اندازه گیری انعطاف پذیری سازمانی: اکتشاف و الگوی عمومی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19984||2014||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technological Forecasting and Social Change, Volume 64, Issue 1, May 2000, Pages 23–38
An organization is flexible if it is capable of multiple responses to its environment. Because changing over from one response to another involves “set-up costs,” flexibility can be regarded as detrimental to efficiency. But in a time of globalization and rapid change in business, companies must attend to agile response (flexibility) as much as to efficiency. Efficiency can be measured by several techniques, including Data Envelopment Analysis (DEA). There is, however, no accepted, operational, and useful measure of organizational flexibility. This article characterizes the properties such a measure would have. Following some scene-setting discussion of the roles of efficiency and flexibility in theories of economics, evolution, and general systems, a general model of (relative) flexibility is proposed, building on Ashby's  definition of the variety that must be generated by a sustainable system. A special case of this model is applied to 10 years of financial data on 44 computer and computer-related companies. Results show that companies scoring high on a flexibility measure achieve more consistent efficiency over the time span studied. Discussion indicates how a flexibility model can complement DEA studies to round out the characterization of corporate performance.
Flexibility means being capable of multiple responses to the firm's environment. There is a cost (in energy or money) in maintaining an inventory of responses, and changing over from one response to another involves set-up costs. A firm can achieve cost efficiencies by cutting back on its inventory of responses (and gambling that the discarded responses will not be needed), for instance by hiring less well-trained personnel. Thus, flexibility and efficiency can be regarded as antithetical or detrimental to one another. But in a time of globalization and rapid change in business, companies must attend to agile response (flexibility) as much as to efficiency. “Agility is survival, being responsive to client requirements,” says Al Walker, Executive VP of Standard Technologies, Inc. . It also involves, he says, shortened response time and real-time assessment of the company's progress. Efficiency can be measured by several techniques, including Data Envelopment Analysis (DEA). But there is no accepted, operational, and useful measure of organizational flexibility. This study characterizes the desirable properties of such a measure. Our focus is on overall corporate performance, and so we develop a measure that can either subsume the flexibility of the detailed functions (machine shop, foreign currency trading, etc.) of the organization, or, if data are available, model them directly.
نتیجه گیری انگلیسی
In this article, we considered the theoretical frameworks of microeconomics, evolution, and general systems as possible homes for a discussion of organizational flexibility, choosing systems theory and especially Ashby's Law of Requisite Variety. We offered a list of criteria for a useful and theoretically defensible model of flexibility, and developed such a model. A rough approximation of the general model was computed, showing encouraging results. The discussion clarified some issues of efficiency and flexibility, and showed how a flexibility model works together with efficiency models, especially DEA models, to round out the characterization of corporate performance. This broader perspective can also help guide the selection of input/output variables in DEA studies. While system theory holds that efficiency and adaptiveness (flexibility) are tradeoffs, it is not possible to define flexibility simply as the inverse of DEA (economic) efficiency. This is because as the number of inefficient years and efficient years in an intertemporal DEA must sum to a constant, there could be no conclusion concerning whether flexibility contributes to long-run efficiency. Also, efficiency can be a cross-sectional calculation, but flexibility must be a longitudinal calculation. We have shown schema that go beyond these limitations and that suggest that increasing flexibility is associated with more consistent efficiency. The general model, while developed in the context of total-enterprise performance, can be applied also to the flexibility of individual functions (e.g., manufacturing or sales activities) within the firm. However, the model requires commensurable data from comparable activities within the firm or across firms, over several time periods. Therefore, for this initial development we focused on corporate-level flexibility because the needed data are found in external financial statements. It remains to sharpen and further test the model of flexibility. This would involve, inter alia, testing various instances of and approximations to the general optimization model. Smaller and newer companies can be expected to show smaller absolute ranges on each financial measure, simply because the magnitudes of these are smaller for smaller firms. While it appears that it does so, the flexibility model must be proven to treat small and large enterprises in a mathematically evenhanded way, and also to bear out the commonsense notion that startup firms can be more flexible than established firms. The flexibility and efficiency constructs should be regressed against additional measures of corporate performance in larger data sets, if sets can be constructed that is not confounded by mergers, acquisitions, life cycle effects and “evolutionary” changes in business models. In the real world we would expect not only the environmental stimuli and organizational responses to be ratio-scale (unlike Ashby's nominal-scaled presentation), but also the “outcomes” and “consequences of outcomes” to be ratio-scale. We would like to have sufficient data to justify abandoning the “uniform distribution” assumption in favor of more suitably bell-shaped distributions of stimuli and responses. These variations are allowed by the general model but were not effected in the approximate version that we computed. The general model could be better tested if a data set encompassing relevant non-discretionary variables could be assembled for the period of time analyzed here, and this seems possible. We hope these arguments set useful parameters for further discussion of organizational flexibility.