Information Systems Frontiers最新文献

Data integration and discovery are open issues in Data Lakes potentially storing hundreds of data sources. The present paper addresses these issues targeting multidimensional data sources, that is sources containing atomic or derived measures aggregated along a number of dimensions, typically derived from raw data for analytical and reporting purposes. Combining semantic models of metadata with existing data-driven techniques, the paper proposes an approach for the discovery of mappings between source metadata and concepts in a reference knowledge graph, enabling the definition of reasoning-based techniques to discover, integrate, and rank data sources relevant to a given analytical query. The efficiency and effectiveness of the approach is discussed by means of experiments on real-world scenarios.

Smart technologies have demonstrated striking outcomes regarding the early diagnosis of diseases and the delivery of the necessary healthcare in the last decade. However, by emphasizing the core fundamentals of social justice and sustainability, together with digitalization and smart technologies that predicate raising productivity and flexibility, Industry 5.0 has proven to achieve more efficient results. Industry 5.0 technologies provide more intelligent ways for human employees and higher efficiency development while also improving safety and performance in many applications. In this research, the contribution is focused on the healthcare and how Industry 5.0 technologies demonstrate several advantages for the healthcare sector, starting with automated and precise disease prediction, moving on to aiding medical personnel in continual surveillance and monitoring and concluding with successful digital automation of smart equipment. The objective of this study is to apply a hybrid multi-criteria decision-making approach under a neutrosophic environment to evaluate the advantages of industry 5.0 technologies in the healthcare sector. Industry 5.0 primary value is to reach human-centric, sustainable, and resilient industries. While Industry 5.0 technologies sub-values regarding the healthcare sector are determined and distinguished according to the 3-main values mentioned previously based on literature. The methodologies applied in this study are: The Analytical Hierarchy approach (AHP) evaluates the main values and sub-values. Subsequently, the effectiveness of industry 5.0 technologies according to their values to the healthcare sector are ranked by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach is constructed under uncertainty based on a neutrosophic environment to achieve accuracy in the evaluation process. The results show that the most influential technology in healthcare are AI and cloud computing, while nano-technology, drone technology, and robots are at the end of the ranking. While validating the suggested technique, outcome comparisons were carried out to demonstrate the benefits of the methodologies. A sensitivity study indicates that adjusting the weightings of the sub-values has no significant effect on the ranking of technologies.

In this paper, we investigate Data Wrangling (DW) pipelines in the form of workflows devised by data analysts with varying levels of experience to find commonalities or patterns. We propose an approach for pattern discovery based on workflow mining techniques, addressing key challenges associated with finding patterns in data preparation solutions. The findings provide insights into the most commonly used DW operations, solution patterns, redundancies, and reuse opportunities in data preparation. The findings were used to create design pattern specifications curated into a catalog in the form of a DW Design Patterns Handbook. The evaluation of the proposed handbook is performed by surveying professionals with results confirming the usefulness of discovered patterns to the construction of DW solutions and assisting data analysts/scientists via the reuse of patterns and best practices in DW.

Abstract

Digital transformation stems from a mix of personal and corporate IT environments and involves integrating digital technologies and business processes in a digital economy. As such, organizations expect that digital transformation improves performance, increases their reach, and ensures better business results. However, there is still a lack of (i) conceptual models to provide an accurate representation of the digital transformation elements; and (ii) standard visualizations of such elements to specific stakeholders. In a previous work, a reference model to support digital transformation initiatives using ArchiMate was proposed. In this paper, we address the second part of this problem regarding the need for specific visualizations. We followed the Design Science Research approach to learn how to identify the needs of the stakeholders, extending and improving the reference model through the design of a set of architectural viewpoints. We used the well-known ArchiSurance case to demonstrate the application of the viewpoints and semi-structured interviews to evaluate the proposal, providing insights to the discussion of the results obtained.

Abstract

Few studies assessed the impact of Covid-19 on the aviation industry from the passengers’ perspective. This study examined how airline passengers’ emotions (positive and negative) and sub-emotions (joy, trust, surprise, anticipation, fear, sadness, anger, and disgust) changed in the pre-and post-Covid-19 periods. 21,463 reviews from 2014–2022 of top 10 luxury airlines were extracted from Skytrax. Using the lens of the Appraisal Theory of Emotion, the analysis revealed an increase in negative emotion and related sub-emotions after the pandemic. Using topic modeling seven similar topics (namely food, staff, customer service, board, flight, crew, and luggage) and four dissimilar topics (entertainment and drink for pre-Covid-19 and wait and Covid-19 for post-Covid-19) were identified. Regression analysis showed that the topics food and entertainment generated significant positive emotion whereas wait and customer service generated significant negative emotion. The results would help the luxury airlines to identify offerings to improve during the recovery after Covid-19.

