Evidence map›Paper›PMID 29734683›Full record

ArticleSensors (Basel, Switzerland)2018

Do Monetary Incentives Influence Users' Behavior in Participatory Sensing?

Ngo Manh Khoi, Sven Casteleyn, M Mehdi Moradi, Edzer Pebesma

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ngo Manh KhoiGEOTEC, Institute of New Imaging Technologies (INIT), Universitat Jaume I, 12071 Castellón, Spain. mngo@uji.es.ORCID 0000-0001-6001-2773
Sven CasteleynGEOTEC, Institute of New Imaging Technologies (INIT), Universitat Jaume I, 12071 Castellón, Spain. sven.casteleyn@uji.es.
M Mehdi MoradiGEOTEC, Institute of New Imaging Technologies (INIT), Universitat Jaume I, 12071 Castellón, Spain. moradi@uji.es.ORCID 0000-0003-3905-4498
Edzer PebesmaInstitute for Geoinformatics, Universität Münster, 48149 Münster, Germany. edzer.pebesma@uni-muenster.de.ORCID 0000-0001-8049-7069

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers.

Indexed as

monetary incentiveparticipatory sensingsmart cityuser behavioruser engagement

Identifiers

PMID29734683
PMCPMC5982840

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.