Employing information communication technologies to enhance qualitative international marketing enquiry
Bibliographic record
Abstract
Purpose Extant international marketing enquiry has been widely criticised for lacking scope and ambition. Typically, empirical investigations have involved single market studies employing quantitative methods and survey techniques. Consequently, researchers have been challenged to embrace greater methodological pluralism and broaden their geographical perspectives. This contribution posits that new information communication technologies (ICT), particularly the internet, can significantly improve the robustness of qualitative and mixed‐method international marketing research. Design/methodology/approach The paper describes and evaluates the application of ICT in a recent cross‐national enquiry into rapidly internationalising small firms. Online sources were used to gather information on 218 internationalising small firms, in Australia, Canada, Ireland and New Zealand. An e‐mail instrument was then administered to verify this data and address information gaps, resulting in 143 usable responses, evenly distributed across locations. Key emerging themes were identified and a representative sub‐sample of 53 firms was selected for further in‐depth investigation via face‐to‐face interviews with CEOs. Findings The authors contend that such technologies can help to refine sample identification and selection procedures, improve response rates and encourage greater respondent “buy‐in” to depth interviews. They also lead to much more targeted lines of enquiry during depth interviews by identifying key research themes and issues, thus enhancing the depth and richness of the insights obtained. Originality/value The paper concludes that novel ICT‐enabled research approaches as described herein are particularly effective because, compared to conventional survey methods, they are more user friendly and better received by subjects.
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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 | 0.145 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".