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In November 2022, the commercial company, OpenAI, launched ChatGPT. Since then, university students have rapidly become regular users of this artificial intelligence (AI) platform. One reason for this is the powerful capability of this generative AI tool to produce textual content, which in many cases, is almost indistinguishable from human-generated content. Another reason is that ChatGPT easily gives anyone access to knowledge. However, there is a problem as the vast majority of its users have no idea how this AI platform works and thus overlook the importance of thinking critically about information communicated in ChatGPT. While some call for banning this generative AI tool, this study aims to provide evidence that science classrooms can become scenarios where students find explicit, concrete, and realistic opportunities to critically evaluate scientific information generated by ChatGPT.

An intervention study was conducted with 55 students (26 females and 29 males, 17–24 years old) during a university Spanish-English bilingual science course taught within an active learning environment. The data consist of the written critiques of the students about Spanish-English bilingual scientific texts produced by ChatGPT.

Results indicate that the intervention had a positive effect on students’ abilities to construct sound arguments in Spanish and in English while judging the quality of scientific texts produced by this AI bot. Moreover, the findings suggest that the intervention enriched students’ skills to make improvements to texts produced by this generative AI tool.

The evidence provided in this study contributes to the exploration of possibilities to help students become critical users of ChatGPT.

We know how to structure violence. The military industrial complex and its auxiliary institutions, such as the private weapons and securities industries, are prime examples. When we use the Galtungian lens and add ecoviolence to Johan Galtung’s classic direct–structural–cultural violence triangle, we get a violence diamond, or perhaps more accurately, a web of violence (see Turpin & Kurtz, 1996). To what we often think of as violence (which is direct), we must add the institutions structured to harm individuals, groups, cultures, and the ecosphere itself. We could, however, build sustainable development with direct, structural, cultural, and ecological nonviolence. We already have institutions that structure nonviolence – or often both violence and nonviolence in contradictory simultaneous ways: families, faith and cultural institutions, and friendship networks. We also have numerous peaceful societies, economies, and cultures that provide models such as Bhutan’s Happiness and Well-being paradigm. Jennifer Turpin and I wrote about the Web of Violence in 1996, which we collectively weave from micro to macro levels of life, but we also need to attend to and weave a Web of Nonviolence. This chapter investigates how we can be more intentional and systematic in weaving a nonviolent web, following a three-step Gandhian process: research, analyse, and mobilise.

As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms’ agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance.

This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling.

The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm’s agility. This paper also found out that industry dynamism moderates the firms’ agility and, ultimately, firms’ performance.

The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism.

The present research is to the best of the authors’ knowledge among the first studies considering a firm’s agility to explore the impact of BA on a firm’s performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility.

This study focuses on case studies to identify the antecedents to the effective use of business intelligence (BI). For decades, research has shown that post-adoption behaviour has a critical role in the success of technology adoption. Efforts have been drawn beyond intention and adoption point to the effective use of technology. In the case of BI adoption, its nature of discretional use adds complexity to the equation. Despite the statistical evidence provided, research has indicated the need for other approaches to shed light on the antecedents of BI’s effective use.

This study employs a systematic review approach to synthesise 20 years of related works from three major research databases. It focuses on qualitative and mixed-method research to identify new avenues in the related topic.

The findings provide the taxonomy of the antecedents. It identifies 40 antecedents grouped into 8 categories: technology, operational support, resources, governance, strategic, individual behaviour, environment and culture. It also highlights several organisation-specific and individual-specific antecedents, such as analytic leadership, business vision, collaborative working culture, organisational learning culture and user trust.

The study provides insight from interpretative case studies. It leverages the relevance of BI’s research to its practices. It offers new avenues in the BI post-adoption studies at the organisational and the individual level of analysis.

This paper focuses on managerial practices in the context of supply chain. It focuses on the innovation of monitoring and control practices and proposes a holistic approach to managing social sustainability in the supply chain, extending the point of view beyond the traditional boundaries of individual factories or their immediate suppliers.

The analysis is based on a systematic review of scientific literature on managerial practices in supply chains, with a specific focus on social sustainability. The primary goal is to identify essential measurement strategies and key indicator factors within this domain.

Our findings highlight that most of scientific literature focuses on qualitative approaches, though quantitative approaches are also used. Despite the extensive research, an under-investigated area is the use of hybrid models for measuring social sustainability in the supply chain.

This framework is designed to identify the main categories of measurement and relative indicators for assessing social sustainability in supply chains.

This research proposes an innovative and integrated framework, leveraging a hybrid approach that addresses the limitations observed in existing management practices. Additionally, it provides directions for future research.

Industrialized construction has brought about expectations of improved productivity in the construction industry. However, the lack of a commonly accepted definition has created confusion regarding the types of development covered by the industrialized construction umbrella. These inconsistent definitions convoluted the discussion on this phenomenon. This study aims to clarify the definition of industrialized construction through a systematic literature review.

This systematic literature review was conducted according to PRISMA principles. Records were gathered from Scopus and Web of Science. Following the scientometric analysis, content analysis was conducted according to the template analysis approach.