The body of knowledge on the benefits of Health Information Exchange (HIE) services primarily consists of studies that are conducted on HIEs in the US. The important legal and economic differences between the US market and the rest of the world, significantly reduce the generalizability of the current insights on HIEs. In this research we intend to examine if the HIE adoption and usage would lead to substantial changes in resource utilization in settings outside of the US, hence improving the generalizability and external validity of the literature on HIE effectiveness. We conducted a longitudinal study of all medical providers in Abu Dhabi, United Arab Emirates between January 2019, and May 2022. Using HIE system log data, and insurance claims data we implemented Difference-in-Differences models to estimate the impact of HIE adoption reducing utilization of laboratory and pathology, and radiology imaging services as well as surgery procedures at both individual clinician and hospital levels. We further implemented panel data regression models to estimate the gradual increase in the benefits of HIE for users. On average, at the hospital level, adoption of HIE services has resulted in 24.7% reduction in pathology and laboratory orders, and 11.2% reduction in radiology imaging. At the clinician level, on average, HIE adoption leads to 7.8% reduction in pathology and laboratory orders, and 5.6% reduction in surgery procedures. These reductions are relative to the predicted increase in outcomes which would have occurred had the HIE not been implemented. We also show that the benefits of HIE happens gradually over time and as users gain more experience with the HIE, they observe better results. While our findings are consistent with the prior literature and show that HIE adoption significantly reduces the utilization of medical services, we uncover that the magnitude of these savings are different from those in the US.

This study examines how different digital strategies influence agility in managing customer demand. We test the effects of digital strategies on three types of digitally-enabled demand management agility–adaptive, resilient, and entrepreneurial. Using a configurational perspective, we conceptualize digital strategies as the synergistic use of IT-driven and business-driven initiatives in selective or collective value chain domains. Configurations are used to outline three digital strategies: supply chain-oriented, marketing-oriented, and value chain-wide. Using data from a survey of 200 firms, we use configurational analysis to test the hypotheses. The results indicate that specialized–supply chain or marketing-oriented–digital strategies may be sufficient to create adaptive and resilient agility. However, a value chain-wide digital strategy is necessary to facilitate entrepreneurial agility. Results also indicate that a specialized digital strategy may suffice in less turbulent environments, but a value chain-wide digital strategy is required to manage demand management disruptions in highly turbulent environments.

Abstract

The demand for information technology outsourcing (ITO) providing large-scale professional services has developed into a mature and essential industry for many organizations. Against this backdrop, managing the internal workplace relationship and organizational commitment levels between the vendor firm providing critical ITO services and the vendor’s employees performing the work for the external client is a significant emerging challenge for all involved parties. Contracts in various formats and structures (transactional or psychological) are often applied as a universal tool to govern the complex ITO relationship. However, the formal or transactional contract may not be enough to ensure employee commitment to position the ITO relationship for success. This paper investigates the impact that the psychological contract has on organizational commitment across different matching patterns between the outsourcing vendor and the vendor's employees. Building upon empirical findings and theoretical perspectives of a systematic grounded theory-based literature review, business observations, and best practices, we develop a theoretical framework that categorizes the matching patterns of the vendor-employee psychological contract into four types: Vendor Dominant, Employee Dominant, Mutual Relational, and Mutual Transactional. Our findings from an analysis of 562 survey participants demonstrate that the employee's relational psychological contract has a higher positive influence on organizational commitment than the employee's transactional psychological contract in both matching and mismatching situations. The implications and future research avenues of study are discussed.

Children affected by attention-deficit hyperactivity disorder (ADHD) exhibit several symptoms characterized by inattention, impulsivity and motor hyperactivity that impair both school performance and everyday life. The BRAVO (Beyond the tReatment of the Attention deficit hyperactiVity disOrder) project dealt with the development of several serious games based on extended reality that help patients improve in self-control, respect for rules, attention and concentration. In order to achieve both logopaedic and behavioural educational goals, serious games were developed concerning three different categories: Topological Categories, Infinite Runner and Planning. Experimental tests conducted over a six-month period assessed the patients’ performance and the emotional impact of the games, also showing a general improvement in cognitive and behavioural functions.

Vast amounts of medical data are generated every day, and constitute a crucial asset to improve therapy outcomes, medical treatments and healthcare costs. Data lakes are a valuable solution for the management and analysis of such a variety and abundance of data, yet to date there is no data lake architecture specifically designed for the healthcare domain. Moreover, benchmarking the underlying infrastructure of data lakes is fundamental for optimizing resource allocation and performance, increasing the potential of this kind of data platforms. This work describes a data lake architecture to ingest, store, process, and analyze heterogeneous medical data. Also, we present a benchmark for infrastructures supporting healthcare data lakes, focusing on a variety of analysis tasks, from relational analysis to machine learning. The benchmark is tested on a virtualized implementation of our data lake architecture, and on two external cloud-based infrastructures. Our results highlight distinctions between infrastructures and tasks of different nature, according to the machine learning techniques, data sizes and formats involved.