The double-edged effects of data privacy practices on customer responses

Requesting personal information in frontline service encounters raises privacy concerns among customers. The proximity contact tracing that occurred during the COVID-19 pandemic provides an intriguing context of information requests. Hospitality venues required contact tracing details from customers, and customer cooperation varied with concerns about privacy. Drawing on gossip theory, we investigate the roles of businesses' data privacy practices and government support in driving customers' responses to contact tracing. Our findings show that perceived transparency of a business's privacy practices has a positive effect on customers' commitment to the business, while perceived control exerts a negative effect on commitment. These effects are mediated by customers' information falsification rather than disclosure, because the former is a sensitive behavioral indicator of privacy concerns. The results also reveal the moderating roles of government support. This research contributes to the customer data privacy literature by demonstrating the distinct effects of perceived transparency and control on commitment and revealing the underlying mechanism. Moreover, the research extends the conceptual understanding of privacy practices from online contexts to face-to-face contexts of frontline service. The findings offer implications for the management of customer data privacy. © 2022 Elsevier Ltd

AN EFFECTIVE HYBRID DATA ANALYTICS TECHNIQUE FOR A 360-DEGREE VIEW OF CUSTOMER DATA

Digital transformation has increased the importance of big data analytics. The growth of customer data generated through multiple sources, such as physical stores, online stores and services, social media platforms and multimedia applications, has created complexity in understanding customer behaviours and other related business patterns, such as sales and revenue growth. This means that organisations need a 360-degree view of their customer data from multiple sources to create customer journey recommendation systems and gain business, sales and revenue insights. Big and quantitative data, such as data collected from face-to-face surveys from customers in physical stores, have played a critical role in organisational decision making. However, big data requires precise analytical methods and techniques to ensure its credibility and the efficiency of its outputs to the related business. This research paper presents a hybrid big data analytics technique that uses multiple sources of big data from online services and quantitative data collected from face-to-face physical semi-government customer service centres. The study model has been implemented in mega projects such as COVID vaccine centres for semi-government organisations in Saudi Arabian. The output of the study is presented a 360-degree customer view for top management and business developers that helps provide a reliable view of and access to the customer information that businesses and organisations need, such as information on the customer journey combined with sales and revenue forecasts. © MCCSIS 2022.All rights reserved.