The Impact of User – Generated Content (UGC) on Product Reviews towards Online Purchasing – A Conceptual Framework

(1)

Procedia Economics and Finance 37 ( 2016 ) 337 – 342

Peer-review under responsibility of Faculty of Business Management, Universiti Teknologi MARA doi: 10.1016/S2212-5671(16)30134-4

ScienceDirect

FIFTH INTERNATIONAL CONFERENCE ON MARKETING AND RETAILING (5TH

INCOMaR) 2015

The Impact of User – Generated Content (UGC) on Product

Reviews towards Online Purchasing – A Conceptual Framework

Azlin Zanariah Bahtarª*, Mazzini Mudaᵇ

ªFaculty of Business Management, Universiti Teknologi MARA (Melaka) Kampus Bandaraya, MALAYSIA

ᵇFaculty of Business Management, Universiti Teknologi MARA, Shah Alam, MALAYSIA

Abstract

Social media is a virtual community that links people around the globe through many websites such as Friendster, MySpace, YouTube, Facebook and Twitter. In 2010, Instagram was introduced as a new addition to this social media family. Since then, many online retailers have started to conduct their businesses on Instagram as they see this new social media as a friendlier channel and easier to monitor compared to Facebook and Blogs. Many buyers would also prefer to purchase from Instagram as well due to less cluttered page, straightforward interface and easier to read feedbacks from other buyers. Feedbacks or also known as User – Generated Content (UGC) happens when previous buyers share their experiences online, which allows others including the potential buyers to read. Based on an extensive literature review, this paper proposes a framework to investigate the influence of UGC on consumer's online product purchase intention. Findings from this study are expected to extend the literature on UGC and give benefits to marketers and online business operators.

Peer-review under responsibility of Faculty of Business Management, Universiti Teknologi MARA.

Keywords: User – Generated Content; eWOM; Instagram; Online Purchase Intention; Marketers.

1.Introduction

The Internet innovation has created a completely new life and everything around us encountered a radical change that is essential in people’s daily lives. Previously things have to be done physically especially in making a purchase.

* Corresponding author. Tel.: +6019-3904986, +606-2857144; fax: +606-2857014.

E-mail address: [email protected]

(2)

Today, consumptions and buying activities can be done on–the–go, no matter where and when as long as consumers are connected to the internet especially when the mobile internet is available now (Muslim, Rezaei, & Abolghasemi, 2014). With the creation of user friendly gadgets and smart phones, conducting online transactions now are effortless (Kaplan & Haenlein, 2010, 2012).

Presently, consumers have a great bond with social media, thus, in making an online purchase decision, consumers rely more on information generated by other users in the social media and networks (Thoumrungroje, 2014)

.

Online consumers believe users will not only speak the good sides of a product but the negative sides as well. This study aims to investigate whether the user – generated content (UGC) can influence online buyers’ intentions. This is based on the premise that the usage of UGC might aid and assist the buyers’ decision-making and sellers’ performances.

Instagram

On 6 October 2010, Instagram was introduced and this new comer of social media channel has recorded to have over 300 million of users as of until December 2014 (Geurin-Eagleman & Burch, 2015). This application allows users to share their photos and 15 seconds videos instantly (Geurin-Eagleman & Burch, 2015; Hu, Manikonda, & Kambhampati, 2014) as the Instagram FAQ (2015) has described this application as “a fun and quirky way to share your life with friends through a series of pictures”. Users today perceived this application as hip, trendy, and up to date. Moreover, the fact that this application is simple and less clutter as compared to Facebook and Blogs, users prefer to spend more time scrolling on the timeline and read the captions, comments and feedbacks prepared and left by other users. Result to this overwhelmed number of users, it attracts many sellers to start their business and conduct their promotion activities on Instagram (Geurin-Eagleman & Burch, 2015).

2.Literature and Hypotheses

2.1. User – Generated Content (UGC)

User – generated content (UGC) or also known as electronic word – of – mouth (eWOM) works exactly like common word–of–mouth (Manap & Adzharudin, 2013) except that it spreads input through an online medium. By definition, UGC refers to any own created material uploaded to the Internet by non–media and it has a greater influence on people’s consumption (Cheong & Morrison, 2008; Dijck, 2009; Jonas, 2010; Krishnamurthy & Dou, 2010; Presi, Saridakis, & Hartmans, 2014) where the contents are generally be shared on social media such as on facebook, YouTube, Twitter and Instagram. Hennig-Thurau, Gwinner, Walsh, & Gremler (2004) defined eWOM/UGC as follows “..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 people and institutions via the Internet”.

