This chapter underlines a critical analysis of current literature material on the topic under study. The section outlines and discusses E-retailer sustainability via electronic word of mouth (eEWM). Additionally, the paper analyses the growth of online reviews services and electronic word of mouth. Analysis of the effects and impacts of online reviews on the e-retailer image, buying intentions and business sales are then presented. Lastly, the researcher examines the consistency of E-retailer and online reviews in providing the same service to every customer.
2.2. E-retailer Sustainability Via Electronic Word Of Mouth
Electronic retailing implies the sales of services and goods over the web (Adjei et al. 2009, p. 634). E-retailing includes business-to-consumer (B2C) and business-to-business (B2B) sales of goods and services through advertising or subscription to web content (Chu and Kim 2011, p. 48 and Chih et al. 2013, p. 660). E-retailing requires business enterprises to transform their traditional business models to websites (Zhang 2015, p. 58). The success of E-tailing requires strong branding (Korfiatis 2012, p. 205). Consequently, internet sites must be easily navigable, engaging and updated to meet consumer’s demands (Mukherjee et al. 2012). Additionally, the company needs to have competitive prices, reliable distribution systems and transparent business practices (Alboqami et al. 2015, p. 339).
One of the strategies used by E-retailers is Electronic WOM (Thadani and Cheung 2012, p. 465). Electronic word-of-mouth (eWOM) is any opposing or affirmative statement made by the actual, prospective or former client which is accessible to many persons via the web (Alboqami et al. 2015, p. 339). Unlike in traditional word-of-mouth in which the message disappears soon after transmission, in eWOM, the message is retained over a period (Zhu and Zhang 2010, p. 134). Also, eWOM wields unprecedented speed of diffusion and scalability in comparison to traditional WOM because it involves a multi-way transmission of information using asynchronous mode (Castañeda et al. 2009, p. 20 & Park et al. 1986, p. 140). Electronic WOM utilises internet reviews, blogs, messages on online groups and social media posts to transmit information(Hennig- Thurau, 2004, p. 38).
In eWOM messages are sent via the internet thus made available to an undetermined number of persons (Adjei et al. 2009, p. 641, Reichelt et al. 2014, p. 70 and Park et al. 2012, p. 253). Thus it is a person-to-person communication occurring over the web or any other electronic form of communication (Venkatesh et al. 2014, p. 100). Moreover, eWOM communication is easily measurable compared to traditional WOM (Bhattacherjee 2001, p. 351 and Mauri and Minazzi 2013, p. 100). In online platforms, word-of-mouth information that is obtainable is far more ample in comparison to information from traditional connections in the offline arena (Kim et al. 2007, p. 494). However, in conventional modes, the sender and the receiver know each other hence increasing the credibility of the information (Huei et al. 2015, p. 15).
The developments of the internet provide a rich ground for the growth and expansion of eWOM (Liu et al. 2012, p. 561). A large number of customers use website tools such as consumer review sites, online discussions boards and forums and weblogs useful modes of marketing (Cao et al. 2011, p. 511). Buyers can post their comments, feelings and reviews of goods and services on the web (Lin et al. 2013, p. 29). Once an e-retailer acquires a customer, the E-retailer usually has information on customer identity which is used to generate potential repeat purchases (Limbu et al. 2012, p. 150). Customizing ability of eWOM increases the acquisition and retention of the consumer hence enhancing business sustainability. However, due to lack of credibility, and the existence of cyber-crime, it is challenging for customers to trust products sold in E-retail platforms (Nelson et al. 2010, p. 25).
2.3. Growth of Online Review Services and Electronic Word of Mouth
Consumer online reviews present new information channel with growing importance and popularity in the distribution of goods and services (Zhang et al. 2010, p. 1336 and (Lada et al. 2009, p. 67). Studies show that 88% of consumers, in 2013, read reviews to gauge the quality of local businesses. In the same year, 39% read online reviews on a regular basis as compared to 12% who did not. In 2012, approximately 90% of online grown-ups utilised search sites to get information on the internet. In 2014, 64% of persons believed that online search is the most credible source of information about organisations and people and 93% of persons make opinion basing on the first ten online searches (Advanced Web Ranking 2014). In 2015, studies found out that online reviews impact 67% of purchasing decision (Charkraborty and Bhat, 2018, p. 57 and Erskine, 2017).
