Assessing industry differences in marketing innovation using multi-level modeling
Bibliographic record
Abstract
Purpose Currently, the bulk of research on marketing innovation focuses on various firm-level dimensions using relationships from the technological (product and process) innovation literature. Research on industry-level differences in marketing innovation is lacking. Testing relationships form the technological paradigm in the context of the marketing innovation paradigm is also lacking. This paper aims to present empirical evidence on both aspects using a large-scale data set. Design/methodology/approach This study uses two large-scale datasets, each consisting of approximately 4,000 Canadian enterprises in 18 industries. The data was collected by Statistics Canada in 2009 and 2012 through its nationwide Survey of Innovation and Business Strategies program. Two widely used theoretical frameworks, resource-based view of the firm and the competitive perspective, are used to generate constructs and hypotheses in relation to marketing innovation. The data was analyzed using multi-level logistic regression. Findings The findings show that industry-level competition is a much more important driver of marketing innovation than firm-level competition. The authors also show that marketing constructs that are significant in the context of technological innovation are also significant for marketing innovation. Research limitations/implications This study extends the firm-level literature by providing evidence of how industry-level dynamics enhances marketing innovation. The study also provides empirical evidence from Canadian enterprises that complement those from other countries. Practical implications A deeper understanding of the drivers of marketing innovation can enable managers to enact innovation strategies that can enhance organizational performance, differentiate themselves and enhance customer engagement and brand image. Originality/value As one of the few studies to examine industry-level differences in marketing innovation, the authors show that disaggregating competition into industry-level and firm-level provides a clearer picture of how competition advances marketing innovation. Additionally, this study is the first of its kind to provide empirical evidence on Canadian enterprises, thereby complementing evidence on marketing innovation from other countries. Thus, this study makes a theoretical and empirical contribution to the emerging marketing innovation literature.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".