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Open AccessOpen Access JUSTC Research Articles: Management Science and Engineering

Pricing strategies of laborer-sharing platform in two transaction modes

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUST-2021-0083
  • Received Date: 20 March 2021
  • Rev Recd Date: 07 April 2021
  • Publish Date: 31 May 2021
    Abstract

  • Abstract

    With the advancement of Internet information technology,the laborer-sharing platform plays a crucial role in promoting the full use of human resources across the whole society. At present, multiple trading modes with different pricing strategies are adopted by the laborer-sharing platform. Considering the heterogeneity of laborers’ abilities, we construct the laborer-sharing platform’s profit functions under the buyer pricing strategy and laborer pricing strategy, and analyze its optimal pricing strategy in the bidding mode.First, our analysis shows that when the mismatch degree between the service of the low-type laborer and the buyer’s task is close to that of the high-type laborer, the laborer pricing strategy is beneficial to the platform. Second,when the task mismatch degree of the low-type laborer is much lower than that of the high-type laborer, the platform’s pricing strategy depends on the buyer’s satisfaction with the completing task. Finally, we compare two transaction models: the bidding mode and the piece mode, and find that under the laborer pricing strategy, the platform’s profit in the bidding mode is not always higher than that in the piece mode.

    Abstract

    With the advancement of Internet information technology,the laborer-sharing platform plays a crucial role in promoting the full use of human resources across the whole society. At present, multiple trading modes with different pricing strategies are adopted by the laborer-sharing platform. Considering the heterogeneity of laborers’ abilities, we construct the laborer-sharing platform’s profit functions under the buyer pricing strategy and laborer pricing strategy, and analyze its optimal pricing strategy in the bidding mode.First, our analysis shows that when the mismatch degree between the service of the low-type laborer and the buyer’s task is close to that of the high-type laborer, the laborer pricing strategy is beneficial to the platform. Second,when the task mismatch degree of the low-type laborer is much lower than that of the high-type laborer, the platform’s pricing strategy depends on the buyer’s satisfaction with the completing task. Finally, we compare two transaction models: the bidding mode and the piece mode, and find that under the laborer pricing strategy, the platform’s profit in the bidding mode is not always higher than that in the piece mode.
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    • References(22)
  • [1]
    Malhotr A, Alstyne V M. The dark side of the sharing economy and how to lighten it. Communications of the ACM, 2014, 57(11): 24-27.
    [2]
    Kassi O, Lehdonvirta V. Online labour index: Measuring the online gig economy for policy and research. Technological Forecasting & Social Change, 2018, 137: 241-248.
    [3]
    Wood A J, Graham M, Lehdonvirta V, et al. Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 2019, 33(1): 56-75.
    [4]
    Philip H E, Ozanne L K, Ballantine P. Examining temporary disposition and acquisition in peer-to-peer renting. Journal of Marketing Management, 2015, 31(11-12): 1310-1332.
    [5]
    Martin C J, Upham P, Budd L. Commercial orientation in grassroots social innovation: Insights from the sharing economy. Ecological Economics, 2015, 118: 240-251.
    [6]
    Hall J V, Krueger A B. An analysis of the labor market for Uber’s driver-partners in the United States. ILR Review, 2018, 71(3): 705-732.
    [7]
    Bimpikis K, Candogan O, Saban D. Spatial pricing in ride-sharing networks. Operations Research, 2019, 67(3): 744-769.
    [8]
    Jiang B J, Yang B C. Quality and pricing decisions in a market with consumer information sharing. Management Science, 2018, 65(1): 272-285.
    [9]
    Sun H, Wang H, Wan Z X. Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity. Transportation Research Part B, 2019, 125: 76-93.
    [10]
    Halaburda H, Piskorski J M, Yildirim P. Competing by restricting choice: The case of matching platforms. Management Science, 2017, 64(8): 3574-3594.
    [11]
    Basu A, Bhaskaran S, Mukherjee R. An analysis of search and authentication strategies for online matching platforms. Management Science, 2019, 65(5): 2412-2431.
    [12]
    Kim J Y, Natter M, Spann M. Pay what you want: A new participative pricing mechanism. Journal of Marketing, 2009, 73(1): 44-58.
    [13]
