2014 International Symposium on Technology Management and Emerging Technologies (ISTMET 2014), May 27 - 29, 2014, Bandung, Indonesia
Understanding factors on the customer intention behavior through Facebook commerce: a conceptual model Aryati Bakri Faculty of Computing University Technology Malaysia Johor, Malaysia
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Seyedeh Marjan Mahdavi Anari Faculty of Computing University Technology Malaysia Johor, Malaysia
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Roliana Ibrahim Faculty of Computing University Technology Malaysia Johor, Malaysia
[email protected] Businesses all over the world, in their plight to satisfy existing customers and acquire new ones have persistently sought ways to address this challenge [3]. With the ever growing population of the Internet and FB in particular, more representation of the mainstream population is now streamed in an inter-networked communication platform of people from diverse backgrounds and regions. This potential not only presents enormous benefits to businesses but also the challenge of how best to fully tap into this virtual global wealth.
Abstract— In this research paper, authors investigated the Facebook users behavior parameters that have effect over the customers shopping intention on Facebook commerce whereby the number of Facebook users is increased every day and a lot of people spend a lot of time on Face book. So, it is a heaven of market for e-trader and it is very significant to understand the behavior of this type of customers for being successful. The research focused on the development of research model to test the impact of Facebook users’ intention behavior factors through shopping on Facebook. The literature review was done to explore the work done on social commerce and Facebook commerce. The authors identify the problem that Facebook with a 1.11billion users worldwide, can be described as a “marketer’s haven” whereas only 10% of retailers sold through Facebook, and 24% had not created a Facebook page. In order to achieve them, a research model is proposed that each factors identified by Literature review and in-depth interview.
Several studies have shown that marketing goods and services in the social media differ from traditional market approaches [4,5,6,7]. Moreover, there seems to be a problem with the storefront, which is Facebook, since consumers seem not to be ready to buy goods and services in Facebook at the scale that retailers are used to sell through traditional ecommerce. In addition, customer behavior through Facebook is different than E-commerce. So, marketing of goods and services in FB demands a proper insight into consumers’ intention behaviour to purchase as well as a good understanding of how technologies affect the traditional ideas behind existing theories and marketing models. It requires managers and marketers alike to find out the processes involved in consumer intention behavior so as to devise marketing strategies to best align with this new mode of business. In order to successfully do this, certain factors must be considered; antecedent variables that influence users behaviour with respect to their intention to make online purchase.
Keywords—Facebook commerce; Intention behavior; online shopping; E-customer
I.
INTRODUCTION
The advent of the Internet has changed dramatically the consumers’ lifestyle particularly the younger generation of consumers and their shopping patterns. Online social networks such as Facebook (FB) facilitate collaboration, interaction and communication between individuals connected to the network from different parts of the globe. From the business perspective, social networks facilitate interaction through connections and communication between clients and marketers, and provide an enabling environment for businesses to expand their scope of business [2].
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Understanding online consumers’ intention behaviour and their intention to use social networks for their online shopping has been the focal point of many businesses especially in the social media such as Facebook. With a whopping 1.11billion users worldwide, Facebook can be described as a “marketer’s haven”. As more people continue to discover and use this
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social outfit on a daily basis, its accelerated growth has made Facebook develop a more representation of the real world people and at the same time, more diverse and interconnected.
Sandelowski [17] stated that through the use of qualitative methods of data collection helps researchers observe and record individual behaviour patterns and compare them to reality without changing the research variables. WMMA at 2011 advocated for the use of qualitative technique as an appropriate method for understanding phenomenal behaviours. The flexibility of data collected through qualitative methods enables the research to be in-depth, consumer-centric, and focused on emerging consumer perspectives rather than a researcher-defined agenda, which is most common with quantitative techniques. Facebook, being a new and highly evolving online social market, demands new unstructured approaches to understanding its complex and highly proliferating and interactive variables and dynamics. This, according to Jepsen and Rodwell [18], has been a major reason why past researchers have failed in their attempt to use structured models and quantitative methods to understand online consumers’ shopping intention.
