Investigation of the role of internet marketing, word of mouth communication and brand awareness on purchasing decisions: An empirical study in online stores
Bibliographic record
Abstract
This research aims to investigate the role of internet marketing, word-of-mouth and brand awareness on purchasing decisions in online stores. The respondent sampling technique used in this research is a non-probability sampling technique, which uses a purposive sampling technique. The responses to this research were from 468 online store consumers. Measurements of “Internet Marketing”, “Word of Mouth Communication”, “Brand Awareness” and “Purchasing Decisions” were carried out using a seven-point Likert scale, ranging from strongly agree (1) to strongly disagree (7). In this research, the data was analyzed using the Partial Least Square (PLS) method with SmartPLS version 3.0 software. The stages of research data analysis are outer model testing, namely unified validity and reliability, inner model testing and hypothesis testing. Based on the results of data analysis, it is concluded that internet marketing, word-of-mouth and brand awareness had positive and significant effects on purchasing decisions. Better internet marketing will improve consumer purchasing decisions, brand awareness plays an important role in consumer purchasing decisions, and consumers will carry out word-of-mouth activities and tell other people about consumer experiences after consuming products, electronic word of mouth can help consumers in making buying decisions. Based on the results of data analysis, the study provides managerial implications as follows: online stores should evaluate the Internet marketing strategy used by the company by looking for information about what is currently trending among the public and forming a special team to carry out Internet marketing strategies to make them more attractive and creative and provide more complete information regarding products are marketed so that they attract consumers to make purchasing decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".