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The purpose of this study is to understand the public value of the government of Kuwait using blockchain technology to develop the capabilities of smart cities.

Research was conducted in Kuwait, where the increased use of blockchain technology has been evidenced in both the private and public sectors. A total of seven IT managers were interviewed to gauge their responses to blockchain and its use in Kuwait ministries.

Blockchain technology offers many benefits for the development of smart cities in Kuwait. This is a statement that received almost mutual agreement amongst all the IT managers interviewed. However, as regards wider acceptance, the majority mentioned that a framework is necessary to better articulate the public value of using blockchain in smart cities in Kuwait.

This paper develops research hypotheses and a framework for articulating the public value of blockchain technology for smart cities in Kuwait.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

The growth of the internet, access to technology and rapid digital transformations have paved the way for developing attack surfaces for individuals and organizations. There is a dire need to provide cybersecurity awareness most effectively. Gamification-based platforms have evolved to make cybersecurity education more engaging and effective. This study explores the gamification platforms available for cybersecurity training and awareness, the extent to which they are used and their benefits and challenges.

PRISMA 2020 was used to conduct the systematic literature review.

The study comprehends the game design elements and their role in the effectiveness of cybersecurity training and awareness. The study unveils that traditional education methodologies are insignificant in cybersecurity awareness, and gamification-based platforms are more beneficial. The paper summarizes the implications of the findings and further postulates future research directions.

This work comprehends the various forms of gamification platforms and frameworks available for cybersecurity training and will motivate further development of gamification platforms. This paper will help academia, private and public organizations and game designers enhance their gamification-based cybersecurity education interventions.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

Based on the Natural resource-based view (NRBV), this study aims to analyze the association between the Sustainable Development Goals related to Gender Equality, Decent Work, Innovation, and Climatic Action in the Food Industry.

A quantitative study is adopted based on the 2019 World Bank Enterprise Survey microdata. The database includes 1,242 food enterprises from 16 European Union countries. We applied logistic regression with cluster robust standard errors.

Despite global efforts to promote decent work and gender equity, the anticipated results have not yet been achieved, suggesting varying performance in different contexts. Food firms, characterized by significant environmental impacts and seasonal tasks, employ diverse Human Resource Management (HRM) strategies based on whether they pursue innovation or environmental objectives. Grounded in the NRBV, our findings underscore the importance of investing in qualified workers and offering attractive wages to meet environmental goals, as well as providing stable contracts for female workers. The NRBV framework also highlights the crucial role of product and process innovations, whether green or not, in achieving climate action objectives.

European policies must be adapted to the human resources characteristics in the food industry, providing specific training on environmental and innovation issues and contributing to more work stability and gender equality.

Our study applies the NRBV to analyze how human resources and product/process innovations can boost environmental preservation in an industry characterized by strong environmental impacts, seasonal tasks, and financial constraints.

Based on the ability-motivation-opportunity (AMO) framework, this paper develops and tests a moderated mediation model to investigate how and when green high-performance work systems (GHPWS) lead to green service innovation (GSI). For this purpose, the current study examines the mediating role of decent work (DW) and the moderating role of climate change skepticism (CSS) in the relationship between GHPWS and GSI.

Overall data collected from 262 Pakistan banking employees analyzed through the SPSS PROCESS macro supports this model.

Findings suggest that GHWPS increased GSI through DW. The positive effect of DW on GSI was particularly high for the employees with low CCS.

Theoretically, this study expands knowledge in sustainable business practices and human resource management. Practically, it offers organizations strategies to integrate sustainability into their operations. This study implies that human resource management must be realigned with the overall organizational sustainability strategy to adopt environmental innovations. Along with the commercial and economic outcomes, this research has implications for public policy by advocating for practices that support environmental sustainability and DW. Societally, it promotes awareness of the importance of sustainability in business, which has a potential ripple effect on public attitudes and improves quality of life through environmental preservation.

This study provides unique insights into the employees’ doubts about anthropogenic climate change and the importance of organizations implementing GHPWS toward cultivating DW and GSI in the context of developing countries.

