The quality of big data marketing analytics (BDMA), user satisfaction, value for money and reinvestment intentions of marketing professionals

被引:7
|
作者
Haverila M. [1 ]
Li E. [2 ]
Twyford J.C. [3 ]
McLaughlin C. [4 ]
机构
[1] Department of Marketing, Thompson Rivers University, Kamloops
[2] Faculty of Management, The University of British Columbia Okanagan, Kelowna
[3] Department of Business and Management, The University of Manchester, Manchester
[4] Department of Marketing, St. Francis Xavier University, Antigonish
关键词
Big data marketing analytics (BDMA); Information quality; Intentions to reinvest; Perceived satisfaction; Technology quality; Value for money;
D O I
10.1108/JSIT-10-2022-0249
中图分类号
学科分类号
摘要
Purpose: The purpose of this paper is to examine how the quality of big data marketing analytics (BDMA) impact the satisfaction, perceived value for money and intentions to reinvest as perceived by marketing managers, i.e. the users of BD. Design/methodology/approach: Survey data was collected with the help of a marketing research company – mainly among Canadian and US marketing professionals with experience in BDMA deployment (N = 236). The structural model was analyzed with partial least squares structural equation modeling. Findings: Findings indicate that the quality of technology has a significant and positive impact on perceived value for money but not on the satisfaction levels of those who use the data (marketing professionals). Furthermore, information quality is significantly and positively related to satisfaction for marketing professionals – but not the perceived value for money. Both perceived value for money and satisfaction are positively linked to intentions to reinvest in big data. Originality/value: This paper examined separately the significance of the technology and information quality of BDMA in assessing its importance on user satisfaction and perceived value for money and, ultimately, on intentions to reinvest among marketing managers. It is noteworthy that the users of the BD (marketing managers) appear to be much more critical of BD than the data generators (BD analysts). © 2023, Emerald Publishing Limited.
引用
收藏
页码:30 / 52
页数:22
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