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This article presents the results of a quantitative analysis of Ukrainian arts and humanities (A&H) research from 2012 to 2021, as observed in Scopus. The overall publication activity and the relative share of A&H publications in relation to Ukraine's total research output, comparing them with other countries. The study analyzes the diversity and total number of sources, as well as the geographic distribution of authors and citing authors, to provide insights into the internationalization level of Ukrainian A&H research. Additionally, the topical spectrum and language usage are considered to complete the overall picture.

This study uses the Scopus database as the primary data source for analyzing the general bibliometric characteristics of Ukrainian A&H research. All document types, except Erratum, were considered. A language filter was applied to compare the bibliometric characteristics of English versus non-English publications. In addition to directly imported data from Scopus, the study employs the ready-to-use SciVal tools to operate with A&H subcategories and calculate additional bibliometric characteristics, such as Citations per Publication (CPP), Field-Weighted Citation Impact (FWCI) and journal quartiles. Information on the country of journal publishers and details on delisted journals from Scopus were obtained from the official Source Title List available on the Elsevier website and the SCImago Journal and Country Rank Portal.

According to the results obtained, the publication patterns for Ukrainian A&H research exhibit dynamics comparable to those of other countries, with a gradual increase in the total number of papers and sources. However, the citedness is lower than expected, and the share of publications in top-quartile sources is lower for 2020–2021 period compared to the previous years. The impact of internationally collaborative papers, especially those in English, is higher. Nevertheless, over half of all works remain uncited, probably due to the limited readership of the journals selected for publication.

This study provides original insights into the bibliometric characteristics of Ukrainian A&H publications between 2012 and 2021, as assessed using the Scopus database. The authors’ findings reveal that Ukraine's A&H publications have higher visibility than some Asian countries with similar population sizes. However, in comparison to other countries of similar size, Ukraine's research output is smaller. The authors also discovered that cultural and historical similarities with neighboring countries play a more significant role in publication activity than population size. This study highlights the low integration of Ukrainian A&H research into the global academic community, evident through a decline in papers published in influential journals and poor citedness. These findings underscore the importance for authors to prioritize disseminating research in influential journals, rather than solely focusing on indexing in particular databases.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The world of work is constantly changing. The COVID-19 pandemic has reinforced working from home, and there is an increasing demand for flexibility regarding the workplace. There is little empirical evidence on the mechanisms and factors that influence employee outcomes, such as productivity and turnover intention, at the workplace and at home. In addition, it is unclear whether the workplace characteristics that influence employee outcomes vary between different nations due to country-specific circumstances. The paper aims to address these two issues.

The research model applied in this study is based on the job demands-resources (JD-R) and environmental demands-resources models using German (n = 429) and USA (n = 507) survey samples. Partial least squares structural equation modelling is used to analyse the influence of workplace characteristics (isolation, family–work interference, equipment/facilities and skill variety) on employee outcomes (satisfaction, burnout, productivity and turnover intention). Additionally, a multi-group analysis is used to explore group differences in the factors influencing satisfaction, burnout, productivity and turnover intention between employees in Germany and the USA.

The results reveal that significant determinants of productivity and turnover intention include isolation, family–work interference, equipment/facilities and skill variety. Isolation and equipment/facilities are identified as the most important demands and resources of the home workplace. Some significant differences are found between Germany and the USA. The positive effect of isolation on burnout is significantly stronger in the USA than in Germany, whereas the positive effect of family–work inference on burnout is stronger in Germany than in the USA. The negative effects visible for the relations between burnout and satisfaction, and satisfaction and turnover intention are stronger in Germany than in the USA. The positive effect of burnout on turnover intention is stronger in the USA compared to Germany.

The study adds empirical evidence to the JD-R theory by analysing the influence of the home workplace characteristics on employee outcomes in different countries for the first time using a multi-group analysis. In addition, the study reveals new insights into the differences between the knowledge workforces in Germany and the USA by uncovering how key factors influence employee outcomes such as productivity and turnover intention, partially carried by varying length of experience in work from home between both of these countries. Insights from this study can support corporate real estate managers to make better decisions on the design of employees’ home workplaces and the integration of work from home into the company’s workspace concept.

