The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed.

Purpose: The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approach: This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.Findings: The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/value: The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

Purpose: Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.Design/methodology/approach: The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach.Findings: A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.Originality/value: This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.

The visions of what constitutes Industry 4.0 is an industry based on gains in efficiency and productivity enhancements supported by integrated, smart information systems. This has caused information systems strategic misalignment that present a severe barrier to national and organizational aspirations. This paper studies the readiness of manufacturing companies for Industry 4.0 by using a case study of Chinese multinational enterprise in the aluminum production sector. The research design follows a rigorous grounded theory approach, which consisted of 41 semi-structured interviews in 7 different company branches. Based on this case study, the paper proposes an IS strategic misalignment model that identifies three levels of misalignment that need to be resolved before the vision of the smart industry can be realized. Six main categories of causes and five main categories of consequences of IS strategic misalignment are presented. This study contributes to the IS alignment literature and provides important implications for the achievement of Industry 4.0 in practice.

Recent years have seen a growing call for use of big data analytics techniques to support the realisation of symmetries and simulations in digital twins and smart factories, in which data quality plays an important role in determining the quality of big data analytics products. Although data quality affecting big data analytics has received attention in the smart factory research field, to date a systematic review of the topic of interest for understanding the present state of the art is not available, which could help reveal the trends and gaps in this area. This paper therefore presents a systematic literature review of research articles about data quality affecting big data analytics in smart factories that have been published up to 2020. We examined 31 empirical studies from our selection of papers to identify the research themes in this field. The analysis of these studies links data quality issues toward big data analytics with data quality dimensions and methods used to address these issues in the smart factory context. The findings of this systematic review also provide implications for practitioners in addressing data quality issues to better use big data analytics products to support digital symmetry in the context of smart factory.