Compared to producer–generated content (PGC) that usually hired endorsers and celebrities to speak the advantages and benefits of the products (Verhellen, Dens, & Pelsmacker, 2013), consumers have turned away from the common promotion practices (Hassan, Nadzim, & Shiratuddin, 2015) as they have perceived UGC as more credible. The shared contents in UGC are based on consumers’ own experiences. As such, it is proven to be more trustworthy, useful and unbiased (Buttle, 1998; Mir & Rehman, 2013; Jonas, 2010; Verhellen, Dens, & Pelsmacker, 2013). Potential consumers trust the content generated by other users in regards to brands and products mainly because they perceive the users do not have any commercial interest (Mir & Rehman, 2013).

Thus, online consumers always rely on the content generated by other users to assist them in making a purchase decision (Bae & Lee, 2011). Based on the above discussion, we propose these hypotheses:

H1 : User – generated content has a positive effect on the perceived credibility H2 : User – generated content has a positive effect on the perceived usefulness H3 : User – generated content has a positive effect on the perceived risk

2.2. Perceived Credibility

Today's online consumers perceived and believed that contents generated by other users as more credible than the content provided by the sellers (Jonas, 2010). The parties/users involved in UGC view each other as a highly

(3)

credible source of information. Ohanian (1990) defined credibility as communicator’s positive characteristics that able to influence receiver acceptance of certain information, and able to provide correct information to other users (Racherla & Friske, 2012). Credibility mainly comprises expertise and trustworthiness. Thus, it can be postulated that credibility may influence internet users’ attitude towards the content generated by others.

There were arguments regarding the credibility of the unknown source where their level of expertise is in question. However, it was proven in the study conducted by Hansen, Jin and Lee (2014) that consumers prefer UGC rather than the traditional promotional methods due to the users’ honest opinion. This statement can also be supported by another study conducted by Manap and Adzharudin (2013). These authors found that this method is increasingly popular in tourism industry and recognised it as an official marketing strategy (Kucukusta, Law, Besbes, & Legoherel, 2015). To conclude, if the consumers positively support and trust the information regardless of who the sources are, the consumers can easily be persuaded to purchase the products (Waldt, Loggerenberg & Wehmeyer, 2009). Thus, we suggest the following hypothesis:

H4 : Perceived credibility has a positive effect on attitude towards UGC

2.3. Perceived Risk

Risk is an uncertainty that normally unavoidable unless online consumers have prior knowledge and experiences to enable them for not making a poor decision (Dai, Forsythe, & Kwon, 2014). A high-risk perceiver would always conduct information seeking by obtaining input from UGC as a common practice especially in purchasing either a new product or an expensive product due to the anxiety involved. (Gemünden, 1985; Brown, Pope, & Voges, 2003; Gefen & Straub, 2004; Shergill & Chen, 2005; Lee & Moon, 2015). Perceived risk is subjective to different users as individuals would have their perceptions on matters and might have past experiences. Perceived risk is defined as consumers’ level of uncertainties regarding the outcome of online purchase decision (Featherman & Pavlou, 2002). However, the risks can be lessened if the online consumers able to get and understand the information about the intended purchase product before making the purchase decision (Gefen & Straub, 2004). There are different types or facets of perceived risk such as performance risk, financial risk, time risk, psychological risk, social risk, privacy risk, source risk and overall risk. Therefore, we suggest this hypothesis:

H5 : Perceived risk has a negative effect on perceived usefulness

2.4. Perceived Usefulness

Perceived usefulness can be a factor that will influence online consumers to respond to the information provided by others, affect their attitudes and lead to purchase the discussed product (van der Heijden, Verhagen, & Creemers, 2003; Horst, Kuttschreuter, & Gutteling, 2007; Kim & Song, 2010; Muslim et al., 2014). Users view information provided by other users as “user-friendly” where the content is easier to understand regardless of the length of the information (Racherla & Friske, 2012).

