Transforming business-to-business marketing from tradition to digitalization: a taxonomic review of current trends, methodologies and future paths
Bibliographic record
Abstract
Purpose Despite the numerous benefits of digitalization, many business-to-business (B2B) firms have yet to rely on data-driven decision-making, wavering the decision to adopt digital marketing practices. Topical scholarship is scattered across disciplines, schools of thought and methodological approaches, leading to an inability to suggest better management practices. This study aims to review the extant B2B marketing digitalization literature and addresses these concerns. Design/methodology/approach This paper conducted a systematic literature review of 96 high-quality articles extracted from the Web of Science database. Thereafter, this paper carried out descriptive statistical and content analyses of these articles. Findings Six primary research streams have been identified, and 16 research propositions have been formulated to comprehensively overview the B2B marketing digitalization landscape. The study delves into the factors and barriers influencing the pace of B2B marketing digitalization, sales lead generation and sales performance. Additionally, it introduces B2B digital value creation frameworks, emphasizing the crucial role of marketing analytics and decision tools in effective B2B marketing. The research also underscores various digitalization strategies aimed at bridging the digitalization gap in B2B companies at both strategic and tactical levels. Finally, the study presents an agenda to stimulate future research on theoretical and managerial topics critical to enriching the field. Originality/value This research outlines 16 research propositions that could be further tested to get more detailed insights into the digitalization of B2B marketing. Additionally, practitioners, authorities and researchers in the field may find this review valuable as it provides a comprehensive overview of current research in the domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".