Information Systems

We present in this paper Phi, a generic microservices-based Big Data architecture dedicated to complex multi-layered systems, that rallies multiple machine learning jobs, stream and batch processing. We show how to apply our architecture to an adaptive e-learning application that adjusts its recommendation to the emotions of the learner on the spot. We deploy our application on the cloud using AWS services, and perform some performance tests to show its feasibility in a realistic environment.

Nowadays, the amount of data that is constantly being generated presents new challenges for the technical and scientific community, such as the challenge of ensuring Complex Event Processing (CEP) in Big Data contexts, which arises to meet current advanced analytical needs. Therefore, some works are dedicated to the design and implementation of integrated CEP systems in the context of Big Data, as it is an example the Intelligent Event Broker (IEB) on which this work is based on. The IEB is a collection of several components that are integrated and validated to create a homogeneous system that will process events in real time in Big Data contexts, focusing on a rule-based approach. Considering the complexity of the IEB in constantly running contexts, it is important to have the ability of monitoring the evolution of the system, to avoid its uncontrolled growth. To accomplish that, we have previously proposed a component named “Mapping and Drill-down System” for the IEB, composed of a Web visualization Platform and a graph database. The main goal of the work presented in this paper is propose an architecture for the Mapping and Drill-down System component to monitor, in real time, the IEB’s execution data, by collecting, processing, and efficiently storing it in a graph database for later visualization through the Web Visualization Platform. The graph database and the Web Visualization platform are the key components of the Mapping and Drill-down System. With this work, it will be easier to understand the behavior of the IEB in constantly running contexts, ensuring its controlled growth and helping the community in the design and development of CEP systems for Big Data contexts, especially in the monitoring component of such complex systems.

Higher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer skills, general universities started to offer various courses on IT, including computer science that was originally offered by technical universities. It is also possible to have selected medical studies not only at medical universities but also in private colleges, e.g., nursing studies. As a result, the current classification of higher education institutions used in official statistics can be revised. The paper shows the experimental work on the use of machine learning methods to classify and cluster higher education institutions in Poland. Different attributes were used to classify the type of institution, including fields of studies, programme orientation and others. The aim of the paper was also to evaluate various machine learning methods in the process of classifying or clustering and validating the associated types of higher education institutions.

The aim of this paper is to present the use of sentiment analysis in both Polish and English languages. This goal is related to the fact that the authors of this article have observed many sentences in both Polish and English, used in social media and on websites in Poland. The paper presents the principles of various inflectional forms that should be used in the preparation of the training dataset being the subject of the analysis. Therefore, one of the goals of this article is to identify possible problems that an analyst of sentiment analysis machine learning methods may misinterpret. The motivation of the study was to see if the same methods could be used to analyse sentiment in different languages. We decided to evaluate the possibility of using one sentiment evaluation mechanism, assuming the use of similarly prepared training sets. In addition, the article shows the principles and differences between these languages, including in terms of the possibility of gender identification based on the text. We presented the results of a case study that showed how machine learning tools treat unstructured data to find the right sentiment and what problems can be identified when delivering text in these two languages. The conducted study also showed the possibility of using Big Data sources, such as comments in the form of comments on websites or social media, in order to correctly identify the sentiment, which is not always the case if the training set is not prepared properly.

In this paper, we study how the application of a dynamic user engagement profiling can influence the efficiency of systems aimed at detecting the user’s propensity to buy a subscription. Specifically, we address a task of identifying the digital media readers who are involved enough in the publisher’s offer to pay for access to the content of a given webpage. We present the user engagement profile updating framework responsible for enriching raw events with time-agnostic temporal features. In particular, we experimentally evaluate the performance of machine learning algorithms for the task of predicting the user propensity to subscribe using the synthetic dataset based on publicly available data streams on users of KKBox’s music service. Additionally, we provide the results of online tests in which the propensity-to-subscribe prediction model is used to control the paywall displays on a digital media website with live traffic. The results of experiments have proven that enrichment of data with engagement profiles leads to higher performance of prediction models than relying just on raw features and tuning the model’s hyperparameters.

Knowledge gained by meta-learning processes is valuable when it can be successfully used in solving algorithm selection problems. There is still strong need for automated tools for learning from data, performing model construction and selection with little or no effort from human operator. This article provides evidence for efficacy of a general meta-learning algorithm performing validations of candidate learning methods and driving the search for most attractive models on the basis of an analysis of learning results profiles. The profiles help in finding similar processes performed for other datasets and pointing to promising learning machines configurations. Further research on profile management is expected to bring very attractive automated tools for learning from data. Here, several components of the framework have been examined and an extended test performed to confirm the possibilities of the method. The discussion also touches on the subject of testing and comparing the results of meta-learning algorithms.

As the blockchain ecosystem gets more mature many businesses, investors, and entrepreneurs are seeking opportunities on working with blockchain systems and cryptocurrencies. A critical challenge for these actors is to identify the most suitable environment to start or evolve their businesses. In general, the question is to identify which countries are offering the most suitable conditions to host their blockchain-based activities and implement their innovative projects. The Blockchain Readiness Index (BRI) provides a numerical metric (referred to as the blockchain readiness score) in measuring the maturity/readiness levels of a country in adopting blockchain and cryptocurrencies. In doing so, BRI leverages on techniques from information retrieval to algorithmically derive an index ranking for a set of countries. The index considers a range of indicators organized under five pillars: Government Regulation, Research, Technology, Industry, and User Engagement. In this paper, we further extent BRI with the capability of deriving the index – at the country level – even in the presence of missing information for the indicators. In doing so, we are proposing two weighting schemes namely, linear and sigmoid weighting for refining the initial estimates for the indicator values. A classification framework was employed to evaluate the effectiveness of the developed techniques which yielded to a significant classification accuracy.

