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To cope successfully with the pressures imposed by a devastating pandemic and other challenges, companies and policymakers need to look at how they conceptualize, define, measure and operationalize “value”. This paper aims to support this conversation.

This study presents a historical review of how the value construct has been conceptualized over time, demonstrating that its history is one of tension and debate with conceptualizations swinging between objective (i.e. the value of something exists independent of the observers) and subjective (i.e. the value of something depends on the personal response of the observer to what is being considered) views over time.

This paper outlines the implications to researchers of value’s low construct clarity, offering suggestions designed to exploit rather than ignore the duality of the value construct. Instead of thinking of the value construct as being subjective or objective, this study recommends that scholars consider value’s objectivity and subjectivity as being interrelated and complementary. The paper recommends that researchers use both quantitative and qualitative methodologies in studying this construct.

A major limitation of this paper is the word count limitation restricting the extent to which this paper could explore a more comprehensive list of the conceptualizations of value throughout history.

This paper presents practitioners with a nuanced understanding of value that should assist those interested in examining the worth of investments with observable expenses but less quantifiable outputs.

The authors have not found a similar analysis of the various conceptualizations of value.

The purpose of this study is to analyze the relationship between academic writing and generative artificial intelligence (AI).

This paper is characterized as exploratory and descriptive, with a qualitative approach. Two approaches were used: the first, a narrative review of the literature with a systematic search from which a data collection stage was carried out using asynchronous interviews by means of an online questionnaire.

The results indicate that generative AI should be seen as a complementary tool for creative and critical academic writing. The data collected also highlighted issues related to academic dishonesty and the new type of plagiarism – plagiarism made possible by technologies – as well as issues of authorship and legitimacy of work carried out with AI and the loss of reflective and critical thinking and creativity.

The considerable increase in resources using AI in all dimensions of human life.

The impact that the use of generative AIs can have on the creation of knowledge and the dissemination of scientific research.

The impact that the use of generative AIs can have on the creation of knowledge and on the dissemination of scientific research.

The need for academia to anticipate the use of AI in academic writing and to incorporate its benefits into this process, especially considering researchers in training.

El objetivo de este artículo es analizar la relación entre la escritura académica y la inteligencia artificial generativa.

Este artículo se caracteriza por ser exploratorio y descriptivo, con un enfoque cualitativo. Se utilizaron dos enfoques: el primero, una revisión narrativa de la literatura con una búsqueda sistemática, a partir de la cual se llevó a cabo una etapa de recogida de datos mediante entrevistas asincrónicas a través de un cuestionario online.

Los resultados indican que la IA generativa debe considerarse una herramienta complementaria para la escritura académica creativa y crítica. Los datos recogidos también pusieron de manifiesto cuestiones relacionadas con la deshonestidad académica y el nuevo tipo de plagio, el plagio posibilitado por las tecnologías, así como cuestiones de autoría y legitimidad del trabajo realizado con Inteligencia Artificial, la pérdida de pensamiento reflexivo y crítico y la creatividad.

La necesidad de que el mundo académico se anticipe al uso de la IA en la escritura académica e incorpore sus ventajas a este proceso, considerando principalmente a los investigadores en formación.

El considerable aumento de los recursos que utilizan la IA en todas las dimensiones de la vida humana.

El impacto que puede tener el uso de las IA generativas en la creación de conocimiento y la difusión de la investigación científica.

El impacto que puede tener el uso de las IA generativas en la creación de conocimiento y la difusión de la investigación científica.

O objetivo deste artigo é analisar a relação entre a redação acadêmica e a inteligência artificial generativa.

Este artigo é caracterizado como exploratório e descritivo, com uma abordagem qualitativa. Foram usadas duas abordagens: a primeira, uma revisão narrativa da literatura com uma busca sistemática, a partir da qual foi realizada uma etapa de coleta de dados usando entrevistas assíncronas por meio de um questionário on-line.

Os resultados indicam que a IA generativa deve ser vista como uma ferramenta complementar para a redação acadêmica criativa e crítica. Os dados coletados também destacaram questões relacionadas à desonestidade acadêmica e ao novo tipo de plágio - o plágio possibilitado pelas tecnologias, bem como questões de autoria e legitimidade do trabalho realizado com a Inteligência Artificial, a perda do pensamento reflexivo e crítico e da criatividade.

A necessidade de a academia antecipar o uso da IA na redação acadêmica e incorporar seus benefícios nesse processo, considerando principalmente pesquisadores em formação.

