Forecasting in Organizations: Reinterpreting Collective Judgment Through Mindful Organizing

Forecasting researchers acknowledge that improving our understanding of forecasting’s organizational aspects could shed light on challenges such as prediction accuracy, forecasting techniques implementation, and forecast alignment between firms’ functions. However, despite the potential of an organizational research program, the literature has often maintained its emphasis on technical aspects or has approached organizational complexity from a functionalistic lens; assuming a concrete reality “out there” that is predictable and exists independently of the participants’ beliefs. Consequently, subjectivity and nuanced organizational dynamics are often disregarded as problematic behavior that needs to be extricated from the forecasting process. Within this context, this article proposes a paradigmatic shift toward a functionalist-interpretive “transition zone” where the inherent subjectivity of human organizations can be incorporated into the forecasting process to describe it more accurately and crucially, refine prescriptions. To bridge the functionalist-interpretive world views, this article brings forward the mindful organizing program, a framework that introduces a nuanced template of groups’ real-life interactions focused on collective interpretive work, the quality of organizational attention, and a particular sensitivity to analyze errors and near misses (Weick, Sensemaking in organizations. Sage, 1995; Weick et al., Research in organizational behavior, Elsevier Science/JAI Press, 1999). The incorporation of these concepts can contribute to the forecasting field from three angles: (a) substantiates the inherent subjectivity of the forecast process where actors can influence prediction outcomes, (b) offers a representation of collective judgment debiasing mechanisms and (c) emphasizes the process of collective learning via error deliberation. Under this approach, achieving forecast accuracy is less critical than unveiling collective learning mechanisms, which will eventually yield higher forecast adaptation levels in the long run.