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The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

Consumers frequently use electronic payments (e-payment) as their first step into formal financial services. The advancement of information and communication technology, on the other hand, has resulted in several achievements for human civilization, altering people’s lives, behaviors and societal measures. This study’s main aim is to investigate issues and identify the factors that are likely to influence customers’ acceptance of implementing e-payment in the Kingdom of Bahrain.

A quantitative research approach was adopted to test the influence of e-payment data security, trust, ease of use, usefulness and accessibility on customers’ acceptance of the service. A questionnaire survey was electronically administered to a purposive sample, and 531 responses were returned, achieving the required sample size for the study. Descriptive statistics analysis was used to ascertain data validity and consistency, and regression analysis was used to test the model’s hypotheses.

The findings of this study demonstrated a high influence of the mentioned factors on the e-payment acceptance of the customers in the Kingdom of Bahrain. The main recommendations are to increase the adoption of e-payment; focus highly on the security factor in e-payment adoption; create a trustworthy e-payment service; strive to make the e-payment services more user-friendly; increase the longevity of the e-payment services by focusing on usefulness; and make e-payment services more accessible.

This study’s potential contribution is to identify the factors that influence e-payment acceptance by customers in Bahrain and draw attention to issues to be considered in adopting new e-payment services.

The core emphasis of the paper is to inspect the relationships between managerial leadership, employee happiness and turnover intentions among academicians of private universities in Punjab.

The proposed study used a descriptive research methodology and a structured instrument to collect responses from individuals (n = 400) using a purposive sampling method. SPSS and partial least squares structural equation modeling (PLS-SEM) are applied to evaluate the data.

The outcomes disclosed that managerial leadership has a substantial effect on employee happiness (ß = 0.591, p < 0.05) and turnover intentions (ß = 0.566, p < 0.05). Besides this, it has been discovered that employee happiness mediates the association among managerial leadership and turnover intentions. The present research is among the few empirical findings that have examined academicians' perspectives on their turnover intentions in private universities.

By concentrating on effective managerial leadership and employee happiness, the analysis will be advantageous for human resource (HR) managers and authorities of private universities to strengthen academician retention. Therefore, the study adds something novel to the corpus of extant literature.

By considering the challenges of Industry 5.0, the purpose of this study is to analyze the role of heuristic factors in the technical qualities and emotions of Millennials and Generation Z (Gen Z) to assess their acceptance of the use of artificial intelligence (AI) devices such as robots. For this purpose, this paper uses the innovative AI device use acceptance (AIDUA) framework. This research evaluates the implications of human–machine interactions for the usage of robots and AI in daily life.

The proposed AIDUA model is tested using data collected from Millennials and Gen Z. First, a principal components analysis technique is used to validate each measure. Second, a multiple regression analysis using IBM SPSS 26.0 is conducted.

The results of this study suggest that human–machine interaction is a part of a complex process in which there are different elements determining individuals’ acceptance of the use of AI devices during daily life. This paper outlines both the theoretical and practical implications. This study enriches the AIDUA model by connoting it with features and emotions belonging to the younger generation. Additionally, this research offers technology companies suggestions for addressing future efforts on technical performance and on the alignments of the expectations of young people in Society 5.0.

First, the originality of this paper lies in highlighting the binary role of emotions in triggering the use of AI devices and robots. Second, the focus on Millennials and Gen Z offers a new lens for the interpretation of longitudinal phenomena in the adoption of AI. Finally, the findings of this paper contribute to the development of a new perspective regarding a “heartly collaborative” approach in Society 5.0.

This study explores the interconnectedness and complexity of risk-varied climate initiatives such as green bonds (GBs), emissions trading systems (ETS) and socially responsible investments (SRI). The analysis covers the period from September 2011 to August 2022, using six indices: three representing climate initiatives and three indicating uncertainty.

To achieve this, the study first examines dynamic lead-lag relations and correlation patterns in the time-frequency domain to analyse the returns of the series. Additionally, it applies an innovative approach to investigate the predictability of uncertainty measurements of climate initiatives across various market conditions and frequency spillovers in the short, medium and long run.

The findings indicate changing relationships between the series, increased linkages during turbulent market periods and strong co-movements within the network. The ETS is recommended for diversification and hedging against uncertainty indices, whereas the GB may be suitable for long-term diversification.

This study highlights the role of climate initiatives as potential hedges and contagion amplifiers during crises, with implications for policy recommendations and the asymmetric effects on market connectedness.

The paper answers questions that previous studies have not and contributes to the literature regarding financial risk management and social responsibility.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

This article introduces the special issue of Learning and Development in Highly-Dynamic VUCA Contexts. The issue reviews the concept of VUCA (volatility, uncertainty, complexity and ambiguity), highlights its implications for the learning and development function and argues that learning and development play a critical role in helping organisations, people and the societal context in which they operate to work within and navigate VUCA contexts.