O aumento considerável de recursos usando IA em todas as dimensões da vida humana.

O impacto que o uso de IAs generativas pode ter sobre a criação de conhecimento e a disseminação de pesquisas científicas.

O impacto que o uso de IAs geradoras pode ter na criação de conhecimento e na disseminação de pesquisas científicas.

This study aims to contribute to the enrichment of the literature by examining the impact of cross-cultural training on expatriates’ adjustment of Nigerian bankers on expatriate assignments in other African countries.

Data on cross-cultural adjustment from expatriate employees in five banks that collectively accounted for over 80 per cent of Nigerian banks with subsidiaries in other African were systematically selected for the investigation. This data was collected quantitatively via a survey instrument. Independent sample t-test, analysis of variance (ANOVA) and regression analysis were deployed in analyzing the data.

The study found that cross-cultural adjustment varied significantly across the different categories of gender, age, marital status, previous expatriate training, previous expatriate experience, and duration of expatriation. The study concludes that in order to attain higher levels of adjustment African banks and other organizations should provide a more comprehensive cross-cultural training program that mirrors the needs of employees following a detailed needs analysis. Also, the training must be sequential and not a one-off approach.

The literature though still nascent is largely focused on expatriate preparation and adjustment for expatriates moving from Western-to-western contexts and very little exists in the literature on how multinationals from Non-Western contexts like Africa prepare their staff for expatriation and its consequent impact on their adjustment. This study aims to enrich the literature by examining the impact of cross-cultural training on expatriates’ adjustment of Nigerian bankers on expatriate assignments in other African countries.

The objective of this research is to develop an solution to water management at the scale of buildings, through the technological resources. Automating analysis using 3D models helps increase efficiency in buildings during the operational phase, consequently promotes sustainability.

This study presents a methodology based on Design Science Research to automate water management at building scale integrating BIM-IoT-FM. Data from smart meters (IoT) and the BIM model were integrated to be applied in facilities management (FM) to improve performance of the building. The methodology was implemented in a prototype for the web, called AquaBIM, which captures, manages and analyzes the information.

The application of AquaBIM allowed the theoretical evaluation and practical validation of water management methodology. By BIM–IoT integration, the consumption parameters and ranges for 17 categories of activities were determined to contribute to fulfill the research gap for the commercial buildings. This criterion and other requirements are requirements met in order to obtain the AQUA-HQE environmental sustainability certification.

Traditionally, water management in buildings is based on scarce data. The practical application of digital technologies improves decision-making. Moreover, the creation of consumption indicators for commercial buildings contributes to the discussion in the field of knowledge.

This article emphasizes the investigation of the efficiency of use in commercial buildings using operational data and the use of sustainable consumption indicators to manage water consumption.

The impact of dependence on self-care on people’s lives is very significant, with consequences for the person and their caregivers. The purpose of this study is to map the evidence on the factors that influence the empowerment of the person dependent on self-care on returning home.

Scoping review according to the criteria proposed by the Joanna Briggs Institute: population (people with a dependence on self-care), concept (factors that influence training) and context (return home after hospitalization in a medical-surgical context). The research was carried out from March 1 to April 30, 2022, in the databases CINAHL and MEDLINE (via EBSCO), Scielo, LILACS, Cuiden and MedicLatina; Gray literature searched RCAAP, DART-Europe and OpenGrey. Studies published in Portuguese, Spanish and English were included, with no time limit.

One hundred and eighty-one articles were obtained, which, after analysis according to the criteria, resulted in seven studies included for review, ranging from 2007 to 2021, with a level of evidence between 2. c and 4. a (according to Joanna Briggs Institute), and two thematic areas/four categories emerging.

The need for information and training, the relationship and proximity with the health-care team, the design of nursing care targeted at the person’s level of dependence, education, gender, type of surgical intervention and postoperative period, physical space and lack of privacy and audiovisual media.

The perception of these factors proves to be important in the person’s training process, with the nurse’s role being highlighted due to their emphasis on the transition home.

This paper aims to contribute to the current debate between the mainstream and the non-mainstream literature on the effect of the growth of finance on the level of income inequality, for which the empirical evidence has also been providing mixed results.

We estimate a linear model and a non-linear model by employing a panel autoregressive distributed lag approach and relying on the dynamic fixed-effects estimator because of the existence of variables that are stationary in levels and stationary in the first differences.

