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Master Thesis

HALMSTAD

UNIVERSITY

Master's Programme in Strategic Entrepreneurship for International Growth, 60 credits

Impact of product involvement and

consumer expertise on online consumer review for consumer purchase intention

International Marketing, 15 credits

Halmstad 2020-05-29

Sinin Tabassum, Md Soud Al Fahad

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Acknowledgment

We would like to remember all the valuable people for their kind support and without their help, it would be impossible to complete the paper. Firstly, we would like to thank our supervisor Klaus Solberg Søilen who teaches us everything about research and strategy. Secondly, our friends and family who give tremendous support and effort to reach us in today’s position. And finally, the questionnaire respondents, without their help it was impossible to answer the research question.

Sinin Tabassum MD. Soud Al Fahad May 29, 2020

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Abstract

Purpose: To investigate the effects of online reviews on consumer purchase intention considering the moderating role of product involvement and consumer expertise.

Methodology: To reach our goal in this paper, we conduct a descriptive study in a deductive way. This is quantitative research in which the relationship between online reviews and consumer buying behavior will be tested. The research strategy of the study is an online survey.

The sample size is 200 respondents considering confidence level 95% and confidence interval 7. Data editor IBM SPSS is used to performing the data analysis.

Findings: High-low product involvement and high-low consumer expertise have an impact on the factor of online review (quality, quantity, and credibility) significantly and it affects the purchase intention of the consumer. The study created a conceptual model, which is adapted from the ELM model that considers expertise, involvement, perceived quality, quantity credibility of online consumer review and intent to purchase. This study found that the effect of review type (quality) on the intention of purchase was stronger for both experts and novice and both high-low involvement products. Depending on the level of involvement, the quantity of review on purchase intention increases but the quantity of review on the intention to purchase did not differ under both low involvement & high expertise. Again, individuals rely on source credibility when product involvement is low. But the credibility of the review did not differ on the purchase of intention under low involvement and low expertise situation.

Research implications: This study applies the ELM model to measure the cognitive factor (review factor) and motivation factor (involvement and expertise) together. This study shows consumers with different levels of involvement and expertise prefer different levels of online review factors. The marketer could classify online review information into different category lines like the attribute-based review, benefit-based review, etc. and based on the analysis, the marketer can make a different plan for a different level of consumer (expert and involved consumer).

Keywords: Quality, quantity, and credibility of review, Product involvement, consumer expertise, elaboration likelihood model (ELM Model).

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List of Abbreviation

High Inv _quality High Involvement and Quality of Review High Inv_ quantity High Involvement and Quantity of Review High Inv_ Credibility High Involvement and Credibility of Review Low Inv _quality Low Involvement and Quality of Review Low Inv_ quantity Low Involvement and Quantity of Review Low Inv_ Credibility Low Involvement and Credibility of Review High Exp _quality High Experience and Quality of Review High Exp_ quantity High Experience and Quantity of Review High Exp_ Credibility High Experience and Credibility of Review Low Exp _quality Low Experience and Quality of Review Low Exp_ quantity Low Experience and Quantity of Review Low Exp_ Credibility Low Experience and Credibility of Review

ELM Elaboration Likelihood Model

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Table of Contents

1 Introduction __________________________________________________ 1 1.1 Background ____________________________________________________ 1 1.2 Problem Discussion ______________________________________________ 3 1.3 Purpose of the study _____________________________________________ 4 1.4 Delimitations ___________________________________________________ 5 1.5 Disposition _____________________________________________________ 5 2 Literature review ______________________________________________ 6

2.1 Web era _______________________________________________________ 6 2.2 Online communities ______________________________________________ 6 2.3 From consumer to prosumer _______________________________________ 7 2.4 Electronic word of mouth (eWOM) _________________________________ 8 2.5 Online consumer Reviews _________________________________________ 9 2.6 Online review factor ____________________________________________ 11 2.6.1 Quality of online review ___________________________________________ 11 2.6.2 Quantity of online review __________________________________________ 12 2.6.3 Credibility of online review _________________________________________ 12 2.7 Elaboration Likelihood Model (ELM)_______________________________ 14 2.8 Consumer Purchase intention: _____________________________________ 15 2.9 Product involvement ____________________________________________ 16 2.9.1 High vs. Low product involvement ___________________________________ 16 2.10 Consumer expertise ____________________________________________ 17 2.11 Frame of reference: ____________________________________________ 18 2.11.1 The quality of reviews: ___________________________________________ 19 2.11.2 The quantity of reviews: __________________________________________ 20 2.11.3 The credibility of reviews _________________________________________ 21 2.11.4 Conceptual Model: _______________________________________________ 22

3 Methodology ________________________________________________ 24 3.1 Research Philosophy ____________________________________________ 25 3.2 Research Approach _____________________________________________ 25 3.3 Methodological Choice __________________________________________ 26 3.4 Research Strategy ______________________________________________ 26 3.5 Sample Profile _________________________________________________ 27 3.6 Time Horizon __________________________________________________ 27 3.7 Data Collection ________________________________________________ 27 3.8 Different Variables _____________________________________________ 28

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v | P a g e 3.9 Measurement of Variables ________________________________________ 28 3.10 Data Analysis _________________________________________________ 29 3.11 Ethical Issues _________________________________________________ 29 4 Analysis and Discussion _______________________________________ 30

4.1 Descriptive Statistics ____________________________________________ 30 4.1.1 Demographics ___________________________________________________ 30 4.1.2 Reliability Analysis _______________________________________________ 33 4.1.3 Correlation ______________________________________________________ 34 4.1.4 Chi-Square Test __________________________________________________ 37 4.2 Binary Logistic Regression Analysis for Hypothesis Testing _____________ 38

