Literature Review

Online reviews and ratings

 

The impact on individual decision-making and product demand

  

 This literature review begins with explaining bounded awareness theory that clarifies group decision-making. Furthermore, researcher describes study boundaries and defines online peer recommendations. The chapter also covers specific theories related to online reviews impact on individual decision-making process and how it assist businesses in reaching specific goals.

 

2.1. Bounded Awareness in Groups

 

Online reviews and ratings are related to behavioural decision making theories. Academics have concluded that information that is discussed in groups influence final decision-making (Bazerman & Moore, 2013: p. 70). Material, that eventually becomes part of a discussion, is bounded by awareness of the group (Bazerman & Moore, 2013: p. 70). Individual benefits because of access to large information pool (Mannix & Neale, 2005) but may be disadvantaged because of unshared information (that does not confirm group norms). This was proved by a number of experiments. For example: Strasser and Titus (1985) found individuals preferred a certain choice when influenced by group in comparison to situation when they chose individually (83% versus 67% respectively).

 

2.2. Boundaries and Definition

 

Enabled by recent technologies such as instant messaging tools, traditional word of mouth (exchange of information about product or service) transformed into electronic word of mouth (Godes et. al., 2005: p. 416). Nowadays, individuals anonymously exchange information that operates in one-to-many nature (compared to one-to-one in traditional WOM) (Godes et. al., 2005: p. 422). Generally, researchers define social interactions as “actions that are (1) taken by individuals not actively engaged in selling the product and (2) has an impact on others” (Godes et. al., 2005: p. 422). Interestingly, this form of WOM is seen more credible than advertising (Allsop et. al., 2004) and has become most influential communication channel (Keller, 2007). As the Internet consumption penetrates everyday life and individuals enter digital age, this becomes a key influencer in decision-making process (Windsor, 2012: p. 24).

 

Specifically, consumer reviews help to choose products and services that best suit unique usage needs (Chen & Xie, 2008).

 

Over the past decade, the phenomenon resulted in various types of marketing communications (Lee et. al., 2008: p. 341). Mangold & Smith (2012: p. 142) identifies several venues such social media (e.g. Facebook, Twitter and various blogs); companies (e.g. Amazon); sites specifically dedicated to customer ratings and reviews (e.g. Epinions, TripAdvisor, Yelp and Zagat) and search engine sites (e.g. Google Maps, Bing Local and Yahoo! Local).

 

In essence, product reviews are everywhere and easily obtained. Customers can get access to many opinions before making any purchase (Mangold & Smith, 2012: p. 146). According to Yugo & Jinhong (2008) online reviews are type of product information created by individuals based on personal usage experiences. It is “a platform for online retailers to collect, distribute and search feedback and comments about participants’ past behaviour” (Resnick et. al. 2000).

 

Product reviews represent an emerging market phenomenon that is becoming vital in consumer purchase decisions (Mangold & Smith, 2012: p. 142). For example: survey of 1,200 people showed 62% value consumer reviews and 70% of shoppers rate friend’s recommendations as important (Consumer Purchasing Report in June, 2011).

 

Online review systems are designed in a number of different ways. For example: Amazon has a 5-star system, a feedback table for each seller and categories in positive, neutral and negative reviews (Chou et. al., 2013: p. 134). User-friendly design such as pleasant display and ease of navigation serves are important aspect in reviews systems’ design (Mudambi and Schuff, 2010).

 

There are a number of intrinsic and extrinsic motivations why consumers consume, contribute and create brand-related content such as online reviews. First, individuals consume eWOM because of information search, entertainment or remuneration (Muntinga et. al., 2011: p. 27). Second, consumers contribute (e.g. share reviews) because of personal identity expression, social interaction and entertainment (Muntinga et. al., 2011: p. 29). Third, they are motivated to create (e.g. write reviews), because of personal identity, social integration, entertainment and empowerment (Muntinga et. al., 2011: p. 33). Similarly, researchers identify motivations such as personal gratification (Dichter, 1966) and altruistic behaviour (Henning-Thurau et al., 2004; Sundaram et al., 1998).

 

However, one needs to be aware of online review limitations. Critiques disagree on its causality effects. Zhu and Zhang (2010: p. 133) state online reviews may represent consumers’ preferences (predicting product sales) but have little effect on purchase decisions. Eliashberg & Shugan (1997) claim online reviews serve as predictors rather than influencers of product sales.

