Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in

Prominent e-commerce platforms allow users to write reviews for the available products. User reviews play an important role in creating the perception of the product and impact the sales. Online reviews can be considered as an important source of e-word of mouth (e-WOM) on e-commerce platforms. Various dimensions of e-WOM on product sales have been examined for different products. Broadly, studies have explored the effect of summary statistics of reviews on product sales using data from various e-commerce platforms. Few studies have utilized other review characteristics as length, valence, and content of the reviews. The sequence of reviews has been hardly explored in the literature. This study investigates the impact of sequence of helpful reviews along with other review characteristics as ratings (summary statistics), volume, informativeness, and valence of reviews on product sales. Hence, a holistic approach has been used to explore the role of summary statistics, volume, content and sequence of reviews on product sales with special emphasis on sequence of reviews. Relevant theories such as message persuasion, cognitive overload and belief adjustment model have also been explored during the construction of the model for review data. The proposed model has been validated using the helpful reviews available on Amazon.in website for various products.

[1]  Yogesh Kumar Dwivedi,et al.  Adoption of online public grievance redressal system in India: Toward developing a unified view , 2016, Comput. Hum. Behav..

[2]  Ling Liu,et al.  Do online reviews affect product sales? The role of reviewer characteristics and temporal effects , 2008, Inf. Technol. Manag..

[3]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[4]  Deepak Khazanchi,et al.  An Empirical Study of Online Word of Mouth as a Predictor for Multi-product Category e-Commerce Sales , 2008, Electron. Mark..

[5]  Mohammad Salehan,et al.  Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics , 2014, Decis. Support Syst..

[6]  E. Sugawara,et al.  Properties of AdeABC and AdeIJK Efflux Systems of Acinetobacter baumannii Compared with Those of the AcrAB-TolC System of Escherichia coli , 2014, Antimicrobial Agents and Chemotherapy.

[7]  N. Malhotra.  Reflections on the Information Overload Paradigm in Consumer Decision Making , 1984 .

[8]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[9]  Miriam J. Metzger,et al.  Trusting expert- versus user-generated ratings online: The role of information volume, valence, and consumer characteristics , 2013, Comput. Hum. Behav..

[10]  Yogesh Kumar Dwivedi,et al.  Using Clickers in a Large Business Class , 2016 .

[11]  Yogesh Kumar Dwivedi,et al.  Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust , 2017, Int. J. Inf. Manag..

[12]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[13]  J. E. Russo,et al.  Biased interpretation of evidence by mock jurors. , 2001, Journal of experimental psychology. Applied.

[14]  Gilad Ravid,et al.  Information overload and the message dynamics of online interaction spaces: a theoretical model and empirical exploration , 2004, IEEE Engineering Management Review.

[15]  Curtis P. Haugtvedt,et al.  Message Order Effects in Persuasion: An Attitude Strength Perspective , 1994 .

[16]  Yogesh Kumar Dwivedi,et al.  An empirical validation of a unified model of electronic government adoption (UMEGA) , 2017, Gov. Inf. Q..

[17]  Starr Roxanne Hiltz,et al.  Structuring computer-mediated communication systems to avoid information overload , 1985, CACM.

[18]  J. Izhavarasi Devi.  Estimating the Helpfulness and Economic Impact of Product Reviews , 2012 .

[19]  Geng Cui,et al.  Terms of Use , 2003 .

[20]  Yogesh Kumar Dwivedi,et al.  Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model , 2017, Information Systems Frontiers.

[21]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[22]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[23]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[24]  Yong Zhang,et al.  Consumer product evaluation: the interactive effect of message framing, presentation order, and source credibility , 2000 .

[25]  V. Dhar,et al.  Does Chatter Matter? The Impact of User-Generated Content on Music Sales , 2007 .

[26]  Yogesh Kumar Dwivedi,et al.  Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk , 2018 .

[27]  Patrick De Pelsmacker,et al.  Balance and Sequence in Online Reviews: The Wrap Effect , 2012, Int. J. Electron. Commer..

[28]  Alain Yee-Loong Chong,et al.  Predicting online product sales via online reviews, sentiments, and promotion strategies , 2016 .

[29]  Xinshu Zhao,et al.  Clutter and serial order redefined and retested , 1997 .

[30]  Charles F. Hofacker,et al.  Primacy and Recency Effects on Clicking Behavior , 2006, J. Comput. Mediat. Commun..

[31]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[32]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[33]  Andreas Munzel,et al.  Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product , 2016 .

[34]  Nathalia Purnawirawan,et al.  Balance and Sequence in Online Reviews: How Perceived Usefulness Affects Attitudes and Intentions , 2012 .

[35]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[36]  D. Kirsh.  A Few Thoughts on Cognitive Overload , 2000 .

[37]  Yogesh Kumar Dwivedi,et al.  Cronfa - Swansea University Open Access Repository , 2017 .

[38]  P. D. Maeyer,et al.  Impact of online consumer reviews on sales and price strategies: a review and directions for future research , 2012 .

[39]  Do-Hyung Park,et al.  eWOM overload and its effect on consumer behavioral intention depending on consumer involvement , 2008, Electron. Commer. Res. Appl..

[40]  Arpita Khare,et al.  An integrated model for predicting consumer's intention to write online reviews , 2017, Journal of Retailing and Consumer Services.

[41]  Chrysanthos Dellarocas,et al.  Using Online Reviews as a Proxy of Word-of-Mouth for Motion Picture Revenue Forecasting , 2004 .

[42]  Atreyi Kankanhalli,et al.  Which ideas are more likely to be implemented in online user innovation communities? An empirical analysis , 2016, Decis. Support Syst..

[43]  Patrick De Pelsmacker,et al.  EXPERT REVIEWERS BEWARE! THE EFFECTS OF REVIEW SET BALANCE, REVIEW SOURCE AND REVIEW CONTENT ON CONSUMER RESPONSES TO ONLINE REVIEWS , 2014 .

[44]  Eun-Ju Lee,et al.  When do consumers buy online product reviews? Effects of review quality, product type, and reviewer's photo , 2014, Comput. Hum. Behav..

[45]  Yogesh Kumar Dwivedi,et al.  Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust , 2015, Journal of Financial Services Marketing.

[46]  Sangwon Park,et al.  What makes a useful online review? Implication for travel product websites. , 2015 .

[47]  Yogesh Kumar Dwivedi,et al.  Citizen's adoption of an e-government system: Validating extended social cognitive theory (SCT) , 2015, Gov. Inf. Q..

[48]  D. Harrison,et al.  Affective verbal learning in hostility: an increased primacy effect and bias for negative emotional material. , 2007, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[49]  Yogesh Kumar Dwivedi,et al.  Citizen's Adoption of an E-Government System: Validating the Extended Theory of Reasoned Action (TRA) , 2015, Int. J. Electron. Gov. Res..

[50]  Yogesh Kumar Dwivedi,et al.  Predicting the “helpfulness” of online consumer reviews , 2017 .

[51]  Ying Zhu,et al.  Examining impacts of negative reviews and purchase goals on consumer purchase decision , 2017 .

[52]  Nan Hu,et al.  Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales , 2014, Decis. Support Syst..