The performance of a building's internal environment, which includes the air temperature, lighting and acoustics, is what determines the quality of the environment inside the building. We present a thermal model for achieving thermal comfort in buildings that makes use of a multimodal analytic framework as a solution to this challenge. In this study, a multimodal combination is used to evaluate several temperature and humidity sensors as well as an area image. Additionally, a CNN and LSTM combination is used to process the image and sensor data. The results show that heating setback and interior set point temperatures, as well as mechanical ventilation based on real people's presence and CO<SUB align=right>2 levels, are all consistently reduced when ICT-driven intelligent solutions are used. The CNN-LSTM model has a goodness of fit that is 0.7258 on average, which is much higher than both the CNN (0.5291) and LSTM (0.5949) models.

This paper presents a Scalable Video Coding (SVC) system over multicast Software-Defined Networking (SDN), which focuses on, transmission management for the sender-receiver model. Our approach reduces bandwidth usage by allowing the receiver to select various video resolutions in a multicast group, which helps avoid a video freezing issue during bandwidth congestion. Moreover, the SVC Multiple Sessions Transmission Bandwidth thresholds Quantised Level Balance (SVC-MST BWQLB) routes different layers of the SVC stream using distinct paths and reduces storage space and bandwidth congestion problems in different video resolutions. The experimental results show that the proposed model provides better display quality than the traditional Open Shortest Path First (OSPF) routing technique. Furthermore, it reduced transmission delays by up to 66.64% for grouped resolutions compared to SVC-Single Session Transmission (SVC-SST). Additionally, the modified Real-time Transport Protocol (RTP) header and the sorting buffer for SVC-MST are proposed to deal with the defragmentation problem.

Differential privacy is usually applied to location privacy protection scenarios, which confuses real data by adding interference noise to location points to achieve the purpose of protecting privacy. However, this method can result in a significant amount of redundant noisy data and impact the accuracy of the location. Considering the security and practicability of location data, an effective differential privacy protection method of location data based on perturbation loss constraint is proposed. After applying the Laplace mechanism under the condition of differential privacy to perturb the location data, the Savitzky-Golay filtering technology is used to correct the data with noise, and the data with large deviation and low availability is optimised. The introduction of Savitzky-Golay filtering mechanism in differential privacy can reduce the error caused by noise data while protecting user privacy. The experiments results indicate that the scheme improves the practicability of location data and is feasible.

Constrained programming is the concept used to select possible alternatives from an incredibly diverse range of candidates. This paper proposes an AI-assisted Backtracking Scheme (AI-BS) by integrating the generic backtracking algorithm with Artificial Intelligence (AI). The detailed study observes that the extreme dual ray associated with the infeasible linear program can be automatically extracted from minimum unfeasible sets. Constraints are used in artificial intelligence to list all possible values for a group of variables in a given universe. To put it another way, a solution is a way of assigning a value to each variable that these values satisfy all constraints. Furthermore, this helps the study reach a decreased search area for smart backtracking without paying high costs. The evaluation results exhibit that the IB-BC algorithm-based smart electricity schedule controller performs better electricity bill during the scheduled periods than comparison approaches such as binary backtracking and binary particle swarm optimiser.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

We introduce a unique inductive bias to improve the reconstruction quality of Neural Radiance Fields (NeRF), NeRF employs the Fourier transform to map 3D coordinates to a high-dimensional space, enhancing the representation of high-frequency information in scenes. However, this transformation often introduces significant noise, affecting NeRF's robustness. Our approach allocates attention effectively by segregating channels within NeRF using attention-based gating units. We conducted experiments on an open-source data set to demonstrate the effectiveness of our method, which leads to significant improvements in the quality of synthesised new-view images compared to state-of-the-art methods. Notably, we achieve an average PSNR increase of 0.17 compared to the original NeRF. Furthermore, our method is implemented through a carefully designed special Multi-Layer Perceptron (MLP) architecture, ensuring compatibility with most existing NeRF-based methods.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

5G mobile communication systems are an in-depth fusion of multi-radio access technologies characterised with frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the user's preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality-of-service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

