Information Technology and Process Performance: An Empirical Investigation of the Interaction Between IT and Non‐IT Resources*
Bibliographic record
Abstract
ABSTRACT Drawing on the resource‐based view, we propose a configurational perspective of how information technology (IT) assets and capabilities affect firm performance. Our premise is that IT assets and IT managerial capabilities are components in organizational design, and as such, their impact can only be understood by taking into consideration the interactions between those IT assets and capabilities and other non‐IT components. We develop and test a model that assesses the impact of explicit and tacit IT resources by examining their interactions with two non‐IT resources (open communication and business work practices). Our analysis of data collected from a sample of firms in the third‐party logistics industry supports the proposed configurational perspective, showing that IT resources can either enhance (complement) or suppress (by substituting for) the effects of non‐IT resources on process performance. More specifically, we find evidence of complementarities between shared business–IT knowledge and business work practice and between the scope of IT applications and an open communication culture in affecting the performance of the customer‐service process; but there is evidence of substitutability between shared knowledge and open communications. For decision making, our results reinforce the need to account for all dimensions of possible interaction between IT and non‐IT resources when evaluating IT investments.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".