NetLogo User Community Models
## WHAT IS IT?
In brief, this simulation is built on as a continuation to the previous model on Talk of the Network (Goldenberg et al., 2001) with an add-on feature.
This simulation will let user observe how information is being spread in the community. For this simulation, members of the community will be stationary. If the collection of information coming from others with strong and weak ties is strong enough to pass their threshold, they will turn from the state of “uninformed” to “informed”. Each member of the community will have its own unique threshold of getting into the informed state. And just as in real life, members will put higher value in the information spread by someone close to them than those who are not directly connected to them.
In general Word-of-Mouth (w-o-m) can be defined as the sharing of information (opinions or recommendations) through communication among people about products and services (Westbrook, 1987). In business area, a lot of companies are aiming to increase their brands knowledge through various word-of-mouth methods – offline (e.g. face-to-face) as well as online (e.g. through SNS). Of course, advertisement also plays a role in the sharing of information by being the initial contact as well as one of the influencers in getting people to know a brand. But, contrary to popular belief, in long-term, advertisement is not as powerful as w-o-m in contributing to the spread of information (Buttle, 1998; Keller & Berry, 2003; Laborie, 2006). Not only brand knowledge, researchers also found that w-o-m influenced almost 70% of customer’s decision to buy (Balter, 2008).
For this simulation, there will be two kinds of relationship defined – strong ties and weak ties. The differentiation of the relationship is based on “the strength of weak ties” theory by Granovetter (1973). By observing the micro-level differences, a lot of differences can be observed on the macro-level phenomena. According to Granovetter, as contrary to weak ties, strong ties are characterized with a more stable, frequent and intimate interaction. Using this logic, in this simulation, strong ties are understood as those who are closely connected to the individual while weak ties are understood as those who are not directly connected to the individual.
As a add-on feature, current simulation will also be observing the effect of different valance of the information (e.g. positive or negative) towards the rate and end-result of the spreading of information. Moreover, according to the result collected by Deitz and Cakim (2005), the number of negative w-o-m overpower positive w-o-m by 1.133 times.
The add-on feature in this simulation is based on a journal article titled "On Braggarts and Gossips: A Self-Enhancement Account of Word-of-Mouth Generation and Transmission" by Angelis M.D. et al. (2012).
## HOW IT WORKS
As have been mentioned before, each member has its TRESHOLD. To start off, user can adjust a set of variables which will be constant throughout the simulation - STRONG-TIES, WEAK-TIES, ADVERTISING and %-OF-POSITIVE-RUMOR.
Besides that, each member also has its own STATE - informed "1" or not "0"). With the implementation of watch-one procedure, user can observe and learn more about the detail of the process from one of the randomly selected member which is being put into the spotlight. From this member, user can track the NO. OF WEAK TIES which can tell us the number of "informed" others in the community which are not directly connected to the member. The other one is to track the NO. OF STRONG TIES which is the number of "informed" others in the surrounding which are directly connected to the member (neighbors).
The probability of a member to move from not knowing to knowing is defined using a formula stated in the journal article (Goldenberg et al., 2001):
prob = (1 - ((1 - alpha) * ((1 - beta-w) ^ j) * ((1 - beta-s) ^ m)))
As there are two kinds of rumor being spread, the probability for both negative (PROB) and positive rumor (PROBX) will be counted seperately. As been mentioned above, negative w-o-m will have more power over positive w-o-m. So, PROBX = PROBX * A with a = the power of negative w-o-m. After that, members can only move from "uninformed" to "informed" only if:
## HOW TO USE IT
As a starter, user can use the sliders provided to adjust the WEAK-TIES, STRONG-TIES, ADVERTISING and %-POSITIVE-RUMOR.
STRONG-TIES: This is to set how strong the effect of others with a strong connection to the member knowing the information towards the probability of the member knowing the information.
After that, user can simply click on the SETUP button then the GO button to begin the simulation.
Members who have moved from "uninformed" to "informed" will be given a blue color code (positive) and red color code (negative). This will not change until the end of the simulation after the division of the percentage of positive rumor.
## THINGS TO NOTICE
User can observe how the number of TOTAL INFORMED PEOPLE and TOTAL UNINFORMED PEOPLE changes throughout the simulation. A "slower" speed is recommended to fully observe the changes as this simulation is quite fast.
Furthermore, user also can see how different percentage of positive rumor can affect the number of people informed. Another thing to observe is the TICK-STOP which will tell user when the highlighted member changes from "uninformed" to "informed".
## THINGS TO TRY
User can try to play around with the sliders. As mentioned before, user can observe how different percentage of positive rumor can affect the end number of people being informed. Besides that, user can also change the power of negative w-o-m over positive w-o-m by changing the setting for a at the code bar.
User may also try out the BehaviorSpace prepared to help them to see the complete quantitative data in Microsoft Excel format. The settings are adjusted according to Goldenberg et al. (2001) except for the percentage of positive rumor. User may also be interested to change the settings to match with the user's needs.
## EXTENDING THE MODEL
One of the important things to be upgraded is the count of ticks. If it's possible to make it a larger count, maybe user will be able to observe the differences in different conditions.
## RELATED MODELS
The previous model of similar topic titled "Talk of the Network" which is based on a journal article titled "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth" by Goldenberg et al. (2001).
## CREDITS AND REFERENCES
Angelis M.D., Bonezzi A., Peluso A. M., Rucker D.D. & Costabile M. "On Braggarts and Gossips: A Self-Enhancement Account of Word-of-Mouth Generation and Transmission". Journal of Marketing Research Vol. XLIX, 551-563. 2012.
Balter, Dave (2008), The Word of Mouth Manual, Vol. 2. Boston: Bzz Pubs.
Buttle FA. (1998). “Word-of-Mouth: Understanding and Managing Referral Marketing,” Journal of Strategic Marketing, 6, 241-254.
Deitz, Sarah, and Idil Cakim. "Online Influence and the Tech-fluentials," July 2005: [URL: http://www.efluentials.com/documents/wommaconferencepaperjuly 132005.pdf].
Goldenberg J., Libai B. & Muller E. "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth". Marketing Letters 12:3, 211-223. 2001. Kluwer Academic Publishers, The Netherlands.
Granovetter MS. (1973). “The Strength of Weak Ties,” American Journal of Sociology, 78(May), 1360-1380.
Keller, Ed, and Jon Berry. The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy. New York: Free Press, 2003.
Laborie, Jean-Louis. "The Theory Behind Engagement and Integration's Early Experience Across Media." Paper presented at ReThink: 52nd Annual Advertising Research Foundation Annual Conference and Expo, March 20-22, 2006: [URL: http://mail.thearf.org/roymorgan/ Engagement/2006.rethink.ARRThe%20Theory.pres.Laborie.pdf].
Westbrook, Robert A. (1987), “Product/Consumption-Based Affective Responses and Postpurchase Processes,” Journal of Marketing Research, 24 (August), 258–70.
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