Customers’ smart lockers switching behaviors in Hanoi: Facilitator and inhibitor factors


Authors

  • Nguyen Thi My Nguyet Thuongmai University
  • Tran Thi Hoang Ha Thuongmai University
  • Nguyen Minh Trang Thuongmai University
  • Nguyen Thi Thuy Chung Thuongmai University
DOI: https://doi.org/10.57110/jebvn.v4i1.273

Keywords:

Last-mile delivery, smart locker, switching behaviors, facilitator factors, inhibitor factors

Abstract

This study applied customer perceived value and innovation resistance theory to examine facilitator and inhibitor factors for switching behaviors from home delivery to smart lockers. Survey data from 327 customers using smart lockers in Hanoi was analyzed by using PLS-SEM. The findings show that inhibitor factors including usage barriers,  value barriers, and traditional barriers  negatively affect switching behaviors toward smart lockers. Meanwhile, facilitors such as functional value, emotional value and environmental value positively impacts switching behaviors. This difference was found between the group of customers based on shopping online frequencies. Thereby, some practical implications are proposed to help enterprises to enhance customers' switching behaviors to use smart lockers in last-mile delivery in the future.

References

Anderson, J. C., Kellogg, J. L., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Campos, J. G. F. de, & Mello, A. M. de. (2017). Transaction costs in environmental purchasing: analysis through two case studies. Journal of Operations and Supply Chain Management, 10(1), 87. https://doi.org/10.12660/joscmv10n1p87-102

Chen, Y. H., & Keng, C. J. (2019). Utilizing the Push-Pull-Mooring-Habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Internet Research, 29(1), 167–193.

https://doi.org/10.1108/IntR-09-2017-0343

Cheng, S., Lee, S. J., & Choi, B. (2019). An empirical investigation of users’ voluntary switching intention for mobile personal cloud storage services based on the push-pull-mooring framework. Computers in Human Behavior, 92, 198–215. https://doi.org/10.1016/j.chb.2018.10.035

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/10.1016/S0022-4359(01)00056-2

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.2307/3150980

Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.

https://doi.org/10.1007/s11747-014-0403-8

Hsieh, J. K., Hsieh, Y. C., Chiu, H. C., & Feng, Y. C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework. Computers in Human Behavior, 28(5), 1912–1920. https://doi.org/10.1016/j.chb.2012.05.010

Huong, T. T., & Thiet, B. N. (2020). Smart locker - A sustainable urban delivery solution: Benefits and challenges in implementing in Vietnam. 12th NEU-KKU International Conference Socio-Economic and Environmental Issues in Development, 1123–1135.

Kushwah, S., Dhir, A., & Sagar, M. (2019). Understanding consumer resistance to the consumption of organic food. A study of ethical consumption, purchasing, and choice behaviour. Food Quality and Preference, 77, 1–14. https://doi.org/10.1016/j.foodqual.2019.04.003

Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69(7), 2432–2439. https://doi.org/10.1016/j.jbusres.2016.01.013

Lin, C. C., & Dong, C. M. (2023). Exploring consumers’ purchase intention on energy-efficient home appliances: Integrating the Theory of Planned Behavior, Perceived Value Theory, and Environmental Awareness. Energies, 16(6). https://doi.org/10.3390/en16062669

Ministry of Information and Communications. (2023). Summary report for the first 6 months of 2023, directions and tasks for the last 6 months of 2023. https://mic.gov.vn/Upload_Moi/2023_01_eng/28.6.-DU-THAO-BC-CONG-TAC-QLNN-6-THANG-DAU-NAM-2023-cua-Bo.pdf

Morar, D., & Dumitrela, D. (2013). An overview of the consumer value literature-perceived value, desired value. International Conference “Marketing - from information to decision”, 6th Edition. https://www.researchgate.net/publication/271585009

Okholm, H., & Basalisco, B. (2013). E-commerce and delivery: A study of the state of play of EU parcel markets with particular emphasis on e-commerce European Commission DG Internal Market and Services. https://doi.org/10.2780/89659

Olsson, J., Hellström, D., & Vakulenko, Y. (2023). Customer experience dimensions in last-mile delivery: An empirical study on unattended home delivery. International Journal of Physical Distribution and Logistics Management, 53(2), 184–205. https://doi.org/10.1108/IJPDLM-12-2021-0517

