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Platform-level consequences of performance-based commission for service providers: Evidence from ridesharing

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Abstract

Ridesharing platforms compensate drivers using a fixed commission system that does not systematically reward effective drivers, which reduces platform engagement. Unsurprisingly, driver transaction activity is intermittent and service unpredictable. Influenced by agency theory, we propose a variable commission that jointly accounts for drivers’ transactions and service performance. To alleviate disengagement, we propose a customer-oriented engagement framework that challenges the notion of the sole monetary focus of drivers. We compare the effects of variable and fixed commission schemes on consequences such as driver net revenue and referral value, mediated by attitudinal outcomes. In a 3-month cluster-randomized field experiment with 3,367 ridesharing drivers across 16 cities and two population tiers, we show improvements in driver satisfaction and emotional connectedness accentuated by goal-oriented feedback. Variable commission with goal-oriented feedback translates to a 24.5% rise in revenue, a 19.5% increase in referral value, and a 43.21% lower churn. A cost–benefit analysis reinforces these results.

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Notes

  1. Intra-component items are equally weighted to avoid a constraint on measures. Taking our approach as an initial exploration, these weights are best determined by platforms based on usage data. The flexibility of altering weights allows the SPES to be generalized to other verticals.

  2. In experimental cities, the platform communicated the change in the commission scheme to the drivers through the mobile app on three occasions: thirteen, seven, and three days before the experiment. The platform also communicated the 25% commission to the control city drivers in the same way and at the same times to avoid confounding bias. On each occasion, the drivers acknowledged receipt of the message in the app.

  3. As the discussed engagement approach suggests, direct contribution is affected by satisfaction only, while indirect contribution is influenced by emotion only. We empirically verified the differences in emotional connectedness and satisfaction to be statistically insignificant in the direct (6) and indirect (7) contribution equations. Similarly, we tested for the potential effects among satisfaction and emotion by adding emotional connectedness (base value) in Eq. 4 and satisfaction (base value) in Eq. 5. Both are statistically insignificant. Finally, since the errors of satisfaction and emotional connectedness could be correlated within an individual, we used seemingly unrelated regression (SUR).

  4. The first stage moderated mediation is derived from the simple equations \(\widehat{M}={a}_{0}+{a}_{1}X+{a}_{2}Z+{a}_{3}XZ+{e}_{m}\) and \(\widehat{Y}={b}_{0}+{b}_{1}M+{b}_{2}X+{e}_{y}\). Substituting \(\widehat{M}\), we have \(\widehat{Y}={b}_{0}+{a}_{0}{b}_{1}+\left({a}_{1}{b}_{1}+{a}_{3}{b}_{1}Z\right)X+{a}_{2}{b}_{1}Z+{b}_{2}X+{e}_{y}+{{b}_{1}e}_{m}\) where \(\left({a}_{1}{b}_{1}+{a}_{3}{b}_{1}Z\right)\) denotes X’s conditional indirect effect on Y and \({b}_{2}\) denotes X’s direct effect on Y. As depicted with \({\upbeta }_{3}\) and \({\upbeta }_{4}\), the direct effects are insignificant. The indirect effects of the treatment on direct and indirect contributions are significant as per Preacher and Hayes’ (2008) bootstrap script test, and their effects are shown in Figures WA.9a and WA.9b. Overall, we observed indirect-only moderated mediations for both the direct and indirect contribution paths.

  5. In the main model, the treatment effect on satisfaction is .486 for drivers with better (p < .01) and -.059 for drivers with worse commission (p < .05), while the treatment effect on emotional connectedness is .502 for the first (p < .01) and .036 for the second group (p < .23).

  6. We asked drivers to pick a range for an adjusted commission and found that approximately 10% variation above and below 25% is the most and 0% is the least preferred choice. Our selection of the 15–35% commission range in the field experiment is also consistent with drivers’ fairness perception.

