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• Article | Proceedings of the Hawaii International Conference on System Sciences

  Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

  Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström

  Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.

  Keywords: big data; data science and analytics management; governance and compliance; organizational systems and technology; anomaly detection; application performance management; Machine learning; enterprise architecture;

  Citation:

  Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836.  View Details

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• Supplement | HBS Case Collection | December 2018

  Amazon Acquires Whole Foods (B)

  Rory McDonald, Sarah Mehta and Shaye Roseman

  This short case, meant for pairing with HBS case 615-013 “AmazonFresh: Rekindling the Online Grocery Market,” explores Amazon’s rationale for acquiring Whole Foods.

  Keywords: Mergers and Acquisitions; Growth and Development Strategy; Technology Industry; Food and Beverage Industry;

  Citation:

  McDonald, Rory, Sarah Mehta, and Shaye Roseman. "Amazon Acquires Whole Foods (B)." Harvard Business School Supplement 619-029, December 2018.  View Details

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• Article | Academy of Management Journal | December 2018

  Ideological Misfit? Political Affiliation and Employee Departure in the Private-Equity Industry

  Y. Sekou Bermiss and Rory McDonald

  Though organizations are increasingly active participants in the political realm, little research has investigated how an organization’s heightened focus on political ideology impacts employees. We address this gap by exploring how an individual’s political ideological misfit with an organization’s prevailing ideology impacts mobility. By tracking the movements of over 40,000 investment professionals in the U.S. private equity industry over 10 years, we investigate the consequences of the ideological misfit that arises when individuals’ political ideologies diverge substantially from the prevailing ideology of their firms. We hypothesize that ideological misfit increases the likelihood of mobility, though the effects vary with liberal/conservative ideological orientation. Polynomial regression analyses reveal that the fit relationship between individual and organizational political ideology deviates from the idealized congruence relationship that underlies prevailing theory. Through a supplemental, qualitative investigation, we propose some potential interpretations for our findings.

  Keywords: Values and Beliefs; Employees; Organizational Culture; Resignation and Termination; Financial Services Industry; United States;

  Citation:

  Bermiss, Y. Sekou, and Rory McDonald. "Ideological Misfit? Political Affiliation and Employee Departure in the Private-Equity Industry." Academy of Management Journal 61, no. 6 (December 2018): 2182–2209.  View Details

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