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NetLogo User Community Models

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Facebook share on group buying decision

by Ong Shu Fang (Submitted: 11/15/2012)

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## WHAT IS IT?

This project aims at modeling the behavior of two types of agents in a social network environment, in particular Facebook. The simulation created will demonstrate the positive effects of electronic word of mouth (eWOM) in influencing online buying behaviour. This project is inspired by Hsiao and Chen’s writings about the effects of eWOM in their journal article titled, "Product judgment and choice: The moderating role of the sense of vitural world." The study shows that in a positive eWOM scenario, the attitude of online members towards a reviewed product will positively affect their intention to purchase. This is similar to the theory of planned behavior (Ajzen, 1985) where people formed intention to carry out the action. The stronger the attitude towards people “sharing” information online, the more likely the person is to perform that behavior. If the subjective norm promotes the idea that “sharing” increase positive attitude in the product, people are more likely to share and in another way increases buying behaviors.

Facebook was selected as the social network in this model as it is an effective marketing tool that is widely utilized by many companies online, primarily due to its relatively lower cost as well as its high population and ubiquity. Facebook “Shares” is a canonical example of what constitutes a positive eWOM. More importantly, positive eWOM arising from Facebook “Shares” can serve as an attestation of the company’s credibility and eminence, reducing uncertainty in consumer decision-making (Ravi et al, 2011).

With the Internet and Facebook rapidly metamorphosing into an important faction of everyone’s life, the group buying industry has risen in prominence as it successfully blends online technology and commerce to bring daily deals conveniently. Despite its burgeoning popularity, there has been little empirical research on the group buying industry. Therefore, this project will shed light on the importance of eWOM in influencing consumer purchase behaviour for this popular industry.

At the start, the model will comprise of red and blue agents, representing fans of the Facebook page for a group buying site and Facebook users who are not fans of the same page (non-fans) respectively. In this model, it is assumed that is that once the fans 'like' the group buying site, new deals from the buying site will be automatically posted and shared on the fan's wall.Facebook shares are perceived to be positive responses, and thus considered as electronic word of mouth (eWOM).

This simulation will demonstrate the positive effects of eWOM in influencing online buying behaviour, which is proxied by the number of non-fans induced into purchasing due to shares on Facebook. In this simulation, the eWOM from “shares” by Facebook by fans is able to influence the non-fans to "share" the post and/or buy the product.

According to a research conducted by Syncapse in 2010, non-fans of a Facebook page has a probability of 27% in sharing a deal. In addition, Chadwick Martin Bailey, a custom market research and consulting firm, carried out a research in 2010 and showed that Facebook fans have a 51% probability of making a purchase upon seeing a share. The probabilities of sharing by non-fans are chosen as threshold values in this model whereas the probability of buying as a result of getting influenced by the number of Facebook shares will be essential in computing the number of buyers.

## HOW TO USE IT

Click the SETUP button to set up the agents.

Click GO to start the simulation. The simulation will identify fans who have not shared the post and ask them to share. Once the fans share, there will be an eWOM effect where the posts will appear on both the fans’ and non-fans’ newsfeed, which will be seen by the non-fans despite not having liked the Facebook page. The buying decision of fans is not the main concern of the model but rather the buying decision of non-fans. Once the non-fans see the share, they will decide whether they will share the post and/or buy the product. Aforementioned, the study by Syncapse (2010) also revealed that fans exert a greater influence on non-fans as compared to non-fans.

The Percentage-Of-Fan slider controls for the total number of fans.

The Strength-of-Buying slider controls the probability of non-fans buying upon viewing the share. For instance, if the slider is set at 0.6, the non-fan has a 60% probability of purchasing the deal upon seeing a share for a particular deal.

The non-fan buy and non-fan no buy monitors represent the total number of buys from non-fan as well as the total number of non-fan who did not buy respectively. In addition, an additional monitor, “Number of Buys”, was incorporated into the simulation to display the total number of buys from both fans and non-fans.

## THINGS TO NOTICE

When you execute SETUP, the red and blue agents are randomly distributed throughout the stimulation. The model made the assumption that all fans will "share" the post. As for non-fans, the loop of connection will break when the non-fans buy or when the neighbours (either fans or non-fans) surrounding this particular non-fan has made their decisions and the interaction link between this non-fan and his neighbours will be broken, resulting in him not sharing and buying.

## THINGS TO TRY

Try changing the Strength-of-buying sliders. The higher the value, the more likely non-fans will purchase the product.

## EXTENDING THE MODEL

Due to the limited size of the online community, expanding the proportion of fans beyond a certain high percentage will not yield practical results pertaining to the effects of eWOM. For instance, increasing the percentage of fans from 20% to 80% will not result in more non-fans enticed into buying due to the significant increase in Facebook “Shares”.
This is attributed by the fact that the number of non-fans under the 80% fan scenario would have dropped drastically, therefore resulting in less non-fans buying the deal.
Despite this limitation, the simulation will be useful in reinforcing the idea that eWOM is indeed imperative in inducing more buys, since fans have a higher probability of purchasing a deal.

Future research endeavours can include other factors that affect consumption behaviour such as sensitivity to prices. It is believed that price plays an imperative role in affecting purchase decision, but due to limited studies on Facebook users, this variable was omitted from the model.

The model can be extended to investigate the effects of eWOM on other aspects of the web economy, such as online auction sites, for instance eBay. It will serve as bedrock for future research efforts in further exploring the positive externalities of eWOM.

## CREDITS AND REFERENCES

Ajzen, I. (1985) ‘From intentions to actions: a theory of planned behavior’ in Kuhl, J. and Beckmann, J. (eds) Action-control: From Cognition to Behavior Heidelberg: Springer.

Chadwick Martin Bailey (2010). Consumers engaged via social media are more likely to buy, recommend. Retrieved from blog.cmbinfo.com/press-center-content/bid/46920/Consumers-Engaged-Via-Social-Media-Are-More-Likely-To-Buy-Recommend

Huang, J., Hsiao, T., & Chen, Y. (2012). The effects of electronic word of mouth on product judgment and choice: The moderating role of the sense of virtual community. Journal Of Applied Social Psychology, 42(9), 2326-2347. doi:10.1111/j.1559-1816.2012.00943.x

Ravi, S.S., Morales-Arroyo. M., and T., Pandey, 2011, “The Emergence of Electronic
Word-of-Mouth as a Marketing Channel for the Digital Marketplace,” Journal of
Information, Information Technology, and Organizations, 6, pp. 41-46.

Syncapse (2010). The value of a facebook fan: An empirical review. Retrieved from http://www,syncapse.com

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