Business Information Systems Workshops

The purpose of the research in this article is to analyze the structure of energy in Germany and compare the obtained data with events occurring in the country and the world. The article reviews the world energy sector and considers the rating of regions by gross energy production. The analysis helps to identify the leading regions in terms of energy production: Asia and Oceania, North America and Europe. The German economy and energy sector were considered, as well as the development of nuclear power in particular and the gradual abandonment from nuclear power plants because of the occurred radiation accidents in the world. It also describes the relevance of data analysis in the energy sector, especially in working with renewable energy sources due to their instability and unpredictability. Using self-organizing Kohonen maps, the data on German energy indicators was analyzed. Basing on the analysis it was concluded that these maps correspond to the changes in the energy policy of Germany.

The problems of “time-to-event’ data analysis explore data of processes with the defined end point of time when an explored event occurs. The methods of survival modelling, hazard analysis, risk evaluation are combined for understanding and exploring these data. In this article the time-to-event modelling addresses in-hospital mortality of stroke patients. The clinical data of stroke cases is explored based on the historical records of neurology department of Clinical Centre in Montenegro. The main aim of the research is to explore survival techniques for getting insights from the stroke clinical data with the goal of identifying impact of the variables for the predictive research. The time-to-event data does not follow the normal distribution, thus limiting application of major methods of analysis. In the article the survival analysis techniques such as Life table, Kaplan–Meier survival plot and the Cox proportional hazards regression model are applied for exploring stroke data.

Vanbreda Risk & Benefits, a large Belgian insurance broker and risk consultant, allocates a substantial amount of time and resources to answer contract related questions from customers. This requires employees to manually search the relevant parameters in the contracts. In this paper, a solution is proposed and evaluated that automatically extracts insurance parameters from contracts using regular expressions and Natural Language Processing. While Natural Language Processing has been used in insurance for optimising premiums, detecting fraudulent claims, or underwriting, limited work has been done regarding parameter extraction. The proposed solution has been developed on 127 different contracts and two different contract types in terms of accuracy and time performance. Moreover, the automatic parameter extraction has been compared to manual parameter extraction. We conclude that automatic parameter extraction using regular expressions achieves better accuracy than manual extraction on top of being significantly faster, allowing Vanbreda Risk & Benefits to invest more time into providing better customer service.

The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients’ medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the representation of medical information as sequences of events, effectively allowing to perform process mining analyses. In this paper, we will present some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19. We show that process mining techniques are able to reconstruct a model of the ICU treatments for COVID patients.

The main purpose of this paper is to represent how knowledge-based Enterprise Model (EM) as problem domain data storage may be used in Information Systems (IS) engineering process. Enterprise Meta-Model (EMM) presented more than two decades ago justifies EM structure. EM stores problem domain data gathered by analyst and this EM structure can be used for project models creation in IS design phase. Unified Modeling Language (UML) models are one of the possible models, which can be generated from EM. These models can be generated through transformation algorithms. To present possibility of UML models generation from EM particular problem domain example is defined in this paper. Presented example demonstrates that data stored in EM is enough for different UML models generation and this paper presents that different UML behavioral models can be generated from EM and can illustrate same problem domain from different perspectives.

So far, the biggest challenge for a comprehensive Business Information Systems (BIS) education curriculum was the fast-changing nature of its target market and the resulting demand for a combination of up-to-date technical knowledge, organization-centred mindset, and adaptive skills. However, advances in pedagogical methods, changes in the skills of high-school graduates, and widening online options in the wake of the COVID-19 pandemic brought on a new set of expectations. This situation may be considered an opportunity to address the threat of potentially increasing mismatch and misalignment between competences required by the IS industry labour market and current training contents offered and methods used by higher education institutions. This paper provides a systematic and comprehensive overview of the challenges BIS programs have to face and address. It considers everyday experiences of BIS educators and current best practices as starting point. Then provides an overview of employer and alumni opinion, as well as reviews up-to-date teaching methods related to teaching soft computer skills. It also considers the requirements and opportunities related to an increasingly online-centred situation. Based on these challenges the paper lays down the foundation for a potential curriculum design approach intended to address all of the above issues in an integrated framework.