The spread of COVID-19 results in disruption, uncertainty, complexity, and ambiguity in all businesses. Employees help companies achieve their aims. To manage human resources sustainably, analyse organisational strategy. This thorough research study attempts to find previously unidentified challenges, cutting-edge techniques, and surprising decisions in human resource management outside of healthcare organisations during the COVID-19 pandemic. The narrative review examined corporate human resource management measures to mitigate COVID-19. Fifteen publications were selected for the study after removing duplicates and applying the inclusion and exclusion criteria. This article examines HR's COVID-19 response. Human resource management's response to economic and financial crises has been extensively studied, but the COVID-19 pandemic has not. This paper reviewed the literature to reach its goal. The results followed the AMO framework for human resource policies and procedures and the HR management system. This document suggests COVID-19 pandemic-related changes to human resource management system architecture, policies, and practises. The study created a COVID-19 pandemic human resource management framework based on the literature. The COVID-19 pandemic had several negative effects, including social and behavioural changes, economic shock, and organisational disruption.

The banking sector has experienced a substantial technological shift that has opened up new and better opportunities for its customers. Based on their technological expenditures, the study assessed the two biggest public Indian banks and the two biggest private Indian banks. The most crucial statistical techniques used to demonstrate the aims are realistic are bivariate correlations and ordinary least squares. This work aims to establish a connection between research and a technology index that serves as a proxy for operational efficiency. The results show that for both public and private banks, the technology index positively influences operational efficiency metrics like IT costs, marketing costs, and compensation costs. This suggests that when the technology index increases, so do IT, marketing, and compensation costs, even though it has been shown that the technology index favourably improves operational efficiency measures like depreciation and printing. This means that the cost to banks is high despite greater investment in technology for these activities.

The proliferation of technology and communication has resulted in increased digitalisation that includes digital payments. This study is aimed at unravelling the relationship between awareness of individuals about the digital payment system and customer satisfaction with digital payments. Two models were developed in this study. First model considers awareness → usage pattern → customer satisfaction. Second model considers usage pattern → customer satisfaction → perception of digital payments. These two alternative models were tested by collecting data from 507 respondents in southern India was analysed using the structural equation modelling. The results indicate that usage pattern acted as a mediator between awareness and satisfaction, and satisfaction acted as a mediator between usage pattern and consumers' perception of digital payments. The implications for theory and practice are discussed.

While digitalisation reforms aiming to enhance the quality of public services were put in place, most stakeholders in developing countries still use paper-based-tendering processes, which are associated with increased costs. To overcome these problems, calls to adopt e-bidding have recently emerged. This study aims to explore the readiness of Moroccan SMEs to adopt e-bidding. To achieve this goal, we proposed an integrated framework combining the TAM and UTAUT models to examine the predictors of SMEs' intention to adopt e-bidding. We empirically tested the conceptual model using a partial least squares (PLS) estimation based on data from 210 SMEs. Our results suggest that effort expectancy, facilitating conditions, and social influence as the key factors influencing SMEs intention to adopt e-bidding. We also suggest firm size as a significant moderator. This will help in improving SMEs' user experience and will also allow a better implementation of e-bidding in Morocco and similar contexts.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

In the era of big data, enterprises have accumulated a large amount of research and development data. Effective management of their precipitated data and safe sharing of data can improve the collaboration efficiency of research and development personnel, which has become the top priority of enterprise development. This paper proposes to use blockchain technology to assist the collaboration efficiency of enterprise R&D personnel. Firstly, the multi-chain blockchain platform is used to realise the data sharing of internal data of enterprise R&D data department, project internal data and enterprise data centre, and then the process of construction of multi-chain structure and data sharing is analysed. Finally, searchable encryption was introduced to achieve data retrieval and secure sharing, improving the collaboration efficiency of enterprise research and development personnel and maximising the value of data assets. Through the experimental verification, the multi-chain structure improves the collaboration efficiency of researchers and data security sharing.

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

In order to reduce the challenges encountered by learners and educators in engaging in educational activities, this paper classifies learners' roles in virtual learning communities, and explores the role of behaviour characteristics and their positions in collaborative knowledge construction networks in promoting the process of knowledge construction. This study begins with an analysis of the relationship structure among learners in the virtual learning community and then applies the FCM algorithm to arrange learners into various dimensional combinations and create distinct learning communities. The test results demonstrate that the FCM method performs consistently during the clustering process, with less performance oscillations, and good node aggregation, the ARI value of the model is up to 0.90. It is found that they play an important role in the social interaction of learners' virtual learning community, which plays a certain role in promoting the development of artificial intelligence.

With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.

