Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Business | en_US |
| dc.contributor.advisor | Ngai, Eric (MM) | en_US |
| dc.creator | Wong, Ping Yiu James | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12856 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Reputation and multiple submission : an empirical study of solvers’ strategic choices in the online labour market | en_US |
| dcterms.abstract | It is observed in the crowdsourcing and online labour market platforms that some solvers opt to make multiple submissions in the same Fixed Price All-Pay Contest, especially those subjective oriented tasks. This strategy demands excessive solver efforts. Especially in the single winner contests, only one of a solver’s multiple submissions can be selected as “the winner”; the rest will be discarded and all the associated efforts are forgone. Recent empirical result suggests that the number of submission by a solver is negatively related to the contest success in open innovation contest. This finding casts doubt on why the solvers would make such the effort choice. If multiple submission cannot lead to higher winning probability, over time solvers may cease to use it as excessive efforts cannot be justified and the platforms should not support it due to the potential welfare loss. There are multiple theories suggesting that increasing the number of submission predict better chance to win, such as simple probability rule, more quantity of idea generation leads to increased quality, and multiple designs enable greater range of solutions for the seeker. This research attempts to reconcile this conflict by developing an overall theoretical model to examine the motivation framework predicting the effort choice of multiple submission and the factors predicting the winning probability for those solvers adopting the strategy. Recognizing the potential choice complexity (in terms of quantity and variety of solutions which is fundamental to crowdsourcing/OLM) by the seeker, this research proposes that the seekers may resort into intuitive mode of information processing and the use of heuristics to reducing the decision efforts. Our findings support that multiple submission alone exhibits curvilinear (inverted U-shaped) relationship to the winning probability. However, after it is moderated by the solver reputation score, a subgroup of multiple submission by a solver increases its saliency which attracts more recognition from the seekers. Since multiple solvers may use the multiple submission strategy in the same contest, a solver’s subgroup of multiple submission has to be salient enough from the rests of solvers to significantly improve winning probability. Our analysis also confirms our findings are context dependent: our finding is more pronounced in a more crowded choice set and/or with less experienced seekers. We expect this research will contribute to the solver strategic behaviour / contest theory / contextual effect research domains by demonstrating the potential contextual effect on the seeker could influence the contest outcome in the crowdsourcing/OLM context and also offer an overall set of solver strategy based on the theoretical model developed. | en_US |
| dcterms.extent | 147 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2017 | en_US |
| dcterms.educationalLevel | D.B.A. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Reputation | en_US |
| dcterms.LCSH | Labor market | en_US |
| dcterms.LCSH | Crowdsourcing | en_US |
| dcterms.LCSH | Contests | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7355.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 9.63 MB | Adobe PDF | View/Open |
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