Fostering brand engagement and value-laden trusted B2B relationships through digital content marketing
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how digital content marketing (DCM) users can be engaged with business-to-business (B2B) brands and determine how such engagement leads to value-laden trusted brand relationships. Design/methodology/approach Through an online survey, data were collected from the email marketing list of a large B2B brand, and the hypothesised research model was analysed using covariance-based structural equation modelling. Findings This paper identifies a bundle of helpful brand actions – providing relevant topics and ideas; approaching content with a problem solving orientation; as well as investing in efforts to interpret, analyse and explain topics through DCM – to foster relationship value perceptions and brand trust. Critically however, cognitive-emotional brand engagement is shown to be a necessary requirement for converting these actions into relationship value perceptions. Research limitations/implications This paper furthers the understanding of the dual role of helpful brand actions in functionally oriented DCM. Additionally, this paper offers evidence of the central role of cognitive-emotional brand engagement in influencing value-laden customer–brand relationships. Practical implications This paper introduces a bundle of helpful brand actions that forms the basis for the dual roles of a brand in enhancing customer value and in fostering brand engagement and building relationships. This approach helps practitioners to steer brand-related perceptions arising from DCM interactions towards building trusted brand relationships. Originality/value This paper contributes to the marketing literature by revealing a potential approach to DCM in managing customer relationships. Instead of focusing solely on the content benefit-usage link to support engagement, this paper reveals the potential of helpfulness as a brand-initiated DCM engagement trigger in engaging customers with the brand, vis-à-vis the content.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".