توسعه یک سیستم هوشمند ترکیبی برای توسعه استراتژی بازاریابی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2856||2000||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 27, Issue 4, January 2000, Pages 395–409
In this paper, the development of a hybrid intelligent system for developing marketing strategy is described. The hybrid system has been developed to: provide a logical process for strategic analysis; support group assessment of strategic marketing factors; help the coupling of strategic analysis with managerial intuition and judgement; help managers deal with uncertainty and fuzziness; and produce intelligent advice on setting marketing strategy. In this system, the strengths of expert systems, fuzzy logic and artificial neural networks (ANNs) are combined to support the process of marketing strategy development. Moreover, the advantages of Porter's five forces model and the directional policy matrices (DPM) are also integrated to assist strategic analysis. In the paper, the software architecture of the hybrid system is discussed in details. Particularly, the group assessment support module, the fuzzification of strategic factors, and the fuzzy reasoning for setting marketing strategy are addressed. In addition, the empirical field work on evaluating the hybrid system is also summarised. The empirical evidence indicates that the hybrid intelligent system is helpful and useful in supporting the development of marketing strategy.
Marketing strategy is the means by which the marketing objectives will be achieved . In recent years, the use of computer-based information systems in the field of strategic marketing has been increasingly highlighted. Researchers have attempted at developing effective information systems in support of marketing strategy development. Decision support systems (DSSs) have been developed to support strategic marketing decisions (e.g., 6, 35, 37, 64, 66 and 67). Pioneering research has been conducted to apply expert systems in strategic marketing planning 3, 7, 12, 13, 14, 34, 38, 41, 42, 44, 62, 63 and 64. Fuzzy logic has been used to model market entry decisions . Pioneering work has also been undertaken by Carlsson  to combine fuzzy logic and hyperknowledge in support of effective strategy formation. A support system for strategic management, called Woodstrat, has been implemented successfully in Finnish forest and wood industries. Carlsson's work 11 and 14 on developing the hyperknowledge system was based upon the idea and theory of creating hyperknowledge environments coined by Chang et al. 68 and 69. Recently, artificial neural networks (ANNs) have also been harnessed to aid the process of strategic marketing decisions 22, 26, 52 and 58. More recently, efforts have been made to build hybrid systems to assist marketing strategy formulation 11, 18 and 70. Researchers have also developed a hybrid artificial intelligence approach to the implementation of trading strategy . A framework for a hybrid intelligent system in support of marketing strategy development has been proposed by Li  and Li et al. , with five objectives: to help strategic analysis; to couple strategic analysis with managers' judgement; to integrate the strengths of diverse support techniques and technologies; to combine the benefits of different strategic analysis models; and to help strategic thinking. The proposed framework provides a pragmatic conceptual framework for the development of computer-based support for marketing strategy formulation and strategic marketing planning. This paper describes the development of a hybrid intelligent system for developing marketing strategy. In the following sections, the background for building a hybrid intelligent system for marketing strategy development is briefly discussed. The software architecture of the hybrid intelligent system is presented in detail. Particularly, the group assessment support, the fuzzification of marketing strategy factors and the fuzzy reasoning for setting marketing strategy are addressed. Moreover, other associated technical details are also examined. In addition, the findings of the field work on evaluating the system are also summarised. Conclusions are drawn and intended future research is outlined in Section 14.
نتیجه گیری انگلیسی
The development of a hybrid intelligent system for marketing strategy development has been described in the paper. Aiming at offering enhanced support, the system has been developed to combine the strengths of an ES, fuzzy logic, an ANN model and decision support technologies. It has also been designed to integrate the benefits of Porter's five forces model and the DPM models in support of strategic analysis. At least, the following salient features have been implemented in the system: 1. It provides managers with an organised method to conduct strategic analysis; 2.It supports the group assessment of marketing strategy factors based upon the opinions of a panel of managers; 3. It employs an ANN forecasting model to help managers assess market growth and market share; 4. It helps the coupling of strategic analysis with managerial judgement; 5. It harnesses fuzzy logic to handle fuzziness and uncertainty in evaluating strategic criteria; 6.It utilises a fuzzy expert system to conduct fuzzy reasoning for developing marketing strategy. The system has been evaluated with marketing directors in five large British companies. Marketing directors' responses to the system were very favourable. Empirical evidence indicates that the hybrid system is useful and helpful in support of the key aspects of marketing strategy development. The advice or outputs generated by the hybrid system were reported to be mostly sound, surprisingly accurate, and clearly reflecting managerial judgement. The system has potential as an effective means for developing marketing strategy. The work and related findings reported here, however, are exploratory and preliminary. Further efforts on improving the system functions and further evaluation work with more industrial users will be a matter of priority of the future work.