Universities have encountered numerous difficulties and challenges during the COVID-19 pandemic. They used various approaches to deal with these challenges. Unfortunately, these experiences are not widely discussed. Therefore, this study provides preliminary insights on how the business information systems department at the Faculty of Organizational Sciences, University of Maribor managed to overcome different challenges and executed the study process completely online in the COVID-19 pandemic. Experiences of conducting several courses at the bachelor and master level are reported in the paper. We also provide some suggestions on how to overcome specific challenges faced by students and lecturers. In the future, we wish to conduct a multiple case study including the viewpoints of lecturers, support staff, and students.

COVID-19 pandemic has taught us how to continue with the day-to-day activities interacting and working from remote locations. In this paper, we have highlighted the positive approach necessary to complete a project with success under this constraint by interacting regularly with the relevant stakeholders keeping focus on the final project deliverables. The salient points with supporting references are chalked out which might be helpful for others to follow if faced with stressful situations that COVID-19 pandemic taught us.

Student centeredness in curriculum development has pedagogical benefits. Learning outcomes describe what competences will be possessed by students after graduating. Degree programs as well as labour market needs may be compared based on such competences. Developing or redesigning Business Information Systems (BIS) degree programs may utilize offers of competitor institutions as best practices. This was the case at a Hungarian university where the process of reforming the BIS program to meet changing market requirements included a review of peers. This paper presents both competitor and labour market analysis to create a baseline how programs offered by peer institutions ranked on Times Higher Education World University Rankings perform.

Accurate weather prediction is a challenging task. It involves large amount of data and computation, which vary dynamically. This paper discusses a novel idea of using Multi Agent System (MAS) for weather forecasting, particularly in the Indian context. The proposed approach incorporates a deep neural network model within MAS with a hybrid ANN algorithm to recognize the static and dynamic weather conditions. The approach uses ensemble prediction to account for indeterminism in weather conditions. Predictions and alerts given by MAS can help the government and local authorities to plan precautions in a timely manner. The paper discusses the implementation challenges and advantages of a MAS Model compared to Numerical Weather Prediction method.

Big Data and its uses are widely used in many applications and fields; artificial information, medical care, business, and much more. Big Data sources are widely distributed and diverse. Therefore, it is essential to guarantee that the data collected and processed is of the highest quality, to deal with this large volume of data from different sources with caution and attention. Consequently, the quality of Big Data must be fulfilled starting from the beginning; data collection. This paper provides a viewpoint on the key Big Data collection Quality Factors that need to be considered every time the data are captured, generated, or created. This study proposes a quality model that can help create and measure data collection methods and techniques. However, the quality model is still introductory and needs to be further investigated.

In a world that is more and more driven by data, decision makers are provided with a huge amount of information. However, while this appears to be a good development, they also face the challenge of getting through those masses to get to the actually important insights. To ease this task for managers that oversee highly complex situations, management information systems (MIS) provide valuable support. While those systems were usually drawing from internal data sources that were rather structured, for several years the paradigm of big data has been on the rise. This however brings not only new possibilities for gaining insights, but also additional challenges. To help in dealing with those, the publication at hand presents a review of the literature that considers the incorporation of big data in MIS and reflects on current trends as well as challenges for future researchers.

Test driven development (TDD) is a practice that aims to improve product quality and maintainability by interweaving the design and implementation with its testing. It is most prominent in the software development domain. However, its usefulness is not undisputed, making it a somewhat controversial topic. Besides giving a short introduction regarding the principles of TDD, the publication at hand motivates and outlines a structured literature review to obtain new insights regarding the perception of TDD in computer science, hopefully contributing to the corresponding discourse. Furthermore, by already conducting the first steps of the review, it provides a first impression regarding the vastness of the potentially relevant literature base and gives a rough indication regarding the extend that is to be expected for the completed work.

This study examines the influence of augmented reality on consumer behaviour in online retailing based on the stimulus-organism-response model. In this context, the affective and cognitive response, and the effect on purchasing behaviour are investigated in more detail. For this purpose, a quantitative study was carried out and analysed using structural equation modelling. The results show a positive influence of the perceived augmentation both on emotions during the use of AR and on the perceived amount of information. The attitude towards the use of AR has the greatest impact on purchasing behaviour, followed by the perceived amount of information. In addition, emotions indirectly effect the purchasing behaviour through its attitude as a mediator.

