Sentiment Analysis through Big Data in online Retail Industry: A Conceptual Quantitative Study on linkage of Big-Data and Assortment Proactive of Online Retailers
PDF

Keywords

Big-Data
Sentiment Analysis
SMART-PLS
Assortment and Sentiment Analysis

How to Cite

Sultan, M. F., Jabeen , M., & Mannan, M. A. (2020). Sentiment Analysis through Big Data in online Retail Industry: A Conceptual Quantitative Study on linkage of Big-Data and Assortment Proactive of Online Retailers: Sentiment Analysis through Big Data in online Retail Industry: A Conceptual Quantitative Study on linkage of Big-Data and Assortment Proactive of Online Retailers. KIET Journal of Computing and Information Sciences, 3(2), 16. https://doi.org/10.51153/kjcis.v3i2.47

Abstract

Big-Data is the recent trend in data sciences prevailing all over the globe. The tool aids significantly in optimization of knowledge and has predominant use in optimization of knowledge and productivity. However, there is lack of understanding of concept and its application in Pakistan as indicated by Gallup Pakistan (2018) and stream of data is going to be doubled in two years’ time Tankard (2012). Therefore, there is a definite need of research which optimizes understanding associated with technology and its application from the context of Pakistan. Hence considering the application of big-data in retail sector this study aims to explore the impact of sentiment analysis through relating impact of big-data with effective assortment s of online stores. Although data has been collected from IT experts associated with online retail sector via quota sampling and SMART-PLS has been incorporated for the purpose of analysis. Results of the study highlights that big-data is perceived as the major tool for the betterment of assortment in online retail stores although data scientist and their applicability might diminish the impact of the use of big-data.

https://doi.org/10.51153/kjcis.v3i2.47
PDF

References

Ab Hamid, M. R., Sami, W., &Sidek, M. M. (2017, September). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. In Journal of Physics: Conference Series (Vol. 890, No. 1, p. 012163). IOP Publishing

Afthanorhan, W. M. A. B. W. (2014). Hierarchical component using reflective-formative measurement model in partial least square structural equation modeling (Pls-Sem). International Journal of Mathematics, 2(2), 33-49

Aktas, E., & Meng, Y. (2017). An Exploration of Big Data Practices in Retail Sector. Logistics, 1(2), 12

Ali, R., Subzwari, M., & Tariq, S. (2016). Impact of Information Technology on Retail Sector in Pakistan. KASBIT Journal of Management & Social Science, 9(1), 63-93.

Andreev, P., Heart, T., Maoz, H., & Pliskin, N. (2009). Validating formative partial least squares (PLS) models: methodological review and empirical illustration. ICIS 2009 proceedings, 193

Ashraf, S., (2013), “Can we Optimize Pakistan via Big Data?”, Tech Juice, https://www.techjuice.pk/can-optimize-pakistan-via-big-data/

Baker, W., Kiewell, D., & Winkler, G. (2014). Using big data to make better pricing decisions. McKinsey Analysis. McKinsey & Company, https://www.mckinsey.com/businessfunctions/marketing-and-sales/our-insights/using-big-data-to-make-better-pricingdecisions#

Benitez, J., Henseler, J., Castillo, A., &Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679

Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-95

Briesch, R. A., Chintagunta, P. K., & Fox, E. J. (2009). How does assortment affect grocery store choice? Journal of Marketing research, 46(2), 176-189

Brown, B. Bughin, J., Byers, A.H.; Chui, M.; Dobbs, R.; Manyika, J. and Roxburgh, C., (2011), “Big Data: The Next Frontier for Innovation, Competition, and Productivity”, Technical Report for Mckinsey& Company, Washington, DC, USA

Cheah, J. H., Memon, M. A., Chuah, F., Ting, H., &Ramayah, T. (2018). Assessing reflective models in marketing research: A comparison between PLS and PLSC estimates. International Journal of Business and Society, 19(1), 139-163

Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142-5156, doi: 0.1080/00207543.2015.1066519

Clark, R., & Vincent, N. (2012). Capacity-contingent pricing and competition in the airline industry. Journal of Air Transport Management, 24, 7-11

Cohen, L., Manion, L., & Morrison, K. (2007). Observation. Research methods in education, 6, 396-412

Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., &Sharit, J. (2006). Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and aging, 21(2), 333.