Many applications, across an array of different industries, access blockchain technology. This paper focuses on the luxury industry and explores how these blockchain-based applications add value. The study incorporates the findings of a Delphi study of luxury sector professionals. Eleven luxury brand core values were identified and validated, and six blockchain application areas along the customer journey were refined for the industry by panelists. The results show that the product authenticity or certification application meets the most important core values for luxury brands, especially in thriving online marketplaces and secondary markets. Responsibility, i.e. the value ranked lowest by the panelists, entails huge demands from the market, namely in the form of corporate social responsibility and sustainability. The study provides the luxury sector with valuations of technology applications according to added value, customer journey phases and concrete use cases as examples. The contribution of the study is that it provides indicators for luxury business strategies when considering taking part in the blockchain networks.

National population census provides the basis for governments’ financial, economic, health and education policies for its populace. It plays a vital role in mapping a country’s growth and financial trajectories and it is the single most valuable and shared resource among government departments and apparatuses. The centralized, traditional methodologies currently in use are faced with several challenges including and not limited to high costs, privacy issues, enumerating unsafe areas and reduced cooperation. This research aims to analyze through a systematic literature review the drawbacks and challenges of the current traditional methodologies used in housing and population census to identify if a decentralized system would assist in mitigating them. The drawbacks identified are population coverage, ethnic and racial discrimination, privacy concerns, census data distribution, cost of census, and cooperation and participation. The research, even though at an embryonic stage, shows that blockchain-based solutions may be a candidate for solving several of the above mentioned challenges while laying at the same time the foundations of our research on blockchain-based systems for tackling with other challenges faced within census such as that of the missing people.

An increasing number of prosumers participate in the energy market, either by offering flexibility or selling surplus energy. This is made possible through EU directives for electricity transactions in smart grids. The directives provide guidelines for individual and aggregated transactions, allowing customers to sell or share electric surplus at the local level or to the national grid. This is seen as an important part of the transition to renewable energy.In this paper, we introduce blockchain as a mechanism for handling decentralized transactions in smart grids. Blockchain technology allows for a flexible peer-to-peer trading mechanism. It can handle transmission and distribution management with energy flow optimization and grid infrastructure security, prosumer and microgrid management with different trading and pricing mechanisms, and interactive load between electric vehicles and grid. We describe blockchain technology, provide a survey of blockchain applications in the energy sector, emphasize the achievements and limitations of this technology in EU research studies and industrial projects, and underline the findings of the Smart-MLA project in this field.

New ventures and private investors are showing increasing interest in innovative forms of fundraising. ICO is the abbreviation of Initial Coin Offering and it represents an innovation in entrepreneurial finance [1]. However, no study has ever developed a taxonomy of academic and not academic discourse related to this type of innovative financial tool. This paper aims to fill this gap by developing a taxonomy to investigate and categorize papers that discuss Initial Coin Offering phenomenon. This study is developed using a mixed methodology. The first stage of the research protocol regards the dataset definition and description. In the second stage we adopted the taxonomy process developed by Nickerson et al. [2]. The purpose of the present work is to develop a taxonomy with a set of dimensions each consisting of a set of characteristics that describes the objects in a specific study. We identified “Research Topics” as set of dimensions. It comprises eight dimensions: field of investigation, focus, actors, token type, extra topic, research issue, ICO phase, blockchain. In the taxonomy process we assigned a single value to every dimension. In the last section, we summarize some preliminary results, providing conclusions and discussions for future research.

Blockchain technology offers a vast amount of possible applications, which include the decentralization of platforms among many others. That decentralization can enable decentralized governance of platforms, which in turn may lead to a decentralized governance structure in which the rights to make decisions about the platform are decentralized. It remains unclear how decentralized the platform governance really is solely based on its possibility and intention to decentralize. We analyze how decentralized the governance structures of in reality occurring platforms are. For this we collected data of 16 platforms which are based on the Ethereum blockchain. These platforms were analyzed in terms of token and transaction distribution as well as transaction behavior. This allowed us to examine the distribution of platform resources associated with the governance structure. As a result, we could identify that not yet decentralized governance structures becomes more decentralized. Additionally, we enhance the knowledge on the development of the decentralization of platform resources.

The Learning Management System (LMS) tries to resolve the multiple challenges that occur due to limits of time, location, and frequency of teacher-student interactions. As a tool in the e- learning process, the LMS provides several benefits that can help overcome problems that often occur during the learning process.However, the current deployment of LMS as a learning medium still has its limitations such as low engagement and motivation, secure documents, certification, and exam verification to replace a cumbersome manual process, and ultimately personalization of features that relevant to students’ requirements. Recently, some new technologies developed as recent trends to tackle the various difficulties and challenges relevant to these issues and obstacles. In this sense, the Gamification design boosted users’ interaction and engagement with the online system by adding a new game concept. Similarly, blockchain technology improves the security of online document exchange, verification, and storage. Ultimately, learning analytics demonstrated to allow the personalization of online platforms based on interactions and data logs. In this work, we studied how combining these strategies might boost the LMS performance and tackle existing issues and problems.To do that, we employed a design science methodology as a rigor innovation strategy in digital innovation. The results reveal encouraging results of the new systems (LMSD) implementation in Dubai’s British university.