The aim of this paper is to investigate the factors and design a conceptual model that influences Facebook users’ intention behaviour so as to be able to understand and determine online consumers shopping intention for emarketers. II.
LITERATURE REVIEW
Even though several (if not all) businesses have registered their presence in this social media, yet, most of them adopt same old traditional strategies and methods in achieving their objectives. Most marketers and businesses still believe in “the more the better” approach whereby their goal is to reach as many people as possible with the same message or marketing campaign as common with the conventional way of marketing in most traditional media [8]. This approach has not yielded much result as the volume of Facebook trade is still marginally low given its huge market potentials. Out of the top 500 retailers, only 10% sold through Facebook, and 24% had not created a Facebook page [1]. The use of advanced technologies has made it possible for businesses and marketers to easily hit their target audience with specific and unique marketing campaigns suitable for each consumer to meet their unique needs. However, there is a sharp contrast between Facebook marketing (or online social market) and traditional markets.
Sands et al. [19] identified some of the factors that should be considered when dealing with online consumers. She suggests that a good understanding of consumer intention behaviour and good insight as to how online technologies challenge the traditional concepts behind existing theories and models must be foreknown. This implies that a good marketer can adapt his or her marketing plans and tactics to suit a specific consumer if aware of that consumer’s decisions making process thereby converting the intention to purchase of the prospective purchaser into an action – an actual purchase.
The relationship between social and commerce networks is at the beginning of its exploration and due to its economic importance and value in consumer behavior research it’s important to be studied [10]. Consumers seem not to be ready to actually buy goods and services in Facebook at the scale that retailers are used to sell through traditional e-commerce.
Numerous researchers such as Hung et al [20], Delafrooz et al. [21], and Ha and Stoel [22] have established that trust definitely effects on approaches in the direction of online shopping. In the perspective of Jarvenpaa et al. [23] exactly displayed that trust has an important consequence on customer’s attitudes for online shopping in different cultures.
According to Baker [11], more consumers now learn about new or existing brands through social media. Harridge and Quinton [11] asserts that consumers gave over 500 billion different impressions about products and services in Facebook alone in 2011, which makes up about one-quarter of the total impression created through all forms of online advertising. Riegner’s [12] studies indicated an increase in percentage of the consumers who were influenced to purchase products or services through Facebook ratings and reviews from 12% in 2009 to 57% in 2011. Nowadays, consumers increasingly discuss about virtually everything regarding their purchase or intention to purchase on Facebook.
It has been exposed that customers will hesitate for online shopping if they do not feel certain, and information of their credit card is protected and secure from potential hackers. Some previous researches such as [24] that conducted on online shopping setting, specifies that customers’ insights of privacy have an important and positive consequence on their trust in online retailer. The quantifiable significance of this matter is exposed by [25], who mentioned that the defense of privacy is the anxiety issue in Internet purchasers. According to several studies by [26, 27] ,the previous online shopping experience intensely effects on purchase intentions. The results also propose that prior experiences purchasing online contribute in decreasing customer’s worries. In the perspective of [28], the possible influence on purchase intention intended for experience properties is fewer clear.
Many of the previous studies on online social consumers’ intention behaviour employed the use of quantitative methods of data collection in identifying factors that influence consumers’ intention behaviour. Cronin, Brady and Hult [13] and Fisher et al. [14], observed that qualitative metrics are best measures for desirable signs of tone, quality and benefits of consumer interaction. According to them, this method of inquiry enables the researcher conduct an in-depth study from a broader perspective [15]. Proctor and Capaldi [16] describes qualitative research as a method through which an individual’s different perceptions can be examined in order to develop a shared meaning that can narrate descriptive information to help the researcher comprehend a socio-cultural phenomenon.
