MooreFBTM7300-11_Final 11-26-2015

Student: Fahmeena Odetta Moore

DIRECTIONS FROM PROFESSOR

The second draft of your literature review is due this week. As a reminder, you are building from previous assignments. Your literature review should include at least 20 scholarly sources and revisions to your literature review based on your instructor’s feedback. The goal of this assignment is to ensure that you have conducted a broad, comprehensive review of the literature so that you may gain a global understanding of the research in your chosen topic area. This knowledge will eventually be useful when you need to identify potential lines of inquiry for your dissertation. Finally, please ensure your draft is in adherence with the publication requirements of the APA Publication Manual (6th ed.) and that your draft is free of typographical and grammatical errors. This is a kind reminder that you should continually proofread your work.

Length: 15-20 pages, with a minimum of 20 peer-reviewed references included in the review (this page length requirement does not include the reference list)

Your paper should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University’s Academic Integrity Policy.

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Running Head: DRAFT LITERATURE REVIEW MOOREFBTM7300-11 1

Draft Literature Review Paper on Online Product Reviews

By Fahmeena Odetta Moore

Northcentral University

December 26, 2015

BTM7300

Professor Kuhn

DRAFT LITERATURE REVIEW MOOREFBTM7300-11 2

Draft Literature Review Paper on Online Product Reviews

There are now millions of reviews on the internet about consumers’ experiences with products

and services. These online product reviews/evaluations are scattered all over the internet on review

websites such as Yelp.com that collect customer reviews for the benefit of consumers, on blogs, on retail

websites such as Amazon.com, and on social media. Yelp.com alone had 90 million reviews as of the

third quarter of 2015. As the following example indicates, a review by the same author may be posted in

several places on the internet. This post by J. Kaufman on Twitter about her hotel stay linked to a blog

post with more detailed information: “We LOVED our first stay at the Walt Disney World Swan. Find

out why! http://www.delightful.life/why-we-love-the-walt-disney-world-swan-hotel/ …” (Kaufman,

2015).

Consumers as well as businesses (manufacturers and service providers) find online product

reviews useful. Studies indicate that more than 60% of consumers read reviews and use them when

deciding whether to purchase products and services (Hu, Koh, and Reddy, 2014, p. 42). Manufacturers

and service providers are interested in online product reviews because: (1) the reviews provide feedback

about their products and services, (2) the reviews provide information about competitors’ products and

services (provide competitive intelligence), and (3) the reviews influence consumers to purchase or pass

over their offerings. From online reviews, companies may make decisions about their products.

Businesses also need to decide how to respond to online reviews which may tarnish their reputation.

The purpose of this paper is to review the literature on online product reviews from a decision-

making perspective. The paper will look at the decisions individuals and companies need to make

regarding online product reviews, how theory explains their decision-making, and also at the information

systems that support their decision making. In the paper, I first review the methodology, which will

include a summary of the articles selected for this literature review. Then I will discuss my decision-

making framework and present the literature using this framework, and conclude with Implications for

Future Research.


Methodology

For this paper, I focused mainly on research articles from peer-reviewed journals published

within the last five years (from 2010 to present). Searches of databases such as ScienceDirect and EBSCO

provided lots of articles on online reviews. I selected articles for different reasons: first, I selected articles

on different aspects of online reviews that were useful in developing a concept map; second, I selected

articles that added knowledge or detail to identified themes or categories; third, I selected additional

articles that utilized different theories to explain decision-making in the online review domain when I

decided on a theoretical, decision-making framework for the literature review; and finally, I selected

articles that provided additional information on specific areas, used theories in a different way, or provide

a different/new viewpoint on a topic.

A total of 42 articles from several disciplines such as marketing, economics, law, and information

technology were used for this literature review. Most used a conceptual framework that was not based on

one predominant theory but pulled theories and findings from the literature. Some used a theoretical

framework grounded by one theory while others used a framework that seemed to be a combination of

theoretical and conceptual. Table 1 below shows the journals the articles are from as well as the

framework and theory utilized in the articles.

Table 1

Articles by Journal and Framework Highlighting the Theories UsedFramework

Used*Journal Article(Author/s) T C T&C Actual

TheoryInformation SystemsDecision Support Systems Bai (2011) X

Hu, Bose, Koh, & Liu (2012) XHu, Koh, & Reddy (2014) XHu, Liu, & Sambamurthy (2011)

X

Lu, Wu, Mao, Wang, & Zhang (2015)

X

Weathers, Swain, & Grover X


(2015)Xu, Liao, Li & Song (2011) XZhang, Zhao, Cheung, & Lee (2014)

X Heuristic–systematic model

Journal of Management Information Systems

Jensen, Averbeck, Zhang, &Wright (2013)

X Language Expectancy Theory

Ma, Khansa, Deng, & Kim (2013)

X Elaboration Likelihood Model

Journal of The Association for Information Systems

Cheung, Sia & Kuan (2012) X Elaboration Likelihood Model

International Journal of Information Management

Zhang, Cheung, & Lee (2014) X Komiak and Benbasat’s trust-based acceptance model that is built upon the Theory of Reasoned Action; the Heuristic-systematic model.

MIS Quarterly Shen, Hu & Ulmer (2015) X Electronic Commerce Research and Applications

Koh, Hu, & Clemons (2010) X Hofstede Theory

Computer Science Procedia Computer Science Bafna & Toshniwal (2013) X Computers in Human Behavior Dou, Walden, Lee, & Lee

(2012)X

Lee & Shin (2014) X Theory of Reasoned Action

Yeap, Ignatius, & Ramayah (2014)

X Fuzzy Analytic Hierarchy Process

Expert Systems With Applications Costa, Ferreira, Brito, Bittencourt, Holanda, Machado, &Marinho (2012)

X

Fuzzy Sets and Systems Portmann, Meier, Cudré-Mauroux, & Pedrycz (2015)

X Fuzzy Set Theory

Communications of the ACM Kugler (2014) XLaw Computer Law & Security Review Hunt (2015) X Franchise Law Journal Gerhards (2015) XMarketing


Marketing Science Berger, Sorensen, & Rasmussen (2010)

X

Gfk-Marketing Intelligence Review Moe & Schweidel (2013) X Journal of Marketing Management Wolny& Mueller (2013) X Theory of

Reasoned Action

Journal of Interactive Marketing van Noort & Willemsen (2012) X Journal of Product & Brand Management

Ullrich & Brunner (2015) X

Consumer & Retailing Journal of Retailing and Consumer Services

Engler, Winter, & Schultz (2015)

X

Journal of Consumer Satisfaction, Dissatisfaction & Complaining Behavior

Huppertz (2014) X Equity Theory

Journal of Consumer Research Moore (2015) XEconomics Economic Insights – Trends and Challenges

Bucur (2014) X

Economic Modelling Chen, Chen, Hu, & Li (2015) X Pacific Economic Review Liang (2013) X Exit–voice

theory/Game theory

Business and Management Business Horizons Gregoire, Salle, & Tripp (2015) X Journal of Business Research Kastanakis &Voyer (2014) X Cornell Hospitality Quarterly Levy, Duan & Boo (2013) X

Park & Allen (2013) X International Journal of Hospitality Management

Baker, Magnini, & Perdue (2012)

X

Tourism Management Sparks, So, & Bradley (2016) X Theory of Kardes

International Journal of Management Cases

Osathanunkul (2015) X

Social Sciences Discourse, Context & Media Zhang & Vasquez (2014) XTotal 5 26 11*T= Theoretical Framework Grounded By One Theory (based on the definition of a theoretical framework in Imenda (2014) and Rocco and Plakhotnik (2009)), C = Conceptual Frameworks Not Based On One Predominant Theory, T&C = Combination of Theoretical and Conceptual Framework.

