Investigating the Effects of the Big Data Analytics on Customer Response in Online Purchases
DOI:
https://doi.org/10.62270/jirms.vi.22Keywords:
Big Data Analytics, Online Business, Customer ResponseAbstract
Purpose: The concept of big data has gained tremendous attention in recent years. Most businesses are now looking for possible ways to understand the effects of big data on business settings and how it can help to achieve a competitive edge for the business. The current study however endeavors to investigate the effects of big data analytics (BDA) in predicting customer response to online purchases.
Design/Methodology/Approach: The survey was conducted and collected data from the students at universities that exist in Peshawar city. A total of 400 students participated in the study for the period of 1st to 30th April 2022. A total of 235 respondents responded accurately and filled out the questionnaire, and the remaining were outliers and excluded from the study.
Results: The results showed that the information search, dynamic prices, recommendation system, and customer services have a significant positive impact on the responses of the customers whereas security concerns, shopping addictions, and group influences have a significant negative impact on responses of the customers.
Contribution: The study will be useful for developing influential policies for the companies by finding the factors affecting customer response in a multi-cultural environment through using BDA. It will also allow the customers to understand their practices more clearly in the era of BIG Data.
References
Ahmad, N., Vveinhardt, J., & Ahmed, R. (2014). Impact of word of mouth on the consumer buying decision. European Journal of Business and Management, 6(31), 394-403.
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. DOI: https://doi.org/10.1007/s12525-016-0219-0
Ambilkar, P., Dohale, V., Gunasekaran, A., & Bilolikar, V. (2022). Product returns management: a comprehensive review and future research agenda. International Journal of Production Research, 60(12), 3920-3944. DOI: https://doi.org/10.1080/00207543.2021.1933645
An, T. (2022). Data mining analysis method of consumer behaviour characteristics based on social media big data. International Journal of Web Based Communities, 18(3-4), 224-237. DOI: https://doi.org/10.1504/IJWBC.2022.125492
Anandarajan, M., Hill, C., & Nolan, T. (2019). Practical text analytics: Springer. DOI: https://doi.org/10.1007/978-3-319-95663-3
Astudillo, C., Bardeen, M., & Cerpa, N. (2014). Data mining in electronic commerce-support vs. confidence. Multidisciplinary Digital Publishing Institute, 9(1), 1-7. DOI: https://doi.org/10.4067/S0718-18762014000100001
Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: Unpacking the effects of volume, variety, and veracity on firm performance. Journal of Product Innovation Management, 38(1), 49-67. DOI: https://doi.org/10.1111/jpim.12545
Chen, & Chi, T. (2021). How does channel integration affect consumers’ selection of omni-channel shopping methods? An empirical study of US consumers. Sustainability, 13(16), 8983. DOI: https://doi.org/10.3390/su13168983
Chen, Q., Feng, Y., Liu, L., & Tian, X. (2019). Understanding consumers’ reactance of online personalized advertising: A new scheme of rational choice from a perspective of negative effects. International Journal of Information Management, 44, 53-64. DOI: https://doi.org/10.1016/j.ijinfomgt.2018.09.001
Dalziel, R. C., & De Klerk, N. (2021). Media and group influence on Generation Y consumers’ attitudes towards beauty products. Spanish Journal of Marketing-ESIC, 25(1), 115-136. DOI: https://doi.org/10.1108/SJME-12-2019-0104
Deepa, N., Pham, Q.-V., Nguyen, D. C., Bhattacharya, S., Prabadevi, B., Gadekallu, T. R., . . . Pathirana, P. N. (2022). A survey on blockchain for big data: approaches, opportunities, and future directions. Future Generation Computer Systems. DOI: https://doi.org/10.1016/j.future.2022.01.017
Del Vecchio, P., Mele, G., Passiante, G., & Serra, D. (2022). Knowledge generation from Big Data for new product development: a structured literature review. Knowledge Management Research & Practice, 1-16. DOI: https://doi.org/10.1080/14778238.2022.2094292
Du, G., & Lin, Y. (2022). Brand connection and entry in the shopping mall ecological chain: Evidence from consumer behavior big data analysis based on two-sided markets. Journal of Cleaner Production, 364, 132663. DOI: https://doi.org/10.1016/j.jclepro.2022.132663
Fernandes, S., Venkatesh, V., Panda, R., & Shi, Y. (2021). Measurement of factors influencing online shopper buying decisions: A scale development and validation. Journal of Retailing and Consumer Services, 59, 102394. DOI: https://doi.org/10.1016/j.jretconser.2020.102394
Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417-428. DOI: https://doi.org/10.1016/j.chb.2018.08.039
Guangting, Z., & Junxuan, Z. (2014). The Study of Impact of" Big Data" to Purchasing Intention. International Journal of Business and Social Science, 5(10), 91-95.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook: Springer Nature. DOI: https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. DOI: https://doi.org/10.1108/EBR-11-2018-0203
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1-16. DOI: https://doi.org/10.1186/s40537-019-0206-3
Hoeper, S., Crosbie, E., Holmes, L. M., Godoy, L., DeFrank, V., Hoang, C., & Ling, P. M. (2022). “The Perfect Formula:” Evaluating Health Claims, Products and Pricing on Cannabis Dispensary Websites in Two Recently Legalized States. Substance Use & Misuse, 1-8. DOI: https://doi.org/10.1080/10826084.2022.2069267