Population growth and increased trash generation are expected to worsen living conditions in LCDs. However, waste management organizations have been hindered by a lack of stakeholder participation and coordination as well as obsolete disposal methods. By automating the collection and transportation of rubbish, current technology only aids to reducing human participation. People could be more engaged in waste management activities if they were remunerated for it. The enabling system should automatically record significant activities and respond appropriately. The blockchain technology could be the solution as it may help waste management by increasing public knowledge, transparency, and stakeholder confidence. This study looks at how the blockchain technology could improve waste management by increasing public awareness, transparency, and trust among stakeholders. By ensuring immutability using a cryptographically secure distributed ledger system, blockchain links people, processes, and technical developments. Through a systematic literature review on blockchain technology deployment in waste management, this research seeks to add to the content of previous studies and to enlighten the path for future studies. Furthermore, the study’s findings will help addressing research gaps on using new technology to conserve the environment. They would also help authorities and politicians to raise awareness and involve stakeholders in social issues.

Cloud computing has become an increasingly attractive option for oil and gas organizations looking to reduce costs while increasing operational excellence. The cloud is not only a tool for faster computing power and higher performance, but also as an instrument to faster application deployment, lower service costs and a step towards digital transformation. This study aimed to determine the level of cloud computing adoption within selected organizations within the oil and gas industry in South Africa. The study also attempted to understand the factors that influence this adoption and how SA oil and gas companies are utilizing cloud technologies, as well as the factors that influence the presumed lack of adoption. This research was guided by the Technology, Organization, and Environment (TOE) framework and used a qualitatively approach, based on semi-structured interviews from seven participants, from four oil and gas companies in SA. The findings of this research show a high level of awareness but low level of adoption of cloud computing. The factors that were found to have a positive and significant influence on the intention to adopt cloud computing included security, relative advantage, compatibility, top management support, vendor support and competition. The factors that were found to be a risk or challenge towards the adoption of cloud computing included complexity, government regulations, organizational readiness, bandwidth, trust and vendor lock-in.

Today, the business requirements of consumer are often very complex, requiring a composition of multiple component cloud services to create new composite value-added applications. In the process of cloud service composition, service composability forms the basis of the efficient development of emerging composite services. Developing such services is a challenging task due to the lack of tools and techniques for understanding the composability relationships among component cloud services, which influence the whole composite service’s efficiency.In this paper, we propose a composability model for cloud services that characterize the composition relationships among services. We propose a set of composability rules which compare the semantic features of cloud services to verify the interconnection of them. Our proposed model deals essentially with functional and technical cloud services aspects. These rules compare the capabilities and requirements of cloud services to determine whether two services are composable.

The effectiveness of e-government projects is hampered by a lack of know-how, reduced funding, and a lack of sound policy initiatives and decisions. Many e-government projects have been devalued because they were designed incorrectly, and effectively transfer existing bureaucracy to the digital world. The purpose of this paper is to explore the factors affecting the acceptance and satisfaction of IS users in e-government. Data was collected by 498 users in the Greek public sector. This study is useful for professionals who design these systems to improve their effectiveness and to carefully consider these variables in the design and usage of IS in the public sector.

The realization of the complexity of the modern societies’ needs and problems leads to an understanding that the current civil societies’ ecosystem could be compared to a network, where every stakeholder is connected to the others and they collaborate by exchanging information, services and engagement. Recent research studies suggest the adoption of a new model, the Quadruple Helix model, in the action plans of smart cities. This model puts the community and the citizens in the command position alongside with the business, research and government stakeholders. It also entails the participation of the citizens not only in the service provision, through monitor and engagement, but also in the policy making, leading to a more bottom-up decision-making system towards Smart Cities 4.0 level. Information and Communication Technologies, and more specifically the Disruptive Technologies, emerge as a major means of achieving digital transformation in order to co-create user-driven decisions for the communities and the smart cities of the future. The aim of this study is to compare existing solutions of digital participation systems, identify the points of convergence and differentiation and extract useful insights for future research and applications.

Literature posits that most data and research on E-Government dominantly focuses on large economies where social, political, organizational and economic aspects of these local contexts significantly differ from other parts of the world. One such part is the group of island states specifically referred to as Small Islands Developing States (SIDS), sharing common challenges of reduced size, diseconomies of scale, impact of climate changes and other challenges. E-Government models should be adapted to local context, and for SIDS this entails understanding the local context so as to formulate a sustainable model. Even though we find studies and models for SIDS, significant differences exists among SIDS that warrant individual approaches. An example of a SIDS is São Tome and Principe, where the government is involved in E-Government initiatives. This is visible at the Ministry of Finance where different interacting Departments have developed systems and software tools to manage business processes. These systems and tools are used for interactions internally and with public and private sectors. However, for implementation, there is a lack of a centralized, interoperable vision or directive. Consequences are high total cost of ownership, subsequent costs with interoperability and maintenance, and, in the end, deficient long-term sustainability. Considering the impact of costs of information technology initiatives to the public budget, financed in its majority by development and bilateral assistance, there is a need for an E-Government model that prescribes directives for a sound, interoperable and sustainable E-Government implementation. We propose the development of E-GovSTP, a framework/model that intends to combine technical considerations and aspects of the local context to formulate guidelines for E-Government implementations in the Ministry of Finance. This artifact shall be developed through sound theoretical foundation, application of established standards and guidelines to areas of privacy and security, interoperability and system and communications. Additionally, the fundamental aspects of the local context (political, social and organizational) shall be factored into the model in order to guarantee the sustainability having in mind existing technical, material and financial constrains the country faces.

