Informatics, Volume 9, Issue 2 (June 2022) – 20 articles

This exploratory study examines the effectiveness of social robots’ ability to deliver advertising messages using different “appeals” in a business environment. Specifically, it explores the use of three types of message appeals in a human-robot interaction scenario: guilt, humour and non-emotional. The study extends past research in advertising by exploring whether messages communicated by social robots can impact consumers’ behaviour. Using an experimental research design, the emotional-themed messages focus on the health-related properties of two fictitious energy drink brands. The findings show mixed results for humour and guilt messages. When the robot delivered a promotion message using humour, participants perceived it as being less manipulative. Participants who were exposed to humourous messages also demonstrated a significantly greater intent for future purchase decisions. However, guilt messages were more likely to persuade consumers to change their brand selection. This study contributes to the literature as it provides empirical evidence on the social robots’ ability to deliver different advertising messages. It has practical implications for businesses as a growing number seek to employ humanoids to promote their services. Full article

One of the key elements to consider in business management is the management of IT (Information Technology) departments by implementing processes as described in the ITIL (Information Technology Infrastructure Library) standard. This is particularly important in industries that are not directly related to ICT (Information and Communication Technologies), such as the tourism industry. In this paper, we present a methodology to sequence the implementation of the ITIL processes in any company to support its development. The methodology is based on an optimization algorithm and the information extracted from a survey. The optimal sequence is built from this information and from the information of the restrictions imposed by the company that implements the ITIL processes. We applied the methodology to a company in the tourism industry. The sequence obtained shows the need to implement operative processes (initial positions in the sequence), and afterwards strategic processes (final positions in the sequence). A comparison with other authors’ proposals shows differences in the order of processes proposed by this methodology. The main conclusion is that it is possible to use the information of companies to efficiently generate an optimal sequence of ITIL processes which enhances their management; this sequence is unique for every company that wants to implement ITIL in the tourism industry, and it is independent from the proposals of other authors who designed generic/non-optimal sequences. Full article

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials. Full article

The pandemic caused by the 2019 coronavirus disease has marked a total change in the development of society. Since then, its effects have been visible in people, both in work, education and psychological areas. There are many jobs and organizations that have set out to identify the reality of people after the pandemic and how the pandemic has affected their daily lives. To do this, countries have organized data and statistics collection campaigns that allow investigating the new needs of people. With this, instruments such as surveys have become more relevant and valid to know what these needs are. However, the analysis processes must guarantee answers that are able to determine the direct impact that each question has on people’s feelings. This work proposes a framework to determine the incidence values of surveys based on their categories and questions and how they capture the reality of people in areas such as education, the impact of work, family and the stress generated by the pandemic. With the results obtained, each element and category that the population considers a consequence of COVID-19 that affects the normal development of life has been identified. Full article

An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users’ needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers’ behaviour by using a decision-making model, which analyses the consumer’s choices and helps the decision-makers to understand their potential clients’ needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making. Full article

The Indonesian spectrum usage fees—the so-called Biaya Hak Pengguna Frekuensi Izin Pita Frekuensi Radio (BHP IPFR)—are currently calculated using a formula determined by the three following main parameters: the frequency band, the country’s economic parameter, and the nationwide population. As spectrum usage fees are proportional to the width of the bandwidth, the current formula would result in an extremely high price when applied to 5G-mmWave private networks, with the cost burden being a direct consequence for the service operator. In this paper, we propose the formulation of a new spectrum usage fee for 5G-mmWave private network implementation in Indonesian industrial areas. To do so, we evaluate the current formula, adopt the framework offered by the ITU-R SM.2012-5 (06/2016), and use an industrial reference index—the Indonesia Industry Readiness Index 4.0 (INDI 4.0) score. We test the proposal by applying the new formula to calculate the 5G-mmWave private network spectrum usage fee for the Jakarta industrial area. The result shows that the new formula gives a lower spectrum usage fee than the current formula, which benefits 5G-mmWave private network service operators. Such savings can be regarded as a government subsidy for the service operators to use in various ways in the industry, providing further economic benefits. Using the input–output model, we prove that despite the proposed new formula brings a lower spectrum usage fee, resulting in a loss in state income, it will lead to a much greater positive impact on the national economic output. Applying the new formula will eventually have a multiplier effect on various sectors and encourage digital economic growth and national digital transformation, especially for vertical industries in Indonesia. This study may serve as a guideline or initial reference for Indonesian policymakers and service operators for applying the CAPEX and OPEX cost of using the new spectrum for 5G-mmWave private network service implementation and estimating the economic multiplier for 5G-mmWave private network service deployment in industrial areas. It can also be used as a benchmark case for other countries to apply spectrum usage fees for private networks in industrial areas. Full article