The article presents the results of the work on the method of intuitive UI and UX personalization of mobile applications. The method is based on the user’s personality profile (Big 5) inferred from the available data on the user’s phone at the time of installation. The user’s personality model was created based on machine learning performed on data from 2,202 people. The proposed method enables personalization from the first contact of the customer with the application. Therefore, it is a significant advantage of the study. Moreover, the method ensures complete data privacy protection since no data about the user is uploaded outside the mobile phone.

The global pandemic, caused by the spread of COVID-19, has altered the way people go shopping. In light of this, Social Media channels are an important means of sharing information about goods and services, and different kinds of brands. Since these channels are of considerable market significance, the authors of this paper decided to describe the results of a survey on how to use Social Media to improve customer relationship management processes in 31 companies. The focus was on digital marketing for micro and small businesses. In addition, an in-depth analysis was conducted of four companies, to determine the challenges and strategies in social customer relationship management adopted by micro and small businesses. The results show that this is still a new policy for micro and small companies, but has a great potential to boost sales, enhance customer loyalty and increase brand awareness. The lessons learned can assist policymakers in taking more suitable measures for strengthening this market sector.

Social Media and Customer Relationship Management (CRM) are already widely used in business settings, but other non-commercial sectors started only recently to adopt them. Among them are Higher Education Institutions (HEIs). Even though research shows positive effects on the quality of services, student satisfaction, and attractiveness towards international students, the adoption is very low. This research in progress reviews the state of research about Social CRM in HEIs and gives an example of the potential of social media for CRM approaches of HEIs by applying Social CRM concepts and techniques for better understanding the negative service experiences of students. By applying analytical Social CRM techniques on large amounts of User-Generated-Content (UGC) in complaint platforms the paper gives insights into problem chains inaccessible with manual methods. Based on the scarce research about Social CRM as well as the demonstrated potential of social media for CRM strategies of HEIs, this paper concludes with a call for further research on Social CRM in HEIs.

Service Innovation plays an important role in research and practice and enabled the surge of new concepts that changes the focus from a product-oriented approach to a service-oriented approach. However, further developments place the customer in the center of company-client relationships. The recent advances in customer data analysis and the positioning of customers as company’s co-creators led to the development of a new concept called Customer-induced Orchestration of Services. The novelty of the topic requires further studies and a deeper understanding of the interdisciplinary concepts around it. This paper identifies the main drivers and concepts, allowing a more holistic view on the topic. The results support further research, as well as the development of a framework or method for the application of Customer-induced Orchestration of Services, which enables more transparency and control for customers.

In recent years, with fast-developing computational capabilities, high-resolution techniques have been widely employed in atmospheric models. Thus, researchers can apply these high-resolution models to produce detailed meteorological scenarios, which empowers studies of urban-scale climatology relying on finer grid spacing. The WRF ARW/Noah LSM/UCM model is often used in urban climate research. However, the default input land surface data in urban areas is not precise enough, especially for the data describing in China. This study was pertinent to the increasing presence of ambiguous modeling practices in urban-scale climatology because of the out-of-date land surface data with insufficient fixes. Given the lack of quality-assured high-spatial-resolution land surface datasets, we produced a high-resolution urban land surface dataset including the land cover, vegetation coverage, urban morphology, artificial impervious area, and anthropogenic heat data for the Hong Kong-Shenzhen area - one of the world’s largest metropolitan areas and a unique pair of twin cities. In short, the high-resolution urban land surface dataset provided a complete set of land surface information required for high-resolution urban climate modeling in the Hong Kong-Shenzhen area, which is exceedingly rare, especially in which the data on detailed urban morphology and anthropogenic heat fluxes.

Open data is an integral part of Smart City projects carried out around the world. A public transport network is widely used when it is safe, well designed and reliable. The development and maintenance of the urban transport are key items in city budgets. Decisions regarding changes and the future organization of the public transport are supported mainly by Intelligent Transport Systems (ITS). There are many challenges, and one of them is the bus electro-mobility revolution. European leaders encourage a faster transformation to sustainable economy by introducing incentives and directives followed by EU funding. As a result cities replace aged fleets of diesel buses with the electric ones. A zero emission buses network is a new technology for public operators. It involves investments in chargers integrated to electric grids and the introduction of new maintenance processes. Each investment project that aims to introduce that innovative eco-friendly solution is preceded by feasibility studies. Total Cost of Ownership (TCO) is one of the crucial measure for making business decision. To calculate a proper configuration of chargers and fleet of buses, knowledge of specific operational conditions is necessary. That includes analysis of Open Data such as route characteristics, weather and dynamic traffic conditions. The paper review the existing literature with regard to utilization of Open Data by public transport operators to analyze scalability and operation efficiency of the electric buses. The Open Data used in the recent studies are characterized, classified and analyzed. Moreover, the examples of Open Data sources and platforms that might be used by decision makers are provided.

