Exploring the Influence of Slack Resources and Absorptive Capacity on Strategic Flexibility Using the Miles and Snow Taxonomy: A Review and Future Research Agenda
Bibliographic record
Abstract
Small business strategy making and development have not been the top priority of discussion in the small business and entrepreneurial literature. Strategy making in a general sense has been axiomatically viewed from a perspective of planning, whereby an unplanned strategy is deemed to be causative enough of the successes or failures of a firm’s strategy and performance outcomes. Simply, there is a lack of studies explicating the effects of slack resources and absorptive capacity (ACAP) on small business-level strategy transitions. The current research approaches small business strategy from a resource-based view of the firm (J. B. Barney, 1986; Feurer & Chaharbaghi, 1994; Robins & Wiersema, 1995) using the theoretical basis of strategic flexibility, which suggests that when business owners are flexible with internal resources (i.e., assets, human capital, information, knowledge, and technology), they are better able to create dynamic strategies that allow tactical changes in its environmental positions and adaptiveness (Matthyssens, Pauwels, & Vandenbempt, 2005; Srour, Baird, & Schoch, 2016; Zhou & Wu, 2010). This study provides three propositions for future research to confirm the impact of slack resources and ACAP on firm strategy flexibility in tumultuous business landscapes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".