Decision-Making Framework

Table 2 below shows the decision-making framework used to organize the findings and

conclusions from the 42 articles selected for this literature review. The framework consists of decisions

by consumers, businesses (manufacturers and service providers), and review websites related to online


reviews as well as systems that support their decision-making. The paper looks at what decisions are

made and how decisions are actually made, including the factors that influence the decision. This means

that the paper focuses on descriptive decision-making/theory (Aliev, Pedrycz, Kreinovich, & Huseynov,

2016).

One goal of the framework is to review the theories used for explaining decision-making by the

actors and their effectiveness. These theories are expected to fall under the umbrella of decision theory –

theory for making decisions when there is uncertainty. The paper will not attempt to model the decision-

making or list all decision-making theories.

Table 2

Decision-Making Framework______________________________________________________________________________

Decisions by the Consumero Reviewing online reviews for purchase decision-makingo Decision to post a review

Decisions by Businesseso Use of online reviews (for marketing, advertising etc.)o How to handle positive and negative online reviews of productso Quality of products to produce & what to offer online

Decisions by Review websiteso Revenue streams/business modelo Motivations/opportunities for fraud

Systems that assist with decision-makingo Systems to monitor online reviewso Sentiment summary/extractiono Authenticity/Recommender systems

______________________________________________________________________________

Decisions by the Consumer

Reviewing online reviews for purchase decision-making. Ads for Angie’s List, a website that

includes customer reviews, tell how users can use the reviews on the website to find a reliable, honest

business to provide a service such as roofing. Online product reviews provide information on the

performance, problems and other aspects of products and services. The product reviews are there to assist

consumers in making decisions about what to purchase, the service to choose, or contractor to hire.


Consumers may have favorite websites for reviewing online product reviews. For products such

as movies, there are a range of options such as: movieratings.com, rottentomatoes.com, amazon.com,

MovieLens, social networking/media website Twitter, and even websites such as Quora.com where users

can ask questions. Some websites such as TripAdvisor specialize in specific areas or genres. TripAdvisor

collects reviews for travel-related services such as hotel stays. Consumers make the decision to start their

information search for particular products or services at a particular website. Research by Yeap, Ignatius,

and Ramayah (2014) found the following order of preference for movie websites: review sites because

they compile ratings; social networking sites; personal blogs, which usually have a one-sided opinion; and

instant messaging sites. The research used the fuzzy Analytic Hierarchy Process method for determining

selections/preferences. Other findings include: (1) for websites, consumers give source credibility (source

trustworthiness and source expertise) higher importance than information quality (relevance, usefulness,

comprehensiveness, accuracy, timeliness), (2) within source credibility, source trustworthiness was more

important than source expertise to consumers, and (3) within information quality, consumers ranked the

attributes as follows: relevance, usefulness, comprehensiveness, accuracy and then timeliness.

Consumers should consider the following when reviewing online product reviews to make a

purchase decision:

(1) The number of reviews available. Depending on what the user is interested in, there may be

hundreds of online reviews or just a few to review. For example, if the user wants to review all restaurants

within a 10 mile radius on Yelp.com, there may be hundreds of reviews to review, maybe hundreds for

each restaurant. If the user wants to review all Caribbean restaurants within a 10 mile radius, there is

likely to be a lot less reviews for fewer restaurants. The number of reviews indicates how popular each

business is and may lead consumers to select a business/product because of its popularity (Zhang, Zhao,

Cheung, & Lee, 2014; Lee & Shin, 2014). For each business, there is likely to be more product

information as well as more diverse views (less bias) when there are a lot of reviews. When there are

more reviews, prospective purchasers of the product are more likely to have the views of different groups

or segments of the population. Underreporting bias, the bias caused because only a section of the


population opted to post a review, is less likely to exist (Koh, Hu, & Clemons, 2010). However, when

there are a lot of reviews, there is a lot more to read and analyze which may be time-consuming.

Consumers may choose to read the most recent reviews or narrow the list of reviews to a few reviews that

have arguments for the product and a few reviews that have arguments against the product. Hu, Koh, and

Reddy (2014) found in their research that the most recent reviews and most helpful reviews provided by

Amazon.com on the first page of an item have a significant relationship with sales and have a larger

impact than average reviews, which suggests that consumers review these highlighted reviews more than

other available reviews. When a website such as movieratings.com has a small number of reviews for a

product (movies), the consumer may decide to look at online reviews posted on other websites hoping for

a larger pool of reviews to review.

(2) How credible the reviewer/poster is. When the consumer reads a review, he or she makes a

judgment about the credibility of the review, which includes deciding whether someone with ulterior

motives posted the review or whether the review is biased. If a review seems to be a sales pitch or

advertisement sponsored by a manufacturer, the consumer is likely to take it with a grain of salt. Research

has focused on what causes consumers to judge a review as credible or authentic.

Jensen, Averbeck, Zhang, and Wright (2013) used the Language Expectancy Theory, a theory of

social influence, as a foundation to explain how consumers evaluate the credibility of anonymous

reviewers using the text comments. Jensen, Averbeck, Zhang, and Wright hypothesized that the

difference between consumers’ expectations and the actual in these three areas affected how they viewed

the credibility of reviewers: lexically complex language (more technical terms, longer words and complex

sentences), two-sidedness, and affect (use of emotion-laded words). For example, if because of the

culture, consumers did not expect overly biased, inflammatory comments but such comments were in an

online review, there would be a negative violation and the consumers would not likely believe the

reviewer is credible. Consumers are more likely to believe the reviewer is credible if there is a positive

violation, i.e. the reviewer performs better than expected. Using only positive reviews of digital cameras,

Jensen, Averbeck, Zhang, and Wright found that when posters are anonymous, they are viewed as more


credible when the post is two-sided or there is less affect in the post because these are different from what

is expected. If a review was one-sided, consumers questioned the motivation of the poster. Posts with

lexically complex language did not lead readers to believe the reviewer/poster was more credible. Jensen,

Averbeck, Zhang, and Wright also found that there was a weak relationship between reviewer credibility

and perception of product quality and that involvement (how important owning the product was to the

consumer) influenced the intention to purchase more than reviewer credibility.

