Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/535559
Title: Exploring determinants of user generated content a consumer behaviour perspective
Researcher: Rawat, Kavita
Guide(s): Kumar, Sunita
Keywords: Economics and Business
Elaboration Likelihood Model (ELM),
Information Acceptance Model (IACM).
Management
Online Review,
Review Helpfulness,
Social Sciences
University: CHRIST University
Completed Date: 2023
Abstract: The advances in digital technology and the Internet have accelerated the growth of the online ecosystem. The ease of access to the Internet by the masses has ensured phenomenal expansion among online users. The past decade newlinewitnessed tremendous growth of online applications, platforms and apps that are newlinehelping to solve complex human needs. The online ecosystem itself witnessed newlinetremendous change, while static information sources have been replaced with dynamic ones that allow online users to participate in the system. The vast information society has transformed from being just the consumer of information to the participant in the generation of the information source. Business finds the exponential growth of online users and their active participation as an opportunity. Business benefits by sensing the market trends quickly in a better newlineway and take timely remedial actions. newlineDespite immense benefits offered by the online mode of business, many challenges have surfaced in recent times on account of ever-increasing technological sophistication and exponential growth of unique and similar newlineproduct offerings and associated reviews. The presence of many similar product offerings and associated reviews creates a technology-induced hurdle, with the potential to impair the rational thought process of consumers, who often search, scan and vote for only the top few reviews of selected products. This has the potential to make aged reviews continuously accumulate votes over time and newlineretain their near top position in the helpful review list, compared to recent quality newlinereviews. The current study applies statistically and scientifically derived newlinehelpfulness scores for ranking reviews and placing them at their appropriate positions. The study derived helpfulness scores enable re-ranking reviews of consumer products. The initial review dataset is constructed from publicly available reviews in Amazon.in.
Pagination: xiv, 222p.;
URI: http://hdl.handle.net/10603/535559
Appears in Departments:Department of Management Studies

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01_title.pdfAttached File193.23 kBAdobe PDFView/Open
02_prelim pages.pdf928.11 kBAdobe PDFView/Open
03_abstract.pdf67.96 kBAdobe PDFView/Open
04_table_of_contents.pdf132.87 kBAdobe PDFView/Open
05_chapter1.pdf204.25 kBAdobe PDFView/Open
06_chapter2.pdf505.42 kBAdobe PDFView/Open
07_chapter3.pdf254.2 kBAdobe PDFView/Open
08_chapter4.pdf687.1 kBAdobe PDFView/Open
09_chapter5.pdf127.16 kBAdobe PDFView/Open
10_chapter6.pdf148.41 kBAdobe PDFView/Open
11_annexures.pdf1.84 MBAdobe PDFView/Open
80_recommendation.pdf337.76 kBAdobe PDFView/Open
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