Information Asymmetry and Search Friction in Early-Stage Researchers Recruitment

Abstract

Early-stage student researcher’s recruitment process includes challenges for both faculty member and student researchers. The theoretical basis of the study draws on concepts in the light of signalling theory and search and matching theory; challenges occurred from information asymmetry and search friction examined in this research. This thesis uses an exploratory mixed method design where Phase 1 involved five semi-structured interviews with 2 active PHD students and 2 active masters students and a faculty member at the University of Vaasa and analysed by content analysis in NVivo, yielding four major categories, namely: fairness of recruitment and the search process, challenges in recruitment, signals and candidate evaluation and time-consuming search. In Phase 2, a quantitative survey was conducted with 100 valid respondents (88 students and 12 professors) from five Finnish universities, which offered the quantitative data on the frequency of the identified perceptions and practices. Qualitative research identifies the challenges in finding research opportunities may not centrally advertised fully and are fragmented and dispersed across informal channels depends on the urgency of the project and ends up with internal hiring. While in quantitative research, students' participants in the ease of finding research positions showed a mean score of 2.77 (N=88). (40.9%) selected the neutral option, 39.7% disagreed regarding how easy it was to discover, and only 19.4% disagreed regarding how easy it was to discover. This indicates that search friction and lack of central listings are major issues in conducting an effective research position search. The finding from interview highlights the that the information asymmetry in candidate evaluation, is research interest and specifically focused on the evaluation of research motivation to complete the research work, that is most central to supervisor selection decisions and least accurately reported by the curriculum vitae, academic transcript, or motivation letter. While survey highlights assessing candidate motivation from application materials, professor respondents (N=12) rated the difficulty with a mean score of 3.67 out of 5. A significant majority (58.3%) rated the difficulty as 4 or higher, with 58.3% giving a score of 5. Only 16.6% felt research motivation assessment was low, suggesting that supervisors generally find it challenging to evaluate motivation from conventional application materials. This supports the qualitative research that motivation is a critical yet less observable quality in candidate assessments. The thesis ends with the recommendations for an AI-enabled platform given four features, the functional needs of which can be described as centralised opportunity feed, verified student competency profiles, AI generated matching and ranking and transparent ranking explanations, directly based on the empirical evidence but interpreted with the theoretical literature.