Cheug, Sia, and Kuan (2012) used the Elaboration Likelihood Model (ELM), a theory of

informational influence, as the foundation of their research to explain judgments of credibility. They

hypothesized that more cognitive processing or more elaboration was required to judge argument quality.

If the message had good arguments, i.e. the argument quality is high, consumers viewed the

message/review as credible. Users may not always read or evaluate the argument quality of every online

review but may rely on “information cues” to pick specific reviews for further review (Cheug, Sia, &

Kuan, 2012, p. 620). Cheung, Sia, and Kuan believed that less cognitive work was required to judge the

credibility of the source (how reputable the source is), consistency, and two-sidedness – factors expected

to affect how consumers viewed the credibility of a review. Cheug, Sia, and Kuan found that argument

quality, source credibility, review consistency, and a balanced view (two-sidedness) all have a positive

effect on review credibility. In line with ELM, consumers with higher expertise and involvement

(engagement and motivation to understand the message) relied more on central cues – argument quality.

However, customers with lower expertise/knowledge and lower involvement did not rely more on

peripheral cues such as source credibility as ELM theorizes. Consumers relied on source credibility and

review consistency when their expertise/knowledge was low and their involvement was high. Unlike

Jensen et al. (2013), Cheug, Sia, and Kuan did not look into whether lexically complex language affected

credibility, but argument quality seems to be related. Jensen et al. (2013) found that reviews with lexically

complex language were not judged to be more credible while Cheug, Sia, and Kuan found that readers

appreciate a review with higher argument quality and judge the reviewer who prepared the review as

credible.


Zhang, Zhao, Cheung, and Lee (2014) applied the Heuristic–Systematic Model to judgments of

the credibility of online reviews for purchase decision-making, which they believed was an improvement

over the Elaboration Likelihood Model. Both of these models are considered dual-process theories. They

hypothesized that the following determine how credible consumers found reviews and their willingness to

purchase the product: (1) argument quality, a systematic and second-order factor that has two dimensions:

perceived informativeness and perceived persuasiveness, (2) source credibility, a heuristic factor, and (3)

perceived quantity of reviews, a heuristic factor. Source credibility and perceived quantity of reviews,

which are not determined from the content of the review, cause a bias about argument quality. Zhang,

Zhao, Cheung, and Lee used data that included only positive reviews from websites in China to test these

hypotheses. The findings supported all the hypotheses.

In their research, Lee and Shin (2014) looked at review quality – quality of review based on the

relevancy, comprehensiveness and accuracy of included product-related information – which seemed to

be closely related to argument quality. They used the Theory of Reasoned Action to hypothesize that a

high quality review will lead readers to have a high evaluation of the product (or a positive attitude

towards the product in line with the review) and because of this evaluation will be more willing to

purchase the product. The research also sought to test hypotheses that higher quality reviews will lead

readers to evaluate the poster/reviewer as well as the website more positively, and also investigate the

effects of product type and the presence of photos on evaluations of the product, poster and website.

Using participants from Seoul, South Korea, the team found that review quality indirectly affected

purchase intention through the evaluation of the product. In other words, a high quality review led readers

to evaluate the product positively which then led them to make a decision to purchase the product. The

type of product may alter this effect, however. If the product is an experience good, which is difficult to

evaluate if you have not tried it, there is both a direct as well as an indirect effect. In addition to deciding

to purchase the product because they judged it to be of high quality (indirect effect), readers’ decision to

try the product was based on “intuition” rather than “thoughtful deliberation” as assumed by the Theory

of Reasoned Action (Lee & Shin, 2014, p. 363). When there is a photo of the reviewer, readers decide to


purchase the product when they have formed their own conclusions about its quality (indirect effect).

Readers had high opinions of posters/reviewers as well as the website after reading higher quality

reviews. However, the presence of reviewers’ photos led readers to rate the website highly after reading a

high-quality review.

Dou, Walden, Lee, and Lee (2012) conducted research on the differences in credibility of reviews

due to the source or people/company the review is from (using a video review with people’s voices and

not faces). The researchers found that customers think a review is more credible if they believe it is from

an average user or independent review site rather than the manufacturer of the product even though it may

not be. If the review is from the manufacturer, customers tend to believe reviewers were paid for their

review and are therefore less credible. Surprisingly, customers did not believe reviews by the independent

review site or the manufacturer displayed more expertise. The researchers noted that a possible

explanation is that the video provided general information rather than detailed information about the

product. This research highlighted the importance of the source of a review and showed that the

displayed, visible or perceived source may be different from the actual or original source. The researchers

suggested that review sites provide information to help customers form conclusions about the original

source of a review, especially for anonymous posts/reviews. Examples of such information include the

number of reviews by a user, date of the user’s last review, and a generic geographic reference.

Kugler (2014) discussed several ways to identify fraudulent reviews or signs of fraudulent

reviews. Clues such as several posts by the same poster within a short time period, the same content and

style in various reviews, and reviewers that change their identities/perspectives in different reviews

indicate that a review is fraudulent. Hu, Bose, Koh, and Liu (2012) were able to identify manipulated

reviews (reviews created by vendors, publishers or authors) using a runs test to measure the randomness

of the reviewers’ writing style. They hypothesized that reviews written by real customers will have a

random writing style (since people have different writing styles) while manipulated reviews will have a

different writing style that is not random.


(3) The weight of the “evidence”. Once the consumer has a set of believable reviews, he or she

may find that they conflict. Consumers need to weigh the arguments presented by reviewers/posters and

make judgments about the quality of the product. Moore (2015) indicated that reviews may contain

explanations of why the consumer selected the product as well as their reactions to the product. These

explanations can provide information for a prospective customer to reconcile reviews (for example, the

prospective customer may determine that a problem identified by a previous customer occurred because it

was at a particular time of day) or predict how the prospective customer will feel about the product. The

reactions may indicate that the customer had high expectations and reviewed the product harshly.

Consumers need to consider the role expectations play in the sentiment expressed in online

reviews. Engler, Winter, and Schulz (2015) determined that ratings/reviews reflect the expectations of

reviewers about the quality of a product (formed from previous ratings/reviews as well as brand

reputation and price) more than the actual quality of the product. The researchers determined that

customer satisfaction depended more on how the product compared to expectations than the actual

performance of the product, and customers did not objectively evaluate products. Their expectations

drove what they see.