4.2.1 The quality of online consumer reviews _______________________________ 38 4.2.2 The quantity of reviews: ___________________________________________ 42 4.2.3 The credibility of review ___________________________________________ 50

4.3 Summary of the analysis: ________________________________________ 57 5 Conclusion __________________________________________________ 59

5.1 Theoretical implication: __________________________________________ 61 5.2 Managerial Implication: _________________________________________ 61 5.3 Limitation and Future research: ____________________________________ 62 References ___________________________________________________ 63 Appendices ___________________________________________________ 79 Appendix I _______________________________________________________ 79 Appendix II ______________________________________________________ 80

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List of Figures

FIGURE 1:DISPOSITION ... 5

FIGURE 2:CONCEPTUAL MODEL ... 23

FIGURE 3:THE RESEARCH ONION. ... 25

List of Tables TABLE 1:DEFINITIONS OF ONLINE CONSUMER REVIEW ... 10

TABLE 2:SUMMARY OF HYPOTHESIS ... 22

TABLE 3:MEASUREMENT OF VARIABLES ... 28

TABLE 4:STATISTICS ... 30

TABLE 5:GENDER ... 30

TABLE 6:AGE ... 31

TABLE 7:PROFESSION ... 31

TABLE 8:ONLINE_USE_NOT ... 32

TABLE 9:PRODUCT INVOLVEMENT ... 32

TABLE 10:CONSUMER EXPERTISE ... 33

TABLE 11:RELIABILITY STATISTICS ... 33

TABLE 12:CORRELATIONS ... 34

TABLE 13:CHI SQUARE TEST STATISTICS ... 37

TABLE 14:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H1(A) ... 38

TABLE 15:VARIABLES IN THE EQUATION FOR H1(A) ... 39

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TABLE 16:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H1(B) ... 39

TABLE 17:VARIABLES IN THE EQUATION FOR H1(B) ... 40

TABLE 18:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H1(C) ... 40

TABLE 19:VARIABLES IN THE EQUATION FOR H1(C) ... 41

TABLE 20:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H1(D) ... 41

TABLE 21:VARIABLES IN THE EQUATION FOR H1(D) ... 42

TABLE 22:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H2(A) ... 43

TABLE 23:VARIABLES IN THE EQUATION FOR H2(A) ... 44

TABLE 24:OMNIBUS TESTS OF MODEL COEFFICIENTS H2(B) ... 45

TABLE 25:VARIABLES IN THE EQUATION FOR H2(B) ... 45

TABLE 26:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H2(C) ... 47

TABLE 27:VARIABLES IN THE EQUATION FOR H2(C) ... 48

TABLE 28:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H2(D) ... 49

TABLE 29:VARIABLES IN THE EQUATION FOR H2(D) ... 49

TABLE 30:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H3(A) ... 51

TABLE 31:VARIABLES IN THE EQUATION FOR H3(A) ... 52

TABLE 32:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H3(B) ... 53

TABLE 33:VARIABLES IN THE EQUATION FOR H3(B) ... 53

TABLE 34:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H3(C) ... 54

TABLE 35:VARIABLES IN THE EQUATION H3(C) ... 55

TABLE 36:OMNIBUS TESTS OF MODEL COEFFICIENTS FOR H3(D) ... 56

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viii | P a g e TABLE 37:VARIABLES IN THE EQUATION FOR H3(D) ... 56 TABLE 38:SUMMARY OF THE HYPOTHESIS TESTING ... 57 TABLE 39:SUMMARY OF CONCLUSION ... 60

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1 Introduction

The introducing chapter will give a background to the research topic, which leads to a problem discussion, presentation of the research problem, thesis purpose, and delimitations

1.1 Background

The Internet has become a major source of customer information, empowerment and an open forum, encouraging electronic word of mouth (e-WOM) for consumers (Constantinides &

Fountain, 2008), thus, the role of Internet has moved from an information transfer channel exclusively to a convergence of technology-mediated social participation channels (Chua &

Banerjee, 2015) lead a new generation interactive online community which has been developed over few years (Constantinides & Fountain, 2008). User-generated online product reviews, one form of e-WOMs, have become an important information source for consumer purchase decisions (Chwen, 2010; Chen & Li, 2010; Dellarocas, 2003). Online reviews mean when customers post positive and or negative reviews of the product on the web and it has read by many people (Daugherty & Hoffman, 2014). It is also called online consumer reviews and describes the opinions, experiences, and assessments of previous customers of products (Lee

& Park, 2008). The online review has now become a new forum where the customer shares their experience and knowledge which affect other consumers buying behavior.

A few years back, consumers purchasing decision was affected by advertisements or professional advice, whereas now, consumers actively share their experience or opinion on online and changes people’s behavior towards the product in a way that they rely on reviews posted on the internet (Dellarocas, 2003; Guernsey, 2000). The traditional search option has now become a substitute to internet-based search, whereby an interaction takes place with strangers and not just with friends like in traditional WOM (Klein & Ford, 2003). That’s why eWOM networks reach a bigger scale by using the internet’s multiple communication capabilities (Dellarocas, 2003). As a marketing tool for businesses and a crucial motivating factor for new product sales, online customer reviews have become increasingly important.

This is because customers can create or break goods by sharing their product impressions with other clients in online customer reviews (Cui et al., 2012).

