Information Systems

Data Warehousing applied in Big Data contexts has been an emergent topic of research, as traditional Data Warehousing technologies are unable to deal with Big Data characteristics and challenges. The methods used in this field are already well systematized and adopted by practitioners, while research in Big Data Warehousing is only starting to provide some guidance on how to model such complex systems. This work contributes to the process of designing conceptual data models for Big Data Warehouses proposing a method based on rules and design patterns, which aims to gather the information of a certain application domain mapped in a relational conceptual model. A complex domain that can benefit from this work is Genomics, characterized by an increasing heterogeneity, both in terms of content and data structure. Moreover, the challenges for collecting and analyzing genome data under a unified perspective have become a bottleneck for the scientific community, reason why standardized analytical repositories such as a Big Genome Warehouse can be of high value to the community. In the demonstration case presented here, a genomics relational model is merged with the proposed Big Data Warehouse Conceptual Metamodel to obtain the Big Genome Warehouse Conceptual Model, showing that the design rules and patterns can be applied having a relational conceptual model as starting point.

Data acquisition is no longer a problem for organizations, as many efforts have been performed in automating data collection and storage, providing access to a wide amount of heterogeneous data sources that can be used to support the decision-making process. Nevertheless, those efforts were not extended to the context of data integration, as many data transformation and integration tasks such as entity and attribute matching remain highly manual. This is not suitable for complex and dynamic contexts where Information Systems must be adaptative enough to mitigate the difficulties derived from the frequent addition and removal of sources. This work proposes a method for the automatic inference of the appropriate data mapping of heterogeneous sources, supporting the data integration process by providing a semantic overview of the data sources, with quantitative measures of the confidence level. The proposed method includes both technical and domain knowledge and has been evaluated through the implementation of a prototype and its application in a particularly dynamic and complex domain where data integration remains an open problem, i.e., genomics.

By means of Digital Libraries, entire archives can be made available to users at a click away; but due to the fact that the representation as accurate and as wide as possible of the events in which Cultural Heritage plays an essential role in understanding the past, the metadata aggregators must use data models that satisfy the demands of information. We present the workflow behind the eCHO Framework, a framework which allows users, on the one hand, to sanitize, normalize and interconnect data represented according to LIDO XML Schema, and on the other hand, to take advantage of the representation of metadata through the event-centric approach of Europeana Data Model. Eventually, it is presented the applicability of the use of this framework in the context of identifying the time periods in which took place the most important events in which the Romanian Cultural Heritage have been involved in, statistics that can be extended to the purpose of identifying the lifestyle of the population.

Currently, it is possible to have machines producing relevant data to be processed in real-time, facilitating the organizational decision-making. In recent works, we proposed a system that integrates Complex Event Processing (CEP) in the Big Data era, trying to make Industry 4.0 systems more pro-active. Due to its complexity when running in industrial contexts, appropriate monitoring mechanisms need to be ensured to prevent the uncontrolled growth of the system. In this context, this work focuses on proposing a system architecture that will enable an innovative monitoring strategy based on graph analysis, namely the Intelligent Event Broker (IEB) Mapping and Drill-down System. In this work, it is proposed an inspection and logging strategy for the IEB that allows to not only continuously inspect the codebase of the system and fuel an ever-growing Graph Database, but also to strategically store log occurrences to know what is continuously happening. For demonstrating the architecture and design rules, we use a context from Bosch Portugal presenting a flowchart and a graph data model, being the latter a mirror of all the implemented IEB components and the relationships between them. This work helps researchers and practitioners in the design and development of CEP systems for Big Data contexts and, especially, the monitoring component of such a complex system.

Big Data technologies and approaches have an important impact on the organization and governance of the enterprise. With such a high volume of structured & unstructured data, real time and mutualization needs, it is quite complicated to keep a high quality of data by respecting the governance rules and best practices. In addition, new team roles and organization must be applied in order to adapt to the new Big Data decisional constraints. In this direction, we present in this paper an overview of DECIDE, a decisional Big Data Methodology. We focus, particularly, on its team workforce, data quality, storage and governance fundamentals, rules and steps.

We live in times where companies and individuals are dealing with extremely large amounts of data coming from all different kind of sources. This data includes a lot of very valuable information, which, most of the time, cannot be inferred at first sight. Therefore, in today’s businesses there is a growing necessity of discovering efficient and useful information out of the data that has been gathered. This is the reason why Machine Learning, a technology that has been developed since mid-20th century, is one of the biggest growing technologies in this last decade, being one of its most popular applications in the field of data. The paper presents an analysis what techniques are available for starting with a Data Science project, how easy they are to implement, and how they can be applied in a real world case. The data that was worked with for this project was gathered from a telecommunications company.

Blockchain technology and its economic, social, and technological implications, have seen significant upsurge among researchers across the globe. Blockchain has revolutionized the concept of transactions by enhancing their security and efficiency. The blockchain technology is primarily associated with Bitcoin but however, the technology has the potential to go far beyond cryptocurrencies across various verticals. In a recent survey performed by Deloitte, more than 53% of the responds across various industries see blockchain technology as a critical requirement for their respective organizations [4]. The hospitality industry is one such domain where blockchain can prove enormously beneficial. The paper explores major application areas that involve the applicability of blockchain technology in the hospitality industry. The work investigates the implications of blockchain technology in enhancing operational efficiency, increased revenue and improved security and privacy for the hospitality industry. The paper establishes a link between blockchain technology and the hospitality sector and subsequently analyses recent works and case studies. A two-step research study has been introduced to present a systematic review of some of the main contributions in the literature that focuses on the integration of the blockchain technology and hospitality industry. The article adds to an interesting concept of blockchain technology, and its current research trends with respect to the hospitality industry and their various areas of application.

