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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. 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.
    2. 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.
    3. 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.
    4. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    5. V. Kumar & Werner Reinartz, 2018. "Customer Relationship Management," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-55381-7, December.
    6. 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.
    7. 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.
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    Cited by:

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    2. R. M. Ammar Zahid & Safia Rafique & Muzammil Khurshid & Wajid Khan & Ikram Ullah, 2024. "Do Women’s Financial Literacy Accelerate Financial Inclusion? Evidence from Pakistan," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 4315-4337, March.
    3. Tanzeela AQIF & Abdul WAHAB, 2022. "Reshaping The Future Of Retail Marketing Through Big Data: A Review From 2009 To 2022," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 14(3), pages 5-24, September.
    4. Omar. A. Alghamdi & Gomaa Agag, 2023. "Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    5. Rizomyliotis, Ioannis & Kastanakis, Minas N. & Giovanis, Apostolos & Konstantoulaki, Kleopatra & Kostopoulos, Ioannis, 2022. "“How mAy I help you today?” The use of AI chatbots in small family businesses and the moderating role of customer affective commitment," Journal of Business Research, Elsevier, vol. 153(C), pages 329-340.
    6. Carles Carreras & Lluís Frago, 2022. "Could a Retail-Less City Be Sustainable? The Digitalization of the Urban Economy against the City," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    7. Tii N. Nchofoung & Guivis Zeufack Nkemgha & Dieu ne Dort Talla Fokam & Arsène Aurelien Njamen Kengdo, 2024. "Achieving the Sustainable Development Goals Through Water and Sanitation: Do Information and Communication Technologies (ICTs) Matter for Africa?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 4383-4407, March.
    8. Youssef, Mayada Abd El-Aziz & Eid, Riyad & Agag, Gomaa, 2022. "Cross-national differences in big data analytics adoption in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    9. Mohammad Imtiaz Hossain & Mosab I. Tabash & May Ling Siow & Tze San Ong & Suhaib Anagreh, 2023. "Entrepreneurial intentions of Gen Z university students and entrepreneurial constraints in Bangladesh," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-34, December.
    10. Chen, Kuan-Yang & Huan, Tzung-Cheng, 2022. "Explore how SME family businesses of travel service industry use market knowledge for product innovation," Journal of Business Research, Elsevier, vol. 151(C), pages 519-530.

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