Impact of eWOM on Consumer Attitude

Attitude is defined as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favour or disfavour” (Eagly and Chaiken 2007, p 582). Studies have found that eWOM has an impact on attitude towards product (Chih et  al. 2013; Huang and Korfiatis 2015; Ladhari and Michaud 2015; Park 2008), brand (Lee et al. 2009; Sandes and Urdan 2013; Wu and Wang 2011), and website (Lee et al. 2009; Chih et al. 2013), which in turn can affect intention to

Volume Type of eWOM Format of

eWOM

Information overload

Satisfaction Confidence in

decision- making Confusion Information

usefulness Purchase intention

Trust Type of review

system ( mobile or web-based) Involvement

Fig. 6.2 Impact of eWOM on information overload and its effects (Sources: Furner and Zinko (2016), Luo et al. (2013), Park et al. (2006), Park and Lee (2008))

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purchase (Cheung and Thadani 2012). For example, using software programs, Huang and Korfiatis (2015) identified that online reviews during a product trial influence formation of product attitude.

It has been found by previous studies that consumers’ attitudes can be influenced by different factors. Figure 6.3 summarizes eWOM factors from previous studies found to influence consumer attitude.

Ladhari and Michaud (2015) found that comments generated on Facebook have an impact on attitudes towards a hotel; positive comments generated positive atti- tudes about the hotel. Park (2008a, b) investigated the effect of cognitive fit on consumers’ attitudes towards products. The results revealed that consumers have more favourable attitudes when there is cognitive fit between the review format and information processing strategy. It concludes that consumers with high involvement have more favourable attitudes towards a product with the matrix review format.

The effect of cognitive fit on attitude towards a product is greater for consumers with high involvement than for the ones with low involvement.

Other studies investigate eWOM impact on attitude towards brand. Wu and Wang (2011) showed that, in the case of eWOM, brand attitude is affected by source

Source credibility

Message format

Quality

Type of message

Volume

Valence

Extrimity

Subjectivity

Involvement Platform type

Attitude Intention to purchase

Fig. 6.3 Impact of eWOM on consumer attitude (Sources: Jeong and Koo (2015), Ladhari and Michaud (2015), Lee et al. (2008, 2009), Park (2008a, b), Park et al. (2006), Sandes and Urdan (2013), Wu and Wang (2011))

6 Impact of eWOM

credibility. Sandes and Urdan (2013), using exploratory and experimental methods, found that exposure to consumers’ comments online can affect brand image. For instance, exposure to negative comments connected to a brand can make the image of the brand look worse, whereas exposure to positive comments about a brand can positively affect perceived brand image. Lee et al. (2009) conducted two studies in order to investigate the impact of valence and extremity of consumer online product reviews on attitude towards brand and website. Results from both experiments showed that extremely positive reviews increase attitude toward brand but even a moderate amount of negativity can vanish this effect. More than that, extremely negative reviews have a stronger impact on attitude towards brand comparing to moderately negative or extremely positive online reviews. The results of this study are in line with negativity and extremity effect, found in the impression formation literature (Fiske 1980; Skowronski and Carlston 1987), which predict that people weigh negative or extreme information more heavily than positive or moderate information (Lee et al. 2009).

While previous researchers studied the impact of eWOM on receivers of infor- mation (Huang and Korfiatis 2015; Ladhari and Michaud 2015; Lee et  al. 2009;

Park 2008a, b), Kim et  al. (2015) investigated the effect of eWOM on senders.

Precisely, they studied the impact of incentivized eWOM on communicator attitude.

Using the “saying is believing” theoretical foundation, the study found that by gen- erating and providing biased recommendations the communicator will believe the biased recommendations. Also, the senders will use these biased recommendations to update their attitude. It was shown that valence and number of opportunities to recommend have an impact on the change in attitude. The findings show that atti- tude of individuals communicating positive (negative) eWOM will become more positive (negative). Also, the more opportunities an individual has to communicate positive (negative) eWOM the more positive (negative) their change in attitude.

Previous studies have considered the moderating role of involvement on con- sumer attitude (Cheung and Thadani 2012; Lee et al. 2008). For instance, Lee et al.

(2008) showed that as involvement increases, the effect of negative eWOM on con- sumer attitude is higher for high-quality eWOM than for low-quality eWOM. Wu and Wang (2011) found that product involvement has a moderating effect on the relationship between positive eWOM messages and brand attitude. Also, when prod- uct involvement is low, even though emotional appeal has a persuasive effect it can- not exceed the effect made by rational appeal. The study conducted by Park et al.

(2006) analyzed the impact of online consumer reviews on information processing which depends on levels of involvement. It found that the number of simple recom- mendations has a positive impact on attitude towards product and purchase intention for low-involvement consumers; however, these recommendations do not change the attitude and intention for high-involvement consumers. For high- involvement con- sumers, the product attitude and purchase intention initially increase then go down gradually with the number of attribute-value reviews, drawing an inverted U shape.

A study conducted by Jeong and Koo (2015) investigated the moderating effect of

online platform on consumer behavior. The findings showed that objective positive and

subjective positive online reviews posted on a customer-generated website will be rated

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higher in terms of attitudes toward the reviewed product than those same reviews

posted on a marketer-generated website. On the other hand, objective negative and

subjective negative online reviews posted on a customer-generated website will be

rated lower than those same reviews posted on a marketer-generated website.