Digital Economy. Emerging Technologies and Business Innovation

The purpose of the study is to develop an approach to constructing multi-modal machine learning models for assessing soft skills of business simulation players using game logs. The study was performed by analyzing the logs of business simulation “Corporate Governance”, which simulates the management of an enterprise in a real market. Within the framework of the study, business simulation is considered not as a learning game that forms competencies, but as a diagnostic one for assessing the players’ soft skills. The approach allows taking into account simultaneously each player’s individual strategy and the overall team scores in the assessment. An approach to the application of machine learning methods for analyzing business simulation logs is proposed, based on constructing a meta-algorithm that takes into account various types of input data. Individual player actions data are considered as action sequences and are treated by text data processing methods. As research implications, this paper presents a new integrated conceptual approach, which can be useful for studies focused on recruitment techniques and employee skills diagnostics. Currently, the player’s competencies are actually measured manually, rather than using tools for automated assessment. It is time-consuming and costly, especially when it is necessary to conduct mass business simulations. This research provides guidance for automating the process of assessing player skills thus delivering benefits of practical importance.

Moroccan industrial enterprises investing in the metallurgical, mechanical and electromechanical industries sector, play a significant role in Morocco’s economic development. The particularity of Moroccan companies investing in this sector is that the projects they are awarded generally require massive investment in the initial phase, and only generate sufficient cash flow in the long term to cover costs and debt servicing, especially when they operate for the benefit of public-sector customers, who are notorious for their late payments to suppliers. As a result, the loans granted have to be long-maturity, with longer grace periods than conventional loans. As a result, these projects do not have the same specific features as those of operating activities within other companies. In this study, some boosting machine learning models were used to predict the funding method of industrial projects. The data contains 5198 projects of some companies operating in the said sector, we found that K Neighbors Classifier, LGBM Classifier and Extra Trees Classifier can produce accurate and reasonably funding decision. Among the three machine learning methods, we find that ExtraTreesClassifier appears to be overall most effective.

In today’s digital age, travelers heavily rely on peer feedback to select hotels for their trips. However, the lack of transparency in hotel rating algorithms raises concerns about their accuracy. To address this issue, a study advocates for a reevaluation of the rating process by prioritizing customer feedback. The study used Sentiment Analysis to analyze over 28,000 reviews from Booking and Tripadvisor, and then converted sentiment scores into a 0 to 10 scale. Comparisons with ratings displayed on the platforms revealed a significant disparity, particularly with Tripadvisor showing higher averages than Booking. The study highlights the need for more transparent and accurate representation of guest sentiments in hotel ratings.

This exploratory study discusses the advantages and potential of blockchain in human resources management. To complement the current knowledge about the multiple applications of blockchain technology in business, this paper conducted an interview with a blockchain expert, who showed how blockchain can be applied to business and particularly to human resources. Then, the authors describe a case study involving the application of blockchain in the human resources department of a company, discussing the business performance achieved with such implementation.

The paper suggests that the adoption of blockchain in a human resources department results in a higher performance for business, considering the costs and time reduction with certain processes in the human resources department. The use of blockchain in human resources is part of the digital transformation movement in businesses and allows better agility to respond to fast market demands. This technology also allows better recruitment of new employees, which is mandatory to stand out in a competitive market.

Employee turnover constitutes a substantial challenge for organizations, exerting an impact on operational continuity and overarching productivity, thereby incurring concealed costs. Consequently, estimating the likelihood of employee turnover emerges as a valuable endeavor for Human Resource Departments. Such forecasting empowers these departments to proactively undertake measures aimed at averting such occurrences, particularly when staff is deemed pivotal to organizational success. Our research endeavors to explore the predicting capacity inherent in personality assessment test scores for anticipating turnover tendencies among candidates and employees. Utilizing a data-driven methodology, our study aspires to forge a robust correlation between personality traits and turnover. Furthermore, our modeling of turnover probability incorporates the influence of demographic information (e.g., gender, age, and education level), as well as performance and work-period metrics. Utilizing several machine learning methods, we cast the problem of predicting turnover tendency of an individual as a binary classification problem and train separate classifiers for candidates and current employees. Our experimental findings indicate that the differentiation between managers and subordinate employees proves advantageous in predicting turnover tendencies, given the requirements of distinct psychological traits at various hierarchical levels.

