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Journal of the Academy of Marketing Science

08-03-2024 | Original Empirical Research

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

Authors: Orhan Bahadır Doğan, V. Kumar, Avishek Lahiri

Published in: Journal of the Academy of Marketing Science

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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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Appendix
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Footnotes
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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Metadata
Title
Platform-level consequences of performance-based commission for service providers: Evidence from ridesharing
Authors
Orhan Bahadır Doğan
V. Kumar
Avishek Lahiri
Publication date
08-03-2024
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-024-01005-0