Search results

Megaproject supply chains involve multiple layers of stakeholders, leading to complex relationships and risks. The role of social interactions within these networks is unexplored. Therefore, an analysis of construction supply chain risk management from the perspective of social networks is essential to identify related stakeholders, their relationships and the social network risk factors.

About 65 risk factors, identified from literature and interviews, informed the development of a questionnaire for the study. Online questionnaires administered in Ghana and South Africa produced 120 valid responses. Feedback from the responses was ranked and assessed to determine the overall social network risk levels using the Normalised Mean and Fuzzy synthesis analysis methods.

About 24 risk factors were identified and classified into six groups: Client/Consultant-related, Community-related, Government-related, Industry Perception-related, Supplier-related and Stakeholder Opportunism. The top five social network risks identified include bribery, supplier monopoly, incomplete design teams, poor communication and lack of collaboration.

The study provides detailed evaluations of social network risks in Africa, and the findings will help in developing strategies to mitigate supply chain disruptions caused by these challenges.

This study contributes to the literature on supply chain risk management by offering context-specific insights into the social network perspective of megaprojects in Africa, which differs from those in developed countries.

Establishing an effective educational system is directly tied to academic leadership, a multifaceted concept that differs from one environment to another. The purpose of this research is to investigate the role of digital leadership (DL) aspects in the enhancement of technical knowledge sharing (KS) and dealing with emotional intelligence (EI) among the teaching faculty of higher education institutions (HEIs).

Following a quantitative and cross-sectional research design, convenient sampling is employed for data collection via a self-administered questionnaire from 320 faculty members of private HEIs in Pakistan.

Structural equation model (SEM) is used for path analysis. The results reveal a positive and significant effect of DL, aspects like visionary leadership (VL), digital citizenship (DC), systematic improvement (SI), on knowledge sharing (KS), and emotional intelligence (EI).

This study has highlighted the significance of DL in private HEIs. The findings of the study imply that institutional heads of higher education institutions (HEIs) can successfully manage the knowledge assets that they have and those of their staff members, ensure the successful adoption of technology and foster product and process innovation that improves organizational performance and integrates successful strategies into the educational system by demonstrating DL aspects. The research also analyzes institutional heads' present leadership strategies to enhance response to technological change and innovations, which are considered fundamental pillars of organizational success. Ultimately, this will extend the literature on adopting DL techniques towards digital transformation in the education system.

This study empirically confirms the role of DL aspects such as VL, DC, and SI towards KS and EI. Most of the research demonstrates the direct impact of DL on EI, whereas the aspects of DL are not directly related to KS and EI. Studies have also shown how DL enhances its role in incorporating leadership in organizations, industries, and education, mainly in Western countries. This research addresses the gap in understanding the direct effects of DL aspects on KS and EI in non-Western countries, particularly within the education sector.

The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.

In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.

In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.

Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.

This study aims to explore the interplay between CO2 emissions, financial development (FD) and foreign direct investment (FDI) in Asia-Pacific and Oceania. It also aims to understand short- and long-term impacts, emphasizing the role of FDI, FD and FD’s moderating effect on the FDI–CO2 relationship.

Using a 21-year panel data set (2000–2020) from 44 countries, the study employs the pooled mean group-autoregressive distributed lag (PMG-ARDL) model supplemented by the Dumitrescu–Hurlin panel causality test. This method assesses the complex dynamics and offers a robust analysis of short- and long-term effects in the Asia-Pacific and Oceanian context.

Long-term results indicate that FDI coupled with FD and FD’s moderating effect on FDI significantly contributes to CO2 emissions. Short-term relationships are more complex and lack statistical significance. FD positively moderates the FDI–CO2 relationship in the long run.

For investors, policymakers and stakeholders in Asia-Pacific and Oceania, the study highlights the importance of considering environmental impacts in investment decisions. The insights into the role of FDI and FD help craft policies and strategies for environmental sustainability.

