transportation Based on Electric Vehicle Concepts: A Brief Overview.”Energy & Environmental Science 3 (6): 689–699. doi:10.1039/C001674H.

Eggers, Felix, and Fabian Eggers. 2011. “Where Have All the Flowers Gone? Forecasting Green Trends in the Automobile Industrywith a Choice-based Conjoint Adoption Model.” Technological Forecasting & Social Change 78 (1): 51–62.

Erkoyuncu, John Ahmet, Rajkumar Roy, Essam Shehab, and Kalyan Cheruvu. 2011. “Understanding Service Uncertainties in Indus-trial Product–Service System Cost Estimation.” The International Journal of Advanced Manufacturing Technology 52 (9–12):1223–1238.

Franke, Nikolaus, Martin Schreier, and Ulrike Kaiser. 2010. “The ‘I Designed It Myself’ Effect in Mass Customization.” ManagementScience 56 (1): 125–140.

Gao, Jie, Yinliang Yao, Valerie C. Y. Zhu, Linyan Sun, and Lin Lin. 2011. “Service-oriented Manufacturing: A New Product Patternand Manufacturing Paradigm.” Journal of Intelligent Manufacturing 22 (3): 435–446.

Graham-Rowe, Ella, Benjamin Gardner, Charles Abraham, Stephen Skippon, Helga Dittmar, Rebecca Hutchins, and Jenny Stannard.2012. “Mainstream Consumers Driving Plug-in Battery-Electric and Plug-in Hybrid Electric Cars: A Qualitative Analysis ofResponses and Evaluations.” Transportation Research Part a: Policy and Practice 46 (1): 140–153.

Green, Erin H., Steven J. Skerlos, and James J. Winebrake. 2014. “Increasing Electric Vehicle Policy Efficiency and Effectiveness byReducing Mainstream Market Bias.” Energy Policy 65 (2): 562–566.

Gruner, K., and C. Homburg. 2000. “Does Customer Interaction Enhance New Product Success?” Journal of Business Research 49(1): 1–14.

Gupta, Sumeet, and Hee W. Kim. 2008. “Linking Structural Equation Modeling to Bayesian Networks: Decision Support forCustomer Retention in Virtual Communities.” European Journal of Operational Research 190 (3): 818–833.

Hackbarth, André, and Reinhard Madlener. 2013. “Consumer Preferences for Alternative Fuel Vehicles: A Discrete Choice Analysis.”Transportation Research Part D: Transport and Environment 25 (12): 5–17.

Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 2011. “Willingness to Pay for Electric Vehicles andTheir Attributes.” Resource and Energy Economics 33 (3): 686–705.

Hodson, Nick, and John Newman. 2009. “A New Segmentation for Electric Vehicles.” McKinsey Quarterly, November 1–5.Jayaraman, Vaidyanathan, Rakesh Singh, and Ajay Anandnarayan. 2012. “Impact of Sustainable Manufacturing Practices on Con-

sumer Perception and Revenue Growth: An Emerging Economy Perspective.” International Journal of Production Research 50(5): 1395–1410.

Mont, O. K. 2002. “Clarifying the Concept of Product–Service System.” Journal of Cleaner Production 10 (3): 237–245.Nambisan, Satish, and Robert A. Baron. 2009. “Virtual Customer Environments: Testing a Model of Voluntary Participation in Value

Co-creation Activities.” Journal of Product Innovation Management 26 (4): 388–406.Okoli, Chitu, and Suzanne D. Pawlowsi. 2004. “The Delphi Method as a Research Tool: An Example, Design Considerations and

Applications.” Information & Management 42 (1): 15–29.Olhager, Jan, and Erik Selldin. 2004. “Supply Chain Management Survey of Swedish Manufacturing Firms.” International Journal of

Production Economics 89 (3): 353–361.Rienstra, Sytze A., and Peter Nijkamp. 1998. “The Role of Electric Cars in Amsterdam’s Transport System in the Year 2015: A Sce-

nario Approach.” Transportation Research Part D: Transport and Environment 3 (1): 29–40.Roberts, Nicholas, Jason Bennett Thatcher, and Varun Grover. 2010. “Advancing Operations Management Theory using Exploratory

Structural Equation Modeling Techniques.” International Journal of Production Research 48 (15): 4329–4353.Skippon, Stephen, and Mike Garwood. 2011. “Responses to Battery Electric Vehicles: UK Consumer Attitudes and Attributions of

Symbolic Meaning following Direct Experience to Reduce Psychological Distance.” Transportation Research Part D: Trans-port and Environment 16 (7): 525–531.

Sweeney, Jillian C., and Geoffrey N. Soutar. 2001. “Consumer Perceived Value: The Development of a Multiple Item Scale.” Journalof Retailing 77 (2): 203–220.

Train, Kenneth E., and Clifford Winston. 2004. “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers.”International Economic Review 48 (4): 1469–1496.

