Consulting Service Adoption Among Small Service Businesses: A DOI-RBV Perspective
Bibliographic record
Abstract
Consulting, in its various forms, supports the survival, development, and competitiveness of small businesses. While prior studies have examined its effects and benefits, research remains limited on how small businesses adopt and engage with these services. This study applies the Diffusion of Innovation (DOI) theory and the Resource-Based View (RBV) to develop a new framework that explains not only the levels of small service firms’ Awareness, Access, and Engagement with consulting services but also how they perceive the benefits and which resources they lack to facilitate adoption. To test the model, a survey of 418 small service firms in the Greater Toronto Area (GTA) was analyzed using covariance-based structural equation modeling (SEM), supported by exploratory and confirmatory factor analyses (EFA and CFA) and partial least squares (PLS) modeling. Results indicate that Awareness strongly influences Access, Perceived Value, and Engagement, while Access further enhances Perceived Value and Engagement. Perceived Value is the strongest driver of Engagement. Resource Constraints, particularly financial and time pressures, moderate these relationships. Awareness also varies by industry, firm size, and network involvement, and referral-based channels are identified as the most trusted sources. The framework provides a nuanced and generalizable lens to understand consulting adoption, capturing both determinants and engagement levels, and can be applied to study the adoption of other innovations (e.g., AI and emerging technologies). The research findings offer practical insights for consultants, support organizations, and policymakers to enhance awareness, strengthen trusted channels, tailor services, and address adoption barriers.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".