医疗旅游
旅游
营销
背景(考古学)
业务
忠诚
卓越
独创性
服务(商务)
消费者行为
概念框架
关系营销
产品(数学)
医疗保健
旅游地理学
晋升(国际象棋)
公共关系
市场营销管理
心理学
社会学
政治学
地理
社会心理学
几何学
政治
考古
法学
社会科学
数学
创造力
作者
Bruno Sousa,Gisela Alves
标识
DOI:10.1108/jhti-05-2018-0032
摘要
Purpose This paper entails a reflection on medical tourism services and guest experiences. The purpose of this paper is to analyze how relationship marketing relates to other relevant variables in consumer’s behavior applied to medical tourism contexts and guest experiences. This study aims at discussing the customer behavior in healthcare management and medical tourism contexts and addresses the predisposition for the destination and the influence of relationship marketing on behavioral intentions. Design/methodology/approach The paper starts from a conceptual framework based on relationship marketing theory. From this theoretical base, the concepts of trust, commitment and cooperation and behavioral intentions are derived. A theoretical model is developed specifying antecedents of satisfaction and loyalty in healthcare management and medical tourism contexts. Findings The conceptual model shows that tourist destinations in the context of healthcare and medical tourism can be managed together with the study of the tourist consumer behavior and should focus on aspects that reinforce relationship marketing to the site, as planning services excellence, communication strategies, promotion services, integrated experiences and combating seasonality. Originality/value This study has already identified that the global movement of tourism is seemingly showing an increased focus on the niche product or niche service. In this case, the question seems to be whether the further growth in demand for healthcare management and medical tourism – as a niche tourism example – products will continue until they take a form of mass tourism. The new vogue of medical tourism forces to challenge and re-visit the power relationships that exist within contemporary tourism and the host–guest relationship.
科研通智能强力驱动
Strongly Powered by AbleSci AI