发表机构
HDFC Bank（HDFC银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出客户关系智能框架，通过调查分析发现CRM和CKM是增强客户参与的主要驱动因素，而MDM作为数据质量基础发挥支持作用。
AI 中文摘要
本研究探讨了客户关系管理（CRM）、主数据管理（MDM）和客户知识管理（CKM）如何共同构成一个客户关系智能（CRI）框架，以增强客户参与（CE）。一项针对零售、医疗保健、IT和电信行业100名参与者的横断面调查，使用Spearman rho相关性和有序逻辑回归（IBM SPSS）进行分析。双变量相关性较弱且不显著（r<0.19，p>0.06）。回归分析确定CRM（beta=0.717，p=0.002）和CKM（beta=0.581，p=0.009）是CE的显著正向预测因子；MDM显示出正向但不显著的直接影响（beta=0.346，p=0.071）。该模型解释了CE方差的20.5%（Nagelkerke R^2=0.205）。平行中介分析（Hayes PROCESS模型4，5000个自助抽样样本）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的间接效应不显著；假设H4未得到支持。在CRI框架内，CRM和CKM是CE的主要驱动因素，而MDM作为基础性数据质量使能者，其战略价值通过其对CRM执行和知识管理的支持作用得以实现。鉴于样本量和横断面设计，研究结果应视为探索性。未来研究应采用更大规模的特定行业样本和纵向设计进行复制，特别是在受数据治理要求影响的受监管BFSI环境中，MDM架构由数据治理指令塑造。
英文摘要
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.
CommentsPreprint submitted to 16th IEEE ICCCNT 2025. 8 pages, 12 tables