Augmented Reality (AR) has found practical applications on popular visual social media platforms where users can apply beauty filters to enhance their appearance. This study aims to provide a deeper understanding of the motivations behind the use of beauty filters by young women in the Tunisian context, where presence on social networks follows global trends. Based on theories of uses and gratifications and self-presentation, we conducted a qualitative study using a triangulation of online observation, semi-structured interviews, and projective techniques. Results reveal nine motivations: Enhancing facial features, enhancing photo’s quality, convenience, purchase decision, self-confidence, social influence, attention seeking, entertainment, pass time and curiosity.

This article addresses two important gaps in the digital platform literature. First, it addresses the lack of a shared conceptualization of digital platforms. Following a literature review, we identified three perspectives explicitly discussed in the literature to define digital platforms. In addition, we discovered an implicit perspective that, although not directly addressed, emerged in the literature. With the help of these perspectives, their scopes, and definition examples, researchers and practitioners can enhance their communication and improve the comparability of future studies. The second gap this paper addresses is the lack of criteria that distinguish e-commerce entities. This paper proposes an e-commerce taxonomy based on both the literature and an empirical sample. This taxonomy offers an overview of the distinctive characteristics expressed by these entities such as “network effects”, “type of network effects”, “information availability for the seller” and “information availability for buyers”. Our taxonomy contributes to the theoretical and empirical understanding of e-commerce entities by providing a structured framework that can be used to study and compare different e-commerce entities systematically.

In response to the evolving dynamics of healthcare, this research underscores the need for robust solutions to facilitate the exchange of clinical terminology, ensuring seamless communication and interoperability across healthcare systems. Terminology servers are pivotal in standardising and managing terminology, ensuring consistent communication and knowledge sharing. We must choose a modern, highly customisable, multilingual terminology server that supports FHIR and standard terminologies. This article offers a comprehensive overview of the challenges and limitations of existing clinical terminology exchange methods. We evaluate the strengths and weaknesses of prominent terminology servers. The findings reveal crucial insights into the current landscape of terminology management solutions, uncovering limitations and potential gaps. As a result, the article concludes with a compelling argument for the need to explore and develop a new enhanced terminology server solution. This exploration responds to the evolving demands of the modern healthcare industry and sets the stage for future advancements in clinical terminology management.

The creation of decentralized data spaces opens up enormous potential for the data economy. The Gaia-X initiative aims to bring values such as data self-sovereignty, trustworthiness, and interoperability to those data spaces. However, enabling financial transactions in data ecosystems supported by data spaces based on Gaia-X is still a topic to be addressed. This paper proposes an architecture for payment services in data spaces based on Gaia-X. This architecture was developed based on use cases from two projects within the Gaia-X 4 Future Mobility project family, but it should also enable the implementation of payment services in other data spaces, with a focus on B2B ecosystems. Requirements were elicited from those use cases, selecting suitable payment methods and defining the relevant criteria for structuring an architecture compatible with the Gaia-X principles. The proposed architecture was verified through stakeholders’ feedback and one test implementation, demonstrating suitability to two Gaia-X B2B data ecosystems that are structured on data spaces with fundamentally distinct infrastructures. The contributions of the proposed architecture add to advancing standardized payment services within data spaces based on Gaia-X and reducing barriers to entry for new participants in such ecosystems.

Accurate patient identification is crucial during admissions in healthcare institutions. Mistaken identity can lead to fatal consequences if patients are treated based on someone else’s medical history. Identifying citizens is generally well-regulated, but accurately identifying foreign, unknown, or anonymous patients is more challenging and often lacks sufficient regulation. Our objective is to develop and assess a coding system for patient identifiers, enhancing the precision of associated health records and offering a robust, adaptable method for identifying patients from diverse backgrounds. We investigated the patient identifier system design in the Estonian National Health Information System (ENHIS) during its shift from the HL7 V3 to the HL7 Fast Healthcare Interoperability Resources (FHIR) communication protocols. This transition involved evolving from an Object Identifier (OID) system to a Uniform Resource Locator (URL) system. We devised an Identifier Domain coding system tailored for patient identification that aligns with our goals and generalised this system as a universal patient identification method. The Design Science methodology, a well-established approach in software engineering and information systems, underpins our research. We tested and illustrated our proposed patient identification coding system using examples from the Estonian Patient Register. This newly developed system enables the identification of all patient types. It is user-friendly, semantically clear, backwards compatible with the OID system, expandable, and aligns with FHIR standards. Our findings can assist in creating interoperable patient identifier systems internationally.

