The Influence of Online Customer Reviews on Purchase Intention: the Role of Non-numerical Factors

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The Influence of Online Customer Reviews on Purchase Intention: the Role of Non-numerical Factors

Abdulaziz Elwalda (Brunel University, UK) Kevin Lu (Brunel University, UK)

Abstract

Significant attention has been recently drawn to the relationship between online customer reviews (OCRs) and purchase intention. Nevertheless, due to the complex nature of the problem, such a relationship is not yet entirely understood. This ongoing study aims, therefore, to examine this relationship by developing an understanding of the impact of the non-numerical attributes of OCRs on customer trust and intentions. A structural equation modelling procedure is applied to the investigation of the impact of OCRs on customer trust and intentions. The findings of this study, in addition to their contribution to the existing literature, will help business communities by providing a better understanding of consumers and how their decisions are affected by OCRs.

Key words: Online customer reviews (OCRs), eWOM, purchase intention, TAM, social support, trust Introduction

With the rapid advancement of technology and the excessive use of the Internet, opportunities for gathering and providing product information have been extended. People are no longer limited to face-to-face word of mouth (WOM) interaction; rather, they communicate through blogs, online discussion forums, chat rooms, Web-based opinion platforms and news groups (Fei, 2011). Around 84% of Internet users in the USA, for example, have participated in online discussions (Horrigan & Rainie, 2001). Such ability to exchange experiences and opinions online is acknowledged as electronic word of mouth (eWOM) (Davis & Khazanchi 2008; Litvin et al. 2008). eWOM, in this regard, might be considered to be an extension and a new form of traditional (offilne) WOM. Hennig-Thurau et al. (2003) were apparently the first to profoundly define the term eWOM, when they cited that eWOM is “any positive or negative statement made by potential, actual, or former customers about a product or company which is made available to a multitude of the people and institutes via the Internet” (Hennig-Thurau et al. 2003. pp. 39). This definition leads to an understanding that eWOM needs to fulfil two main requirements in order to be considered as such; first, it must be an online statement, comment, or review about a product/ service or company, regardless of its valence, and secondly, it could be provided by any individual, notwithstanding their experience of the product or service.

Through the rise of virtual communities, a new type of eWOM has emerged, namely online customer reviews (OCRs), or as they are sometimes referred to “user generated content” (Bae & Lee 2011). ORCs are defined as evaluative information generated by customers and posted on company or third party web sites (Mudambi & Schuff 2010). OCRs are viewed as one of the most popular (Purnawirawan et al. 2013) and important (Sen & Lerman 2007) forms of eWOM. According to eMarketer report (2008), 61% of customers referred to online reviews, blogs or other forms of consumer feedback before making a purchase. Senecal and Nantel (2004) find that consumers who consult an online endorsement are twice as likely to select products as those who do not.

The need for this study arises from the growth of firms’ concerns about the effect of OCRs on their sales. OCRs are considered to be the new element in the marketing communication mix (Chen & Xie, 2008) and have become a crucial source of feedback (Dwyer, 2007). In addition, firms can use online reviews as a tool for understanding customers’ attitudes towards their products (Dellarocas et al., 2007) and, hence, to assist them in developing their manufacturing, distribution and marketing strategies accordingly (Zhang et al., 2012). This study, therefore, will address such needs by

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investigating the effect of OCRs usfulness, credibility, presence, and social support on customers’ trust and puchase intentions. Futhermore, it will contribute to the existing marketing literature and will also provide new insights for further studies.

To date, there has been little discussion in the literature about the impact of OCRs on consumer decisions (Christodoulides & Michaelidou, 2012). Stephen and Galak (2012) contend that the way earned media affects customer decisions is not well understood. In addition, contextual measures such as numerical ratings have been the main focus of most OCR studies (e.g. Chevalier & Mayzlin 2006; Duan et al. 2008); non-numerical factors, for instance, credibility, usefulness, and social support, on the other hand, have been relatively ignored. Pan and Zhang (2011), furthermore, argue that despite the wide use of OCRs and their great ability to attract customers, little is yet known about the extent of their helpfulness.

