Determination of factors affecting customer satisfaction towards “maynilad” water utility company: A structural equation modeling-deep learning neural network hybrid approach

Ardvin Kester S. Ong, Yogi Tri Prasetyo*, Mariela Celine C. Sacro, Alycia L. Artes, Mariella Phoemela M. Canonoy, Guia Karyl D. Onda, Satria Fadil Persada, Reny Nadlifatin, Kirstien Paola E. Robas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The Maynilad Water Services Inc. (MWSI) is responsible for supplying water to the west zone of Metro Manila. The utility provides service to 17 cities and municipalities which frequently experience water interruptions and price hikes. This study aimed to identify the key factors affecting customer satisfaction toward MWSI by integrating the SERVQUAL dimensions and Expectation Confirmation Theory (ECT). An online questionnaire was disseminated to 725 MWSI customers using the snowball sampling method to obtain accurate data. Ten latent were analyzed using Structural Equation Modeling and Deep Learning Neural Network hybrid. It was found that Assurance, Tangibles, Empathy, Expectations, Confirmation, Performance, and Water consumption were all factors affecting MWSI customers' satisfaction. Results showed that having an affordable water service, providing accurate water bills, on-time completion of repairs and installations, intermittent water interruptions and professional employees contribute to the general satisfaction. MWSI officials may utilize this study's findings to assess further the quality of their services and design effective policies to improve. The employment of DLNN and SEM hybrid showed promising results when employed in human behavior. Thus, the results of this study would be beneficial when examining satisfaction to utilities and policies among service providers in different countries. Moreover, this study could be extended and applied among other customer and service-focused industries worldwide.

Original languageEnglish
Article numbere13798
JournalHeliyon
Volume9
Issue number3
DOIs
Publication statusPublished - Mar 2023

Keywords

  • Deep learning neural network
  • Expectation-confirmation theory
  • Servqual
  • Structural equation modeling
  • Water utility

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