Search results

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

The purpose of this paper is to examine the influence of clients' trust, opportunism and adaptation on contractual (non)cooperation with a mediating role of coordination in the construction industry.

A questionnaire was used to collect data from employees of the Pakistani construction industry. Smart partial least square (SmartPLS) has been used for analyzing the data of 270 respondents from construction projects.

The results of the SmartPLS indicate that (1) Trust and contract coordination positively while opportunism negatively influence contractor's contractual cooperation. (2) Contract adaptation and contract coordination positively influence the noncontractual cooperation of the contractor. (3) Moreover, contract coordination positively mediates the relationship between trust and noncontractual cooperation, but negatively mediates the relationship between opportunism and contract adaptation and noncontractual cooperation.

The findings of this research suggest several policy implications for administrative authorities, project managers and policymakers. These authorities need to focus on clients' trust, opportunism and adaptation because these factors significantly influence contract coordination and cooperation in the construction industry. Emphasizing these factors will enable project managers to gain economies of scale and mitigate project failure.

To the best of the authors’ search and knowledge, they did not find any study examining the mediating role of coordination between trust, opportunism, adaptation and cooperation in the construction industry. Hence, the present study advances their understanding in the field of project management and construction business.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

This study aims to analyze the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioral intention of using mobile health applications, especially during and after the COVID-19 pandemic.

A survey was developed using an online survey platform and distributed to Indonesian consumers for three weeks, and 149 usable responses were obtained. The principal component analysis, linear regression and analysis of variance tests were performed to test the validity and reliability of the measurement model and the hypothesized relationships among constructs.

Surprisingly, unlike previous studies on IT adoption, the findings show that social influence has no significant impact on behavioral intention. Facilitating conditions have a very weak to almost no significant impact on behavioral intention to use mobile health applications.

This research is conducted during pandemic COVID-19 where using mobile health apps is a must. In the future this research can be expanded as comparison study after the pandemic COVID-19 stated.

The result implies that digital technologies adoption intention is strongly affected by performance expectancy and effort expectancy, with performance expectancy as the most significant predictor. Nonetheless, the interaction of performance expectancy, effort expectancy, social influence and facilitating conditions influences behavioral intention significantly. Therefore, social influence and facilitating conditions are still important even with very insignificant effects.

To improve consumers’ behavioral intention to use mobile health applications, application providers should promote mobile health applications as useful telemedicine tools by primarily focusing on the application performance and usage experience.

This paper aims to determine whether there is a relationship between intellectual capital and social capital and internal control weaknesses and assess the relationship between the variables of intellectual capital and social capital and internal control weaknesses.

The statistical population consists of 1,309 firm-year observations from 2014 to 2020. The research hypothesis is tested using statistical methods, including multivariate, least-squares and fixed-effects regression.

The results demonstrate a negative and significant relationship between intellectual capital, social capital and internal control weaknesses. The study also finds that increased intellectual and social capital quality improves human resource utilization, control mechanism, creativity and firm performance. The results also show that intellectual capital and social capital enhancement will reduce internal control weaknesses in the upcoming years.

This paper is the pioneer study on the relationship between intellectual capital and social capital and internal control weaknesses in Iran, carried out separately and in exploratory factor analysis. This paper considers intellectual capital components for theoretical factor analysis, including human capital, structural capital and customer capital. Internal control weakness is assessed based on financial, non-financial and information technology (IT) weaknesses.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study has two objectives: to explore the factors that influence student self-efficacy regarding engagement and learning outcomes in a business simulation game course and to compare the difference between hierarchical and general teaching methods.

From September 2021 to May 2022, a questionnaire was administered to 126 students in a business simulation game course at the Zhongshan Institute, University of Electronic Science and Technology of China. Data were analyzed using nonparametric paired samples tests and linear regression.

The results showed that student self-efficacy, engagement and learning outcomes were significantly higher with the hierarchical teaching method than with the general teaching method. There were also differences in the factors that influenced self-efficacy regarding learning outcomes between the two teaching methods. With the general teaching method, student self-efficacy did not directly affect learning outcomes, but did so indirectly by mediating the effect of engagement. However, with the hierarchical teaching method, self-efficacy directly and significantly affected learning outcomes, in addition to indirectly affecting learning outcomes through student engagement.

Compared with the control group experimental research method, the quasi-experimental research method can eliminate the influence of sample heterogeneity itself, but the state of the same sample may change at different times, which is not necessarily caused by the hierarchical teaching design.

Based on the results of this study, teachers can apply hierarchical teaching according to student ability levels when integrating business simulation games. The results of this study can inspire teachers to protect student self-confidence and make teaching objectives and specific requirements clear in the beginning of the course, and also provide an important practical suggestion for students on how to improve their course performance.

The research results can be extended to other courses. Teachers can improve students' self-efficacy through hierarchical teaching design, thus improving students' learning performance and also provide reference value for students to improve their learning performance.

This study built a model based on self-system model of motivational development (SSMMD) theory, comparing factors that affect student self-efficacy regarding learning outcomes under different teaching methods. The model enriches the literature on SSMMD theory as applied to business simulation game courses and adds to our understanding of hierarchical teaching methods in this field. The results provide a valuable reference for teachers that can improve teaching methods and learning outcomes.