Although the composition of individuals can strongly affect the success of professional collaboration, organizations often struggle with their so-called social matching decisions. For example, when recruiting new people to an organization, the decision-making is often reduced to intuitively matching individuals based on vague descriptions of projects or positions. The role of technology in recruiting is typically confined to gathering and presenting simple candidate profiles. We argue that many issues in recruitment boil down to lack of understanding the process of decision-making from social matching perspective, covering aspects like identification of relevant selection criteria and choice of the most suitable candidate. To better understand the appropriate roles of information technology (IT) in this domain, we interviewed 21 expert matchmakers, such as HR specialists and headhunters. Based on qualitative analysis of their experiences, we provide a bottom-up framework of the decision-making stages in recruitment, focusing on the pertinent challenges from the perspective of social matching. The findings indicate that, particularly, the epistemic asymmetry between the recruiter and candidates regarding the expected qualities calls for deliberation throughout the decision-making process. Matchmakers also struggle between contradictory ideals of agility and holistic decision-making. Based on the findings and relevant literature, we propose six roles that IT could play in social matching decisions in recruitment.
Organizations’ hiring processes are increasingly shaped by various digital tools and e-recruitment systems. However, there is little understanding of the recruiters’ needs for and expectations towards new systems. This paper investigates recruitment chatbots as an emergent form of e-recruitment, offering a low-threshold channel for recruiter-applicant interaction. The rapid spread of chatbots and the casual nature of their user interfaces raise questions about the perceived benefits, risks, and suitable roles in this sensitive application area. To this end, we conducted 13 semi-structured interviews, including 11 interviews with people who are utilizing recruitment chatbots and two people from companies that are developing recruitment chatbots. The findings provide a qualitative account of their expectations and motivations, early experiences, and perceived opportunities regarding the current and future use of chatbots in recruitment. While chatbots answer the need for attracting new candidates, they have also introduced new challenges and work tasks for the recruiters. The paper offers considerations that can help to redesign recruitment bots from the recruiter’s viewpoint.
Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.
The team composition of a project team is an essential determinant of the success of innovation projects that aim to produce novel solution ideas. Team assembly is essentially complex and sensitive decision-making, yet little supported by information technology (IT). In order to design appropriate digital tools for team assembly, and team formation more broadly, we call for profoundly understanding the practices and principles of matchmakers who manually assemble teams in specific contexts. This paper reports interviews with 13 expert matchmakers who are regularly assembling multidisciplinary innovation teams in various organizational environments in Finland. Based on qualitative analysis of their experiences, we provide insights into their established practices and principles in team assembly. We conceptualize and describe common tactical approaches on different typical levels of team assembly, including arranging approaches like “key-skills-first”, “generalist-first” and “topic-interest-first”, and balancing approaches like “equally-skilled-teams” and “high-expertise-teams”. The reported empirical insights can help to design IT systems that support team assembly according to different tactics.
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