徐琪 張慧賢
摘 要: 眾包競(jìng)賽作為新興的社會(huì)互動(dòng)行為下的商業(yè)模式,創(chuàng)意眾包通過(guò)匯集公眾的知識(shí)、技能、信息,幫助企業(yè)直接面對(duì)消費(fèi)者,全面創(chuàng)新產(chǎn)品設(shè)計(jì)和開(kāi)拓銷(xiāo)售市場(chǎng),充分利用互聯(lián)網(wǎng)優(yōu)勢(shì)來(lái)應(yīng)對(duì)市場(chǎng)快速變化的需求。所研究的是固定獎(jiǎng)金機(jī)制下的眾包競(jìng)賽,發(fā)包方的目的為最大化創(chuàng)意方案的最高質(zhì)量。首先,根據(jù)全支付拍賣(mài)的相關(guān)理論建立發(fā)包方和接包方雙方多屬性的效用模型,然后運(yùn)用Stackelberg博弈的方法得出均衡解,并分析競(jìng)賽參與人數(shù)、獎(jiǎng)金、接包方的得分和接包方的創(chuàng)意努力成本參數(shù)等對(duì)眾包雙方的最優(yōu)策略的影響。研究表明:因?yàn)榻影酱嬖诰馔稑?biāo),競(jìng)賽人數(shù)的增加會(huì)使接包方為競(jìng)賽所付出的努力先增加后減少,而且創(chuàng)意努力的成本參數(shù)越低,其期望效用越高;關(guān)于發(fā)包方獎(jiǎng)金金額的設(shè)置,因?yàn)榻影降哪芰τ邢蓿?jiǎng)金不能無(wú)限地激勵(lì)接包方,導(dǎo)致獎(jiǎng)金的增加會(huì)使發(fā)包方的效用先增加后減少;發(fā)包方通過(guò)設(shè)置最低得分可以防止接包方聯(lián)合低價(jià),導(dǎo)致發(fā)包方無(wú)法得到高質(zhì)量的創(chuàng)意方案。
關(guān)鍵詞: 眾包競(jìng)賽;多屬性;獎(jiǎng)金;參賽人數(shù);全支付拍賣(mài)
中圖分類(lèi)號(hào): F 224
文獻(xiàn)標(biāo)志碼: A
Abstract: As a business model under the emerging social interaction behavior, crowdsourcing gather public knowledge, skills, and information to help companies directly face consumers, fully explore innovative product design and sales markets, and make full use of Internet advantages to respond market demand. This paper explores crowdsourcing contest which is under a fixed bonus mechanism. The purpose of crowdsourcing is to maximize the best quality. First, we establish utility function both the contestant and the Crowdsourcing company, and then the optimal solutions to Stackelberg game and then analysis results show that: the increase in the number of contestants will increase the effort for the competition and then decrease;the lower the creative effort cost of the contestants, the higher the expected utility; due to the limited ability of contestants, bonuses can′t infinitely motivated contestants, so as the bonus increases, crowdsourcing company′s expected utilities increases first and then decrease.
