Level of Involvement and the Influence of Persuasive Strategies in E-commerce: A Game-Based Approach
Bibliographic record
Abstract
Research has shown that persuasive strategies are more effective in bringing about a change in attitude or behavior when they are tailored to individuals or groups of similar individuals. Several domains such as exercise and health domains use the demographic data of users to tailor influence strategies such as their age, gender, and culture. However, in domains such as e-commerce where the users’ demographic data is unknown, there is a need to identify other factors that can be used to tailor persuasive strategies. To contribute to research in this area, this work-in-progress paper investigates the use of shoppers’ level of involvement in the shopping process as a potential factor for tailoring persuasive strategies in e-commerce. We present preliminary results from a game-based study that compares the response to Cialdini's persuasive strategies for people with high and low levels of involvement. Our results suggest that people with high levels of involvement in the shopping process are influenced differently from those with low level of involvement, making level of involvement a potential trait that can be used in tailoring persuasive strategies in e-commerce. The shoppers who are highly involved in the shopping process responded to more authority messages compared to other strategies, while those with low level of involvement responded to more commitment messages than other strategies. Also, the highly involved shoppers shopped for healthier foods for themselves and a child while they shopped the least healthy for a significant other while the low involved shoppers shopped healthier for their significant other and less healthy for themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".