Integrating Organizational Reputation Mechanism to Decision-Making Processes: Facebook Case

Year 2019, Volume: 17 Issue: 34, 301 - 322, 13.09.2019

Volkan Yüncü Üzeyir Fidan

https://doi.org/10.35408/comuybd.425271

Cited By: 2

Abstract

Reputation is an intangible asset stemming from the rational and perceptual evaluations of different stakeholder groups regarding an organization. It is also a valuable asset helping organizations gain a competitive advantage as well as being a crucial tool through which organizations protect themselves particularly in turbulent times such as the one Facebook Inc. has recently been going through. As it has turned out that personal data of up to 87 million users was obtained by an analytics firm without users’ permission, the company have had quite hard times and this also led to significant reputational damage that is hard to fix. For instance, in response to the revelation of the scandal, lawmakers and regulators in the U.S. and U.K. increased their scrutiny of the social media giant and furious users across the world who are protestingithave launched "Delete Facebook" campaigns. Hence, this survey conducted in the wake of the news of the scandal attempts to determine whether the people would continue to use their social media accounts or not by utilizing the perceived reputation scale. The analysis was done through Decision Trees technique and the rules that affect the perception of the participants and their preferences are revealed. Participants' reputation perceptions are mapped and the probability value of each decision is calculated by the Naive Bayes algorithm. Accordingly, in the decision tree diagram, thirteen rules were obtained. Then, the probability values of each decision made by the Bayesian classifier were calculated and the output of the decision tree diagram was tested. As a result, each rule obtained from the Decision Tree diagram has the same result as the Bayes probability values. 

Keywords

Reputation management, organizational reputation, naive bayes, decision trees

References

• A.G. Asuero., A. Sayago., A. G. González (2006) The Correlation Coefficient: An Overview, Critical Reviews in Analytical Chemistry, 36:1, 41-59
• Andrew R. Leach and Valerie J.Gillet (2003 ). An Introduction to ChemoinformaticsBy (University of Sheffield, U.K.). Kluwer Academic Publishers:  Dordrecht. 2003
• Barnett, M.L., Jermier, J., Lafferty, B. (2006). Corporate reputation: the definitional landscape. Corporate Reputation Review, 9(1).
• Barney, J. B. (1986), “Strategic factor markets: Expectations, luck, and business strategy”, 
Management Science, 32(10), pp. 1231-1242.
• Bounsaythip, C., Runsala, R. E., 2001. Overview of Data Mining for Customer Behavior Modeling. VTT İnformation Technology.
• Breiman, JH Friedman, RA Olshen, and CJ Stone (1984) Classification and Regression Trees. Wadsworth Inc.
• Brown, Tom J., Peter A. Dacin, Michael G. Pratt, David A. Whetten (2006), Identity, intended image, construed image, and reputation: An interdisciplinary framework and suggested terminology, Journal of the Academy of Marketing Science, 34(2), 99-106.
• Carroll E. Craig (2013). The handbook of communication and corporate reputation WestSussex: John Wiley &Sons Publication.
• Chen, C. and Otubanjo, O. (2013). A functional perspective to the meaning of corporate reputation, The Marketing Review,13(4), 329-345.
• Cravens, K., Goad Oliver, E., Ramamoorti, S., 2003. The reputationindex: measuring and managing corporate reputation. European Management Journal 21 (2), 201-212.
• Davies A. (2006). Best practice in corporate governance: Building reputation and sustainable success, Hants: Gower Publishing.
• Davies, G., Chun, R., da Silva, R. et al. Corp Reputation Rev (2001) 4: 113. https://doi.org/10.1057/palgrave. crr.1540137
• Fombrun, C.;Gardberg, N.,Sever, J. J. The Reputation QuotientSM: A multi-stakeholder measure of corporate reputationBrand Management (2000) 7: 241. https://doi.org/10.1057/bm.2000.10
• Fombrun, C. J., Ponzi, L. J.; Newburry, W. (2015). Stakeholder tracking and analysis: The RepTrak® system for measuring corporate reputation. Corporate Reputation Rewiew, 18(1).
• Fombrun, C. J.ve Riel, V. (1997). The reputational landscape, Corporate Reputation Review 1(2). DOI: 10.1057/palgrave.crr.1540008
• Helm, S., (2005). Designing a Formative Measure for Corporate Reputation,Corporate Reputation Review Volume 8, Issue 2, pp. 95-109., 2005, Doi: 10.1057/palgrave.cr r.1540242
• Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29 (2), 119–127.
• Lange, D.; Lee, P.M.; Dai, Y. (2011). Organizational reputation: A review. Journal of Management, 37 (1), 153-184
• Orre, R. (2003). On Data Mining and Classification Using a. Bayesian Confidence Propagation Neural Network, doctoral dissertation, Stockholm.
• Ponzi L. J.; Fombrun C. J.; Gardberg N.A. (2011). RepTrak TM Pulse: Conceptualizing and validating a short-form measure of corporate reputation. Corporate Reputation Review, 14 (1).
• Pruzan, P. (2001). Corporate reputation: image and identity: Corporate Reputation Review, 4 (1), 50-64.Quinlan, J.R. Induction of Decision Trees, 1986, Machine Learning 1:81-106.
• Roberts, P. W. and Dowling, G. R. (2002), Corporate reputation and sustained superior financial performance. Strat. Mgmt. J., 23: 1077-1093. doi:10.1002/smj.274
• Sarstedt, Marko, Wilczynski, Petra and Melewar, T.C., (2013), Measuring reputation in global markets—A comparison of reputation measures’ convergent and criterion validities, Journal of World Business, 48, issue 3, p. 329-339Shkolnikov, A., Leachman, J.,Sullivan, J.D. (2004, December 27). The Business Case for Corporate Citizenship. Report No. 0410. Center for International Private Enterprise, USA.
• Sumer, H. vePernsteiner, H. (2014). İtibaryönetimi, İstanbul: Beta.
• Walsh G.; Beatty S. (2007). Customer-based reputation of service firm: Scale development and validation. Journal of the Academy of Marketing Science 35 (1).
• Wang, Y.; Li, Y.; Song, Y.; Rong, X.; Zhang, S. Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree. Algorithms 2017, 10, 124.