Special Issue "Technology-Enabled Interdisciplinary Learning in Economic/Business Studies"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Samo Bobek
E-Mail Website
Chief Guest Editor
Faculty of economics and business, University of Maribor, SI-2000 Maribor, Slovenia
Interests: e-business; e-learning; digital transformation
Prof. Dr. Simona Sternad Zabukovsek
E-Mail Website
Assistant Guest Editor
Faculty of economics and business, University of Maribor, SI-2000 Maribor, Slovenia
Interests: acceptance of business information systems; e-learning; digital transformation
Prof. Dr. Jarmila Zimmermannová
E-Mail Website
Assistant Guest Editor
Department of Sustainable Development, Moravian Business College Olomouc, 77900 Olomouc, Czech Republic
Interests: economic policy; economic instruments of environmental policy; sustainability
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Teaching paradigms in the area of economic and business studies are changing, and therefore, learning approaches developed by educators are transforming accordingly with recent developments. There are two major areas of change. Teaching is becoming more and more technology-enabled, not only based on e-learning solutions and platforms but also enhanced with the use of solutions and tools used to make the content of the courses more real-life-oriented. Solutions and tools used are business solutions (e.g., enterprise resource planning (ERP) solutions and customer relationship management (CRM) solutions ), solutions from other disciplines like GIS that support spatial analysis of economic data, and tools used to implement recent gaming approaches to economic and business studies. Technology-enabled learning in business studies is closely connected with interdisciplinary competencies built among students using different solutions and tools, many of which come from other disciplines.

On the other hand, technology-enabled learning develops the digital skills of students. To achieve better results and sustainable study programs, these technologies and solutions used to have to be accepted by educators and by students on the advanced level. Moreover, such technologies also have positive environmental effects. There is a need for further understanding of innovative technology-enabled teaching approaches and best practices to facilitate higher attitudes of educators and students.

The topics of this issue include, but are not limited to, the following:

  • Digital transformation of economic/business studies;
  • E-learning approaches and technologies;
  • Interdisciplinary competencies in economic/business studies;
  • ERP-solution-enabled teaching;
  • CRM-solution-enabled teaching;
  • Spatial analysis of economic data in teaching;
  • Tools and technologies for gaming approach in economic/business studies;
  • Technology-enabled teaching acceptance;
  • Student attitudes toward technology-enabled learning;
  • Environmental aspects of e-learning and digital transformation of economic/business studies;
  • Multidisciplinary approaches for sustainable study programs.           

Prof. Dr. Samo Bobek
Chief Guest Editor

Prof. Dr. Simona Sternad Zabukovsek
Prof. Dr. Jarmila Zimmermannová
Assistant Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • interdisciplinary learning
  • technology-enabled learning
  • digital transformation
  • technology acceptance
  • business and economics studies

Published Papers (1 paper)

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Research

Article
Remote Sensing for Short-Term Economic Forecasts
Sustainability 2021, 13(17), 9593; https://doi.org/10.3390/su13179593 (registering DOI) - 26 Aug 2021
Viewed by 177
Abstract
Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that [...] Read more.
Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that arises for instance from the time needed to collect and process the data. To improve the forecasts by reducing the delay, it is of interest to find alternative data sources that provide information on economic activity without significant delays. Among others, satellite images are thought to assist here. This paper addresses the potential of earth observation imagery for short-term economic forecasts. The study is focused on the estimation of investments in the construction sector based on high resolution (HR) (10–20 m) and very high resolution (VHR) (0.3–0.5 m) images as well as on the estimation of investments in agricultural machinery based on orthophotos (0.1 m) simulating VHR satellite imagery. By applying machine learning it is possible to extract the objects of interest to a certain extent. For the detection of construction areas, VHR satellite images are much better suited than HR satellite images. VHR satellite images with a ground resolution of 30–50 cm are able to identify agricultural machinery. These results are promising and provide new and unconventional input for economic forecasting models. Full article
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