Danah, B., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.

De Mauro, A., Greco, M., Grimaldi, M., & Nobili, G. (2016). Beyond data scientists: a review of big data skills and job families. Proceedings of IFKAD, 1844-1857

Ducange, P., Pecori, R., &Mezzina, P. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22(1), 325-342.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.

Fazl-e-Haider, S. (2018 February 19). Booming Growth in Pakistan’s Retail Sector. Economist, http://www.pakistaneconomist.com/2018/02/19/booming-growthpakistans-retail-sector/

Friedrich, O. Stoler, P. Moritz, M. & Nash, J. N. (1983).Machine of the year: The computer

moves. Time Magazine, 121(1). 14–28

Gallup Pakistan, (2018), “Big Data Analysis Reports”. Retrieved from http://gallup.com.

pk/polls/gallup-history-project/big-data-analysis/

Gielens, K., Gijsbrechts, E., & Dekimpe, M. G. (2014). Gains and losses of exclusivity in grocery retailing. International Journal of Research in Marketing, 31(3), 239-252

Glass, R., & Callahan, S. (2014). The Big Data-driven business: How to use big data to win customers, beat competitors, and boost profits. John Wiley & Sons.

Grewal, D., & Levy, M. (2007). Retailing research: Past, present, and future. Journal of retailing, 83(4), 447-464.

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of qualitative research, 2(163-194), 105.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications

Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. sage publications

Hair, J. F., Ringle, C. M., &Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12

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

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(3), 414-433

Hair, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152

Hajirahimova, M. S., &Aliyeva, A. S. (2017a). About Big Data Measurement Methodologies and Indicators. International Journal of Modern Education and Computer Science, 9(10), 1

Hajirahimova, M. S., &Aliyeva, A. S. (2017b). Big Data Initiatives to Developed Countries. Problems of information society, 1, 10-19, doi: 10.25045/jpis.v08.i1.02

Henseler, J., Ringle, C. M., &Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.

Howe, K. (2014). Beyond big data: How next-generation shopper analytics and the internet of everything transform the retail business. Cisco, 1-10

IBM Institute of Business Value IBM, (2018), “Analytics: The real-world use of big data in retail”, How innovative retailers extract value from uncertain data, https://www-935.ibm.com/services/us/gbs/thoughtleadership/big-data-retail/

IDC, (2014), “Executive Summary: Data Growth, Business Opportunities, and the IT Imperatives”, Retrieved from https://www.emc.com/leadership/digitaluniverse/2014iview/executivesummary.html

Integreon Insight (2012). “Big just got bigger”. Grail Research, 1–17 (Retrieved from http://www.integreon.com/pdf/Blog/Grail-Research-Big-Data-Just-Got-Bigger_ 232.pdf)

Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). II. More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13-30

Jun, S. P., & Park, D. H. (2017). Visualization of brand positioning based on consumer web search information: Using social network analysis. Internet Research, 27(2), 381-407.

Kaur, R., & Jagdev, G. (2017). Big Data in retail sector-an evolution that turned to a revolution. International Journal of Research Studies in Computer Science and Engineering (IJRSCSE), 4(4), 43-52

Khan, A., (2017, July 31), “Trading data”, Retrieved from https://www.thenews.com.pk/magazine/money-matters/194614-Trading-data

Khan, M. W., Khan, M. A., Alam, M., & Ali, W. (2018). Impact of Big Data over Telecom Industry. Pakistan Journal of Engineering, Technology & Science, 6(2), 116-126, http://dx.doi.org/10.22555/pjets.v6i2.1958

Kivunja, C., & Kuyini, A. B. (2017). Understanding and applying research paradigms in educational contexts. International Journal of Higher Education, 6(5), 26-41

Kshetri, N. (2016). Big data's big potential in developing economies: impact on agriculture, health and environmental security. CABI. Wallingford, doi10.1079/9781780648682.0000, ISBN 9781780648682

Latif, Z., Tunio, M. Z., Pathan, Z. H., Jianqiu, Z., Ximei, L., &Sadozai, S. K. (2018, March). A review of policies concerning development of big data industry in Pakistan: Subtitle: Development of big data industry in Pakistan. In Computing, Mathematics and Engineering

Technologies (iCoMET), 2018 International Conference on (pp. 1-5). IEEE.

