Customer relations management research: an assessment of sub field development and maturity
Bibliographic record
Abstract
MIS research into electronic commerce customer relations management (ECCRM) has received a great deal of attention over the last five years. This study investigates the status and maturity of this emerging sub field through an exhaustive literature review from all available conference proceedings and journals from the first published article (1984) until the end of the first quarter 2000. Results of the study of 211 articles indicate a number of interesting trends, which should be of concern to IS researchers in this area and in MIS on the whole. First, exploratory surveys dominate the research literature, which in itself may be problematic. However, even more troubling is the fact that the majority of the survey instruments were either not validated, or the authors did not think it important to include validation procedures within their papers. Second, little theory has been developed and few empirical studies use hypothesis testing. Third, there is little cumulative tradition emerging, as each study develops a new conceptual model, new constructs and new instruments. Finally, the news is not all bad; on the whole ECCRM literature has employed a wide range of research methods, constructs and variables. Although the sub field of ECCRM is young, it is growing rapidly and professional activity within the IS research community illustrates its emerging importance.
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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 | 0.057 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".