Typology Analysis of Performance Models of Small and Medium-Size Enterprises (SMEs)

A number of firm performance models are available. Reviewing these models and pointing out their individual strengths and weaknesses, would help both academic researchers and professional users to understand and appreciate how and when to use these various models. The theoretical models for Small and Medium-size Enterprise (SME) performance can be divided into two categories: firm dynamics theories and performance prediction models. In the first part of this paper we review, in a condensed manner, the most relevant firm dynamic theories, i.e. SME's performance models. These include: Stochastic Theories, Learning Model Theories and Hazard Modeling Theories. In the second part of this paper, we examine the performance prediction models of SMEs, which include Z-Scores, ZETA-Scores, Neural Networks (NN) and the SIV® models, among others. The strengths and weaknesses of each of these models are exposed and discussed.     

Type-2 TOPSIS: A Group Decision Problem When Ideal Values are not Extreme Endpoints

In the traditional TOPSIS, the ideal solutions are assumed to be located at the endpoints of the data interval. However, not all performance attributes possess ideal values at the endpoints. We termed performance attributes that have ideal values at extreme points as Type-1 attributes. Type-2 attributes however possess ideal values somewhere within the data interval instead of being at the extreme end points. This provides a preference ranking problem when all attributes are computed and assumed to be of the Type-1 nature. To overcome this issue, we propose a new Fuzzy DEA method for computing the ideal values and distance function of Type-2 attributes in a TOPSIS methodology. Our method allows Type-1 and Type-2 attributes to be included in an evaluation system without compromising the ranking quality. The efficacy of the proposed model is illustrated with a vendor evaluation case for a high-tech investment decision making exercise. A comparison analysis with the traditional TOPSIS is also presented.     

ASPEM as the New Topographic Analysis Tool for Small and Medium-Sized Enterprises (SMEs) Performance Models Utilization

Quantifying and measuring small firm performance is vital in our understanding of how internationalization influences firm performance. That is also important when attempting to grasp the mechanisms of the internationalization processes. There are few methods used for the evaluation of performance of Small and Medium-sized Enterprise (SMEs). These methods can be either macro or micro economic in nature. Hazard Modeling, Stochastic Models, and Learning Models are examples of macro economic models while Z-Scores, ZETA-Scores, Neural Networks, and the SIV^® model are examples of micro models. Choosing the most suitable performance model is an essential step in order to maximize our knowledge in relation to firm performance. Utilizing SMEs performance measures without thinking about the category of model, will bias the outcome of the majority of SMEs studies. However, using firm performance diverse models in an efficient manner requires strategic thinking. In this paper, we are re-introducing a tool that can accommodate that aspect. Abouzeedan (2002) designated the new tool: the Arena of SMEs Performance Models or an ASPEM diagram. The horizontal axis in the diagram indicates the Information Intensity Requirements of the model. The vertical axis indicates the Coverage Intensity of the model varying from an individual firm up to a whole group of firms. By allocating each of the SMEs performance models, at the suitable region of the ASPEM Diagram, researchers can better build a sound strategy for the application of these methods.