Modern society consumes a huge amount of energy, making the energy industry highly important across the globe. Customers are supplied with the electricity via the energy grid, as part of the utility value chain and pay on per-unit consumed basis. Thus, grid operations and energy prices have little effects on actual energy demand because grid imbalances frequently arise rapidly over very short periods of time, due to imprecise forecasts or unexpected events. Non-predictable renewable energy sources variable generation raises crucial challenges in grid management, making grid defection a rapidly increasing challenge to traditional energy markets. Blockchain technology has been studied to overcome these problems for application in the smart energy grid, and experts agree that it has the potential to change the electricity market. Blockchain and distributed ledger technologies can promote a transparent, secure and decentralized transactions network that will allow new innovative business solutions. Although, the integration of blockchain into the smart energy grid poses some challenges and prohibits the widespread use of blockchain technology in the energy sector. Therefore, in this paper a market analysis was conducted, to investigate the parameters that affect the large-scale adoption of blockchain in smart energy grids. The first part of the paper is setting up the scene, introducing the blockchain and smart grid fundamentals, as well as presenting blockchain’s potential impact on different energy use cases. On the second part of the paper the market analysis is presented, providing blockchain technology’s market opportunities within the energy grid. The paper ends with a description of threats and market challenges that the technology has to address in order to get through the hype, prove its economic, social and technological potential and eventually be accepted in the mainstream.

The Raft consensus algorithm constitutes a widely-used algorithm not only in the broader area of distributed systems, ut also in private/permissioned blockchains such as Hyperledger Fabric. A Raft-based distributed system (RDS) strongly relies on leader election, which involves a number of time-related parameters. In the Raft-related literature, the process according to which those parameters are set is an under-researched area. Specifically, the use of the uniform distribution is the dominant approach. Motivated by this realization, in this work, we focus on these time parameters proposing the notion of “leadership uniformity” in combination with a series of performance metrics. Leadership uniformity is based on the desirable characteristic of having equality among the nodes who serve as leaders. The proposed performance metrics are straightforward adaptations of widely-used measurements from broad disciplines such as estimation theory. The experimental results of this work justify the appropriateness of the proposed notion of leadership uniformity. Specifically, the best performance was yielded by the utilization of normal distribution from which the time parameters under investigation were drawn.

In this study, we investigate whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem are related to bitcoin price and we model bitcoin price volatility taking into consideration public awareness. We take a middle-of-the-road approach, by using a simpler – and thus less data demanding - proxy for public awareness compared to studies that have used complex models that include many parameters to capture the relationships and factors in the ecosystem, but at the same time, a richer approach compared to approaches that simply use the volume of searches for “bitcoin” and its “price” as a proxy in their models. Specifically, we use six different Google Trends queries as proxies in our models: three searches for positive incidents, and three for negative ones. We employ a dataset with monthly price data that covers the time period from September 1st 2011 to December 31st 2019 and we use GARCH and EGARCH models to test whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem is related to bitcoin price and to model price volatility. Results show that majority of our proxies of public awareness are significantly related to price. Moreover, our EGARCH model has detected an asymmetry pertaining to the price volatility’s reaction to price news, specifically an “anti-leverage effect”, that is, the price volatility is more sensitive to good financial news rather to bad news. In addition, we detected a significant effect of both old and novel news.

Creating an online electronic voting system that ensures coercion-resistance and end-to-end verifiability at the same time, has constituted a real challenge for a long period of time. The notion of coercion-resistance was first introduced by Juels, Catalano, and Jakobsson (JCJ) in 2005. Since that time, several research papers have appeared to address the main issue of JCJ scheme (the quadratic complexity of verifying credentials). The majority of these systems have been based on the availability of a secure web bulletin board. Despite this widespread requirement, the notion of an append-only web bulletin board remains vague, and no method of constructing such a bulletin board has been proposed in those papers. Our paper fills the gap and proposes an end-to-end verifiable e-voting protocol based on Blockchain technology. In this research work, we propose a Blockchain-based online electronic voting protocol that ensures all the security requirements expected from secure and democratic elections. Our proposal is inspired from the scheme proposed by Araùjo and Traoré in 2013, which is based on the work of JCJ and has a linear complexity. Called LOKI Vote, our scheme is practical for large scale elections and ensures a strong privacy for voters by using a variety of cryptographic primitives. Additionally, our protocol enhance the complexity of the old coercion resistant systems by using a new mix network, called Low Latency Anonymous Routing Protocol, which is characterized by a lower complexity and a higher level of security. Finally, we formally prove the security of LOKI Vote using the automated verification tool, ProVerif, and the Applied Pi-Calculus modeling language.

