The effects of digital marketing, word of mouth, and service quality on the purchase decisions: An empirical study of food SMEs products
Bibliographic record
Abstract
The purpose of this study is to analyze the influence of digital marketing, word of mouth, and service quality on purchasing decisions through a quantitative questionnaire using an accidental sampling method. The research is designed to find out the relationship between the influence of digital marketing, word of mouth and service quality on consumer purchasing decisions using a quantitative approach method. The variables in this study consist of independent variables and dependent variables. The variables studied include digital marketing, word of mouth, service quality and consumer purchasing decisions. The research was conducted at food SMEs in Jakarta, Indonesia. Sources of data in this study were primary data including consumer responses to digital marketing, word of mouth, service quality and purchasing decisions obtained from the results of distributing online questionnaires. The sample size used in this study was 680 people. The data collection method used in this study was an online questionnaire distributed by social media. The data were analyzed using SPSS software and structural equation modeling (SEM) with SmartPLS software tools. The results of this study indicate that the higher the digital marketing, word of mouth, and service quality, the higher the purchasing decision. SMEs must further optimize the use of digital marketing in marketing their companies such as uploading interesting content on one of the existing social media. From a word-of-mouth point of view, companies must promote more to their closest circle of benefits what they get from using SEMs products. Service quality must continue to provide excellent service to consumers or customers so that these consumers feel comfortable and satisfied.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".