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Vol. 7 No. 1 (2018): JITA - APEIRON

Velimir Štavljanin, Milica Jevremović

COMPARISON OF PERCEIVED INTERACTIVITY MEASURES OF ACTUAL WEBSITES INTERACTIVITY

Original scientific paper

DOI: https://doi.org/10.7251/JIT1701042S

Abstract

Interactivity is a concept of enormous importance for digital marketing. It was recognized as a key feature of website, a hub of all digital marketing activities. But, almost all interactivity measures were conceptualized one or two decades ago. In the meantime, technological novelties changed the face of websites. Also, a number of interactivity features increased exponentially. Those changes had a huge impact on practice and could influence user’s perception of interactivity. Aim of this paper is to explore whether several selected existing measures of perceived interactivity could cope with those changes. Paper reports a study in which two websites of low and high interactivity were developed and in an experimental setting as stimuli used to test three perceived interactivity measures. Results show that all measures estimated perceived interactivity of a high interactivity website better than of a low interactivity website. Also, results show that particular dimensions of a model could be used to estimate overall interactivity.

Keywords: website interactivity, perceptual interactivity, actual interactivity, interactivity measures, website design.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.