DRIVE-THRU TRANSFORMATION: ELEVATING CUSTOMER SATISFACTION WITH DIGITAL TWINS

Authors

  • TITO WIRA EKA SURYAWIJAYA UNIVERSITAS DIAN NUSWANTORO http://orcid.org/0000-0002-8894-4828
  • YEHUDA MARCEL LANAWAANG UNIVERSITAS DIAN NUSWANTORO
  • MAHMUD MAHMUD UNIVERSITAS DIAN NUSWANTORO
  • THANT THET OO KHANT UNIVERSITAS AIRLANGGA

Abstract

In the modern business landscape, traditional methods of understanding consumer behavior are constrained by time, cost, and depth limitations. The emergence of Digital Twins, which are virtual replicas created through real-time data integration and advanced algorithms, has fundamentally transformed how we perceive consumer behavior and market responses. This study aims to address challenges related to data security and privacy, exploring potential solutions within the context of applying Digital Twins in drive-thru services. Using a quantitative approach with a sample size of 2000 American drive-thru service customers, the research employs SEM-PLS for data analysis. Online questionnaires are distributed through Triaba to collect targeted data. The findings indicate that while process efficiency in drive-thru services has little impact on service quality, Digital Twins can still lead to waiting times that challenge customer expectations. Although limited to America, this research confirms the significant role of Digital Twins in enhancing drive-thru service efficiency and customer satisfaction, providing valuable insights for users and system integrators during implementation.

Author Biographies

TITO WIRA EKA SURYAWIJAYA, UNIVERSITAS DIAN NUSWANTORO

Department of Management

YEHUDA MARCEL LANAWAANG, UNIVERSITAS DIAN NUSWANTORO

Department of Communication Sciences

MAHMUD MAHMUD, UNIVERSITAS DIAN NUSWANTORO

Department of Management

THANT THET OO KHANT, UNIVERSITAS AIRLANGGA

Master of Management Department

References

Abdi, H. (Ed.). (2021). New perspectives in partial least squares and related methods (Re-Prints). Springer.

Alcaraz, C., & Lopez, J. (2022). Digital Twin: A Comprehensive Survey of Security Threats. IEEE Communications Surveys & Tutorials, 24(3), 1475–1503. https://doi.org/10.1109/COMST.2022.3171465

Attaran, M., & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. https://doi.org/10.1016/j.dajour.2023.100165

Esteban-Bravo, M., & Vidal-Sanz, J. M. (2021). Marketing research methods: Quantitative and qualitative approaches. Cambridge University Press.

Gregori, M. (2023). Advanced Measurement and Sampling for Marketing Research.

Hair, J. F. (Ed.). (2014). A primer on partial least squares structural equations modeling (PLS-SEM). SAGE.

Hammar, K., & Stadler, R. (2023). Digital Twins for Security Automation. NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1–6. https://doi.org/10.1109/NOMS56928.2023.10154288

Han, Y., Li, Y., Li, Y., Yang, B., & Cao, L. (2023). Digital twinning for smart hospital operations: Framework and proof of concept. Technology in Society, 74, 102317. https://doi.org/10.1016/j.techsoc.2023.102317

Heluany, J. B., & Gkioulos, V. (2023). A review on digital twins for power generation and distribution. International Journal of Information Security. https://doi.org/10.1007/s10207-023-00784-x

Hu, W., Zhang, T., Deng, X., Liu, Z., & Tan, J. (2021). Digital twin: A state-of-the-art review of its enabling technologies, applications and challenges. Journal of Intelligent Manufacturing and Special Equipment, 2(1), 1–34. https://doi.org/10.1108/JIMSE-12-2020-010

Human, C., Basson, A. H., & Kruger, K. (2023). A design framework for a system of digital twins and services. Computers in Industry, 144, 103796. https://doi.org/10.1016/j.compind.2022.103796

Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002

Kurvinen, E., Kutvonen, A., Ukko, J., Khadim, Q., Hagh, Y. S., Jaiswal, S., Neisi, N., Zhidchenko, V., Kortelainen, J., Timperi, M., Kokkonen, K., Virtanen, J., Zeb, A., Lamsa, V., Nieminen, V., Junttila, J., Savolainen, M., Rantala, T., Valjakka, T., … Mikkola, A. (2022). Physics-Based Digital Twins Merging With Machines: Cases of Mobile Log Crane and Rotating Machine. IEEE Access, 10, 45962–45978. https://doi.org/10.1109/ACCESS.2022.3170430

Lahrichi, A., Siena Gore, & Rosenberger, K. (2020). Systems Engineering Project for Designing a Drive-Thru. https://doi.org/10.13140/RG.2.2.36414.00323

Le, T. V., & Fan, R. (2024). Digital twins for logistics and supply chain systems: Literature review, conceptual framework, research potential, and practical challenges. Computers & Industrial Engineering, 187, 109768. https://doi.org/10.1016/j.cie.2023.109768

