A Human Capital Perspective on Behavioral Factors Affecting Customers’ Acceptance of Crowd Logistics: A Systematic Literature Review
Keywords:Crowd Logistics, Human Capital, Self-efficacy, Systematic Literature Review
This paper provides a systematic review of the customer’s behavioral deliberations that govern their choice to participate in a crowd-logistics market. Little attention has been given to the crowd logistics platforms and small businesses. This paper presents the analysis of customers' crowd logistics adoption from a behavioral perspective. The study applied a systematic literature review method and examined previous literature on crowd logistics from Science Direct and Taylor and Francis. Systematic literature review involve the principles of transparency, rigor, and replicability, which bring significant benefits over traditional literature reviews through greater objectivity. The findings suggest that customers’ ability to use technology, safety considerations, desire to make social connections, trust, and convenience are the factors that influence people’s decisions to participate in the crowd-logistics market. The findings of this study are valuable for empirical research in a particular setting and strengthen the body of knowledge on the adoption or acceptance of the crowd-logistics concept.
Agatz, N., Erera, A., Savelsbergh, M. and Wang, X. (2012). ‘Optimization for dynamic ride-sharing: a review’. European Journal of Operational Research, 223 (2), 295–303.
Alnaggar, A., Gzara, F., & Bookbinder, J. H. (2021). Crowdsourced Delivery: A Review of Platforms and Academic Literature. Omega (United Kingdom), 98.
Baldi, M. M., D. Manerba, G. Perboli, and R. Tadei. (2019). “A Generalized Bin Packing Problem for Parcel Delivery in Last-Mile Logistics.” European Journal of Operational Research, 274 (3), 990–999.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioural change. Psychological Review, 84, 191-215.
Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency. American Psychologist, 37, 122-147.
Bates, O. A., Friday, J., Allen, T., Cherrett, F., McLeod, T., Bektas, T. and Nguyen, T. (2018). “Transforming Last-Mile Logistics: Opportunities for More Sustainable Deliveries.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
Belk, R., (2014). You are what you can access: sharing and collaborative consumption online. Journal of Business Research, 67, 1595–1600.
Bellotti, V., Ambard, A., Turner, D., Gossmann, C., Demkova, K. and Carroll, J.M. (2015). A muddle of models of motivation for using peer-to-peer economy systems. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems – CHI ’15. ACM Press, New York, USA, 1085– 1094.
Buldeo Rai, H., S. Verlinde, J. Merckx, and C. Macharis. (2017). Crowd Logistics: An Opportunity for More Sustainable Urban Freight Transport? European Transport Research Review, 9 (3), 39.
Cebeci, M.S., Tapia, R.J., Kroesen, M., de Bok, M. and Tavasszy, L. (2023). The effect of trust on the choice for crowd-shipping services. Transportation Research Part A.
Deloison, T., E. Hannon, A., Huber, B., Heid, C., Klink, R. and Wolff, C. (2020). The Future of the Last-Mile Ecosystem. Cologny: World Economic Forum.
Hamari, J., Sjöklint, M. and Ukkonen, A. (2016). The sharing economy: why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67, 2047– 2059.
Harrington, L. (2019). Change at the Speed of the Customer: How E-commerce is Accelerating Logistics Innovations.
Joanna Briggs Institute. (2001). An introduction to systematic reviews Changing practice: evidence-based practice information sheets for health professionals, (1), 1–6.
John, S. P. (2013). Influence of Computer Self-Efficacy on Information Technology Adoption. International Journal of Information Technology, 19, 1-13.
Johnston, M.P. (2014). Secondary Data Analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries, 3, 619-626.
Karsten, R., Mitra, A. and Schmidt, D. (2012). Computer Self-Efficacy: A Meta-Analysis. Journal of Organizational and End User Computing, 24, 54-80.
Khan K.S. (2003). Five steps to conducting a systematic review. Journal of the Royal Society of Medicine, 96, (3), 118–21.
Kunze, O. (2016). Replicators, Ground Drones and Crowd Logistics A Vision of Urban Logistics in the Year 2030. Transportation Research Procedia, 19, 286 – 299.
Le, T. V. and Ukkusuri, S. V. (2019). Modelling the willingness to work as crowd-shippers and travel time tolerance in emerging logistics services. Travel Behaviour and Society, 15, 123–132.
Li, S., Wu, W., Xia, Y., Zhang, M., Wang, S. and Douglas, M.A. (2019). How do crowd logistics platforms create value? An exploratory case study from China. International Journal of Logistics Research and Applications, 22 (5), 501-518.
Loar, E. A. (2018). Computer Self-Efficacy Revisited. https://doi.org/10.9743/JIR.2018.4
Macharis, C. and Kin, B. (2017). The 4 A’s of sustainable city distribution: innovative solutions and challenges ahead. International Journal of Sustainable Transportation, 11, 59–71.
Mallett, R., Hagen-Zanker, J., Slater, R. and Duvendack, M. (2012). The benefits and challenges of using systematic reviews in international development research. Journal of Development Effectiveness, 4(3), 445–455.
Mehmann, J., Frehe, V. and Teuteberg, F. (2015). Crowd Logistics—A Literature Review and Maturity Model. In Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg International Conference of Logistics (HICL) (Vol. 20, pp. 117-145). Berlin: Epubli GmbH.
