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April 30, 2026
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April 30, 2026
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April 28, 2026
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Forecasting Technology Trends through the Gap Between Science and Technology: The Case of Software as an E-Commerce Service

Foresight and STI Governance. 2021. Vol. 15. No. 2. P. 12–24.
Daim T., Bukhari E., Bakry D., VanHuis J., Yalcin H., Wang X.

Identifying technology trends can be a key success factor for companies to be competitive and take advantage of technological trends before they occur. The companies always work to plan for future products and services. For that, it is important to turn to methods that are used for technology forecasting. These tools help the companies to define potential markets for innovative new products and services. This paper uses text mining techniques along with expert judgment to detect and analyze the near-term technology evolution trends in a Software as a Service (SaaS) case study.  The longer-term technology development trend in this case is forecasted by analyzing the gaps between science and technology. This paper contributes to the technology forecasting methodology and will be of interest to those in SaaS technology. Our findings reveal five trends in the technology: 1) virtual networking, 2) the hybrid cloud, 3) modeling methodologies, 4) mobile applications, and 5) web applications. Among the results achieved, we can summarize the interesting ones as follows: it is possible to say that traditional information systems are now evolving into online information systems. On the other hand, the use of a licensing model based on subscriptions triggers the change in perpetual licensing models. The product range that has evolved towards mobile technologies has put pressure on information storage technologies and has led to the search for new methods especially in the development of database systems. 

Research target: Economics and Management
Language: English
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Keywords: text miningtechnology trendstechnology forecasting e-commercesoftware as a servicetechnological trajectory
Publication based on the results of:
Development of a methodological base for a comprehensive study of global trends using neural network language models (2021)
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