Our findings confirm that finance, economic growth, educational attainment and degree of trade openness have a positive long-term effect on the level of income inequality in the European Union countries, whilst government spending has a negative impact in the short term.

Our findings imply that policy makers should rethink the functioning of the financial system in order to restore a supportive relationship between finance and income inequality and adopt public policies that are more in favour of the poor in order to constrain the growth of income inequality in the European Union countries.

To the best of our knowledge, this is the first paper that, simultaneously, focuses on the European Union countries, assesses the nexus between finance and income inequality, uses three different variables as proxies for the level of income inequality (the Gini coefficient, the top 1% income share and the top 10% income share), measures the variables that are proxies for the level of income inequality in terms of pre-tax and pre-transfer values and as post-tax and post-transfer values, takes into account four different variables as proxies for the role of finance (credit, credit-to-deposit ratio, liquid liabilities and stock market capitalisation) and identifies the long-term and short-term determinants of income inequality.

This study proposes an assessment framework for improving smart building performance in the broader context of smart city development, considering dimensions like environmental sustainability, building characteristics, intelligence, computation management and analytics. The framework is crafted to guide future research, aligning with the growing emphasis on sustainability and intelligence in evolving urban landscapes within smart cities.

In the initial phase, the concepts of “Smart City” and “Smart Buildings” are analyzed through a systematic literature review, considering the impact of governance on city sustainability and growth, along with the role of public policies in transforming buildings and cities. The empirical research evaluates innovation levels in small and medium-sized European cities, proposing a new framework with validated dimensions and sub-dimensions. This validation involves input from international experts through a Focus Group.

The key research findings validate the new proposed assessment framework for smart buildings within smart city development. The experts’ insights align with and support the dimensions identified in the bibliographic research, providing a comprehensive understanding of the role of smart buildings in sustainable urban development.

This framework not only provides insights for a new model with specific dimensions and sub-dimensions but also serves as a guide for formulating strategies and policies to enhance innovation in these settings. The value of this approach is strengthened by the validation and consolidation process involving international experts in the field.

This paper aims to explain and demonstrate how the Repertory Grid Technique (RGT) and Honey’s Content Analysis (HCA) can make new contributions to the field of Operations and Supply Chain Management (OSCM). The proposition involves integrating these complementary analyses to fortify the rigor of qualitative research and establish robust data analysis protocols to identify the main attributes of interviewees regarding a phenomenon while understanding in their perspective how these attributes impact the desired analysis outcome.

This study uses examples with rich empirical data from 40 interviewees across two organizations. The examples use a protocol that allows the grouping of meanings from different knowledgeable individuals and capturing relevant constructs related to an outcome.

The combination of RGT and HCA permits researchers to effectively identify and analyze the constructs individuals and groups utilize to comprehend the subject matter under investigation. Consequently, these techniques present a structured means to conduct grounded theory investigations and interpretive research, thereby enabling the iterative development of the preliminary conceptual models necessary for OSCM field advancement.

We present two examples in which the protocol is applied to the field of OSCM. These examples illustrate that the techniques provide valuable opportunities for OSCM research, particularly for addressing the limitations related to sample size. Ultimately, RGT and HCA complement quantitative methodologies by uncovering nuanced variations and micro-foundations within firm- and network-level phenomena, offering insights essential for advancing our understanding of OSCM dynamics in specific contexts.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

This paper aims to investigate how emerging market multinational enterprises (EMNEs) are integrating and expanding into other emerging market host countries, focusing on Chinese mining companies in Peru.

Adopting a qualitative approach, an in-depth analysis of two Chinese state-owned enterprises’ strategies was conducted, building on stakeholder theory and the business ecosystem perspective.

This study reveals a reliance on high-level political lobbying rather than localized engagement strategies. However, findings point to increasing grassroots resistance among local stakeholders, undermining EMNEs’ bargaining power.

This paper argues for a paradigm shift toward inclusive, cooperative “translocal governance” approaches as empowered communities gain voice. Key contributions include advancing theoretical understanding of changing stakeholder relationships and power configurations in emerging countries, underscoring the rising significance of microlevel sociocultural embeddedness for MNE success and highlighting practical imperatives for EMNEs to embark on rapid localization strategies in Latin America. By elucidating multilayered integration realities in Peru, this interdisciplinary study yields contextualized insights and enriches perspective on the conditions and pathways for EMNEs to build sustainability in Global South emerging market environments.