In 2016, approximately 84% of persons trusted personal recommendations as much as online reviews and 74% of clients believed a local business more after viewing positive reviews. Every one-star increase in the Yelp rating resulted in 10% increase in revenue (Yelp 2018). The popularity of online reviews increased drastically in 2017. A study by Erkan and Chris found out that, 5% never used the internet to search for local business, 21% used it two to five times and 17% for between six to ten times. Also, 15% use the internet every month, 11% every week and 16% multiple times a week (see figure 1 below) (Erkan and Chriss 2016, p. 1). Hence, examining consumer reviews have become a fundamental part of selecting a local business (Wan 2014, p. 179).
Figure 1
Number of Times of Using the Internet to Search for Local Businesses in 2016
Note. From ‘Electronic Word of Mouth, 2006’ (Erkan and Chriss 2016, p. 1)
With the increase in online surveys, eWOM has experienced significant growth and popularity over the years. As shown in figure 2 below, the most prominent influence on customer decisions is knowledgeable family friends and colleagues (34%), mainstream media (27%) followed by social media (19%) (Fleishman Hillard Global Intelligence 2017). The rise in internet usage has amplified consumers ability to get impartial opinions on merchandises (Prendergast and Yuan 2010, p. 687).
Figure 2
Most Credible Sources of Campany Information
Note: From ‘Data by Wolny and Mueller 2013’ (Wolny and Mueller 2013’ 2013, p. 562)
2.4. Impact of Online Reviews on Brand Image
The brand identity refers to the perception the customer has on a brand (Venkatesh et al. 2014, p. 12 and Sohn 2014, p. 146). Attitude or perception towards a product provides significant moderation of consumer behaviour (See-To et al. 2014, p. 189). The idea of attitude has been examined in social psychology (Erdem and Louviere 2002, p. 20). Psychological theories agree that attitudes substantially influence the overall responses to a given product either favourably or unfavourably (Rauniar et al. 2014, p. 6).
Consumers assess products and form opinions towards them(Keller, 2003, p. 596). Brand assessment is the analysis of the company identify its influence on consumers purchasing behaviour (Huei et al. 2015, p. 14). Studies show that electronic WOM affects pre-usage attitudes together with delayed and immediate services and goods perceptions (Jalilvand et al. 2012, p. 140). Consumers general attitude towards goods and services is a continuous learning process and is affected by social groups, known influences, personalities, evidence, former consumer behaviour and experiences (Ramkissoon and Uysal 2011, p. 537).
Product reviews before purchases are one of the most vital aspects of communication by eWOM (Ferreira et al. 2012, p. 430). Hence, electronic WOM judgmentally influences clients product reviews and their buying prospects (Silke and Mangold 2011, p. 38). Brand image is vital for companies acquisition decisions, coalitions, product price, success and competitive advantage (Ramkissoon and Uysal 2011, p. 538). An empirical study by Reza Mohammad and Samiei Neda shows that eWOM considerably affects company branding and indirectly results to consumers intent to purchase its products (see figure 3 below) (Reza and Samiei 2012, p. 9) and Torlak et al. 2014, p. 63). Thus, the company’s branding image includes customers evaluations and experiences related to the identity (Prendergast and Yuen 2010, p. 688).
Figure 3
Interaction between eWOM, Purchase Intentions and Brand Image
Note. ‘Factors influencing brand image,’ (Reza and Samiei, 2012, p. 9).
2.5. Effects of Online Reviews on Purchasing Intentions
Purchase intent is one of the critical elements of purchaser cognitive behaviour and can depict how buyers intents to purchase a particular product or brand (Ahuja et al. 2007, p. 156). According to Torlak et al. purchase intents refers to, consumers conscious plan to acquisition a good or service (Torlak et al. 2014, p. 63). Several researchers incorporate purchase intention as a critical indicator of the success of online advertisement (Kim et al. 2007, p. 496, Kim and Kang 2014, p. 22 and Huei et al. 2015, p. 16 and Rivera et al. 2015, p. 250). Several other studies concur that buyers attitudes towards a brand or a product affect purchase intentions (Ahuja et al. 2007, p. 150, and Alboqami et al. 2015, p. 340).
Purchasing is directly proportional to attitudes towards E-retailer products. Behavioural intention gets influenced by beliefs, subjective norms and perceived behavioural controls towards actions (Elwalda et al. 2016, p. 310, Erkan and Chriss 2016, p. 8 and Huei et al. 2015, p. 20). Purchasing intention increases with the ubiquity of favourable reviews and reduces with negative online reviews (Huei et al. 2015, p. 16). Thus the effect of eWOM on purchase prospects may be explicated through brand image. Consumers consider reviews obtained from eWOM and utilise them to form brand image perceptions (Torlak et al. 2014, p. 65).