Moreover, as purchasing online is essentially an interaction between the individual and the e-vendor, and since the behaviour of the e-vendor cannot be assured or monitored, such interaction involves more risk and social uncertainty than offline shopping (Lee & Turban, 2001; Reichheld & Schefter 2000). Jarvenpaa et al. (1999) point out that in a relationship where the trustor has no full control over the trustee’s actions, trust plays a central role. Building trust is hence a vital factor in online shopping, and cannot be underestimated. Reichheld and Schefter (2000) argue that trust is the main attribute of any relationship, and it is even superior to economic causes such as discount prices. Similarly, Kim et al. (2012) find that customers tend to value perceived trust more than perceived price in an online store.

Accordingly, it is suggested that more extensive research should be conducted to cover all the possible antecedents of e-commerce trust (Ba & Pavlou 2002). Moreover, the findings of Utz et al. (2012) propose that, in order to improve online trust models, it is necessary to add consumer generated content. Therefore, the aim of this study is to investigate such a gap in the marketing literature by developing an understanding of the impact of OCR non-numerical factors “credibility, usefulness, and social support” on customer trust and intention. Moreover, to properly understand the total impact of OCRs on purchase intention and, consequently, sales, it is necessary to first understand their influence on trust, since the factors which affect the process are likely to influence the outcome.

Theoretical Background

The term ‘OCR’ refers to customer-generated information and recommendations presented online by customers about a product. This information comprises customers’ experiences, evaluations and opinions (Bae & Lee, 2011). Product websites such as consumerreview.com, epinions.com and consumerreasearchinc.com are examples of online customer review platforms that cover a wide variety of product categories (Sen & Lerman 2007). In addition, there are other types of OCR platforms; for example, retailers’ websites (amazon.com), brand websites (fourms.us.dell.com) and personal blogs (Twitter.com) (Lee & Youn 2009). Individuals tend to view such OCR platforms as trustworthy (Sen & Lerman 2007) since they are created by customers who are not perceived to strive for manipulation (Bickart & Schindler 2001). Additionally, customers provide indirect feedback by delivering an honest evaluation of the advantages and disadvantages of a product (Park et al. 2007). Most recently, OCRs have been examined as a form of electronic word of mouth (eWOM) contact that influences customer behaviour (Chatterjee 2001; Chen & Xie 2008; Lee et al. 2011). However, OCRs differ from eWOMs in a number of important ways. Firstly, an eWOM can be generated either by consumers or marketers, whereas OCRs are only generated by consumers (Cheong & Morrison 2008). Moreover, individuals on social networking sites such as Facebook or Twitter generally control who has access to their information. In contrast, OCRs are usually posted on e-retailer websites such as eBay and Amazon, where there is no restriction, making them accessible to users. Finally, eWOM information is ordinarily communicated directly to specific receivers such as via a tweet on Twitter.com. OCRs, on the other hand, are publicly communicated (Clare 2012). Taking these

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differences into account, Bae and Lee (2011) argue that OCRs can still be considered to be a type of eWOM communication.

Unlike offline transactions, online purchases are not conducted face to face, thereby increasing the level of risk and uncertainty. As indicated previously, Jarvenpaa et al. (1999) point out that in a relationship where the trustor has no control over the trustee’s actions, trust plays a central role. Building trust is therefore a vital factor in the online shopping context, and cannot be underestimated. Hsu et al. (2013) highlight that more reliable product information is needed in an online context to support buying decisions. Such information is offered by OCRs and is considered to be credible and trustworthy (Bickart and Schindler (2001). Bickart and Schindler (2001) find that product information provided in online discussion forums has a greater impact than seller-created information. It is claimed, hence, that OCRs can be used as a tool to gain customer trust (Baek et al. 2012).

