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Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance

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  • Ying, Song
  • Sindakis, Stavros
  • Aggarwal, Sakshi
  • Chen, Charles
  • Su, Jiafu

Abstract

Much of the research on big data analytics has been centered on technical or system development. Research has been carried out on the usage of big data analytics to understand customer relationships and experience, amongst others. Still, there is a lack of research in the retail industry considering big data management, examining the impact on customer satisfaction and organizational performance in the retail sector. Retailers explore analytics to gain a unified picture of their customers and operations across the store or online channels and make strategic decisions contributing to the growth of the retail industry. Thereof, this study has been conducted by majorly focusing on the Singapore retail industry to clarify the feasibility of big data management analytics. Quantitative research method was employed involving 500 participants from the retail industry of Singapore. The results of the study stated that amongst the different big data analytics utilized within the retail industry of Singapore, social media analytics had been majorly answered by the participants. Future researchers can study about the upcoming retail trends in Singapore and how the effects of big data analysis changed in the past few years and deal with the unexpected future recessions in the retail industry within Singapore.

Suggested Citation

  • Ying, Song & Sindakis, Stavros & Aggarwal, Sakshi & Chen, Charles & Su, Jiafu, 2021. "Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance," European Management Journal, Elsevier, vol. 39(3), pages 390-400.
  • Handle: RePEc:eee:eurman:v:39:y:2021:i:3:p:390-400
    DOI: 10.1016/j.emj.2020.04.001
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Mojtaba Vaismoradi & Hannele Turunen & Terese Bondas, 2013. "Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study," Nursing & Health Sciences, John Wiley & Sons, vol. 15(3), pages 398-405, September.
    3. V. Kumar & Werner Reinartz, 2018. "Customer Relationship Management," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-55381-7, December.
    4. John A. Aloysius & Hartmut Hoehle & Soheil Goodarzi & Viswanath Venkatesh, 2018. "Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes," Annals of Operations Research, Springer, vol. 270(1), pages 25-51, November.
    5. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    6. V. Kumar & Werner Reinartz, 2012. "Customer Relationship Management Issues in the Business-To-Business Context," Springer Texts in Business and Economics, in: Customer Relationship Management, edition 2, chapter 13, pages 261-277, Springer.
    7. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
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    7. Mesbaul Haque SAZU & Sakila Akter JAHAN, 2022. "How Big Data Analytics Impacts the Retail Management on the European and American Markets?," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(6), pages 62-72, June.
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