Authors :
Arthur Rafael T. Bregondo; Marx Janzen B. Lizardo; Grace Lorraine Intal; Gloren S. Fuentes
Volume/Issue :
Volume 11 - 2026, Issue 6 - June
Google Scholar :
https://tinyurl.com/3e83fer6
Scribd :
https://tinyurl.com/2p9j2km7
DOI :
https://doi.org/10.38124/ijisrt/26jun990
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The increasing reliance on digital platforms has transformed automotive service management, necessitating more efficient and user-friendly solutions. This study focuses on enhancing the customer experience at LR Almazan Auto Shop Co. Inc. by prototyping a web-based service management platform that streamlines service requests and provides seamless access to vehicle data. Employing the Design Thinking methodology across five phases—Empathize, Define, Ideate, Prototype, and Test—the research identifies key strategies for improving user satisfaction and operational efficiency. A usability evaluation was conducted with 65 respondents (50 customers, 10 service staff, and 5 administrators) using the System Usability Scale (SUS) and the User Experience Questionnaire Short Version (UEQ-S). The proposed system achieved excellent usability scores, with mean SUS ratings of 83.95 (customers), 87.75 (service staff), and 89.50 (administrators). Comparative analysis projects a potential 30-40% improvement in overall customer satisfaction and significant projected gains in appointment scheduling efficiency, communication transparency, and paperwork handling. Aligned with global efforts to drive industry innovation and digital transformation (UNSDG 9) [20], this study contributes to the modernization of automotive service workflows while integrating security considerations to ensure a reliable and efficient platform.
Keywords :
Automotive Service, Web-Based System, Service Management, System Usability, User Experience, Design Thinking.
References :
- Arabi Moef, Samir Lamouri, Robert Pellerin, Simon Tamayo-Giraldo, Edison Tobon-Valencia, and Romain Eburdy. 2019. Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs. International Journal of Production Research 58, 5 (2019), 1384–1400. https://doi.org/10.1080/00207543.2019.1636323
- Aristoteles, A. P. Aristio, S. M. Febyanti, L. Junaedi, and A. S. Septiananda. 2024. Structural Model for Analyzing the Impact of Social CRM on Customer Relationship Performance in Automotive Manufacturing Company. Procedia Computer Science 234 (2024), 861–868. https://doi.org/10.1016/j.procs.2024.03.073
- B. P. Siahaan and E. A. Prasetio. 2022. Understanding customer insights through big data: Innovations in brand evaluation in the automotive industry. The Asian Journal of Technology Management 15, 1 (2022), 49–66. https://doi.org/10.12695/ajtm.2022.15.1.4
- D. Gonçalves, M. Bergquist, S. Alänge, and R. Bunk. 2022. How Digital Tools Align with Organizational Agility and Strengthen Digital Innovation in Automotive Startups. Procedia Computer Science 196 (2022), 107–116. https://doi.org/10.1016/j.procs.2021.11.079
- F. F. Fahmi and M. Alwy. 2020. Design of Virtual Automotive Showroom with Augmented Reality Technology Using The Smartphone. IOP Conference Series: Materials Science and Engineering 1003 (2020), 012149. https://doi.org/10.1088/1757-899x/1003/1/012149
- K. Rababah, H. Mohd, and H. Ibrahim. 2011. Customer Relationship Management (CRM) Processes from Theory to Practice: The Pre-implementation Plan of CRM System. International Journal of e-Education, e-Business, e-Management and e-Learning 1, 1 (2011), 22–27. https://doi.org/10.7763/IJEEEE.2011.V1.4
- J. L. M. Pérez, J. León, Y. C. Castilla, S. Shahrazad, V. Anjos, T. Adão, M. López, E. Peres, L. Magalhães, and D. G. Gonzalez. 2023. A cloud-based 3D real-time inspection platform for industry: a case-study focusing automotive cast iron parts. Procedia Computer Science 219 (2023), 339–344. https://doi.org/10.1016/j.procs.2023.01.298
- K. Teplická, S. Khouri, T. Mudarri, and M. Freňáková. 2023. Improving the Quality of Automotive Components through the Effective Management of Complaints in Industry 4.0. Applied Sciences 13, 14 (2023), 8402. https://doi.org/10.3390/app13148402
- Lee-Sang Jung and Dae-Hyun Jung. 2018. Design of Integrated Database for CRM in Automobile Maintenance Industry. Journal of Korean Computer Information Society 23, 5 (2018), 55–63. https://doi.org/10.9708/jksci.2018.23.05.055
- M. Esteller-Cucala, V. Fernandez, and D. Villuendas. 2020. Evaluating Personalization: The AB Testing Pitfalls Companies Might Not Be Aware of - A Spotlight on the Automotive Sector Websites. Frontiers in Artificial Intelligence 3 (2020). https://doi.org/10.3389/frai.2020.00020
- M. Rumez, D. Grimm, R. Kriesten, and E. Sax. 2020. An overview of automotive service-oriented architectures and implications for security countermeasures. IEEE Access 8 (2020), 221852–221870. https://ieeexplore.ieee.org/abstract/document/9285284
