The prevalence of social media transforms e-business into social commerce and facilitates consumers' engagement in cross-cultural social commerce. However, social commerce operations encounter unpredictable challenges in cross-cultural business environment. It is vital to further investigate how contextual elements affect consumers' trust and their engagement when they are exposed to the complexity of cross-cultural business environment. The stimuli-organism-response paradigm is employed to examine how the two dimensions of social media usage influence consumers' engagement in cross-cultural social commerce. The current study surveyed 2,058 samples from 135 countries, and the regression analysis results illustrate the mechanism whereby informational and socialising usage of social media positively influences consumers' engagement in social commerce through consumers' trust toward social commerce websites. Additionally, the associations between two aspects of social media usage and consumers' trust towards social commerce are negatively moderated by cultural distance. Both theoretical and practical implications are also discussed.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

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.

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.

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 propose that technological, service-dominant and design logics must interplay for an IT artefact to succeed. Based on data from a project aiming at a B2B platform for manufacturing small and medium enterprises (SMEs) in Europe, we explore these three logics in an agile software design context. By using an inductive approach, we theorise about what is needed for the alignment of the three logics. We contribute with a novel theoretical lens, the Framework for Adaptive Space. We offer insights into the importance of continuously reflecting on all three logics during the agile software design process to ensure mutual understanding among the agile team and the B2B platform end-users involved.

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.

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.

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.

This article studies the application of artificial intelligence technology in natural resource planning and management. This article first introduces the background of NR and AI intelligent technology, then conducts academic research and summary on NR planning management and AI intelligent technology. Then, an algorithm model based on multi-objective intelligent planning algorithm is established. Finally, simulation experiments are conducted, and experiments summary and discussion are provided. The experimental results show that the average efficiency value of the four stages of NR planning and management before use is 5.25, and the average efficiency value of the four stages of NR planning and management after use is 7. The difference in the average efficiency value before and after use is 1.75. It can be seen that the use of AI intelligent technology can effectively improve the efficiency of natural resource planning and management.

As IOT develops drastically these years, the application of cloud computing in many fields has become possible. In this paper, we take low-voltage current transformers in power systems as the research object and propose a TCN-BI-GRU power measurement method that incorporates the signal characteristics based on the transformer input and output. Firstly, the basic signal enhancement extraction of input and output is completed by using EMD and correlation coefficients. Secondly, multi-dimensional feature extraction is completed to improve the data performance according to the established TCN network. Finally, the power prediction is completed by using BI-GRU, and the results show that the RMSE of this framework is 5.69 significantly lower than other methods. In the laboratory test, the device after being subjected to strong disturbance, its correlation coefficient feature has a large impact, leading to a large deviation in the prediction, which provides a new idea for future intelligent prediction.

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.

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.

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.

Software start-ups generally use development practices that are adapted to their agile and innovative environment. However, these practices, although consolidated, may not be the best ones for a specific context. This paper aims to present a set of guidelines for software development in start-ups. It also aims to show the results of three studies to validate and refine the proposed guidelines: a confirmatory survey, a focus group, and an expert panel. The participants were actors from both the industry and the academia. The results revealed that the guidelines obtained a positive perception from the participants of both contexts. Based on their approval, we can infer that those guidelines can increase the quality of products generated by start-ups and the chances of success for these organisations. Besides, the need for some improvements has been identified, and they will be implemented in the next version of the guidelines.

A number of studies have applied social network analysis (SNA) to show that the patterns of social interaction between software developers explain important organisational outcomes. However, these insights are based on a single network relation (i.e., uniplex social ties) between software developers and do not consider the multiple network relations (i.e., multiplex social ties) that truly exist among project members. This study reassesses the understanding of software developer networks and what it means for performance in software development settings. A systematic review of SNA studies between 1990 and 2020 across six digital libraries within the IS and management science domain was conducted. The central contributions of this paper are an in-depth overview of SNA studies to date and the establishment of a research agenda to advance our knowledge of the concept of multiplexity on how a multiplex perspective can contribute to a software developer's coordination of tasks and performance advantages.

The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

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.

This article explores dual affiliated graduate students that conduct research involving both universities and firms, which we conceptualise as a form of academic engagement, e.g., knowledge networks. We explore what they do during their studies, and their perceptions about their contributions to the firm's capacities for technology and innovation. So far, university-industry interactions in engineering are less researched than other fields, and this qualitative study focuses upon one department of Electrical Engineering in Sweden. First, we define and describe how the partner firms and universities organise this research collaboration as a form of academic engagement. Secondly, we propose a conceptual framework specifying how graduate students act as boundary-spanners between universities and firms. This framework is used for the empirical analysis, when exploring their perceptions of impact. Our results reveal that they primarily engage in problem-solving activities in technology, which augment particularly the early stages of absorptive capacities in firms.

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.

The face of the hospitality sector in India is continuously changing and in times of career transitiveness, it is important to know the factors that support a successful career. The current research aims to explore the relationship between career planning, employee optimism, career adaptability and career satisfaction in the Indian hospitality sector. The study included 283 employees from Indian hospitality sector. Additionally, the study used SEM and bootstrap method to measure the dual mediating relationship between career planning, employee optimism dimensions, career adaptability dimensions, and career satisfaction in Indian setting. The results indicated that optimism dimensions and career adaptability dimensions partially mediate the relationship between career planning and career satisfaction in Indian hospitality sector. The study suggests useful implications for academia and industrial purpose. The limitations and future research avenues have been discussed. The study would contribute to the sparse literature on employee optimism, career planning, career adaptability and subjective career success. It would contribute to the social cognitive career theory (SCCT).