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The effect of economic incentives and cooperation messages on user participation in crowdsourced public transport technologies


Transport data is crucial for transport planning and operations. Collecting high-quality data has long been challenging due to the difficulty of achieving adequate spatiotemporal coverage within a representative sample. The increasingly integrated use of Information and Communication technologies in transport systems offers an opportunity to collect data using non-traditional methods. Crowdsourcing applications are an example where a community of users shares information about their travel experience. However, crowdsourcing applications depend on a critical mass of users providing feedback. We conducted a large-scale field experiment to examine the effect of economic incentives (a lottery for free trips) and cooperation messages (asking users to help the community) to encourage users to share reports about bus stop conditions using a crowdsourcing app. We found that offering an economic incentive increased the participation rate almost three times compared to a control group, which did not receive any message. This positive effect lasted for several weeks but decreased over time, especially for users who had not made reports prior to the experiment. This incentive also increased the number of reports shared by users. Using a cooperation message, with or without the economic incentive, also increased the participation rate compared to the control group, but adding a cooperation message decreased the effect of a standalone economic incentive.

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  1. Other examples include Wikipedia, in which only 0.2% of active US visitors are active contributors (Nielsen 2006). In this case, even though few contributors may provide high-quality information, there is concern about inequality (e.g., gender) (Nielsen 2006; Torres 2016). Similarly, only a small fraction of buyers provide an online review despite the fact that online shoppers highly value online reviews of products from many different consumers (NationMaster 2019).

  2. One reason that economic incentives may backfire is the so-called “crowding-out of intrinsic motivation”, in which economic incentives reduce the chance of a desired behavioral change by undermining people’s intrinsic motivation—i.e., their desire to perform a task for its own sake without any economic reward (Frey and Oberholzer-Gee 1997; Gneezy and Rustichini 2000; Schwartz et al. 2015, 2020).

  3. For a further review of the literature on cooperation, see Klein and Ben-Elia (2016).

  4. The literature on the effect of economic incentives on socially desirable behavior has been mixed, showing that their effect may depend on how incentives are structured and delivered (Gneezy et al. 2011; Kamenica 2012; Schwartz et al. 2019).

  5. We used almost the entire database of active users at the time of the experiment, and excluded only a few hundred users who participated in a pilot test.

  6. Even though push notifications could be received even if the app was not being used, sending them when users were more likely to use the app increased the chance that they had some information to share.

  7. The messages showed the amounts in Chilean pesos (CLP), but we show them here in U.S. dollars (USD) using the prevailing conversion rate at the time of the experiment.

  8. We oversampled the experimental conditions with an economic incentive based on the results from a pilot (“Appendix 2” provides information about the sample size and the statistical power analysis).

  9. We also found that the percentage of users who uninstalled the app during the campaign was small and very similar across conditions: economic incentive (0.58%), cooperation message (0.54%), both (0.47%), and control (0.57%).

  10. We excluded outlier observations with an extremely high number of reports—over the 99.5th percentile—to avoid a strong influence from very few observations. For completeness, in the “Appendix 4”, we show an analysis that includes these observations.

  11. Compared to the negative binomial model, the Poisson distribution does not assume overdispersion of the count data. In our case, there is overdispersion as the unconditional mean number of reports is much lower than its variance for each experimental condition.



  14. Regarding report quality, only a small percentage of reports may be considered dubious (i.e., more users rejected the report instead of confirming it) out of all the reports made during the campaign: economic incentive (5.5%), cooperation message (8.4%), both (6.9%), and control (11.8%).

  15. The economic incentive condition had two possible specific messages, which could be seen only if people opened the economic incentive notification—one with the economic incentive specific message and another with the both specific message. Because few people likely read the messages in the app, we found no sizable differences between messages for people who received the economic incentive notification, so we decided to present the results based on the notifications only.


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We would like to acknowledge two anonymous reviewers for their helpful and comprehensive comments and suggestions. This research was partially funded by ANID-PFCHA/Doctorado Nacional/2017 Grant 21170750, Fondecyt Grant 1191104, Fondecyt Grant 1191745, ANID-PIA/BASAL Grant AFB180003, and MIPP (ICS13_002 ANID).

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All authors contributed to the formulation of the study goal, methodology, formal analysis, and the research and investigation process. JA and CM performed the data collection process and creation of the initial draft. CM and DS performed the experimental design and implementation of the computer code for the analysis. The revision and edition of the final draft were performed by JA, MM, and DS. Finally, the management and coordination responsibility for the submission of the paper was performed by JA.

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Correspondence to Jacqueline Arriagada.

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Claudio Mena, Marcela Munizaga, and Daniel Schwartz declare that they have no conflict of interest. Jacqueline Arriagada declares that she is involved with Transapp as co-founder. Transapp has provided the data for this study.

