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Interpersonal Conflicts in the Unit Impact the Service Quality Rated by Customers: The Mediating Role of Work-Unit Well-Being.

Miriam BenitezJosé M León-PerezAlejandro OrgambídezFrancisco J Medina
Published in: International journal of environmental research and public health (2021)
Social dynamics at work are crucial for understanding how internal processes in an organization are related to their performance and productivity. Following the Service-Profit Chain (SPC) theory, this study analyses, at the work-unit level, how interpersonal conflicts are related to service quality in the hospitality and tourism industry through the shared experience of well-being in the work unit. In other words, we examine the mediating role of two main aspects of work-related well-being in the unit (job satisfaction and burnout) on the relationship between interpersonal conflicts in the unit and customers' perceptions of service quality. To do so, we conducted a cross-sectional survey study that collected data from 398 service employees (91 work units) and 1233 customers from three and four-star hotels with restaurant in Spain. Using path analysis in Structural Equation Models, our results supported a full mediation model at the work-unit level: interpersonal conflicts in the work unit are related to customers' service quality perceptions through the work-unit's well-being (job satisfaction and burnout). Therefore, our findings extend the SPC theory by integrating group dynamics and employees' experiences, which should be enhanced through occupational health-oriented policies and practices to increase service quality. In this sense, this study has implications for the development of intervention programs aiming at improving the occupational well-being and quality of service in hospitality and tourism settings.
Keyphrases
  • healthcare
  • mental health
  • public health
  • primary care
  • quality improvement
  • machine learning
  • climate change
  • social support
  • social media
  • deep learning
  • artificial intelligence
  • big data