The analysis of 121 articles revealed four main themes related to industrialized construction: 1) the construction concept, 2) construction methodologies, 3) systematization, rationalization and automatization and 4) societal and industrial change processes. Definitions of industrialized construction can be analyzed with seven clusters: 1) prefabrication, 2) standardization, 3) sector, 4) integration, 5) manufacturing practices, 6) technological investment and 7) none. Based on the content analysis, the proposed definition is: industrialized construction is the adoption of practices that minimize project-specific work in construction from the start of the design to the end of the building’s life cycle.

This study proposes a definition for industrialized construction following content analysis of broadly sampled literature. The proposed definition can provide a basis on which developments in the construction industry can be reflected.

Over the last 2 decades, supply chain sustainability research has become a highly dynamic and fruitful study area. This field has garnered significant attention due to its potential to reshape decision-making processes within supply chains. At the same time, the practical side of supply chain operations remains intensely competitive in today’s business landscape. Furthermore, the current academic research aims to outline effective strategies for achieving sustainability across supply chains, particularly in the manufacturing sector. In response to these challenges, this research has conducted an integrated multi-criteria decision-making approach to evaluate sustainable supply chain performance from the triple bottom line perspective, including financial, environmental, and social performance.

The initial stage involves selecting the crucial criteria (short-term and long-term) and alternatives for sustainable supply chain performance (SSCP) from experts and conducting an in-depth literature review. Initially, there were 17 criteria, but after a pilot test with co-authors and online discussions with experts, the number of criteria was subsequently reduced to 9. In the second phase, the Best-Worst Method (BWM) was applied to rank and prioritize the criteria. The third and final stage examined the causal relationship between the identified criteria, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique.

Based on BWM analysis results, the top three criteria in terms of prominence are: (1) return on investment (ROI), (2) product quality, and (3) manufacturing lead time. Out of the three alternatives, financial performance (FP) is the most crucial dimension for SSCP, followed by environmental performance (ENP) and social performance (SP). On the other hand, the DEMATEL approach showed that work health and safety (short-term criterion), asset utilization (long-term criterion), energy consumption (long-term criterion), waste disposal (long-term criterion), manufacturing lead time (short-term criterion), and on-time delivery (short-term criterion) are categorized within the cause group, while criteria such as return on investment (ROI) (long-term criterion), customer-service level (short-term criterion), and product quality (long-term criterion) fall into the effect group.

The proposed study has certain drawbacks that pave the way for future research directions. First, it is worth noting the need for a larger sample size to ensure the reliability of results, the potential inclusion of additional criteria to enhance the assessment of sustainability performance, and the consideration of a qualitative approach to gain deeper insights into the outcomes. In addition, fuzziness in qualitative subjective perception could be imperative when collecting data to ensure its reliability, as translating experts’ perceptions into exact numerical values can be challenging because human perceptions often carry elements of uncertainty or vagueness. Therefore, fuzzy integrated MCDM frameworks are better suited for future research to handle the uncertainties involved in human perceptions, making it a more appropriate approach for decision-making in scenarios where traditional MCDM methods may prove insufficient.

The proposed framework will enable decision-makers to gain deeper insights into how various decision criteria impact SSCP, thus providing a comprehensive evaluation of SSCP that considers multiple dimensions, such as financial, environmental, and social performance within the manufacturing sector.

The proposed study is the first empirical study to integrate both BWM and DEMATEL approaches to evaluate sustainable supply chain performance in the manufacturing context.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.

This chapter explains how and why the knowledge economy will increase the demand for knowledge engineering. It defines and traces the evolution of knowledge engineering. It identifies the two components of knowledge engineering – elicitation and representation. It discusses the increased importance of tacit knowledge, specifically know-what and know-how, for organizations and companies. The increased demand for knowledge engineering calls for increased number of knowledge engineers. Knowledge engineering will expand beyond its current homes in systems development and cognitive science. The MASK methodology is an important intermediary between formal knowledge engineering and the methods needed to develop natural language and conceptual modeling for the knowledge economy.

The purpose of this study is to investigate the role of foreign aid in the Western Balkans countries’ economic growth between 2009 and 2021.

This paper uses a panel data approach to examine the effects of foreign aid on economic growth in the region and incorporates a random-effects model to accommodate the unique cross-country variations and time-specific factors, as well as a pooled OLS and fixed-effects model for a comprehensive, comparative analysis.

The in-depth regression analysis shows that foreign aid has not had a significant impact on the economic growth of the region. Further evidence suggests that trade openness exhibited a significant positive correlation with economic growth, while gross capital formation, although positively associated, did not significantly impact it, indicating the complexity of its role in the region’s economies.

The analysis presented in this study has significant practical implications, particularly for policymakers in the Western Balkans. Given the region’s ambitions for European Union membership and the challenges of high unemployment and inflation, understanding the role of foreign aid is crucial.

This research provides a unique contribution to the field of development economics by examining foreign aid effectiveness within the context of a region often overlooked in the literature. The analysis also offers fresh insights into the complex dynamics of foreign aid and its implications for policy and development strategies.