Park, M., Jun, J., & Park, H. (2017). Understanding mobile payment service continuous use intention: An expectation - Confirmation model and inertia. Quality Innovation Prosperity, 21(3), 78–94. https://doi.org/10.12776/QIP.V21I3.983

Quan, N. H., Binh, N. T., & Ly, B. T. (2022). Impact of smart locker use on customer satisfaction of online shoppers in Vietnam. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01428-6

Ringle, Sarstedt, & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii. https://doi.org/10.2307/41410402

Román-Augusto, J. A., Garrido-Lecca-Vera, C., Lodeiros-Zubiria, M. L., & Mauricio-Andia, M. (2023). How to reach green word of mouth through green trust, green perceived value and green satisfaction. Data, 8(2). https://doi.org/10.3390/data8020025

Sam, S., & Sheth, J. (1989). Consumer resistance to innovations the marketing problem and its solutions. The Jounal of Consumer Marketing, 6(2), 5–14. https://doi.org/10.1108/EUM0000000002542

Sánchez-Franco, M. J., & Roldán, J. L. (2005). Web acceptance and usage model: A comparison between goal-directed and experiential web users. Internet Research, 15(1), 21–48. https://doi.org/10.1108/10662240510577059

Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0

Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Barriers toward purchasing from online travel agencies. International Journal of Hospitality Management, 89. https://doi.org/10.1016/j.ijhm.2020.102593

Thanh, K. (2022, July 29). Last-mile delivery services battle over market share. https://www.sggp.org.vn/doanh-nghiep-chuyen-phat-nhanh-day-manh-dich-vu-gia-tri-gia-tang-post645667.html

Vakulenko, Y., Shams, P., Hellström, D., & Hjort, K. (2019). Online retail experience and customer satisfaction: The mediating role of last mile delivery. International Review of Retail, Distribution and Consumer Research, 29(3), 306–320. https://doi.org/10.1080/09593969.2019.1598466

Van Duin, J. H. R., De Goffau, W., Wiegmans, B., Tavasszy, L. A., & Saes, M. (2016). Improving home delivery efficiency by using principles of address intelligence for b2c deliveries. Transportation Research Procedia, 12, 14–25. https://doi.org/10.1016/j.trpro.2016.02.006

Wang, X., Zhan, L., Ruan, J., & Zhang, J. (2014). How to choose “last mile” delivery modes for e-fulfillment. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/417129

Wetzels, M., & Odekerken, G. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and Empirical Illustration. Management Information Systems Quarterly - MISQ, 33(1), 177-195. https://doi.org/10.2307/20650284

Xie, W., Chen, C., & Sithipolvanichgul, J. (2022). Understanding e-commerce customer behaviors to use drone delivery services: A privacy calculus view. Cogent Business and Management, 9(1). https://doi.org/10.1080/23311975.2022.2102791

Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. In Journal of the Association for Information Systems, 12(12).

Yuen, K. F., Wang, X., Ma, F., & Wong, Y. D. (2019). The determinants of customers’ intention to use smart lockers for last-mile deliveries. Journal of Retailing and Consumer Services, 49, 316–326. https://doi.org/10.1016/j.jretconser.2019.03.022

Yusoff, F., Mohamad, F., Tamyez, P., & Panatik, S. (2023). Adoption of parcel locker in Malaysia: Literature review and research agenda. Global Business and Management Research: An International Journall, 15(2s), 1–17.

Zarei, M. M., Chaparro-Pelaez, J., & Agudo-Peregrina, Á. F. (2020). Identifying consumer’s last-mile logistics beliefs in omni-channel environment. Economic Research-Ekonomska Istrazivanja, 33(1), 1796–1812. https://doi.org/10.1080/1331677X.2020.1760914

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A Means-End Model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302

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Published

25-02-2024

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How to Cite

Nguyen Thi My Nguyet, Tran Thi Hoang Ha, Nguyen Minh Trang, & Nguyen Thi Thuy Chung. (2024). Customers’ smart lockers switching behaviors in Hanoi: Facilitator and inhibitor factors. VNU JOURNAL OF ECONOMICS AND BUSINESS, 4(1), 78. https://doi.org/10.57110/jebvn.v4i1.273

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Original Article