An empirical investigation of intelligent agents for e-business customer relationship management: a knowledge management perspective.
Bibliographic record
Abstract
Izak Benbasat MIS Division, Faculty of Commerce and Business Admin. University of British Columbia 2053 Main Mall, Vancouver, BC, V6T1Z2, Canada Phone: (604)822-8396, Fax: (604)822-0045 Email: benbasat@commerce.ubc.ca Abstract: Using a knowledge management perspective, this paper investigates new and efficient ways of applying intelligent agents to e-business customer relationship management. Intelligent agents, as well as knowledge-based systems or expert systems, as a branch of applied artificial intelligence not only predate the recent surge of interest in knowledge management, but also stand out as a well-established means for implementing certain aspects of knowledge management. Intelligent agent technologies make it easier to codify, store, share, and transfer certain kinds of knowledge. Based on the IS literature on explanations and decisional guidance for knowledge-based systems, this paper argues that transferring appropriate knowledge from an organization’s staff to its partners and customers can facilitate efficient customer relationship management (e.g., improving customer trust). It is suggested that three types of knowledge – “How Explanations”, “Why Explanations”, and “Decisional Guidance” – be embedded in intelligent agents, and transferred to the agent’s users. A laboratory experiment is proposed to test if, in which aspects, and to what extent these types of knowledge will increase customer trust.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".