Enticing the IT crowd: employer branding in the information economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop an instrument to measure employer branding in the information age. Firms increasingly migrate from matter-intensive business models to information-intensive models, where value lies in information rather than the physical objects. This shift has, in turn, led to a change in employee work skills. This is particularly true in the information technology (IT) sector, where firms rely on a limited supply of skilled labor. Employer branding, a firm’s reputation as a place to work, is an important strategy to attract and retain employees. Design/methodology/approach From the literature, the authors developed and refined an instrument to measure key value propositions of employer brands. The potential IT employees surveyed in the study were students enrolled in the disciplines of computer science and information systems at a comprehensive university in North America. The study went through three stages resulting in an instrument for psychometric properties. Findings This research revealed eight employer branding value propositions that future IT employees care about. These dimensions are important for both IT firms and industries competing for skilled IT labor to understand and manage. Originality/value This paper extends the work of Berthon et al. (2005) on employer branding to the information intensive age and particularly the IT sector. It allows executives to manage and measure their employer brand so as to maximize competitive advantage in attracting and retaining skilled employees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it