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| Title: | 群體顧客期望控制―在發展型服務提供者環境下以粒子群演算法為基礎之協同互動設計
PSO-based collaborative interaction design For group expectation control in low-moderate competence service providers |
| Authors: | 郭瑞麟 |
| Contributors: | 苑守慈
郭瑞麟 |
| Keywords: | 顧客滿意度
粒子群演算法
協同式互動設計
Customer Satisfaction
Particle Swarm Optimizatio
Collaborative Interaction Design |
| Date: | 2009 |
| Issue Date: | 2016-05-09 15:18:13 (UTC+8) |
| Abstract: | 隨著服務體驗經濟時代的來臨，服務提供商所面臨到的環境是愈來愈競爭及逐漸是轉由消費者所主導的型式，因此服務提供商如何去滿足顧客的需求並且達成更高的滿意度便是主要的目標之一。特別是在發展型服務提供者的環境下，他們需要去考量自已本身較不充足的服務能力及資源來設計及提供服務給顧客，而這樣的條件之下，他們也很難在短時間及時的去改善並且提供穩定的服務品質。因此，本研究提出一套是基於顧客期望理論及粒子群演算法的架構下所發展的協同式互動設計機制，希望協助發展型服務提供商解決他們所面臨的問題及創造出更大的服務價值。
本研究將協同式互動設計機制應用在會展產業的服務環境底下，並利用模擬實驗的方式去驗證此機制的有效性及鞏固性。協同式互動設計機制共有四大模組：(1)顧客偏好識別模組 (2) 粒子群期望因子選擇模組 (3) 情境式旅程抉擇模組 及(4) 服務執行模組。本研究設計此機制時考量了加入顧客間互動的能力來幫助發展型服務提供商進行更有效的服務互動並且執有效的顧客群體期望控制的目標，以便在減輕服務提供商所付出的成本之下，還能達成良好的顧客滿意度。而本研究的研究貢獻為幫助發展型服務提供商解決他們所面臨的挑戰，並且在有限的資源和能力底下，仍然可以使得他們保持與高能力服務廠商之間的競爭;而另一貢獻為在整體的服務環境底下，能讓所有的參與角色都能夠得到最大的價值，而形成一個高效能的服務生態系統。
With the progressive advancement of the technology and fiercely-competitive environment in recent years, customers have paid more attention to the issue that how diversity and rich the service experience could satisfy their needs; in other words, the service providers must acquire the competitive advantage among other service competitors by pondering on that how to deliver the qualified service offerings in every service encounter to achieve the objective of customer satisfaction. On the other hand, many research findings noted that customers’ service quality evaluation in service encounter were influenced by the comparison between the customer expectation toward service and the service performance that they perceived; therefore, managing the customer expectation becomes the vital part concerning the customer satisfaction. Furthermore, the shortcomings of the low-moderate competence service providers is that they could not provide the constant qualified service offerings to customers in each service interaction in terms of the reason for lesser service capability and resource.
Consequently, this study propose the collaborative interaction design approach which based on the Particle Swarm Optimization(PSO) algorithm to generate the dynamical service interaction among the service providers and customers for the low-moderate competence service providers and aids them to control their group customers’ expectation by collaborating with customers; in other words, the service effort of the service provider could be lightened by engaging the customer capability and the service offerings could be enhanced to provider for customers. Therefore, this study utilizes the four modules in the research framework to achieve the aforementioned objective. Ultimately, the expected contributions of this study are two-folds: (1) Aid the low-moderate competence service providers to improve the service experience for customers on the restriction of lesser service capability. (2) Utilize the PSO algorithm to decide the determinants that effectively influence on customers’ expectation considering the whole benefits among stakeholders. Hence, the collaborative interaction design proposed in this study has conspicuous benefits for the low-moderate competence service providers to preserve the competitive advantage by providing the well-design exemplar to let them follow. |
| Reference: | [1] An-Tien Hsieh. (2004). Participative customers as partial employees and service provider workload. International Journal of Service Industry Management, 15(2), 187-199.
[2] Alderfer, C. P. (1969). An empirical test of a new theory of human needs. Organizational behavior and Human Performance, 4, 142-175.
[3] A. Hevner, S. March, J. Park, and S. Ram, “Design Science Research in Information Systems,” Management information Systems Quarterly, Vol. 28, No. 1, March 2004, pp. 75-105.
[4] Boulding, W., Kalra, A., Staelin, R. & Zeithaml, V.A. (1993). A dynamic
process model of service quality: from expectations to behavioral intentions. Journal of Marketing Research, XXX(February), 7-27.
[5] Bailey, J. J., Gremler D. D., & McCollough M. A. (2001). Service encounter
emotional Value: The dyadic influence of customer and employee emotions. Services Marketing Quarterly, 23(1), 1-25.
[6] Bettencourt, L. A. (1997). Customer voluntary performance: Customers as partners in service delivery. Journal of Retailing, 73(3), 383-407.
