Consumer hierarchical value map modeling in the healthcare service industry

Estimating consumer value is useful for comprehending consumers' need. Most research of consumer value focus on discussing single attribute but are incapable of exploring multi attributes to explore its entire connotation including attributes, consequences and values. However, it is important to understand what attribute do consumer care and their linkage in marketing decisions. The current study applies Means-End Chain (MEC) to model a healthcare consumer Hierarchical Value Map (HVM) as 3-tiers with line segments connecting related nodes at each level for understanding how consumers transform service attributes into their own value. The current study includes conducting the consumer categorization processes and Second order Confirmatory Factor Analysis (SCFA) to test the goodness-of-fit of consumer value model and analyze relationships between consumer value and satisfaction. Then, apply hard laddering to collect data for constructing a hierarchical value map. The study not only enhances the empirical insufficiency in results and value level of consumer value, but also confirms Holbrook's (1994c, 1996, 1999) eight types of consumer value including efficiency, excellence, status, esteem, play, aesthetics, ethics and spirituality on healthcare institutions. The results reveal that each attribute of excellence value is completely linked in three levels. On the other hand, the spirituality value has the weakest links in consumer cognitive structure where the number of linkage between attribute-result and result-value is the least. Finally, satisfaction in value level is linked with waiting time of outpatient services, administration procedures, registration service, range of services, attitude/courtesy of nurses, physician expertise and treatment effect in attribute level. These findings are relevant to issues concerned with healthcare in Taiwan and other developing nations which serve as an important reference for the healthcare service industry.