Some reviews may contain information that reconciles conflicting reviews. Weathers, Swain, and

Grover (2015) described new and different information a review may contain that audiences find helpful

in reducing uncertainty and also reducing ambiguity about the product. Such information include:

references to other brands (brand comparisons), references to other reviews (where the reviewer may

agree or disagree with comments in other reviews or reconcile conflicting reviews), describing usage

situations (information on how the reviewer uses the product), and listing of product features. In their

model that also included balance (for balanced, two-sided reviews), claims of expertise, and valence, the

researchers found that: (1) referring to other brands in reviews made the reviews more helpful, with a

stronger effect for experience goods, (2) referring to other reviews in a way that builds consensus made

reviews more helpful, with a stronger effect for experience goods, (3) disagreeing with information in

other reviews made the reviews less helpful, (4) describing usage situations made reviews more helpful,


and (5) listing product features generally increases the odds of a review being helpful for search goods

and reduces the odds of a review being helpful for experience goods.

Some prospective customers may place more emphasis on negative reviews. This means that they

pay more attention to negative reviews than positive ones (Zhang, Cheung, & Lee, 2014). Negative

reviews are more likely to be truthful assessments of the product. They also indicate what problems can

occur.

The opinion/rating expressed in online reviews could conflict with information about the product

from other sources, including the brand reputation. For example, the paper by Zhang and Vásquez (2014)

looked at reviews where the customer rated 4- and 5-star hotels only one and two stars. The ratings in the

reviews were lower than the “official” ratings – ratings on the Forbes/Mobil scale. In their research, Park

and Allen (2013) selected cases that had higher TripAdvisor ratings than the official (Forbes/Mobil)

rating of the hotels. For example, the two luxury hotels were rated 4-stars on the Forbes/Mobil scale and

five bullets on TripAdvisor. Customers may view “official” ratings by experts as more trustworthy

because experts are less likely to engage in opportunistic and fraudulent behavior. Research by Yeap,

Ignatius, and Ramayah (2014) found that consumers preferred movie reviews written by critics and

experts.

Hu, Liu, and Sambamurthy (2011) found in their research that there is much manipulation in

reviews which caused the sentiment in reviews to vary over time, i.e. the reviews reflected one sentiment

at one time and a different sentiment at a later time. Hu, Liu, and Sambamurthy showed that the

average/mean numerical rating of a product was first high and then later decreased, which meant that it

did not consistently measure quality. After some time, ratings/reviews reflected the true quality of the

product because there was less manipulation by vendors, publishers and authors. Hu, Liu, and

Sambamurthy found that when there is much manipulation/fraud in online reviews, consumers use price

as a measure of the quality of the product. In other words, the consumer no longer trusts the reviews to

provide accurate information and look to the product price to gauge the product’s quality. In such cases, a

higher price will result in higher sales – not the usual relationship between price and sales as indicated by


the demand curve. Hu, Liu, and Sambamurthy found that as time progresses, there were changes in the

relationship between price and sales, some of which were statistically insignificant.

Zhang et al. (2014) looked at how trust affects consumers’ purchase intention, especially when

there are inconsistent reviews. Zhang et al. based their research on Komiak and Benbasat’s trust-based

acceptance model that is built upon the Theory of Reasoned Action as well as the Heuristic–Systematic

Model. They found that customers’ evaluations of the competence, benevolence and integrity of an online

retailer (cognitive trust or the consumers’ judgement that the online retailer is a good, reliable business

from the online reviews) positively impacted emotional trust (attitude and emotional feelings about

relying on the online retailer) which led to an increase in the willingness to purchase from the online

retailer. Zhang et al. also found that: (1) when there are inconsistent reviews, emotional trust is more

likely to predict whether the consumer purchases from the online retailer (purchase intention), and (2)

females are better integrators of inconsistent information in online reviews than males, and the

relationship between emotional trust and purchase intention was stronger for female consumers.

(4) Responses provided by companies. Prospective customers may look at the responses to

complaints in reviews to judge whether an issue was an isolated event or regular occurrence. As indicated

in Sparks, So, and Bradley (2016), customers also use responses to online reviews to judge how

concerned a business is about customers as well as its trustworthiness. In line with the Theory of Kardes,

Sparks, So, and Bradley believed that customers use responses as a cue to what is actually occurring and

to draw conclusions about the organization. Customers took cues from the existence of responses to

online reviews, timeliness of responses and the human voice in responses.

All in all, consumers have their own criteria for deciding on a particular product or service. A

consumer who decides to utilize online reviews may set requirements such as “I want a contractor who

has had no negative complaints” or “I don’t mind if there is a negative review for a service, but there has

to be only positive reviews within the last three months” or “I want the cell phone with the best camera so

I will pay attention to what reviewers say about the camera” or “I want the best service within a 5 mile

radius.”


Decision to post a review. The writing of reviews is no longer left to professionals or experts

such as professional movie critics and restaurant critics. The average consumer now posts comments or

thoughts on products and services online (Jensen et al., 2013). Research has looked at the motivations for

and reasons why consumers choose to write online reviews. Motivations discussed in papers by Moe and

Schweidel (2013) and Koh, Hu, and Clemons (2010) seem like the reason for deciding to post (or

deciding not to post) a review.

Research by Moe and Schweidel found that consumers that had either a very positive or very

negative experience with a product are more likely to post a review online. These consumers are

motivated to tell others and make the decision to post a review about a good or bad experience. Some of

the other findings by Moe and Schweidel were: (1) consumers were more likely to post when other

ratings were predominantly positive, (2) less-active posters were more likely to post when there was

consensus among posts and also likely to adjust their opinion upward when previous ratings were more

positive (a form of “peer” pressure or influence), and (3) more active posters were less likely to be

influenced by other posts and, in fact, wanted to be perceived as experts. These more active posters are

motivated by the attention and reputation that posts may provide, a finding corroborated by recent

research by Shen, Hu, and Ulmer (2015).

In their research, Koh, Hu, and Clemons considered the role culture plays in the decision to post

an online review. Using Hofstede’s theory, they hypothesized that reviewers in a collectivist culture, such

as the culture in China, are less likely (not motivated) to post a review if they have strong negative

opinions about the product because they would not fit in. Reviewers in an individualistic culture such as

the culture in the United States (US), which values freedom of expression, are more likely to post a

review with a strong negative opinion. The researchers found that unlike Chinese reviewers, US

reviewers are willing to go outside the boundary of the community to voice an opinion that is more

negative than the consensus. This difference in culture is also discussed in the paper by Kastanakis and

Voyer (2014), which states that people in cultures such as in the United States “want to be authentic,

which means they place the highest value on personal goals and individual freedom to express the ‘true


self’” (Kastanakis & Voyer, 2014, p. 427). On the other hand, people in collectivist cultures want to

consider the wishes and needs of others and consider how their actions reflect on the group.

