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Information Matters: an Empirical Study of the Efficiency of On-Demand Services

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Abstract

On-demand services through Internet platforms, e.g. ride-sharing, food take-out services, have emerged as a new business model. In these businesses, customers place orders on Internet platforms and get services fulfilled offline in a timely manner. In this paper, we examine the factors that affect the efficiency of on-demand food take-out services. Besides operational and road factors, we highlight the role of information integration of the ordering platform and the logistics platform. Our results show that information integration of the two platforms significantly increases service efficiency. Through integration, the logistics platform can optimize delivery dispatch based on more comprehensive and accurate historical and real-time demand and delivery information, avoiding suboptimal and short-sighted decisions. We also find that the efficiency of on-demand services depends much more on the information integration and the operational efficiency of the service provider than road conditions. We discuss the theoretical and practical implications for the business model of on-demand services.

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References

  • Barlow, A., & Li, F. (2005). Online value network linkages: integration, information sharing and flexibility. Electronic Commerce Research and Applications, 4, 100–112.

    Article  Google Scholar 

  • Brette, O., Buhler, T., Lazaric, N., & Marechal, K. (2014). Reconsidering the nature and effects of habits in urban transportation behavior. Journal of Institutional Economic., 10(3), 399–426.

    Article  Google Scholar 

  • Chen, D. Q., Mocker, M., Preston, D. S., & Teubner, A. (2010). Information systems strategy: reconceptualization, measurement, and implication. MIS Quarterly, 34(2), 233–259.

    Article  Google Scholar 

  • Dettenbach, A.M.C., & Ubber, S. (2015). Managing disruptions in last mile distribution. The Forty-eighth Hawaii International Conference on System Sciences, Hawaii, USA, 2015.

  • Dong, S., Xu, S. X., & Zhu, K. X. (2009). Information technology in supply chains: the value of IT-enabled resources under competition. Information Systems Research, 20(1), 18–32.

    Article  Google Scholar 

  • Ellison, A. E., Bliemer, M. C. J., & Greaves, S. P. (2015). Evaluating changes in driver behaviour: a risk profiling approach. Accident Analysis and Prevention, 75, 298–309.

    Article  Google Scholar 

  • Gosain, S., Malhotra, A., El Sawy, O. A., & Chehade, F. (2005). Coordinating for flexibility in e-business supply chains. Journal of Management Information Systems, 21(3), 39–45.

    Google Scholar 

  • Gurvich, I., Lariviere, M., & Moreno, A. (2016). Operations in the on-demand economy: Staffing services with self-scheduling capacity. Working paper. Available at SSRN: http://ssrn.com/abstract=2336514.

  • Hoogendoorn, R.G., Tamminga, G., Hoogendoorn, S.P., & Daamen, W. (2010). Longitudinal driving behavior under adverse weather conditions: Adaptation effects, model performance and freeway capacity in case of fog. The Thirteenth International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Portugal.

  • Hübner, A., Kuhn, H., & Wollenburg, J. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing. International Journal of Retail & Distribution Management., 44(3), 228–247.

    Article  Google Scholar 

  • Jun, J. (2010). Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic. Transportation Research Part C, 18, 599–610.

    Article  Google Scholar 

  • Kieu, L.M., Bhaskar, A., & Chung, E. (2012). Bus and car travel time on urban networks: Integrating bluetooth and bus vehicle identification data. The Twenty-fifth ARRB Conference: shaping the future: linking policy, research and outcomes, Perth, Australia, 2012.

  • Konur, D., & Geunes, J. (2011). Analysis of traffic congestion costs in a competitive supply chain. Transportation Research Part E, 47, 1–17.

    Article  Google Scholar 

  • Kulp, S. C., Lee, H. L., & Ofek, E. (2004). Manufacturer benefits from information integration with retail customers. Management Science, 50(4), 431–444.

    Article  Google Scholar 

  • Lansdown, T. C., & Saunders, S. T. (2012). Driver performance, rewards and motivation: a simulator study. Transportation Research Part F, 15(1), 65–74.