- P. Ramteke, T. Kolhe, S. Thakare, R. S. Chauhan, P. Neole, and M. Panjwani. 2022. Design and development of web based "Automotive management System." International Journal of Scientific Research in Engineering and Management 6, 3 (2022). https://doi.org/10.55041/IJsREM11837
- Q. Ma and H. Hassan. 2023. Multimodal discourse analysis of automotive websites. Applied Mathematics and Nonlinear Sciences 0, 0 (2023). https://doi.org/10.2478/amns.2023.2.00158
- R. Fletcher, A. Mahindroo, N. Santhanam, and A. Tschiesner. 2020. The case for an end-to-end automotive-software platform. McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-case-for-an-end-to-end-automotive-software-platform
- S. C. W. Ngangi and A. J. Santoso. 2019. Customer Acceptance Analysis of Customer Relationship Management (CRM) Systems in Automotive Company using Technology Acceptance Model (TAM) 2. Indonesian Journal of Information Systems 1, 2 (2019), 133–146. https://doi.org/10.24002/ijis.v1i2.1993
- S. Kraus, S. Durst, J. J. Ferreira, P. M. Veiga, and N. Kailer. 2022. Digital transformation trends in service industries. Service Business 16, 1 (2022), 1–24. https://doi.org/10.1007/s11628-022-00516-6
- S. Llopis-Albert and F. Rubio. 2021. Impact of digital transformation on the automotive industry. Technological Forecasting and Social Change 162 (2021), 120343. https://doi.org/10.1016/j.techfore.2020.120343
- S. N. Samsudin, B. Abdullah, and N. Yusoff. 2022. Customer Satisfaction and Service Experience in Big Data Analytics for Automotive Service Advisor. In 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, Shah Alam, Malaysia, 84–89. https://doi.org/10.1109/I2CACIS54679.2022.9815482
- Toyota Motor Philippines Corporation. n.d. myTOYOTA PH - Apps on Google Play. Retrieved from https://play.google.com/store/apps/details?id=tmp_notifsvl_devvl.app
- United Nations. 2023. Goal 9 | Department of Economic and Social Affairs. Retrieved from https://sdgs.un.org/goals/goal9
- V.-A. Briciu, A. Briciu, C.-A. Tudor, and C. Coman. 2022. Analyzing romanian automotive companies' websites to evaluate the online employer of choice and branding characteristics. SWS Scholarly Society Online Library 09, 01 (2022). https://doi.org/10.35603/sws.iscss.2022/s10.094
- Y.-C. Liu and B. Kim. 2020. V-WorkGen: Virtual Workload Generation Tool for Connected Automotive Services. In 2020 IEEE International Conference on Big Data. IEEE, Atlanta, GA, USA, 1780–1783. https://doi.org/10.1109/BigData50022.2020.9378025
- A. Bangor, P. T. Kortum, and J. T. Miller. 2008. An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction 24, 6 (2008), 574–594. https://doi.org/10.1080/10447310802205776
- J. Brooke. 1996. SUS: A Quick and Dirty Usability Scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, and A. L. McClelland (Eds.), Usability Evaluation in Industry. Taylor and Francis, London, UK, 189–194.
- M. Schrepp, A. Hinderks, and J. Thomaschewski. 2017. Design and Evaluation of a Short Version of the User Experience Questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence 4, 6 (2017), 103–108. https://doi.org/10.9781/ijimai.2017.09.001
- T. Brown. 2008. Design Thinking. Harvard Business Review 86, 6 (2008), 84–92. Retrieved from https://hbr.org/2008/06/design-thinking
The increasing reliance on digital platforms has transformed automotive service management, necessitating more efficient and user-friendly solutions. This study focuses on enhancing the customer experience at LR Almazan Auto Shop Co. Inc. by prototyping a web-based service management platform that streamlines service requests and provides seamless access to vehicle data. Employing the Design Thinking methodology across five phases—Empathize, Define, Ideate, Prototype, and Test—the research identifies key strategies for improving user satisfaction and operational efficiency. A usability evaluation was conducted with 65 respondents (50 customers, 10 service staff, and 5 administrators) using the System Usability Scale (SUS) and the User Experience Questionnaire Short Version (UEQ-S). The proposed system achieved excellent usability scores, with mean SUS ratings of 83.95 (customers), 87.75 (service staff), and 89.50 (administrators). Comparative analysis projects a potential 30-40% improvement in overall customer satisfaction and significant projected gains in appointment scheduling efficiency, communication transparency, and paperwork handling. Aligned with global efforts to drive industry innovation and digital transformation (UNSDG 9) [20], this study contributes to the modernization of automotive service workflows while integrating security considerations to ensure a reliable and efficient platform.
Keywords :
Automotive Service, Web-Based System, Service Management, System Usability, User Experience, Design Thinking.