This study aims to examine differences in the perceived value of luxury as drivers of luxury purchase intentions between individualist and collectivist cultures (at a country level) and consumers of Muslim versus Christian religious backgrounds. Moreover, this study investigates how consumers’ acculturation to the global consumer culture (AGCC) impacts their perceived luxury values.

The authors conducted two online survey studies. The first study compares Muslim consumers in Kuwait versus Muslims consumers in the UK. The second study compares the UK Muslim sample to a UK Christian sample. The authors collected data from 600 and 601 respondents, respectively. Partial least square structural equation modeling was used to test this study’s research hypotheses.

The perceived personal values of luxury primarily drive consumers’ luxury purchase intentions. The hedonic value of luxury impacts luxury purchase intentions significantly more for Muslims in the UK than in Kuwait. No significant differences were observed between religions. Consumers’ AGCC exerts a positive impact on all included perceived luxury values and more strongly impacts perceived uniqueness for Muslims than for Christians.

The paper builds on an integrative luxury values framework to examine the impact of luxury values on consumers’ purchasing intentions by studying the moderating effect of culture and religion on these relationships. The study is partly set in Kuwait, an understudied country, and investigates a Muslim minority in the UK.

This study aims to contribute to strategic crisis communication research by exploring international media representations of third sector crises and crisis response; expanding the range of crisis types beyond transgressions; and developing a framework that integrates framing and crisis communication theory.

Quantitative content analysis was applied to identify patterns in crisis reporting of 18 news media outlets in Canada, Germany, India, Switzerland, UK and US. Using an inductive framing approach, crisis coverage of nonprofit organizations (NPOs) and intergovernmental organizations (IGOs) between 2015 and 2018 was analyzed across a wide range of crises, including but not limited to prominent cases such as Oxfam, Kids Company, or the Islamic Research Foundation.

The news media in six countries report more internal crises in the third sector than external crises. The most frequent crisis types were fraud and corruption, sexual violence/personal exploitation and attacks on organizations. Exploratory factor analysis revealed three components of crisis response strategies quoted in the media, conditional rebuild, defensive and justified denial strategies. Causal attributions and conditional rebuild strategies significantly influenced media evaluations of organizational crisis response. Three frames of third sector crises were detected; the critique, the damage and the victim frame. These frames emphasize different crisis types, causes, crisis response strategies and evaluations of crisis response.

The study reveals the particularities of crises and crisis communication in the third sector and identifies factors that influence mediated portrayals of crises and crisis response strategies of nonprofit organizations (NPOs) from an international comparative perspective. The findings have relevant implications for crisis communication theory and practice.

This paper aims to analyse how financial literacy (FL) is conceptualised and operationalised in the Indian context.

A systematic literature review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Thirty-six articles published between 2010 and 2020 were considered for analysis. The FL conceptualisation was examined based on knowledge, ability, skill, attitude and confidence elements. The FL operationalisation was analysed using the modified version of the Organisation for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment (PISA) 2012 model for organising the domain for an assessment framework.

The findings indicate that, despite offering operationalisation details of the FL, 13 out of 36 studies did not include a conceptual definition of FL. Of the 23 studies that mentioned a conceptual definition, 87% are primarily focused on the “knowledge” element and only 39% have combined knowledge, ability/skill and attitude elements in defining FL. As in the developed countries, the Indian studies also preferred investment/saving-related contents in their FL measures. The volume of content focusing on the financial landscape is meagre amongst the FL measures used in India and developed countries. The survey instruments of most studies have been designed in the individuals’ context but have failed to measure the extent to which individuals apply the knowledge in performing their day-to-day financial transactions. Further, it was found that 20 out of 36 studies did not convert the FL level of their target groups into a single indicator or operational value.

To the best of our knowledge, this is the first study that explores the FL’s assessment practices in India. Further, this study offers new insights by comparing the contents of FL measures used in Indian studies with those used in developed countries.