 

Additionally, Zhu and Zhang (2010: p. 133) suggest interested parties can easily manipulate online forums. As companies can anonymously post reviews to praise products and increase awareness (Mayzlin, 2006), potential buyers may heavily discount reviews and ratings (Zhu and Zhang 2010: p. 133).

 

2.3. Online Reviews Impact on Individual Decision Making Process

 

Extended problem solving and habitual decision making depends on the importance of what consumer buys and how much effort one is willing to put into the decision. (Solomon, 2009: p. 149). Many choices fall in the middle. Two factors dictate the type of decision-making, namely involvement and perceived risk. (Solomon, 2009: p. 149). Level of involvement refers to perceived consequences of the purchase. For instance: buying soap involves habitual decision-making. Perceived risk may be present if the product is expensive or hard to understand, for example: buying a new phone includes extended problem solving. There are five types of risks such as time, financial, social, psychological and physical risk (Solomon, 2009: p. 149). Therefore, habitual decision making involves low involvement products and holds low level risk, and extended problem solving concerns high involvement products and includes high level of risk.

 

Online review systems help consumers to make better purchase decisions (Chou et. al., 2013: p. 134). They assist in learning about product specifications and negative aspects of a product or service (e.g. Goldsmith & Horowitz, 2006; Schindler & Bickart, 2005). Alternatively, online reviews may serve as a form of heuristics that benefit consumers in making quicker but as good decisions (e.g. Duan et al., 2008; Park et al., 2007).

 

Previous researcher applied the elaboration likelihood model (Petty et al. (1983) to understand the influence of online reviews. Alternatively, heuristic-systematic model may also be applied in the aims of understanding the impacts of online reviews (Zhang et al., 2014: p. 79). More recent studies (e.g. Luca, 2016) applied Bayesian hypothesis and Heuristics model. These are, essentially, similar theories that explain ways in which consumer process information.

 

2.3.1. Extended Problem Solving

 

Systematic information processing indicates people consider all relevant information before forming a judgement in decision-making (Todorov et al. 2002: p. 196). This implies participants pay significant amount of mental effort when evaluating the argument assessing their validity (Chaiken, 1980). A number of researchers found that consumers are affected by the quality of online reviews. For instance: Park et al. (2007) found that consumers are affected by quality of online reviews when processing information through central route. Cheung et al. (2008) investigated central route factors and quality of online reviews using four dimensions: relevance, timeliness, accuracy and comprehensiveness. Additionally, Lee et. al. (2008: p. 343) concluded that reviews with simple recommendations are weaker than attribute-specific comments.

 

Nevertheless, individuals engage in systematic information processing only when they have sufficient motivation, ability and cognitive resources (Chen & Chaiken, 1999). Additionally, product needs to be relevant. Relevance refers to the degree of congruence between information and what consumer requires when evaluating product with online reviews and ratings (Lee et. al., 2008: p. 342). Reliability (trustfulness) is also an important factor when engaging in systematic problem solving. If the receiver is not convinced of credibility, online reviews and ratings may not have an impact on consumer behaviour, in fact, may result in negative consumer perceptions (Lockie, 2015: p. 32).

 

2.3.2.  Habitual Decision Making

 

Heuristic information processing theory suggests consumers may automatically apply simple decision rules or rules of thumb when reaching quick judgements (Chaiken & Ledgerwood, 2012). Scholars studied several dimensions associated with peripheral route to persuasion. Large quantity of reviews (Park et al., 2007), source trustworthiness (Cheung et al. 2008) and average rating (Luca, 2016) were shown to be effective heuristics in consumer decision-making.

 

Generally, there are four types of heuristics. (1) Availability heuristics is related to ability recalling occurrence of event that is readily available in memory (Tversky & Kahneman, 1973). (2) Representativeness heuristics refers to tendency to look for traits that correspond with previously formed stereotypes (Bazerman & Moore, 2013: p. 8). In (3) confirmation heuristics, people usually interpret evidence in a way that supports the conclusions in the outset (Bazerman & Moore, 2013: p. 8). (4) Affect heuristics is defined as “a judgment that follow an emotional evaluation that occurs before any higher-level of reasoning takes place” (Kahneman, 2003). Online reviews may serve as any general type of heuristic.

