Abstract
This paper explores the factors that affect employees' service recovery performance from the perspectives of internal management. In addition, this paper combines the techniques of linear multi-variable statistical analysis and nonlinear fuzzy neural network methodology to analyse data and validate hypotheses. This paper selects travel agents who have frequent interactions with customers and provide intangible services. The empirical finding indicates that the more authoritative leaders have a more negative effect on employees' service recovery performance. Strong corporate culture works against employees' service recovery performance. Employees with high self-efficacy have better service recovery performance. The important characteristic of this paper is that it combines multi-variable analysis and nonlinear fuzzy neural network methodology in its empirical approach. Meanwhile, the factors concerning internal management within an organisation are examined, and measures to enhance employees' service recovery performance are proposed. In other words, these are responses to improve service recovery performance of employees on a preventive basis within an organisation. This is very different from the consumers' perspectives as seen in most of the previous literature. Past studies tend to focus on the amendments and responses of service recovery from the perspectives of consumers, and a vast majority of the case studies examine western enterprises. This paper studies small and medium enterprises in Taiwan and illustrates the factors that affect employees' service recovery performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.