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Forecasting models for the diffusion of technological innovations

Tsontaki Maria

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URI: http://purl.tuc.gr/dl/dias/D4B1BF1F-E6F6-4BB4-B0A1-89A542052AE4
Year 2017
Type of Item Diploma Work
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Bibliographic Citation Maria Tsontaki, "Forecasting models for the diffusion of technological innovations ", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2017 https://doi.org/10.26233/heallink.tuc.69476
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Summary

Nowadays, just 4% of the New Products, that enter the Market, survive. Why is that, 96% of the Technological Innovations disappear?The answer is found in the insufficient Business Management and the unsuitable Marketing. At this point exactly, the Science of Technological Forecasting comes in. As Imperfect and Uncertain Technological Forecasting is, so necessary it is in the contemporary era of the rapid Technological Change. Technological Forecasting is an integral and necessary part of the Business Management and consequently, of the Business Marketing. More specifically, every decision of Resource Allocation needs Technological Forecasting. Technological Forecasting forecasts Future Sales, the Product Life Cycle and the Need of Developing New Products. With this knowledge, Technological Forecasting assists the timely Scheduling of the Business Management and Marketing. As a result, Business Strategies are Optimized and consequently, Profit is Maximized while at the same time, Loss is Minimized.The present study focuses on the High Technology Innovations, which are classified into eight key Technological Domains. Forecasting Models are divided into Single Models and Hybrid Forecasting Models. Case Studies are being investigated in terms of the Technological Domain they fall into, the Forecasting Method, the Input Variables, the Forecasting Horizon, the Comparison with other Forecasting Methods and the Evaluation Measures. Coding Data in Tables. Identification of the key Technological Forecasting Methods and presentation of their Characteristics, their Advantages and Disadvantages. The Optimal Single Forecasting Models are detected. The Models that form Optimal Hybrid Forecasting Models are also detected. The Optimal Combinations between Single Models, that form Optimal Hybrid Forecasting Models, are highlighted. The Optimal Single Models as well as the Optimal Hybrid Models, per Technological Domain, are identified.There is no Universally Optimal Forecasting Model, that forecasts with the highest possible Accuracy always, in every situation. On the contrary, the Optimal Forecasting Model depends on, the Innovation itself and the Technological Domain it falls into as well as, the Input Variables that are used, the Forecasting Horizon, the Amount of Historical Data and the Country in which the Diffusion takes place, or even the Geographical Area within the Country.

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