Bibliographic record
Abstract
Purpose The internet has become mainstream in everyday communications and transactions. This research aims to provide a segmentation analysis for the online market based on the various uses of the internet. Design/methodology/approach A review of the online consumer segmentation literature is first conducted. Survey method and cluster analysis techniques are used in the empirical study. A sample of 407 participants that belonged to a large consumer panel adequately responded to an online survey and provided their pattern of internet use, internet experience, and psychological characteristics. Findings The analysis shows that the online consumers form three global segments: the basic communicators (consumers that use the internet mainly to communicate via e‐mail), the lurking shoppers (consumers that employ the internet to navigate and to heavily shop), and the social thrivers (consumers that exploit more the internet interactive features to socially interact by means of chatting, blogging, video streaming, and downloading). Subsequent χ2 and ANOVA tests illustrate that consumers from these segments exhibit significantly divergent demographic and experience profiles. Research limitations/implications The results indicate that online consumers differ according to their pattern of internet use. The results have external and ecological validity; however, they lack the control provided in a laboratory experiment. Future research should examine if the findings can be replicated using behavioral measures. Practical implications Practitioners that plan to follow a resource‐based approach should consider the distinctive characteristics of the online market segments for an optimal allocation of marketing expenditure. Marketing and advertising strategies can be developed according to the customer's online segment. Further, online marketers can use the demographic and experience profiles to predict their customer's segment. Originality/value This paper is the first to perform a segmentation analysis to the online consumer market according to internet use pattern. The results show that usage can reliably be used as a segmentation base. Managerial and theoretical implications are furnished.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".