Exploration of Social Media Capabilities for Recruitment in SMEs: A Multiple Case Study
Bibliographic record
Abstract
Abstract This study aims to explore social media capabilities for recruitment in the context of SMEs from the recruiters’ perspective. The conceptual framework is based on a perspective of the RBV that aims to concentrate specifically on the development of IT capabilities in the use of social media for recruitment purposes. In doing so, this study focuses on the following research questions: How do SMEs use social media for recruitment and what are their particularities? What are the capabilities needed to take advantage of social media for recruitment in SMEs? Have these social media capabilities been developed in SMEs? To answer these questions and build an emergent theory about these specific challenges of the digital era, we conducted an interpretive multiple case study in three Canadian SMEs using social media in their HR practices for at least three years. It was found that there are four main patterns that explain the use of social media for recruitment in SMEs. First, social media is not the first choice when it comes to choosing a recruitment tool. Second, the use of social media for recruitment is not a structured activity. Third, recruiters use social media the same way they do in their own life. Finally, marketing people are often involved in recruitment practices on social media. These patterns stem from the fact that SMEs have shortcomings in their social media capabilities in general and more specifically in recruitment where gaps exist in terms of knowledge, skills, and attitudes. To our knowledge, this study is the first to explore the use of social media for recruitment and to propose an integrated framework to evaluate social media capabilities. Through the identification and the discussion of a series of practices concerning e-HRM, our results are also helpful in a digital context where SMEs are struggling to keep up with the pace of adoption and use of IT in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".