Scholars have reiterated the potential of mobile government solutions to extend the adoption of electronic government services in the context of the proliferation of mobile phones across various countries. This study aimed to understand the challenges faced by municipalities in the implementation, deployment and maintenance of m-government applications. The research followed an interpretive case study strategy and collected data from a municipal entity and its citizens, focusing on the m-government services of the entity. The findings indicate that various stakeholders are key to the successful delivery and uptake of m-government services. The municipal entity faced various challenges including ICT skills shortages, budget overruns, system interoperability and constrained resource capacity. From the citizens’ perspective, the study noted concerns with the training of users, service resolution delays as well as privacy and security concerns. Several recommendations for consideration on m-government projects are put forth based on the lessons from the case study.

This study investigates why the Corona-Warn-App, which was meticulously designed in Germany to interrupt the COVID-19’s chain of infection, was not installed by the majority of the population and therefore failed to achieve what it was created to do. We collect natural language data by scraping 70,529 related comments from Twitter, and apply sentiment analysis to understand the content. We distinguish negative comments into two categories: technical issues, e.g. crashes and errors, and trust-related issues, e.g. concerns about privacy protection. After a more detailed manual check, we find that some criticisms of the app are not accurate. Surprisingly, more than 40% of trust-related denunciations are based purely on misinformation spread by users. For example, a user complains about a violation of data privacy, when, in fact, the app is fully GDPR-compliant. Our study provides evidence for the intentional promulgation of misinformation to lower trust in life-saving technologies during a pandemic, and calls for a more careful evaluation of the technology’s performance.

Digital Marketing, and specifically, targeted marketing online is flourishing in recent years, and is becoming evermore precise and easy to implement, given the rise of big data and algorithmic processes. This study assesses users’ perceptions regarding the fairness in algorithmic targeted marketing, in conditions of scarcity. This is increasingly important because as more decisions are made by data-driven algorithms, the potential for consumers to be treated unfairly by marketers grows. Awareness of users’ perceptions helps to create a more open, understandable and fair digital world without negative influences. Also, it may help both marketers and consumers to communicate effectively.

Mobile dating applications have changed how romantic relationships are pursued. However, despite the popularity of these applications, users have expressed numerous privacy and security concerns that warrant further investigation. This study explored how users’ locus of control influences the actualisation of affordances to mitigate privacy and security concerns. Through a qualitative study based on 12 semi-structured interviews, seven propositions are formulated. The study contributes to MDA literature and affordance theory. The propositions articulate that users can actualise affordances from a network of tools to mitigate privacy and security concerns given their locus of control.

The choice of software for implementing online learning has always been one of the fundamental problems in education sciences. Efficiency and quality of education largely depend on the properties of the tool (software) that the teacher uses. The COVID-19 pandemic has led to the rise in numbers of users of e-learning tools. Decision makers had to choose which available product their corporation, university or school would use. After several months of widespread implementation of different e-learning software, users are ready to give an evaluation. The aim of this paper is to provide such evaluation on MS Teams, which can be obtained by applying Technology Acceptance Models. Among the set of Technology Acceptance Models developed in science and verified in practice, the Unified Theory of Acceptance and Use of Technology (UTAUT) deserves special attention due to its flexibility and large predictive power. We propose an enriched UTAUT model for MS Teams, which adds two new variables to the original: Product Superiority (PS) and System Comprehensiveness (SC). This paper presents the development of Technology Acceptance Models as a software evaluation method, followed by the presentation of hypotheses and description of the research method used. The research was carried out using the questionnaire distributed among university teachers from northern Poland. We present the analysis of the results along with the conclusions formulated on their basis. At the end, we highlight the interpretative limitations and indicate further research directions.

Presence is one of the most important psychological constructs for understanding human-computer interaction. It is especially important for applications that use advanced techniques of iteration between humans and computers, such as Virtual Reality (VR) applications. The paper addresses the problem of difficulty for users to experience a sense of presence in VR applications, especially when the worlds they are transported to are unknown to them. It presents the experiments made for three selected mobile application together with their results and analysis.

This paper examines how a positive online customer experience (OCE) can lead customers to consent retailers to collect their personal data and to receive personalized services in exchange. The paper addresses a gap in literature by acknowledging the customer’s perspective while most of the prior literature has overemphasized the benefits of customer data for the firm. The aim of this paper is to provide a literature review to inform understanding of the antecedents and consequences of the willingness to share information (WSI). The paper offers four important contributions for both academics and practitioners. First, it adds to understanding of the importance of OCE and WSI for customer loyalty in e-commerce. Second, the paper examines the e-store antecedents of WSI by drawing on existing literature. Third, it proposes the potential consequences of WSI and constructs a conceptual framework for future testing. Finally, the paper proposes managerial implications.

Traditionally requirements for Information Systems are considered as functional and non-functional. However, with current omnipresent Digital Technologies, we believe that new requirements dealing with individuals’ well-being are emerging. We call them sentient requirements using the term from the animal rights protection field. In this paper, we analyze the existing literature to understand better the deep nature of humans’ interactions with digital technologies and we introduce sentient requirements. It is based on a literature review including scientific and science-fiction literature. We apply these requirements to improve user experience in museums through a visiting game as a use case. Our proposal could be used by researchers and practitioners to enforce the design of Information Systems in various application fields to provide a better interaction between humans and digital technologies.

Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations. While factual explanations can be helpful, humans typically do not ask why a particular prediction was made, but rather why it was made instead of another prediction, i.e., humans are interested in counterfactual explanations. While research in explainable AI produced several promising techniques to generate counterfactual explanations, directly applying them to predictive process monitoring may deliver unrealistic explanations, because they ignore the underlying process constraints. We propose LORELEY, a counterfactual explanation technique for predictive process monitoring, which extends LORE, a recent explainable AI technique. We impose control flow constraints to the explanation generation process to ensure realistic counterfactual explanations. Moreover, we extend LORE to enable explaining multi-class classification models. Experimental results using a real, public dataset indicate that LORELEY can approximate the prediction models with an average fidelity of 97.69% and generate realistic counterfactual explanations.

In the Higher Education Sector (HES), we see increasingly Artificial Intelligent (AI) agents in the form of chatbots or interactive virtual agents indistinguishable from people and a unique example of human-machine interaction using natural language processing. They are becoming one of the main technological tools to ensure accreditation and e-learning, while providing better adaptive learning. This conceptual paper aims to examine the factors that affect the intention to use AI agents/chatbots for adaptive learning in HEI from a sociomateriality perspective taking into consideration the mcdonaldization effect. An extended UTAUT (Unified Theory of Acceptance and Use of Technology) model is proposed to be evaluated in the HES context.

Innovation requires a specific management approach and it seems that emerging technologies are able to provide additional value to innovators for better handling the unknown and unpredictable environment in which innovation is developed. The innovation process is the best known methodological and systematic way for innovations to be developed and whether artificial intelligence is already used and how exactly in this process, is the focal point in this study. The research was motivated by the frequency of innovation's failure during development and the diverse case studies in the literature in which artificial intelligence has been used to support the successful development outcome. We used a bibliometric analysis for sheding the light and bringing more understanding for the new managerial techniques through artificial intelligence as part of the innovation management in the last 20 years research. The results of this study are particularly important for innovation managers who are not first adopters and need more analysis of the application of artificial intelligence, the outcomes, benefits and disadvantages of this use as part of the innovation development and innovation process. The paper contributes by summarizing the current research on the topic and outlines the research agenda for its further evolution.

Heart disease (HD) is one of the utmost serious illnesses that afflict humanity. The ability to anticipate cardiac illness permits physicians to deliver better knowledgeable choices about their patient's wellbeing. Utilizing machine learning (ML) to minimize and realize the symptoms of cardiac illness is a worthwhile decision. Therefore, this study aims to analyze the effectiveness of some supervised ML procedures for detecting heart disease in respect to their accuracy, precision, f1-score, sensitivity, specificity, and false-positive rate (FPR). The outcomes, which were obtained using python programming language were compared. The data employed in this investigation came from an open database of the National Health Service (NHS) heart disease which originated in 2013. Through the machine learning (ML) technique, a dimensionality reduction technique and five classifiers were employed and a performance evaluation between the three classifiers- principal component analysis (PCA), decision tree (DT), random forest (RF), and support vector machine (SVM). The NHS database contains 299 observations. The system was evaluated using confusion matrix measures like accuracy, precision, f1-score, sensitivity (TPR), specificity, and FPR. It is concluded that ML techniques reinforce the true positive rate (TPR) of traditional regression approaches with a TPR of 98.71% and f-measure value of 68.12%. The true positives rate which is the same as the sensitivity was used to evaluate the accuracy of the classifiers and it was deduced that the PCA + DT outperformed that of the other two with a sensitivity of 98.71% and since the value is on the high side, this implies that the classifier will be able to accurately detect a patient with HD in his or her body.

Organizations have implemented business intelligence (BI) applications to realize a variety of organizational benefits. However, a majority of BI application implementation projects are unsuccessful. One of the main reasons for the failure is end user resistance to adopt BI applications that their organization chose. Therefore, it is important to understand how to facilitate individual adoption of BI application. This study proposes a technology acceptance model for the systematic evaluation of administrative activities in public hospitals, using the example of a (BI) software package. The proposed model is based on an expanded Technology Acceptance Model (TAM) and previous surveys of IT implementation success factors and reviews of relevant change management variables.

The appearance of pandemics of various kinds that have shaken the world and transformed health paradigms has led many organizations and states to review their health strategies to ensure sustainable assistance to the population. Organizations are turning more towards a sustainable digital transformation, which considers multiple dimensions, including health. This study presents a topic-oriented mapping of a range of conceptual and practice-based efforts and strategies implemented in the virtual health paradigm. The systematic literature review conducted since the first insights in 1995 reveals the eagerness related to the digital transformation of health care and the popularization of digital health strategies. The resolutions of our study will enrich the emerging literature on virtual health in a wide range of settings.

Facial treatments, even for aesthetic purposes, often involve unnecessary patient risk due to treatment by unexperienced practitioners and/or a lack of standardized procedures. We develop a software to support and standardize facial treatments based on knowledge of experts. The prototype utilizes WebGL and 3D-equipment, but it also focuses affordability to make its wide-spread use more likely. It aims to help in treatment planning along with professional self-development. In this paper, we describe the underlying problem, a possible medical model as a solution, the prototype architecture, and how the prototype is utilized in the treatment process. Finally, we conduct a test of the prototype.