Socially, this research emphasizes the necessity of a balanced approach to economic development, considering the potential long-term environmental consequences. Policymakers and stakeholders may use these findings to guide discussions and actions to achieve sustainable and socially responsible development in this dynamic region.

The findings contribute original insights into the essential relationships among FDI, FD and CO2 emissions in a diverse region like Asia-Pacific, enhancing the understanding of environmental implications in regions experiencing rapid economic growth.

This study aims to explore students’ perceptions of the use of an artificial intelligence-generated content avatar (AIGC avatar) within a learning management system (LMS).

This qualitative research involved seven postgraduate students. Data were collected through individual, in-depth interviews. The videos of the AIGC avatar, created using Leonardo, ChatGPT and Heygen, were uploaded to the LMS to communicate with students for the purposes of a welcome note, assignment guide, assignment feedback, tutorial reminders and preparation as well as to provide encouragement and study tips. Students were interviewed at the end of the semester.

The findings of this study indicated that the majority of participating students held positive perceptions regarding the use of the AIGC avatar in the LMS. They reported that it enhanced their perceived instructor’s social presence and motivation to learn. The assignment guide and feedback were particularly valued by the participants. While some students noted the AIGC avatar’s lack of naturalness, others appreciated the clear and professional speech it delivered.

The study was confined to seven students from a single course at one institution, which may limit the generalizability of the findings. Future research could involve a larger and more diverse group of participants.

The findings may offer education providers an alternative solution for engaging students in an LMS.

This study highlights the potential of AIGC avatars to replace text-based communication in LMS and enhance students’ perceived instructor social presence.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.

A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.

The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.

This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.

The purpose of this study is to explore the knowledge, attitude and practices (KAP) of Industry 4.0 between the academicians and industry players in construction engineering, further suggesting a mechanism to narrow the gap between the distinct parties.

This study was conducted through structured online and face-to-face interviews, using KAP survey, and semi-structured interviews. This constructive research was conducted among Malaysian construction industry players and academicians from the construction engineering department in public universities.

The findings exhibit the similarities and differences of KAP between academics and industry on Industry 4.0 in construction engineering. In general, both categories of respondents have displayed more similarities than differences in all aspects, except for knowledge. The better knowledge profile of Industry 4.0 among the academicians reflects the nature of the academic works that constantly seek new knowledge, thus suggesting the establishment of an industry-academic (I-A) knowledge equilibrium framework to leverage the knowledge profile between both parties.

This exploratory study that showcases the perspective of the academia and industry practitioners on Industry 4.0 acts as a cornerstone for bridging the gap between the two distinct sectors within the same field.

The gap between the academic and industry was highlighted, further establishing the I-A knowledge equilibrium framework that could also be applied to other fields of study.

The originality of this paper was the profiling of the KAP of Industry 4.0 for the academicians and industry players in construction engineering, further distinguishing the gap between both parties.

To build long-term relationships and gain a competitive edge, marketers need to provide customers with unique and distinct experiences that they cannot find in other companies. According to the literature, after-sales service helps to achieve these goals. By modeling the linkages between after-sales service, service quality, customer attitude and purchase intention, this study aims to understand how customers perceive after-sales service in decision-making in kitchen appliance industry.

Through purposive sampling, 324 respondents, primarily female, answered a structured questionnaire about their perception of after-sales service for kitchen appliance products. Previously well-established, validated scale measures from the extant literature were used. The responses were gathered using a seven-point Likert scale.

According to the findings, after-sales service quality is vital in kitchen appliance buying decisions. Accordingly, the higher the quality of service perceived by the customer, the more favorable the brand's attitude and purchase intention will be. Additionally, brand reputation was found to be an essential moderator between customer attitude and purchase intention, suggesting that the reputation of the kitchen appliance brand plays a positive and significant role in consumers’ purchase intentions.

It is well known that after-sales service plays a crucial role in current business scenarios, but empirical research on kitchen appliances has been scarce. This study aims to fill a void in the existing literature by investigating the relationships between after-sales service, after-sales service quality, customer attitude and purchase intention in the domain of kitchen appliances.

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.