How electronic word-of-mouth influences intention to use parcel lockers services: Evidence from an integrated TAM-TPB model
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DOIhttp://dx.doi.org/10.21511/im.22(4).2026.01
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Article InfoVolume 22 2026, Issue #4, pp. 1–13
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Type of the article: Research Article
Abstract
The rapid growth of online shopping, coupled with the increasing demand for flexible parcel collection, has promoted parcel locker services (PLKS) as an effective last-mile logistics solution. This study examines the extended role of electronic word-of-mouth (eWOM) within an integrated TAM-TPB framework in explaining consumers’ intention to use PLKS. A questionnaire survey was conducted primarily in Hanoi and Ho Chi Minh City, Vietnam, between November and December 2025. The analysis was based on 362 valid responses from online shoppers who had not previously used PLKS. PLS-SEM was employed to test the proposed hypotheses. The findings reveal that eWOM has a positive and statistically significant association with the core components of TPB, particularly attitude (β = 0.569; p = 0.000) and perceived behavioral control (β = 0.582; p = 0.000). Attitude was identified as the strongest predictor of intention to use parcel locker services (β = 0.491; p = 0.000). Furthermore, eWOM was positively associated with intention to use both directly and indirectly through the statistically significant mediating roles of attitude (β = 0.28; p = 0.000) and Perceived Behavioral Control (β = 0.083; p = 0.000). However, the results do not support the mediating role of subjective norms in the relationship between eWOM and intention to use (β = 0.024; p = 0.073). The findings offer managerial implications for locker service providers, highlighting the importance of investing in digital communication strategies, fostering positive customer reviews and feedback, and ensuring transparent and accessible service information to strengthen consumers’ adoption intentions.
Acknowledgments
The authors received no financial support or external funding for conducting this research or preparing the manuscript. All analyses and interpretations were carried out independently by the authors.
- Keywords
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JEL Classification (Paper profile tab)M31, L87, D12
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References51
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Tables6
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Figures1
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- Figure 1. Research framework
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- Table 1. Characteristics of survey respondents
- Table 2. Reliability and convergent validity result
- Table 3. Heterotrait-monotrait ratio (HTMT) – Matrix
- Table 4. Fornell-Larcker criterion
- Table 5. Direct and indirect effects on intention to use
- Table A1. Construct, measurement items and sources
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- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
- Al-Dmour, H., Aloqaily, A., Al-Qaimari, R., & Al-Hassan, M. (2021). The effect of the electronic word of mouth on purchase intention via the brand image as a mediating factor: an empirical study. International Journal of Networking and Virtual Organisations, 24(2), 182-199.
- Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2023). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 31(3), 1293-1308.
- An, H. S., Park, A., Song, J. M., & Chung, C. (2022). Consumers’ adoption of parcel locker service: protection and technology perspectives. Cogent Business & Management, 9(1), 2144096.
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
- Azhar, M., Ali, R., Hamid, S., Akhtar, M. J., & Rahman, M. N. (2022). Demystifying the effect of social media eWOM on revisit intention post-COVID-19: an extension of theory of planned behavior. Future Business Journal, 8(1), 49.
- Baber, A., Thurasamy, R., Malik, M. I., Sadiq, B., Islam, S., & Sajjad, M. (2016). Online word-of-mouth antecedents, attitude and intention-to-purchase electronic products in Pakistan. Telematics and Informatics, 33(2), 388-400.
- Bambauer-Sachse, S., & Mangold, S. (2011). Brand equity dilution through negative online word-of-mouth communication. Journal of Retailing and Consumer Services, 18(1), 38-45.
- Chang, S.-H., & Chou, C.-H. (2018). Consumer Intention toward Bringing Your Own Shopping Bags in Taiwan: An Application of Ethics Perspective and Theory of Planned Behavior. Sustainability, 10(6), 1815.
- Chuong, H. N., Uyen, V. T. P., Ngan, N. D. P., Tram, N. T. B., Tran, L. N. B., & Ha, N. T. T. (2024). Exploring a new service prospect: customer’ intention determinants in light of utaut theory. Cogent Business & Management, 11(1), 2291856.
- Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
- Cong, M. V. H., Nguyen, C. H., Nhu, L. T., & Tran, T. T. (2024). A study on the impacts of safety and security on consumer’s intention to use electronic wallets in Hanoi. Innovative Marketing, 20(4), 85-99.
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
- Dayal, E., Khuntia, L., Lakshay, L., Buldeo Rai, H., & Pani, A. (2025). Behavioral intention to use parcel lockers in the last mile and underlying linkages with travel modal choice. Research in Transportation Economics, 111, 101568.
- East, R., Romaniuk, J., Chawdhary, R., & Uncles, M. (2017). The impact of word of mouth on intention to purchase currently used and other brands. International Journal of Market Research, 59(3), 321-334.
- ElSemary, M., Eman, N., Deselnicu, D. C., & Haddad, S. S. G. (2025). An Empirical Study on the Determinants of Customers’ Intentions to Switch to Smart Lockers as a Trending Last-Mile Logistics Channel. Logistics, 9(4), 177.
- Encarnación, T., & Amaya, J. (2025). Determinants of parcel locker adoption for last-mile deliveries in urban and suburban areas. Transportation Journal, 64(1), e12031.
- Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768-775.
- Goh, S., Ho, V., & Jiang, N. (2015). The effect of electronic word of mouth on intention to book accommodation via online peer-to-peer platform: Investigation of theory of planned behaviour. The Journal of Internet Banking and Commerce S, 2, 2-7.
- Google (2025). e-Conomy SEA 2025 report.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications.
- Hasan, B. (2010). Exploring gender differences in online shopping attitude. Computers in Human Behavior, 26(4), 597-601.
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
- Hwang, J., Kim, I., & Gulzar, M. A. (2020). Understanding the eco-friendly role of drone food delivery services: Deepening the theory of planned behavior. Sustainability, 12(4), 1440.
- Iyer, R., & Griffin, M. (2021). Modeling word-of-mouth usage: A replication. Journal of Business Research, 126, 512-523.
- Jakobsen, M., & Jensen, R. (2015). Common Method Bias in Public Management Studies. International Public Management Journal, 18(1), 3-30.
- Jalilvand, M. R., & Samiei, N. (2012a). The effect of electronic word of mouth on brand image and purchase intention: An empirical study in the automobile industry in Iran. Marketing Intelligence & Planning, 30(4), 460-476.
- Jalilvand, M. R., & Samiei, N. (2012b). The impact of electronic word of mouth on a tourism destination choice: Testing the theory of planned behavior (TPB). Internet Research: Electronic Networking Applications and Policy, 22(5), 591-612.
- Kahr, M. (2022). Determining locations and layouts for parcel lockers to support supply chain viability at the last mile. Omega, 113, 102721.
- Khan, F. A., Shah, A. A., Ali, S. M. F., Khan, A., Mangi, S., Poplani, S., Barkat, M., Firdous, S., & Ali, T. (2024). The Theory of Planned Behavior as a Mediator between Electronic Word-of-Mouth and Sustainable Fashion Purchase Intentions: a Pakistani Context. Archives of Management and Social Sciences, 1(3), 100-113.
- Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for information Systems, 13(7), 2.
- Lai Ying, H., & Chung, C. M. Y. (2007). The effects of single-message single-source mixed word-of-mouth on product attitude and purchase intention. Asia Pacific Journal of Marketing and Logistics, 19(1), 75-86.
- Lee, H., Min, J., & Yuan, J. (2021). The influence of eWOM on intentions for booking luxury hotels by Generation Y. Journal of Vacation Marketing, 27(3), 237-251.
- Liao, W.-L., & Fang, C.-Y. (2019). Applying an Extended Theory of Planned Behavior for Sustaining a Landscape Restaurant. Sustainability, 11(18), 5100.
- López, M., & Sicilia, M. (2014). eWOM as Source of Influence: The Impact of Participation in eWOM and Perceived Source Trustworthiness on Decision Making. Journal of Interactive Advertising, 14(2), 86-97.
- Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
- Ngan, L. T. T., Phu, N. T., Anh, N. H., Thu, N. N. A., Thuan, N. T. M., & Uyen, H. T. T. (2026). Unraveling factors that drive online consumers’ intention to use smart parcel lockers for last-mile delivery. Journal of Marketing Theory and Practice, 34(3), 638-651.
- O’Reilly, K., MacMillan, A., Mumuni, A. G., & Lancendorfer, K. M. (2016). Extending our understanding of eWOM impact: The role of source credibility and message relevance. Journal of Internet Commerce, 15(2), 77-96.
- Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. An updated guide and practical guide to statistical analysis, 1(1), 1-72.
- Sasidharan, A., & Venkatakrishnan, S. (2024). Intention to Use Mobile Banking: An Integration of Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM). KSII Transactions on Internet and Information Systems, 18(4), 1059-1074.
- Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.
- Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
- Tsai, Y.-T., & Tiwasing, P. (2021). Customers’ intention to adopt smart lockers in last-mile delivery service: A multi-theory perspective. Journal of Retailing and Consumer Services, 61, 102514.
- Tugiman, N. (2022). Electronic word-of-mouth (ewom): how social media influencers affect consumers’ purchase intention. International Journal of Law, Government and Communication.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
- Wang, X., Yuen, K. F., Wong, Y. D., & Teo, C. C. (2018). An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. The International Journal of Logistics Management, 29(1), 237-260.
- Won, D., Chiu, W., & Byun, H. (2022). Factors influencing consumer use of a sport-branded app: the technology acceptance model integrating app quality and perceived enjoyment. Asia Pacific Journal of Marketing and Logistics, 35(5), 1112-1133.
- Yan, J., Kim, Y., & Heo, J. (2019). Using a Technology Acceptance Model to Empirically Examine Chinese Consumers’ Intention to Use Intelligent Express Lockers. Journal of the Korean SCM Society, 19(2), 107-114.
- 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.
- Yusoff, F. A. M., Mohamad, F., Tamyez, P. F. M., & Panatik, S. A. (2023). Do I need to use it? Factors influencing the intention to adopt automated parcel lockers as last-mile delivery services. Acta Logistica (AL), 10(4), 567.
- Zhou, M., Zhao, L., Kong, N., Campy, K. S., Xu, G., Zhu, G., Cao, X., & Wang, S. (2020). Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery. Journal of Retailing and Consumer Services, 52, 101911.