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Appendix 1: Notifications received by users on their phones

Messages sent to users. From left to right: Economic incentive, Cooperation message and Both conditions. Users had to press the notification (at the top of the figure) in order to read the full message.Footnote 15

figure a

Details of the notifications and messages sent to users, translated to English:

  Economic incentive Cooperation message Both
Notification Participate for CLP$10,000 in bip! credits! Help other commuters! Help and participate for CLP$10,000 in bip! credits!
  Campaign: Win by reporting your bus stop Campaign: Help by reporting your bus stop Campaign: Help and win by reporting your bus stop
Condition-specific message Participate in this campaign between [dates] by sending reports about a bus stop, and you will be participating in a draw for three bip! credits of CLP$10,000 By participating in this campaign between [dates] by sending reports about a bus stop, you will be helping to improve other peoples’ trips, as well as the public transport system Participate in this campaign between [dates] by sending reports about a bus stop, and you will be helping to improve other peoples’ trips and the public transport system. You will also be participating in a draw for three bip! charges of CLP$10,000
Common message Remember that to report a bus stop near your location, you must first click on the bus stop on the map, and then on the yellow button that will appear at the bottom of the screen. Thanks for being part of our community

Appendix 2: Sample size

To identify the sample size required for each treatment, we perform a statistical power analysis based on the results of a pilot. This pilot showed that the cooperation message had the smallest effect compared to the control (0.8 p.p. from 1.1%). Therefore, we required approximately 6000 individuals in each of these experimental conditions to have a 95% statistical power (we added a few hundred people because some phones may have changed or not be working). The rest of the sample was evenly distributed to detect differences between the economic incentive and both conditions, and to be able to split the economic incentive condition into two additional conditions for people who opened the economic incentive notification. For the latter, the message section in the app either repeated the text from the notification (i.e., offering an economic incentive) or also included the text from the cooperation message. We expected the difference to be small, if detectable, because few people may use the message section in the app. Therefore, the final sample was 11,164 for each of these three groups (the Both condition and the two inside the economic incentive one). Consistently, Table 4 shows no significant difference between the texts in the message section for the economic incentive condition (p > 0.2 for all models).

Table 4 Estimation of the effect of each experimental condition on participation rate using a logit model

Appendix 3: Estimation of the effect of each experimental condition on participation rate using a linear probability model

  All Previous contributors Previous Lurckers All with interactions
Economic incentive 0.039*** 0.079*** 0.031*** 0.024***
(0.003) (0.010) (0.003) (0.003)
\(<0.001\) \(<0.001\) \(<0.001\) \(<0.001\)
Cooperation message 0.010** 0.035** 0.004 -0.002
(0.003) (0.013) (0.003) (0.004)
0.005 0.005 0.169 0.538
Both 0.027*** 0.062*** 0.019*** 0.013***
(0.003) (0.011) (0.003) (0.003)
\(<0.001\) \(<0.001\) \(<0.001\) \(<0.001\)
Contributors-economic incentive     0.085***
Contributors-cooperation message     0.068***
Contributors-both     0.080***
Constant 0.013*** 0.044*** 0.007** 0.013***
(0.002) (0.009) (0.002) (0.002)
\(<0.001\) \(<0.001\) 0.002 \(<0.001\)
p values for pairwise comparisons
Economic incentive versus cooperation message \(<0.001\) \(<0.001\) \(<0.001\) \(<0.001\)
Economic incentive versus both \(<0.001\) 0.043 \(<0.001\) \(<0.001\)
Cooperation message versus both \(<0.001\) 0.016 \(<0.001\) \(<0.001\)
R\(^2\) 0.006 0.008 0.006 0.027
Observations 46,516 8136 38,380 46,516
  1. \(^+p<0.10\), *\(p<0.05\), **\(p<0.01\), ***\(p<0.001\)
  2. All columns show standard errors between parentheses and p values in italics

Appendix 4: Estimation of the effect of each experimental condition on the level of user contribution using different models and different analyses

Negative binomial    
  Without exclusion With Poisson With bus reports
Economic incentive 1.231** 0.235* 0.322
(0.408) (0.112) (0.430)
0.003 0.036 0.453
Cooperation message 0.517\(^+\) 0.088 0.685
(0.289) (0.139) (0.693)
0.074 0.523 0.323
Both 1.526* 0.234* 0.226
(0.590) (0.119) (0.639)
0.010 0.048 0.724
Constant \(-\) 2.43*** 0.752*** 0.108
(0.231) (0.108) (1.039)
\(<0.001\) \(<0.001\) 0.916
Economic incentive \(-\) 18.1*** \(-\) 1.33*** \(-\) 0.28
(0.348) (0.118) (0.106)
\(<0.001\) \(<0.001\) 0.239
Cooperation message \(-\) 0.63\(^+\) \(-\) 0.50*** 0.106
(0.371) (0.143) (0.164)
0.089 \(<0.001\) 0.739
Both \(-\) 1.21\(^+\) \(-\) 1.05*** 0.164
(0.656) (0.124) (3.695)
0.065 \(<0.001\) 0.588
Constant 0.345 4.196*** 3.695
(0.317) (0.113) (1.030)
0.277 \(<0.001\) \(<0.001\)
Ln alpha 3.965***   2.454*
(0.125)   (0.037)
\(<0.001\)   0.037
alpha 52.751   11.641
(6.637)   (13.72)
Log-likelihood \(-\) 11865.09 \(-\) 11057.26 \(-\) 2431.907
Observations 46,516 46,438 46,516
  1. \(^+p<0.10\), *\(p<0.05\), **\(p<0.01\), ***\(p<0.001\)
  2. All columns show standard errors between parentheses and p values in italics

Appendix 5: Estimation of the effect of each experimental condition on the level of user contribution using a Zero-inflated negative binomial model

See Tables 5, 6 and 7.

Table 5 Estimation of effects of each experimental condition on the level of all users’ contributions over time
Table 6 Estimation of effects of each experimental condition on the level of Previous Contributors’ contributions over time
Table 7 Estimation of effects of each experimental condition on the level of previous lurkers’ contributions over time

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Arriagada, J., Mena, C., Munizaga, M. et al. The effect of economic incentives and cooperation messages on user participation in crowdsourced public transport technologies. Transportation (2022).

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  • Crowdsourcing app
  • Public transport
  • Transport data
  • Field experiment
  • Contribution