2.6. Overall Effect of Internet Reviews on Business Sales
In the 21st
century, many researchers have studied the effect of electronic WOM on the bottom line and come up with mixed findings (Erkan and Chriss 2016, p. 8). The numbers of studies analysing the efficiency of eWOM have declined in the recent years suggesting strong comprehension of the phenomenon. Nevertheless, it is unclear if eWOM significantly influences business sales. These varying results hinder the development of dependable insights to help marketers make informed decisions (Rosario 2016, p. 297). On average, electronic word of mouth positively correlates with sales (Lin and Chen 2013, p. 30). However, the effects differ across product, platform and metric factors. There are several eWOM platforms, social media sites such as blogs, facebook and discussion forums and review sites such as Epinions and Yahoo, e-commerce platforms such as eBay and Amazone.com.
Consumers often evaluate online platforms basing on the additional information provided (Knoll 2014, p. 10). Of importance are trustworthiness and signals of homophily (Ladhari and Michaud 2015, p. 37). Electronic WOM is useful when consumers believe that the sender is reliable. As such, information about the e-tailer aids consumers in evaluating whether the sender is trustworthy and the eWOM is relevant thus correlating with increased sales (Lin and Fang 2006, p. 1210 and Berger 2014, p. 586). Also, displaying data in a structured and visible way produces a more significant positive impact on product sales. However, it takes time for consumers to trust an e-retailer (Mauri and Minazzi 2013, p. 107).
Product features also impact the effectiveness of electronic WOM on business sales. Functional risks are higher for services and new products as compared to goods and services that are already popular (See-To and Ho 2014, p. 190). Also, the functional risk is prevalent in hedonic goods and services, which are enjoyable and pleasant and appeal to people senses (Charo et al. 2015, p. 45). Financial risk refers to the money that consumers will forego for the product if fails or does not meet the expectations (Rahman et al. 2015, p. 148). When faced with high financial risk, customers will rely profoundly on electronic WOM (Fang and Lin 2006, p. 1215).
Moreover, E-retailers with two negative reviews on the front page of the search outcomes risk losing 44% of clients. Three negative articles on a first search query may lead to 59% loss in customers. Other studies reveal that more than 50% of adults in the US have Googled someone before conducting business with them (Erskine 2017). Such statistic depicts a positive impact of online assessments on business sales volumes.
2.7. Reliability of Online Review Services
Consumer-generated online product reviews are becoming an indispensable constituent of e-commerce. The reviews serve several purposes. One of the function is to assist online consumers in evaluating services and products before making buying choices (Cui et al. 2012, p. 39). The other function is to let consumer familiarise with services or products even if they do not have the intention of purchase (Berger 2014, p. 586). Studies conducted in the past show that online shopping trains consumers to seek for reviews on the web before purchasing any product (Rahman et al. 2015, p. 148). Also, online reviews have a substantial effect not only on commodities and new products but also on services (Wan 2014, p. 179). As such, E-retailers that provide quality goods and services, and have positive online reviews, have a high chance of making consistent high profits.
However, according to Bright Local, an Internet-based research company, the trust that many buyers place in online reviews might be deceiving (Bright Local 2017). The company found out that, approximately 21% of Americans review products that they have never tried. Some of the motives behind such actions include dislike or like of the E-retailer or just posting spoof reviews. Naturally, every company should expect some negative reviews for an altruistic reason (Wan 2014, p. 179). Such reviews can negatively affect the reputation of the company hence reducing the reliability of online review services. Other studies show that, in USA and Canada, there was a consistent increase in number of consumers who use online customer reviews for local business from 2011 to 2014(see figure 4 below) (Marketing Charts 2014).
Figure 4
Consumer use of Online Customer Reviews for Local Business from 2011 to 2014
Note. From ‘Consumer use of Online Customer Reviews for Local Business,’ Marketing Charts (2014).
There is also an increasing concern regarding review manipulation by interested players like product manufacturers (Mauri and Minazzi 2013, p. 107). Since manipulation of online reviews has substantial financial rewards, companies with strong financial capacities outcompete those with fewer resources by promoting their products (Wolny and Mueller 2013, p. 562 and Bae and Lee 2011, p. 255). Hence E-retailers have to assist shoppers to filter the reports and recognise quality ones. For instance, amazone.com introduced vote filtering mechanism to help consumers make decisions (Ahuja et al. 2007, p. 156).