It is contended that, in online communities, trust might be built though the sharing of knowledge and experience (Flavian & Guinalıu 2005; Hajli & Khani 2013; Zhao & Lavin 2012). An eMarketer report (2007) highlights that consumers trust websites featuring reviews more than professional guides. Hajli and Khani (2013), additionally, find that the level of trust for new products is increased by social word of mouth. Awad and Ragowsky (2008) establish that online WOM quality is an antecedence of e-commerce trust and Chen (2011) similarly finds that loyalty is greatly influenced by electronic word of mouth. Furthermore, Stewart (2003) mentions that trust is transferable within online communities and Lee et al. (2011) opine that trust could be transferred to an unknown target from a trusted target if the former is considered to be associated with a trusted source. Accordingly, OCRs might possibly build trust in the e-vendor, firstly since they are posted by a related experienced party, and secondly because they are usually perceived to be credible and trustworthy. Moreover, the social presence of a website, which could be established by the mere existence of OCRs (Kumar & Benbasat 2006), is found to have a positive correlation with consumer trust (Choi et al. 2011; Gefen & Straub 2004). Drawing on the effect on purchase intention, in recent years there have been a number of attempts to identify the relationship between OCRs and firms’ performance. Although it is widely accepted that OCRs have an impact on marketing performance measures, especially purchase intention (for example, Clemons et al. 2006; Chevalier & Mayzlin 2006; Duana et al. 2008; Gauri et al. 2008; Gruen et al. 2006; Huang & Chen 2006; Hu et al. 2008; Lee & Youn 2009; Stephen & Galak 2012; Tirunillai & Tellis 2012; Zhu & Zhang 2010), mixed outcomes have been concluded. For instance, Moe and Trusov (2011) reveal that the valenced dynamics of online reviews of fragrance and beauty products could have a direct impact on sales. On the other hand, the nature of online ratings of digital microproducts was found not to play a decisive role in customer buying decisions (Amblee & Bui 2012). In the movie industry, however, the valence of online reviews was demonstrated to be a vital predictor for box office revenue (Chintagunta et al., 2010). Similarly, Dellarocas et al. (2007) outline that the valence of OCRs might serve as a rate of decay of a movie’s external publicity. Amblee and Bui (2012), in contrast, argue that the valence of online reviews is not a dependable predictor of sales. Likewise, debate continues about the effect of the volume of OCRs on firms’ performance. Amblee and Bui (2012) investigate the impact of eWOM on sales of digital microproducts and conclude that the number of recommendations and messages are significantly associated with sales. Moe and Trusov (2011) highlight that customer-generated product ratings of fragrance and beauty products have a direct effect on sales and Chevalier and Mayzlin (2006) establish the same results in the book industry, since a positive correlation between the number of online reviews and book sales was found on amazon.com. In the video games industry, in addition, the number of reviews of each online game was found to exert a positive impact (Zhu & Zhang, 2010). Duan et al. (2008) have also obtained consistent outcomes from their previous studies, as they conclude that the volume of online WOM is related to box office revenue. Furthermore, Olbrich and Holsing (2011) analysed clickstream data, which included more than 1.5 million products, and reveal that the greater the rating, the greater the possibility of purchase.

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In contrast, different results were reported on the effect of the volume of online reviews on sales. Clemons et al. (2006) determine that the number of ratings has no significant effect on sales growth in the craft beer industry. Chintagunta et al. (2010) disclose that future box office performance is not greatly affected by the volume of online WOM, and Dellarocas et al. (2007) found that the association found between the total weekly box office and the total weekly volume of reviews is likely to function as a proxy of product sales. Duan et al. support this finding in their study (2008) in which they demonstrate that the ratings of OCRs do not play a significant role in box office revenue and just serve as a forecast.

Methodology Procedure

This study aims to investigate the impact of OCRs on customer trust and intention. To achieve such an aim, a quantitative approach will be employed. Based on previous studies, it is aimed to collect 500 questionnaires.

Once the data is collected, structural equation modelling (SEM) will be performed. SEM is a very powerful technique that is widely used for model testing in market research. In this study, SEM will be used to test the model validity and the casual relationships among the latent variables. Firstly, the relationship between the variables will be examined to ensure the validity of the constructs. Secondly, a path analysis will be performed in which the hypothesised relationships among variables are tested with a linear equation system.

Measures and pilot test

The items of the questionnaire are adopted and developed from the literature, after screening and redefinition against the netnographic research. All the constructs “Perceived usefulness, Perceived ease of use, Perceived control, Source expertise, Source trustworthiness, Emotional Support, Informational support, Perceived enjoyment, Social presence, and Sense of belonging” are measured on a 5-point rating of agreement scale (1= strongly disagree to 5 = strongly agree).