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade. Full article

Making smart and informed decisions often requires the integration and analysis of large amounts of data. However, integrating these data is rarely straightforward, mainly because of heterogeneities in data structure and format. In this study, we focus on two widely used data formats by municipalities to store digital maps of their infrastructure: Computer-Aided Design (CAD) and Geographic Information Systems (GIS). While most municipalities still maintain infrastructure data in CAD format, many have started converting them to GIS since GIS includes geographical coordinates. However, the inherent differences between these two formats pose challenges to accurately converting information from CAD to GIS. The main goal of this study is to develop a procedure to help municipalities to perform CAD-to-GIS conversion. To that end, potential problems in CAD-to-GIS conversion were first identified through interviews with practitioners at different U.S. municipalities and through a literature review. Taken together, we propose the C2G framework to streamline the conversion process while minimizing information loss. The framework consists of five stages, and the execution of this framework and tasks involved in each stage are explained. Moreover, we apply the framework to real-world underground stormwater infrastructure data obtained from the University of Illinois at Chicago (UIC) to illustrate the framework’s applicability. The case study explains details about the technical difficulties we encountered in the process and provides recommendations to circumvent those difficulties. The results from the case study showed that the C2G framework was able to successfully convert CAD data to GIS data. Although the framework is developed specific to the needs of CAD/GIS practitioners in the US municipalities, it can be adopted in most CAD-to-GIS conversion situations. The information learned during the interviews supports the need for a standard CAD-to-GIS conversion process. The contribution of this study is to fill this gap by developing a generalized framework to carry out CAD-to-GIS conversion which only requires basic knowledge of CAD and GIS. Full article

Social media is the most popular canvas to engage with art. In this study, we provide a different angle, on how an artistic installation on a world-renowned monument—such as Paris’ Arc de Triomphe—can emotionally affect viewers and potentially increase the popularity of the artwork. We collected N = 7078 Instagram and N = 3776 Twitter posts of the Arc de Triomphe as wrapped (installation) and unwrapped using APIs. As engagement indicators for several supervised machine learning experiments, we chose the total number of likes, comments, shares, text sentiment, and so on. Our findings revealed that people were captivated by the poetic installation. Based on the results, we discovered that the sentiments of triumph and surprise prevailed in datasets of the Arc de Triomphe as unwrapped. The same sentiments of triumph and surprise were most prevalent in datasets as wrapped, as well, but with higher scores. Furthermore, we have provided evidence of public art experience and engagement in the social media era. This research, we believe, will be useful in future studies of social media through the lens of public art and popularity. We hope that our findings will stimulate future research in the fields of art curatorship, cultural heritage management, marketing and communication, aesthetics, and culture analytics. Full article

