IS Application Capabilities and Relational Value in Interfirm Partnerships
Bibliographic record
Abstract
This study examines how capabilities of information systems (IS) applications deployed in the context of interfirm relationships contribute to business performance. We propose that these capabilities augment the relational value that a firm derives from its business partners—channel partners and customer enterprises—in the context of the distribution channel. Two cospecialized relational assets are considered as key to realization of relational value—knowledge sharing and process coupling. Hypotheses linking two IS capabilities (IS flexibility and IS integration) to the relational asset dimensions, and ultimately to firm performance, are proposed. The research model is tested based on data collected through a survey of business units of enterprises embedded in customer and channel partner ties in the high-tech and financial services industries. We find that IS integration with channel partners and customers contributes to both knowledge sharing and process coupling with both types of enterprise partners, whereas IS flexibility is a foundational capability that indirectly contributes to value creation in interfirm relationships by enabling greater IS integration with partner firms. We find that two types of relational assets are significantly associated with business performance—knowledge sharing with channel partners and process coupling with customers—pointing to underlying mechanisms that differentially leverage resources of different types of channel partners. Implications for theory development and practice based on these findings are proposed.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".