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A hybrid approach for personalized service staff recommendation

Author

Listed:
  • Wei-Lun Chang

    (Tamkang University)

  • Chien-Fang Jung

    (Tamkang University)

Abstract

In this study, we established a novel set of service procedures that epitomize the human-centered spirit of service. By using self-organizing maps and collaborative filtering recommendation, we developed a mechanism that links the two service procedures of selecting service staff members and how customers decide tip amounts based on perceived value. Through the proposed mechanism, the recommender system could effectively predict customer preferences regarding service staff members and assign suitable members for delivering services. In addition, this study integrated the service experiences of previous customers with local tipping cultures for calculating recommended tip amounts for the reference of customers. Under this mechanism, the customer-centered spirit can be completely integrated into service procedures for effectively enhancing customer satisfaction, increasing the job satisfaction of employees, and producing a virtuous cycle of service quality improvement.

Suggested Citation

  • Wei-Lun Chang & Chien-Fang Jung, 2017. "A hybrid approach for personalized service staff recommendation," Information Systems Frontiers, Springer, vol. 19(1), pages 149-163, February.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:1:d:10.1007_s10796-015-9597-7
    DOI: 10.1007/s10796-015-9597-7
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    Cited by:

    1. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
    2. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.
    3. Shivam Gupta & Sachin Modgil & Choong-Ki Lee & Uthayasankar Sivarajah, 2023. "The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry," Information Systems Frontiers, Springer, vol. 25(3), pages 1179-1195, June.
    4. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.
    5. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.

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