We use a systematic literature review methodology to answer the following questions pertaining to smart cities and blockchain: (i) Why was blockchain chosen as the solution? (ii) What blockchains are being considered for use in smart cities and why? and (iii) What blockchain based applications are being researched for smart cities? Our results - based on 45 peer-reviewed academic studies all published in journals that met pre-defined search criteria - show that increased security, privacy, and trust are the reasons most cited in the literature for the use of blockchain for smart cities. Consortium, Hybrid, Private, and Public blockchains are discussed with respect to their suitability for smart cities applications, and finally, we discuss smart cities blockchain applications from the literature using a taxonomy based on the framework defined by Silva et al. [40]. In conclusion, this study highlights the current blockchain challenges and future research opportunities, including the need to change the current mindset of centralized control and trusted third parties to a more participative engagement model across smart cities.

The so-called disruptive technologies play an important role in shaping the next generation of digital government: Government 3.0. This new stage places the focus on the data-driven and evidence-based decision and policy making. The prerequisite in achieving this stage is the seamless access to government data. The use of blockchain supports the interoperability-by-default concept in the creation of public services. At the same time blockchain is addressing another important problem governments facing across all over the world, namely, low level of citizens’ trust. In this paper, the authors review literature and projects on blockchain as a tool for improving interoperability and trust in Government 3.0 and they outline the issues for further research in the area, taking into consideration the knowledge collected in existing projects and the opinions of experts in the domain. The research needs are synthesized a) by analysing recent EU-funded projects involving blockchain and b) by drawing a future scenario, which is evaluated by experts to formulate further research needs. Finally, fifteen research needs are identified for blockchain in digital government.

Scholarly and commercial literature indicates several applications of Blockchain Technology (BCT) in different industries e.g. health, finance, supply chain, government, and energy. Despite abundant benefits reported and growing prominence, BCT has been facing various challenges across the globe, including low adoption by organizations. There is a dearth of studies that examined the organizational adoption of blockchain technology, particularly in Australia. This lack of uptake provides the rationale to initiate this research to identify the factors influencing the Australian organizations to adopt BCT. To achieve this, we conducted a qualitative study based on the Technology, Organization, Environment (TOE) framework. The study proposes a theoretical model grounded on the findings of semi-structured interviews of blockchain experts in Australia. The proposed model shows that the organizational adoption of blockchain is influenced by perceived benefits, compatibility, and complexity, organization innovativeness, organizational learning capability, competitive intensity, government support, trading partner readiness, and standards uncertainty.

The digital transformation of public services has been traditionally one of the main targets of digital government research and practice; however, it has focused mainly on the digital transformation of complex public services, based on the development of highly sophisticated and costly information systems (IS) for this purpose. Nevertheless, considerable public value can be generated through the digital transformation of widely used simpler public services as well, as it can result in huge savings of both public servants’ and citizens’ time, as well as improvements in the quality of these services. Furthermore, due to the financial resource constraints that governments of most countries face, it is important that this is implemented at a low cost, adopting a ‘frugal innovation’ approach. In this direction this paper: a) describes the ‘frugal’ low-cost digital transformation of a widely used simple public service in Greece, the ‘certification of authenticity of signature’, which is applied in two special forms, the ‘formal declaration’ and the ‘authorization’; b) evaluates these novel e-services, based on an extension of ‘Diffusion of Innovation’ theory with an additional trust-related dimension, using both qualitative and quantitative techniques, and finally drawing interesting conclusions of wider interest and applicability.

This paper aims to discuss current approaches to smart city rankings following the main thesis that two decades of e-government benchmarking should be used as a source of inspiration on how to evaluate smart government. We use critical analysis of selected smart-city and two major e-government rankings for this purpose. As our findings show, smart city rankings are lacking for several reasons: there is no consensus on what a smart city is, there are no defined development stages, smart city rankings tend to use quantity indicators or concentrate on the supply-side, and they often suffer from dimension or company biases and often lack methodological transparency.

This study aimed to identify the user acceptance determinants of a revenue services agency mobile filing application. The study used the UTAUT model which was adapted by introducing privacy and trust factors to understand taxpayers’ perceptions towards the mobile filing application. Data was collected through a survey conducted with taxpayers. The results of the study provide empirical evidence on m-Government and in particular the adoption of mobile filing of tax returns. The results highlight that prior experience with using electronic filing systems has a direct effect on the intention of taxpayers to accept and use the revenue services’ mobile filing application to submit their tax returns. These effects are moderated by certain facilitating conditions, effort expectancy and social influence from important stakeholders in a citizen’s context. The study puts forward recommendations towards enhancing the uptake and impact of mobile government services.

An important part of electronic Government is the provision of high quality Public Services (PS) to citizens. Towards this goal, the European Commission has proposed the Core Public Service Vocabulary (CPSV), as a PS data model to be used across the public sector. CPSV is adequate for use in the case of simple PS however its effectiveness is questionable in the case of complex PS. A complex PS is one having many (often complicated) rules interrelating its concepts, e.g. dictating citizens have to submit different documents to invoke a PS based on their profiles and circumstances. The aim of this paper is to investigate the use of Knowledge Graphs (KG) for providing personalized information on PS modeled using CPSV. For this purpose, we develop and evaluate a KG for PS “Get a passport” in Greece as a proof-of-concept to study mapping of CPSV to KG. For simplicity, we limit our scope to developing a KG that can provide the input documents and the relevant cost required for obtaining a passport by citizens based on their profile and circumstances. Free software GRAKN.AI was employed for the development of the KG.

