Defining IT “Business Value” Under Conditions of Economic Uncertainty
Bibliographic record
Abstract
Investment in Information Technology (IT) has typically been justified as playing a crucial role in assisting business and other Organisations in conducting their business in a more efficient and effective way. The implied “value” that results from such investments is known as “IT business value” and its definition and measurement under conditions of economic austerity and uncertainty is the main subject of this paper. The question is why, under such conditions, many Organisations fail to realize the positive impacts expected from IT investment, which by itself is then rather scarce and difficult to attain. To answer this question we concentrate in this paper on the issues of IT business value measurement and more specifically we attempt to answer the research question of how best to define the “business value” of IT and what factors may affect it. The paper first puts forward the main definitions used for both “IT” and “Business value” in the literature. It then goes on to present and critically examine the most prominent of the existing methodologies for measuring “IT Business value” again by resorting to a relevant literature search. Then, we examine the special influencing factors that are at work in times of economic austerity and uncertainty and puts forward a framework for analyzing IT Business value under conditions of economic austerity. This framework is presented in terms of its elements and a description of their main characteristics and measures (metrics). Finally, before the conclusions, a list of the critical success factors for IT investment is presented which is based on a previous published work of the author.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".