To explore the computer-aided translation technology based on the intelligent algorithm of edge computing. This paper presents the research on computer-aided translation technology based on edge computing intelligent algorithm. In the K-means computer edge algorithm, it analyses the traditional way of average resource allocation and the way of virtual machine allocation. In the process of online solution, we have a more detailed understanding of the data information at the edge, and also avoid the connection relationship between network users and the platform, which has a certain impact on the internal operation efficiency of the system. The network user group is divided into several different types of existence through K-means computer algorithm, and various information resources are counted according to their own characteristics. Computer-aided translation technology can significantly improve the quality of translation, improve the translation efficiency, and reduce the translation cost.

With the development of urban construction, its spatial evolution is also influenced by behavioural actors such as enterprises, residents, and environmental factors, leading to some decision-making behaviours that are not conducive to urban public space and environmental design. At the same time, some cities are vulnerable to various factors such as distance factors, transportation factors, and human psychological factors during the construction of public areas, resulting in a decline in the quality of urban human settlements. Urban public space is the guarantee of urban life. For this, in order to standardise urban public space and improve the quality of urban living environment, the standardisation of the environment of urban public space is required. The rapid development of intelligent algorithms and fuzzy control provides technical support for the environmental design of urban public spaces. Through the modelling of intelligent algorithms and the construction of fuzzy space, it can meet the diverse.

Intelligent digital virtual technology has become an indispensable part of modern construction, but there are also some problems in its practical application. Therefore, it is necessary to strengthen the design of intelligent building image processing systems from many aspects. Starting from image digital processing methods, this paper studies the digital twin virtual design scene construction method and related algorithms, converts the original image into a colour digital image through a greyscale algorithm, and then combines morphological knowledge and feature point extraction methods to complete the construction of a three-dimensional virtual environment. Finally, through the comparison of traditional image processing effects with smart building images based on digital twins and BIM technology, the results show that the optimised image processing results have higher clarity, sharper contrast, and a sensitivity increased by 5.84%, presenting better visual effects and solving the risk of misjudgement caused by inaccurate image recognition.

Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.

Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company's own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency's expected dilution rate and the published stock market dilution rate are both around 6%.

The application of computer image digitisation has realised the transformation of people's production and lifestyle, and also promoted the development of the construction industry. This article aims to realise the research on architectural decoration design and production under computer network environment and promote the ecological development of indoor and outdoor design in the construction industry. This article proposes to use virtual reality technology in image digitisation to guide architectural decoration design research. In the comparative analysis of the weight of architectural decoration elements, among the calculated weights of secondary elements, the spatial function has the largest weight, which is 0.2155, and the landscape has the smallest weight, which is 0.0113. Among the three-level unit weights, the service area has the largest weight, which is 0.0976, and the fence frame has the smallest weight, which is 0.0119.

Elections hold immense significance in shaping the leadership of a nation or organisation, serving as a pivotal moment that influences the trajectory of the entity involved. Despite their centrality to modern democratic systems, elections face a significant hurdle: widespread mistrust in the electoral process. This pervasive lack of confidence poses a substantial threat to the democratic framework, even in the case of prominent democracies such as India and US, where inherent flaws persist in the electoral system. Issues such as vote rigging, electronic voting machine (EVM) hacking, election manipulation, and polling booth capturing remain prominent concerns within the current voting paradigm. Leveraging blockchain for electronic voting systems offers an effective solution to alleviate the prevailing apprehensions associated with e-voting. By incorporating blockchain into the electoral process, the integrity and security of the system could be significantly strengthened, addressing the current vulnerabilities and fostering trust in democratic elections.

E-government serves as a vital channel for citizen interactions with the public sector, where user enjoyment is of paramount importance. To date, few studies have comprehensively examined the determinants of citizen enjoyment in e-government. To address this research gap, we administered a survey and gathered data from 363 Australian residents using myGov for tax filing. Our analysis revealed a pronounced discrepancy between reported enjoyment and the intention to continue using the services. Although users demonstrated a strong intent to use e-government services, this intent did not uniformly align with enjoyment. Additionally, informed by self-determination theory, we developed and tested an e-government service enjoyment model to study the impacts of effort expectancy, technophilia, technology humanness, and engagement in fostering user enjoyment. Unexpectedly, the results showed that information privacy concerns, commonly seen as a deterrent in e-government adoption, did not significantly affect enjoyment. Our findings advance the discourse on e-government service improvement.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

This study investigates the well-being of healthcare professionals in India, with a specific focus on the detrimental effects of the pandemic on their mental and physical health, including stress, burnout, and fatigue. This research examines the roles played by calling, mindfulness, and compassionate love as essential resources in promoting the well-being of healthcare professionals. Utilising structural equation modelling (SEM), the results reveal a significant cause and effect relationship between calling, mindfulness, and compassionate love and their influence on overall well-being. Furthermore, the study identifies a noteworthy parallel mediation effect, demonstrating that mindfulness and compassionate love serve as mediators in the relationship between calling and well-being. This research offers practitioners invaluable insights into the effective utilisation of mindfulness and compassionate love practices to enhance the overall well-being of healthcare professionals.