According to several researchers such as [29,30], consuming organization arrangement, they illustrated the effect of product category on online behavior and established that customers desire to use the Internet to purchase search products rather than the experience goods. Price in documents remained particularly the incentive for initial e-shoppers supposing to discover the cheapest prices from e-tailers by way of a payment intended for their risk
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taking. According to [31] this may be altering currently; since as recognized the online values have increased in spite of the circumstance that the buying cost is quiet one of the clients’ main anxieties. Nevertheless, based on several studies previous experience through an e-brand or worth credit of a product affects the customers’ opinions approximately about the significance of the buying price.
comparison purposes [36]. Details of interviews results and analysis will be discussed in the next section. In this study, the students of University Technology Malaysia (UTM) are used as sample subject, because in ecommerce studies, some researchers suggested that using university students as sample subjects is common and they recommend that college students signify a large percentage of web users and are applicable subjects [37, 38]. Also, Walczuch and Lundgren [39] discussed that students are suitable and representative sample subjects in e-retailing research because (1) they can access to Internet freely and have the chance to use this channel for communication and marketable transactions; and (2) they have rich experience and strong reasons for online shopping.
Empirical evidence reveals that income, gender, race, education, and occupation are socio-demographic factors that influence the use of Facebook. Hanson [32] suggested that online marketers need to closely observe existing and prospective consumer demographics. Most of the studies reviewed predominantly used TPB and TAM, and very few used TRA to identify the factors influencing consumers’ online intention behaviour. Our findings reveal that even though TPB was widely used by many studies, the introduction of DTPB, an extended (and improved) version of TPB was more suitable. It is important to understand the contribution and differences of each model in explaining online consumer intention behaviour. Although TAM and TPB derive from TRA, there are differences between these models’ approach to understanding behaviour. Empirical tests comparing TAM, TPB and DTPB, showed that the three models achieved similar fit to the data and that all three models are commendable in terms of their ability to explain overall behaviour, although subjective norm and perceived behavioural control added slightly to the prediction of behaviour. However, the results also showed that DTPB had better explanatory power over TPB and TAM, when behavioural intention is considered [33, 34].It should be noted that TAM is more parsimonious than TPB, and therefore more useful in researches focused on achieving an overall understanding of behaviour [33]. On the other hand, DTPB sacrifices parsimony but provides better insight into the determinants of behavioural intention and actual behaviour [33]. Therefore, it is reasonable to conclude that DTPB is the most appropriate for the purpose of the present study. III.
So, the respondents of this research were sixteen UTM students. According to interview with them, they believe that gender and education are two important factors, which are related to intention to buy online through Facebook. The age of all respondents is between 20 and 45. The gender is female and male, the educational level are students of bachelor, master and PhD degree. IV.
FINDING AND DISCUSSION
The finding describes the dimensions, factors and variables, which are related to each dimension according to literature. Firstly, perceived usefulness, perceived ease of use and compability are related to attitude toward behavior of respondents. Secondly, subjective norm factors are consisted of interpersonal influence and internal influence. Thirdly, perceived behavioural controls are factors such as selfefficiency and faciliting conditions. In addition, these above mentioned factors are related to the theory of DTPB. Fourthly, the other factors are investigated from literature, which defined them as the main and important factors for knowing about ecustomer intention behavior toward F-commerce. These factors are trust, privacy, demography, previous experience, type of products and price. The most of dimensions are related to DTPB according to the literature. The trust, privacy, demography, previous experience, type of products and price factors are found according to the previous related literatures. So, in continues the opinion of Facebook users about the factors which are influenced their intention behavior to purchase toward Facebook is analyzed.
METHODOLGY
An in-depth interview was conducted to answer the research question of this study. In qualitative research, the use of theory is much more varied and it comes at the beginning and provides a lens that shapes what is looked at and the questions asked [35]. In this study, the theory of DTPB is used to provide the answer to the research question.
