Know Yourself and Know Your Enemy: An Analysis of Firm Recommendations and Consumer Reviews in a Competitive Environment

Abstract

Positive Review Volume Cumulative count of positive reviews posted to the focal book up to time t-1 Negative Review Volume Cumulative count of negative reviews posted to the focal book up to time t-1 Positive Helpful Vote Ratio of Helpful vote to Total vote for positive reviews posted to the focal book up to time t-1 Negative Helpful Vote Ratio of Helpful vote to Total vote for negative reviews posted to the focal book up to time t-1 Review Rating Average Average review rating posted to the focal book up to t-1 Review Rating StdDev Standard deviation of review ratings posted to the focal book up to t-1 StdDevofRating Agreement StdDev of the ratings of positive reviews made by all focal reviewers to other books up to t-1 Ratio Reviewer Agreement Ratio of the count of reviewer agreement for the focal book divided by the count of reviewer agreement for the competing book up to t-1 MIS Quarterly Vol. 38 No. 3-Appendices/September 2014 A1

Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013

“…By using the Hausman test (Hausman 1978) an instrumental variable estimation can be performed to investigate whether there are some missed variable biases in the regression model (Jabr & Zheng 2014). According to the Hausman test, under null hypothesis, the specified endogenous regressors can be considered as exogenous.…”

Will video be the next generation of e-commerce product reviews? Presentation format and the role of product type

“…This is a first study on effects of video reviews that we hope will encourage more research that can identify, at a finer grained level, factors that affect consumer opinions through presentation formats of reviews. Online review is a very important topic in IS research as evidenced by recent studies (Aggarwal et al 2012;Cheung et al 2012b;Jabr et al 2014;Zhang et al 2014). Our study contributes to this growing literature by highlighting the role of the presentation format.…”

Flexible Query Answering Systems 2015

“…The size of recommendation set K ranged from 5 to 15, that is, K ∈ {5, 6,7,8,9,10,11,12,13,14 where S is the collection of records in test set and AP (s) @K can be computed by:…”

“…In marketing practice, clickstream data are commonly used to quantify the customers' search behaviors [8,9,16]. Usually, the clickstream data provide information about the sequence of pages or the path viewed by the consumers as they navigate websites [18].…”

“…Since search could reflect consumers' purchase intentions and affect their choices online [9], it is worthy of deep exploration and has attracted a lot of interest from both academia and practitioners. Moreover, as Bhatnagar & Ghose (2004) [3] indicated, consumers exhibit differences in their search patterns, i.e, time spent per search episode and search frequency, which are attributed to product categories and consumer characteristics.…”