Literature has shown the prominence of extracting social media data to understand public opinion. However, there are little works on how these opportunities can be realized and the challenges in exploiting the opportunities in the transportation industry. Further, data quality and availability using social media may vary according to different demographics due to population size and languages used. Additionally, most of the related prior studies that show the opportunities of using social media data were conducted in North and South America. With this proposition, we seek to investigate the challenges of using Twitter data with text mining techniques for understanding users’ opinions and sentiment through a case study of using the data to assess public transportation service performance specifically in a Malaysian context. Our findings indicate that social media data can only be useful in generating reasonable insights if users could input informative words for forming discussed topics to derive opinion, and incline towards a certain sentiment with adjectives. The findings also identified the need for a more proficient dictionary to classify multilingual tweets. Our research provides original evidence proving the potential of using social media data to assess public transportation services performance which may vary depending on the demographics of the social media users.

With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.

Within a smart city concept, it is possible to combine a large number of information sources that has spatio-temporal characteristics. The complexity of such a combination lies in the high heterogeneity of information, the need to use spatial and temporal characteristics, as well as formats for presenting such information. To date, there are information platforms for smart city sources organization that allow combining heterogeneous data sources, however, in the process of combining, human participation is still required to establish an unambiguous correspondence and process space-time characteristics. The paper proposes an approach based on a microservice architecture, in which each data source is mapped to a microservice, which presents an ontological data model of the source. When forming a query, data is sampled from sources and integrated based on spatial characteristics for subsequent analysis. As an example, the paper provides an analysis of data on road accidents in St. Petersburg, Russia since 2019 in order to determine accident-dangerous sections of roads and the main causes of accidents. The result is accidents clusters obtained by combining accidents data, road types and weather conditions in the accidents area.

In 2020 the new ITIL v4 standard was introduced. ITIL standardization had and still has a big influence on how IT-Service Management is seen and performed in practice. Thus, the new standard is expected to have a high impact as well. A key element of ITIL v4 is the strong focus on Stakeholder Value in Service Design. Yet apart from ITIL, stakeholder orientation is a current trend in business analysis. This work provides a Systematic Literature Analysis with regard to approaches that allow value modeling for IT-Services. As a result, no approach that fits all requirements inherent in the ITIL v4 standard and IT-Service Design could be identified. However, a set of requirements that should be considered when developing methods, notations, and tools for IT-Service Value modeling is derived.

Digital transformation is of paramount importance for companies nowadays but successfully doing so proves to be a challenging task. Researchers have proposed a myriad of guidelines on how to tackle digital transformation projects, but most of these are quite general and limited work has been conducted to compile these. In this paper, we aim to consolidate the numerous guidelines into one framework and expand upon each guideline with practical examples. The preliminary framework was validated by expert questionnaires and expanded with their recommendations on how to implement these guidelines in practice. In total, we list 78 guidelines structured in three levels: abstract, general, and practical. In future work, the preliminary framework will be further improved through surveys with special attention to real-life practical examples. This work can aid researchers and practitioners dealing with digital transformation.

The interaction between security management in public and private organisations includes complex challenges. In particular in critical infrastructure sectors, there is a need for instruments that enable the holistic and overarching management of private and public providers. Cross-organisational structures and processes should be defined, but are difficult to establish in federal governmental structures due to different legislative levels and scopes. The paper investigates this challenge using Germany and the Free Hanseatic City of Bremen as example.The study proposes the development of an “Enterprise Architecture Framework” integrating and overarching the organizational structurers for both, a federal state, its municipalities and the (private) critical infrastructure providers in these municipalities. The main contributions of this paper are based on the results of an interview study. The interview partners were representatives of enterprises and public bodies covered by the federal IT security regulations. The contribution of the paper is the identification of security management challenges for services of general interest and how to increase the resilience of public service providers. Cybersecurity management in the context of public institutions is in focus.

