Consumer characteristics as drivers of online information searches
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate consumer characteristics (gender, subjective knowledge of product category, susceptibility to social influence, attitude to internet shopping and internet use) having a bearing on the proportion of online information searches conducted using personal and impersonal information sources, and to explore which of these factors impact the use of electronic word-of-mouth (eWOM). Design/methodology/approach A real-time longitudinal design is used to survey 274 consumers about their information searches when shopping for high involvement goods. Findings Susceptibility to normative social influence and internet use prove the main drivers of the inclination to resort to the internet to conduct searches using personal information sources. Subjective knowledge also positively impacts the proportion of time spent online conducting searches using personal information sources. Men and consumers with a positive attitude to internet shopping use a greater proportion of impersonal online information sources. Complementary analyses show that the use of eWOM is driven by almost all consumer characteristics (except gender) investigated. Originality/value By using a real-time longitudinal approach, this study directly addresses calls for more research into information searches by investigating multi-channel source use in actual purchase situations and minimizing bias relating to forgotten information, while facilitating the collection of more valid data on consumer information search behaviour. The paper also ranks as one of the first to revisit the drivers of the proportion of online information sources in personal and impersonal sources in the era of Web 2.0.
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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".