Digital marketing adoption and success for small businesses
Bibliographic record
Abstract
Purpose This paper aims to examine small business’ participation in digital marketing and to integrate the do-it-yourself (DIY) behavior model and technology acceptance model (TAM) so as to explore the motivations and expected outcomes of such participation. Design/methodology/approach Data from 250 small business owners/managers who do their own digital promotion are collected through an online survey. Structural equation modeling is used to analyze the relationships between the models. Findings The results contribute to the understanding of small business’ digital marketing behavior by finding support for the idea that the technological benefits may not be the only motivators for small business owner/managers who undertake digital marketing. Moreover, and perhaps more importantly, the authors find that the DIY behavior model applies to small business owner/managers who must perform tasks that require specialized knowledge. Research limitations/implications The limitations of this research are that the motivations to undertake digital marketing are limited to those contained in the DIY and TAM models, and the sample may not be representative of all owners and managers who perform digital marketing for their small businesses. Therefore, future research is needed to determine if further motivations to conduct digital marketing exist and whether other samples produce the same interpretations. Originality/value This study presents empirical evidence supporting the application of the DIY model to a context outside of home-repair and extends the understanding of digital footprint differences between large and small businesses.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".