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The purpose of this paper is to survey existing information security policy (ISP) management research to scrutinise the extent to which manual and computerised support has been suggested, and the way in which the suggested support has been brought about.

The results are based on a literature review of ISP management research published between 1990 and 2017.

Existing research has focused mostly on manual support for managing ISPs. Very few papers have considered computerised support. The entire complexity of the ISP management process has received little attention. Existing research has not focused much on the interaction between the different ISP management phases. Few research methods have been used extensively and intervention-oriented research is rare.

Future research should to a larger extent address the interaction between the ISP management phases, apply more intervention research to develop computerised support for ISP management, investigate to what extent computerised support can enhance integration of ISP management phases and reduce the complexity of such a management process.

The limited focus on computerised support for ISP management affects the kind of advice and artefacts the research community can offer to practitioners.

Today, there are no literature reviews on to what extent computerised support the ISP management process. Findings on how the complexity of ISP management has been addressed and the research methods used extend beyond the existing knowledge base, allowing for a critical discussion of existing research and future research needs.

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

Business intelligence (BI) is a combination of computer systems and managerial processes to support decision-making. The balanced scorecard is a kind of business intelligence tool for performance measurement and management control aimed at balancing financial and non-financial as well as short- and long-term measures. The sustainable balanced scorecard is a modification of the original balanced scorecard developed to expressly consider governance, social, environmental and ethical issues, and therefore to allow sustainability concepts to be included within the strategy and the management of the organization. However, although the sustainable balanced scorecard is one of the most suitable tools for integrating sustainability within management, there are few examples of how to develop and implement it which can be used as reference models. To help solve this problem, this paper proposes a methodology for the development of a sustainable balanced scorecard, considering different phases such as planification, analysis, design or computer tool implementation, and describes the findings of three case studies.

The research was conducted using the qualitative multiple-case study method. This made it possible to establish the methodological issues regarding the performance and reporting of this study. Therefore, the research method for the conceptualization and execution of the case studies was divided into seven phases: definition of research goals and questions; proposed theoretical model; identification of units of analysis; case selection; definition of research methods and resources; fieldwork; data collection, classification of information and triangulation; formulation of the enhanced theory, model or methodology; and verification of the rigour and quality of the study.

Paper shows a methodology organized in phases, activities and tasks that allow a sustainable balanced scorecard to be planned, designed, built, computerized and controlled in order to integrate sustainability within the management systems of organizations.

This study contributes to the currently emerging sustainable balanced scorecard literature and practice and, more generally, to research on sustainability measurement and management. The methodology for sustainable balanced scorecard development and implementation showed in this paper contributes to the management and information systems theory because it makes it possible to overcome the shortcomings identified to date: it considers all the sustainability dimensions; it describes all the project life-cycle activities; it encourages stakeholders' participation; and it has been proved to work in real situations.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

To promote access to their services, health-care companies provide various communication channels to their customers (beneficiaries) to enable the receipt of requests, such as authorization for examinations, procedures and hospitalizations. Under the approach of innovation studies, the management of customer relationship channels for health-care companies is characterized as a knowledge-intensive business service (KIBS). The purpose of this study is presenting innovation as a strategy to increase customer service productivity, as well as the monitoring of the quality of the service, the generation of health information for beneficiaries and compliance with the regulation set by the Brazilian National Health Agency (ANS).

The study is characterized as an applied research, as it proposes solutions to problems faced by supplemental health-care companies using the strategy of action research, i.e. an independent, social research with an empirical basis.

The result of this study shows that a computerized health-care system can increase productivity by 21.96%, and it presents an innovative solution for health-care companies to guarantee the process of meeting the demands and requests of their beneficiaries, ensuring the compliance with ANS regulations.

These results can be replicated to other healthcare companies and contribute to those seeking innovation, increased productivity and quality improvements in their services.

This work was also motivated by the lack of lstudies in the areas of health-care companies in Brazil.

The business intelligence (BI) research witnessed a proliferation of contributions during the past three decades, yet the knowledge about the interdependencies between the BI process and organizational context is scant. This has resulted in a proliferation of fragmented literature duplicating identical endeavors. Although such pluralism expands the understanding of the idiosyncrasies of BI conceptualizations, attributes and characteristics, it cannot cumulate existing contributions to better advance the BI body of knowledge. In response, this study aims to provide an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones.

This paper reviews 120 articles spanning the course of 35 years of research on BI process, antecedents and outcomes published in top tier ABS ranked journals.

Building on a process framework, this review identifies major patterns and contradictions across eight dimensions, namely, environmental antecedents; organizational antecedents; managerial and individual antecedents; BI process; strategic outcomes; firm performance outcomes; decision-making; and organizational intelligence. Finally, the review pinpoints to gaps in linkages across the BI process, its antecedents and outcomes for future researchers to build upon.

This review carries some implications for practitioners and particularly the role they ought to play should they seek actionable intelligence as an outcome of the BI process. Across the studies this review examined, managerial reluctance to open their intelligence practices to close examination was omnipresent. Although their apathy is understandable, due to their frustration regarding the lack of measurability of intelligence constructs, managers manifestly share a significant amount of responsibility in turning out explorative and descriptive studies partly due to their defensive managerial participation. Interestingly, managers would rather keep an ineffective BI unit confidential than open it for assessment in fear of competition or bad publicity. Therefore, this review highlights the value open participation of managers in longitudinal studies could bring to the BI research and by extent the new open intelligence culture across their organizations where knowledge is overt, intelligence is participative, not selective and where double loop learning alongside scholars is continuous. Their commitment to open participation and longitudinal studies will help generate new research that better integrates the BI process within its context and fosters new measures for intelligence performance.

This study provides an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones. By so doing, the developed framework sets the ground for scholars to further develop insights within each dimension and across their interrelationships.