Understanding the career dynamics of IT professionals in digital transformation times: a systematic review of career anchors studies
Bibliographic record
Abstract
The concept of career anchors has long been a reference model to guide Human Resources Management (HRM) practices within the information technology (IT) discipline. However, as the digital transformation phenomenon grows increasingly disruptive, the misalignment of human resources is becoming more apparent as IT professionals are faced with mixed job demands requiring multidisciplinary skillsets. Along with the lack of workforce diversity and high turnover rates, these HRM challenges are impacting career dynamics and talent management practices. A systematic literature review of 20 empirical studies reveals three broad themes: debunking the dual-ladder construct of traditionally opposing technical and management career paths, fostering a diverse workforce through a variety of demographic profiles, and understanding the response strategies of IT professionals. While career anchors proved to be a useful model, it falls short in the context of the current structural changes of professional career choices and talent requirements, which requires a more diverse and dynamic model. This finding leads to a new research agenda emphasizing the study of Business Technology Management (BTM). This new concept refers to an emerging transdisciplinary profession, uniting Project Management (PM), Information Systems (IS) and IT competencies within a common body of knowledge for leading digital transformation projects.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".