Wolny and Mueller (2013) researched motivations for engaging in electronic word-of-mouth

(eWOM), including the posting of reviews, and linked these motivations to the decision to post. They

investigated the motives for posting online comments and engaging in other forms of electronic eWOM

communication (liking, sharing, tweeting etc.) about fashion brands on social networking sites Facebook

and Twitter. First, the research by Wolny and Mueller applied Dicther’s ideas on motivations for face-to-

face word-of-mouth using some ideas from other papers that applied Dichter’s concepts to electronic

word-of-mouth. Wolny and Mueller found that fashion involvement (the long-term interest in fashion)

and brand involvement (long-term interest in a brand or company) – both consumer traits – were the

primary reasons why consumers engaged in eWOM communication about fashion. Product involvement

(the shorter-term interest in a product, specifically the negative or positive experience with a product) as

well as self involvement (interest in attention and recognition from posts) and the need for social

interaction also explained the decision to engage in fashion-related eWOM communication. The

researchers found that concern for others and advice seeking were not important factors, which conflicted

with findings by other researchers. Second, from the Theory of Reasoned Action, Wolny and Mueller

considered the effect of attitude (consideration of consequences) and subjective norm (desire to conform

to the views of important people in life) on motivation. Attitude and subjective norm are not really

motivating factors but involve the application of reason (weighing of options etc.) as one would do when

making a decision. The researchers found that attitude and subjective norm “influenced the link between

consumer traits and eWOM engagement” (Wolny & Mueller, 2013, p. 575).

How easy or difficult it is for consumers to create and/or write an online review is also expected

to affect the decision to post a review. The process to create a review on a website could be time

consuming or complicated. For example, for new users the process may involve setting up a new account

on a website. It could also be difficult to craft a message on the experience with a product or service.

When there are existing posts, it may be easier to agree with other reviews (reference other reviews) or


repeat the information in other reviews. However, an online review that duplicates or plagiarizes another

review is considered a suspicious or fraudulent review. In their research into fraudulent reviews, Hu,

Bose, Kou, and Liu found and reported an example of a review that plagiarized the contents of another

review. Research has shown that prior/existing reviews influence customers, i.e. customers form

expectations of products and services from prior reviews which then affect reviews they create after

consumption of the product or service (Engler, Winter, & Schulz, 2015; Ma, Khansa, Deng, & Kim,

2013). It is unclear whether the influence of the prior reviews makes writing of new reviews easier.

Research by Ma, Khansa, Deng, and Kim (2013) that looked at how consumer characteristics as well as

characteristics of online reviews cause the consumer to be influenced by prior reviews suggests that even

if there are prior reviews to assist with the writing of a new review, some consumers choose not to rely on

them, but contribute independent reviews. Using the Elaboration Likelihood Model, Ma, Khansa, Deng,

and Kim hypothesized that female consumers, consumers with more experience with the product,

geographically mobile consumers, and consumers with more friends (social connectedness) would be less

influenced by prior reviews, less “pressured” by prior reviews, or “rely less on prior reviews” (Ma,

Khansa, Deng, & Kim, 2013, p. 300). Also, users who write longer reviews with more information about

how and where the product was used as well as more active users with short time intervals between

reviews would be less influenced by prior reviews. The findings supported all the hypotheses. Less

reliance on prior reviews suggests that the consumers are comfortable with writing about their

experiences, maybe because of their motivation. Ma, Khansa, Deng, and Kim noted that the writing of

independent reviews by geographically mobile consumers seemed like a contradiction since these

consumers are busy and it would be easier to check what others have written so that the review could be

completed quickly. Ma, Khansa, Deng, and Kim offered the explanation that these consumers likely

appreciate reviews since they rely on them so make an effort to contribute their experiences.

Some online reviews include complaints by dissatisfied customers. Happertz (2014) applied the

equity theory (effort model) to consumers’ decision to complain, stating that consumers will complain if

the effort or cost to complain is less than the benefit. Posting online does not require much effort and


there is a low probability of a successful resolution. But, the posts will discourage others from supporting

the business.

Gregoire, Salle, and Tripp (2015) analyzed complaints by customers and came up with six types

of social media complaints, which shed some light on the reasons for some complaints. The six types of

social media complaints are the ways consumers went about their complaining when they experienced a

problem with a company’s product and service: (1) directly contacting the company online, (2) sharing

positive thoughts about service recovery or company’s handling of an issue, (3) badmouthing the

company’s product or service without contacting the company, (4) seek help from a third party such as a

consumer agency when they have difficulty resolving the situation themselves, (5) spreading negative

publicity to get revenge, and (6) posts that help a competitor to take advantage of a difficult company

situation. When there was a problem that the company was not handling to the customer’s satisfaction,

the customer seemed to want to affect the company in a negative way (want consequences for the

company because of their behavior).

Some consumers complain because they hope to be compensated by the business (Baker,

Magnini, & Perdue, 2012). Baker, Magnini, & Perdue determined that a customer’s attitude towards

complaining, their assertiveness or confidence, as well as their oppositional cultural behavior influenced

opportunistic complaining. Customers are also more likely to submit opportunistic complaints if they

believe they were mistreated or not treated fairly, i.e. they “experienced lower levels of distributive,

procedural and interactional justice” (Baker et al., 2012, p. 297). Kugler (2014) mentioned another way

customers may seek to obtain compensation or special benefits from a business: threaten to post a

negative review unless a business provides special treatment, a form of extortion. Thus, the motivation

and reason for posting may be to receive compensation in cash or in kind. However, whether or not

consumers decide to post some reviews will depend, in part, on the expected consequences. If there is a

large risk of loss because the company will sue, the consumer is not likely to post a review.

In summary, whether or not a consumer decides to post a review may depend on: (1) the

experience with the product or service, (2) consequences of posting, (3) whether the consumer believes he


or she can add something to reviews already available because information is missing or what is posted

does not reflect the truth, (4) how the business handles an offline complaint when something goes wrong.

Some consumers will prefer anonymity if they have negative information to share. Some may have

photos, videos or other evidence to support their post.

Decisions by Businesses

Use of online reviews (for marketing, advertising etc.). Businesses seem to view online

product reviews as one form of advertising. Research by Hu et al. (2011) found that vendors, publishers

and authors advertise new products (books) using online reviews. The researchers found that average

rating from reviews of products by consumers decreased over time not only because of self-selection

(differences between early and late reviewers of the products), but also because of manipulation by

vendors, publishers or authors. Early reviewers included vendors, publishers and authors that gave their

products artificially high ratings to boost sales. Over time, there was less manipulation. Among other

findings, the researchers also found that vendors that sell lower quality products and receive low average

consumer rating are more likely to manipulate online reviews. The paper by Gerhards (2015) included a

similar finding, stating that statistics show that “businesses with fewer reviews, bad reviews, or changing

patterns of competition” are more likely to manipulate reviews. In addition to creating fraudulent reviews

to advertise new products, businesses are also motivated to create fake reviews to counteract negative

reviews online which could hurt their reputation. Or a business may either create fake reviews or buy

positive reviews in an attempt to influence customers, including customers of their competitors (Hunt,

2015). Hunt (2015) cited example cases. Samsung and Belkin manipulated online reviews. Fox News told

employees to post fake reviews to promote and defend the company online.