Low back pain is a leading cause of disability worldwide, putting a significant strain on individual sufferers, their families, and the economy as a whole. It has a significant economic impact on the global economy because of the costs associated with healthcare, lost productivity, activity limitation, and work absence. Self-management, education, and adopting healthy lifestyle behaviors, such as increasing physical activity, are all widely recommended treatments. Access to services provided by healthcare professionals who provide these treatments can be limited and costly. This evaluation study focuses on the application of the MyRelief serious game, with the goal of addressing such challenges by providing an accessible, interactive, and fun platform that incorporates self-management, behavior change strategies, and educational information consistent with recommendations for managing low-back pain, based on self-assessment models implemented through ontology-based mechanics. Functional disability measured using the Oswestry Disability Questionnaire showed the statistically significant (p < 0.001) improvement in subjects’ self-evaluation of their health status. System Usability Scale (SUS) test score of 77.6 also suggests that the MyRelief serious game can potentially influence patient enablement. Full article

The complexity in collaborative work is mainly related to the difficulty in social interaction, which generates low levels of understanding among participants about what they should do and about the problem to be solved, resulting in problems in the motivation to generate true collaboration. Therefore, in the search to improve collaborative work and encourage this collaboration, it is necessary to implement strategies that promote the construction of shared understanding and obtain better group results. However, building it becomes a challenge due to the factors that influence it and how little is known about its construction. In this sense, to improve collaborative work, as a result of a research process, the THUNDERS process is proposed, which provides a set of elements to build shared understanding in problem-solving activities and with heterogeneous group formation. Specifically, this paper presents the results of the statistical validation of THUNDERS through the Student’s t-test, which was used in an exploratory study in the educational field in two Colombian universities, where learning styles were considered for the formation of groups; having groups that used the process and other control groups that did not use it, the collaborative activity consisted of determining the scope of a process line simulating a software development company. According to the results obtained in the context of this study, it can be considered that THUNDERS encourages and improves shared understanding when people in a group work collaboratively to solve a problem. In addition, elements for improvement were identified that should be incorporated in further stages of this research so that the process allows for an easy and guided construction of shared understanding in any application context. Full article

Data integration is one of the most challenging tasks for digital collections whose data are stored across various repositories. Data integration across digital repositories has several challenges. First, data heterogeneity in terms of data schema and data values usually occurs across diverse data sources. Second, heterogeneity in data representation and semantic issues are among the problems. The same data may appear in different repositories with varied data representations, i.e., metadata schema. Recent research has focused on matching several related metadata schemas. In this paper, a metadata integration framework is proposed to support digital repositories in socio-cultural anthropology at the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC), Thailand. The proposed framework is defined based on the Metadata Lifecycle Model (MLM). It utilizes non-procedural schema mappings to express data relationships in diverse schemas. A case study of metadata integration over the SAC digital repositories was conducted to validate the framework. The SAC common metadata schema was designed to support data mapping across 13 digital repositories. The SAC “One Search” system was developed to exemplify the system implementation of the framework. Evaluation results showed that the proposed metadata integration framework can support domain experts in socio-cultural anthropology in unified searching across the repositories. Full article

Based on many years of experience as a management consultant in different industries and corporate structures and cultures, the motivation to use digital transformation in connection with variable corporate goals—such as fluctuating workloads, agile response to customer inquiries, and ecological and economic sustainability—results in a process or a product to be developed that intelligently adapts to market requirements and requires forward-looking leadership. Using an AI-based methodical analysis and synthesis approach, the high consumption of economic and human resources is to be continuously monitored and optimization measures initiated at an early stage. The necessary information technology with its infrastructure and architecture is the starting point to accompany the agility and changeability of corporate goals. Researching the relevant documents begins with writing the panorama or the state of knowledge on the topic. This article is about the IT infrastructure based on the requirements for an architecture and behavior that a versatile, agile company needs to accompany the constantly changing framework conditions of the market. The technology used and the available resources, including the human resources, need to be adapted as early as possible. Data now represent the most valuable asset on Earth and future industrial manufacturing systems must maximize the opportunity of data usage. Low-level data must be transformed to make them useful in supporting intelligent decision-making, for example. Furthermore, future manufacturing systems must be highly productive, adaptable, absent of error, and kind to the environment and to local communities. The all-important design should minimize the waste of material, capital, energy, and media. Herein, we discuss the fulfilling of agile customer requirements involving adaptable and modulated production processes (related to the ‘agile manufacturing’ and ‘digital transformation’ perspectives). Full article