A pilot study was firstly conducted to test the questionnaire on a relevant sample, representing the main population of the study. A total of 50 responses were collected. Respondents were aged 19- 40 and the sample was 66% male and 34% female. Coefficient alpha (Cronbach alpha) and item-to-total correlation were used to test the reliability and validity of the items (Churchill 1979) (see Table 1). Nunnally (1978) proposes that a score of 0.5 to 0.6 is acceptable to ensure the item’s reliability. All the above constructs, however, achieved a Cronbach alpha of greater than 0.613. In addition, item-to-total correlation was used to test the relationship of a particular item to the behaviour of the others in the same set. Edgett (1991) suggests that items should achieve an item-to-total correlation of 0.3 or above. Hence, items that scored less than 0.03 will be deleted.

Table 1 Reliability analysis for the study variables

Item Item-total

correlation

Cronbach’s alpha

Perceived Usefulness 0.874

Online customer reviews improve my online shopping performance. 0.682 Online customer reviews enhance my online shopping effectiveness. 0.650

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5

Online customer reviews increase productivity when shopping online.

0.710

I find using online customer reviews is useful. 0.640 Online customer reviews make it easier for me to shop online. 0.724 Online customer reviews make it easier for me to search and find

information about products.

0.600

Online customer reviews allow me to accomplish my task more quickly.

0.600

Perceived Ease of Use 0.815

Online customer reviews services are not complicated. 0.564 Online customer reviews services are not confusing. 0.592 Online customer reviews services do not take a lot of work as a

reader.

0.396

Online customer reviews services do not require a lot of effort. 0.451 I find it easy to understand online customer reviews. 0.770 My interaction with online customer reviews is clear and

understandable.

0.720

Perceived Control 0.779

I feel more in control of online shopping when using online customer reviews.

0.653

Through online customer reviews I have a direct influence on finding the information I need

0.611

Online customer reviews enable me to get a grip on the necessary information.

0.590

Online customer reviews give me more control over the service process.

0.616

Source Expertise 0.714

In general, people who post online reviews are usually knowledgeable about the product being reviewed

0.615

In general, people who post online reviews are experts in evaluating the product being reviewed.

0.788

Source Trustworthiness 0.718

People who post online reviews are generally trustworthy. 0.711 People who post online reviews are generally reliable. 0.790 I believe online customer reviews to be true. 0.698

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Social Presence 0.743

There is a sense of human contact in online customer reviews. 0.437 There is a sense of the personal touch in online customer reviews. 0.459 There is a sense of sociability in online customer reviews. 0.312 There is a sense of human warmth in online customer reviews 0.757 There is a sense of human sensitivity in online customer reviews. 0.640

Sense of Belonging 0.845

I feel a sense of belonging when I use online customer reviews. 0.716 I enjoy being a part of the virtual community. 0.748 I completely trust others in the virtual community of online reviews. 0.811 I am very committed to the virtual community of online reviews. 0.846 Overall, the virtual community of online reviews has a high level of

morale.

0.799

Perceived Enjoyment 0.655

I find the experience of reading online customer reviews is enjoyable.

0.642

I find the experience of reading online customer reviews is pleasant. 0.513 Online customer reviews are kind of entertaining. 0.700

Online customer reviews are interesting. 0.689

Emotional Support 0.764

Online customer reviews are usually comforting and encourage me. 0.679 I usually feel online customer reviews are on the side of the

customer.

0.705

Online customer reviews express interest and concern in my well-being.

0.699

Online customer reviews have a sense of private feelings. 0.504

Informational Support 0.788

Online customer reviews offer suggestions when they are needed. 0.706 Online customer reviews offer information to overcome problems. 0.801 Online customer reviews help me to discover the cause of problems

and provide me with suggestions.

0.697

Subjective Norms 0.752

People who are important to me think that I should shop from e-vendors that provide online customer reviews.

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People who influence my behaviour think I should shop from e-vendors that provide online customer reviews.

0.605

Attitude 0.862

I like the idea of using the Internet to shop from e-vendors that provide online customer reviews.

0.706

I think positively regarding shopping online from e-vendors that provide online customer reviews.

0.698

The idea of using the Internet to shop from e-vendors that provide online customer reviews is appealing.