 

First, online review heuristics could be associated with quantity of reviews provided. Duan et al. (2008) claim consumers are influenced by the number of reviews instead of the nature of reviews themselves. Zhang et al. (2014: p. 82) state that large volume of online reviews is an important heuristics cue and consumer perceptions are shaped by the popularity of products within online review sites. Researchers elaborated that reviews serve as clear numerical indicators, informing that the product is worth buying (Zhang et al., 2014: p. 82). Lee et. al. (2008: p. 343) contributes to this view claiming that high number of people with similar options result in high levels of conformity.

 

Second, perceived source credibility may also serve as a form of heuristic. Chaiken (1980) defined source credibility as consumers’ “overall perceptions regarding the credibility of review sources rather than content of online reviews”. Recent research shows that credibility of results act as an effective decision rule that helps customers to form quick decisions (Zhang et al., 2014: p. 82). Specifically, Luca (2016) discuss the impact of elite reviews in Yelp website. It serves as a source of perceived expertise and trustworthiness (Petty et al. 1983).

 

However, individuals apply heuristics only when they have certain knowledge or learned experience about the cue (availability), when they can remember or activate the cue (accessibility) and when they perceive cue to be relevant or correct with particular judgemental tasks (applicability) (Zhang et al., 2014: p. 80). 

 

2.4. Online Reviews Impact on Product Demand (End Goals)

 

Online reviews may also be seen as means to reach business goals. Essentially, peer recommendation is a form of online retail merchandising (Chaffey and Ellis-Chadwick, 2012: p. 410). Presenting relevant information helps to boost key measures of site performance such as conversion rates and average order value (Chaffey and Ellis-Chadwick, 2012: p. 410).

 

Leading third party review providers such as Bazaarvoice claim eWOM serve as effective merchandising tools increasing online conversion rates (Barton, 2006: p. 47). Specifically, Bazaarvoice found that with eWOM customers converted at higher than average rates and spent considerably more per online order than non-recipients (Barton, 2006: p. 47). For instance, CompUSA achieved 60% higher conversion rate and 50% higher order value (Barton, 2006: p. 48).

 

Interestingly, different kinds of online reviews have specific effects on sales. An improvement in positive eWOM is associated with increase in sales (e.g. Chavallier & Mayzlin, 2006). In contrast, negative reviews lead consumer to avoid a potential purchase (e.g. Mangold & Smith, 2012: p. 144).

 

2.4.1. Positive and Negative Recommendations

 

A number of studies show positive online reviews have advantageous effects on sales. For example: Mangold & Smith (2012: p. 144) found that positive reviews have the potential to reduce consumer uncertainty, converting them from a ‘non-purchase’ to ‘purchase’. Livingston (2002) claims that positive feedback increases probability of sale. Precisely, sellers with 675 positive comments earned 10% more than of initial price (Livingston, 2002). Furthermore, a social commerce firm Reevoo states positive online reviews and ratings uplift sales by 18%. (Consumer Purchasing Report in June, 2011). Company also identified the impact of reviews quantity. According to the report, “50 or more reviews per product could lead to a 4.6% increase in conversion rates” (Consumer Purchasing Report in June, 2011).

 

In contrast, previous research shows how online reviews may disadvantage product sales. For example: Mangold & Smith (2012: p. 144) claim “negative reviews may lead consumer avoiding a potential purchase”. Furthermore, Lucking-Reley et. al. (2000) state negative feedback reduces willingness to pay. Specifically, moving from 2 to 3 stars results in an 11% decrease in price (Lucking-Reley et. al., 2000).

 

2.4.2.  Situational Factors

 

Reviews and ratings may have different effects in different product types and categories. Duan et al. (2008) looked at reviews and ratings in box office sales and concluded they have little effect on product purchases. Furthermore, Vermeulen and Seegers (2009) investigated eWOM effects in hotel industry identifying lower reviews effects on well know hotels. Luca (2016) investigated the impact on independent and chain restaurants and summarised reviews have higher effects on independent restaurants.

 

Moreover, previous studies investigated price effects. Chou et. al. (2013: p. 147) found that consumers buying behaviour is affected when the price is high. In contrast, “when the price was low, online review configuration did not have an impact on the purchase decisions” (Chou et. al., 2013: p. 147). Researchers concluded that low prices are associated with low risk and thus it was easier to make a decision without additional product attributes (such as eWOM) (Chou et. al., 2013: p. 147).

 

The impact of online reviews and ratings also varies with product life cycles. Consumers are more likely to depend on reviews and ratings in the early stage of product life cycle (Mangold & Smith, 2012: p. 144). During the later stages, participants glean information from other sources (Hu et al., 2008).

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