In the contemporary highly competitive digital commerce market, it is easy to miss new online ventures being launched and those that go away. The success of such a venture is highly dependent on the quality of an e-commerce platform. Even more so in the case of dropshipping-based business models, where similarly-profiled businesses are intensively working on acquiring customers to sell goods delivered by the same distributors. Many criteria, frameworks, and models for assessing e-commerce platforms were developed to date, yet applying those directly to dropshipping ventures leave some important areas unexplored. The study scrutinizes the factors that make an e-commerce platform stand out to effectively attract customers in a highly competitive market. Design Science Research approach was used to deliver a dropshipping-oriented evaluation model extension. Business cases for new criteria and proposals for e-commerce platform features designed to address inefficiencies in IT solutions currently operating in the market were provided.

The aim of current research is to investigate the impact of e-learning on the performance of accounting students at universities in the Kingdom of Bahrain. A questionnaire was distributed to accounting employees and students. The results indicated that e-learning enhancing students’ performance and their employability skills. This study recommends that the higher education institutions of the Kingdom of Bahrain expand the e-learning context and connect learners with accounting professions.

The online sales channels of fashion stores, like the physical stores, aim to capture and retain customers. In the case of online sales channels, such as e-commerce fashion websites, success depends on the confidence that customers have in their use and reputation, which can be assessed through customers’ intentions to convey positive opinions about the website. Studies in the literature state that the design of the website, both in terms of its visual aspect and usability, contributes to increasing the confidence and positive word of mouth (WOM) intentions defined as information and/or rumour sharing between individuals of customers. This study seeks to validate this hypothesis regarding fashion e-commerce websites. To this end, a survey-type study was conducted involving 220 customers of e-commerce fashion websites. The results of the study support the hypothesis that website design contributes to generating trust in it, and consequently positive WOM intentions.

Influencing systems and persuasive technology (PT) should give their users a positive experience. While that sounds attractive and many rush implementing novel ideas things such as gamification, a serious professional and scientifically rich discussion is needed to portray a holistic picture on technology influence. Relatively little research has been aimed at exploring the negative aspects, outcomes, and side effects of PT. Therefore this research aims at addressing this gap by reviewing the existing knowledge on dark patterns, demonstrating how intended Pt designs can be critically examined, introducing the Visibility-Darkness matrix to categorize and locate dark patterns, and proposing a Framework for Evaluating the Darkness of Persuasive Technology (FEDPT). The framework is instrumental for designers and developers of influential technology, as it clarifies an area where their products and services can have a negative impact on well-being, in other words, can become harmful to the users.

A key problem in social media is the identification of influential nodes and the analysis of how these nodes are reflected in the graph structure evolution. Influence prediction is an important issue in social networks. Most of the existing methods aim to predict interactions between individuals for static networks, ignoring the dynamic feature of social networks. In order to solve this problem, we propose a new approach to detect influential nodes taking into consideration the structural and semantic evolution of social networks. First, we find the influential nodes within a period of time by using an incremental algorithm. Then, by exploring the structural and semantic aspects of social networks, we predict the future influential nodes.

The exponential growth of online peer-to-peer (P2P) platforms paved the way for large-scale commerce between individuals. This work aims to evaluate the determinants of the intention to use these platforms by the sellers for the transaction of products and services, contributing to fill a gap in the literature. The results confirmed all the hypotheses of the research model, allowing us to conclude that the intention to use is strongly explained by performance expectation, habit, trust, and innovativeness. From the study it is possible to draw relevant implications for the academic world, as well as for the management of P2P platforms.

The continuously improving Internet penetration in the continent, coupled with the increasing number of smartphone users in Africa has been considered as the reasons for the adoption of social media among students and other adolescents. Even though this development has been recognizing in the literature, only a few studies have investigated the acceptance, use, and retention of social media for academic purposes. However, findings of prior studies suggest that the use of social media has an influence on academic performance. To address the lack of knowledge on the adoption of social media among students, this study aims to explore the factors that are related to students’ acceptance and use of social media. We attempt to extend the Technology Acceptance Model by integrating relational engagement, Perceived Satisfaction, as well as the Perspective of the Use of Social Media in Education. The proposed theoretical model was evaluated using quantitative data collected from 460 students in Cameroon. We applied PLS-SEM technique to test the hypotheses and the theoretical model. Implications of the findings, as well as future research directions, are presented.

Deep Learning (DL) modeling has been a recent topic of interest. With the accelerating need to embed Deep Learning Networks (DLNs) to the Internet of Things (IoT) applications, many DL optimization techniques were developed to enable applying DL to IoTs. However, despite the plethora of DL optimization techniques, there is always a trade-off between accuracy, latency, and cost. Moreover, there are no specific criteria for selecting the best optimization model for a specific scenario. Therefore, this research aims at providing a DL optimization model that eases the selection and re-using DLNs on IoTs. In addition, the research presents an initial design for a DL optimization model management framework. This framework would help organizations choose the optimal DL optimization model that maximizes performance without sacrificing quality. The research would add to the IS design science knowledge as well as the industry by providing insights to many IT managers to apply DLNs to IoTs such as machines and robots.