This paper aims to analyse the Financial Intelligence Units (FIUs) of Canada, Australia, The Netherlands and India, focussing on key internal and external processes, such as the exchange of information, operations and compliance with Financial Action Task Force (FATF) recommendations. The paper relies on secondary sources to compare and assess the practices and strategies employed by FIUs within these jurisdictions.

The paper relies on secondary sources to compare and assess the practices and strategies used by FIUs within these jurisdictions.

The ability to combat money laundering and the financing of terrorism (AML/CFT) in countries is influenced by several internal and external factors, including the efficiency of their FIUs’ and compliance with FATF recommendations. The analysis of FIUs across the countries demonstrates a raft of multifaceted challenges and concerns. Yet, when it comes to compliance with FATF’s recommendations, shared concerns emerge, hinting at the complex interplay between country-specific operations and global compliance standards. The paper recommends enhancements to the FIUs’ operational efficiency and overall effectiveness in combating financial crimes.

The paper’s findings are limited to openly available data (such as annual reports and internet sources) for the respective countries. The paper relies on the transparency of FIUs through public media, focusing on comparing and analysing the FIUs of only four specific countries, which limits the generalisations of the findings.

This paper is significant for policymakers and FIU authorities, as they strive to improve the effectiveness of their units and assess their performance in alignment with international standards. The comparative analysis of the FIUs of India, Australia, Canada and The Netherlands provides valuable insights and recommendations that can inform policymakers and operational strategies towards enhancing how FIUs function globally.

This paper offers a unique comparative analysis of the FIUs of India, Australia, Canada and The Netherlands. Its findings have practical implications for policymakers and FIU authorities towards enhancing performance against international AML/CFT standards and promoting global cooperation.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

This paper discussed quantifying architect, engineer and contractor (AEC) professionals' cross-work culture productivity by comparing between Malaysian and United Kingdom (UK) projects during industrialized building project delivery. This study addressed the second part of a mixed method research design study.

This study hypothesized that with understanding of cultural work knowledge between professionals during design phase coupled with competent technological support, productivity can be improved. It utilized Cognitive Organizational Theory (COT) protocols to test conceptual models in SimVision®. Organizational structure, project intensity, and statistical validations parameters were performed to obtain the reliability and generalization of the result.

This study found that with Building Information Modeling (BIM) technology intervention, the handling of exception, coordination and decision-making time could be improved, resulting in better project performances. The result also indicated that in choosing organizational fit, national culture factor needed to be considered; otherwise, organizational change would be unacceptable. By changing the operational process from intensive to reciprocal task intensity with BIM technology intervention, the effect on productivity would be similar to changing hierarchical organizational structure to flatter organizational structure.

Project discrepancies issues are limitedly discussed due to companies' confidentiality. The paper only focuses on understanding the effects of human factors during the integrated project delivery phase.

The findings could support developing countries' professionals to collaborate effectively with developed countries' professionals.

The development of the project's cultural knowledge experimentations will provide guidance to teams involved in international projects from developed and developing countries in pursuing joint ventures in project deliveries in either country successfully.

This study aims to fill the gap on proenvironmental behavior studies among consumers in bottom of the pyramid (BOP) market by examining the relationship between values, beliefs and norms among Ghanaian consumers.

A sample of 324 Ghanaian consumers (responses rate of 64%) was used to estimate the model. The data was analyzed using maximum likelihood robust approach of Lavaan package for structural equation modeling in R.

The findings show that there is no significant relationship between values and the new ecological worldview, which also does not affect awareness of environmental consequences. However, awareness of consequences, likely based on personal experiences, significantly impacts perceived ability to reduce threats to the environment. Ghanaian consumers are mainly influenced by personal experiences with local environmental consequences that affect their perceived ability to reduce threats to their environment and their willingness to engage in proenvironmental behavior. The study supports the argument that proenvironmental behavior is the result of complex decision-making that might be influenced by a country’s economic situation, infrastructure, culture and institutions.

This study focuses on a single country in Africa. More studies are needed among other base of the pyramid countries, and in comparison to developed countries’ consumers.

The study adds to the limited knowledge regarding sustainable consumption in the neglected context of the bottom of the pyramid market.