Investigating measurement richness effect on the relationship between information technology use and individual performance
Bibliographic record
Abstract
Whether Information Technology (IT) use leads to better individual performance has always been an intriguing topic in IS field. However, not many studies examined the Information Technology use/individual performance relationship given the significance of the topic. Researchers and practitioners simply assumed that more IT use lead to better individual performance. A review of the literature presented a different, rather conflicting, picture than the conventional wisdom. The current study thus aims at investigating IT use/individual performance relationship by focusing on the measurement issue i.e. how different richness level measurement of IT use and individual performance affects the use/individual performance relationship. A questionnaire was used to collect data to test the hypotheses. A total number of 261 account managers from two Canadian banks completed the survey regarding their use of new system at the bank. Our results show that, for the most part, use is significantly and positively related to individual performance. However, depending on the measures used, IT use is sometimes significantly but negatively related to individual performance, or there is no significant relationship between the two. Our results are presented in a matrix putting IT use and individual performance in relationship based on different richness level of use and performance measures. Our results helps validate and integrate previous research by providing a comprehensive map in terms of measurement issue. This research helps interpret and compare prior research on use/performance relationship. Results are also of great use to practitioners to assess and examine the benefits of implementing new IT. ii
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".