The literature lacks evidence on the acceptability of AI conversational agents (chatbots) and the motivations for their adoption in healthcare industry. This paper aims to examine the acceptance of these chatbots based on the UTAUT model in Online Health Communities (OHCs) and to explore what kind of impact these particular features have on the users’ intentions, and the actual use of these communities. Based on a quantitative methodology approach, we rely on the UTAUT model to study OHCs users’ behavior and intentions towards such AI conversational agents/chatbots. The study shows that the UTAUT has proved to be a strong and reliable model for evaluating the adoption and application of AI conversational agents (chatbots) in OHCs. A questionnaire was employed to collect data, and respondents are chosen using the cluster sampling approach. On a 7 Likert scale, respondents were asked to select which choice best suited their reaction to any of the topics presented. A total of 632 answers from 62 countries were received, with 443 of them being complete. Many tests were used to examine the data such as the bivariate and multivariate analysis. Since the returned p-value for most of the hypotheses tested was 0.05, the majority of the hypotheses tested were accepted. Findings showed the interrelations between AI conversational agents/chatbots and OHCs on users’ Behavioral Intention (BI). The main constructs of the UTAUT model (Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions) had a significant impact on the participants’ BI and Usage Behavior (UB) for AI conversational agents/chatbots in OHCs. As for moderators, gender and age had no effect on BI and UB. Understanding the main factors that have a significant impact on users’ intentions to use chatbots in OHCs determines the significance of those results.

We provide actionable guidance to organizations needing to comply with the European General Data Protection Regulation (GDPR). We use a data processing pipeline – Data collection, Data protection, and Data operations – to structure the discussion around regulation requirements (with references to specific articles and recitals), socio-technical challenges, and applicable security best practices and techniques. Ensuring compliance is critical since fines for infringements can mount up to 4% of the total worldwide annual turnover of the organization in the preceding financial year.

As financial services and operations expand, financial fraud is on the rise. Despite the use of preventative and security measures to reduce monetary fraud, criminals are constantly acquiring and developing new ways to circumvent fraud detection systems, posing a challenge to quantitative methods and predictive approaches. As a result, new methodologies must be researched and tested to leverage the insights gained from the study to assist further incorrect fraud forecasting and the establishment of fraud discovery schemes with extra measures to alleviate distrustful events. Naïve Bayes (NB) is a significant Machine Learning (ML) classifier that is not yet explored in the literature, unlike the use of common ML techniques like Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and the likes. This paper, therefore, explores the use of a technique yet to be employed for credit card fraud detection (CCFD) namely Naïve Bayes. The classifier was compared using a confusion matrix for performance matrices like accuracy, precision, recall, f-measure, and ROC-AUC. It was discovered that NB outperformed most of the ML classifiers employed in state-of-the-art compared with an accuracy of 97.99%, recall of 98.02%, the precision of 99.97%, f-measure 98.98%, and FPR of 0.1971.

South African institutions of higher education suffered serious disruptions during the COVID-19 pandemic which, resulted in migrating most teaching and learning activities to various online platforms, of which many depended on the open web. This has the potential to expose lecturers and students to cyber security threats and risks. As such cyber security awareness (CSA) becomes important. This study investigated the CSA among preservice teachers pursuing a Bachelor of Education studies in Further Education and Training (FET) at a university in Cape Town, South Africa. The purpose of the study was to gain an insight into CSA among preservice teachers who had been using digital technologies to support learning during the COVID-19 pandemic. An electronic questionnaire was administered to a random sample of 300 preservice teachers. The findings show that preservice teachers were limited in their awareness of cyber security threats and risks likely to affect their use of various digital technologies for remote learning. Furthermore, preservice teachers implemented basic strategies to mitigate basic cyber security threats and attacks. These basic strategies were found not to be sufficient for advanced attacks. The study concluded that lack of proper CSA and knowledge among preservice teachers presented them with challenges in solving threat attacks associated with denial-of-service (DoS), data theft and phishing when using personal digital devices.

The COVID-19 pandemic and the proliferation of online learning have made preventing unethical behaviors an additional task for many teachers. This article investigates the specificity of unethical behavior in online learning and formulates proposals for actions to eliminate this phenomenon. The authors assume that the Internet, as a tool for finding information and communication, enables students to engage in unethical behavior, especially at the stage of assessing the acquired knowledge. The article presents a spectrum of unethical behaviors in online learning carried out via electronic media. The historical perspective of the development of online education in connection with the development of the Internet is presented. The research was carried out using the method of a designed survey among full-time and part-time undergraduate and graduate students of selected universities in northern Poland. A set of ten most common unethical behaviors is determined, then the frequency of their occurrence is examined, and the relationships between them are defined. The article ends with the classification of students’ unethical behaviors in online learning, proposed on the basis of the conducted research.

Cybersecurity has been identified as a major challenge confronting the digital world, neglecting cybersecurity techniques during software design and development increases the risk of malicious attacks. Thus, there is a need to make security an integral part of the agile information system development process. In this exploratory study, we empirically explore the agile security practices adopted by software developers and security professionals. Data was collected by conducting ten semi-structured interviews with agile practitioners from seven companies in the United Kingdom (UK). The study was conducted between August–November 2020. An approach informed by grounded theory was used for data analysis including Open coding, Memoing, Constant comparison and Theoretical saturation. The security practices identified in this study were categorized into roles, ceremonies and artefacts and mapped onto the different phases of the Software Development Lifecycle (SDLC). We discovered practitioners use five artefacts: security backlog documentation, software security baseline standards, security test plan templates, information security and security audit checklists; and that there are more artefacts than roles and ceremonies. Also, while most practitioners rely on automated tools for software security testing, only one practitioner mentioned conducting security tests manually. These practices that we have identified comprise a novel taxonomy which form the main research contribution of this paper.

