How Good Is the Professional's Aptitude in the Conceptual Understanding of Change Management?
Bibliographic record
Abstract
As information technology (IT) functions and their professionals become partners in managing the information resource of the organization, contributors to the strategic planning process and major players in the business, rather than appendages which can be outsourced, new skills and competencies are needed for IT personnel. In particular, the proposition that IT specialists will have to function more like change agents has been echoed by a number of writers. However, there has been no prior empirical research that explicitly measures the degree of knowledge that IT specialists possess about fundamental concepts in the management of change in organizations. The present study offers to fill that gap. Data were collected using a survey instrument, the Managing Change Questionnaire, which was mailed to over 2,200 Canadian IT specialists. Of the sample, 18% returned completed questionnaires. ANOVA and t-test were used to identity differences among categories of respondents. Overall, IT practitioners' scores were acceptable but not particularly impressive. Results indicate that most IT specialists could pass the test regarding their knowledge of the concepts underlying organizational change management, and in the techniques needed to implement such a process, but they were not outstanding in that knowledge. Further, senior IT managers and systems/business analysts demonstrated a better grasp of many of the issues inherent in organizational change efforts than did technical personnel. Implications of these results for research as well as practice and educational programs in IT are discussed.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".