Due to longevity, the world population is getting older; this leads to an enormous number of patients with chronic diseases. Their vulnerability in facing viral and bacterial diseases, in particular in the case of the outbreak of the coronavirus, promotes teleconsultation since the latter reduces their physical interactions and consequently, their chance of contamination. Teleconsultation is considered more economical, comfortable, and practical compared to face-to-face consultations. Due to the criticality of the data and process in teleconsultation, there are numerous concerns, in particular in terms of reliability and security. Moreover, similar to all financial systems; the transparency concerns are also prominent in teleconsultation. To this end, we propose HapiFabric, a teleconsultation framework based on Hyperledger Fabric. Our proposed framework exploits this blockchain technology to improve security, reliability, and transparency of teleconsultation workflows. Without losing generality, we prioritize the elderly and patients with chronic diseases in HapiFabric becasue of their vulnerability. Our innovative teleconsultation workflows cooperate with a telemonitoring service to provide comprehensive medical care at the patients’ homes. We exploited Hapicare, an existing healthcare monitoring system with self-adaptive coaching using probabilistic reasoning, as one of the main participants of HapiFabric which provides telemonitoring services. Moreover, HapiFabric has other participants, namely patients, doctors, insurance, and auditors. We have opted for off-chain data storage of medical data using the InterPlanetary File System (IPFS). We evaluate the HapiFabric framework using two scenarios.

Evaluating the utility of Human-Machine Systems’ User Interfaces is not trivial. Several evaluation methods can be used to investigate if the behaviour of the user interface complies with best practices of Human-Machine Interface Design. Even when is possible to agree on which methods to use to conduct the evaluation, defining the utility requires evaluating the interface under analysis toward the company’s goals, or mission. This paper investigates how the utility, perceived by end users of interfaces, can be captured by a research instrument, as well as be represented by a structured approach based on Usability evaluation and Balanced Score Cards methodology. This is an alternative demarche for accessing the Usability of a Software System, and the main goal is helping designers and administrators to maintain and improve their systems.

Business Analytics (BA) has attracted great interest among firms of most sectors worldwide, as it enables a more advanced and valuable exploitation of firms’ data assets, beyond operations, for the supporting of decision-making. However, though numerous firms take some first steps in this area, most of them make limited use of BA in some of their activities, and cannot advance to a more extensive adoption of BA throughout their activities, so they do not exploit the full potential of it. For this reason, some first research has been conducted on BA adoption and factors affecting it, however more research is required on this topic. Our study makes a contribution to this research stream, by investigating empirically the effect of the extent of enterprise systems (such as ERP, CRM and SCM ones) adoption, as well as the degree of development of firm’s ICT capabilities, distinguishing between technological and management ones, on the extent of BA adoption. It has been based on the Technology, Organization and Environment (TOE) framework. We have used data collected from 363 Greek firms from both manufacturing and services sectors through a questionnaire, from which ordinal regression models are estimated. It has been concluded that both the adoption of enterprise systems, as well as the development of firm’s ICT capabilities, and especially the ICT management capabilities, affect positively the extent of BA adoption.

The purpose of this qualitative study was to provide an insight into how small business enterprises from a South African township evaluated cloud business intelligence solutions and the challenges faced. The study found that despite limited knowledge in security evaluation and lack of easy-to-use techniques for small business enterprise, decision makers conducted basic and unsystematic evaluation of cloud business intelligence prior to adoption. Unsystematic security evaluation was mainly on data security, access control functionalities such as authentication, cloud service providers’ security, trust and reliability and financial risks. The study concluded that an easy-to-use security evaluation framework for cloud business intelligence solution tailored for small business enterprises was a necessity to overcome challenges among enterprises in South African townships in order to enhance good security practices when adopting cloud services.

As problems with health workers’ acceptance and satisfaction are now regarded among the most significant barriers to the diffusion of IS within health settings, the purpose of this paper is to examine which factors affect the level of satisfaction of medical and nursing staff with the use of information systems in the 424 Military Hospital in Northern Greece and especially the impact of gender and age on users’ satisfaction. A total of 257 questionnaires were collected from 3 clinics. Results show that the participants in the survey are satisfied with the usefulness of the Information System as well as the ease of use of the Information System to a large extent. However, the respondents expressed little satisfaction with the provision of the necessary instructions for the execution of the work, but a better level of satisfaction with the ability of the technical support staff to provide quality services.

A significant application of microarray gene expression data is the classification and prediction of biological models. An essential component of data analysis is dimension reduction. This study presents a comparison study on a reduced data using Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) feature selection dimension reduction techniques, and evaluates the relative performance evaluation of classification procedures of Support Vector Machine (SVM) classification technique. In this study, an accuracy and computational performance metrics of the processes were carried out on a microarray colon cancer dataset for classification, SVM-RFE achieved 93% compared to ANOVA with 87% accuracy in the classification output result.

This research study aims to highlight the main weak and strong points of existing telemedicine technologies as well as to propose the creation of a new, innovative and financially efficient system of telemedicine which can be used in the maritime industry. In addition to main applications and details of the new system, the article describes and expounds on necessary equipment as well as personnel training.

