Selection of crowdsourcing formats: simultaneous contest vs sequential contest
| DOI | https://doi.org/10.1108/IMDS-09-2017-0433 |
| Date | 04 February 2019 |
| Pages | 35-53 |
| Published date | 04 February 2019 |
| Author | Wanjiang Deng,Xu Guan,Shihua Ma,Shan Liu |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Selection of crowdsourcing
formats: simultaneous contest vs
sequential contest
Wanjiang Deng
School of Management, Huazhong University of Science and Technology,
Wuhan, China
Xu Guan
School of Economics and Management, Wuhan University, Wuhan, China
Shihua Ma
School of Management, Huazhong University of Science and Technology,
Wuhan, China, and
Shan Liu
School of Management, Xi’an Jiaotong University, Xi’an, China
Abstract
Purpose –The online crowdsourcing has been widely applied in the practice. The purpose of this paper is to
investigate the all-pay auction contest in crowdsourcing, wherein a seeker posts a task online and the solvers
decide whether to participate in the contest and in what extent to spend efforts on their submissions.
Design/methodology/approach –The authors specifically consider two classic contest formats:
simultaneous contest and sequential contest, depending on whether the solver can observe the prior solvers’
submissions before making her own effort investment decision or not. They derive both seeker’s and solver’s
equilibrium decisions and payoffs under different contest formats, and show that they vary significantly
according to the number and the average skill level of solvers.
Findings –The results show that a solver would always invest more on her submission under simultaneous
contest than under sequential contest, as she cannot confirm how other solvers’submissions would be.
This subsequently intensifies the market competition and brings down a solver’s average payoff under
simultaneous contest. Although the simultaneous contest gives rise to a higher expected highest quality of all
submissions, it also requires the seeker to spend more search cost to identify the best submission. Therefore,
when the number of solvers is high or the average skill level is low, the seeker prefers sequential contest to
simultaneous contest. The results also show an analogous preference over two formats for the platform.
Originality/value –This paper investigates two formats of all-pay auction contest in crowdsourcing and
evaluates them from the perspective of solvers, seekers and platforms, respectively. The research offers many
interesting insights which do not only explain the incentive mechanisms for solvers under different contest
formats, but also make meaningful contributions to the seeker’s or the platform’s adoption strategies between
two alternative contest formats in crowdsourcing practice.
Keywords Crowdsourcing, Game theory, All-pay auction, Sequential contest, Simultaneous contest
Paper type Research paper
1. Introduction
Crowdsourcing is an innovative business mode started in 2005. It is defined as the process
that a company obtainsnew ideas or services from a large group of people (usually from an
online community) rather than from its employees or suppliers (Jeff, 2006). Thanks to
the rapid development of internet and mobile technologies (Dynich and Wang, 2016),
today crowdsourcing becomes increasingly popular in many different categories, including
Industrial Management & Data
Systems
Vol. 119 No. 1, 2019
pp. 35-53
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-09-2017-0433
Received 29 September 2017
Revised 26 December 2017
Accepted 2 March 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The authors sincerely thank the department editor, Alain Chong, the senior editor and two anonymous
reviewers for their constructive comments and suggestions that improve the paper. The authors have
also benefited from the support of National Natural Science Foundation of China (71871167, 71472069,
71402126, 71471141).
35
Selection of
crowdsourcing
formats
prediction markets, innovation contests, design contests, experimentation, outsourcing
manual tasks and social commerce (Zhou et al., 2013). Crowdsourcing certainly helps the
company to acquire knowledge, creativity, skill and working hours from those open crowds
more easily. Accordingto the report from “The State of Crowdsourcingin 2015,”in the last 10
years, more than 85 percentof those world-famous enterprises haveused crowdsourcing and
they are three times more likely to launch crowdsourcings via the crowdsourcing platform
than via other social medias[1].
Let us take Taskcn.com (www.taskcn.com/), the biggest crowdsourcing online platform
in China, as an example to briefly illustrate the process of the crowdsourcing contest. A firm
first announces a task on Taskcn.com and sets a reward to attract potential candidates to
submit their solutions. Since both the task and the reward are observable on Taskcn.com,
each candidate can decide whether to participate in the contest by evaluating the possible
reward and the cost of submission. After all the participants submit their solutions to the
platform, the firm assesses their qualities/performances and the participant with best
solution (highest quality) will win the guaranteed reward exclusively from the firm. Building
upon, we apply the all-pay auction (Liu et al., 2014) to model that crowdsourcing contest,
which typically consists of three key roles: seeker, solver and platform. In line with the
concept from Terwiesch and Xu (2008), seeker refers to the firm who proposes a task, solver
refers to the user who submits solution toward the task and platform is an independently
operated third-party company, such as Taskcn.com and Amazon Mechanical Turk
(www.mturk.com/mturk/welcome).
Although the all-pay auction has been widely adopted by different platforms, one feature
varying significantly among these platforms is the observability of submissions. That is, on
some platforms (e.g.99designs.com), solvers have to sign a non-disclosure agreement to keep
their submissions secret, which means none of the solvers can observe others’submissions
before submitting her own solution. However, on some other platforms (e.g. Taskcn.com), the
contents of early submissions are freely posted online, so the solvers who arrive later can
always observe the prior submissions. Therefore, we can divide the all-pay auction into two
formats depending on the observability of solvers’submissions. The firstone is defined as the
simultaneous contest, in which each solver has to independently and privately submit her
solution online without knowing others’submissions. The second is def ined as the sequential
contest, whereinthe solvers arrive sequentiallyand a late-arriving solver can alwaysobserve
the early-arriving solvers’submissions and then decide howmuch effort to spend on her own
submission. Building upon these two contest formats, we will investigate the following
research questions:
RQ1. Given a certain contest format, what are the solvers’equilibrium decisions of effort
investment on their submissions and what is the expected highest quality among
all the submissions?
RQ2. Which contest format is the better option from either the solver’s, seeker’s or the
latform’s perspective?
RQ3. When the seeker can voluntarily determine the number or the average skill level of
solvers in the contest, what is his optimal decision and how does this influence the
seeker’s payoff under two contest formats?
To answer the above questions, we build a stylized all-pay auction contest model between
a single seeker (he) and multiple solvers (she). This paper contributes to the existing
literature by deriving some novel ideas from the comparison between two crowdsourcing
formats, i.e., the simultaneous contest and the sequential contest, as well as provides
useful managerial implications for the seeker’sortheplatform’sadoptionstrategiesof
contest formats in practice.
36
IMDS
119,1
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