The Impact of Human Capital and Organizational Characteristics on the Business Value of Information Technology
Bibliographic record
Abstract
In order for a company to operate effectively within today’s marketplace, an information system (IS) represents a necessary business asset in terms of efficiency and productivity. Still, despite the ongoing advances in technology, an IS stands out as an expensive asset due to the amount of change that it brings to organizational life. Its real value, however, must be examined in terms of its interaction with other resources of the firm. Hence, it is necessary to understand the factors that affect the business value of information technology (BVIT). This research addresses the human capital characteristics and organizational characteristics of a firm, resources that are potentially complementary with IT, and their impact on BVIT. The employees’ diversity and knowledge and the company’s organizational climate and structure represent variables that are expected to affect BVIT. This research uses the resource-based view of the firm as a framework for examining IS, while modeling human capital and organizational characteristics as resources of the firm. It also utilizes concepts from the literature on employee diversity and shared knowledge in order to develop theories and hypotheses about the phenomenon. The resulting hypotheses are built into a research model that is tested using Partial Least Squares, with the relevant data deriving from a large database of Canadian firms that was collected by Statistics Canada in 2005 using the Workplace and Employee Survey. The results show that both resources – human capital and organizational characteristics – impact the business value of IT. These findings have many implications for research and practice, and they contribute to the advancement of knowledge within the field of information systems.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".