استراتژی شرکت، نیروهای محیط زیست، و اندازه گیری عملکرد: یک سیستم پشتیبانی تصمیم گیری وزن دهی با استفاده از روش نزدیک ترین همسایه k
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|12367||2003||14 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 25, Issue 3, October 2003, Pages 279–292
The choice of performance measures is critical to formulating strategies. This paper investigates the relationship among corporate strategies, environmental forces, and the Balanced Scorecard (BSC) performance measures. Corporate strategies are explored within the framework of Miles and Snow's taxonomy, where they are categorized into prospectors, defenders, analyzers, and reactors. The relative weights for each performance measure are calculated by the use of the Analytic Hierarchy Process. A sample of 219 companies can confirm the link between corporate strategies, environmental forces, and the weights of the BSC performance measures. These weights shift depending on the nature of challenges companies face. In the light of this empirical evidence, a decision support system is proposed to help retrieve the BSC weights of the companies with similar characteristics. In order to measure the proximity between companies, a k-nearest neighbor technique is employed. This system can help find the weights of the performance measures for particular strategies.
Companies are shaped by their performance measure. Performance measures play a critical role in formulating corporate strategies, evaluating accomplishments, and compensating organizational members. Traditional performance measures are financial. They tend to be myopic and short-term oriented. The financial aspect is only a part of the whole system of a firm. Companies need to leverage their hidden assets. In particular, knowledge is becoming more important in the new economy. Knowledge is non-financial and intangible. Therefore, non-financial measures have been employed to measure such knowledge assets (Dekker and de Hoog, 2000, Kitts et al., 2001, Lee et al., 1995, Liebowitz and Wright, 1999 and Wilkins et al., 1997). The need for measuring knowledge components has motivated the need for a variety of performance measurement methods. The Balanced Scorecard (BSC) is one of them. The BSC attempts to integrate all the interests of the key stakeholders—shareholders, customers, and employees, on a scoreboard (Kaplan & Norton, 1996). The beauty of the BSC is that it seeks for a balance between financial and non-financial measures. These diverse interests are categorized into financial, customer, internal business process, and innovation and learning measures. Companies have to determine the relative importance (i.e. weights) of BSC measures so that they can better identify which measures to focus on and which to ignore. These weights can shift depending on the nature of challenges companies face. However, relatively little is known about how to determine the weights. The weights allotted to particular measures are likely to differ according to corporate strategies. For example, prospectors may differ from defenders in determining the weights of performance measures. Furthermore, because companies are thriving in different environments, the weights need to accommodate the potential of environmental variables, such as dynamism, heterogeneity, and hostility (Miller & Friesen, 1983). Recently, several articles admit that, to enhance performance, the strategy pursued by the organization needs to fit into the organizational structure and its evaluation systems (Stathakopoulos, 1998). Olson and Slater (2002), in particular, echo that corporate strategies are linked to the relative importance of the BSC performance measures. This paper addresses the following research issues: (i) What is the effect of corporate strategic choices on the weighting of the BSC performance measures? (ii) What are the important environmental variables in determining these weights? (iii) What is the potential capability of the k-nearest neighbor technique in measuring the weights of performance measures? (iv) How does our proposed weighting decision support system work? The rest of this paper is organized as follows: The next section reviews the BSC and the related corporate variables such as strategy, dynamism, and heterogeneity. Section 3 describes measurements including the weighting method. Section 4 analyzes empirical results to explore how the weights of the BSC performance measures vary depending on strategy, dynamism, and heterogeneity. 5 and 6 explore a system for finding the weights among the BSC measures. Section 7 concludes the paper and suggests areas for further research.