Innovative IT Use and Innovating with IT: A Study of the Motivational Antecedents of Two Different Types of Innovative Behaviors
Bibliographic record
Abstract
The paper distinguishes two different types of innovative behaviors involving information technology (IT): innovative IT use (IU) and innovating with IT (IwIT). While the former focuses on changing the technology and the work process to better support one’s existing work goals, the latter focuses on using IT to develop new work-related goals and outcomes. Drawing on Parker’s theory of proactive behavior, this paper compares the motivational antecedents and consequences of these two innovative behaviors enabled by IT. Our model hypothesizes that three generic types of motivation differentially affect IwIT versus IU. The paper also explores the moderating role of slack resources on the effect of motivation on the two innovative behaviors. Data from a survey of 427 IT users from North American companies show that social motivation affects IwIT (but not IU); intrinsic motivation is positively related to IU (but not IwIT); and internalized extrinsic motivation affects both IU and IwIT. Further, the results indicate that the moderating role of slack resources on different motivational paths is not a one-size-fits-all effect, that is, slack in IS resources only moderates the relationship between intrinsic motivation and IwIT. We also differentiated the consequences of IwIT from IU. The post hoc analysis shows that IwIT is significantly related to individual mindfulness at work, but IU is not. The paper contributes to IS research by offering a rich conceptualization of IwIT and examining its motivational antecedents and consequences in comparison to IU.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".