Examining the interdependent role of digitalization and external search breadth in driving service innovation in SMEs
Bibliographic record
Abstract
Purpose This study aims to examine the impact of digitalization on service innovation performance in small and medium-sized enterprises (SMEs), focusing on how external search breadth moderates this relationship. The goal is to understand how digital transformation and external knowledge sourcing interact to influence innovation outcomes. Design/methodology/approach The study is based on a quantitative analysis of a survey conducted with 489 Canadian SMEs. Probit regression models are used to examine the curvilinear (inverted U-shaped) relationship between digitalization and service innovation, and how these changes when external search breadth is introduced as a moderating variable. Findings The results confirm an inverted U-shaped relationship between digitalization and service innovation in SMEs. However, when external search breadth is high, the relationship changes to a U-shape, indicating that digitalization’s impact on innovation depends on the extent of external knowledge sourcing. Digitalization enhances service innovation but shows diminishing returns when over-applied without appropriate external search breadth. Research limitations/implications The study’s findings are based on a sample of Canadian SMEs, limiting the generalizability to other contexts. Future research could explore longitudinal data to assess changes over time. Practical implications SME managers should balance their investments in digitalization with external knowledge sourcing to maximize innovation performance, avoiding over-reliance on one approach. Originality/value This study extends the literature by providing empirical evidence on the dual effect of digitalization and external search breadth on service innovation, specifically in SMEs. It introduces the concept of a shape-flip in the relationship between digitalization and innovation, contingent on external search breadth, a novel contribution to the field.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".