We see the importance of Online reviews in every industry. Its importance cannot be ignored from restaurants, hotels, accessories, clothes to computer apps, users visit frequently to online review sites such as Yelp, Google Maps, and TripAdvisor to warn them (Kaemingk, 2019). In

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2 | P a g e practice, 93% of consumer buying decisions in the USA are influenced by online reviews in 2019 (Kaemingk, 2019). According to a survey, 82% of consumers in Europe read online local brand reviews, with 52% of eighteen to fifty-four years old suggesting that they are only reading reviews (Murphy, 2019). Usually, it takes at least 40 reviews before consumers put their confidence in the overall ranking (Bowman, 2019). Another US-based consumer survey found that 3.3 is the minimum star rating in which brand customers should participate (Podium, 2017). Luca (2016) claims that, with a one-star rise in the online rating site such as Yelp, revenue rises by 5-9 %. According to an anonymous online survey of people in 2015, 64% of tech buyers enjoy reading at least 6 reviews before buying (Capterra, 2015). When making an online order in Sweden in 2018, 67 percent of people choose other customer reviews for their buying decision (Tankovska, 2019). A survey of the views of small and medium-sized enterprises (SMEs) of the effect of user feedback on Sweden's sales in 2016 showed that 30%

of responding SMEs completely accepted that their online platform feedback had a major impact on their sales (Statista, 2016). As per a March 2019 survey in the U.S., 68 percent of respondents said they paid attention to star ratings when assessing a brand or retailer and another 61 percent said they also found the quantity of reviews significant (Clement, 2020). As shown in a survey carried out in Indonesia in July 2019, 69.3% of respondents used products/brands reviewed by an influencer in social media (Müller, 2019). From a survey conducted in India during the pre-festive season, when purchasing goods online, reviews, and ratings always went through at 50 percent (Keelery, 2019). A statistic conducted in Europe in 2017 indicates that 58.4 % of respondents said they used online ratings and reviews to find out about personal care products and cosmetics, making this the most popular online source of information (Sabanoglu, 2018).

There have been several types of research on the topic of online review. Previous research addressed primarily the average impact of online reviews such as general reasons for material on the review website (Burtona & Khammash, 2010; Henning & Walsh, 2003; Lee, 2013). Or expectations and obstacles (Dellarocas, 2003). More descriptive researches are focused on the average online rating or the amount, qualitative variables such as the quality or variation of reviews and their effect on sales or buying behavior (Floh et al., 2013) or scope or duration of online reviews (Chua & Banerjee, 2015; Zhu & Zhang, 2010) or, the blogger's success (Huang, 2015) or perception.

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3 | P a g e 1.2 Problem Discussion

As a marketing force for businesses, online customer reviews have become increasingly important and a primary motivating factor for new product sales. This is because, in online consumer reviews, consumers have the power to make or break goods by sharing their product impressions with other customers. (Cui et al., 2012). Previous research (Adjei et al., 2012) explains the positive effects of C2C experiences in reducing the confusion of customers about products and services and increasing the perception of customers about the business. This could lead to higher revenues and increased profit for the firm. Such C2C interactions created more loyal customers, increase the company’s brand value, provides the idea of new product development, and decreased customer support cost (ibid). C2C interactions affect customer satisfaction and this may affect the brand image of companies. Due to unhappy customers' intention to disseminate negative product information, there is the possibility of damaging the reputation of companies. (Adjei et al., 2012).

The customer gets new information about the product or service that is used or experienced by an unknown user (Park, et al., 2007). If users perceive online reviews as having quality features, including credibility, and feel that the platform contains significant amounts of information, they will see the website as useful to their purchasing decisions (Filieri, 2015; Khammash &

Griffiths, 2011; Wang et al., 2007). Many customers tend to view product information as more credible and accurate in online reviews compared to seller-created information (Dellarocas et al., 2007, Hu et al., 2008). Because of these characteristics, multiple studies have tested online consumer reviews to influence consumer buying behavior and can play an important role in raising product sales by influencing consumer purchasing intentions (Chevalier & Mayzlin 2006; Park & Kim 2008). In literature, the effect of online review has been studied from the point of view of the valence of online review (Zou, 2011), types of reviews (Nina, 2015), overload information in reviews (Park, 2007). the importance of quantity, quality, and credibility of online consumer reviews affecting consumer buying behavior (Brennan, 2000;

Chatterjee, 2001; Chen 2004;), and the effect of online reviews on consumer buying behavior in the case of high-low involvement product ( Park, 2007) and high-low expertise of consumer (Zou, 2011) has been studied.

Lindmark (2015) has suggested finding out the effect of online review in a quantitative way.

Most of the existing literature concentrated on independent knowledge dimensions, such as accuracy or reliability, and analyzed those factors separately (Park et al., 2007; Cheung et al., 2008). Many studies have integrated product engagement and consumer experience separately

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4 | P a g e as important variables in the online context to clarify customer behaviors (Park & Kim, 2008;

Zou et al., 2011). Filieri (2019) has suggested adopting a survey to determine the effect of online review perceptual influences.

Therefore, it would be interesting to investigate how the quantity, quality, and credibility of online consumer reviews the effects the consumer buying behavior considering high-low involvement product and high-low consumer expertise.

1.3 Purpose of the study

Information quality, quantity, source credibility are the important characteristics of online review. (Filieri, 2015). The quantity and quality of online consumer reviews are important factors affecting consumer buying behavior. The popularity of products can be reflected by the number of online reviews of products (review quantity) because fair to say that review quantity shows the number of consumers who have brought the product (Chatterjee, 2001, Chen, 2004).

The content of online reviews (review quality) varies from short to long and from subjective to objective since there is no standard format (Chatterjee, 2001).