A. Attitude toward behavior Factors: The factors related to attitude toward behavior are perceived ease of use, perceived usefulness and compatibility. Then of respondents agree about the effect of perceived ease of use and usefulness on their intention to purchase behavior through Facebook meanwhile, 12 of them are disagree about the factor of compability on their behavior. For example, some selected answers of the responses are:
This research study includes of three main phases. The contribution of phase A is the factors that are related to ecustomer intention behavior through Facebook commerce and using in-depth interview for finding new factors and justifying the identified factors from literature. Phase B is data collection and analysis. The outcome of phase B is a model to investigate major factors for online customer intention behavior through Facebook commerce. Finally, for achieving objective of Phase C qualitative method will be used to offer the best model for emarketer to predict online customer intention behavior through Facebook commerce. After the interview was conducted, the data were analyzed using Atlas ti.6 for coding purposes. The codes were applied to themes for finding similar ideas and case
“-The company page is clear and easy understandable. -The product information is difficult to search. - It is an inconvenient to logging-on to company homepage.
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- It’s enjoyable and fun.
-I tried to buy through Internet but it needs a high internet speed for loading photos, so, It took a long time.”
-It is just for communication and sharing photo, no market tool.
D. Behavioral intention All the interviews analyzed for finding other factors that respondents said about behavioral intention. They express “Trust” has very important factor for them to buy through Facebook. Also, they mentioned about their privacy, because their opinion about Facebook is a private virtual place. So, they worried about their private pictures and information. In addition, having previous experience more encourage them to try this new virtual market. Moreover, they talked about the type of products that they prefer to buy online. So, tangible and intangible goods are important. Also, they mentioned the most encouragement factor to buy through Facebook is price. Based on their opinion, good discount and getting voucher are the advantages of purchasing online. Finally, their behavioral intention is categorized by their gender and education levels. Based on the result, female prefer to buy through Facebook rather than male and they have more intention too. Also, educated people such as PhD level more intended to purchase through Facebook. So, this section of society should be the main goal for e-marketer for increasing their benefit.
- It does not waste time travelling to shops. -Free sample are available. -I never try for buying something from Internet. -I heard it has some app for marketing. -Marketing and Facebook cannot combine. “ B. Subjective norm The factors related to subjective norm are interpersonal and external influences. As a result of in-depth interview, 13 of the respondents mention that interpersonal influences encourage them to use Facebook as a marketing tool, although three of respondents believe that their don’t have any internal influence to purchase through Facebook meanwhile, four of respondents mentioned external influence such as friends comment or advertisements don’t have any intention for them to buy through Facebook. Therefore, it is reasonable that the main factors of subject norm are internal and external influence. For example, some selected answers of the responses are:
Therefore, the main extra factors have more effective on intention behavior of customer toward F-commerce are: Gender, Age, Price, Product type, Trust, Privacy, Previous experience and some constructs of DPTB.
“ -My friend’s suggestions have very influence on me to try their experience. - I never trust anyone when I want to pay money.
V.
-I purchase online a lot and I search on the Internet, I think personally attended to buy online.
CONCLUSION
In conclusion, a conceptual model based on DTPB is developed. The conceptual model draws upon the idea credited to [34] that DTPB beliefs can be decomposed into multidimensional constructs. In addition trust, previous experience and product attribute are integrated into the model based on the literature review and empirical findings support them as major determinants of consumer intention behavior; finally product type, price, gender, education and privacy integrated to the model based on the interviews analyzing. Therefore, this proposed model in fig 1 will attempt to clearly identify the factors that influence online consumers intention behaviour using a qualitative method of data collection to identify influencing factors and the proposed model within the context of a well-established behavioural theory so as to enable marketers and retailers understand FB users’ online shopping intentions behaviour.
- I am not familiar with Facebook shopping. Maybe my friends if tried this before and they are satisfied, I try to shop one time. “ C. Perceived behavioral control Perceived behavioral control factors are Self-efficiency and faciliting conditions. From the result, it can be clearly seen that most of the respondents believe that self-efficiency play an important role in their intention behavior for purchasing through Facebook; (12 responders). Although, they didn’t mention faciliting condition related to their intention behavior, because most of them claim user of Facebook have enough faciliting conditions such as access to the Internet, having computer and other things. Therefore, based on their opinion, the main factor of perceived behavioral control is selfefficiency for encouraging them to purchase online toward Facebook. For example, some selected answers of the responses are: “-I am using internet every day, so, for purchasing I think I don’t need any extra facility that cause I don’t buy. - I am living in Iran and I don’t have Master or Visa card, so, I don’t have enough facility to purchase. - I always buy online through Internet and I love it. So, I think I have enough skill to purchase online.