0.563

I like shopping online from e-vendors that provide online customer reviews.

0.806

I enjoy shopping online from e-vendors that provide online customer reviews.

0.676

Purchase intention 0.724

I strongly recommend others should shop from e-vendors that provide online customer reviews.

0.488

I would be willing to provide my credit card details when purchasing from an online retailer that provides online customer reviews.

0.363

In the future, I will frequently shop from e-vendors that provide online customer reviews.

0.617

I would be willing to provide information to an online retailer that provides online customer reviews.

0.600

Trust 0.852

Shopping from e-vendors that provide online customer reviews is unreliable.

0.769

Shopping from e-vendors that provide online customer reviews cannot be trusted; there are just too many uncertainties.

0.711

In general, I cannot rely on an Internet vendor that provides online customer reviews to keep the promises they make.

0.649

Perceived Risk

Social Risk 0.821

The thought of buying products/ services from e-vendors that provide online customer reviews causes me concern because some friends would think I was just being showy.

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Some people whose opinions I value cause me to think that shopping from e-vendors that provide online customer reviews is foolish.

0.473

Buying products/ services from e-vendors that provide online customer reviews adversely affects other people’s opinions of me.

0.413

Time Risk 0.848

Shopping from e-vendors that provide online customer reviews could create more time pressure on me that I do not need.

0.724

Shopping from e-vendors that provide online customer reviews could lead to an inefficient use of time.

0.780

Shopping from e-vendors that provide online customer reviews take too much time.

0.652

Financial Risk 0.874

Purchasing from e-vendors that provide online customer reviews is an inappropriate way to spend my money.

0.711

If I bought from e-vendors that provide online customer reviews, I would be concerned that the financial investment I make would make not be wise.

0.791

Buying from e-vendors that provide online customer reviews does not provide value for the money I spent.

0.745

If I bought from e-vendors that provide online customer reviews, I would be concerned that I really would not get my money’s worth.

0.622

Performance Risk 0.613

When I consider buying products/ services from e-vendors that provide online customer reviews, I worry about whether they will perform as they are supposed to.

0.384

If I were to buy products/ services from e-vendors that provide online customer reviews, I would be concerned that they would not provide the level of benefits that I would be expecting.

0.165

I am not confident about the ability of products/ services that I would buy from e-vendors that provide online customer reviews to perform as expected.

0.268

Physical Risk 0.822

One concern I have about shopping from e-vendors that provide online customer reviews is that eyestrain could result from looking at the computer.

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I am concerned that using the Web to shop from e-vendors that provide online customer reviews may lead to uncomfortable physical side effects such as poor sleep quality, and backache.

0.663

I am concerned about the potential health-related risks associated with buying from e-vendors that provide online customer reviews.

0.755

Psychological Risk 0.815

The thought of buying from e-vendors that provide online customer reviews makes me psychologically uncomfortable.

0.660

The thought of buying from e-vendors that provide online customer reviews gives me a feeling of unwanted anxiety.

0.632

The thought of buying from e-vendors that provide online customer reviews causes me to experience unnecessary tension.

0.719

Security Risk 0.709

Shopping from e-vendors that provide online customer reviews is an insecure way to send sensitive information.

0.482

If you buy from e-vendors that provide online customer reviews, your credit card details are likely to be stolen.

0.555

Overall, shopping from e-vendors that provide online customer reviews is an unsafe method of transmitting sensitive information.

0.547

Conclusion

This study examines the effects of non-numerical attributes of OCRs on consumer trust and purchase intention. An integrated framework is developed, through which OCRs’ effects on purchase intention are understood. In addition, it contributes to the existing literature by arguing that a larger number of variables need to be considered in the conceptual framework for modelling the relationships between OCRs and marketing performance measures; thus, a greater number of related factors can be considered in a rational and logical way. It is believed, therefore, that the findings of this study will be valuable for both research and business communities. It will help business communities by providing a better understanding of consumers and how OCRs affect their decisions. It will also assist firms to gain useful insight and feedback, which can lead to an increase in customer trust, and will also provide them with a valuable tool for observing customer perceived risk and attitudes towards their products and, therefore, enable them to adopt the appropriate marketing strategies.

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