FCM-based customer expectation-driven service dispatch system

Maintaining long-term customer loyalty has been an important issue in the service industry. Although customer satisfaction can be enhanced with better service quality, delivering appropriate services to customers poses challenges to service providers, particularly in real-time and resource-limited dynamic service contexts. However, customer expectation management has been regarded as an effective way for helping service providers achieve high customer satisfaction in the real world that is nevertheless less real-time and dynamic. This study designs a FCM-based customer expectation-driven service dispatch system to empower providers with the capability to deal effectively with the problem of delivering right services to right customers in right contexts. Our evaluation results show that service providers can make appropriate decisions on service dispatch for customers by effectively managing customer expectations and arranging their contextual limited resources and time via the proposed service dispatch system. Meanwhile, customers can receive suitable service and obtain high satisfaction when appropriate services are provided. Accordingly, a high-performance ecosystem can be established by both service providers and customers who co-create value in the dynamic service contexts.

[1]  W. Renhart,et al.  Pareto optimality and particle swarm optimization , 2004, IEEE Transactions on Magnetics.

[2]  Gary W. Loveman,et al.  Putting the Service-Profit Chain to Work , 1994 .

[3]  Luis V. Dominguez,et al.  Channel Evolution: A Framework for Analysis: , 1992 .

[4]  J. Elbrond,et al.  Towards integrated production planning and truck dispatching in open pit mines , 1987 .

[5]  Daniel P. Siewiorek,et al.  Practical solutions for QoS-based resource allocation problems , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[6]  Stephen L. Vargo,et al.  The Service-dominant Logic of Marketing: Dialog, Debate, and Directions , 2006 .

[7]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[8]  Ray W. Coye,et al.  Managing customer expectations in the service encounter , 2004 .

[9]  Kin Keung Lai,et al.  A dynamic approach to multiple-objective resource allocation problem , 1999, Eur. J. Oper. Res..

[10]  Leyland Pitt,et al.  Management of Customer Expectations in Service Firms: A Study and a Checklist , 1994 .

[11]  Ingoo Han,et al.  Fuzzy cognitive map for the design of EDI controls , 2000, Inf. Manag..

[12]  Paul E. Green,et al.  Designing Pareto optimal stimuli for multiattribute choice experiments , 1991 .

[13]  Kok Kiong Tan,et al.  Evolutionary tuning of a fuzzy dispatching system for automated guided vehicles , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Chris Cannings,et al.  A Resource Allocation Problem , 1994 .

[15]  Florentin Smarandache,et al.  FUZZY COGNITIVE MAPS AND NEUTROSOPHIC COGNITIVE MAPS , 2003, math/0311063.

[16]  Marco Pranzo,et al.  An Advanced Real-Time Train Dispatching System for Minimizing the Propagation of Delays in a Dispatching Area Under Severe Disturbances , 2009 .

[17]  Michael D. Reilly,et al.  Value-Percept Disparity: an Alternative to the Disconfirmation of Expectations Theory of Consumer Satisfaction , 1983 .

[18]  Giovanni Acampora,et al.  Distributing emotional services in Ambient Intelligence through cognitive agents , 2011, Service Oriented Computing and Applications.

[19]  Weihua Gui,et al.  An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity , 2002, IEEE Trans. Neural Networks.

[20]  Stephen L. Vargo,et al.  Evolving to a New Dominant Logic for Marketing , 2004 .

[21]  Chou-Yuan Lee,et al.  Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Daniel P. Siewiorek,et al.  A resource allocation model for QoS management , 1997, Proceedings Real-Time Systems Symposium.

[23]  Karl Marx,et al.  Theories of Surplus Value , 1951 .

[24]  B. Nicoulaud Problems and Strategies in the International Marketing of Services , 1989 .

[25]  K. E. Clow,et al.  Managing consumer expectations of low‐margin, high‐volume services , 1995 .

[26]  Toshihide Ibaraki,et al.  Resource allocation problems - algorithmic approaches , 1988, MIT Press series in the foundations of computing.

[27]  Rogelio Oliva,et al.  Cutting Corners and Working Overtime: Quality Erosion in the Service Industry , 2001, Manag. Sci..

[28]  N. Nohria,et al.  Are leaders portable? , 2006, Harvard business review.

[29]  A. Parasuraman,et al.  The nature and determinants of customer expectations of service , 1993 .

[30]  F. Frei,et al.  Breaking the trade-off between efficiency and service. , 2006, Harvard business review.

[31]  Soe-Tsyr Yuan,et al.  Design of the Customer Expectation Measurement Model in Dynamic Service Experience Delivery , 2010, Pac. Asia J. Assoc. Inf. Syst..

[32]  Giovanni Acampora,et al.  On the Temporal Granularity in Fuzzy Cognitive Maps , 2011, IEEE Transactions on Fuzzy Systems.

[33]  A. Parasuraman,et al.  A Conceptual Model of Service Quality and Its Implications for Future Research , 1985 .

[34]  John P. Lehoczky,et al.  Practical Solutions for QoS-Based Resource Allocation , 1998, RTSS 1998.

[35]  R. B. Woodruff,et al.  Expectations and norms in models of consumer satisfaction. , 1987 .

[36]  Tad S. Golosinski,et al.  Off-Highway Haulage in Surface Mines , 1989 .

[37]  Jonathan Lee,et al.  Modeling uncertainty reasoning with possibilistic Petri nets , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[38]  Socrates Litsios A Resource Allocation Problem , 1965 .

[39]  Horacio J. Marquez,et al.  A stochastic optimization approach to mine truck allocation , 2005 .

[40]  Soe-Tsyr Yuan,et al.  Modeling service experience design processes with customer expectation management: A system dynamics perspective , 2010, Kybernetes.

[41]  An-Pin Chen,et al.  Development of a decision support system for service delivery , 1993 .

[42]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[43]  Kenneth E. Clow,et al.  The antecedents of consumer expectations of services: an empirical study across four industries , 1997 .

[44]  C. Meyer,et al.  Understanding customer experience. , 2007, Harvard business review.

[45]  Kut C. So,et al.  Price and Time Competition for Service Delivery , 2000, Manuf. Serv. Oper. Manag..

[46]  Naoki Katoh,et al.  Resource Allocation Problems , 1998 .

[47]  Rita Di Mascio,et al.  A method to evaluate service delivery process quality , 2007 .

[48]  L. L. Thurstone,et al.  Fechner's law and the method of equal appearing intervals. , 1929 .

[49]  Bengt Carlsson,et al.  Surplus Values in Information Ecosystems , 2002, Int. J. Inf. Technol. Decis. Mak..

[50]  Christos G. Cassandras,et al.  Optimal dispatching control for elevator systems during uppeak traffic , 1997, IEEE Trans. Control. Syst. Technol..