The study aims to conduct an empirical research to examine how green human resource management (GHRM) can influence sustainable organizational performance (SOP) in terms of financial performance (FP) and operational performance (OpP), both directly and through employee green behavior (EGB), in the backdrop of the Indian information and communication technology (ICT) sector.

The study is based on primary data collected through a questionnaire survey from 387 employees engaged in the ICT sector. Confirmatory factor analysis validates the measurement model, while structural equation modeling (SEM) using the partial least squares (PLS) method examines the hypothesized relations.

Results confirm that GHRM significantly contributes to EGB, FP and OpP, with a theoretical underpinning concerning resource-based view (RBV) and ability-motivation-opportunity (AMO) concepts. The study also confirms that connections of GHRM with both FP and OpP are significantly and positively mediated by EGB.

The study provides evidence-based implications of different GHRM practices toward EGB, FP and OpP, which are highly pertinent to strategists, policymakers, managers and supervisors in implementing GHRM for adding value to both operational and financial statements while protecting nature and natural resources through the green efforts of employees.

The study bridges a significant theoretical and practitioner gap by providing a deeper understanding of GHRM as a sustainable human resource management (HRM) model in the context of the Indian ICT sector. It empirically explores the connections between GHRM and OpP, which is a crucial dimension of SOP, and highlights noteworthy implications for promoting EGB, an individual micro-sustainability dimension directed toward the achievement of broader organizational goals, which are essentially oriented to macro-sustainability.

The study aims to examine diabetic patients’ switching intentions toward self-monitoring blood glucose devices, incorporating perceived usefulness and perceived ease of use, with switching cost as a moderator.

A self-administered questionnaire was developed using established measuring scales. Data from 321 respondents was collected and analyzed using the partial least squares structural equation modeling approach.

Results indicate that perceived usefulness and perceived ease of use influence switching intention. Switching cost does not moderate the perceived usefulness, perceived ease of use and switching intention. This study reveals important information for healthcare practitioners and device manufacturers.

The significance and originality of this study stem from its pioneering investigation into the switching intentions of diabetic patients regarding self-monitoring glucose devices through the application of the technology acceptance model, thereby addressing a notable gap in the existing literature on diabetes management and technology adoption, which has lacked comprehensive examination of patients’ transition behaviors in this specific context.

This study introduces a novel approach to conducting importance-performance analysis (IPA) and importance-performance competitor analysis (IPCA) by utilizing online reviews as an alternative to traditional survey-based marketing research.

In order to conduct IPA and IPCA utilizing online reviews, the following three steps were executed: (1) Extract key attributes of the product/service with latent Dirichlet allocation (LDA) topic modeling. (2) Measure the importance of each attribute with keyword analysis. (3) Measure the performance of each attribute with KoBERT-based sentiment analysis.

By utilizing LDA, we were able to identify significant attributes identified by real users’ reviews. The approach of evaluating attribute importance using keyword metrics offers the advantage of straightforward computations, reducing computational expenses and facilitating intuitive metric assessments. The evaluation of BERT-based performance involved adapting pre-trained language models to the specific analysis domain, resulting in substantial time savings without compromising accuracy, ultimately bolstering the dependability of the metrics. Lastly, the case study’s findings indicate a growing emphasis on the aesthetic aspects of mobile banking apps in South Korea while highlighting the pressing need for enhancements in critical areas such as stability and security, which are particularly pertinent to the finance industry.

Previous studies have limitations in assessing significance solely based on sentiment scores and review ratings, resulting in an inability to independently measure satisfaction and importance metrics. This research addresses these limitations by introducing a keyword frequency-based importance metric, enhancing the accuracy and suitability of these measurements independently. In the context of performance measurement, this study utilizes pre-trained large language models (LLMs), which provide a cost-effective alternative to previous methods while preserving measurement accuracy. Additionally, this approach demonstrates the potential for industry-wide competitive analysis by enabling comparisons among multiple competitors. Furthermore, the study extends the application of review data-based IPA and IPCA, traditionally used in the tourism sector, to the evaluation of financial mobile applications. This innovation expands the scope of these methodologies, indicating their potential applicability across various industries.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.