Perceived usefulness is defined as the degree to which a person believes that the use of a system would improve one performance (Davis, 1989; Karahanna & Straub, 1999; Muslim et al., 2014). In this study, perceived usefulness is operationalized as the overall wealth of usefulness of the message or information shared by other users on Instagram. For this study, usefulness is also associated with quick, efficient, and ease (Racherla & Friske, 2012). The contents posted on the social media are based on users own experiences. The shared information can facilitate the online consumers to reduce their perceived risks in making decisions (Horst et al., 2007; Racherla & Friske, 2012; Muslim et al., 2014). Other than that, UGC also assists the online consumers in getting all the information in lesser time and obtaining prompt feedback direct from the sources (Davis, 1989; Featherman & Pavlou, 2002; Racherla & Friske, 2012). Hence, we postulate this hypothesis.

H6 : Perceived usefulness has a positive effect on attitude towards UGC

2.5. Attitude towards UGC

Customer’s favourable attitude will lead to buying intention and attitude of a consumer rapidly changes according to behaviour, motivation, demographic, reference groups, satisfaction/dissatisfaction, purchasing scenario,

(4)

selling environment, innovation of product, and technology advancement (Li & Zhang, 2002; Laforet & Li, 2005; Jun & Jaafar, 2011). Shergill & Chen (2005) mentioned in their study where a pleasant and joyful experience in online shopping will make the buyers have a positive attitude. Previously, in traditional setting, ambient and environment of the retail outlet will be able to attract the consumers. Therefore, for online shopping, the layout should not be too dull. As mentioned earlier, the interface of Instagram is not as cluttered as much any other social media. This will make browsing for information generated by other users more convenient and online shopping experience will be more satisfying and less stressful. Positive consumer’s attitude towards content generated by others and browsing experience will enhance the probability of purchase.

H7 : Positive user attitude towards UGC will influence buying intention

2.6. Online Purchase Intention

Typically, consumers today would usually log on to social media to seek for product information and previous customers’ feedbacks before they decide the purchasing as they rely more on the content generated by other users especially on Instagram (Brown et al., 2003, Horst et al., 2007; Harris, 2012; Racherla & Friske, 2012). This is due to other users usually have the experiences on the intended purchase products, which will assist the potential buyers in making their purchase decision. Uncountable photos and videos of the products can be found on the Instagram. The reviews are uploaded either by the sellers or by other users. Consumers would usually purchase an item after they have read through all the personal informatio generated by other users on the platform and convince enough with what they have analysed (Horst et al., 2007).

2.7. Conceptual Model

Commonly, consumers would search for product information prior to the purchasing and that is what being suggested in previous study conducted by Mir & Rehman (2013) in which the researchers had combined various theories to come out with their framework such as; Technology Acceptance Model, Theory of Reasoned Action and Theory of Planned Behavior. With modification and changes, the researchers have restructured the framework by adding two other variables; Perceived Risks and Online Purchase Intention. In researches conducted by Featherman & Pavlou (2002) and Horst, Kuttschreuter, & Gutteling (2007), it was found that UGC will help to reduce the perceived risks whereby the information generated will assist the potential consumers in making the right choice.

Figure 1: Conceptual Model

User – Generated Content Perceived Credibility Attitude towards UGC Perceived Usefulness Perceived Risk Online Purchase Intention H1 H2 H3 H4 H6 H5 H7

(5)

3.Methodology

To collect data on the proposed model, a self-administered questionnaire accompanied by copies of printed version of the Instagram interface will be used. In this study, a non-probability sampling frame for the stipulated population will be undertaken based on a Quota Sampling Procedure approach. The approach is chosen, as it would able to facilitate the researchers in understanding the online purchase intention of consumers from various geographical areas and backgrounds. The printed copies of the Instagram interface will assist the respondents in answering the questionnaires. Respondents will be instructed to peruse the printed copies for a few seconds before filling up the questionnaire. The answered questionnaires will be analysed using Statistical Package for Social Science (SPSS) and Structural Equation Model (SEM).

