Customer Process Management: A Systematic Literature Review
Bibliographic record
Abstract
Customer Process Management (CPM) is not a new term. This phrase has been emerged in academic article since 1999. The CPM expression has received increasing attention from researcher, consultants, practitioner from technology and application firms. The consultants and practitioners introduced customer process management software during 2003-2008 since most Customer Relationship Management (CRM) initiatives failed. The following years, CPM attracted less attention from academics as well as practitioners, until Prof. Michael Rosemann in 2014, a Business Process Management (BPM) leading expert, called for new BPM research directions. One of his suggestions was to conduct research in CPM, which will complement the dominating current view of BPM. To the best of our knowledge, there is no literature review article to examine CPM. Therefore, the research question of this paper: What is the current state-of-the-art research on CPM in academic literature? This research questions will be breakdown into two research objectives. First, present an overview of existing academic literature on CPM. Second, develop potential topics for future research in this field. The results of this study show that there are many opportunities to conduct research in this area.
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".