The critical success factors of customer relationship management (CRM) technological initiatives
Bibliographic record
Abstract
Customers are any organizations' best assets. As an increasing number of organizations realize the importance of becoming more customer-centric in today's competitive economy, they are also discovering that they must deliver knowledge about their customers, products, and services internally (i.e across multiple organizational functions) and externally (i.e at all customer touch points). Therefore, enterprise executives are interested in knowing the Critical Success Factors that will drive their Customer Relationship Management (CRM) technological initiatives. CRM technological initiatives help foster a customer-centric business strategy, the diffusion of knowledge, a unified face to all customers, and a holistic view of customers. There is no empirical research, to our knowledge, that delves into an understanding of the Critical Success Factors behind CRM technological initiatives. Nor has it been demonstrated that different profiles of Critical Success Factors exist for specific CRM technological initiatives such as Customer Support and Service (CSS), Sales Force Automation (SFA), and Enterprise Marketing Automation (EMA). This thesis compiles the Critical Success Factors of CRM technological initiatives using empirical data from 101 organizations across Canada. The Partial Least Squares (PLS) Structural Equation Modeling method was used to analyze the collected data. A comparison between 57 adopters of CRM technology and 44 non-adopters of CRM technology indicates that the levels of strategic perceived benefits, top management support, and knowledge management capabilities differ between these two independent groups. The core finding of this study reveals that technological readiness, alone, does not lead to successful CRM technological initiatives. Possessing knowledge management capabilities emerges as the most significant critical success factor of CRM technological initiatives and is strongly related to technological readiness. Top management support is significant for all CRM technological initiatives with the exception of the SFA CRM Infrastructure.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".