Evaluating End-User Satisfaction of Retail Minimarket Applications Using the Net Promoter Score Method
DOI:
https://doi.org/10.70356/josapen.v4i1.90Keywords:
End-User Satisfaction, Retail Minimarket Applications, Net Promotor Score, Information Systems EvaluationAbstract
The rapid adoption of digital applications in retail minimarkets has made end-user satisfaction a critical factor influencing customer loyalty and continued usage. This study evaluates end-user satisfaction of a retail minimarket application using the Net Promoter Score (NPS) method, which measures users’ willingness to recommend the application to others. A quantitative approach was employed by distributing an NPS-based questionnaire to active users of the application. Respondents were classified into promoters, passives, and detractors based on their ratings. The results show that 52.0% of users were promoters, 27.3% were passives, and 20.7% were detractors, resulting in an NPS value of 31.3. This positive score indicates that the application is generally well received and demonstrates a satisfactory level of user loyalty. However, feedback from passive and detractor users reveals opportunities for improvement in system performance and feature completeness. Overall, the study confirms that the NPS method is an effective and practical tool for assessing end-user satisfaction in retail minimarket applications.
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References
A. L. Roggeveen et al., “Forging meaningful consumer-brand relationships through creative merchandise offerings and innovative merchandising strategies,” vol. 97, pp. 81–98, 2021, doi: https://doi.org/10.1016/j.jretai.2020.11.006.
R. Saha, U. Jangbahadur, R. Sharma, M. Sinha, and S. Ahlawat, “Acta Psychologica The psychological and emotional factors of gamification and customer experience . A systematic literature review and future research agenda,” Acta Psychol. (Amst)., vol. 260, no. October, p. 105659, 2025, doi: https://doi.org/10.1016/j.actpsy.2025.105659.
Y. Jiang and N. Stylos, “Technological Forecasting & Social Change Triggers of consumers ’ enhanced digital engagement and the role of digital technologies in transforming the retail ecosystem during COVID-19 pandemic,” Technol. Forecast. Soc. Chang., vol. 172, no. November 2020, p. 121029, 2021, doi: https://doi.org/10.1016/j.techfore.2021.121029.
D. Varastika, M. E. Wijaya, M. Shannon, C. Aurelie, P. W. Handayani, and H. Fitriani, “Telematics and Informatics Reports Mobile passport success in indonesia based on the DeLone – McLean information systems success model,” Telemat. Informatics Reports, vol. 20, no. September, p. 100251, 2025, doi: https://doi.org/10.1016/j.teler.2025.100251.
A. M. Shaheen, A. R. Ellien, A. A. El-ela, and A. M. El-rifaie, “Electric vehicles with renewables integration in electrical power systems : A review of technologies , uncertainties and optimization allocations,” Unconv. Resour., vol. 9, no. October 2025, p. 100252, 2026, doi: https://doi.org/10.1016/j.uncres.2025.100252.
T. D. Putri, “Heliyon Intelligent transportation systems ( ITS ): A systematic review using a Natural Language Processing ( NLP ) approach,” HLY, vol. 7, no. 12, p. e08615, 2021, doi: https://doi.org/10.1016/j.heliyon.2021.e08615.
H. Aguinis and J. Burgi-tian, “ScienceDirect Measuring performance during crises and beyond : The Performance Promoter Score,” Bus. Horiz., vol. 64, no. 1, pp. 149–160, 2021, doi: https://doi.org/10.1016/j.bushor.2020.09.001.
V. Norouzi, “Predicting e-commerce CLV with neural networks_ The role of NPS, ATV, and CES,” J. Econ. Technol., vol. 2, no. March, pp. 174–189, 2024, doi: https://doi.org/10.1016/j.ject.2024.04.004.
T. Thuy, A. Ngo, H. Khanh, L. Chau, N. Phuc, and N. Tran, “Acta Psychologica The impact of AI-powered service on customer continuance usage intention in E -retailing : An extended expectation confirmation model,” Acta Psychol. (Amst)., vol. 262, no. December 2025, p. 106095, 2026, doi: https://doi.org/10.1016/j.actpsy.2025.106095.
M. Arce-urriza and R. Chocarro, “Journal of Retailing and Consumer Services From familiarity to acceptance : The impact of Generative Artificial Intelligence on consumer adoption of retail chatbots,” vol. 84, no. January, 2025, doi: https://doi.org/10.1016/j.jretconser.2025.104234.
S. Mahmudur, J. Carlson, N. H. Chowdhury, S. P. Gudergan, M. Wetzels, and D. Grewal, “Share of time in omnichannel retailing : Definition and measurement,” vol. 101, no. April, pp. 279–297, 2025.
W. Liu, Y. Yuan, Y. Jiang, and J. Mou, “Reforming the SERVQUAL model for accommodation sharing services : A mixed-method approach,” Data Inf. Manag., vol. 9, no. 4, p. 100102, 2025, doi: https://doi.org/10.1016/j.dim.2025.100102.
Y. Tri et al., “Acta Psychologica Cognitive perceptions and behavioral loyalty in telecommunication services : Integrating SERVQUAL and CSI in the Philippine context,” Acta Psychol. (Amst)., vol. 259, no. June, p. 105298, 2025, doi: https://doi.org/10.1016/j.actpsy.2025.105298.
V. P. Aggelidis and P. D. Chatzoglou, “Hospital information systems : Measuring end user computing satisfaction ( EUCS ),” J. Biomed. Inform., vol. 45, no. 3, pp. 566–579, 2012, doi: https://doi.org/10.1016/j.jbi.2012.02.009.
K. E. Conroy et al., “Journal of Mood & Anxiety Disorders Change in psychosocial target processes during prenatal depression intervention : A randomized clinical trial,” vol. 12, no. November, 2025.
M. S. Hadi, A. Q. Lawey, T. E. H. El-gorashi, and J. M. H. Elmirghani, “Big data analytics for wireless and wired network design : A survey,” Comput. Networks, vol. 132, pp. 180–199, 2018, doi: https://doi.org/10.1016/j.comnet.2018.01.016.
M. D. A. L. Rafi, A. S. M. M. Hoque, J. Maua, and M. F. Mridha, “NLP-driven Customer Segmentation: A Comprehensive Review of Methods and Applications in Personalized Marketing,” Data Sci. Manag., 2025, doi: https://doi.org/10.1016/j.dsm.2025.09.002.
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