IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1833-d742845.html
   My bibliography  Save this article

Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis

Author

Listed:
  • Tien-Hsiang Chang

    (Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan)

  • Kuei-Ying Hsu

    (Department of Marketing Management, Shu-Te University, Kaohsiung City 824, Taiwan)

  • Hsin-Pin Fu

    (Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan)

  • Ying-Hua Teng

    (Graduate Institute of Management, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan)

  • Yi-Jhen Li

    (Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan)

Abstract

In this study, we explore the needs of different valuable customer groups for service quality and how limited resources are allocated to enhance service quality. Accordingly, we propose a hybrid multi-criteria decision-making (MCDM) tool that uses fuzzy synthetic evaluation (FSE) in combination with the analytic hierarchy process (AHP) to help companies enhance understanding of quantitative data (the weights of the factors that affect service quality) and qualitative information to identify valuable customers. Fifty-three experts and 304 consumers at convenience stores (CVS) comprise the data set. We employed the AHP to obtain index weights in the second step of FSE and conducted FSE to determine the importance of various valuable customer groups. The results demonstrate that different valuable customer groups have dissimilar perceptions and feelings about service quality. The findings indicate that customers between “20 to 29 years old” are the most valuable customer group and that most consumers do not care much about “problem solving”. The analysis is distinct from extant work in that it examines the effect of receiving service quality from a consumer viewpoint, as we conducted a comprehensive analysis from both customer and expert perspectives.

Suggested Citation

  • Tien-Hsiang Chang & Kuei-Ying Hsu & Hsin-Pin Fu & Ying-Hua Teng & Yi-Jhen Li, 2022. "Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis," Sustainability, MDPI, vol. 14(3), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1833-:d:742845
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1833/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/3/1833/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Getasew Taddese & Severine Durieux & Emmanuel Duc, 2021. "Sustainability Performance Evaluation of Faceshield Bracket Manufacturing by Using the Analytic Hierarchy Process," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    2. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    3. Necati Ertekin & Michael E. Ketzenberg & Gregory R. Heim, 2020. "Assessing Impacts of Store and Salesperson Dimensions of Retail Service Quality on Consumer Returns," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1232-1255, May.
    4. Diogo Rodrigues & Radu Godina & Pedro Espadinha da Cruz, 2021. "Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    5. Lee, Kiljae & Yu, Chunyan, 2018. "Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 28-44.
    6. Katarzyna Pietrucha-Urbanik & Janusz R. Rak, 2020. "Consumers’ Perceptions of the Supply of Tap Water in Crisis Situations," Energies, MDPI, vol. 13(14), pages 1-20, July.
    7. Fan-Yun Pai & Chien-Ping Chen & Tsu-Ming Yeh & Massoud Metghalchi, 2017. "The effects of promotion activities on consumers' purchase intention in chain convenience stores," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 12(4), pages 413-432.
    8. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marko Šostar & Vladimir Ristanović, 2023. "An Assessment of the Impact of the COVID-19 Pandemic on Consumer Behavior Using the Analytic Hierarchy Process Model," Sustainability, MDPI, vol. 15(20), pages 1-31, October.
    2. Jincang Yang & Xueqin Dong & Sishi Liu, 2022. "Safety Risks of Primary and Secondary Schools in China: A Systematic Analysis Using AHP–EWM Method," Sustainability, MDPI, vol. 14(13), pages 1-15, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    2. Józef Ober & Janusz Karwot, 2021. "Tap Water Quality: Seasonal User Surveys in Poland," Energies, MDPI, vol. 14(13), pages 1-22, June.
    3. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    4. Arzum Akkaş & Nachiketa Sahoo, 2020. "Reducing Product Expiration by Aligning Salesforce Incentives: A Data‐driven Approach," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1992-2009, August.
    5. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    6. Thitinan Pholsook & Warit Wipulanusat & Poomporn Thamsatitdej & Sarawut Ramjan & Jirapon Sunkpho & Vatanavongs Ratanavaraha, 2023. "A Three-Stage Hybrid SEM-BN-ANN Approach for Analyzing Airport Service Quality," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    7. Li, Siping & Zhou, Yaoming & Kundu, Tanmoy & Sheu, Jiuh-Biing, 2021. "Spatiotemporal variation of the worldwide air transportation network induced by COVID-19 pandemic in 2020," Transport Policy, Elsevier, vol. 111(C), pages 168-184.
    8. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
    9. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    10. Guo, Xiongfei & Chen, Jing, 2023. "Manufacturer’s quality improvement and Retailer’s In-store service in the presence of customer returns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    11. Udoinyang G. Inyang & Okure O. Obot & Moses E. Ekpenyong & Aliu M. Bolanle, 2017. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification," Modern Applied Science, Canadian Center of Science and Education, vol. 11(9), pages 151-151, September.
    12. Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.
    13. Pagliari, Romano & Graham, Anne, 2019. "An exploratory analysis of the effects of ownership change on airport competition," Transport Policy, Elsevier, vol. 78(C), pages 76-85.
    14. Lin Fang & Fengping Wu, 2020. "Can Water Rights Trading Scheme Promote Regional Water Conservation in China? Evidence from a Time-Varying DID Analysis," IJERPH, MDPI, vol. 17(18), pages 1-14, September.
    15. Aihua Long & Pei Zhang & Yang Hai & Xiaoya Deng & Junfeng Li & Jie Wang, 2020. "Spatio-Temporal Variations of Crop Water Footprint and Its Influencing Factors in Xinjiang, China during 1988–2017," Sustainability, MDPI, vol. 12(22), pages 1-15, November.
    16. Dawid Szpak, 2020. "Method for Determining the Probability of a Lack of Water Supply to Consumers," Energies, MDPI, vol. 13(20), pages 1-16, October.
    17. Jing-Wei Liu & Che-Wei Chang & Yao-Ji Wang & Yi-Hui Liu, 2022. "Constructing a Decision Model for Health Club Members to Purchase Coaching Programs during the COVID-19 Epidemic," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    18. Yu Zhou & Shanshan Shi & Shaohua Wang, 2022. "A Multi-Agent Model-Based Evolutionary Model of Port Service Value Network and Decision Preferences," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    19. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    20. Katarzyna Pietrucha-Urbanik & Barbara Tchórzewska-Cieślak & Mohamed Eid, 2021. "A Case Study in View of Developing Predictive Models for Water Supply System Management," Energies, MDPI, vol. 14(11), pages 1-25, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1833-:d:742845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.