Customers can't touch goods in virtual communities or meet online review senders to build trust. Consequently, as consumers read product information, feedback, and recommendations, they need to rely on their own experience and participation to assess the credibility of online reviews. EWOM credibility is the degree to which customers interpret accurate, true, or reliable product information, feedback, or recommendations (Brennan, 2000).

Information processing for purchase intention is affected by the involvement with the product (Petty,1984). In academic research, we have found many models to understand consumer buying behavior such as Howard-Sheth model (1969), Engel-Kollat-Blackwell model (1978), Nicosia model (1966), elaboration likelihood model (1981).

Some research (Celsi, 1988) showed that the contact impact of positive online reviews could vary greatly due to the different rates of information processing by review recipients, which rely on the product knowledge acquired by consumers (also called customer expertise).

From the above discussions, the main purpose of the study is to gain a deep understanding on online reviews effects on consumer purchase intention considering the moderating role of product involvement and consumer expertise and to help businesses understand that online customer reviews can affect business activities, such as making a marketing plan, product sales

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5 | P a g e and company credibility. We have come up with below research questions which will be interesting to investigate:

RQ. 1. What is the effect of the quality of online reviews on consumer purchase intention considering product involvement and consumer expertise?

RQ. 2. What is the effect of the quantity of online reviews on consumer purchase intention considering product involvement and consumer expertise?

RQ. 3. What is the effect of the credibility of online reviews on consumer purchase intention considering product involvement and consumer expertise?

To ensure a strong theoretical foundation, this study uses the elaboration likelihood model (ELM). In the meta-analysis of existing research on the effect of online review, the ELM model and its factors were found most predictive in determining the favorable attitude of information processing of consumers (Bodoff & Shuk Ying, 2014). The ELM model indicates that depending on customer participation, the same information can be interpreted in different ways (Zhou, 2012). This model helps to identify how consumers adopt the information and influence buying behavior. That’s why we have decided to use the ELM model to reach the purpose of the study.

1.4 Delimitations

The study will not consider the online review platform used by the consumers which has impact on the online consumer review. And the study is also not separated whether the online review is initiated by the consumers or the companies. As our study purpose is ‘what’ ‘is the effect of quality/quantity/credibility online review on the consumer buying behavior considering the impact of product involvement and consumer expertise and to that, we had collected data from mass internet user which give us the result of effectiveness.

1.5 Disposition

The structure of the paper is presented in below figure

Figure 1: Disposition

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2 Literature review

The chapter presents earlier research in the thesis subject of online consumer reviews and its influence on consumer purchase intention and moderating role of product involvement and consumer expertise; it is divided into three parts of Customer-to-customer (C2C) Interactions and Electronic word-of-mouth (eWOM), Online consumer review and factors of online review and product involvement and consumer expertise.

2.1 Web era

Marketers use different tools to promote their products, including ads, direct marketing, the Web or digital marketing, sales promotion, personal selling, and publicity or public relations (Belch & Belch, 2007). By revolutionizing business practices and social relations, the commercial Internet opened up a whole new world of opportunities for consumers and corporations with the help of the web (Thackeray et al., 2008). A new generation of online tools, apps, and methods, such as forums, wikis, online communities and virtual worlds, commonly known as Web 2.0, has been created over the last few years (Constantinides &

Fountain, 2008). Interactive communication on the Internet produces a distinctive atmosphere, where Web 2.0 promotes creativity and user collaboration (Murugesan, 2007). According to O’Reilly (2009), Web 2.0 is more dynamic and collaborative than its predecessor; it is a set of innovations, business strategies, and social trends. This is now the era of web 3.0 which is a new platform created from the concept of web 2.0 (Tsaplin et al., 2013). Web 3.0 (2010-2020) is described as the development of high-quality products and services by creative people using Web 2.0 technologies as an enabling framework (ibid). With the help of web 3.0, the consumer can understand the context of knowledge that suits their needs exactly, instead of providing and viewing content on the website (Erragcha et al.,2014).

2.2 Online communities

Online discussion communities have become a widely used interaction medium, allowing for conversations across a wide variety of topics and contexts (Bateman et al, 2011). Many companies are looking for the best way to effectively use online communities because good use of new technologies and online communities will help a business on various aspects such as enhancing brand awareness or boosting profit by reducing costs and growing sales revenue (Norris, 2002). An online community is a set of individuals who communicating within a virtual environment, have a purpose, are technologically supported, and are guided by

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7 | P a g e standards and policies (Preece, 2001). Online communities are a platform where customers with a shared interest in goods, services, or brands connect to obtain information, such as buying advice on specific products (Kozinets, 1999; Cothrel, 2000). An online community is a specific group that shares a common purpose centered on a structured and dynamic network of relationships among participants (Muniz & O'Guinn, 2001; Pelat & Cabot, 2016). Online communities linked to consumption are those networks of people whose online interactions concentrate on shared enthusiasm and knowledge for a specific consumer activity or related group of activity connections (Kozinets, 1999, Kozinets et al., 2010). Several companies are starting to create online communities featuring content, games, and social networks (Mulhern, 2009). Companies are starting to realize that engaging with their customers through an online customer community can be a great way to build customer trust, loyalty, and advocacy”

(Khefacha, 2013). Rob Howard, Zimbra Inc's founder, described three online communities’

forms (Rob, 2011):

Direct community: Equipped and operated by corporations themselves. The organization' have access to all databases of data and customer profiles.

Managed community: These are groups that the company developed and operated, which operate on consumer-facing social networking sites such as Facebook, Twitter, or LinkedIn.