Tung, L. L. 2004. “Service Quality and Perceived Value’s Impact on Satisfaction, Intention and Usage of Short Message Service(SMS).” Information Systems Frontiers 6 (4): 353–368.

Vinodh, S., and Dino Joy. 2012. “Structural Equation Modelling of Lean Manufacturing Practices.” International Journal of Produc-tion Research 50 (6): 1598–1607.

Wang, Yonggui, Hing-Po Lo, and Yongheng Yang. 2004. “An Integrated Framework for Service Quality, Customer Value, Satisfac-tion: Evidence from China’s Telecommunication Industry.” Information Systems Frontiers 6 (4): 325–340.

Wang, Kangzhou, Zhibin Jiang, Na Li, and Na Geng. 2013. “Optimal Production and Admission Control for a Stochastic SOMSystem with Demands for Product and PSS.” International Journal of Production Research 6: 1–19.

International Journal of Production Research 5511

Dow

nloa

ded

by [

Shan

ghai

Jia

oton

g U

nive

rsity

] at

21:

16 1

3 D

ecem

ber

2014

Williams, Andrew. 2007. “Product Service Systems in the Automobile Industry: Contribution to System Innovation?” Journal ofCleaner Production 15 (11–12): 1093–1103.

Yan Bo. 2013. “Study on Customer Satisfaction Evaluation of the New Car Quality.” Engineering Master dissertation., Shanghai JiaoTong University.

Yang, Z., and R. T. Peterson. 2008. “Customer Perceived Value, Satisfaction and Loyalty: The Role of Switching Costs.” Psychologyand Marketing 21 (10): 799–822.

Yee, Rachel W. Y., Andy C. L. Yeung, and T. C. Edwin Cheng. 2010. “An Empirical Study of Employee Loyalty, Service Qualityand Firm Performance in the Service Industry.” International Journal of Production Economics 124 (1): 109–120.

Zeithaml, V. A. 1988. “Consumer Perceptions of Price, Quality and Value: A Means-end Model and Synthesis of Evidence.” Journalof Marketing 52 (7): 2–22.

Zhang, Xiang, Chen Ye, Rongqiu Chen, and Zhaohua Wang. 2011. “Multi-Focused Strategy in Value Co-creation with Customers:Examining Cumulative Development Pattern with New Capabilities.” International Journal of Production Economics 132 (1):122–130.

Zhang, Yong, Yifeng Yu, and Bai Zou. 2011. “Analyzing Public Awareness and Acceptance of Alternative Fuel Vehicles in China:The Case of EV.” Energy Policy 39 (11): 7015–7024.

Appendix 1All measures used a LIKERT scale ranging from 1 = complete unconformity to 7 = complete conformity.

(1) Information sharing(a) I actively express my demand information in the process of buying.(b) I actively express my purchase plan in the process of buying.(c) I actively learn it when the company shares their features.

(2) Cooperative production(a) I involved in the inspection of rental car condition before and after my usage.(b) I participate in selecting the new network node and the new types of the cars.(c) I will put my opinions to the company after the lease.

(3) Personal communication(a) I discuss with the staff when we check the car.(b) I will contact the company in time if some problems happen during the usage.(c) The staff still keeps in touch with me after the lease.

(4) Technical quality(a) The cruising range is enough?(b) The max speed can meet my expectation?(c) The charging time is acceptable?(d) The charging network can satisfy my needs?(e) The energy consumption per one hundred kilometers is acceptable?

(5) Functional quality(a) The guarantee deposit is acceptable?(b) Unit maintenance cost during lease term is acceptable?(c) The service is enthusiastic and initiative?(d) The lease contract is clear enough?(e) The company has a mature assessment system of the car condition.

(6) Company image(a) The store image is very good?(b) The company’s reputation is very good?(c) The societal values approve the electric car rental.

(7) Emotional value(a) It is convenient for me to use the rental service.(b) It is fashionable for me to use the rental service.(c) It is comfortable for me to use the rental service.

(8) Social value(a) I make my contribution to environmental protection.(b) I make my contribution to improve the traffic condition.(c) Others admire my behavior of renting an electric car.

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(d) Renting an electric car to help me make a good impression to others(9) Price effect

(a) Renting an electric car can save the maintenance cost.(b) Renting an electric car can save the money to buy a car.(c) Renting an electric car can avoid the cost of disposal.

(10) Production function(a) I can select different types of cars according to different situations.(b) I can select different lease term flexibly.(c) The company can provide sufficient customer service (maintenance, driving service, etc.).(d) It is very convenient to give back the car or make a contract extension.

(11) Customer satisfaction(a) I’m satisfied with the company’s product and service.(b) There is nothing to complain.(c) The company’s service and product can meet my expectation.

(12) Post-purchase intention(a) I will continue to renting electric cars when I need.(b) I will purchase the service again at the same company.(c) I will recommend it to my friends.

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