We expect many autonomous vehicles in the modern cities of the future. These, in order to ensure the safety of other road users, will have to be equipped with perception systems enabling the detection and identification of obstacles, and the determination of the resulting threat. Currently, these two key aspects are carried out by systems that properly deal with only one of them at a time. Therefore, the article presents the concept of using UWB (Ultra-wideband) technology as a component of the ADAS (Advanced Driver Assistance Systems) to detect and identify road users. The paper also presents a test of driving a car equipped with a UWB tag at city speeds. As a result, the accuracy of the acquired position that can be obtained in such conditions is presented. Then, the obtained data was filtered with the use of simple filters, and the obtained results indicate the possibility of a significant improvement in positioning accuracy.

Despite the growth of Artificial Intelligence (AI) implementation in operational workplace processes, there is limited understanding of its effects at the micro-individual and meso-team levels in government sector organisations. This study examines the impacts of implementing AI within a single case study of a governmental entity in the United Arab Emirates (UAE). Qualitative data was collected through semi-structured interviews with 15 participants and analysed using thematic analysis. The findings identify AI implementation as an incremental process requiring organisational leaders’ adequate understanding of change management. AI has a considerable influence on the individual as it improves the ability of employees to carry out their work and increases their autonomy, competence, relatedness and ultimately productivity. It also enhanced team performance through efficient team communication and cooperative decision-making. The findings add to our understanding of the effects of AI implementation at the individual and team levels and signal the need for organisations to embrace change management approaches to support the transition to AI-enabled operations.

IT systems are an integral part of modern cities, managed by public administration units, equipped with tools allowing for taking efficient and accurate actions. The idea of creating smart cities, equipped with innovative technological solutions, is increasingly becoming the subject of strategic assumptions of many cities around the world. This means that a smart city is not only a vision of a well-managed city responding to the changing needs of its inhabitants, but also real activities that are becoming better and better suited to the specific economic, environmental and social conditions of a given place. The goal of this paper is to identify and find actions for better management of smart governance and smart citizens. To accomplish this goal, the survey was conducted among 280 Polish cities. The questionnaire was filled by public administration staff responsible for smart cities strategy and concept. The results allow to formulate conclusions that most of the actions oriented to smart governance are related to e-services, social participation, social media and civic budget. In the case of smart citizens, workshops and training were the most frequently indicated. The so-called city labs in the context of smart citizens were indicated mainly by representatives of large cities. Moreover, it has been proved that smart governance and smart citizens are the domain of big cities rather than medium and small units.

This paper discusses how Information, Communication and Technology (ICT) systems interact with and impact creativity in one higher education institution at Saudi Arabia. It adopts a qualitative case study methodology and utilizes the organizational creativity theory, which guides the data collection and analysis. The study finds that personality, cognition, and motivation play a role in the impact of ICT on creativity in a major Saudi university. It is useful and beneficial for organizations to understand how ICT impacts creativity in order to be competitive and ensure growth in their industries. Through ICT, universities have become more innovative in their administrative and academic functions; this is important for stakeholders in order to gain benefits from ICT systems in key areas, such as creativity. This study is useful in enabling university managers and employees, as well as ICT specialists, to gain an overall view of the consequences of implementing ICT in universities in a country like Saudi Arabia.

To democratize or not to democratize, this is not the problem anymore for the enterprises that consider democratizing their enterprise AI practice; the problem that these enterprises face nowadays, is how to successfully democratize their enterprise AI. In this paper we conduct a systematic literature review to provide an in-depth analysis of the success factors and the challenges of democratizing the artificial intelligence practices in the enterprises, we also build on this review and propose a framework for the enterprise AI democratization that suggests a set of the success factors and challenges. The research design of this paper is to conduct a systematic literature review by including 41 papers as an initial set of studies for review; we screen the papers and implement inclusion and quality checks on these studies, and we qualify 15 papers for the final review. The key findings of this paper, from the systematic literature review, list a set of success factors and challenges that enterprises should consider to strengthen or to avoid. We propose these factors in a form of proposed framework suggesting four categories: strategy, enterprise architecture, data, and trust. Because of the publication specification and limitation, we limited the scope of our primary studies to a limited set to match the constraints and limitations. The paper includes implications for the academic literature review and the extraction of factors that can impact the process of the enterprise artificial intelligence democratization, and the need to increase the awareness of the enterprise AI practices in order to overcome the challenges that might prevent enterprises from having a successful enterprise AI. While there are some efforts to assess and review the success factors and challenges of the AI practices in general, one of the major findings of the literature review conducted is that there is evident research gap in the literature on the perception and associated factors of artificial intelligence. This paper seeks to fill this gap.

Digital transformation has been an interesting concept from the organizational perspective for a long time. The benefits of a successful digital transformation can take your organization into the next step by providing an increased organizational growth, aid market reachability by penetrating new and exciting markets, or enable your business operations to function to a greater extent than before with higher efficiency and lower lead times. Digital transformation is however a complex and diverse concept that means to integrate new innovations by using digital technology into the organization with the need of making greater organizational changes to succeed. This research, has explored how different organizational aspects like structural, technological and cultural ones can impact the success of a digital transformation during an ERP system change. A case study has been conducted in a company to identify how the current structural, technological, and cultural aspects influence the current digital transformation in that the company. The data was collected through interviews with employees having managerial roles and from internal documents of company and was analyzed using thematic analysis. The results show an agile approach, a more decentralized structure and high readiness for change, along with a transparent communication between management and co-workers to be beneficial for a successfully digital transformation.

