Information system capabilities and emergent competitive strategies: An investigation of the strategic fit of supply chain management information systems
Bibliographic record
Abstract
This study develops a model for analyzing fit between a firm's competitive strategies and the capabilities of their Supply Chain Management Information Systems (SCM IS). Concepts such as configurational theory, the resource based view of the firm, and emergent strategies and capabilites--all of which are underutilized in current IS literature--ground the study theoretically. A positivist case study of five manufacturers is used to explore the constructs and identify appropriate measures for operationalizing the model. The developed model enables IS planners to quickly analyze their firm's competitive strategy patterns and determine the ideal level of support required for each SCM IS capability. Firms can improve the effectiveness of their IS and reduce the risk and cost of misfits, by implementing information systems that fit their emergent competitive strategies. The developed model is a significant improvement over traditional models that advocate aligning information systems with a firm's intended strategies or their current functional requirements, both of which change more frequently than a firm's emergent competitive strategy patterns. The case study investigations yielded several important findings. First, Miles and Snow's (1978) competitive strategy typology proved useful for classifying a firm's emergent competitive strategy patterns and reducing the complexity of analysis. However, the qualitative evidence more strongly supported the use of Conant et al .'s (1990) multi-dimensional questionnaire measure of competitive strategy type rather than Miles and Snow's (1978) paragraph measure. Second, existing conceptualizations of IS capabilities were not well suited to analyzing SCM IS specifically. The findings support the conceptualization of SCM IS capabilities as the level of support provided for: operational efficiency, operational flexibility, planning, internal analysis, and external analysis. Finally, the empirical results strongly supported modeling the strategic fit of a firm's SCM IS as the amount the perceived level of support provided for each SCM IS capability was less than the theoretically ideal level, rather than the more common approach of modeling strategic fit as the absolute deviation between perceived and theoretically ideal levels.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".