This empirical study investigates the mediating role of two components of psychological capital (PsyCap), namely self-efficacy and optimism, in the context of the relationship between transformational leadership (TL), work engagement (WE), and innovative work behaviour (IWB). The study was conducted among IT professionals with a minimum of three years of experience employed in Chennai, India. Data collection was executed using a Google Form, and both measurement and structural models were examined using SPSS 25.0 and AMOS 23.0. The findings of this study reveal several significant relationships. Firstly, transformational leadership (TL) demonstrates a robust positive association with work engagement (WE). Furthermore, work engagement (WE) positively correlates substantially with innovative work behaviour (IWB). Notably, the study underscores that two crucial components of psychological capital, specifically self-efficacy and optimism, mediate the relationship between transformational leadership (TL) and work engagement (WE). These findings carry valuable implications for IT company managers. Recognising that transformational leadership positively influences both work engagement and employees' innovative work behaviour highlights the pivotal role of leaders in fostering a productive and innovative work environment within IT organisations.

The purpose of this research is to investigate how genuine leaders impact the creativity and innovative behaviour (IWB) of information technology (IT) employees. It also examines the impact of perceived organisational support as a mediator in the correlations between authentic leadership as well as innovative behaviours. This study explores the influence of authentic leadership via the employee's IWB using aspects from social exchange theory as well as social cognitive theory. The data was collected from a sample of 487 employees of the IT sector in India. The partial least square method is applied to test the structural relationship of the research framework. Findings reveal that authentic leadership positively impact innovative work behaviour and perceived organisation support mediates authentic leadership and IWB. Additionally, when organisations and leaders support the employees and value their creative thinking then the employee replicates IWB in the organisation. The practical and theoretical implications are discussed.

The study investigated the influence of ebullient supervision on employee commitment in a Ghanaian public university through the mediating role of employee engagement. The simple random sampling technique was used to draw 302 administrative staff of the university to respond to the self-administered questionnaire on the constructs. Furthermore, the partial least square structural equation technique was deployed to test the research hypotheses in the study. The results showed that ebullient supervision had a significant positive relationship with employee commitment and employee engagement. The findings further revealed that employee engagement positively correlated with employee commitment. Finally, the study's findings established that employee engagement partially mediated the link between ebullient supervision and employee commitment. The study emphasised that various supervisors in a university's administration should create an environment that favours fun where subordinates can form ties with one another.

Physical and social aspects each make up a separate part of servicescape. Together, these make up the servicescape. Although previous research has frequently investigated these aspects separately, the purpose of this study is to simultaneously find out the impact of both aspects within the casual dining restaurants' context. In total, 462 customers in Delhi were polled for this study, and structural equation modelling was used to analyse the data. According to the results, both the social and physical parts of the servicescape have the ability to affect how satisfied customers are, which in turn can affect how they behave in the future.

Drawn from technology acceptance model, this study establishes a theoretical framework for the analytical interpretation of factors affecting job-seekers intention to use e-recruitment websites. Using the data obtained from 379 respondents in India, ten hypotheses derived from the experimental model are evaluated using a structural equation modelling technique. Vividness, perceived usefulness (PU), and attitude have been shown to have a significant positive impact on the behavioural intentions (BIs) of job-seekers, although perceived ease of use (PEOU) did not. Furthermore, perceived internet stress (PIS) is observed to be a significant antecedent PEOU; and PEOU is of PU. Such findings broaden our knowledge of e-recruiting in various ways and offer qualitative insights into the potential impact of website functionality on the attractiveness of job-seekers.

