Developing customer analytics capability in firms of different ages: Examining the complementarity of outside-in and inside-out resources
Bibliographic record
Abstract
Customer analytics capability remains underdeveloped among firms despite its potential for enhancing competitiveness. Previous research has predominantly focused on inside-out organizational factors as drivers of customer analytics capability. This paper examines the role of outside-in resource, the complementarity between outside-in and inside-out resources, and their boundary conditions. Specifically, we study how customer orientation culture (an outside-in resource) complements data-driven culture (an inside-out resource) in firms of different ages to drive customer analytics capability and subsequently, firm performance. Using survey data obtained from Canadian firms, we find that customer orientation is not only positively related to customer analytics capability but also reinforces the effect of data-driven culture. We further find that the conditional effect of customer orientation becomes stronger as firm age increases. In particular, among older firms, the impact of data-driven culture is greatest when customer orientation is high, but it becomes nonsignificant when customer orientation is low. We also link these relationships to firm performance using mediation and moderated mediation analyses. Overall, the results suggest that achieving customer analytics excellence and resultant competitive performance requires marketing to continuously act as customer champions and advocate data analytics efforts to ensure the firm embraces an outside-in orientation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".