    Article  Google Scholar 

  • Legner, C., & Schemm, J. (2008). Toward the inter-organizational product information supply chain – evidence from the retail and consumer goods industries. Journal of Association for Information System, 9, 119–150.

    Article  Google Scholar 

  • Lia, F., Nocerino, R., Bresciani, C., Colorni, A., & Luè, A. (2014) Promotion of e-bikes for delivery of goods in European urban areas: An Italian case study. The Fifth Transport Research Arena Conference: Transport Solutions from Research to Deployment, Paris, France.

  • Macro, A. D., Cagliano, A. C., Mangano, G., & Perfetti, F. (2014). Factor influencing logistics service providers efficiency’ in urban distribution systems. Transportation Research Procedia, 3, 499–507.

    Article  Google Scholar 

  • Patnayakuni, R., Rai, A., & Seth, N. (2014). Relational antecedents of information flow integration for supply chain coordination. Journal of Management Information Systems, 23(1), 13–49.

    Article  Google Scholar 

  • Pu, Y., Krishnamurthy, I., & Peter, F. (2016) Mean field equilibria for competitive exploration in resource sharing settings. WWW '16 Proceedings of The Twenty-fifth International Conference on World Wide Web, Canton of Geneva, Switzerland.

  • Quak, H., Nesterova, N., & Rooijen, T. V. (2016). Possibilities and barriers for using electric-powered vehicles in city logistics practice. Transportation Research Procedia., 12, 157–169.

    Article  Google Scholar 

  • Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225–246.

    Article  Google Scholar 

  • Schneider, M. (2015). The vehicle-routing problem with time windows and driver-specific times. Journal of Management Information Systems, 250(1), 101–119.

    Google Scholar 

  • Subramani, M. (2004). How do suppliers benefit from information technology use in supply chain relationships? MIS Quarterly, 28(1), 45–73.

    Article  Google Scholar 

  • Tallon, P. P., & Pinsonneault, A. (2011). Competing perspectives on the link between strategy information technology alignment and organizational agility: Insight from a mediation modal. MIS Quarterly, 35(2), 463–486.

    Article  Google Scholar 

  • Talyor, T.A. (2016). On-demand service platforms. Working paper. Available at SSRN: http://ssrn.com/abstract=2722308.

  • Tsapakis, I., Cheng, T., & Bolbol, A. (2013). Impact of weather conditions on macroscopic urban travel times. Journal of Transport Geography, 28, 204–211.

    Article  Google Scholar 

  • Unrau, D., & Andrey, J. (2006). Driver response to rainfall on an urban expressway. Transportation Research Record Journal of the Transportation Research Board, 1980(1), 24–30.

    Article  Google Scholar 

  • Vlahogianni, E. I., & Karlaftis, M. G. (2012). Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures. Nonlinear Dynamics, 69, 1949–1963.

    Article  Google Scholar 

  • Wong, C. W. Y., Lai, K. H., & Cheng, T. C. E. (2011). Value of information integration to supply chain management: Roles of internal and external contingencies. Journal of Management Information Systems, 28(3), 161–200.

    Article  Google Scholar 

  • Yang, Y., Yao, E., Yang, Z., & Zhang, R. (2016). Modeling the charging and route choice behavior of BEV drivers. Transportation Research Part C, 65, 190–204.

    Article  Google Scholar 

  • Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2013). Designing vehicle routes for a mix of different request types, under time windows and loading constraints. European Journal of Operational Research, 229, 303–317.

    Article  Google Scholar 

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (91646125), Beijing Natural Science Foundation (9172017), National Natural Science Foundation of China (71872200). The study was also supported by Strategic Research Grant (7004776) from City University of Hong Kong.References

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Correspondence to Ling Ge.

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Dai, H., Ge, L. & Liu, Y. Information Matters: an Empirical Study of the Efficiency of On-Demand Services. Inf Syst Front 22, 815–827 (2020). https://doi.org/10.1007/s10796-018-9883-2

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  • DOI: https://doi.org/10.1007/s10796-018-9883-2

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