This paper reviews and assesses classical and novel methods and tools towards engaging students and workforce in the concepts of information security and privacy. We investigate the theoretical basis for deploying a game-based approach for security/privacy learning and awareness, and assess state-of-the-art tools and methods that could be used as part of a challenge-based or game-based framework for learning, including serious games, CTF platforms, escape rooms, puzzle/interactive books and Alternate Reality Games (ARGs), while also identifying key-elements and important aspects that should be taken into consideration when designing a security and privacy learning/awareness program. For each of the above approaches and tools’ categories, we highlight their potential for using them for education and awareness of information security and privacy.

Big data analytics in healthcare present a potentially powerful means for addressing public health emergencies such as the COVID-19 pandemic. A challenging issue for health data to be used, however, is the protection of privacy. Research on big data privacy, especially in relation to healthcare, is still at an early stage and there is a lack of guidelines or best practice strategies for big data privacy protection. Moreover, while academic discourse focuses on individual privacy, research evidence shows that there are cases such as mass surveillance through sensing and other IoT technologies where the privacy of groups needs also to be considered. This paper explores these challenges, focusing on health data analytics; we identify and analyse privacy threats and implications for individuals and groups and we evaluate recent privacy preserving techniques for contact tracing.

Signature is a biometric trait that has piqued the interest of researchers. This is due to its high rate of acceptability. Offline signature in particular, has been around for a while and hence its suitability as a biometric trait. This paper proposes an offline signature recognition system using a multiple algorithm approach. The system accepts handwritten signature, filters the signature and crops the signature region. The Local Binary Pattern (LBP) of the signature image is then obtained. After this, Grey Level Co-occurrence Matrix (GLCM) is applied. Statistical features are then extracted. The difference in the stored features and the extracted features was obtained. The output is compared with a threshold for discrimination. This research aims at improving the performance of offline signature recognition using its textural features. The designed system gave an FRR and FAR of 8.6%, 4.6% respectively for MYCT signature database and 8.8%, 5.2% for GPDS signature database.

The confidentiality and safety of patient records is a significant concern for medical professionals. So protections must be placed to guarantee that illegal individuals do not have access to medical images (Patient’s description). Hence, the objective of this study is to secure digital medical images being transmitted over the internet from being accessed by an intruder. The study, therefore, proposed a modified Least Significant Bit (LSB) algorithm implemented on a MATLAB 2018a programming environment, and the proposed system was compared with the existing system using three performance metrics which are PSNR. MSE and SSIM. The result showed that the proposed approach outperformed the current standard methods by producing a more robust, high capacity, and highly imperceptible stego image. The comparative analysis conducted also showed that the PSNR valve is higher, and MSE value is lower when compared with existing systems. It was concluded that the projected technique accomplishes excellently in making the medical image transmitted to be more secured, robust, and invisible, thereby making the communication to be unnoticeable by an intruder or attacker.

Recent advances in the field of speaker recognition have proved to highly outperform algorithms. However this performance degrades when limited data are presented. This paper presents examples on how Support Vector Machines (SVM) can improve speaker recognition for short utterance data duration. The main contribution in this approach is the use of new vectors when training and testing data are limited. We show how different kernels function of SVM can be used to validate the new approach with different speakers from different databases.

Safety is a vital aspect of aviation industry, and emphasis has been made by all stakeholders in the industry to ensure aviation safety. Strict safety and regulatory procedures are adapted during all phases of aviation including design and development, manufacturing, operations, maintenance and ground services. Still, accidents and incidents persist in aviation, resulting in loss of human life and huge losses to airlines and aircraft OEMs. Artificial intelligence is an evolving domain, which has gained lot of importance during the last decade, predominantly due the capacity of AI systems to handle and process huge amount of data and implement complex algorithms. This paper is indented to improve the aviation safety with the prudent use of artificial intelligence. The paper focuses on how the effects of the factors like pilot fatigue, adverse weather and false warnings, which affect aviation safety, can be mitigated with the use of artificial intelligence.

Nowadays, the use of creativity in business has been increasing drastically because it has been perceived to be important for the market to come up with new ways, focused on answers to the problems proposed by the users. Several different creativity techniques can be used in a myriad of contexts. One of the most important techniques is the SCAMPER technique, which is based on reorganizing, modifying, adding, and eliminating information. An automated system will provide answers and solutions to creativity problems and contribute to minimizing the cost of innovation in companies. The aim of this paper is, therefore, to design an architecture system for a creative information system based on the SCAMPER creativity technique, thus building an automated system of this technique.

Advancements in research tools and databases have accelerated the scientific research life cycle. However, the chronological gap between published research, research in progress and emerging research topics is shrinking, thus putting pressure on researchers to find novel research ideas. The Literature Review (LR) process is a fundamental process that can identify gaps in the research literature and stimulate new research ideas. While many researchers adopt different methodologies conducting LR, there is no methodology that can comprehensively unveil innovative research ideas. This research aims to develop a search by concepts framework. The framework involves the use of Natural Language Processing (NLP), Knowledge Graphs (KGs), and Question Answering systems (QA) to ease finding relevant concepts related to a certain scientific topic along with associated files and citations that would in return maximize the efficiency of the scientific research. The framework also allows researchers to visualize the connection between different concepts similar to the cognitive imaging of the human mind.

