This study presents an analytic model to support the general public in evaluating digital currency exchange platforms. Advances in technologies have offered profitable opportunities, but the general public has difficulty accessing appropriate information on digital currency exchange platforms to facilitate their investments and trading. This study aims to provide a decision support system using analytic models that will guide the public in deciding the appropriate digital currency exchange platform for trading and investment. The overarching objective is to support the public in embracing the new era of a dependable, trustworthy, and sustainable digital society. Particularly, this study offers an analytics model that compares numerous well-known digital currency exchange platforms based on the opinions of 34 human expert members on six main criteria to identify the most suitable platform. In this study, the analytic hierarchy process approach, which is a multiple-criteria decision-making method, and Expert Choice software were used for decision support. Using pairwise comparisons of exchanges with respect to the criteria in the software, the weight of each exchange was determined, and these weights became the basis for prioritizing the exchange platform. This study provides valuable insight into how an analytics-driven expert system can support the public in selecting their digital currency exchange platform. This work is an integral part of an effort to help disruptive digital technology become widely accepted by the general public.

This study aimed to identify the critical factors and items affecting the productivity of sustainable human resources in a Railway Operation Company based on the perceptions of employees and managers in the Human Resources Department. The study was motivated by research which was applied in terms of the objectives of the study and a descriptive survey was employed as the method. The statistical population of the current study consisted of all employees and managers of the Human Resources Department of the company. Random sampling was employed to collect data and the sample size was 191 people according to Morgan’s Table. Methods including the correlation coefficient, multivariate regression, and factor analysis were employed for data analysis. The findings highlight the main factors and items affecting labor productivity in the Urban and Suburban Railway Operation Company as perceived by the Human Resources Department, which were mainly related to human resources management and could be attributed to motivation and requirements for their effective contribution to the improvement of public welfare. Organizational Attitude and Culture, Leadership Style, and Bonus and Ergonomics were extracted as factors affecting productivity or as independent variables. This study is the first study that has aimed to discuss the perceptions of the Human Resources Department active in a company. As such, the study highlights the standpoint of the main decision makers in the Urban and Suburban Railway Operation Company with regard to labour productivity in the urban and suburban sector.