Other factors influencing the reliability of online reviews include subjectivity, readability, informativeness and linguistic correctness in the reviews (Baek et al. 2012, p. 103). Reviews by self-described expert induce a perception of helpfulness. At time longer review outcomes are not useful in attracting customers because they are tedious to read (Wolny and Mueller 2013, p. 562). Inherent bias caused by the self-selected behaviour of consumers may also negatively affect a trustworthy E-retailer thus making online reviews unreliable. Since marketers with the power to control eWOM messages may deceive clients, not all consumer trust online reviews (Huei et al. 2015, p. 14 and Hyun et al. 2011, p. 436).
2.8. Consistency in Providing the Same Service to Every Customer
Establishing consistency for consumer’s self-concepts and brand image is vital. Consumers may display various personal concepts in different social contexts and across geographical divides (Wan 2014, p. 179). Consumers require consistency in quality of the products. Delivering reliability is imperative for both the consumers and the E-retailers. Inconsistencies frustrate consumers as they become unsure of the quality of the products. Studies demonstrate that buyers are more likely to re-contact E-retailers that consistently provide quality products (Berger 2014, p. 589). Thus, giving inconsistent answers may have regulatory and financial ramifications on a business.
Most E-retailers are multichannel-led (Erskine 2017). Using of different e-channels provides challenges in delivering consistent information to consumers due to inability to manage data flow. A study conducted in 2015, in the US, depicted that, only 52% of E-retailers offer answers across the digital channels that they operate (Bright Local 2017). A significant number of E-retailers do not respond to consumers enquires. Only 2% of companies provided consistent response across the online platforms in which they operate (Adjei et al. 2009, p. 634). Hence there is a need to centralise knowledge in the knowledge base to support all channels. E-retailers should prepare, review and update one set of information thus reducing complexity effort and chances of errors.
References
Adjei, M., Noble, S., Noble, C (2009). The influence of C2C communications in online brand communities on customer purchase behaviour. Journal of the Academy of Marketing Science, 38(5), 634-653.
Advanced Web Ranking. (2014). The impact of blended search results on organic CTR in 2014. Retrieved 2018, from Advanced Web Ranking: https://www.advancedwebranking.com/google-ctr-study-2014.html
Ahuja, R. D., Michels, T. A., Walker, M. M., & Weissbuch, M. (2007). Teen perceptions of disclosure in buzz marketing. Journal of Consumer Marketing, 24 (3), 151 - 159.
Alboqami, H., Al-Karaghouli, W., Baeshen, Y., Erkan, I., and Ghoneim, A. (2015). Electronic word of mouth in social media: the typical characteristics of retweeted and favourited marketer-generated content posted. Int. J. Internet Marketing and Advertising, Vol. 9, No. 4, 338-358.
Bae, S. and T. Lee (2011). Product type and consumers’ perception of online consumer reviews. Electronic Markets, 21(4), 255-266.
Baek, H., J. Ahn and Y. Choi (2012). Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues. International Journal of Electronic Commerce, 17(2), 99-126.
Berger, J. (2014). Word-of-Mouth and Interpersonal Communication An Organizing Framework and Directions for Future Research. Journal of Consumer Psychology, 24 (4), 586–607.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly (25)3, 351-370.
Bright Local. (2017). Local Consumer Review Survey. Retrieved 2018, from Bright Local: https://www.brightlocal.com/learn/local-consumer-review-survey/
Cao, Q., W. Duan and Q. Gan (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
Castañeda, J., Rodríguez, M. and Luque, T. (2009). Attitudes' hierarchy of effects in online user behaviour. Online Information Review, 33(1), 7-21.
Charkraborty, U, and Bhat, S. (2018). The Effects of Credible Online Reviews on Brand Equity Dimensions and Its Consequence on Consumer Behavior. Journal of Promotion Management, 24(1), 57-82.
Charo, N., Sharma, P., Shaikh, S., Haseeb, A. and Sufya, M. (2015). Determining the Impact of Ewom on Brand Image and Purchase Intention through Adoption of Online Opinions. International Journal of Humanities and Management Sciences (IJHMS), 3(1), 41-46.
Cheung, C. and Thadani, D. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461-470.
Chih, W., Wang, K., Hsu, L. and Huang, S. (2013). Investigating Electronic Word-of-Mouth Effects on Online Discussion Forums: The Role of Perceived Positive Electronic Word-of-Mouth Review Credibility. Cyberpsychology, Behavior, and Social Networking 16(9), 658-668.
