Authentic omnichannel: Providing consumers with a seamless brand experience through authenticity
Bibliographic record
Abstract
Abstract Omnichannel represents a customer‐oriented distribution paradigm through which retailers can deliver a seamless customer experience and create an authentic brand narrative that is communicated to customers across diverse touchpoints. Despite the increasing relevance of the omnichannel approach, research on how omnichannel can affect the customer experience remains scant. This research consists of a qualitative study and three experimental studies. Drawing from signaling theory, we contend that the signal congruency established by omnichannel—where all the channels are aligned and convey a consistent message to customers—can enhance consumers' purchase intention and perceptions of brand authenticity. We further investigate the role of brand authenticity as a mediator of the relationship between multichannel customer experience (seamless vs. nonseamless) and purchase intention, as well as of brand untrustworthiness as a moderator of the relationship between multichannel customer experience and brand authenticity. The results show that a seamless multichannel customer experience has a significant main effect on purchase intention and that participants in the seamless multichannel customer experience condition perceive the brand as more authentic than those in the nonseamless multichannel customer experience condition. Both the mediation and moderation hypotheses are supported. These findings enhance the literature on signaling theory and omnichannel. They also provide insightful implications for retailers in terms of managing the omnichannel customer experience. Overall, this study integrates the research areas of brand authenticity and omnichannel and provides valuable insights by indicating how seamlessness can boost consumers' perception of brand authenticity. Furthermore, the study advances our knowledge by investigating the impact of brand authenticity as both a result of the omnichannel customer experience and a predictor of purchase intention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".