Hung, S.-W., Cheng, M.-J., & Lee, C.-J. (2021). A new mechanism for purchasing through personal interactions: Fairness, trust and social influence in online group buying. Information Technology & People, 35(5), 1563-1589. DOI: https://doi.org/10.1108/ITP-05-2020-0329
Kshetri, N. (2014). Big data׳ s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145. DOI: https://doi.org/10.1016/j.telpol.2014.10.002
Le, T. M., & Liaw, S.-Y. (2017). Effects of pros and cons of applying big data analytics to consumers’ responses in an e-commerce context. Sustainability, 9(5), 798. DOI: https://doi.org/10.3390/su9050798
Liao, S.-H., & Yang, C.-A. (2021). Big data analytics of social network marketing and personalized recommendations. Social Network Analysis and Mining, 11(1), 1-19. DOI: https://doi.org/10.1007/s13278-021-00729-z
Liu, X., Shin, H., & Burns, A. C. (2021). Examining the impact of luxury brand's social media marketing on customer engagement: Using big data analytics and natural language processing. Journal of Business Research, 125, 815-826. DOI: https://doi.org/10.1016/j.jbusres.2019.04.042
Livingston, M. D., & Haardörfer, R. (2019). A gentle introduction to mediation and moderation. The Journal of Pediatrics, 214, 246-248. DOI: https://doi.org/10.1016/j.jpeds.2019.08.066
Lo, H.-Y., & Harvey, N. (2012). Effects of shopping addiction on consumer decision-making: Web-based studies in real time. Journal of behavioral addictions, 1(4), 162-170. DOI: https://doi.org/10.1556/JBA.1.2012.006
Lv, H., Shi, S., & Gursoy, D. (2022). A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. Journal of Hospitality Marketing & Management, 31(2), 145-175. DOI: https://doi.org/10.1080/19368623.2021.1937434
Maichum, K., Parichatnon, S., & Peng, K.-C. (2016). Application of the extended theory of planned behavior model to investigate purchase intention of green products among Thai consumers. Sustainability, 8(10), 1077. DOI: https://doi.org/10.3390/su8101077
Malhotra, D., & Rishi, O. (2021). An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics. Journal of King Saud University-Computer and Information Sciences, 33(2), 183-194. DOI: https://doi.org/10.1016/j.jksuci.2018.02.015
Mariani, M., & Borghi, M. (2021). Are environmental-related online reviews more helpful? A big data analytics approach. International Journal of Contemporary Hospitality Management, 33(6), 2065-2090. DOI: https://doi.org/10.1108/IJCHM-06-2020-0548
Mariani, M., & Matarazzo, M. (2021). Does cultural distance affect online review ratings? Measuring international customers’ satisfaction with services leveraging digital platforms and big data. Journal of Management and Governance, 25(4), 1057-1078. DOI: https://doi.org/10.1007/s10997-020-09531-z
Müller, A., Brand, M., Claes, L., Demetrovics, Z., De Zwaan, M., Fernández-Aranda, F., . . . Steins-Loeber, S. (2019). Buying-shopping disorder—is there enough evidence to support its inclusion in ICD-11? CNS spectrums, 24(4), 374-379. DOI: https://doi.org/10.1017/S1092852918001323
Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., . . . De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data Advances in intelligent data analysis and applications (pp. 309-325): Springer. DOI: https://doi.org/10.1007/978-981-16-5036-9_30
Oesterreich, T. D., Anton, E., Teuteberg, F., & Dwivedi, Y. K. (2022). The role of the social and technical factors in creating business value from big data analytics: A meta-analysis. Journal of Business Research, 153, 128-149. DOI: https://doi.org/10.1016/j.jbusres.2022.08.028
Ryen, V., Soylu, A., & Roman, D. (2022). Building Semantic Knowledge Graphs from (Semi-) Structured Data: A Review. Future Internet, 14(5), 129. DOI: https://doi.org/10.3390/fi14050129
Shamim, S., Yang, Y., Zia, N. U., & Shah, M. H. (2021). Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings. Computers in Human Behavior, 121, 106777. DOI: https://doi.org/10.1016/j.chb.2021.106777
Song, J., Yoo, S., Yang, J., Yun, S., & Shin, Y. (2022). Network analysis based on big data in social media of Korean adolescents’ diet behaviors. PLoS one, 17(8), e0273570. DOI: https://doi.org/10.1371/journal.pone.0273570
Sumbal, M. S., Tsui, E., Irfan, I., Shujahat, M., Mosconi, E., & Ali, M. (2019). Value creation through big data application process management: the case of the oil and gas industry. Journal of Knowledge Management, 23(8), 1566-1585. DOI: https://doi.org/10.1108/JKM-02-2019-0084
Trifu, M. R., & Ivan, M. L. (2014). Big Data: present and future. Database Systems Journal, 5(1), 32-41.
Trom, L., & Cronje, J. (2019). Analysis of data governance implications on big data. Paper presented at the Future of Information and Communication Conference. DOI: https://doi.org/10.1007/978-3-030-12388-8_45
Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data, 3-20. DOI: https://doi.org/10.1007/978-3-319-67925-9_1
Yu, W., Wong, C. Y., Chavez, R., & Jacobs, M. A. (2021). Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture. International Journal of Production Economics, 236, 108135. DOI: https://doi.org/10.1016/j.ijpe.2021.108135
Zhang, H., Zang, Z., Zhu, H., Uddin, M. I., & Amin, M. A. (2022). Big data-assisted social media analytics for business model for business decision making system competitive analysis. Information Processing & Management, 59(1), 102762. DOI: https://doi.org/10.1016/j.ipm.2021.102762
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Copyright (c) 2022 Wisal Ahmad, Cedric Aimal Edwin, Muhammad Faheem
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