Chu, S. and Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47-75.
Cui, G., H.-K. Lui and X. Guo (2012). The Effect of Online Consumer Reviews on New Product Sales. International Journal of Electronic Commerce, 17(1), 39-58.
Elwalda, A., Lu, K., & Ali, M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, 56, 306-319.
Erdem, T., Swait, J. and Louviere, J. (2002). The impact of brand credibility on consumer price sensitivity. International Journal of Research in Marketing, 19(1), 1-19.
Erkan, I. and Chriss, E. (2016). The influence of eWOM in social media on consumers purchase intentions: An Extended approach to information adoption. Computers in human behaviour, 30, 1-9.
Ferreira, J., Raposo, M., Gouveia Rodrigues, R., Dinis, A. and do Paço, A. (2017). 20 Online Reputation Statistics That Every Business Owner Needs To Know. Retrieved 2018, from Forbes: https://www.forbes.com/sites/ryanerskine/2017/09/19/20-onlinEputation-statistics-that-every-business-owner-needs-to-know/#4f4f5934cc5c
Fleishman Hillard Global Intelligence. (2017). Most Trusted Sources of Company Information.
Hennig- Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52.
Huei, C., Mee, L., and Aik, N. (2015). A Study of Brand Image, Perceived Service Quality, Patient Satisfaction and Behavioral Intention among the Medical Tourists. Global Journal of Business and Social Science Review, 2(1), 14-26.
Hyun, S. and Kim, W (2011). Dimensions of Brand Equity in the Chain Restaurant Industry. Cornell Hospitality Quarterly, 52(4), 429-437.
Jalilvand, M., Samiei, N., Dini, B. and Manzari, P. (2012). Examining the structural relationships of electronic word of mouth, destination image, tourist attitude toward destination and travel intention: An integrated approach. Journal of Destination Marketing & Management, 1(1-2), 134-143.
Keller, K. (2003). Brand Synthesis: The Multidimensionality of Consumer Knowledge. Journal of Consumer Research, 29, 595-600.
Kim, E., Sung, Y., & Kang, H. (2014). Brand followers' retweeting behaviour on Twitter: how brand relationships influence brand electronic word-of-mouth. Computers in Human Behavior, 37, 18-25.
Kim, J., Kim, M., and Lennon, S. (2007). Information components of apparel Retail websites: task relevance approach. Journal of Fashion Marketing and Management, 11(4), 494-510.
Knoll, J. (2015). Advertising in social media: a review of empirical evidence. International Journal of Advertising, ((ahead-of-print, 1-35).
Korfiatis, N., E. García-Bariocanal and S. Sánchez-Alonso García-Bariocanal and S. Sánchez-Alonso. (2012). Evaluating the content quality and helpfulness of online product reviews: The interplay of review helpfulness vs review content. Electronic Commerce Research and Applications, 11(3), 205-217.
Lada, S., Harvey Tanakinjal, G. and Amin, H. (2009). Predicting intention to choose halal products using the theory of reasoned action. Islam Mid East Fin and Mgt, 2(1), 66-76.
Ladhari, R. and Michaud, M. (2015). eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions. International Journal of Hospitality Management, 46, 36-45.
Lee, E. & Shin, S. Y. (2014). When do consumers buy online product reviews? Effects of review quality, product type, and reviewer's photo. Computers in Human Behavior, 31(1), 356-366.
Lien, C., Wen, M., Huang, L. and Wu, K. (2010). Antecedents and outcomes of consumer environmentally friendly attitudes and behaviour. Journal of Marketing Management, 26(13-14), 1319-1344.
Limbu, Y., Wolf, M. and Lunsford, D. (2012). Perceived ethics of online E-retailers and consumer behavioural intentions. Jnl of Res in Interact Mrkting, 6(2), 33-154.
Lin, C., Wu, Y. and Chen, J. (2013). Electronic word-of-mouth: the moderating roles of product involvement and brand image. International conference on technology innovation and Industrial Management, 29-47.
Lin, T. and Fang, C. (2006). The Effects of Perceived Risk on the Word-of-Mouth Communication Dyad. Social Behavior and Personality, 34(10), 1207–16.
Liu, M., Chu, R., Wong, I., Zúñiga, M., Meng, Y. and Pang, C. (2012). Exploring the relationship among affective loyalty, perceived benefits, attitude, and intention to use co-‐branded products. Asia Pacific Journal of Marketing and Logistics, 24(4), 61-582.