Key words: crowdsourcing; multiple attributes; bonus; number of participants; all pay auction
本文運(yùn)用多屬性逆向拍賣(mài)的相關(guān)理論,分析創(chuàng)意方案的質(zhì)量屬性,建立發(fā)包方和接包方雙方的效用模型,研究競(jìng)賽接包方的投標(biāo)策略、發(fā)包方的獎(jiǎng)金策略,以及接包方和發(fā)包方雙方的期望收益。
1 創(chuàng)意眾包模型構(gòu)建
1.1 模型描述及假設(shè)
創(chuàng)意眾包方案針對(duì)不同的產(chǎn)品或服務(wù)等要求,具有多維質(zhì)量屬性,如時(shí)裝產(chǎn)品具有時(shí)尚性、流行性、舒適性、創(chuàng)新性等特點(diǎn)。創(chuàng)意眾包競(jìng)賽首先是發(fā)包方通過(guò)眾包網(wǎng)站公布創(chuàng)意方案設(shè)計(jì)的要求、創(chuàng)意方案多維質(zhì)量屬性競(jìng)賽打分規(guī)則、方案的最低得分以及獎(jiǎng)金數(shù)目;之后每個(gè)競(jìng)賽參與者根據(jù)打分規(guī)則和自身效用,進(jìn)行創(chuàng)意方案設(shè)計(jì)并以密封方式投標(biāo)創(chuàng)意方案;發(fā)包方根據(jù)評(píng)分規(guī)則,計(jì)算每個(gè)參賽接包方的投標(biāo)方案的質(zhì)量屬性得分,得分高的接包方獲得獎(jiǎng)金;眾包網(wǎng)站將發(fā)包方已定的獎(jiǎng)金數(shù)目發(fā)放給獲勝的接包方。據(jù)此流程,本文首先構(gòu)建發(fā)包方及競(jìng)賽接包方效用函數(shù),然后建立參賽接包方最優(yōu)投標(biāo)模型與眾包方獎(jiǎng)金激勵(lì)模型。本文所用的符號(hào)及其說(shuō)明如表1所示。
4 結(jié)論
眾包在提升企業(yè)創(chuàng)新能力上扮演了越來(lái)越重要的角色,在學(xué)術(shù)界也成為研究熱點(diǎn)之一。本文所研究的是固定獎(jiǎng)金機(jī)制下的眾包競(jìng)賽,發(fā)包方的目的是為了獲得創(chuàng)意方案的最高質(zhì)量,在多屬性逆向拍賣(mài)模型的基礎(chǔ)上構(gòu)建了眾包模型,得到了Stackelberg博弈均衡解,發(fā)現(xiàn)了競(jìng)賽參與人數(shù)的增加,會(huì)使接包方對(duì)于眾包競(jìng)賽所付出的努力先增加后減少。此外,獎(jiǎng)金會(huì)刺激接包方有更多的參賽熱情,在一定程度上可以激勵(lì)接包方為眾包競(jìng)賽付出更多努力。但是因?yàn)榻影侥芰Φ南拗?,使接包方?duì)于眾包競(jìng)賽所付出的努力有限制,導(dǎo)致獎(jiǎng)金金額超過(guò)一定的數(shù)值后獎(jiǎng)金增加會(huì)降低發(fā)包方的效用。本文關(guān)于參與人數(shù)和獎(jiǎng)金的討論也是為了眾包雙方能夠更好地實(shí)施和利用眾包。
參考文獻(xiàn):
[1] DAVID E, AZOULAY-SCHWARTZ R, KRAUS S. Protocols and strategies for automated multi-attribute auctions[C]// International Joint Conference on Autonomous Agents & Multiagent Systems: Part. ACM, 2002:77-85.
[2] 孫亞輝, 馮玉強(qiáng). 多屬性密封拍賣(mài)模型及最優(yōu)投標(biāo)策略[J]. 系統(tǒng)工程理論與實(shí)踐, 2010, 30(7):1185-1189.
[3] 曾憲科,馮玉強(qiáng).逆向多屬性拍賣(mài)投標(biāo)策略及收益性分析[J].管理科學(xué)學(xué)報(bào),2015,18(9):24-33.
[4] ARCHAK N, SUNDARARAJAN A. Optimal design of crowdsourcing contests[J]. ICIS 2009 proceedings, 2009: 200.
[5] MOLDOVANU B, SELA A. The optimal allocation of prizes in contests[J]. American Economic Review, 2001, 91(3):542-558.
[6] YANG Y, CHEN P Y, PAVLOU P A. Open innovation: An empirical study of online contests[C]// International Conference on Information Systems, Icis 2009, Phoenix, Arizona, Usa, December. DBLP, 2009: 13.
[7] CHEN Y, TECK-HUA H O, KIM Y M. Knowledge market design: A field experiment at Google answers[J]. Journal of Public Economic Theory, 2010, 12(4):641-664.
[8] TAYLOR C R. Digging for Golden carrots: An analysis of research tournaments[J]. American Economic Review, 1995, 85(4):872-890.
[9] CHE Y K, GALE I. Optimal design of research contests[J]. American Economic Review, 2003, 93(3):646-671.