Digitalization becomes a key strategy for the success of businesses, which in today’s critical times, are under remarkable pressures and diffused uncertainty. The rapid pace of digitization is forcing deep changes in the modus operandi of organizations. This phenomenon is even more so true for accounting organizations considering that, by implementing blockchain, RPA, cloud, big data, cybersecurity, and AI, accountants might have the most digitized workplace of all. The purpose of this paper is to explore how these emergent technologies are contributing to the digital transformation of accounting firms. Based on a qualitative approach, the methodology consists of a thematic analysis of the academic literature to reveal the synergic effect of the most disruptive emergent technologies for accountancy firms. In addition to the topic of research, the originality of this study is ensured by the fact that it presents both technical and conceptual information, easily digestible for academicians and practitioners skilled in the ICT field, or not. The paper is intended to be a building brick for the literature related to this topic.

Nowadays, Information Technology (IT) is part of virtually every business, and companies that cannot keep the pace with new technologies will disappear over time. Due to their nature of specific activities and exceeding other areas, professional accounting and auditing services can improve their performance through Robotic Process Automation (RPA). Furthermore, RPA can contribute to increasing the credibility of the accounting profession, as well as streamlining the activity in order to comply with the requirements imposed by professional standards but with much lower costs. This study is based on a review of the literature and through an exploratory approach opens a discussion on the concept of RPA and customises it in the field of professional accounting services by analysing robotics models specific to accounting and audit.

Decision trees classifiers are simple and prompt data classifiers as supervised learning means with the potential of generating comprehensible output, usually used in data mining to study the data and generate the tree and its rules that will he used to formulate predictions. One of the major challenges for knowledge discovery and data mining systems stands in developing their data analysis capability to discover out of the ordinary models in data. The excellence of a university is specified among other concerns by its adapting competence to the constant changing needs of the socio-economic background, the quality of the managerial system based on a high level of professionalism and on applying the latest technologies. This article represents an implementation of a J48 algorithm analysis tool on data collected from surveys on different specialization students of my faculty, with the purpose of differentiating and predicting their choice in continuing their education with post university studies (master degree, Ph.D. studies) through decision trees.

Exams failure among university students has long fed a large number of debates, many education experts seeking to comprehend and explicate it, and many statisticians have tried to predict it. Understanding, predicting and preventing the academic failure are complex and continuous processes anchored in past and present information collected from scholastic situations and studentspsila surveys, but also on scientific research based on data mining technologies. In the current article the authors illustrate their experiments in the educational area, based on classification learning and data clustering techniques, made in order to draw up the studentspsila profile for exam failure/success.

The purpose of this study is to analyze the factors that are contributing to the remarkable growth of the m-commerce sector. The article examines eight variables, including socioeconomic (Internet access, mobile users, mobile Internet penetration rates, card payment transactions, consumer confidence, Internet use: selling goods or services) and macroeconomic (GDP and wage), that are considered to influence global m-commerce growth. The Generalized Method of Moments (DPD/GMM) was used to analyze a panel of data that covers the years 2011 through 2020, on a sample of 42 developed and developing countries. The empirical findings show that wages, GDP, consumer confidence index, card payment transactions, mobile users, Internet access and Internet use (selling goods and services) have a positive impact on m-commerce, whereas the mobile Internet penetration rate has a negative impact. Using a sizable and representative panel data set on socioeconomic and macroeconomic indicators, this research advances the state of the art in customer comprehension of these topics. The study’s novel contribution is the incorporation of under-researched m-commerce drivers into empirical analysis. Detailed policy recommendations are provided, with an emphasis on practical, implementable measures to enhance the m-commerce industry.