What Motivates Marketing Innovation and Whether Marketing Innovation Varies across Industry Sectors
Bibliographic record
Abstract
Innovativeness is one of the fundamental instruments of growth strategies that provide companies with a competitive edge. Only a few recent studies have examined marketing innovation and the factors that might encourage its adoption. This study investigates the factors that motivate marketing innovation and examines whether the occurrence of marketing innovation varies across industry sectors. This study uses data from surveys and a nationwide census conducted by Statistics Canada. They include: the Survey of Innovation and Business Strategies (SIBS) 2009, the Survey of Innovation and Business Strategies (SIBS) 2012, the Business Registry (BR) and the General Index of Financial Information (GIFI). Multilevel (random-intercept) logistic regression modelling is employed. The results show that if a firm has a strategic focus on new marketing practices, maintains marketing within its enterprise, acquires or expands marketing capacity, has competitor and customer orientations, and adopts advanced technology then it is more likely to carry out marketing innovation. However, breadth of long-term strategic objectives and competitive intensity do not have significant impacts on marketing innovation. In addition, product innovation and organizational innovation occur simultaneously with marketing innovation, but process innovation may not. Lastly, the occurrence of marketing innovation is found to vary across industry sectors. The theoretical and empirical implications of the results are discussed within this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".