Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective
Received: 6 March 2024 | Revised: 23 March 2024 | Accepted: 31 March 2024 | Online: 8 April 2024
Corresponding author: Muhammad Nafees Khan
Abstract
This study aims to systematically review the literature on the impact of big data and knowledge management on customer interactions and consumption patterns from an applied science perspective. A comprehensive search strategy was implemented in seven scientific publication databases. The inclusion criteria consisted of original research articles published in English, excluding gray literature, book chapters, and conference proceedings. A total of 400 articles were retrieved, and 40 articles met the inclusion criteria after two rounds of screening. The selected articles were analyzed following a mixed-method approach incorporating qualitative and quantitative data analysis techniques. Thematic analysis was deployed to identify recurring themes and patterns in the articles, while descriptive statistics were used to summarize the study characteristics. The data analysis showed that big data and knowledge management significantly affect customer interactions and consumption patterns, with most studies focusing on the retail and banking sectors. The findings of this study have several theoretical and practical implications. From a theoretical point of view, this review contributes to the growing body of literature on the intersection of big data, knowledge management, and consumer behavior. From a practical perspective, the results can inform policymakers and practitioners on leveraging big data and knowledge management in order to improve customer interactions and consumption patterns.
Keywords:
big data, customer knowledge management, customer interaction, consumption pattern, consumer behavior, innovation, artificial intelligenceDownloads
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