Over recent decades, advanced economies have been characterised by reduced rates of productivity. In this article, we advance the hypothesis that one of the potential causes of this trend might be the new knowledge capitalisation practices. Capitalisation of intangible assets is justified by the limited exhaustibility of knowledge, which implies its slow obsolescence, and hence, having the potential of being capitalised to reflect its prolonged period of contribution to productivity. However, the capitalisation of an increasing proportion of the assets that initially were accounted for as labour or intermediate inputs is having a direct effect on increasing capital and theoretical output and reducing total factor productivity (TFP). Our empirical analysis based on US-listed firms shows that the capitalisation of knowledge strongly reduces both the levels of TFP, and because of its fast increase in the last two decades, its rates of growth.

Intellectual assets, especially its relational forms, have become increasingly important to explain a firm's innovation. To examine relational forms of intellectual assets (IA), this study theoretically and empirically advances a concept of alliance management capability (AMC) to explain the value creation and capture aspects of a firm's innovation process. The concepts of value-creating alliance experiences (VCAE) and value capturing alliance experiences (VCPAE) were introduced in which a firm's ability to learn from these alliance experiences increases the firm's ability to discover and govern partnerships that bring the firm's innovations to market. Hypotheses were developed and empirically examined in the biotechnology industry. A contribution of this study is that a firm's VCAE and VCPAE introduce a greater 'openness' to a firm's innovation process. This openness enables a firm to better adapt and respond to the opportunities of the market and thus impact a firm's competitive advantage to innovate.

The notion of creative slack purposefully refers to the notion of organisational slack proposed by Penrose (1959), who suggested that managers in organisations always have some stock of unused resources that inevitably accumulate when developing projects and are the primary factors determining the growth and innovation of the firm. In this contribution, we aim at adding a new dimension to the notion of organisational slack. Our view is that in many innovative organisations the slack of unused ideas is essentially a creative one, which is accumulated in diverse communities through multiple projects. This creative slack is a key intangible asset and a source of knowledge creation and innovation. To explain how organisations may benefit from exploiting the creative slack accumulated by communities, we rely on the analysis of two case studies, that of the Hydro-Québec Research Institute (IREQ), and of Ubisoft Montreal.

Science-based university spin-offs face considerable technology and market uncertainty over extended periods of time, increasing the challenges of commercialisation. Scientist-entrepreneurs can play formative roles in commercialising lab-based scientific inventions through the formation of well-endowed university spin-offs. Through case study analysis of three science-based university spin-offs within a biotechnology innovation ecosystem, we unpack the impact of <i>pre-formation</i> intangible assets of academic scientists (research excellence, patenting, and international networks) and their entrepreneurial capabilities on spin-off performance. We find evidence that the pre-formation entrepreneurial capabilities of academic scientists can endow science-based university spin-offs by leveraging the scientists' pre-formation intangible assets. A theory-driven model depicting the role of pre-formation intangible assets and entrepreneurial capabilities in endowing science-based university spin-offs is developed. Recommendations are provided for scholars, practitioners, and policymakers to more effectively commercialise high potential inventions in the university lab through the development and deployment of pre-formation intangible assets and entrepreneurial capabilities.

Pandemic has compelled the entire world to change their way of life and work. To control the infection rate, academic institutes deliver education online similarly. At least one smartphone is available in every home, and students use their smartphones to attend class. The study investigates the link between smartphone usage (SU) and academic performance (AP) during the pandemic. 490 data were obtained from various institutions and undergraduate students using stratified random sampling. These components were identified using factor analysis and descriptive methods, while the relationship of SU and AP based on gender classification was tested using Smart-PLS-SEM. The findings show that SU has a substantial relationship with academic success, whether done in class or outside of it. Even yet, the study found that SU and AP significantly impact both male and female students. Furthermore, the research focuses on SU outside and within the classroom to improve students' AP.

Work-life balance (WLB) is a widely explored topic in the academic discourse. The researchers are trying to find strategies to effectively balance their work and home responsibilities for women in management. This study aims to analyse how gender roles and inequalities shape the strategies of female academics in higher education institutions. Eighteen faculty members participated in the semi-structured interviews. The trustworthiness of qualitative inquiry was ascertained by using triangulation, thick descriptions, and peer reviews. Three major themes emerged from the analysis: emotional, religious and social strategies. Despite available support, faculty noted challenges in managing work and family roles and fighting with gender stereotypes. This research adds to the emerging concept of WLB literature from the developed countries' viewpoint. It also shows how WLB discourse varies from Western sensibilities and collaborates with the previously established strategies that female academics formulate in WLB.