4.Conclusion

Word – of – mouth (WOM) and User – Generated Content (UGC) are not new research areas as many studies had been conducted since 1960’s and the early of year 2000 respectively. UGC is an activity of online users self – express themselves through sharing their views, opinions and feedbacks on what they have consumed or used. The contents are usually shared on social media because they believe the shared information will aid others in making their purchase decisions. The previous studies mainly focus on people seeking for information in assisting themselves in making a purchase decision and also share their views on other’s videos on YouTube but rarely on other social media mediums. Thus, this proposed study aims to understand how UGC can influence Instagram users to purchase products promoted and reviewed on Instagram and will content provided by unknown users influence one’s decision to purchase. Judging at the current economic scenario with uncountable numbers of Instagram sellers, inputs from others will definitely influence a person’s decision to purchase.

References

Bae, S., & Lee, T. 2011. Product Type and Consumers’ Perception of Online Consumer Reviews. Electronic Markets, 21(4), 255–266.

Brown, M., Pope, N., & Voges, K. 2003. Buying or Browsing?: An Exploration of Shopping Orientations and Online Purchase Intention. European Journal of Marketing, 37(11/12), 1666–1684.

Buttle, F. A. 1998. Word of Mouth: Understanding and Managing Referral Marketing. Journal of Strategic Marketing, 6(3), 241–254.

Cheong, H. J., & Morrison, M. A. 2008. Consumers’ Reliance on Product Information and Recommendations found in UGC. Journal of Interactive Advertising, 8(2), 1–30.

Dai, B., Forsythe, S., & Kwon, W. S. 2014. Impact of Online Shopping Experience on Risk Perceptions and Online Purchase Inten tions: Does Product Category Matter? Journal of Electronic Commerce Research, 15(1), 13–24.

Davis, F. D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quality, 13(3), 319–340. Featherman, M. S., & Pavlou, P. A. 2002. Predicting e-Services Adoption: A Perceived Risk Facets Perspective. In 8 th American Conference on

Information System, (Vol. 9, pp. 1034–1046).

Dallas. Gefen, D., & Straub, D. W. 2004. Consumer Trust in B2C Commerce and the Importance of Social Presence: Experiments in e-Products and e-Services. Omega, 32(6), 407–424.

Dijck, J. Van. (2009). Users Like You? Theorizing Agency in User-Generated Content. Media, Culture & Society, 31(1), 41–58.

Gemünden, H. G. 1985. Perceived Risk and Information Search. A Systematic Meta-Analysis of the Empirical Evidence. International Journal of Research in Marketing, 2(2), 79–100.

Geurin-Eagleman, A. N., & Burch, L. M. (2015). Communicating via photographs : A Gendered Analysis of Olympic Athletes’ Visual

Self-presentation on Instagram. Unpublished Journal. Sport Management Review. Sport Management Association of Australia and New Zealand.

Harris, C. G. 2012. An Evaluation of Search Strategies for User-Generated Video Content. In Proceedings of First International WWW Worshop on Crowdsourcing Web Search (Vol. 842, pp. 31–35).

Hassan, S., Nadzim, S. Z. A., & Shiratuddin, N. 2015. Strategic Use of Social Media for Small Business Based on the AIDA Model. Procedia - Social and Behavioral Sciences, 172, 262–269.

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.

Horst, M., Kuttschreuter, M., & Gutteling, J. M. 2007. Perceived Usefulness, Personal Experiences, Risk Perception and Trust as Determinants of Adoption of EGovernment Services in The Netherlands. Computers in Human Behavior, 23(4), 1838–1852.

Hu, Y., Manikonda, L., & Kambhampati, S. (2014). What We Instagram: A First Analysis of Instagram Photo Content and User Types.

Association for Advancement of Artificial Intelligence.

Jonas, J. R. O. 2010. Source Credibility of Company - Produced and User - Generated Content on the Internet: An Exploratory Study on the Filipino Youth. Philippine Management Review, 17, 121–132.

Jun, G., & Jaafar, N. I. 2011. A Study on Consumers’ Attitude towards Online Shopping in China University of Malaya. International Journal of Business and Social Science, 2(22), 122–133.

(6)

Kaplan, A. M., & Haenlein, M. (2010). Users of the World, Unite! The Challenges and Opportunities of Social Media. Business Horizons, 53(1), 59–68.

Kaplan, A. M., & Haenlein, M. (2012). Social Media: Back to the Roots and Back to the Future. Journal of Systems and Information Technology,

14(2), 101–104.

