2010
DOI: 10.1016/j.aei.2009.06.003
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Management and forecast of dynamic customer needs: An artificial immune and neural system approach

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Cited by 40 publications
(18 citation statements)
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“…In addition, service firms are constantly seeking to provide better quality services and to create new markets by relying on innovation (Berry et al 2006). Recent changes in service innovation have arisen for reasons related to the diversification of customer needs, trends in convergence services, and the increased competition and international specialization caused by globalization (Chong and Chen 2010;Redondo-Cano and Canet-Giner 2010). Thus, service firms should focus on the interactions with their external environments as a critical factor for the access, acquisition, and development of new ideas (Caloghirou et al 2004).…”
Section: Introductionmentioning
confidence: 99%
“…In addition, service firms are constantly seeking to provide better quality services and to create new markets by relying on innovation (Berry et al 2006). Recent changes in service innovation have arisen for reasons related to the diversification of customer needs, trends in convergence services, and the increased competition and international specialization caused by globalization (Chong and Chen 2010;Redondo-Cano and Canet-Giner 2010). Thus, service firms should focus on the interactions with their external environments as a critical factor for the access, acquisition, and development of new ideas (Caloghirou et al 2004).…”
Section: Introductionmentioning
confidence: 99%
“…Service innovation plays a critical role in bringing competitive advantage under the severe competition circumstance with dynamic customer requirements in the services sector [34,6]. How enterprises develop new services is an important research topic in service innovation and service design [21,23,5].…”
Section: Introductionmentioning
confidence: 99%
“…Many analytical models have been employed to forecast the dynamics of quality attributes. Some commonly adopted methods include statistical models, such as compositional double exponential smoothing method [11], fuzzy trend analysis [12] and artificial intelligence (AI) models [13]. Different models have different requirements on the amount of historical data to be used, and the prediction accuracy depends on the property of the data.…”
Section: Introductionmentioning
confidence: 99%