Browsing by Author "Cojuharenco, Irina"
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- Strike a happy medium: the effect of it knowledge on venture capitalists' overconfidence in it investmentsPublication . Singh, Harpreet; Aggarwal, Rohit; Cojuharenco, IrinaIn this article, the effect of IT knowledge on the overconfidence of venture capitalists (VCs) in their IT investments is examined. Our findings show that the effect of IT knowledge on overconfidence is nonlinear. VCs with moderate levels of IT knowledge are least overconfident. At the same time, VCs with moderate levels of IT knowledge are most resistant to the biasing effects of past successes. Past failures show a negative association with overconfidence independent of the level of the VC's IT knowledge. Finally, the negative association between stakes and VC overconfidence is stronger with greater levels of IT knowledge. These results shed light on the highly disputed role of IT knowledge in the domain of IT investments.
- Tell me who, and I’ll tell you how fair: a model of agent bias in justice reasoningPublication . Cojuharenco, Irina; Marques, Tatiana; Patient, DavidA salient and underresearched aspect of un/fair treatment in organizations can be the source of justice, in terms of a specific justice agent. We propose a model of agent bias to describe how and when characteristics of the agent enacting justice are important to justice reasoning. The agent bias is defined as the effect on overall event justice perceptions of specific agent characteristics, over and above the effect via distributive, procedural, and interactional justice. For justice recipients to focus on agent characteristics rather than on the event being evaluated in terms of fairness is an unexplored bias in justice judgments. Agent warmth, competence, and past justice track record (entity justice) are identified as agent characteristics that influence justice judgments. Agent characteristics can influence overall event justice perceptions positively or negatively, depending on the ambiguity in terms of justice of the event and on its expectedness from a particular justice agent. Finally, we propose that agent bias is stronger when justice recipients use intuitive versus analytic information processing of event information. Our model of agent bias has important theoretical implications for theories of organizational justice and for other literatures, as well as important practical implications for organizations and managers.