Participating community: These are communities that individuals or groups of users create and maintain, usually on c social networking sites, but sometimes also with proprietary software

2.3 From consumer to prosumer

The Prosumer concept extracted from the words producer and consumer can qualify as a consumer of the post-modern age (Șahin & Dogdubay, 2017). Prosumer’s term was coined by Alvin Toffler (Kaplan & Haenlein, 2010; Toffler, 1980) and he said that the term is a combination of the word’s "producer" and "consumer,". It initially intended for the proactive role consumers would play when products were mass-customized in the manufacturing process (Vikram, 2016). Nowadays, consumers do not choose to purchase products and services that they use only for practical benefits, they scrutinize more, have more awareness, derive interest from the products and services they encounter, and even involve them in the production processes of these products and services. (Șahin & Dogdubay, 2017). Prosumer want to be involved in the production of the product they consume and they tend to take part in any part of the process that is consumed and presented to them, and they are exploring, researching,

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8 | P a g e recommending, self-describing and producing value through the product or service they consume (Izvercianu et al., 2012).

The term “prosumer” isn’t a new one, it is been around the marketing world for years (Gunelius, 2010). But in today’s world of the social web, it has taken on a new importance that business leaders and marketers cannot ignore (Fine et al., 2017). Since the social web grew and resources such as Twitter, blogs, Facebook, and YouTube permitted communication to move quicker and farther than ever before (Gunelius, 2010). The Internet and the Web 2.0 revolution laid the groundwork for a new paradigm that empowered customers around the world, enabling users to emerge from uninformed shoppers into discerning connoisseurs, from passive customers to active producers (Lehdonvirta, 2012). The term “prosumer” has transformed from meaning

“professional consumer” to meaning “product and brand advocate” (Gunelius, 2010). Instead of simply "consuming" products, people become the voices of those products and have a major impact on the success or failure of businesses, products, and brands, particularly through their social network involvement (Potra et al., 2014). Prosumers are the online influencers not only recognized by corporate leaders and marketers but also acknowledged, trusted, and built relationships for their products and brands to flourish. (Gunelius, 2010). In the digital world, prosumers possess several features: they are knowledgeable customers, internet users who adopt collaborative technology or engage in product or service prototype, design, testing, and have some impact on their social network. (Izvercianu & Şeran, 2011). The idea of the

"prosumer" has emerged with the rise of eWOM, which explains how consumers have become much more prominent because of their willingness to share their product or service experiences with a large number of people freely, quickly, and easily (Gunelius, 2010). Much of the material on social networking sites such as Facebook, Instagram, and Twitter, as well as review platforms including TripAdvisor, Google Reviews and Yelp, comes from user postings, making these users to prosumers (Siuda & Troszynski, 2016).

2.4 Electronic word of mouth (eWOM)

Online product reviews have considered one of the most important forms of electronic word- of-mouth (eWOM) in shaping customer attitudes and making buying decisions smoother.

(Plummer, 2007). Web 2.0's innovations allow consumers to share their perceptions, thoughts, and feedback about products, services, or brands in the form of online reviews with other consumers. (Filieri, 2015). eWOM occurs in a virtual context, while conventional WOM usually occurs face-to-face or one-on-one context. (King et al., 2014). In traditional WOM, the

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9 | P a g e participants are close by and can draw on a wealth of social and contextual cues. On the other hand, eWOM connects people and creates a network with them in online communities where interactions are more accessible. (Kozinets et al., 2010). Social networking networks are considered truly relevant to eWOM platforms. (Canhoto & Clark, 2013; Erkan & Evans, 2014;). EWOM social media information can emerge in several different ways, users may actively post about brands and their goods or services. (Ibid). eWOM is viewed as' any statement made by prospective, current or former consumers about a product or service made available via the Internet to a multitude of persons and institutions (Hennig-Thurau et al., 2003.

King et. al, 2014). The eWOM enables customers to communicate socially, share product- related knowledge, and make informed buying decisions through computer-mediated communications (Blazevic et al. 2013; King et al., 2014). Electronic word-of-mouth is influential in today's dynamic market and has the ability to influence consumer decision behavior (Xie et al., 2011). EWOM was without doubt a strong marketing force (Cheung &

Thadani, 2012). Online customer reviews, one form of eWOM contact, influence consumer behavior through normative attitudes, stating whether other consumers like a product or dislike it (Park & lee, 2008; Senecal & Nantel, 2004).

2.5 Online consumer Reviews

Online reviews have an impact on e-commerce sales and product awareness (Lee & Shin, 2014;

Lee & Youn, 2009). Individuals are not only seeking online feedback to gain approval from others but also to engage in an online community and learn what's new in the market (Weathers et al., 2015). Expertise has also been found that consumers accept online reviews for social reassurance (Qi et al., 2016). While the reviews are the work of outsiders, they are regarded as a credible source of information (Hu et al., 2012).

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10 | P a g e Table 1: Definitions of Online Consumer review

Authors Definition

Lee, Park, & Han, (2008) and Park & Lee, (2008) and Park, Lee & Han (2007).

Online consumer reviews including prior consumer product experiences, observations, and opinions play two aspects, information, and recommendation.

Chen, & Xie, (2008). Online consumer product review as a modern type of word- of-mouth information is an emerging industry trend that is playing an increasingly valuable role in customer buying decisions. The online customer review is a modern medium of product information, with rising prominence and

significance.

Zhan et al. (2009) Online product reviews contain critical information about consumer perceptions and their experience related to the product.

Zhang et al. (2014) Online consumer reviews are seen as a significant source of information that provides assistant to customers for purchasing decisions. Online reviews have been spread through various online media, including websites of online retailers, social networking sites, discussion forums, blogs, and online review sites.

