Omnichannel management capabilities in international marketing: the effects of word of mouth on customer engagement and customer equity
Bibliographic record
Abstract
Purpose The main purpose of this study is to fill the research gap on how B2B global service firms integrate dynamic capabilities within their omnichannel management to influence positive word of mouth (WOM), customer engagement (CE) and customer equity. Design/methodology/approach Drawing on the dynamic capability and WOM theories, a model has been developed that defines the subjects of the empirical test. The paper reports on data collected from 312 service-oriented global firms in Australia, through a cross-sectional survey. Data were analyzed using structural equation modeling. Findings The findings suggest that content management (i.e. information consistency, source trustworthiness and endorsement) and concerns management (i.e. privacy, security and recovery) capabilities are the two significant antecedents of positive WOM within a B2B omnichannel setting in international marketing. The findings also confirm the key mediating role of CE between positive WOM and customer equity. Originality/value The findings extend dynamic capability theory in the context of international marketing by linking WOM, CE and customer equity. The findings add further theoretical rigor by establishing the nomological chain between positive WOM and customer equity, in which CE plays a key mediating role.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".