مقاله انگلیسی رایگان در مورد اثر انگیزه های مبتنی بر عملکرد بر کیفیت پیش بینی مدیریت – الزویر 2015

Understanding forecasts is important because of their pervasiveness in business decisions such as budgeting, compensation, and financial reporting. Inaccurate forecasts can reduce the effectiveness of the production planning process and negatively impact production efficiency, cost management, and ultimately firm performance (e.g., Bruggen, Grabner, & Sedatole, 2013). To increase the chance of obtaining accurate forecasts from an agent, a principal needs to be careful in designing the management control system that elicits such forecasts from the agent (e.g., Osband, 1989). One such control system that is commonly used is the planning and budgeting system of a firm (Merchant & Van der Stede, 2012). Within the planning and budgeting system, an important design choice is the level of aggregation at which the principal elicits forecasts from the agent. In practice, firms vary considerably in the level of aggregation of the information elicited by the planning and budgeting system (Merchant & Van der Stede, 2012). For example, top management can request that divisional managers prepare either an aggregated forecast (e.g., forecast total sales for the division) or a disaggregated forecast (e.g., forecast sales for individual products within the division) (see Kahn, 1998 and Lapide, 2006). Although managers are likely to prepare both disaggregated and aggregated forecasts for internal decision-making purposes, the level of forecast aggregation required by the budgeting system will determine which forecast is more salient to them. Further, research on the anchoring and adjustment bias suggests that managers likely anchor on the numbers in the forecast that are most salient to them (Bromiley, 1987; Tversky & Kahneman, 1974). Therefore, the level of aggregation at which the principal elicits forecasts from the agent should affect managers’ forecasts even when both types of forecasts are prepared. Although economic theory suggests that a rational agent will provide the same forecast of a summary performance measure regardless of the level of forecast aggregation (or forecast type), psychology theory suggests that forecast type will influence the quality of the agent’s forecasts, where forecast quality can refer to both the accuracy and optimism (or bias) in a forecast. We investigate how a control system design choice—forecast type—interacts with incentives to affect two dependent measures of forecast quality: forecast accuracy and forecast optimism. Forecast accuracy refers to the degree of closeness between a forecast and the actual outcome.