Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study

• 成果类型: Article
• 署名作者: Aggarwal, Rohit; Midha, Vishal; Sullivan, Nicholas
• 署名单位: Utah System of Higher Education; University of Utah; Illinois State University; University System of Ohio; University of Dayton
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.1053
• 发表日期: 2024
• 关键词: hiring decisions; trust; GENDER; credibility; INFORMATION; self; overconfidence; satisfaction; perceptions; Mediation

摘要

Online professional networks (OPNs) are an increasingly common tool used by recruiters to find and vet qualified job candidates for open positions. These sites allow users to publish recommendations given by other users to supplement their profile information and add credibility to the information provided. OPN recommendations provide a rich source of information to recruiters. Unlike recommendations shared in other ways (non-OPN recommendations), OPN recommendations are publicly accessible, and candidates have full control over which recommendations they show to others. Despite the role OPN profiles play in the recruiting process, there has been a lack of research into the specific effect of OPN recommendations, how the impact of OPN recommendations differs from non-OPN recommendations, and how they can best be used to maximize the likelihood of candidates receiving interviews. First, we theoretically conjecture why recommendations that reveal a candidate's weakness will have different effects on the likelihood of interview depending on whether the weakness is expected or unexpected and how that difference depends on how the recommendation is presented (OPN versus non-OPN). Then, we establish that the effectiveness of a recommendation is higher when presented as a non-OPN recommendation than as an OPN recommendation and show that OPN recommendations benefit more from discussing an expected weakness than non-OPN recommendations. Data were collected from a field study leveraging the candidate tracking system of a large recruitment firm.