主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院

中国管理科学 ›› 2016, Vol. 24 ›› Issue (4): 94-101.doi: 10.16381/j.cnki.issn1003-207x.2016.04.011

• 论文 • 上一篇    下一篇

招聘服务供应链中合作广告博弈

梁昌勇1,2, 侯静怡1,2, 傅为忠1   

  1. 1. 合肥工业大学管理学院, 安徽 合肥 230009;
    2. 合肥业大学过程优化与智能决策教育部重点实验室, 安徽 合肥 230009
  • 收稿日期:2014-09-09 修回日期:2015-04-26 出版日期:2016-04-20 发布日期:2016-04-29
  • 通讯作者: 侯静怡(1974-),女(汉族),甘肃会宁人,合肥工业大学管理学院,工程师,博士生,研究方向:供应链管理、服务科学、人力资源管理,PPP管理等,E-mail:wdhjy2003@sina.com. E-mail:wdhjy2003@sina.com
  • 基金资助:

    国家自然科学基金资助项目(71331002,71271072)

Cooperative Advertising Game in Recruitment Service Supply Chain

LIANG Chang-yong1,2, HOU Jing-yi1,2, FU Wei-zhong1   

  1. 1. College of Management, Hefei University of Technology, Hefei 230009, China;
    2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
  • Received:2014-09-09 Revised:2015-04-26 Online:2016-04-20 Published:2016-04-29

摘要: 本文研究求职者投递简历量不确定性的条件下由网站招聘服务商和用人企业组成的招聘服务供应链中的合作广告对招聘效用的影响。网站服务商首先确定招聘套餐价格和广告赠送率,用人企业根据套餐及广告,确定本企业对求职者提供的职位搜索价格。运用斯坦伯格博弈模型进行合作广告效用预测显示:(1)对于用人企业,广告效用增加,求职者对本单位的招聘职位搜索价格增加,企业期望简历量和期望效用增加。(2)对于网站服务商,固定广告赠送率可确定职位价格;固定职位价格可确定广告赠送率;提高广告赠送率可激励用人企业选择更高价位广告。将上述理论运用于企业与智联招聘、前程无忧和新安人才网三家网站建立招聘服务供应链时的斯坦伯格决策,可从企业、服务商和供应链三个角度综合做出决定-若企业在安徽省内招聘则优选新安,在全国招聘则优选智联,可保证企业招聘效果和服务供应链效用最优。上述方法为中小企业选择招聘服务商提供参考,也为人才管理理论研究拓展新方向。

关键词: 服务供应链, 招聘, 合作广告, 斯坦伯格博弈, 人才管理

Abstract: Suitable talents are critical for enterprises to develop market, to amplify advantage and to get comparative competition. Web recruitment advertisement is one of the most important strategy means for global enterprises to compete for excellent talents. To improve recruitment effect, an Enterprise Employer (EE) and several Recruitment Web Servicers (RWSs) constitute a Service Supply Chain (SSC) to share cooperative advertising for attracting expected applicants to offer resumes. First, the RWSs design their own serving packages, including recruitment position price (w), advertisement density (x) and advertisement sharing rate (t). Secondly, the EE selects appropriate RWS(s) by predicting amount of applicants' resumes to be received from different RWSs. Finally, EE pays for recruitment package and collects resumes with continuous website advertising during a suitable period. RWSs help EE with necessary services according to SSC contract. Supposing the amount of applicants' resumes (y) to be received by EE is a decreasing function of searching price (p) for applicants, but an increasing function of website advertisement density (x) with advertising marginal effect diminishing. That is, y(p,x)=yi-αp+r√x, wherein yi is the initial resumes amount without advertisement, α is price sensitivity and r is advertisement coefficient. Five theory propositions are proved to be correct using Steinberg Game to predict cooperative advertising effectiveness. The results are as follows:(1) For EE, the higher advertising effectiveness is, the lower searching prices for job seekers, the more candidates will apply for vacancies by offering resumes.(2) For RWS, the more sharing ratio is,the higher advertisement density EE wants to purchase. RWS can determine recruitment position price with sharing ratio fixed. Meanwhile, RWS can also determine sharing ratio with recruitment position price fixed. With the above Steinberg game theory of sharing cooperative advertising, one EE in Anhui province is predicting different parameters to compare three RWSs, i.e., Zhaopin.com, 51job.com and Goodjobs.cn to form a recruitment service supply chain. The real data are from the Anhui EE practice where the second writer is in charge of the recruitment for a year. The results show that Goodjob.cn is best for hiring candidates in Anhui province and Zhaopin.com is the best when hiring talents in national wide with the expected utility of RSSC maximum. The method in the paper is helpful for many SMEs to select RWSs according to recruitment package and is also contributive to talent management theory.

Key words: service supply chain, internet recruitment, cooperative advertising, steinberg game, talent management

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