Fei et al. (2011) Online reviews are web introduction and product or brand assessments but not ads; they are usually text, image, multimedia, and combination. Recently the most popular category is text reviews.

Fan & Miao, (2012). Customers can use electronic word of mouth in various forms of online consumer reviews that can be used to encourage them to make buying decisions on e-commerce.

Online product reviews usually consist of three components, namely the pros and cons specifically relating to a product's perceived strengths and disadvantages, related product scores, and formless feedback and suggestion. As shown in a study conducted by Weber Shandick (2012) With KRC Research, 65 percent of buyers bought was motivated by a consumer review to choose a brand that was not part of their original choice (Floyd et al., 2014). When consumers browse online, learn about products, and compare different alternatives, they are likely to find and accept multiple reviews of other consumers ' online products (Mudambi & Schuff, 2010). Moreover, as per a survey from market research company Nielsen (2012), 70 percent of customers say they trust online reviews of the product (Floyd et al., 2014). For instance, women are more likely to read reviews for convenience and quality and risk management reasons, where the use of online reviews by men depended on their expertise level (Kim et al., 2011). It is assumed that the number of online product feedback improves brand awareness and generates higher sales. (Anderson & Salisbury, 2003; Liu 2006).

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11 | P a g e Online reviews on sales may differ greatly depending on the cultural orientations and self- constructions of consumers (Floyd et al., 2014). When online product reviews concern items marked by greater product involvement, the effect on sales elasticity is substantially greater than when products with lower involvement are evaluated (Dou et al., 2012). Online product reviews have a greater effect on sales elasticity when provided by a critic, appear on a non- seller website, and provide valence details in the evaluation (Floyd et al., 2014).

Research shows that online product reviews are a significant source of business intelligence that helps managers and advertisers consider consumers ' expectations and preferences (Chung

& Tseng, 2012). These online product reviews provide details of user preferences, feedback, and suggestions that serve as a significant source of business intelligence, helping managers and marketers understand consumers more effectively (Ibid). Customer reviews for a wide variety of product categories are available online nowadays (Hu et al., 2012). Online product reviews given by customers who have never purchased goods have become a significant source of information for buyers and advertisers on the quality of the products (Weathers et al., 2015).

Opinion sharing platforms provide open, simple communication networks to communicate and collect customers' opinions and preferences for various products (Qi et al., 2016). Since customers become more and more centered on online reviews to make buying decisions, the product's sales become dependent on word of mouth (WOM) it induces (Lee & Shin, 2014).

As a result, companies can attempt to manipulate online product reviews to boost their sales (Zhu & Zhang, 2010). The source's perceived credibility is a critical factor in evaluating the power of word-of-mouth information particularly as web-based communication (You, et al., 2015). There are two dimensions of credibility: competence, or to what extent the communicator is perceived as a source of credible claims, and trustworthiness, or to what extent the communicator is perceived as a source of unbiased claims (Weathers et al., 2015). Online reviews can also be a powerful marketing communications promotion tool (Dellarocas, 2003).

This tool has been used by companies and vendors because it offers a cheap and impact way to meet their clients (Dellarocas et al., 2007).

2.6 Online review factor 2.6.1 Quality of online review

Previous studies examine how readers judge the quality of the content in online reviews and whether the reviews are useful. (Cheng & Ho, 2015). The quality of the review was often described as the persuasive strength of the message and was commonly measured in terms of

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12 | P a g e its relevance, timeliness, accuracy, and comprehensiveness. (Cheung & Thadani, 2012;

Cheung et al., 2008). Several researchers analyzed the quality of online customer review content in terms of relevance, reliability, understandability, and sufficiency (Bailey &

Pearson,1983; Lee et al., 2008; Petty & Cacioppo, 1983; Petty & Cacioppo, 1984). Users may rate reviews quality as being very helpful, helpful, somewhat helpful, not helpful, and off-topic (Mackiewicz, 2010). Mudambi and Schuff (2010) have analyzed customer reviews on Amazon.com and find that quality reviews, rating reviews and product attributes (experience products and search products) influence the attitude of the reader towards the usefulness of reviews.

2.6.2 Quantity of online review

In the field of product reviews, a customer can find a large quantity of reviews in the online platform to read and often from unknown sources (Gottschalk & Mafael, 2017; Lee & Lee, 2004). In addition, the literature argues that such reviews are often more trustworthy than traditional sources in the printed press (Jiang & Benbasat, 2004; Korfiatis, 2007; Garcia et al., 2012). A consumer will be less likely to face pressure from knowledge on decision support (i.e.

product reviews) when he/she has the option of how many reviews to read (Hu & Krishen, 2019). If the quantity of reviews does not exceed cognitive capability, the user should pay more attention to the information provided and mentally organize and incorporate the information into his / her current knowledge systems. (Mayer & Moreno, 2003). Furthermore, it is argued in the literature that these reviews are sometimes more trustworthy than traditional sources in the printed press (Jiang & Benbasat 2004; Korfiatis et al., 2012). The quantity of review is simply defined as the overall number of comments (Cheungand & Thadani 2010). Whereas, Duan et al., (2008), has indicated that based on available review numbers or the length of the reviews, the quantity of online reviews can be measured. Amazon allows users to quantify their sentiments of review in the form of star ratings, and the higher the rating of a given review, the more favorable their sentiments are (Gerdes Jr. et al., 2008; Li et al., 2013).

2.6.3 Credibility of online review

Credibility refers to the degree to which information is considered believable by a receiver (Eisend 2006; Cheung et.al, 2009). Credibility is a dynamic and multifaceted phenomenon with two key components relevant to the source being trust and expertise. (Wathen & Burkell, 2002).