Although virtual teams (VTs) have been around for over two decades, there are no studies explicitly examining their members’ well-being. Motivated, therefore, by a knowledge gap in the VT literature, and a practical need to understand well-being in this context due to the Covid-19 pandemic which has led to an unprecedented transition into virtual working, in this paper, we draw on 14 interviews and present initial findings of a comparative case between two European organizations involving different types (global vs. local) of VTs (Phase 1). Using the job demands-resources (JDR) model as our theoretical lens, we make the following contributions: We identify the situated character of job demands and resources among our participants, explaining how VT members experience simultaneously increased job demands and reduced job resources, which, in combination, may substantially impair their well-being. We also find that understandings of demands and resources are idiosyncratic and vary depending on prior individual experiences of VT members. We discuss initial theoretical and practical contributions of Phase 1 of our study and outline our next steps (Phases 2 and 3).

In recent years, organizations and researchers have become increasingly interested in digital transformation. Technology has found its way into the lives of customers, but at the same time is disrupting industries by enabling organizations, that have embraced it, to gain more and more competitive advantages. However, technology is only one factor of a successful digital transformation strategy, where culture, management, human resources etc. also play an important role. While digital transformation has been researched over the years from multiple points of view, limited studies have focused in detail on the impact of technology on digital transformation. Questions such as how fast an organization should adapt to the evolution of technology, which technologies should be preferred and finally, in which cases technology might delay the digital transformation process, remain unanswered. The present paper aims to fill this gap by conducting a systematic literature review of 74 related articles, based on the Webster & Watson methodology, followed by a concept analysis of technology related themes in digital transformation. The results of the analysis reveal that technology does not only act as an enabler or driver of digital transformation, but can also be a barrier of it. While we contribute with our paper to the research body in digital transformation, at the same time, we identify potential research gaps that leave space for further investigation.

Since the late 1990s, audio industry has been subject to severe changes, due to the advent of new technologies such as the mp3 compression codec and the related arrival of peer2peer sharing platforms. The latter ones were replaced by streaming platforms which now drive the digital transformation in this industry. However, there is still turmoil on how digital transformation in this field again facilitates new forms of business. Thus, there is an ongoing change regarding the way value is created and captured within a new market structure. The main objective of our work is to map this new market structure and the new ways of value creation considering the recent developments in the audio industry. Based on a literature review we derive our first drafts of both models which were modified across two focus group workshops afterwards. Moreover, expert interviews are employed to evaluate and continuously modify our models for the current as well as future audio industry. The two models presented in this paper, capture the current value creation mechanisms in the audio industry and provide insights to better understand the future developments facilitated by digital technologies for academia and in practice.

The paper discuss how organizational agility can affect the knowledge management processes in organization, especially in case of ongoing COVID-19 pandemics changes. The research problems are to show a link between knowledge management and agility in organization and to examine a COVID-19 pandemics impact on both knowledge management in organization and organizational agility. We carefully examined a literature from knowledge management-related, recognized scientific journals. We searched journal articles from 1994–2021 and divided the results into 3 time periods to show the historic view on knowledge management in agility, the current view in 2015–2019 and latest time period (2020–2021) to show the impact of COVID-19 pandemics on knowledge management and organizational agility. The study shows the relation of knowledge management and organizational agility with some diversity of research scopes related with COVID-19 pandemics. The insights of our research may be useful for organizations in transforming their knowledge management processes in dynamically changing environment.

Digital transformation requires FinTech organizations to be agile and apply innovative approaches and flexible architectures that allow the delivery of new digital services to their clients, partners, and employees. To consolidate this perspective, this paper proposes an agile approach using organization modeling techniques to illustrate all management processes and disciplines in microservices-based FinTech software development. On the one hand, agile methods are development processes to drive the system life cycle in terms of incremental and iterative engineering techniques. On the other hand, microservices architecture offers nimble, scalability, and faster deployment life-cycle and the ability to provide solutions using a blend of different technologies. Typically, such methods are well-suited for implementing and adapting software processes management to cope with stakeholders’ requirements and expectations immediately into the development life cycle.

Robotic Process Automation (RPA) originally entered the field of information systems as one of those disruptive innovations that will, among other automation solutions, have a profound effect on job descriptions and work itself in near future. One of the sectors that will be revolutionized – and are in fact already changing – are financial and human resource (HR) services, such as accounting, billing, and payroll services. According to statistics, Finnish companies operating on the administrative and support services sector make little use of service robots. Companies that have initially adopted the technology do not necessary reach its full potential. We explore what factors create challenges for the continued adoption of robotic process automation by investigating two companies and using interpretive case study as our research method. The two companies are service centers. They operate on the public sector in Finland and provide financial and HR services for their owner-clients. The findings of this study include insights into the key factors that affect continued adoption of RPA, and these point towards an expanded model of Innovation-Decision Process where iterations of a new decision are triggered by new ideas on how to use the innovation.

Agility spread from the bottom of organizations, carried by software professional acting as grassroots in promoting and integration of agility, until the practices and approaches got foothold in the organizations. This is unique in comparison with other major transformation of the industry such as that of the capability maturity model (CMM). This paper aims at understanding this agile phenomenon and the underlying informal structure and dynamics. The paper suggests to theorizes this as agile pockets of professionals playing an important role in the agile transformation. Especially four arch types of agile pockets are suggested to be crucial in pivotal situations of the transition. The theory and arguments are based in the well-known theory Communities of practice by Etienne Wenger [21, 22], but needs more development and empirical validation.