There are consequences to these decisions that companies must consider. Laws such as the

Federal Trade Commission Act apply. Hunt (2015) spoke of a case in the U.S. where 19 businesses used

the services of overseas reputation enhancement companies to post fake reviews. The Federal Trade

Commission (FTC) investigated and the defendants were found guilty of misleading and deceptive

conduct. Another example is the persecution of Samsung for manipulation of online reviews. There may


be other consequences to posting fake reviews to sites such as Yelp which allows companies to pay for

separate advertising on the website.

How to handle positive and negative online reviews of products. Reviews may be very

negative and/or may have complaints about a product or service that are available for all to see. Reviews

may question the product quality or exaggerate a problem not likely to occur again. Research by Levy,

Duan, and Boo (2013) looked at the types of complaints included in reviews of hotels in Washington, DC

from ten websites. They found that customers complain of problems in just about all aspects of hotel

service in reviews. Companies have to decide how and when to handle or respond to posts and reviews

online.

There are a lot of things to consider when deciding whether to respond and how. For a business,

too little handling/responses to complaints may cost the company customers while too much responding

could be a waste of resources (Liang, 2013). Businesses need to consider whether a complaint is

opportunistic and how the response or reaction will affect future opportunistic complaining, reactions of

customers, as well as their operations in areas such as financial status and culture (Baker et al., 2012).

When deciding whether to respond to an individual review, companies may look at the identity of the

reviewer, expertise of the reviewer, the reason behind the post, how believable the post is, and its

potential influence on customers. Businesses are more likely to take action if a review would negatively

impact its reputation, sales and/or profits. However, the effect of a negative review on sales is not always

as expected. One would expect a negative review to negatively impact or harm sales, but Berger,

Sorensen, and Rasmussen (2010) found in their research that negative publicity (which may come from

negative reviews) may increase awareness of products that are not well known and thus help to increase

sales. They found that negative publicity is likely to harm sales when the product is well known because

of the influence of the negative information on customers.

There are the parts of an online review to consider and their effect on the consumer. Hu et al.

(2014) looked at how different parts of a review on Amazon.com – such as title, numeric rating, text

comment/body – would impact sales. The researchers found that the sentiment in text comments have a


direct impact on sales rank (used as a proxy for demand) while the numeric rating does not directly

impact sales but affects it through the sentiment in text comments. Sentiments in the body of the review

have a larger impact on sales than sentiment in the title of the review. Surprisingly, moderate sentiments

have a stronger effect on sales than strong sentiments. The researchers also found that the most recent and

most helpful reviews have a more significant impact on sales than the average review because customers

are more likely to use them.

A company may seek identification of reviewers or the removal of posts (Gerhards, 2015). Or a

company may respond to a negative review with a positive customer review. Research by Ullrich and

Brunner (2015) found that, for particular brands, responding to a negative review with a positive customer

review was more effective than a positive brand response. Another strategy is to take legal action against

reviewers/commenters. Gerhards (2015) noted that lawsuits against customers can backfire because of the

negative publicity from them.

The expected or “normal” way to respond to a customer review is to post a response to the

review. Websites such as TripAdvisor have a section for a response to a review. On platforms such as

Twitter, responses have a character limit but attachments/pictures may be used.

Research by Levy et al. (2013) provided information on what is usually included in a response.

The research found that hotels in Washington, DC responded to over two-thirds of very negative one-star

reviews posted on 13 websites and most responses included apologies, appreciation, correction, and an

explanation. The researchers noted that their findings conflicted with previous research that found

responses were generic, negative and not timely.

However, research by Zhang and Vásquez (2014) found that responses posted on TripAdvisor are

generic to some extent. These researchers used a discourse perspective to determine the elements or

moves included in negative one-star and two-star responses. They found that responses have common

moves (or a common structure) that are similar to moves in business correspondence. The typical

sequence of ten moves discovered in the research is: [Openingpleasantries] followed by [Gratitude]

followed by [ApologizeforsourcesofTrouble] followed by one or more of [Proof ofAction],


[AcknowledgeComplaints/Feedback], [RefertoCustomerReviews], and

[AvoidanceofReoccurringProblems], then followed by [InvitationforaSecondVisit], then followed by

[Solicitresponse], then followed by [Closingpleasantries]. Responses provided by the majority of Chinese

hotels in the study (50/80) referred back to the customer complaint either by briefly mentioning the

problem in the response or providing an explanation for the problem. Hotels, however, did not have much

of a conversational human voice when responding to negative reviews. The majority of responses used

pronouns such as ‘we’ and ‘us’ and, if signed, identified the author by a position title or department name.

Research by Park and Allen (2013) found that companies have different approaches and strategies

to respond to online reviews. In their research that looked at responses by hotels, they found that some

hotels regularly responded to both positive and negative reviews while others irregularly responded to

only negative reviews and others responded to no reviews. From specific cases looked at, the researchers

found that frequent responders believed that online reviews are accurate and authentic and customers pay

attention to them, and were more collaborative in the development of responses. One of the frequent

responders utilized a strategic approach which meant the hotel sought to have a relationship with

customers and also used feedback to improve operations. This was above and beyond the problem-solving

approach used by other hotels – an approach that used responses to online reviews as a mechanism to

solve a reported problem quickly and efficiently. Park and Allen proposed that hotels decide on an

approach to using and responding reviews – whether problem-solving or strategic.

vanNoort and Willemsen (2012) thought the approach or strategy was important and considered

reactive or proactive strategies in their research. Some of their findings include: (1) consumers viewed

brands that reacted to negative posts (i.e. posted a response when the customer requested one) favorably

regardless of whether the response was posted on a consumer-generated platform or brand-generated

platform, (2) consumers viewed brands that were proactive in responses to negative posts (i.e. posted a

response when the customer did not request a response) favorably when the response was posted on a

brand-generated platform, (3) consumers thought a response post had a more conversational human voice

when it was reactionary regardless of whether the response was posted on a consumer-generated platform


or brand-generated platform, (4) consumers thought a proactive response post had a more conversational

human voice when it was posted on a brand-generated platform, and (5) consumers prefer companies to

respond to negative electronic word of mouth rather than remain silent.

Gerhards (2015) noted that the best course of action may be to prevent disputes with customers.

Instead of focusing on the best way to respond, the company should take measures to prevent customers

from posting negative reviews in the first place. This includes “proactively addressing customer concerns

offline through private email and telephone calls” (Gerhards, 2015).