Michel, S., Bootz, J. and Bessouat, B. (2022). Possible futures of crowd logistics for manufacturers: results of a strategic foresight study. Journal of Business and Industrial Marketing.
Miller, J., Nie, M. and Stathopoulos, A. (2017). Crowdsourced urban package delivery: modelling traveller willingness to work as crowd-shippers. Transportation Research Record: Journal of the Transportation Research Board, 2610, 67–75.
Mladenow, A., Bauer, C. and Strauss, C. (2015). Crowdsourcing in Logistics: Concepts and Applications Using the Social Crowd. Proceedings of the 17th International Conference on Information Integration and Web-Based Applications & Services, Brussels, 11-13 December 2015, 1-8.
Mladenow, A., Bauer, C. and Strauss, C. (2016). “Crowd Logistics”: The Contribution of Social Crowds in Logistics Activities. International Journal of Web Information Systems, 12, 379-396.
Neuman, W. L. (2011). Social research methods. Boston: Allyn and Bacon.
Paloheimo, H., Lettenmeier, M. and Waris, H. (2016). Transport reduction by crowdsourced deliveries – a library case in Finland. Journal of Cleaner Production, 132, 240–251.
Panda, R., Verma, S. and Mehta, B. (2015). Emergence and acceptance of sharing economy in India. International Journal Online Mark. 5, 1–17.
Punel, A., Ermagun, A. and Stathopoulos, A. (2018). Studying determinants of crowd-shipping use. Travel Behaviour and Society, 12, 30–40.
Punel, A. and Stathopoulos, A. (2017). Modelling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research, 105, 18-38.
Rai, H. B., Verlinde, S. and Macharis, C. (2018). Shipping outside the Box. Environmental Impact and Stakeholder Analysis of a Crowd Logistics Platform in Belgium. Journal of Cleaner Production, 202, 806-816.
Rai, H. B., Verlinde, S., Merckx, J. and Macharis, C. (2017). Crowd Logistics: An Opportunity for More Sustainable Urban Freight Transport? European Transport Research Review, 9, 1-13.
Rayle, L., Dai, D., Chan, N., Cervero, R. and Shaheen, S. (2016). Just a better taxi? A survey-based comparison of taxis, transit, and ride-sourcing services in San Francisco. Transp. Policy 45, 168–178.
Romero, E., Wagner, C., Zhuhadar, L. and Wyatt, R. (2009). Web-Based Technology Use and Computer Self-Efficacy as Predictors of Faculty Perceptions of Support for the Implementation of eLearning. In Proceedings of the International Conference on Mobile, Hybrid, and On-Line Learning, 28-34). IEEE.
Rougès, J.-F. and Montreuil, B. (2014). Crowdsourcing delivery: new interconnected business models to reinvent delivery. In: 1st International Physical Internet Conference, 1–19.
Salazar, M.K. (1991). Comparison of Four Behavioral Theories A Literature Review. AAOHN Journal, 39, (3), 128-135.
Savelsbergh, M., Van Woensel, T., 2016. 50th anniversary invited article—city logistics: challenges and opportunities. Transportation Science, 50, 579–590.
Shaheen, S.A., Chan, N.D. and Gaynor, T. (2016). Casual carpooling in the San Francisco Bay Area: understanding user characteristics, behaviours, and motivations. Transportation Policy, 51, 165–173.
Teo, T., Hwee, J. and Koh, L. (2010). Assessing the Dimensionality of Computer Self-Efficacy among Pre-Service Teachers in Singapore: A Structural Equation Modeling Approach. International Journal of Education and Development Using Information and Communication Technology, 6, 7-18.
Tokar, T., Williams, B.D. and B. S. Fugate. (2020). I Heart Logistics—Just Don’t Ask Me to Pay For It: Online Shopper Behavior in Response to a Delivery Carrier Upgrade and Subsequent Shipping Charge Increase. Journal of Business Logistics, 41 (3), 182–205.
Tranfield, D., Denyer, D. and Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14, 207–222.
Tricco, A.C. (2011). The art and science of knowledge synthesis. Journal of Clinical Epidemiology, 64 (1), 11–20.
Wang, F., Ma, X. and Liu, J. (2019). Demystifying the Crowd Intelligence in Last Mile Parcel Delivery for Smart Cities. IEEE Network, 33 (2), 23–29.
Wanga, Y., Wang, Y., Huangc, G.Q. and Lin, C. (2023). Public acceptance of crowdsourced delivery from a customer perspective. European Journal of Operational Research.
Yuen, K.F., Wang, X., Mac F. and 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.
Zhang, Y., Xiang, C., Li, L. and Jiang, H. (2021). Evolutionary game analysis and simulation with system dynamics for behavioural strategies of participants in crowd logistics. The International Journal of Transportation Research, 13(7), 540-554.
Zhou, M., Zhao, L., Kong, N., Campy, K.S., Xu, G., Zhu, G., Cao, X. and Wang, S. (2020). Understanding consumers’ behaviour to adopt self-service parcel services for last-mile delivery. Journal of Retailing and Consumer Services, 52.
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