[雙語(yǔ)翻譯]服務(wù)質(zhì)量外文翻譯--生產(chǎn)力與服務(wù)質(zhì)量影響服務(wù)業(yè)的因素（英文）

1、 Procedia - Social and Behavioral Sciences 109 ( 2014 ) 487 – 491 1877-0428 © 2014 The Authors. Published by Elsevier Ltd. Selection and peer review under responsibility of Organizing Committee of BEM 2013. d

2、oi: 10.1016/j.sbspro.2013.12.494 ScienceDirect2nd World Conference On Business, Economics And Management -?WCBEM 2012 Productivity and Service Quality: Factors Affecting in Service Industry Serdar Durdyeva, Ali Ihtiyarb

3、*, Syuhaida Ismailc, Fauziah Sh. Ahmadd, Nooh Abu Bakare abcde University Technology Malaysia Kuala Lumpur, Jalan Semarak, 54100, Kuala Lumpur, Malaysia Abstract As part of its aim, this study is going to identify system

4、atically the factors that can often affect productivity and perceived service quality directly or indirectly. Undertaking an in-depth literature review first identified the factors. The final analysis model was built t

5、hrough a statistical analysis conducted with the chosen factors. The results of the analysis indicate that the workforce cluster (e.g. skills and experiences of the workforce) and the work management cluster (e.g. comm

6、unication between parties) have greater impact than the financial management cluster (e.g. reworks) and logistics (e.g. delivery on time). This research focuses on the qualitative perspective of customers on productiv

7、ity and perceived service quality. Although the process of translating qualitative opinions into quantitative data is a matter for debate, the result of this research, when compared to other quantitative studies, can b

8、e used to establish a strategy and an action plan for managing productivity and perceived service quality. Finally, some recommendations have been provided for addressing key factors affecting productivity and perceive

9、d service quality in HIS in Turkey. Keywords: Productivity; Service Quality; Turkey; Home Improvement; 1. Introduction Within service studies, productivity has been one of the most significant issues from an economic p

10、oint of view. It is easy to understand the reasons of the service productivity importance. The concept becomes a key player in the economic growth of developing economies, because of its contribution to the economy and

11、improvement of the living standards. In addition, the productivity of services is not only important in itself, but also because services influence the productivity of other economic sectors. All these factors help ser

12、vices to play a key role in fulfilling the competitiveness of ambitious objectives, full employment and social cohesion and environmental sustainability in any nation. Therefore, as part of its aim, this research seeks

13、 to evaluate factors affecting productivity and perceived service quality (PSQ), which is limited to the Home Improvement Sector (HIS) in Turkey. The research would provide the critical factors, which store, department

14、 and post-sale managers could focus on in order to achieve the significant improvement in the productivity and provide a high service quality. By this way, resources could be optimally disbursed addressing the few case

15、s responsible for the remarkable portion of the productivity issues. * Corresponding Author: Ali Ihtiyar. E-mail address: aliihtiyar@gmail.com Available online at www.sciencedirect.com© 2014 The Authors. Published

16、by Elsevier Ltd. Selection and peer review under responsibility of Organizing Committee of BEM 2013.Open access under CC BY-NC-ND license.Open access under CC BY-NC-ND license.489Serdar Durdyev et al. / Procedia - Soci

17、al and Behavioral Sciences 109 ( 2014 ) 487 – 491 provided with softcopies of the original questionnaire, respectively. Respondents in pilot testing process were asked to comment on the readability, accuracy and c

18、omprehensiveness of the questionnaires. The Cronbach’s alpha coefficient (α) was used to determine the questionnaire reliability, because items, which are used to form a scale (Likert scale), construction at the group

19、level and reliability of each item at the individual level has to be evaluated. A α exceeding 0.9 indicates high reliability, α between 0.9 and 0.7 indicates an acceptable level of reliability, and α below 0.35 indicat

20、es low reliability. For the pilot test, Cronbach’s α of 0.916 was achieved, and the corrected scale consisted 27 structural survey questions representing 27 factors of productivity and PSQ in Turkish HIS. For the quant

21、itative surveys, emails were sent to the target population (customers) in the database inviting them to participate in the online survey. The survey was hosted on a web-based survey platform. Several reminders were sen

22、t at monthly intervals to the potential respondents, to improve the response rate. Only 354 usable feedbacks were received by the cut-off date and this represented about 71.54% of total 495 email invitations that have b

23、een sent. Finally, factor analysis was applied as an appropriate data analysis technique. Factor scores were gained from the defined clusters that were used as independent variables for further analysis. 4. Findings T

24、he factor analysis performed on the 27 items to measure productivity and PSQ of HIS. According to the results of factor analysis, 27 factors were reduced to five main clusters of productivity and PSQ with eigenvalues hi

25、gher than 1.0. Table 1 represents the five extracted clusters, as well as the loading scores of each factor. The reliability analysis of 27 factors at the personal level resulted in a value of 0.916, which verifies the

26、 reliability of the model used in this study. According to the demographic background of the study, 82.53% of participants graduated from tertiary education (university: 52.47%, postgraduate: 30.06%), in this respect,

27、 level of education of the participants’ shows the reliability of the responses of this study. On the other hand, 75.13% of total participants are above the 30 years old and 77.18% of the contributors have a regular in

28、come of at least $1100. Furthermore, the frequencies of shopping and their consuming amount in a time among the customers play a critical role for understanding, analysing and evaluating the buying process. Monthly sho

29、pping frequencies of the participants shows that 58.06% of them making shopping at least six times, which is $28 per shipping. In this study, factor analysis was performed to verify that the four clusters that grouped

30、 the factors were statistically significant. In Table 1, the number of clusters represents the number of groups of factors. Table 1. Factor loading scores Factors α Loadings Workforce Cluster 0.811 Skills and experien

31、ces of the labour 0.923 Training and education 0.876 Motivation 0.813 Length of time in industry 0.764 Length of time in company 0.692 Absenteeism 0.608 Accounted of Variance 0.329 Eigenvalue 8.672 Work Mana

32、gement Cluster 0.855 Communication between parties 0.838 Frequent changings in management style 0.794 Store congestion 0.783 Career opportunities 0.663 Performance monitoring and control 0.597 Accounted of Varia