Quality of products to produce & what to offer online. Online product reviews provide

feedback about a company’s products/services as well as the products and services of competitors. The

feedback influence the design and quality of the products manufactured (or services provided) and even

where to sell certain products.

With analysis, companies can glean lots of information from online reviews. Differences in tastes

and preferences, including sensory perception (visual and auditory perceptions) could be a reason for

differences in sentiment expressed for some products in different countries. Kastanakis and Voyer (2014)

found that several cultures believe their “cultural” smell is better and comprehend their own culturally

familiar melodies better than the melodies of other cultures. Some products could have strong negative

opinions/sentiment in one country and strong positive sentiment in another. Tastes and preferences in

different countries/cultures could lead to lots of reviews for some products (because the products are very

popular) and little reviews for others (because they are not well liked or popular). In this case, the absence

of reviews would be a signal for companies that a product is not well liked.

Chen, Chen, Hu, and Li (2015) looked at which products (high or low quality) manufacturers

would choose to offer online. They found that the firms’ decision on whether to offer high or low quality

products in the online market (rather than in the physical market) depends on the marginal cost of

production. If the marginal cost decreases as quality improves, firms will pool high quality products with

low quality products in the online market, which means that the overall/average quality of products


available online will be higher than in the physical market.

Decisions by Review Websites

There have been lots of complaints against Yelp and other websites accusing them of engaging in

fraudulent behavior to maximize profits. During 2008 and 2014, the FTC received 2,046 complaints

against Yelp (Gerhards, 2015). Gerhards (2015) discussed lawsuits against Yelp. In one example Levitt v.

Yelp, a group of companies claimed that Yelp manipulated reviews and wrote negative reviews to get

them to purchase advertising on its website. There is still the question of whether Yelp and other websites

are choosing to create fraudulent reviews and attempt to extort money from manufacturers. It seems like

the websites have the opportunity to commit fraud – they own the websites and the content posted by

consumers. The business model used by websites may shed some light on whether the websites have the

motivation or inclination to commit fraud. Some websites such as Angie’s List are membership-based.

Consumers pay for access to the website. Yelp is not membership-based. The website’s revenues are from

advertising by businesses on its website – an advertising business model (Osathanunkul, 2015, p. 41). Just

this year, Yelp recorded a profit for the first time ever that was mostly due to an income tax allowance

(Mac, 2015). Yelp has also had problems with businesses that create fake reviews. Gerhards (2015)

mentioned lawsuits initiated by Yelp against reputation management businesses that promise clients to

display only positive reviews on Yelp by getting rid of negative reviews. The business model of Yelp and

review websites is an area that needs further research.

Systems That Assist With Decision-Making

Systems to monitor online reviews. As indicated above, companies need to make decisions on

whether to respond to online reviews and how. This requires monitoring online reviews to monitor the

reputation of products and services and the company. There are systems to assist companies with the

monitoring of online reputation, which involves keeping up with posts and reviews on websites such as

Yelp and TripAdvisor, posts on blogs, and posts on social media sites such as Twitter and Facebook – a

tedious and time consuming task if done manually. In the hospitality industry, for example, there are

commercially available reputation management systems such as Revinate and Brand Karma to help hotels


track and respond to online reviews and aggregate reviews, among other things (Levy, Duan, and Boo,

2013, p. 51). In their examination of hotel cases, Park and Allen (2013) found that one hotel used an

online monitoring tool/service that provided a ranking score that indicated how the hotel compared to

others in brand engagement.

There has been recent research on systems to collect information from the internet to provide

management with information needed for reputation management. Bucur (2014) proposed a software

system that would collect content from the web pages using a spider, determine the customer opinion or

sentiment (whether positive, negative or neutral opinion) from the extracted comments as well as the

polarity of opinions, and then make the information available for company decision making. Bucur’s

proposed architecture included the publicly available SentiWordNet 3.0 (http://sentiwordnet.isti.cnr.it/) as

a component that would extract the sentiment applying sentiment analysis at both the document level and

sentence level. In their research, Costa et al. (2012) developed a program (web mining software) to mine

blog posts and extract the sentiment. The research team used available technologies such as Lucene

(reusable code developed by the Apache organization that performs searches and other functions) and

Weka.Portmann, Meier, Cudré-Mauroux, and Pedrycz (2015) developed an interesting Fuzzy Online

Reputation Analysis (FORA) framework (based on fuzzy set theory) for companies to search for posts

and comments online and then organize collected tags into a concept model that provides information

about reputation.

Sentiment summary/extraction. The systems proposed in the above research included the

extraction/determination of sentiment from opinion data. There has been recent research focusing on the

extraction or summarization of the sentiment from online comments. Bai (2011) improved upon other

sentiment extraction approaches by using a two-stage Markov Blanket Classifier that considers word

dependencies when determining the sentiment from a document. This method involved finding the

parsimonious words in a document that would indicate the sentiment and together with the dependencies

between sets of words selected, determine the sentiment of a new document. Bai compared her method

with the feature selection algorithms in other research and found that her results were better than most of

http://sentiwordnet.isti.cnr.it/


the other results. Bafna and Toshniwal (2013) developed a method for feature-based summarization of

sentiment from a large set of product reviews downloaded from the internet. The researchers used

sentence-level extraction (rather than document-level) to obtain the sentiment about specific product

features or attributes from product reviews. The paper mentioned that the system was for consumers’ use

in making purchasing decisions, but it is also useful for companies.

Xu, Liao, Li, and Song (2011) looked at extracting sentiment from statements comparing a

company’s products to competitor’s products. The method used was a two-level Conditional Random

Fields (CRF) with unfixed dependencies, an improvement over other methods.

Authenticity/Recommender systems. Given the motivations of consumers, manufacturers as

well as review websites, authenticity of online reviews is a real concern. Bing Liu, author of Sentiment

Analysis and Opinion Mining, said in 2012 that about a third of all online reviews are fake (Kugler, 2014,

p. 20). Fake reviews – reviews that are not from real customers, intended to influence behavior – may be

created by individuals, business employees or consultants, promoters/marketing professionals and even

automated bots (Hunt, 2015). Consumers do not want to be misled by fake reviews when making

purchasing decisions. Businesses do not want fake reviews to publicize untruths about their products to

drive customers away.

It is difficult to readily identify fraudulent reviews. Anonymous reviews and reviews created by

users with fake names or accounts are questionable but they may be valid reviews created by persons who

wish to remain anonymous. Consumers tend to believe that they can determine which reviews are

fraudulent but research shows this is not the case (Hunt, 2015, p. 6; Hu, Liu, Sambamurthy, 2011, p. 624).

Websites such as Amazon.com use software to recommend specific reviews to users. Amazon

provides the most helpful reviews based on users’ indication of reviews that are helpful (Hu, Koh &

Reddy, 2014). The most helpful reviews are more likely to be authentic reviews because several users

found the information provided to be credible and useful. Amazon also provides the most recent reviews.