[7] Bagozzi, Richard P. (1995). Reflections on relationship marketing in consumer
markets. Journal of the Academic of Marketing Science, 23 (Fall), 272-277.
[8] Burke, Jonathan L. (1995) "Existence of a Pareto-optimal equilibrium in nearly-stationary overlapping-generations economies Economic Theory," Vol. 5 Issue 2, p247-261.
[9] Claycomb, C., Lengnick-Hall, C.A. &, Inks, L.W. (2001). The customer as a productive resource: a pilot study and strategic implications. Journal of Business Strategies, 18(1), 47-68.
[10] Clerc, M. (1999). The swarm and the queen: towards a deterministic and adaptive
particle swarm optimization. Proc. 1999 Congress on Evolutionary Computation,Washington, DC, 1951-1957.
[11] Czepiel, J.A., Solomon, M.R., Surprenant, C.F. & Gutman, E.G.. (1985). Service
encounters:an overview.Chapter1 in Czepiel, J.A., Solomon, M.R. and
Surprenant, C.F. (Eds). The Service Encounter, Lexington Books, Lexington, MA, 3-15.
[12] Frances X. F. (2006). Breaking the Trade-Off Between Efficiency and Service. Harvard Business Review, 84(11), 92-101.
[13] Gruen, T. W. (1995). The outcome set of relationship marketing in consumer
markets. International Business Review, 4(4), 447-69.
[14] Gremler, D. D. & Gwinner, K. P. (2000). Customer-Employee rapport in
service relationships. Journal of Service Research, 3(1), 82-104.
[15] Hill, D.J. (1986). Satisfaction and consumer services. Advances in Consumer
Research, 13, 311-5.
[16] Jim Spohrer, Stephen K. Kwan. (2008). Service science, management, engineering, and design (SSMED) : An emerging discipline - outline & references. Unpublished Working Paper.
[17] Johnston, R. & Clark, G. (2001). Service operations management, Prentice-Hall, Harlow.
[18] James L. Walker. (1995). Service encounter satisfaction: conceptualized. Journal
of service marketing 9(1), 5-14.
[19] Kennedy, J. and Eberhart, R.C. Swarm intelligence. Morgan Kaufmann Press.
[20] Keh, H. T. and C. W. Teo. (2001). Retail customers as partial employees in
service provision: A conceptual framework. International Journal of Retail and Distribution Management 29(8), 370-78.
[21]Krieger, Abba M., Green, Paul E., and Marketing Letters (1991) "Designing Pareto Optimal Stimuli for Multiattribute Choice Experiments" Nov91, Vol. 2 Issue 4, p337-348
[22] Levitt, T. (1983). The marketing imagination, The Free Press. New York:NY.
[23] Morris, B. & Johnston, R. (1988 ). Dealing with inherent variability: the difference between manufacturing and service. International Journal of Operations & Production Management, 7(4).
[24] Mehrabian, A. & Russell, J.A. (1974). An approach to environmental psychology, Cambridge, Mass.: MIT Press.
[25] National Chengchi University, Service Science Research Center. (2009). [The marketing study of the 2009 auto tronics show].Unpublished raw data.
[26] Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985). A conceptual model of
service quality and its implications for future research. Journal of Marketing, 49(Fall), 41-50.
[27] Parasuraman, A., Berry, L.L. & Zeithaml, V.A. (1991). Refinement and
reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420-50.
[28] Parasuraman, A., Berry, L. L. & Zeithaml, V. A. (1991). Understanding customer
expectations of service. Sloan Management Review, 32(3), 39-48.
[29] Parasuraman, Zeithaml, V. A. & A., Berry, L. L. (1988). SERVQUAL: A
multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(Spring), 12-40.
[30] SSRC. (2009 ). 全體國外買主調查報告：2009台北國際自行車展、台北國際車用電子展. Taipei: Service Science Research Center, National Chengchi University.
[31] Shi, Y. and Eberhart, R.C. (1998). A modified particle swarm optimizer. IEEE
International Conference on Evolutionary Programming, Alaska, May 4-9.
[32] Walker, J., & Baker, J. (2000). An exploratory study of a multi-expectation
framework for services. Journal of Service Marketing 14(5), 411-431.
[33] Hsieh, Y. H., & Yuan, S. T. (2009). Exquisite technology for services: A case study on an Ubiquitous 2.0 exhibition service system. Unpublished Working Paper. Service Science Research Center, NCCU.
[34] Yen-Hao Hsieh and Soe-Tsyr Yuan "A Quantitative Approach to Measure Customer Expectation for Service Innovation within Service Experience Delivery," The 11th International Conference on Electronic commerce (ICEC), Taipei, Taiwan, August 12-15, 2009
[35] Zeithaml, V.A., Berry, L.L. & Parasuraman, A. (1993). The nature and determinants of customer expectations of service. Journal of the Academy of Marketing Science, 21(1), 1-12. |
| Description: | 碩士
國立政治大學
資訊管理學系
96356013 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0096356013 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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