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Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal’s literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation.

The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio.

With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that “Saritas” is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, “Blackman” and “Richardson” are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability.

Several noteworthy research implications are provided by the bibliometric study of Foresight J. “Saritas” is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that “Blackman” and “Richardson” are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects’ ongoing relevance can reveal information about the consistency and development of scholarly interests over time.

Foresight J’s bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal’s influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers.

The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal’s articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development.

This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J’s history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J.

This paper aims to examine the impact of the assurance and advisory role of internal audit (ADRIA) on organisational, human and technical proactive measures to enhance cybersecurity (CS).

The questionnaire was used to collect data for 97 internal auditors (IAu) from the Gulf Cooperation Council countries. The authors used partial least squares (PLS) to test the hypotheses.

The results show a positive effect of the ADRIA on each of the organisational proactive measures, human proactive measures and technical proactive measures to enhance CS. The study also found a positive effect of the confirmatory role of IA on both human proactive measures and technical proactive measures to enhance CS. No effect of the confirmatory role of IA on the organisational proactive measures is found.

This study focused on only three proactive measures to enhance CS, and this study was limited to the opinions of IAu. In addition, the study was limited to using regression analysis according to the PLS method.

The results of this study show that managers need to consider the influential role of IA as a value-adding activity in reducing CS risks and activating proactive measures. Also, IAu must expand its capabilities, skills and knowledge in CS auditing to provide a bold view of cyber threats. At the same time, the institutions responsible for preparing IA standards should develop standards and guidelines that help IAu to play assurance and advisory roles.

To the best of the authors’ knowledge, this is the first study of its kind that deals with the impact of the assurance and ADRIA on proactive measures to enhance CS. In addition, the study determines the nature of the advisory role and the assurance role of IA to strengthen CS.

The purpose of this study is to propose an improved framework for managing Private Participation in Infrastructure ICT (PPI-ICT) projects in the context of developing countries as the requirements to manage them are different in several aspects.

The framework has been proposed based on an exhaustive literature review and statistical analysis of the PPI-ICT projects’ data set using logistic regression, F-test and student’s t-test. The proposed framework was also applied to the PPI-ICT projects.

The framework is an extension to NTCP (novelty, technology, complexity and pace) approach by including extrinsic factors such as income of the country, climate risk, religious diversity, political stability, regularity quality and control of corruption. The proposed framework was used to analyze project characteristics and their external conditions in the context of developing countries. Based on the analyses, the authors have presented a detailed set of recommendations for project managers, practitioners and governments to improve the success rate of these projects.

The major contribution of this study is the framework, which encompasses the NTCP model as well as extrinsic characteristics of PPI-ICT projects. The proposed framework is meant to assist the project managers to comprehend the project characteristics and its external environment to identify an adequate approach for managing projects successfully.

Values have long guided special education services and supports for students with extensive support needs; over the past four decades, those values have been backed by research evidence demonstrating the critical nature of values related to inclusive education, self-determination, and seeking strengths and assets. In this chapter, we investigate these values and their supporting research, documenting strengths and needs in extant research. We emphasize the need to continue to embrace and maintain these values while pursuing research that addresses research gaps while centering the priorities, perspectives, and preferences of people with extensive support needs.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.

This research aims to examine the effects of corporate digital transformation on firm value, with a particular focus on the mediating roles played by cost leadership and differentiation strategies.

This study employs word frequency analysis to create corporate digital transformation indicators and determine how corporate digital transformation impacts firm value. The data used in the analysis comes from 2,056 listed manufacturing enterprises in China between 2010 and 2019.

This study demonstrates that digital transformation has a favorable impact on firm value, and that cost leadership strategy and differentiation strategy significantly mediate the relationship between both of them.

This study utilized word frequency analysis to assess the state of corporate digital transformation. It lacked a more thorough description of internal production processes, operational efficiency, and the pace of digital transformation.

The results of this study can not only promote the digital transformation and firm value, but also provide a theoretical basis for enterprises to choose a reasonable competitive strategy in the digital transformation.

This study contributes significantly to the field of firm value research by including digital transformation as a fundamental component. Furthermore, it investigates how cost leadership strategy and differentiation strategy play mediating roles, providing a new perspective and explanatory mechanism for understanding the influence of digital transformation on firm value.

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.