The contributions to this special issue propose a novel learning and development framework that will inform L&D as the provision of training, learning and development activities in organisations within highly dynamic VUCA contexts and ensuring a strong external focus including organisational, people, community, economic and societal sustainability.

We, the authors, propose seven features of a strategic sustainability L&D function and L&D professional role that are a fit with highly dynamic VUCA contexts.

The proposed framework has important implications for the way in which L&D is structured, its key priorities and plans and the competencies of L&D professionals to add value to all stakeholders. We also emphasise that the work on the L&D function in highly dynamic VUCA contexts needs to be broader and move beyond a performance orientation.

The proposed strategic sustainability role for the L&D function expands theoretically our understanding of how L&D can have impacts at the nexus of the organisation and highly dynamic VUCA contexts, in addition to broadening the constellation of stakeholders that it potentially enhances.

This article examines the direct effect of total quality management (TQM) practices on organizational performance (OP) and innovation (INN), as well as their indirect effect through organizational learning (OL) as a mediating variable. In addition, this survey examines company size as a contextual variable in the relationship between TQM and outcome variables.

A conceptual framework is proposed to test causal links between TQM, OP, INN and OL. To empirically test this framework, 110 questionnaires were collected from large Tunisian quality-certified industries, and 167 questionnaires were collected from small and medium-sized industries (SMIs) in order to examine the effect of company size. In this order, a multigroup analysis (MGA) is performed.

Using the structural equation modeling technique, seven hypotheses are investigated. The results reveal that TQM has a direct and significant positive effect on OP and INN. Data analysis shows that there is a significant positive effect between TQM and OL, while OL positively influences OP and INN. Furthermore, the results illustrate a mediating effect of OL between TQM and OP and INN. The results reveal that large industries and SMIs show significant differences in the relationship, including the conceptual model.

The conceptual framework can be used by practitioners for effective implementation of TQM practices to simultaneously improve operational performance, quality performance and INN. This study also focuses on the role of OL in large-scale industries.

While the relationships between TQM, INN and OP have been examined separately in previous studies, this study examines the relationship between these variables in a unique model in Tunisian industries, including OL as a mediating variable. In addition, it is one of the few studies that considers firm size as a contextual variable and provides an analysis of its effect on the relationships between these variables. This study presents new data and empirical insights into the relationship between these variables.

This study aims to investigate how abusive supervisor affects knowledge hiding (KH). Specifically, this study investigates employee silence as a mediating mechanism between abusive supervision and employees’ KH. Further, psychological ownership is examined as a buffer between abusive supervision and employee silence.

KH has become a major issue for both manufacturing and service sectors. Therefore, this study collected data from 322 employees working in manufacturing and service sectors through “Google Forms” during COVID-19. The respondents were contacted through LinkedIn platform between January and July 2021.

This study noted that when employees working in high-power distance cultures perceive their leaders/supervisors as abusive, they avoid confrontation and engage in silent behavior, which positively affects their KH behavior. However, employees with a high level of psychological ownership are less likely to respond to their abusive supervisors through silence because such employees feel a greater sense of belongingness and prefer to benefit their organization.

This study used a cross-sectional design that restricts causality. However, the findings of this study suggest management to focus on leadership style to minimize KH at the workplace.

To the best of the authors’ knowledge, this study is the first to explore the underlying mechanism (employee silence) and boundary condition (psychological ownership) to explain the association between abusive supervision and KH.

In the presence of informal sector in the country, designing an energy policy and the pursuit of higher economic growth become challenging for emerging economies. These economies are usually resource starved, and the presence of underground economy leads to faulty estimates of energy demand. The authors explore the energy–growth nexus in the presence of underground economy for Pakistan, an emerging economy host to large informal sector and facing recurring energy crises.

The authors evaluate the impact of underground economy on energy demand in the presence of explanatory variables, including official gross domestic product (GDP), foreign direct investment and financial development. The authors first assess the influence of official economy on the consumption of energy. The authors investigate how energy consumption is influenced solely by underground economy. Finally, the authors evaluate the impact of true GDP on the energy consumption. The authors employ combined cointegration method of Bayer and Hanck (2013) and then apply vector error correction model.

The results reveal that official GDP, underground economy and true GDP positively and significantly affect energy consumption in both short and long run. Similarly, financial development as well as foreign direct investment enhance energy consumption. The authors find unidirectional causality between energy consumption and official GDP variables (OGDP → EC), underground economy (UE → EC) and true GDP variables (TGDP → EC) in the long run. The authors observe bidirectional causality in the short run between energy consumption and official GDP (OGDP ↔ EC) and true GDP (TGDP ↔ EC).

To the best of the authors' knowledge, no study examines the causal relationship of energy consumption and underground economy. Overall, the findings assist policymakers to consider and implement different energy-related policies considering the significant role of underground economy for energy consumption in Pakistan.