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[13] Cronin Jr, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218. [14] Fisher, E. S., Bynum, J. P., & Skinner, J. S. (2009). Slowing the growth of health care costs—lessons from regional variation. New England Journal of Medicine, 360(9), 849-852. [15] Barnham, C. (2010). Qualis: The qualitative understanding of essence. International Journal of Market Research, 52, 757-773. doi:10.2501/S1470785310201648 [16] Proctor, R. W., &Capaldi, E. J. (2008). Qualitative research methods. Why science matters: Understanding the methods of psychological research. Oxford, UK: Blackwell. doi:10.1002/9780470773994.ch8 [17] Sandelowski, M. (2000 a). Combining Qualitative and Quantitative Sampling, Data Collection, and Analysis Techniques in Mixed‐Method Studies. Research in nursing & health, 23(3), 246-255. [18] Jepsen, D. M., &Rodwell, J. J. (2008). Convergent interviewing: A qualitative diagnostic technique for researchers. Management Research News, 31, 650-658. doi:10. 1108/01409170810898545 [19] Sands, S., Harper, E., & Ferraro, C. (2011). Customer to noncustomer interactions: Extending the social dimension of the store environment. Journal of Retailing and Consumer Services, 16, 438-447. doi:10.1016/j.jretconser.2011.06.007 [20] Hung, S.-W., Cheng, M.-J., & Chen, P.-C. (2012). Reexamining the factors for trust in cultivating online customer repurchase intentions: The moderating effect of perceived waiting. International Journal of HumanComputer Interaction, 28, 666-677. [21] Delafrooz, N., Paim, L. H., Haron, S. A., Sidin, S. M., & Khatibi, A. (2009). Factors affecting students’ attitude toward online shopping. African Journal of Business Management, 3(5), 200-209. [22] Ha, S., &Stoel, L. (2008). Promoting consumer-retailer relationship building: Influence of consumer trustworthiness of customer loyalty program Brinkmann keting. Journal of Customer Behaviour, 7(3), 215229. [23] Jarvenpaa, S. L., Tractinsky, N., and Vitale, M. .Consumer trust in an Internet store,. Information Technology and Management (1), 2000, pp. 45.71 [24] Chiu, C.-M., Lin, H.-Y., Sun, S.-Y., & Hsu, M.-H. (2009). Understanding customers' loyalty intentions towards online shopping: An integration of technology acceptance model and fairness theory. Behaviour& Information Technology, 28, 347-360. [25] Casalo, L. V., Flavian, C., & Guinaliu, M. (2007). The influence of satisfaction, perceived reputation and trust on a consumer's commitment to a Website. Journal of Marketing Communications, 13, 1-17. [26] Gefen D, Karahanna E, Straub DW. Inexperience and experience with online stores: the importance of TAM and trust. IEEE Trans. Eng. Manage. 2003;50(3):307. [27] Dennis, C., Merrilees, B., Jayawardhena, C., & Wright, L. T. (2009). Econsumer behavior. European Journal of Marketing, 43, 1121-1139 [28] Y.-Y., Han, S., Kwak, H., Moon, S. and Jeong, H. (2007), ―Analysis of topological characteristics of huge online social networking servicesǁ‖, Proceedings of the 16th International Conference on World Wide Web, Banff,ACMPress, New York, NY, pp. 835-44. ine," Journal of Computer-Mediated Communication, 10 (1). [29] Li, D. H., Browne, G. J., & Wetherbe, J. C. (2006). Why do Internet users stick with a specific Web site? A relationship perspective. International Journal of Electronic Commerce, 10, 105141. [30] Girard, T., Korgaonkar, P., & Silverblatt, R. (2003). Relationship of type of product, shopping orientations, and demographics with preference for shopping on the Internet. Journal of Business & Psychology, 18, 101120. [31] Wu, J., Arturo, E., & Gaytan, A. (2013). The role of online seller reviews and produce price on buyers’ willingness-to-pay: A risk perspective. European Journal of Information Systems, 22, 416-433. [32] Schiffman, L. G., Hansen, H., & Kanuk, L. L. (2008). Consumer behaviour: A European outlook. Pearson Education.