Karahanna, E., & Straub, D. W. 1999. The Psychological Origins of Perceived Usefulness and Ease-of-Use. Information & Management, 35(4), 237–250.

Kim, H., & Song, J. 2010. The Quality of Word-of-Mouth in the Online Shopping Mall. Journal of Research in Interactive Marketing, 4(4), 376– 390.

Krishnamurthy, S., & Dou, W. 2010. Note from Special Issue Editors: Advertising with User - Generated Content: A Framework and Research Agenda. Journal of Interactive Advertising, 8(2 (Spring)), 1–4.

Featherman, M. S., & Pavlou, P. A. (2002). Predicting e-Services Adoption: A Perceived Risk Facets Perspective. In Eigth American Conference

on Information System (Vol. 9, pp. 1034–1046). Dallas.

Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived Usefulness, Personal Experiences, Risk Perception and Trust as Determinants of Adoption of E-Government Services in The Netherlands. Computers in Human Behavior, 23(4), 1838–1852.

Kucukusta, D., Law, R., Besbes, A., & Legoherel, P. (2015). Re-Examining Perceived Usefulness and Ease of Use in Online Booking: The Case of Hong Kong Online Users. International Journal of Contemporary Hospitality Management, 27(2), 185–198.

Mir, I. A., & Rehman, K. U. (2013). Factors Affecting Consumer Attitudes and Intentions toward User - Generated Product Content on YouTube.

Management & Marketing Challenges for the Knowledge Society, 8(4), 637–654.

Laforet, S., & Li, X. 2005. Consumers’ Attitudes towards Online and Mobile Banking in China. International Journal of Bank Marketing, 23(5), 362–380.

Lee, H.-H., & Moon, H. 2015. Perceived Risk of Online Apparel Mass Customization: Scale Development and Validation. Clothing and Textiles Research Journal, 33(2), 115–128.

Li, N., & Zhang, P. 2002. Consumer Online Shopping Attitudes and Behavior: An Assessment of Research. AMCIS 2002 Proceedings, 74, 508– 517.

Manap, K. H. A., & Adzharudin, N. A. 2013. The Role of User Generated Content (UGC) in Social Media for Tourism Sector. In West East Institute International Academic Conference Proceedings (pp. 52–58). Istanbul, Turkey.

Mir, I. A., & Rehman, K. U. 2013. Factors Affecting Consumer Attitudes and Intentions toward User - Generated Product Content on YouTube. Management & Marketing Challenges for the Knowledge Society, 8(4), 637–654.

Muslim, A., Rezaei, S., & Abolghasemi, M. 2014. User Satisfaction with Mobile Websites: The Impact of Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Trust. Nankai Business Review International, 5(3), 258–274.

Ohanian, R. 1990. Construction and Validation of a Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness. Journal of Advertising, 19(3), 39–52.

Presi, C., Saridakis, C., & Hartmans, S. 2014. User-Generated Content Behaviour of the Dissatisfied Service Customer. European Journal of Marketing, 48(9/10), 1600–1625.

Racherla, P., & Friske, W. 2012. Perceived “Usefulness” of Online Consumer Reviews: An Exploratory Investigation Across Three Services Categories. Electronic Commerce Research and Applications, 11(6), 548–559.

Shergill, G. S., & Chen, Z. 2005. Web-Based Shopping : Consumers ’ Attitudes Towards Online Shopping in New Zealand. Journal of Electronic Commerce Research, 6(2), 79–94.

Thoumrungroje, A. (2014). The Influence of Social Media Intensity and EWOM on Conspicuous Consumption. Social and Behavioral Sciences, (November 2012), 7–15.

Van der Heijden, H., Verhagen, T., & Creemers, M. 2003. Understanding Online Purchase Intentions: Contributions from Technology and Trust Perspectives. European Journal of Information Systems, 12(1), 41–48.

Verhellen, Y., Dens, N., & Pelsmacker, P. De. 2013. Consumer responses to Brands Placed in YouTube Movies: The Effect of Prominence and Endorsers Expertise. Journal of Electronic Commerce Research, 14(4), 287–303.

Waldt, D. Van Der, Loggerenberg, M. Van, & Wehmeyer, L. 2009. Elebrity Endorsements Versus Created Spokespersons in Advertising: A Survey Among Students. SAJEMS NS, 12(1), 100–114