An Exploratory Study: “Blind-Testing” Consumers How They Rate Helpfulness of Online Reviews

Amazon.com uses the helpfulness vote (H-Vote) on consumer product reviews to signal quality. The top reviews with the best H-Vote are thus prominently displayed so that consumers readily use them for their purchase decision-making. However, the influence of those H-Votes may not be the same depending on contexts. We conducted a pilot survey experiment by using 108 consumers to investigate. The results showed that consumers were not swayed by a positive or negative H-Vote for a top favorable review in Amazon.com. However, consumers significantly voted positively for a top unfavorable (or critical) review in Amazon.com when a negatively-biased H-Vote was shown. To further explore the underlying factors, we deconstructed the YES/NO-based H-Vote with a Likert-scale-based helpfulness rating for (a) product learning and (b) purchase decision-making. While the difference in H-Vote did not affect the learning rating, it did impact the decision rating under a false-negative H-Vote. Implications and future research directions are discussed

Not only Online Review but also its Helpfulness is Manipulated: Evidence from Peer to Peer Lending Forum

Online reviews have become proposed as useful information for consumers to make decision. Meanwhile, review manipulation will weaken the credibility of online reviews. Except manipulating the review text and rating, we propose that review helpfulness, an important signal for consumer to filter the reviews, could also be manipulated. This study thus explores the existence of review helpfulness manipulation and the relationship between firm quality and review manipulation. Based on a dataset from a review forum in www.wdzj.com which is the leading and largest portal of peer to peer lending industry in China, we get the following interesting results. First, due to the manipulation of review helpfulness, a manipulated positive review is more likely to receive higher helpfulness, while a manipulated negative is more likely to get lower helpfulness. Second, a manipulated review tends to be lower quality in terms of readability and word count, which are found as positive predictors for review helpfulness. Third, high quality firms tend to manipulate more positive reviews, and at the same time high quality firms will receive more negative manipulated reviews. This study extends current understanding about online review manipulation, thereby providing theoretical and practice implications

Current State of the Digital Deception Studies in IS

Digital deceptions exist on the Internet in various forms and for different purposes. The purpose of this study is to understand the current state of the digital deception research in IS discipline. Based on our review and analysis of the selected digital deception articles published in IS journals and conference proceedings, we discussed various perspectives of digital deceptions, such as the media, types of deception, deceivers, motivations, and victims. The results of our study indicate that deception phenomena are severely under-researched in IS discipline. The study provides suggestions for future research

Reading Consumer Reviews to Confirm My Expectations: The Accelerated Impact of Confirmation under Extreme Review Tones

This study examines how the initially perceived product value affects a consumer’s purchase intention after he/she reads various tones of online reviews. It proposes that the associations among the initially perceived product value, the level of confirmation made by reading reviews and final purchase intention would differ across review tones that; 1) when the tone is extreme, the impact of confirmation will be stronger than when the tone is moderate and 2) when the tone is favorable, the impact of initially perceived product value will be stronger than when the tone is critical. The survey was conducted to 276 online shopping mall users in Korea and most of hypotheses were supported. This study emphasizes that the impact of online review should be discussed together with the level of expectation that a customer had before reading online reviews, because the customers have to go through searching and screening processes before reading online reviews

Internet Fraud: Information for Teachers and Students

Internet fraud takes a number of forms with the responsible individuals changing tactics rapidly to avoid detection. The perpetrators rely on telemarketing, emails, as well as presenting themselves personally to unsuspecting people. The evolution of internet marketing as well as ecommerce and the ease of connectivity create increasing opportunities for fraudsters while at the same time placing more unsuspecting internet users at risk of falling prey to these schemes. There exists a thriving economy online with large sums of money changing hands online. It is therefore important for any internet user to easily identify when they are exposed to internet fraud schemes and as such avoid being a victim

Revisiting Self-Selection Biases in E-Word-of-Mouth: An Integrated Model and Bayesian Estimation of Multivariate Review Behaviors

This paper studies the consumer self-selection bias in the e-word-of-mouth (eWOM) systems, e.g. consumer review websites. Under Bayesian framework, this study extends our understanding of this bias and discovers two new sources through developing a system of structural models of consumer review behaviors tested by a large data set. Our model and results provide evidences that the timing and content of a review introduce significant amount of bias into ratings in a simultaneous fashion. Specifically, we find that after controlling for various exogenous effects the two sources of bias persist: a subsequent rating is positively associated with the time interval between two consecutive reviews by the same consumer, and is negatively associated with the length of a review. Clearly, our findings confirm that modern eWOM systems have notable flaws despite of their mechanical advantages. We further discuss the possible mechanisms as well as the economic impact underlying these findings