Cloud computing is used to a greater extent in today’s organizations and enables organizations to obtain on-demand network access to IT services. When cloud computing is adopted in an organization, the IT governance becomes more challenging, because organizations need to address business and IT-related processes as well as managing risks and maintaining the relationship with cloud computing vendors. This research aims at finding how cloud computing service model specifically Software as a Service (SaaS) influence IT governance structures, processes and relational mechanisms in a public organization. For this purpose a case study was conducted in a Swedish municipality and the data was collected through interviews with IT managers and from internal documents of municipality and was analyzed using thematic analysis. The results of this study shows that SaaS influences the IT governance structure by improving roles and responsibilities definition and speeds up the decision-making processes. Moreover, the communication with the vendors is more efficient due to the use of SaaS.

Digital transformation is a sine qua non in the business operations in private and public sector organizations. However, in achieving digital transformation in public organizations of significance importance are the cultural barriers. Previous studies on the barriers in digital transformation have mainly focused on private organizations, and less attention has been given to cultural barriers in digital transformation in public organizations. Therefore, this research has focused on the case of a public organization like Inland Revenue Department (IRD) in Sri-Lanka to identify the cultural barriers in digital transformation. The data was collected through semi-structured interviews, and from internal documents (organizational publications and annual performance reports of IRD), and was analyzed using thematic analysis. The research has identified a number of twenty-one cultural barriers that were classified into five themes. Out of those twenty-one, twelve were recognized as new cultural barriers in digital transformation in public organizations. The results of this research are important for both researchers in this area as well as the managers in public organizations to focus their efforts, mitigate the identified cultural barriers, and improve the digital transformation implementation in their organizations.

The extant literature on digital transformation, as an emerging phenomenon, has grown in volume during the last decades. As organisations continue to embrace digital transformation, in pursuit of improved efficiency of their business processes as well as provision of better services and products to their customers, managing the necessary changes have become challenging for leaders. One of these challenges is the alignment between the IT strategy—including the introduction of new digital technologies—with the overall organisational strategy, also referred to as strategic alignment. Even though both digital transformation and strategic alignment have attracted the attention of IS researchers, there is a paucity of research exploring how strategic alignment issues play a role in the digital transformation processes undertaken by today’s organisations. To address this gap, in this study we present the findings of an empirical qualitative study conducted in two countries. Our results indicate that the action organisations take to improve their strategic alignment is dependent on how far they have come to introduce new technologies, reconfigure their business processes, and redefined their overall organisational strategy. The study provides insights on how leaders plan and implement changes in response to the changes in external environment as well as the internal organisational dynamics.

The paper addressed the specific roles and corresponding capabilities required by Chief Information Officers (CIOs) to facilitate customer value development and organizational competitiveness and performance in the Fourth Industrial Revolution (4IR). The objective was to develop a CIO management framework with specific 4IR roles and capabilities. The study was an exploratory and theoretical literature synthesis that followed an interpretivist philosophy with qualitative analysis. It was evident that CIOs should fulfil the roles of customer value developer, technology entrepreneur and life-long learner in the 4IR. The paper made an original contribution to knowledge by developing a CIO framework for managing the 4IR comprising specific CIO roles and capabilities that were directly relevant to the 4IR. It also had value for CIOs and other industry leaders by highlighting the importance of the 4IR and its innovative technologies and the position of CIOs whose responsibility it is to convert the 4IR into value.

The study focuses on large-scale planned organizational and IT changes because of their typical high costs and risks to organizations. The research gap was insufficient research relating to change management based on the ontological view that change and constancy exist in cohesion. The study contended that a change and constancy management approach would be worthwhile for addressing unintended negative consequences of changes. Thus, the aim was to empirically investigate these changes and determine whether actively managing change and constancy together could mitigate unintended negative consequences of changes and increase change success. A predominantly qualitative questionnaire survey was administered, and the resulting data were analyzed qualitatively and quantitatively. The data provided evidence of both change and constancy in these contexts, the failure rates reported in the literature, unintended negative consequences and their potential severity and the approach being worthwhile to mitigate any costly chasm between change conceptualization and actualization.

The increasing competition in the construction industry requires companies to cooperate and be actively involved in the dynamic management of multiple relationships and supply chains. To facilitate such dynamic supply chain management, some enterprises implement internet of things (IoT) technology and its smart devices. Through the utilisation of IoT technologies in supply chain cooperation, some enterprises have achieved the co-creation of values. However, few researchers evaluate the impacts of IoT implementation on the achievement of value co-creation in the Chinese construction context. To fill these gaps, this study concentrates on exploring the role of IoT implementation in enhancing value co-creation in terms of competency alignment (CA), behavioural alignment (BA), process alignment (PA) and congruence of expectation (CE) in Chinese supply chain collaboration. The data that informs the methodology is collected through a questionnaire and the findings illustrate that IoT implementation positively correlates with CA, BA, PA and CE. The paper concludes by summarising the study’s findings and outlining the managerial implications and opportunities for future study.

