<i>Review:</i> The Resource-Based View and Information Systems Research: Review, Extension, and Suggestions For Future Research1
Bibliographic record
Abstract
Information systems researchers have a long tradition of drawing on theories from disciplines such as economics, computer science, psychology, and general management and using them in their own research. Because of this, the information systems field has become a rich tapestry of theoretical and conceptual foundations. As new theories are brought into the field, particularly theories that have become dominant in other areas, there may be a benefit in pausing to assess their use and contribution in an IS context. The purpose of this paper is to explore and critically evaluate use of the resource-based view of the firm (RBV) by IS researchers. The paper provides a brief review of resource-based theory and then suggests extensions to make the RBV more useful for empirical IS research. First, a typology of key IS resources is presented, and these are then described using six traditional resource attributes. Second, we emphasize the particular importance of looking at both resource complementarity and moderating factors when studying IS resource effects on firm performance. Finally, we discuss three considerations that IS researchers need to address when using the RBV empirically. Eight sets of propositions are advanced to help guide future research.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".