Effectiveness and downside risks of employing firm controlled agents to calm down online firestorms
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Effectiveness and downside risks of employing firm-controlled agents to calm down online firestorms. 12 th OUIWS, Harvard Business School, 2014. by Nik Franke, Thomas Funke, Peter Keinz, and Alfred Taudes. Phenomenon and research question.

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12 th OUIWS, Harvard Business School, 2014

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Effectiveness and downside risks of employing firm controlled agents to calm down online firestorms

Effectiveness and downside risks of employing firm-controlled agents to calm down online firestorms

12th OUIWS, Harvard Business School, 2014

by

Nik Franke, Thomas Funke, Peter Keinz, and Alfred Taudes


12 th ouiws harvard business school 2014

Phenomenon and research question

In this project, we explore a strategy to calm down emerging online firestorms.

Online firestorms

“… a sudden discharge of large quantities of [...] complaint behavior against a [...] company [...] in social media networks.“ 1

Major threat to a company’s reputation2

Research questions:

1.) Can agents of protection effectively help to calm down online firestorms?

2.) Which factors influence their effectiveness?

NikFranke: hey guys, calm down a bit. singtel is setting up the 4G network to improve their service to all of us...so they do care for us…and by the way: i‘ve never experienced any problems …i‘drecommendtheirservicetomyfriends ...


12 th ouiws harvard business school 2014

Method

We used case-based agent-based modeling to investigate the RQs.

Step 1: Building the model

Literature review to derive a general model on opinion diffusion3

Case study to gain data about a real-life online firestorm

Step 2: Validating the model

Simulation (1st model)

# of peers participating

Real conflict

time

Variation of - number & roles agents- community characteristics

Step 3: Running experiments

t1

t3


12 th ouiws harvard business school 2014

Preliminary findings & next steps

Agents of protection can significantly affect online firestorms.

  • Robust patterns across all scenarios:

  • Agents of protection

  • reduce intensity, speed, and duration of an online firestorm (p<.001)

  • increase the average attitude towards the company after an online firestorm (p<.001)

No agents of protection

t3

10 opinionleaders as agents of protection

t1

t3

  • Next steps:

  • Extending the model to account for effects triggered by un-covered agents of protection

  • Attempt to find “optimal” infiltration strategy


12 th ouiws harvard business school 2014

Thank you for your attention!

Literature:

1)

Pfeffer, J., T. Zorbach, and K. M. Carley. "Understanding online firestorms: Negative word-of-mouth dynamics in social media networks." Journal of Marketing Communications 20.1-2 (2014): 117-128.

2)

Stich, L., G. Golla, and A. Nanopoulos. "Modelling the spread of negative word-of-mouth in online social networks." Journal of Decision Systems 23.2 (2014): 203-221.

3)

Hegselmann, R, and U. Krause. "Opinion dynamics and bounded confidence models, analysis, and simulation." Journal of Artificial Societies and Social Simulation 5.3 (2002).

Miller, K.D., F. Fabian, and S. Lin. "Strategies for online communities." Strategic Management Journal 30.3 (2009): 305-322.


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