整合客户管理与数据治理，发现关系管理与知识管理是提升客户参与的关键。
Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement
- 构建客户智能框架，融合CRM、MDM与CKM协同机制。
- CRM和CKM显著正向预测客户参与度，解释20.5%的方差。
- MDM虽无直接效果，但通过支持其他系统间接发挥作用，适合企业战略规划者。
本研究探讨客户关系管理（CRM）、主数据管理（MDM）与客户知识管理（CKM）如何共同构成客户关系智能（CRI）框架以增强客户参与（CE）。对零售、医疗、IT及电信行业100名参与者进行横断面调查，采用斯皮尔曼等级相关与有序逻辑回归分析（IBM SPSS）。双变量相关性较弱且不显著（r<0.19, p>0.06）。回归分析显示，CRM（beta=0.717, p=0.002）与CKM（beta=0.581, p=0.009）是CE的显著正向预测因子；MDM虽呈正向趋势但不显著（beta=0.346, p=0.071）。模型解释了20.5%的CE变异（Nagelkerke R²=0.205）。平行中介分析（Hayes PROCESS Model 4，5000次自举抽样）未发现MDM通过CRM（IE=0.021，95% BC CI [-0.072, 0.121]）或CKM（IE=0.032，95% BC CI [-0.061, 0.126]）对CE的显著间接效应，假设H4未获支持。结果表明，在CRI框架中，CRM与CKM是客户参与的主要驱动力，而MDM作为基础数据质量保障，其战略价值体现在对CRM执行与知识管理的支持上。鉴于样本量与横断面设计，结论属探索性，未来研究应在更大规模、分行业的纵向设计中复现，尤其在受监管的BFSI领域，因数据治理要求影响MDM架构。
原文摘要 · Abstract (English)
This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute a Customer Relationship Intelligence (CRI) framework for enhanced Customer Engagement (CE). A cross-sectional survey of 100 participants across retail, healthcare, IT, and telecommunications sectors was analysed using Spearman rho correlation and ordinal logistic regression (IBM SPSS). Bivariate correlations were weak and non-significant (r<0.19, p>0.06). Regression identified CRM (beta=0.717, p=0.002) and CKM (beta=0.581, p=0.009) as significant positive predictors of CE; MDM showed a positive but non-significant direct effect (beta=0.346, p=0.071). The model explained 20.5% of CE variance (Nagelkerke R^2=0.205). Parallel mediation analysis (Hayes PROCESS Model 4, 5,000 bootstrap samples) found no significant indirect effects of MDM on CE via CRM (IE=0.021, 95% BC CI [-0.072, 0.121]) or CKM (IE=0.032, 95% BC CI [-0.061, 0.126]); Hypothesis H4 was not supported. CRM and CKM emerge as the principal drivers of CE within the CRI framework, while MDM functions as a foundational data quality enabler whose strategic value is realised through its enabling effect on CRM execution and knowledge management. Findings should be treated as exploratory given the sample size and cross-sectional design. Future research should replicate with larger sector-specific samples and longitudinal designs, particularly in regulated BFSI contexts where MDM architecture is shaped by data governance mandates.
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