The effect of individuals’ analytical orientation and capabilities on decision quality and regret

Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.

A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions

We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.

Against generalisation: Data-driven decisions need context to be human-compatible

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

The transformative impact of big data applications in sport marketing: current and future directions

PurposeAs big data (BD) has increasingly become an important tool for managers and researchers to transform sport management practices, the purpose of this research is to highlight diverse data sources and modern analytical techniques that will leverage BD as a means to advance scholarship in sport management.Design/methodology/approachA comprehensive review of existing BD literature in sport management outlines new perspectives on BD research method and the application of BD in sport management.Findings First, through a thorough review of the literature, a domain-specific conceptualization that incorporates the field's mission and priorities was developed. Second, potential data sources and different types of analytical opportunities was identified, highlighting strategies for developing methodological approaches that leads to novel research questions. BD analytics can allow for more flexibility in improving methodological capability to analyze data and, thus, provide more granular and predictive insights. Finally, this paper concludes with a discussion of BD's impact on three domains of sport management, whereby the organizations yield data-driven decisions.Originality/valueBD has the potential to transform the sport management operations and bridges the research-practice gap. BD research in sport management is instrumental for accumulating new knowledge and/or testing existing theories, either in a deductive fashion or by taking an inductive approach, as the field embarks to advance scholarship.

Urban IoT ontologies for sharing and electric mobility

Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains.

Learning Analytics for Primary and Secondary Schools

Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.

Learning Analytics Tool for Bulgarian School Education

Nowadays, schools use many information systems to automate their activities for different stakeholders’ groups – learning management systems, student diary, library systems, digital repositories, financial management and accounting systems, document processing systems, etc. The huge amount of data generated by the users of these systems, led to increased interest in the collection and analysis of data to encourage students to achieve higher results, teachers to provide personalized support and school managers to make data-driven decisions at all levels of school, and stimulates research into the application of Learning Analytics (LA) in schools. The paper presents a LA model and a software prototype of the LA tool designed for the needs of Bulgarian school education from the perspective of different stakeholder groups (students, teachers, class teachers, parents, school managers, inspectors from evaluation agencies), aiming to improve school methods of approaching and analyzing learning data. The tool allows stakeholders to track data for students’ learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. Research and experiments with the model and the LA tool under consideration are conducted based on the information infrastructure of a typical Bulgarian school.

Quantifying Bridge Element Vulnerability over Time

The bridge has been a crucial element of the transportation system of the U.S.A. for many years. The National Bridge Inventory (NBI) reported more than 615,000 national bridges in 2018. Maintaining and fixing bridges is a crucial task for transportation agencies to keep the road network connected. Louisiana, which has 12,899 bridges, was selected as the study site for this study. The American Road and Transportation Builders Association (ARTBA) reported in 2019 that 13% of all Louisiana bridges were classified as structurally deficient. This study applies a data mining algorithm, the empirical Bayes geometric mean (EBGM) method, to identify critical patterns of the bridge inventory condition at element level as a measure of vulnerability, using NBI rating data from 2015 to 2018. It finds that severe condition is highly associated with the following elements, regardless of their structural importance: bridge joints, and “bridge rail timber,”“bearing other,” and “superstructure floor beam reinforced concrete” elements. Poor condition is highly associated with elements like “top flange reinforced concrete,”“bearing movable,” and “superstructure floor beam reinforced concrete.” The quantification scores developed in this study could help transportation agencies and bridge engineers to identify more easily the key element or combination of elements associated with poor or severe condition, so that they can make data-driven decisions in maintaining and repairing the most needed bridge elements.

COVID-19 and its threat to refugees in Africa

Although it is widely accepted that coronavirus disease 2019 (COVID-19) has adversely affected the Global South’s most vulnerable refugee communities, they have received little attention. There have been gaps in testing, which is fundamental to treat and isolate patients and make data-driven decisions to protect the refugee community. Therefore, it is imperative to holistically implement policies to curtail COVID-19 in refugee camps to ensure that refugees are safe and protected from the pandemic. Processes for timely diagnosis and treatment, quick isolation and contact tracing are essential to keep refugees safe. Furthermore, it is crucial to encourage protective behaviours and raise awareness about hygiene and social prevention to dampen disease transmission. Refugees in the Global South have been disproportionately affected by the consequences of the COVID-19 pandemic, facing financial hardship and social injustice throughout. Refugees in Africa have also faced threats to their security, being subjected to torture, disappearance, or even killings in their host countries. The pandemic has exposed gender inequalities, with females being the most affected, and health inequities in the refugee community in Africa. There is a need for international organizations like the African Union, United Nations (UN) agencies, non-governmental organizations (NGOs), and other stakeholders to take serious action regarding the refugee situation in Africa. Food aid for refugees in Africa should be increased as quickly as possible and refugees’ security must be guaranteed. Of equal importance, there must be justice for the death or disappearance of refugees. It is imperative to end discrimination against refugees and support the promotion of gender equity.