This article focuses on the digital transformation in Morocco in recent years, with particular emphasis on information and communication technologies (ICTs) and their indicators. The practical analysis, based on Principal Component Analysis (PCA), provides a better understanding of the impact of ICT on the digital divide in the country. The analysis reveals that digital skills, investment in telecommunications and Internet access are the main factors influencing Morocco’s digitization. Although the country has made progress in these areas, challenges remain, including gaps in the development of digital skills.

To reduce the digital divide and foster a data-driven digital economy, measures are needed such as improving digital literacy, promoting digital innovation, adopting digital technologies and training qualified personnel. This analysis provides an overview of the current state of digital transformation in Morocco, and highlights the opportunities for balanced and inclusive development in a country striving to adapt to the challenges and opportunities of the digital economy.

This study aims to explain the latest digital technology trends in pig production in agricultural establishments that can be applied in a municipality in Mexico. The incorporation of technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain has proven to increase biosecurity measures in pig farms, contributing to the prevention of associated diseases and improving the quality of meat production for the well-being of society. Based on this understanding, we have formulated a prototype proposal relevant to Mexico. Our proposal was structured using methodologies that included field visits to two pig farms located in Zapopan, Jalisco, interviews with specialists in veterinary medicine, and a review of relevant literature to understand the current global scenario regarding the digitalization of pig farms in search of the best practices to achieve a “smart farm”.

Our results indicate the viability of integrating digital technologies into pig farming and the potential to expand this approach to other farms in Mexico. Specifically, we propose the implementation of IoT devices for monitoring NH3-CO2 levels and temperature, a mobile application for farm surveillance, and Blockchain technology for tracking the supply chain of raw materials and pharmaceuticals. Additionally, a microfluidic biosensor can be utilized for early detection of swine stress.

To facilitate the widespread adoption of these technologies, we suggest promoting this type of initiative within the livestock industry and encouraging the integration of practices that prioritize technological advancements and animal welfare.

Rapid technological advancements and increased connectivity have given rise to a new economic era, where digitalization plays a key role in the economic growth of nations. This article studies the effect of innovation and digitalization on the economic growth of four countries in the North African region, namely, Algeria, Tunisia, Morocco and Egypt over the period 2007 to 2022.

Using panel data analysis based on two approaches, panel least squares with a fixed effects regression estimator, and robust ordinary least squares (ROLS), dependence tests, showed that the Innovation and digital transformation have a positive and significant effect on improving the economic growth of our studied sample. However, significant differences exist for each country: it is the ICT development index, which has a significant impact on economic growth in Morocco, while in Tunisia, it is digital investment which contributes the best to economic growth. As for Egypt and Algeria, the results showed that it is rather the degree of innovation in these two countries that has a strong and significant effect on the level of economic growth.

This study explores how business sophistication, and digital technologies influence the global innovation index, focusing on how entrepreneurial orientation mediates this relationship in emerging nations’ institutions. The rise and extensive adoption of digital technology in these areas enhances the innovation capacity of organizations. Businesses with a robust entrepreneurial orientation improve their capacity for innovation. The use of smooth transition autoregression techniques revealed a significant correlation between international markets, digital technology, and innovation in nine emerging nations from 2011 to 2022. A growth in a company's entrepreneurial drive strengthens the connection between digital technology and worldwide markets. The study uses a quantitative approach and an applied research technique, collecting data using a descriptive survey.

This research investigates the effectiveness of mobile money in addressing liquidity challenges in Morocco through an empirical analysis utilizing an Autoregressive Distributed Lag model. Examining monthly data spanning from 2020 to 2022, we analyze both short- and long-term dynamics. Our findings reveal that mobile money provides short-term relief by mitigating immediate liquidity demand, in accordance with transaction convenience theory. However, its long-term impact diminishes, indicating limitations as a standalone solution. Surprisingly, we observe that higher levels of cash circulation also correlate with increased long-term liquidity demand, potentially driven by precautionary motives. We emphasize the necessity for further research integrating behavioral perspectives. We recommend a multifaceted strategy for policymakers, which includes promoting financial literacy to maximize the immediate advantages of mobile money, understanding cash preferences to foster collaboration between both systems, and conducting further research to fully understand the long-term impact of mobile money on Morocco’s financial system.