Credibility proceeds from an association between source characteristics, receiver characteristics, and the media (ibid). Perceptions of credibility impact a receiver’s purpose to

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13 | P a g e modify his or her attitude based upon the information obtainable (Hovland et al., 1953; Lim &

Van, 2015).

Consumers evaluate the credibility of online product reviews to mentor their buying decisions.

(Jiménez & Mendoza, 2013). Online review sites are also criticized for their credibility as the information shared on the online review platforms usually does not go through any robust empirical verification editorial process (Johnson & Kaye, 2002). Several researchers argue that online information is more credible than information from other more conventional media (Park et al., 2007; Senecal & Nantel, 2004), as the information is shared by experienced users who are known to be credible information sources (Gretzel et al., 2007; Park et al., 2007).

Credibility is one of the concerns regarding online consumer reviews; and high creditability reviews have a positive impact on the degree to which consumers adopt information (Cheung et al., 2009). Obviously, online reviewers are conscious of credibility and its prominence (Mackiewicz, 2010). Therefore, more credible reviews for a product would definitely have a greater impact on the willingness of the customer to purchase the product (Zhao et al., 2013).

The heritage of product review credibility has begun to shed light on how prospective customers evaluate online reviews. (Jensen et al., 2013). The earlier findings of a study confirm the positive relationship between online product review credibility and purchasing intention (Jiménez & Mendoza, 2013). The high credibility of the reviewers significantly enhanced perceptions of product quality. (Jensen et al., 2013). The credibility of an online review of a search product is measured by the level of detail in the review, while the level of review agreement determines the credibility of an online review of an experienced product (Jiménez

& Mendoza, 2013). the results indicate that, as heuristic cues, the variance of the reviews is used to determine the credibility and usefulness of online reviews of search and experience products (Ibid). Customer rating and reviewer credibility are more essential for a low-priced product and experience goods. (Baek et al., 2012).

Academic research findings indicate that consumers use information quality dimensions related to the review message to determine the credibility of a source: the more detailed and complete the information contained in a review is applicable to customer needs, the more credible the communication source would be viewed (Filieri, 2015). If a source is lacking in credibility, it will lose effectiveness and not be very persuasive (Ibid). Furthermore, The findings of another study show that the existence of personal identifying information (e.g., names, residency status,

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14 | P a g e and stay date) may boost the perceived credibility of online customer reviews, which in turn will improve online review persuasiveness (Xie et al., 2011).

2.7 Elaboration Likelihood Model (ELM)

Petty and Cacioppo (1981) introduced the Elaboration Likelihood Model (ELM) concept into academic literature ((Kitchen et al., 2014). The elaboration likelihood model (ELM) offers a theoretical perspective in the psychology literature on how attitudes develop and alter over time (Fan & Miao, 2012). The ELM model was established during the days of mass media marketing in the 1980s (Kitchen et al., 2014). In ELM's view, persuasion takes place through two routes of influence: the central route and the peripheral route (Salehan, & Kim, 2016). The central route stimulates critical thinking in the audience about the message's argument and needs a high degree of cognitive effort (Angst & Agarwal, 2009). In comparison, the peripheral route to persuasion takes place by positive and negative signals and undergoes less cognitive effort (Petty & Cacioppo, 1986; Salehan, & Kim, 2016). The elaboration likelihood model (ELM) indicates that consumers shift their attitude through a dual-route involving the main and peripheral routes (Petty & Wegener, 1999). The central route addresses knowledge- claims like the quality of information, which needs more investment in the effort (Zhou, 2012). The peripheral route, in comparison, processes information cues such as reputation and requires less effort (Petty & Wegener, 1999). If users prefer central or peripheral routes is dictated by the elaboration likelihood, which includes motivation and ability (Zhou, 2012).

Filieri and McLeay (2014) used an elaboration likelihood model to identify the factors that lead to the adoption of consumer information, such as product ranking, information accuracy, value- added information, information relevance, and information timeliness. ELM can also explain the effect of the number of reviews depending on the level of expertise(Fan & Miao, 2012).

According to ELM, consumers with low expertise are more likely to focus on a peripheral cue such as the number of arguments, while consumers with high expertise are more likely to engage in effortful cognitive activity through the central route, and they focus on the argument quality (Park et al., 2007). By integrating the cognitive fit theory and ELM, the previous study examines that consumers with different levels of expertise prefer different types of review messages (based on cognitive fit theory), and the effect of cognitive fit on purchase intention is stronger for experts than for novices (Teng et al., 2014).

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15 | P a g e 2.8 Consumer Purchase intention:

Purchase intention is a form of consumer behavior and defines "the deliberate decision or intention of a customer to make an effort to purchase a product” (Lu et al., 2014). Purchasing intention is the aim of a consumer to purchase a specific brand, which has been the subject of enormous attention (Chang & Liu, 2009). The purchase intention has been used to determine the probability of buying a product and higher buying intention signifies a growing desire to purchase the product (Hsu et al., 2017). Consumers make purchasing decisions rely on a systematic method of product information collection, assessment, and integration (Shirin &

Kambiz, 2011). Kotler et al. (1999) argued that there is a predominant "stimulus-response"

model and the black box (including consumer feature and decision processing) principle of behavioral science reaction when a customer makes a purchasing decision behavior (Lin &

Chen, 2006). Fill (1999), states that there are five phases of the general process by which consumers intend to purchase and execute them, this process includes problem identification, knowledge quest, alternative assessment, purchasing decision and post-purchase evaluation. In addition, Chen & Barnes (2007) argued that the purchasing intention scale consists of the following three items: (1) consumer planning to purchase the product in the future; (2) consumer intending to purchase the product in the future; and (3) consumer predict to purchase the product in the future.

