The effect of marketing capabilities on marketing agility and perceived performance
Bibliographic record
Abstract
Purpose This study utilizes the principles of dynamic capabilities to examine the interrelationships among marketing agility, marketing capabilities and perceived market and financial performance. It further investigates the mediating effect of marketing agility on perceived performance through marketing capabilities. It offers a novel perspective on how organisations derive value from implementing big data marketing analytics (BDMA) programmes. Design/methodology/approach A cross-sectional online survey of 236 marketing professionals in the United States and Canada was conducted using SurveyMonkey. The data were analysed with SPSS, and the structural model was examined using partial least squares structural equation modelling (PLS-SEM). Findings The results indicated that marketing agility positively predicted marketing capabilities, which subsequently had a favourable effect on organisational performance. Therefore, marketing agility allows marketers to execute their roles more effectively, ultimately contributing to firm success. The results showed that the marketing capabilities construct mediated the relationship between marketing agility and performance. Originality/value The findings confirm the critical and joint importance of dynamic marketing agility and ordinary marketing capabilities for excellence in market and financial performance within the dynamic market context of BDMA. By employing a structural equation model, this research demonstrates the relationships among all three variables, thereby enhancing our understanding of mediation and effect sizes and how these constructs work together to improve firm performance.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".