Mobile banking applications, also known as M-Banking apps, have become increasingly popular in recent years. As such, it is important to ensure that these apps are continuously monitored to remain competitive and efficient. The objective of this study is to propose a novel approach for assessing M-Banking apps and generating constructive recommendations for potential enhancements. The methodology employed involves firstly a systematic evaluation of the M-Banking app across various app platforms and social media channels. Secondly, an examination of the M-Banking app is conducted, focusing on understanding how customers engage with each of its features. Thirdly, a survey is administered to capture feedback from the M-Banking app users, identify their needs, and comprehend their overall experiences. Through following these precise steps, this paper provides an overview of the proposed evaluation approach, the criteria used for assessment, and recommendations for resolving identified issues. Results provide recommendations for the optimization of the M-Banking app’s user interface, functionality, security, and marketing strategies. By implementing these recommendations, an app can become a more user-friendly, secure and personalized, contributing to customer retention and attracting new users.

The rapid diffusion of technology and internet use has led banks to consider introducing digital payment services such as internet banking. This paper seeks to test the impact of internet banking adoption on the banking sector’s financial performance in Morocco for the period 2015–2022. An Autoregressive Distributed Lag (ARDL) model was employed to evaluate the short-run and long-run impact of internet banking. Secondary data was gathered from Central Bank of Morocco, High Commission for Planning and Centre Monétique Interbancaire. We used ROA to represent the banking sector’s financial performance and internet banking transactions as a proxy for internet banking. Based on the analysis provided by the ARDL model, internet banking is significant and negative in the short-run which suggests that its adoption hurts the banking industry’s profitability. However, in the long-run, there’s evidence of a positive but weak impact. We conclude that internet banking is a long-term investment that initially decreases the profitability of the banking sector but contributes to the financial performance in the long-run.

This paper's goal is to show how the price of Bitcoin affected the SP500 and CAC40 stock market indices. It will do this by using the symmetric autoregressive distributed lag (ARDL) and the asymmetric nonlinear autoregressive distributed lag (NARDL) models to find the cryptocurrency's mixed effect on global stock market indices. We used two control indices, the gold price, and the crude oil price, from October 2010 to April 2023. Based on stock market indices, the ARDL method suggests that Bitcoin holdings have grown steadily since the COVID-19 crisis. But the NARDL data shows that when Bitcoin goes down, both stock market indices become more volatile. Governments must prioritize monetary digitization by fostering the widespread adoption of digital currencies and transactions, like cryptocurrencies, in light of recent events like the COVID-19 pandemic and the conflict between Russia and Ukraine.

With the rapid development of technology, robotization, Big Data and Artificial Intelligence occupying the forefront of the scientific and media scene, marketing as a field of study and research has undergone a major transformation. This paper traces the advent of a new phenomenon based on artificial intelligence; whose protagonists are virtual influencers promoting well-known brands on Instagram. Through a qualitative study involving focus groups, the authors aim to provide a better understanding of potential followers’ attitudes and perceptions towards these AI-driven strategies in digital marketing, highlighting the fact that consumers resist to the influence that social robots try to exert on them.

Despite acknowledging the novelty and technological impact of virtual influencers, participants expressed concerns over authenticity, emotional connection, and the Uncanny Valley effect. The study suggests that while virtual influencers are reshaping marketing practices, they do not yet seem have the same power over consumers as human influencers.

Search engine bias is a manifestation of the broader societal system, which is interconnected with discriminatory, prejudiced, or biased behaviors of various social groups. This research primarily addresses the issue of manifested gender prejudiced in search engine results. This study focuses on investigating potential variations in nudity ratings across nine EU-27 member states from three culturally varied search engines, namely Google, Yandex, and Baidu.

Using three distinct search engines, we successfully gathered a total of one hundred images of individuals of both genders from nine distinct nations that are part of the EU-27. Subsequently, the nudity ratings of the 5400 photos were calculated, and the results were compared using appropriate statistical analysis methods. Based on the data, we can infer that there is a statistically significant disparity in the ratings provided by search engines for the nudity of acquired images. With certain cross-cultural, cross-national, and gender exceptions, the findings demonstrate a statistically significant difference between the search engines regarding the nudity results of the gathered photographs. Google is characterized by its progressive stance as a search engine, while Baidu and Yandex have shown a more conservative orientation.

Increasing global stakeholder interest in sustainable business practices has led to heightened demands from stakeholders for companies to disclose the environmental impacts resulting from business activities. As a result, companies that use artificial intelligence have begun giving inaccurate sustainability reports in their corporate communications; nevertheless, this has increased the provenance of many challenges. This study aims to explore the dark side of artificial intelligence in green marketing within companies. Qualitative research was carried out through a series of interviews with Tunisian company managers. Five key themes were identified: greenwashing, deepfake, data management, privacy risk, and overconsumption. The results provide a basis for future quantitative research and offer valuable insights for practitioners tasked with navigating the intersection of Artificial intelligence and green marketing.