Biologically Inspired Approaches to Strategic Service Design
借鉴自然选择和群体智能机制，通过遗传算法和粒子群优化处理联合实验数据，帮助管理者设计最优服务组合并实现本地化调整，以匹配异质消费者需求。
This article introduces nature-inspired modeling to strategic service management. It determines optimal service diversification through an evolutionary mechanism of natural selection and population genetics as well as a model of cooperative behavior and collective intelligence in swarms. Specifically, we design and implement Genetic and Particle Swarm Optimization algorithms to stated-preference data derived from a conjoint experiment measuring consumer preferences for service attributes in a retail setting. The proposed procedure provides key insights to strategic service management such as optimal service design, optimal mix of service offerings in terms of consumer demand, and local adaptation of service portfolios. It demonstrates how diversification meets heterogeneous customer preferences and how localized solutions address cross-country differences. The findings suggest that variation in service portfolios elevates customer utility, in the sense that diversified offerings better match heterogeneous customer needs. In an intuitive fashion, consumer diversity is such that a uniform service portfolio is inferior to differentiated offerings, especially with regard to salient service attributes. The results also illustrate that localized diversification strategies are necessary for multistore, multimarket operations. Our method has valuable implications for managers aiming to improve how they design their services. A new tool is introduced which handles tangible and intangible service elements and allows service design optimization by predicting which elements create the most compelling service contexts from a customer perspective. The tool also facilitates localized diversification decisions by adapting critical service attributes to local markets. Bio-inspired models shed new light on marketing phenomena and reveal opportunities for empirical research.