  7. Using an analogy from Uber’s annual report, drivers increased from 464,681 in 2015 to 750,000 in 2018 in the US, an annual increase of 95,106 (Hall and Krueger, 2018; Uber Technologies, 2019). As per the 2019 annual report, driver referral incentives are accounted as customer acquisition costs, which totaled $136 M (Million) in 2018. Payments to drivers attributed to non-driving activities (e.g., marketing expenses for driver acquisition) exceeded the cumulative revenue earned since the inception of the relationship with the driver by $837 M in 2018. Conservatively, the cost of acquisition per driver is $136 M/95,106 = $1,430 with only referral incentives, or $837 M/95,106 = $8,801 including marketing expenses. In our field experiment, 305 drivers churned in three months from four tier A control cities compared to 171 in four tier A treatment cities. Acquisition savings for the difference (134) amounts to 134*1,430 = $191,620 in three months for tier A and $72,930 for tier B. The combined cost savings amount to $264,550.

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Acknowledgements

We thank Editor John Hulland and the anonymous review team for their valuable guidance during the revision process.We thank Renu for copyediting the manuscript.

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  1. Orhan Bahadır Doğan, V. Kumar and Avishek Lahiri have contributed equally to this manuscript.

Authors and Affiliations

  1. Division of Marketing and Supply Chain Management, Price College of Business, The University of Oklahoma, Norman, OK, USA

    Orhan Bahadır Doğan

  2. Goodman School of Business, Brock University, St. Catharines, ON, Canada

    V. Kumar

  3. MICA, Ahmedabad, Telav - Ghuma Rd, Shela, Gujarat, 380058, India

    V. Kumar

  4. Huazhong University of Science and Technology (HUST), Wuhan, China

    V. Kumar

  5. Department of Innovation, Management, and Marketing, University of Stavanger Business School, University of Stavanger, Stavanger, Norway

    Avishek Lahiri

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Dr. Doğan is an Assistant Professor of Marketing at The University of Oklahoma; Dr. Kumar is a Professor of Marketing, and the Goodman Academic-Industry Partnership Professor at Brock University; Distinguished Fellow of MICA, and the Chang Jiang Scholar of Huazhong University of Science and Technology (HUST); Dr. Lahiri is a Postdoctoral Fellow in Marketing at the Stavanger Business School.

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Appendix 1: Determining the platform’s commission using SPES

Appendix 1: Determining the platform’s commission using SPES

At the time of this study, the focal ridesharing platform took 25% commission from the revenue drivers generate with each ride, and the remaining 75% is the driver’s compensation. We use SPES to alter this commission based on the individual driver’s performance relative to all the platform’s available drivers in a city, throughout the month. A higher SPES translates to a lower platform commission, and thereby to a higher driver compensation. The commission needs to revolve closer to the rate in practice (i.e., 25%) to preserve feasibility and the platform’s revenue generation targets. Considering the platform’s financial constraints and management guidelines, a 10% adjustment threshold above and below 25% (i.e., 15 to 35% commission range) is set.

In mapping SPES to commission, taking two decimals into account, we first match the possible SPES (i.e., varying from 1.00 to 5.00, i.e., 400 values) with possible commission (varying from 15.00 to 35.00%, i.e., 2,000 values). Given that there are five times more values in commission compared to SPES, we consider every 0.01 increase in SPES as 0.05% reduction from 35% in platform’s commission (e.g., for \({{\text{SPES}}}_{{\text{it}}}\)=1,3, and 5, commissions are set as 0.35, 0.25, and 0.15, respectively, operationalized as [35-(\({{\text{SPES}}}_{{\text{it}}}\)-1)\(*\) 5]). This setting ensures the platform’s average commission in the treatment condition to remain the comparable to the fixed commission (i.e., 25% commission on an average).

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Doğan, O.B., Kumar, V. & Lahiri, A. Platform-level consequences of performance-based commission for service providers: Evidence from ridesharing. J. of the Acad. Mark. Sci. (2024). https://doi.org/10.1007/s11747-024-01005-0

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  • DOI: https://doi.org/10.1007/s11747-024-01005-0

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