Marketing Charts. (2014). Consumer use of Online Customer Reviews for Local Business. Retrieved 2018, from Marketing Charts: https://www.marketingcharts.com/industries/pharma-and-healthcare-43855/attachment/brightlocal-use-of-online-customer-reviews-july2014
Mauri, A. and Minazzi, R. (2013). Web reviews influence on expectations and purchasing intentions of potential hotel customers. International Journal of Hospitality Management, 34, 99-107.
Mukherjee, A., B. Liu and N. Glance (2012). Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st international conference on World Wide Web, ACM.
Nelson, M., Shaw, M., and Strader, T. (2010). Sustainable e-Business Management.
Lima, Peru: Springer.
Park, C.W., Jaworski, B.J. and Maclnnis, D.J. (1986). Strategic Brand Concept-Image Management. Journal of Marketing, 50, 135-145.
Park, J., Bin, G., and HoonYoung, L. (2012). The Relationship Between E-retailer-Hosted and Third-Party Hosted WOM Sources and Their Influence on E-retailer Sales. Electronic Commerce Research and Applications, 11 (3), 253–61.
Prendergast, G., K., D., & Yuen, S. Y. V. (2010). Online word of mouth and consumer purchase intentions. International Journal of Advertising, 29(5), 687-708.
Rahman, A., and Asrarhaghighi, E. (2015). Consumers and Halal cosmetic products: knowledge, religiosity, attitude and intention. Journal of Islamic Marketing, 6(1), 148-163.
Ramkissoon, H., & Uysal, M. S. (2011). The effects of perceived authenticity, information search behaviour, motivation and destination imagery on social, behavioural intentions of tourist. Current Issues in Tourism, 14(6), 537-562.
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30.
Reichelt, J. Sieverti, J. and Frank, J. (2014). How Credibility Affects eWOM Reading: The Influences of Expertise Trustworthiness, and Similarity on Utilitarian and Social Functions. Journal of Marketing Communications, 20 (1), 65–81.
Reza, M., and Samiei, N. (2012). The Effect of Electronic Word of Mouth on Brand Image and Purchase Intention. Marketing Intelligence & Planning, 30(4), 1-10.
Rivera, M., Gregory, A. and Cobos, L. (2015). Mobile application for the timeshare industry. Journal of Hospitality and Tourism Technology, 6(3), 242-257.
Rosario, A., Sotgiu, F., Valck, K. and Bijmolt, T. (2016). The effect of electronic word of mouth on sales: A meta-Analytic review of platform, product and metric factors. Journal of Marketing Research, 63, 297-318.
See-To, E. W. K., & Ho, K. K. W. (2014). Value co-creation and purchase intention in social network sites: the role of electronic word-of-mouth and trust - a theoretical analysis. Computers in Human Behavior, 31(1), 182-189.
Silke, B., Mangold, S. (2011). Brand equity dilution through negative online word-of-mouth communication. Journal of Retailing and Consumer Services, 18, 38–45.
Sohn, D. (2014). Coping with information in social media: the effects of network structure and knowledge on the perception of information value. Computers in Human Behavior, 32, 145-151.
Torlak, O., Ozkara, B., Tiltay, M., Cengiz, H., and Dulger, M. (2014). The Effect of Electronic Word of Mouth on Brand Image and Purchase Intention: An Application Concerning Cell Phone Brands for Youth Consumers in Turkey. Journal of Marketing Development and Competitiveness, 8(2), 61-68.
Venkatesh, V., Wallin, J., Castro, J, and Lewis, J. (2014). Educational, Psychological, and Behavioral Considerations in Niche Online Communities. Scopus.
Wan, Y. (2014). The reliability of online review helpfulness. Journal of Electronic Commerce Research, (15)3, 178-189.
Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers' motives to engage in electronic word-of-mouth communication through social media platforms. Journal of Marketing Management, 29(5-6), 562-583.
Yelp. (2018). Nielsen: 4 out of 5 Yelp users visit the site when preparing to spend money. Retrieved 2018, from Yelp, Inc: https://www.yelpblog.com/2013/06/nielsen-4-out-of-5-yelp-users-visit-the-site-when-preparing-to-spend-money-at-a-local-business
Zhang, J. Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336-1341.
Zhang, Y. (2015). The Impact of Brand Image on Consumer Behavior: A Literature Review. Open Journal of Business and Management, 58-62.
Zhu, F. and Zhang, X. (2010). Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics. Journal of Marketing, 74(2), 133-148.