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.

Leedy, P. D., & Ormrod, J. E. (2005). Practical Research Planning and Design . New Jersey:

Pearson Merrill Prentice Hall.

Lycett, M. (2013). ‘Datafication’: making sense of (big) data in a complex world. European

Journal of Information Systems, 22(4), 381–386

Maheshwari, A. (2014). Business Intelligence and Data Mining. Business Expert Press.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.Mckinsey& Company, Washington, USA

Matsa, D. A. (2011) Competition and product quality in the supermarket industry, The Quarterly Journal of Economics, 126, 1539–91. doi:10.1093/qje/qjr031 Volpe, R., Okrent, A. and Leibtag, E. (2013) The effect of

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.

Mkansi, M., & Acheampong, E. A. (2012). Research philosophy debates and classifications: students’ dilemma. Electronic journal of business research methods, 10(2), 132-140

Mugenda, A. (2003). Research methods Quantitative and qualitative approaches by Mugenda. Nairobi, Kenya.

Mukherjee, S., & Shaw, R. (2016). Big Data–Concepts, Applications, Challenges and Future Scope. International Journal of Advanced Research in Computer and Communication Engineering, 5(2), 66-74.

New Desk. (2020, May 13). Profit, https://profit.pakistantoday.com.pk/2020/05/13/pakistan-retains-its-place-in-msci-emerging-markets-index/

Oracle (2012). Big data for the enterprise. Oracle White Paper, 1–14, http://www.oracle.com/us/products/database/big-data-forenterprise- 519135.pdf.

Oso, W. Y., & Onen, D. (2009). A general guide to writing research proposal and report. Jomo Kenyatta Foundation.

Pantano, E., Giglio, S., & Dennis, C. (2019). Making sense of consumers’ tweets. International Journal of Retail & Distribution Management, 47(9), 915-927.

Pathirage, C. P., Amaratunga, R. D. G., & Haigh, R. P. (2008). The role of philosophical context in the development of research methodology and theory. The Built and Human Environment Review, 1(1), 1-10

Pecori, R. (2016). S-Kademlia: A trust and reputation method to mitigate a Sybil attack in Kademlia. Computer Networks, 94, 205-218.

Ravand, H., &Baghaei, P. (2016). Partial least squares structural equation modeling with R. Practical Assessment, Research, and Evaluation, 21(1), 1-16, https://doi.org/10.7275/d2fa-qv48

Rejeb, A., Rejeb, K., & Keogh, J. G. (2020). Potential of Big Data for Marketing: A Literature Review. Management Research and Practice, 12(3), 60-73

Ridge, M., Johnston, K. A., & O'Donovan, B. (2015). The use of big data analytics in the retail industries in South Africa. African Journal of Business Management, 9(19), 688-703

Salvador, A. B., & Ikeda, A. A. (2014). Big data usage in the marketing information system. Journal of Data Analysis and Information Processing

Santoro, G., Fiano, F., Bertoldi, B., &Ciampi, F. (2019). Big data for business management in the retail industry. Management Decision, 57(8), 980-1992

Saunders, M. N. K., Lewis, P., Thornhill, A., & Bristow, A. (2015). Understanding research philosophies and approaches: Research methods for business students

Saunders, M., Lewis, P. & Thornhill, A. (2007). Research methods. Business Students 4th edition Pearson Education Limited, England

Schultz, J., (2017, October 10), “How Much Data is Created on the Internet Each Day?” https://blog.microfocus.com/how-much-data-is-created-onthe-internet-each-day