    Gneezya A, Gneezy U, Riener G, et al. Pay-what-you-want, identity, and self-signaling in markets. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(19): 7236-7240.
    [14]
    Schmidt K M, Spann M, Zeithammer R. Pay what you want as a marketing strategy in monopolistic and competitive markets. Management Science, 2014, 61(6): 1217-1236.
    [15]
    Chen Y X, Koenigsberg O, Zhang J Z. Pay-as-you-wish pricing. Marketing Science, 2017, 36(5): 780-791.
    [16]
    Hirshleifer J. On the economics of transfer pricing. The Journal of Business, 1956, 29(3): 172-184.
    [17]
    Burdett K, Shi S Y, Wright R. Pricing and matching with frictions. Journal of Political Economy, 2001, 109(5): 1060-1085.
    [18]
    Aviv Y, Pazgal A. Optimal pricing of seasonal products in the presence of forward-looking consumers. Manufacturing & Service Operations Management, 2008, 10(3): 339-359.
    [19]
    Zhang J Z,Netzer O, Ansari A. Dynamic targeted pricing in B2B relationships. Marketing Science, 2014, 33(3): 317-337.
    [20]
    Caldentey R, Liu Y, Lobel I. Intertemporal pricing under minimax regret. Operations Research, 2016, 65(1):104-129.
    [21]
    Syam N B, Kumar N. On customized goods, standard goods, and competition. Marketing Science, 2006, 25(5): 525-537.
    [22]
    Syam N B, Pazgal A. Co-creation with production externalities. Marketing Science, 2013, 32(5): 805-820.
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    [1]
    Malhotr A, Alstyne V M. The dark side of the sharing economy and how to lighten it. Communications of the ACM, 2014, 57(11): 24-27.
    [2]
    Kassi O, Lehdonvirta V. Online labour index: Measuring the online gig economy for policy and research. Technological Forecasting & Social Change, 2018, 137: 241-248.
    [3]
    Wood A J, Graham M, Lehdonvirta V, et al. Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 2019, 33(1): 56-75.
    [4]
    Philip H E, Ozanne L K, Ballantine P. Examining temporary disposition and acquisition in peer-to-peer renting. Journal of Marketing Management, 2015, 31(11-12): 1310-1332.
    [5]
    Martin C J, Upham P, Budd L. Commercial orientation in grassroots social innovation: Insights from the sharing economy. Ecological Economics, 2015, 118: 240-251.
    [6]
    Hall J V, Krueger A B. An analysis of the labor market for Uber’s driver-partners in the United States. ILR Review, 2018, 71(3): 705-732.
    [7]
    Bimpikis K, Candogan O, Saban D. Spatial pricing in ride-sharing networks. Operations Research, 2019, 67(3): 744-769.
    [8]
    Jiang B J, Yang B C. Quality and pricing decisions in a market with consumer information sharing. Management Science, 2018, 65(1): 272-285.
    [9]
    Sun H, Wang H, Wan Z X. Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity. Transportation Research Part B, 2019, 125: 76-93.
    [10]
    Halaburda H, Piskorski J M, Yildirim P. Competing by restricting choice: The case of matching platforms. Management Science, 2017, 64(8): 3574-3594.
    [11]
    Basu A, Bhaskaran S, Mukherjee R. An analysis of search and authentication strategies for online matching platforms. Management Science, 2019, 65(5): 2412-2431.
    [12]
    Kim J Y, Natter M, Spann M. Pay what you want: A new participative pricing mechanism. Journal of Marketing, 2009, 73(1): 44-58.
    [13]
    Gneezya A, Gneezy U, Riener G, et al. Pay-what-you-want, identity, and self-signaling in markets. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(19): 7236-7240.
    [14]
    Schmidt K M, Spann M, Zeithammer R. Pay what you want as a marketing strategy in monopolistic and competitive markets. Management Science, 2014, 61(6): 1217-1236.
    [15]
    Chen Y X, Koenigsberg O, Zhang J Z. Pay-as-you-wish pricing. Marketing Science, 2017, 36(5): 780-791.
    [16]
    Hirshleifer J. On the economics of transfer pricing. The Journal of Business, 1956, 29(3): 172-184.
    [17]
    Burdett K, Shi S Y, Wright R. Pricing and matching with frictions. Journal of Political Economy, 2001, 109(5): 1060-1085.
    [18]
    Aviv Y, Pazgal A. Optimal pricing of seasonal products in the presence of forward-looking consumers. Manufacturing & Service Operations Management, 2008, 10(3): 339-359.
    [19]
    Zhang J Z,Netzer O, Ansari A. Dynamic targeted pricing in B2B relationships. Marketing Science, 2014, 33(3): 317-337.
    [20]
    Caldentey R, Liu Y, Lobel I. Intertemporal pricing under minimax regret. Operations Research, 2016, 65(1):104-129.
    [21]
    Syam N B, Kumar N. On customized goods, standard goods, and competition. Marketing Science, 2006, 25(5): 525-537.
    [22]
    Syam N B, Pazgal A. Co-creation with production externalities. Marketing Science, 2013, 32(5): 805-820.
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