Kugler (2014) and Hunt (2015) provided information on recommendation software used by Yelp

to automatically detect fraudulent or questionable reviews using algorithms. The Yelp system filter out


fraudulent accounts based on signs such as the same IP address used for several reviews and bias in

reviews. The remaining reviews – reviews that are believed to be authentic – are recommended to

customers. The Yelp system seems to be a new direction for recommender systems. In their survey of

recommender systems, Lu, Wu, Mao, Wang, and Zhang (2015) looked at techniques used by

recommender systems for their recommendations. Based on the techniques, the systems are more likely to

display reviews that are authentic and recommend products and services that are a good fit for the user.

But, there was no mention of filtering based on an assessment of the authenticity of reviews.

Hunt (2015) mentioned that there are commercially available algorithms for detecting and

identifying fraudulent reviews along with other non-commercial solutions. Cornell University has a non-

commercial solution (available at http://aclweb.org.anthology/P/P11/P11-1032.pdf) for detecting fake

reviews.

Theories

Table 3 below shows the actual theories utilized or referenced in articles included in this paper.

Some areas, such as the decision to post by the consumer, were full of theories used to explain behavior.

Others did not have theories. A few theories were used to explain the behaviors in different areas. For

example, the Theory of Reasoned Action was used to explain consumers’ product evaluations and

willingness to purchase a product from the quality of an online review and also used to link the

motivation for posting an online review to the decision to post.

The Theory of Reasoned Action, the Equity Theory, and Exit-Voice/Game Theory seem more

like traditional decision theories that involve the evaluation of alternatives, the weighing of options,

and/or the consideration of consequences. Theories such as the Language Expectancy Theory involve

reaching a conclusion based on observations. Other theories such as the Hofstede theory on culture do not

seem like they are related to decision-making, but the research shows that it is important for

understanding human behavior that drives decision-making.

http://aclweb.org.anthology/P/P11/P11-1032.pdf


Table 3

Actual Theories from Papers with Theoretical Frameworks

Theory Paper That Included The Theory

How Theory Used Variables

Theory of Reasoned Action

Lee & Shin (2014) Used to explain product evaluations and willingness to purchase a product from the quality of an online review

Review quality (relevancy, comprehensiveness, and accuracy), product quality, evaluation of poster and website, type of product.

Zhang, Cheung, & Lee (2014)

Used Komiak and Benbasat’s trust-based acceptance model that is built upon the Theory of Reasoned Action to show that customers’ cognitive trust (based on judgment of a business) impacted emotional trust and purchase intention.

Cognitive trust (competence, benevolence and integrity), emotional trust, gender.

Wolny and Mueller (2013)

Effect on motivation Attitude (consideration of consequences), subjective norm (desire to conform to views of important people)

Heuristic-Systematic Model

Zhang, Zhao, Cheung, and Lee (2014)

Used to explain judgments of review credibility for decision-making

Argument quality, source credibility, perceived quantity of reviews

Zhang, Cheung, & Lee (2014)

Used to determine how systematic processing will occur when reviewing inconsistent information in online reviews and how it will affect trust and purchase intention.

Cognitive trust (competence, benevolence and integrity), emotional trust, gender.

Elaboration Likelihood Model

Cheung, Sia, and Kuan (2012)

Used to explain which reviews consumers find credible.

Argument quality, credibility of source, consistency, two-sidedness.

Ma, Khansa, Deng, & Kim (2013)

Used to determine how prior/existing reviews will influence new/subsequent reviews

Gender (female or male), product experience level, geographic mobility, social connectedness, longer reviews, time interval between reviews.

Language Expectancy Theory

Jensen, Averbeck, Zhang, and Wright

Used to explain how consumers determine the

Lexically complex language, two-sidedness,


(2013) credibility of anonymous reviewers.

affect.

Hofstede Theory Koh, Hu, & Clemons (2010)

Used to explain behavior differences due to culture.

Motivation to post a review.

Theory of Kardes Sparks, So, & Bradley (2016)

Used to explain customers use some information as a cue to what is actually occurring and to draw conclusions about a business.

Existence of responses to online reviews, timeliness/speed of responses, human voice in responses.

Equity Theory Huppertz (2014) Used to explain the decision to complain.

Effort and benefit.

Fuzzy Set Theory Portmann, Meier, Cudré-Mauroux, & Pedrycz (2015)

Used to capture the vagueness or imprecision in human language from the internet.

Fuzzy Online Reputation Analysis (FORA) framework.

Yeap, Ignatius, & Ramayah (2014)

Applied fuzzy set theory to Analytic Hierarchy Process (AHP) to express uncertain comparison ratios used for decision making as fuzzy sets or fuzzy numbers. Applied to the selection of a website/platform.

Website preferences – source credibility (source trustworthiness and source expertise) and information quality (relevance, usefulness, comprehensiveness, accuracy, timeliness).

Exit-Voice theory/Game theory

Liang (2013) Used to model customer complaint management.

Multi-agent communication model.

Conclusion and Implications for Future Research

Consumers, businesses and review websites make decisions related to online product reviews.

For consumers, online product reviews are a resource to aid in the decision to purchase a product or

service. Using online product reviews for purchase decision-making involves many small decisions such

as and judging the credibility of reviews and deciding which reviews to review. Consumers also make the

decision to post a review, which is closely related to the motivation for posting. Businesses decide how to

use reviews and may decide to create fraudulent reviews, which has consequences. Businesses need to

decide how to handle reviews, especially those that have the potential to harm their reputation. This

decision involves strategy. And, businesses may use feedback from online product reviews to decide on


the quality of products to produce and what to offer online. Review websites manage and control the

website as well as the database/set of reviews so deciding how the website will operate is within their

purview. This decision is based on the selected business model. Several theories were used to explain

decision-making mostly by consumers. These theories vary in their focus and approach.

There are many systems that support decision-making by consumers and businesses in important

ways, some are commercially available. These systems could be improved to better support consumers

and businesses. For example, instead of providing just the most helpful and most recent reviews for the

benefit of consumers seeking reviews to assist with the purchase decision, the recommender system could

also provide the top 5 positive reviews and top 5 negative reviews.

One limitation of this research is the focus on specific areas in the online review domain that

affected the selection of articles. For example, there are lots of articles available on the impact of online

reviews on sales, but those articles were not selected. Future research could include a wider selection of

articles. Another limitation of this research is the focus on recent articles which meant that the literature

review was not historical.

The paper identified several issues and areas that need additional research such as the business

models used by review websites and other businesses in the online review domain. In addition, the mix of

theories used to explain decision making in the domain could be used to develop a general theory similar

to the paper by Aliev, Pedrycz, Kreinovich, and Huseynov (2016).


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MooreFBTM7300-11_Final 11-26-2015

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