Aiming to retain their position in the marketplace, organizations are constantly enhancing research and development-based digital innovation activities in order to constantly develop new products and deploy new technologies. However, innovative trends and products are prone to failure, leading to undesired repercussions. In addition, when evaluating a product life-cycle, many decision-makers confront unprecedented challenges related to the estimation of potential disruptive innovation. To address this gap and to tackle the opportunities of digitalization, we conduct quantitative study to investigate the usage of research and development activities that can represent a main economic driver for new product/service development. A new approach for predicting innovative technology-based product success is proposed using Neural Networks models and based on the analysis of patents, publications and technologies revenues which are considered major key performance indicators in measuring technology-based product power. The proposed methodology consists of two main steps: forecasting patents and publications growths separately for a specific candidate technology using a common predictive Neural Network regression model, then integrating the results into a Multi-dimensional Neural Network classifier model in order to predict future revenue growth for this candidate technology. The present methodology is applied using two different types of Neural Networks for comparison purpose: “Wide and Deep Neural Networks” and “Recurrent Neural Networks”. Consequently, addressing this estimation represents a decision support and a crucial prerequisite step before proceeding with investments, where organizations can improve decision making in innovative technology-based product/service development. The findings show that the Recurrent Neural Networks models achieve higher prediction accuracy, and outperform the Wide and Deep Neural Networks, proving to be a more reliable model that can enhance digital innovation development.

Purpose - The purpose of this paper is to present a systemic review of the how outsourcing affects small and medium enterprises (SMEs). The paper achieves that purpose by focusing on outsourcing functions such as support activities, accounting, primary and back-office activities.Methodology - This paper used secondary data. It relies on studies done by other researches to collect information related to the topic. This is presented through the competency theory, the transaction cost theory and the social view theory. The conceptual framework presented also relies on data collected from secondary sources.Findings - The results of this research show that outsourcing in SMEs helps to improve business performance. Some SMEs are outsourcing their core activities to cut down on costs and offer better services.Implications - The implications of the findings is that startups and SMEs will use the research to identify the best theories to rely on when outsourcing. The findings can also be used in framing policies for outsourcing.Originality/value - The originality of the research is that it takes a new approach to address the issue of outsourcing in SMEs. It looks at the concept holistically instead of focusing on one function of outsourcing.

Purpose – This study will examine to find out the solutions for the people who purchase certain products that are linked to a certain brand and at the same time, they are biased towards their sources, whereas, these sources are affiliated and endorsed by such brands.Methodology – This study is based on quantitative research design method utilising convenience sampling method based on the existing literature in the field of business management.Findings – The findings of this study will be congruent with the results of the previous literature, proving that the positive relationship among the different dependent and independent variables exist, respectively.Implications – This study will examine the association between brand attractiveness, brand loyalty and celebrity endorsement in relation to the customer’s attitude while purchasing a product, in the field of marking and business management.Originality/value – This study is developed on the existing literature that will help the readers in understanding the consumer’s attitude towards a brand source.

The purpose of the paper is to identify the key success factors that determine the perception of university information system based on latent dimensions of DeLone and McLean IS Success Models. These dimensions were identified on the basis of empirical data gathered on a sample of 759 university students and staff members. Two-group structural equation sub-models are constructed in the analysis of the measurement equivalence and estimation of two types of models: IS Success Model and Updated IS Success Model with feedback loop. The results show that parameters of IS Success Model differ significantly across groups, indicating the system quality for students, and information quality for staff members, as key factors shaping the satisfaction and individual and organizational impact of university information system. It is also noticeable that it was not possible to estimate sub-models of Updated IS Success Model due to unacceptable values of the stability index.

The management of Information Systems (IS) projects occupies a prominent place in research given the need for continuously improve projects efficiency and efficacy. In the case of developing countries, this is even more important because projects success rates are typically lower than those of the so-called developed countries. Projects success is vital to development, because the countries not only need to use the scarce resources available in the best possible way, but also must gain trust from populations and investors to continue ensuring financing for future projects. Note that developing countries’ governments many times depend on foreign investment to undertake large projects, for instance in the construction or IS infrastructure sectors. However, there are few known studies about the success of IS projects in developing countries. To help fill this gap, we carried out a questionnaire-based survey in four countries. The focus of our survey was on IS projects from the public sector. This enabled to identify quite low levels of success, as well as an urgent need for training and education programmes on project management.

While bringing business and computer science into an improved alignment using the theoretical foundations of information and computation is one of the main aims of information science, improved design knowledge from other interdisciplinary research fields like human-computer interaction (HCI) could advance different information system (IS) design thinking and processes. Since structuring the IS design process for a sustainable result is challenging, a HCI viewpoint and focus on IS design could be beneficial due to the multi and interdisciplinary nature of HCI. In this paper an iterative design process for sustainable IS design conceptualized from HCI is proposed. The resulting design process highlighted the role of HCI in building knowledge in information science. This was achieved by showing the influence of different design choices on user behavior and in that way contributing towards generating reusable designs in different phases of the sustainable IS design process.

Radio Frequency Identification (RFID) Technology is a part of supply chain systems but has not been fully integrated in the shipping industry to date. Port and terminal management teams already make use of this technology to verify cargo information, reduce waiting times and prevent bottlenecks. The adoption of RFID technology in the shipping industry can provide invaluable real-time information about a ship’s crew and cargo. This study deals with RFID-based solutions concerning issues of cargo security and handling, as well as tracking of the crew in emergency situations. Although some maritime companies have upgraded their fleet with modern management systems, there is still much to be gained by the wide use of more RFID applications in shipping. We will expound on some of the most useful RFID applications in the maritime sector and discuss their respective advantages and disadvantages.