An exploratory study of alignment issues of IT acceptance with professionals in a project setting
Bibliographic record
Abstract
The information technology (IT) literature has demonstrated a link with business performance and effectiveness when IT and business objectives are aligned. However, project settings and joint-venture projects in particular, challenge the way alignment has been conceptualized, exposing other sources of alignment. Projects require a quick diffusion of IT to many different stakeholders with different individual and group interests representing many professions with different professional affiliations. Coordinating the diverse groups of individual expertise that often have short stays and weak allegiance to the project is quite difficult. These factors lead to many types of incentive problems in learning and using new IT necessary for project success. This potentially leads to individuals rejecting or bypassing the use of mandated information technology when the individual's personal, group or professional interests do not coincide with project objectives. As a result, a joint venture project setting challenges the underlying assumptions of Diffusion of Innovation Theory and Technology Acceptance Model, thus providing an important contribution for theoretical development in the IT diffusion literature. Therefore, an important research question for IT diffusion, arising from project situations, is: How are individual professional and project incentives aligned in order to diffuse the necessary IT to achieve coordination and project success? The results of this exploratory study conclude that alignment issues affecting an individual's IT acceptance on joint venture projects originates from many unexplored sources. These five alignment issues include objectives, work output, work value, technology expectations, and peers and are required by the individual, their originating company, the project, and the owner company. Each of these alignment issues reveals different aspects of a complex negotiated order to IT diffusion.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".