From fun-lovers to institutionalists: uncovering pluralism in IT occupational culture
Bibliographic record
Abstract
Purpose The study aims to explore whether there is diversity of occupational culture among IT workers. Prior work conceptualizes IT occupational culture (ITOC) as based around six distinctive values (ASPIRE) but has not explored whether there is variation in ITOC. Design/methodology/approach Survey data from 496 New Zealand IT workers was used to create factors representing IT occupational values based on the ASPIRE tool. Hierarchical cluster analysis and discriminant analysis were applied to identify distinctive segments of ITOC. Findings Four ITOC segments were identified: fun-lovers, innovators, independents and institutionalists. These differed in the relative emphasis ascribed to the ITOC values with each segment being distinguished by 1–2 dominant values. Segment membership varied according to level of responsibility and birth country. Institutionalists and innovators had higher concern about organizational and IT issues than fun-lovers and independents. Job satisfaction was lowest among innovators and highest along institutionalists. Research limitations/implications This study challenges the concept of a unified ITOC, suggesting that ITOC is pluralistic. It also theorizes about interactions between ITOC, individual motivation and values and national culture. Practical implications Management needs to be cognizant of the fact that IT occupational culture is not homogeneous and different IT occupational segments require unique management approaches, and that their own values may not match those of others in IT work. By understanding ITOC segments, managers can tailor support, assign tasks appropriately and design teams to optimize synergies and avoid conflict. Originality/value This study reveals the existence of ITOC segments and theorizes about the relationship of these to innovation-orientation, job satisfaction, individual motivation, work styles and national culture. The combination of cluster and discriminant analysis is a valuable replicable inductive method that is underrepresented in Information Systems (IS) research.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".