How Do Big Data Resources Affect Customer Service Performance? The Chain Mediating Role Based on Customer Information Quality and Customer-oriented Ability

Abstract

Abstract:

In the era of the digital economy, big data plays a more and more important role in customer-centric organizations. However, there is a lack of theoretical research and empirical analysis on the path of big data resources to promote customer service performance. Based on the resource-based view and the dynamic capability theory, this paper constructs a chain intermediary model of “big data resource-customer information quality-customer orientation capability-customer service performance”, and discusses the moderating role of the process complexity in the model. The results are as follows: (1) Big data resources have a positive impact on the customer service performance. (2) The customer information quality and the customer-oriented ability play a partial mediating role between big data resources and the customer service performance. (3) The customer information quality and the customer-oriented ability play a chain intermediary role in the positive impact of big data resources on the customer service performance. (4) The complexity of the customer service process can strengthen the influence of big data resources on the customer service performance, and then moderate the chain mediation. This paper reveals how big data resources can create higher customer service performance, and provides enlightenment for the big data practice of commercial banks.

Key words: Big Data Resources, Customer Service Performance, Customer Information Quality, Customer Oriented Capability, Process Complexity

摘要：

数字经济时代,大数据在以客户为中心的组织中发挥越来越重要的作用,但现有研究对大数据资源促进客户服务绩效提升的路径缺乏理论研究与实证分析。本文以资源基础观和动态能力理论为基础,构建“大数据资源—客户信息质量—客户导向能力—客户服务绩效”链式中介模型,并探讨服务流程复杂性在该模型中的调节作用。针对商业银行分支机构员工收集了247份有效问卷进行实证检验,研究结果表明:(1)大数据资源对客户服务绩效存在正向影响;(2)客户信息质量与客户导向能力分别在大数据资源与客户服务绩效之间起部分中介作用;(3)客户信息质量与客户导向能力在大数据资源对客户服务绩效的正向影响中发挥链式中介作用;(4)客户服务流程复杂性通过强化大数据资源对客户信息质量的影响进而对链式中介作用起调节作用。本研究揭示了大数据资源如何创造更高的客户服务绩效,为商业银行的大数据实践提供启示。

关键词: 大数据资源, 客户服务绩效, 客户信息质量, 客户导向能力, 流程复杂性

CLC Number:

F832.33

FANG Ying, YE Li. How Do Big Data Resources Affect Customer Service Performance? The Chain Mediating Role Based on Customer Information Quality and Customer-oriented Ability[J]. Collected Essays on Finance and Economics, 2021, 37(12): 103-113.

房颖, 叶莉. 大数据资源如何影响客户服务绩效?——基于客户信息质量与客户导向能力的链式中介作用[J]. 财经论丛, 2021, 37(12): 103-113.

Figures/Tables 9

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变量	Cronbach’s a	AVE	CR	KMO	1	2	3	4
1.大数据资源	0.844	0.581	0.847	0.760
2.客户信息质量	0.882	0.717	0.883	0.742	0.385^**
3.客户导向能力	0.913	0.779	0.914	0.757	0.343^**	0.427^**
4.客户服务绩效	0.937	0.833	0.937	0.767	0.328^**	0.409^**	0.394^**
5.流程复杂性	0.863	0.718	0.884	0.697	0.020	0.765^**	0.226^**	0.237^**

变量	Cronbach’s a	AVE	CR	KMO	1	2	3	4
1.大数据资源	0.844	0.581	0.847	0.760
2.客户信息质量	0.882	0.717	0.883	0.742	0.385^**
3.客户导向能力	0.913	0.779	0.914	0.757	0.343^**	0.427^**
4.客户服务绩效	0.937	0.833	0.937	0.767	0.328^**	0.409^**	0.394^**
5.流程复杂性	0.863	0.718	0.884	0.697	0.020	0.765^**	0.226^**	0.237^**

路径	间接效应估计	95%置信区间
路径	间接效应估计	下限	上限
总计间接效应	0.347	0.171	0.548
具体间接效应分解
DAR→CIQ→CSP	0.204	0.087	0.405
DAR→COC→CSP	0.079	0.031	0.167
DAR→CIQ→COC→CSP	0.064	0.022	0.131

路径	间接效应估计	95%置信区间
路径	间接效应估计	下限	上限
总计间接效应	0.347	0.171	0.548
具体间接效应分解
DAR→CIQ→CSP	0.204	0.087	0.405
DAR→COC→CSP	0.079	0.031	0.167
DAR→CIQ→COC→CSP	0.064	0.022	0.131

调节变量	路径:大数据资源→客户信息质量→客户导向能力→客户服务绩效
	间接效应	95%置信区间
	间接效应	下限	上限
高流程复杂性	0.109	0.055	0.202
低流程复杂性	0.021	0.009	0.046
差异	0.088	0.044	0.170