The market launch and regulative assessment of ICT-based medical devices in Europe is very complex due to a multitude of regulations to be considered during requirements engineering and product management. Additionally, there are no established standards, best practices or support tools how to launch medical devices on the market. The paper is part of a project aiming for methodical support for medical device launch and assessment, and is dedicated to investigating problem relevance. To understand the processes and requirements three case studies were analysed and the necessary processes and requirements were matched towards enterprise architectures (EA). Based on this finding, we argue that EA could be a suitable way to visualize and recommend required processes and structures for medical device management. The main contributions of our work are (a) a literature analysis of EA use in health care and especially for telemedicine, (b) results from use case analysis investigating the business perspective from inside three health tech companies and (c) the analysis of problems of telemedicine integration into EA.

One of the main limitations of blockchain systems based on Proof of Work is scalability, making them unsuitable for e-commerce and small payments. Currently, one of the principal directions to overcome the scalability issue is to use the so-called “layer two” solutions, like Lightning Network, where users can open channels and send payments through them. In this paper, we propose a Probabilistic Logic model of Lightning Network, and we show how it can be adopted to compute several properties of it. We conduct some experiments to prove the applicability of the model, rather than providing a comprehensive analysis of the network.

The bill of lading (B/L) is one of the most important documents in international trade. Current advances started to adopt public blockchain technology for digitization but lacking privacy. We raise the research question, if a private blockchain is suitable to solve this shortcoming. To answer this research question, we followed the design science research methodology. We designed an architecture and evaluated it via implementation, using Hyperledger Fabric. First results show general feasibility and privacy enhancement but reveal a high complexity and difficult extendibility of Hyperledger Fabric. Preliminary results of a focus group shows that the approach peaked interested by practitioners.

This paper is the first to examine the reaction of the cryptocurrency market to COVID-19 in terms of volatility resilience. Seven GARCH-type models are used to measure, predict, and audit the volatility behavior of the most eminent cryptocurrencies that represent almost 60% of the total crypto market namely, Bitcoin (BTC), Ripple (XRP), Litecoin (LTC), Monero (XMR), Dash (DASH), and Dogecoin (DOGE). The in-sample period extends from January 1, 2015 up to November 30, 2019 and the out-of-sample period covers the COVID-19 period spanning from December 1, 2019 up to April 6, 2021. Results showed that CGARCH (1,1) and GARCH (1,1) are the prevailing models to forecast the volatility of Bitcoin and Ripple respectively in both the in- and out-of-sample periods and that advanced GARCH models appear to better predict asymmetries in cryptocurrencies’ volatilities pre and post COVID-19. Also, the COVID-19 contributed in significantly affecting the volatility of Bitcoin, Ripple, Monero and Dash.

The paper applies innovation dynamics equations to analyze audience dynamics of instant payment systems (IPS) behavior. The research shows that dynamics of IPS are well described by the Ricatti equations, which are generalizations of the Bass basic model of innovation diffusion taking into account different patterns of audience behavior. As proved by IPS experience in Britain, Sweden and other countries, commodification of fast payment services allows for rigorous description of the IPS dynamics. Quantitative estimates are obtained for the degree of cooperative customer behavior and the typical time of system growth. However, these parameters may differ for different transactions types within the same system due to their different business nature. We also show that all systems may be described by the generalized trajectory of evolution and demonstrate that described systems with different payment operations types are located on different stages of this trajectory what reflects their maturity and operation nature. The results can be used for qualitative and quantitative assessment of IPS customers behavior and for short- and medium-term projections. We also discuss some future improvements such as including of competition of different IPSs for countries in which more than one IPS run simultaneously.

Supply chains are increasingly adopting industry 4.0 technologies to meet exceeding stakeholder expectations. Blockchain technology offers an opportunity to facilitate the digital transformation of supply chains. Supply chains can benefit from the characteristics of blockchain including through transparency, traceability, and immutable data, to enable for example quality, sustainability, provenance, and safety. Adoption considerations for blockchain are important to ensure needs are met in the early adoption stages and further stages of deployment. This study aims to explore the adoption considerations for blockchain across supply chain domains reported in the literature, focusing on adoption factors and readiness. Research methodology used is a meta-analysis of literature review studies on blockchain adoption in supply chains to identify themes. The review identified 102 papers from four databases, and 33 are selected for analysis, identifying 64 blockchain adoption factors. Security, system integration, trust, scalability, costs, and traceability are found to be important blockchain adoption factors for supply chain. The adoption factors show a spread over a people-process-technology framework. Limitations of the research and areas for future research are highlighted.