Seetharaman, A., Niranjan, I., Tandon, V. and Saravanan, , A. S., (2016), “Impact of Big Data on the Retail Industry” Journal of Corporate Ownership & Control, 14(1), 506-518

Sekaran, U. and Bougie, R., (2016), “Research Methods For Business: A Skill Building Approach”, John Wiley & Sons, 1-448, ISBN 1119165555, 9781119165552

Shankar, V. (2019). Big Data and Analytics in Retailing. NIM Marketing Intelligence Review, 11(1), 36-40

Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107

Sijtsma, K. (2009). Over misverstandenrond Cronbachs alfa en de wenselijkheid vanalternatieven. Psycholoog, 44(11), 561

Sileyew, K. J. (2019). Research Design and Methodology. In Text Mining-Analysis, Programming and Application. Intech Open, https://www.intechopen.com/books/cyberspace/research-design-and-methodology

Spiess, J., T'Joens, Y., Dragnea, R., Spencer, P., &Philippart, L. (2014). Using big data to improve customer experience and business performance. Bell labs technical journal,18(4), 3-17.

State Bank of Pakistan, (2014), “‘Supermarkets and Retail Shops”, Research Report on ‘Supermarkets and Retail Shops’ Segment”, http://www.sbp.org.pk/departments/ihfd/Sub Segment%20Booklets/Supermarkets%20and%20Retail%20Shops.pdf

Stoicescu, C. (2016). Big Data, the perfect instrument to study today’s consumer behavior. Database Syst. J, 6, 28-42.

Surbakti, F. P. S., Wang, W., Indulska, M., & Sadiq, S. (2020). Factors influencing effective use of big data: A research framework. Information & Management, 57(1), 103146

Tankard, C. (2012). Big Data Security. Network Security Newsletter, Elsevier, ISSN 1353-4858

Tavakol, M., &Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53-55, doi 10.5116/ijme.4dfb.8dfd

Thakuriah, P. V., Tilahun, N. Y., & Zellner, M. (2017). Big data and urban informatics: innovations and challenges to urban planning and knowledge discovery. In Seeing cities through big data (pp. 11-45). Springer, Cham.

Thau, B., (2016, March 2), “Retail pricing strategies getting a makeover from data analytics”, IBM Big Data and Analytics Hub, https://www.ibmbigdatahub.com/blog/retail-pricingstrategies-getting-makeover-data-analytics

Valchanov, I. (2017). Is data science really a rising career? https://www.quora.com/Isdata-science-really-a-rising-areer/answer/IliyaValchanov

Vargas-Sánchez, A., do Valle, P. O., da Costa Mendes, J., & Silva, J. A. (2015). Residents' attitude and level of destination development: An international comparison. Tourism Management, 48, 199-210.

Vijayarani, S., & Sharmila, S. (2016, August). Comparative analysis of association rule mining algorithms. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 3, pp. 1-6). IEEE

Vikas, D. & Nadir, Z. (2014).Big data and social media analytics. A Cambridge Assessment publication, 18, 36-1,www.cambridgeassessment.org.uk/research-matters/

Voleti, S., Kopalle, P. K., & Ghosh, P. (2015). An inter-product competition model incorporating branding hierarchy and product similarities using store-level data. Management Science, 61(11), 2720-2738.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.

Werner, K., Lerzan, A., Yakov, B., Kristina, H., Sertan, K., Francisco, V. O., Marianna S., David, D. and Babis, T., (2017) "Customer engagement in a Big Data world", Journal of Services Marketing, 31(2), 161-17

Zanini, N., & Dhawan, V. (2015). Text Mining: An introduction to theory and some applications. Research Matters, 19, 38-45

Zeng, J., & Glaister, K. W. (2018). Value creation from big data: Looking inside the black box. Strategic Organization, 16(2), 105-140

Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.Erevelles, S., Nobuyuki

Žukauskas, P., Vveinhardt, J., &Andriukaitien?, R. (2018). Philosophy and paradigm of scientific research. Management Culture and Corporate Social Responsibility, 121.