The contemporary urban environment faces such challenges as overloaded traffic, heavy pollution, and social problems, etc. The concept of the “smart city” allows solving some of these issues. One of the opportunities provided by the smart city is the development of micro-mobility and sharing services; contributing to the optimization of transport flows and decreasing carbon footprints. This study investigates the factors affecting the development of e-scooter sharing services and the attitudes of young urban residents towards using these services. The research applied a PLS-SEM (partial least squares structural equation modeling) analysis performed in SmartPLS3.7 software. The data were collected via focus groups and surveying a population aged 18–35. The authors partially based the research on the UTAUT model (the unified theory of acceptance and use of technology), taking such constructs as “intention to use”, “anxiety”, “attitude toward use”, “effort expectancy”, and “social influence”; they also introduced the new unique variables “internal uncertainty”, “e-scooter design”, “experience”, “perceived safety”, “infrastructure quality”, and “motivation to physical activity”. The main finding of the study was determining that the latent variables attitude towards sharing, anxiety, internal uncertainty, JTBD (jobs to be done), and new way of thinking have a direct or indirect effect on the intention to ride e-scooters in the future and/or to use sharing services. The obtained results permit making recommendations to businesses, municipal authorities, and other stakeholders on developing e-scooter sharing services as a contribution to the advancement of the smart city. Full article

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility. Full article

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field. Full article

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems. Full article

The aim of this study is to synthesize the rapidly increasing literature on privacy and security risk of digital payment. By reviewing 591 studies, the literature on this topic was evaluated using a bibliographical approach to highlight the intellectual development of the field and recommend potential research directions in this still-emerging field. According to our assessment, academics have continued to focus on perceived privacy and security, while more multigroup analyses based on subdimensions of risk are needed. In addition, the vast majority of studies have not considered the inter-relationship between risk attributes. We analyse the potential causes of the lack of research diversity and provide additional suggestions to improve digital payment research in the future. This study will be valuable for academics, analysts, regulators, practitioners, and investors. Full article

This study validates and extends the latest unified theory of acceptance and use of technology (UTAUT2) with the privacy calculus model. To evaluate the adoption of healthcare and e-government applications, researchers have recommended—in previous literature—the application of technology adoption models with privacy, trust, and security-related constructs. However, the current UTAUT2 model lacks privacy, trust, and security-related constructs. Therefore, the proposed UTAUT2 with the privacy calculus model is incorporated into four constructs: privacy concern, perceived risk, trust in the smart national identity card (SNIC), and perceived credibility. Results from a survey data of 720 respondents show that habit, effort expectancy, performance expectancy, social influence, hedonic motivation, and price value are direct determinants that influence behavioral intentions to use. Results also revealed that behavioral intentions, facilitating conditions, habits, perceived risks, and privacy concerns are direct predictors of ‘use behavior’. The authors also analyzed the interrelationships among the research constructs. The extended model may lead toward establishing better innovative e-health services to cover the desires of the citizens through the use of health information applications embedded in an all-in-one card. Full article

We review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; the DoD’s assessment was rushed, suppressing alternative risk perceptions. We begin by contrasting the lack of success found in a case study from the commercial sphere (Facebook’s use of machine intelligence to find and categorize “hate speech”). Then, after the DoD case study, we draw a comparison with the Department of Energy’s (DOE) mismanagement of its military nuclear wastes that created health risks to the public, DOE employees, and the environment. The DOE recovered by defending its risk determinations and challenging risk perceptions in public. We apply this process to autonomous human–machine systems. The result from this review is a major discovery about the costly suppression of risk perceptions to best determine actual risks, whether for the military, business, or politics. For autonomous systems, we conclude that the determinations of actual risks need to be limited in scope as much as feasible; and that a process of free and open debate needs to be adopted that challenges the risk perceptions arising in situations facing uncertainty as the best, and possibly the only, path forward to a solution. Full article