Intention to purchase belongs to users of online reviews who intend to purchase a product already reviewed. (Thomas et al., 2019). Whenever consumers purchase goods, they frequently refer to reviews left by people who previously purchased the product to guide their purchasing intentions (Liang, 2016; Hsu et al., 2017). Consumers also rely on online reviews to shape purchasing intentions (Thomas et al., 2019). Today, online reviews are among the most influential sources of information for consumers when forming a purchase decision (Lee and Shin, 2014) and provide great benefits to them (Hamby et al., 2015). Most significantly, they encourage consumers who are geographically scattered to express independent opinions on products and services and to help them make informed purchase intentions (Racherla et al., 2013). Ruiz-Mafe et al., (2018) claims that customers who find the information useful in an online review would have higher purchase intentions. Research shows that the credibility of reviews can have a positive impact on customer purchasing intentions (Fan & Miao, 2012; Lee et al., 2011). Whereas, Yang et al. (2016) claimed that the quantity of the review does not significantly affect the intention of the consumer to purchase. While reading positive overall

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16 | P a g e reviews, those associated with high-quality reviews judged the product more positively, resulting in a stronger purchase intention. (Lee, & Shin, 2014).

2.9 Product involvement

In marketing and consumer behavior research, product involvement has been considered an individual difference variable (Laurent & Kapferer,1985; Park & Keil, 2019). Some marketing literature suggests that an individual’s product involvement may be directly related to his or her commitment to the product (Quester & Ai, 2003; Park & Keil, 2019). Product involvement was identified to be significantly associated with consumer attributes knowledge or understanding, perceptions of product value, brand perceptions and preferences, perceptions of advertising, and customer risk perceptions. (Dholakia, 2001; Zhao, 2003).

According to Samson (2010), product involvement is the degree of customer engagement in a product category on a continuous basis. Product involvement is commonly characterized as a consumer's enduring perception of the importance of a product category, based on that consumer's intrinsic needs, values, and interests (Zaichkowsky, 1985: Floyd et al., 2014).

Previous studies demonstrated that the level of involvement might influence the attitudes and behaviors of consumers. (Wu, 2002; Chao & Chen, 2016). Product involvement enhances the relationship between electronic word of mouth and consumer behavior. (Chao & Chen, 2016).

Word-of-mouth (WOM) affects satisfaction, loyalty, and competitiveness, the degree of product involvement determines the extent to which consumers are engaged in WOM (Schindler & Bickart, 2005; King et al., 2014).

Laaksonen (1994) and Han and Kim (2017) have found that product involvement had a major impact on the cognitive and behavioral responses of consumers including memory, focus, processing, quest, brand interaction, satisfaction, early adoption, and leadership of opinion.

Research indicates that product involvement is closely related to customer expectations and attitudes (Lastovicka, 1979; Han & Kim, 2017), time spent researching the goods, or attention and commitment in buying the products. (Shirkhodaee & Rezaee,2014).

2.9.1 High vs. Low product involvement

Careful product information search usually occurs with high-involvement products as it is the riskier purchase decision because this kind of product is often high-value or more expensive than general products (Polyorat & Buaprommee,2013). In contrast, the low-involvement product is relatively unimportant, inexpensive, or lack of emotional attachment (Cho, 2010;

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17 | P a g e Rahtz, & Moore, 1989). The participants in the high involvement condition read and processed the product information more carefully while the participants in the low-involvement condition did not (Shu & Yi, 2006). Consumers with higher product involvement are more likely to perceive attribute differences, to place higher importance on the product, and to possess greater commitment in their brand choices (Howard & Sheth, 1969; Floyd et al., 2014). Further, higher product involvement motivates consumers to search for more information and spend a greater amount of time making an optimal decision (Clarke & Belk, 1978; Floyd et al., 2014).

Individuals tend to judge information about high involvement products systematically rather than heuristically (Dou et al., 2012). When the involvement is low, however, individuals rely on peripheral cues from a stimulus such as a source credibility, sympathy with the source, the number of arguments (Lee et al., 2008). According to the level of consumer involvement, perceived product popularity as being more important than perceived informativeness of the review information set for low-involvement consumers (Park & Lee, 2008). Low-involvement consumers find the role of recommended more important than the role of informer, but high- involvement consumers regard the role of the informer is more important than the role of the recommended (Park & Lee, 2008).

2.10 Consumer expertise

Consumer expertise performs a major moderating role in consumer buying decisions, with a positive moderating or negative moderating influence. (Cheung et al., 2014). Biswas et al., (2006) believed that the perceived 'expert' relates to a substantial amount of knowledge on a particular topic rather than a generic level of knowledge. Prior research showed that individuals with various levels of expertise appeared to use specific routes of information processing to process persuasive information. (Simpson et al., 2008). Usually, knowledgeable consumers (with high expertise) were typically less sensitive to interpersonal market control and preferred to rely on their own purchasing decision experience (Bearden et al., 2001). Consumer expertise and engagement are typically postulated as regulators that influence whether consumers take central or peripheral route processing when making purchasing decisions (Cheung et al., 2014).

Park and Lee (2008), for instance, have noticed that, when consumers had a higher degree of engagement, they were more likely to participate in an effortful cognitive activity through the central route. If customers had a lower degree of engagement during information collection;

they continued to focus on peripheral cues. (Park & Lee, 2008). Several studies show a favorable relationship between the level of expertise and the WOM (Zou et al., 2011), whereas others indicate a negative relationship (Park & Kim, 2008; Zou et al., 2011). Depending on

References

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