Fig. 1. An conceptual model for customer intention behavior through Facebook commerce.
REFERENCES [1]
Stambor, Z. (2011). As social commerce spreads, half of American adults say they use social networks. Retrieved from http://www.internetretailer.com/2011/08/30/half- american-adults-saythey-use-social-networks [2] Carter, B. (2006). Advertisers dip toe into virtual world. Retrieved from www.deakin.edu.au/arts-ed/apprj/articles/11-miller-lammas.pdf [3] Kotler, P. (2000). Marketing management: The millennium edition (p. 29). Upper Saddle River, NJ: Prentice-Hall. [4] Adjei, M. T., Noble, S. M., & Noble, C. H. (2010).The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the Academy of Marketing Science, 38, 634-653 [5] Castańeda, J. A. (2011). Relationship between customer satisfaction and loyalty on the Internet. Journal of Business and Psychology, 26, 371-38 [6] Chattaraman, V., Kwon, W.-S., & Gilbert, J. E. (2012). Virtual agents in retail web sites: Benefits of simulated social interaction for older users. Computers in Human Behavior, 28, 2055-2066. [7] Andrews, L., & Bianchi, C. (2013). Consumer Internet purchasing behavior in Chile. Journal of Business Research, 66, 1791-1799. [8] Foster, M., Francescucci, A., & West, B. (2010). Why users participate in online social networks. International Journal of e-Business Management, 4(1), 3-19. doi:10. 3316/ijebm0401003 [9] Valles, C., & Petrova, A. (2012). Perceived risks as adoption barriers of Facebook commerce: Exploring Facebook users’ perceptions (Doctoral dissertation, Jönköping University). [10] Baker, B. (2009). Your customer is talking - to everyone: Social media is the new channel for Customer connection. New York: Information management. [11] Harridge, M., & Quinton, S. (2012). Virtual snakes and ladders: social networks and therelationship marketing loyalty ladder. The marketing review, 3, 87-102. [12] Riegner, C. (2012). Word of Mouth on the Web: The Impact of Web 2.0 on Consumer Purchase Decisions. Journal of Advertising Research, 12, 436-447.
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[33] Lin, C. A. (2006). Interactive media technology and electronic shopping. In C. A. Lin, & D. J. Atkin (Eds.),Communication technology and social change: Theory and implications (pp. 203-222). Mahwah, NJ: Erlbaum. [34] Taylor, S., Todd, PA (1995), "Understanding information technology usage: a test of competing models", Information Systems Research, Vol. 6 No.3, pp.144-76. [35] J.W. Creswell, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches,” 3rd ed., Thousand Oaks, CA: Sage Publication, 2008. [36] H. Boeije, “A purposeful approach to the constant comparative method in the analysis of qualitative interviews,” Qual. Quant., pp.391409, 2002. [37] Cyr, D., Hassanein, K., Head, M. and Ivanov, A., “The Role of Social Presence in Establishing Loyalty in e-Service Environments,” Interacting with Computers (19:1), 2007, 43-56. [38] Mouakket, S., “The effect of exogenous factors on the technology acceptance model for online shopping in the UAE,” International Journal Electronic Business (7:5), 2009, 491-511 [39] Walczuch, R. and Lundgren, H., “Psychological antecedents of institutional-based consumer trust in e-retailing,” Information & Management (42), 2004, 159-177
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