Lizar, Y., Mal Novizam, D., Butar-Butar, M. S., & Guci, A. (2023). Tren Global Penelitian Tentang Digital Twin: Analisis Bibliometrik. Indonesian Journal of Computer Science, 12(6). https://doi.org/10.33022/ijcs.v12i6.3513

Luna, N. (2023, August 23). An AI-powered bot could take your next drive-thru order at one of these top fast-food restaurants. https://www.businessinsider.com/fast-food-chains-like-white-castle-turn-to-voice-bots

Magno, F., Cassia, F., & Ringle, C. M. (2022). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal. https://doi.org/10.1108/TQM-06-2022-0197

Maheshwari, P., Kamble, S., Belhadi, A., Venkatesh, M., & Abedin, M. Z. (2023). Digital twin-driven real-time planning, monitoring, and controlling in food supply chains. Technological Forecasting and Social Change, 195, 122799. https://doi.org/10.1016/j.techfore.2023.122799

Patandianan, M. A., & Assidiq, F. M. (2022). PENERAPAN DIGITAL TWIN UNTUK MENGURANGI DAMPAK BENCANA. Riset Sains Dan Teknologi Kelautan, 95–99. https://doi.org/10.62012/sensistek.v5i2.24236

Poth, C. N. (2021). Research ethics. Sage Publications.

Saris, W. E. (2021). Design, evaluation, and analysis of questionnaires for survey research (Second Edition (Republished)). Wiley.

Stavropoulos, P., & Mourtzis, D. (2022). Digital twins in industry 4.0. In Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology (pp. 277–316). Elsevier. https://doi.org/10.1016/B978-0-12-823657-4.00010-5

Suryawijaya, T., & Aqmala, D. (2023). Transforming Consumer Experience Through The Application Of Augmented Optimization Marketing In Retail Marketing Strategy. Strategic Management Business Journal, 3(02), 211–224. https://doi.org/10.55751/smbj.v3i02.73

Suryawijaya, T. W. E., Utomo, M. T. R. S., & Rahayuningtyas, T. E. (2023). Self-Service Optimization: Comprehending Customer Satisfaction. Jurnal Manajemen, 14(1), 203. https://doi.org/10.32832/jm-uika.v14i1.9791

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576. https://doi.org/10.1007/s00170-017-0233-1

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229

Timperi, M., Kokkonen, K., Hannola, L., & Elfvengren, K. (2023). Impacts of digital twins on new business creation: Insights from manufacturing industry. Measuring Business Excellence, 27(3), 433–448. https://doi.org/10.1108/MBE-09-2022-0104

Triaba, S. (2022). Triaba Survey. Triaba. https://www.triaba.com/

VanDerHorn, E., & Mahadevan, S. (2021). Digital Twin: Generalization, characterization and implementation. Decision Support Systems, 145, 113524. https://doi.org/10.1016/j.dss.2021.113524

Vrabi?, R., Erkoyuncu, J. A., Butala, P., & Roy, R. (2018). Digital twins: Understanding the added value of integrated models for through-life engineering services. Procedia Manufacturing, 16, 139–146. https://doi.org/10.1016/j.promfg.2018.10.167

Westfall, P. H., & Arias, A. L. (2020). Understanding regression analysis: A conditional distribution approach. CRC Press, Taylor & Francis Group.

Whitenack, L., & Mahabir, R. (2022). A Tool for Optimizing the Efficiency of Drive-Thru Services. 2022 Systems and Information Engineering Design Symposium (SIEDS), 151–156. https://doi.org/10.1109/SIEDS55548.2022.9799310

Wu, W., Shen, L., Zhao, Z., Harish, A. R., Zhong, R. Y., & Huang, G. Q. (2023). Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics. Journal of Industrial Information Integration, 33, 100443. https://doi.org/10.1016/j.jii.2023.100443

Zhong, Y., & Moon, H. C. (2020). What Drives Customer Satisfaction, Loyalty, and Happiness in Fast-Food Restaurants in China? Perceived Price, Service Quality, Food Quality, Physical Environment Quality, and the Moderating Role of Gender. Foods, 9(4), 460. https://doi.org/10.3390/foods9040460

Downloads

Published

2024-07-09

Issue

Vol. 3 No. 1 (2024): JMDB (Jurnal Manajemen dan Dinamika Bisnis)

Section

Articles

License

Authors who publish with this journal agree to the following terms:
  1. The journal allow the authors to hold the copyright without restrictions and allow the authors to retain publishing rights without restrictions. 
  2. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
  5. Creative Commons License
    This work is licensed under a Creative Commons Attribution 4.0 International License.  Which means you are free to:
    • Adapt — remix, transform, and build upon the material for any purpose, even commercially.