Search results

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial contributions of University Social Responsibility (USR) projects toward achieving the sustainable development goals (SDGs) in rural settings. The objective of this research is to analyze the outcomes of these case projects on the SDGs and, consequently, to propose a theoretical model aimed at enhancing student participation in achieving SDGs through USR programs.

This study adapted the methodology comprising SDGs contribution analysis and participant observation. The SDGs contribution analysis unfolds in three steps. First, the authors conducted evaluation of the 2030 Agenda for Sustainable Development. This step involved a thorough analysis of all 17 SDGs and their respective targets to establish a foundational understanding. Second, analyzed the outcomes of the case projects to examine the specific contributions of case projects toward achieving the SDGs. Third, the information from the preceding steps is analyzed to determine the extent of the case projects’ contributions to the SDGs.

The study proposes a theoretical model to enhance student engagement in achieving SDGs, emphasizing student empowerment, community partnership and robust evaluation methods aligned with SDGs and social impacts. This model could offer guidance for higher education institutions (HEIs) globally on utilizing USR programs to contribute to the SDGs, while simultaneously enriching student learning experiences through practical engagement and empowerment.

This model can be enhanced and validated by applying more rigorous scientific methods. For instance, conducting surveys on students and community participants of events and activities, utilizing a statistically rigorous approach such as pre-post testing, can analyze the effectiveness of these programs on various SDG-related variables (e.g. awareness of SDGs). Additionally, exploring the relationships between the tested variables can be a potential research direction. For instance, examining whether community engagement can positively increase the social impacts of USR projects, or whether student empowerment can enhance community capacity building, are important issues worthy of discovery.

This model emphasizes the pivotal role of student empowerment, advocating for an educational approach that not only enhances students’ proficiency in community development but also potentially shapes their career trajectories, as evidenced by the case projects examined in this study. In essence, this model offers HEIs a structured pathway to enrich student engagement in realizing SDGs through USR initiatives. It posits student empowerment as the foundational element, fostering a learning environment where students gain valuable skills and insights into community development, potentially guiding their future professional endeavors. This research provides practical direction for those HEIs implementing USR projects, which will increase the positive impacts brought by HEIs, especially for the students and local community.

To the best of the authors’ knowledge, no previous studies have proposed a theoretical model specifically designed to engage students in achieving SDGs through USR programs in a rural context. The significance of this study lies in its potential to serve as a guide for higher education institutions globally, enabling them to effectively leverage USR programs to contribute toward SDGs. This makes the study an invaluable resource for researchers, policymakers and educators who are committed to fostering sustainability.

The purpose of this study is to generate a better understanding of the nature of Corporate Social Responsibility (CSR) communication on social media. In fact social media are one of the most effective communication channels in contemporary business settings. Due to their inner characteristics, they should be the ideal channel for communicating CSR topics. Over the last 15 years, a variety of researches discussed the interplay between CSR and social media, resulting in a rather tangled body of knowledge.

This study proposes a scoping review of published academic literature at the intersection of these two research bodies (i.e. CSR and social media communication) to shed light on the nature of the communication, strategic challenges and practices, as well as their key factors and impact.

To date, academic research related to the nature of CSR on social media is fragmented and blurry. This research sheds light on the challenges of interactive social media communication in CSR and presents an account of key strategies, tactics and possible impacts for these challenging communication practices.

This research generates a better understanding of the nature of CSR communication on social media, strategic challenges and practices, as well as their key factors and impact. It is the tentative of generating a synthetic account of relevant literature in the field.

This study aims to investigate the influences of Al₂O₃ mass fraction on the corrosive wear and electrochemical behaviors of FeAl–xAl₂O₃ coatings.

FeAl–xAl₂O₃ coatings were prepared on S355 steel by laser cladding to improve its corrosive wear and electrochemical properties.

The average coefficients of friction and wear rates of FeAl–xAl₂O₃ coatings are decreased with the Al₂O₃ mass fraction, and the Al₂O₃ plays a positive role in the corrosion wear resistance. Moreover, the charge transfer resistance of FeAl–xAl₂O₃ coatings is increased with the Al₂O₃ mass fraction, showing the FeAl–15%Al₂O₃ coating has the best corrosion resistance. The findings show the corrosion resistance of FeAl–15%Al₂O₃ coating is the highest among the three kinds of coatings.

Al₂O₃ was first added into FeAl coatings to further improve its corrosive wear and electrochemical properties by laser cladding.

This study aims to investigate the causes of leakage in radial oil seals under dynamic eccentricity, elucidate the influence of operating parameters on leakage failure and develop methods for predicting and preventing such leakage.

Based on the principle of cam motion and considering viscoelasticity, develops a motion model of the compression and release of the shaft seal and proposes a method to determine its failure. In addition, this study quantifies the leakage gap and formulates a quantitative calculation model to accurately determine the location and shape parameters of the leakage gap.

Leakage gaps predominantly occur during the release phase of the shaft seal. Their presence can be identified by comparing the descending times of the seal and the shaft during this phase. An increase in rotation speed and eccentricity heightens the likelihood of gap formation, with both the dimensions and leakage rate of the gap increasing as these factors escalate. Eccentricity, in particular, has a more pronounced effect on gap formation.

This study clarifies the failure mechanisms of radial oil seals under dynamic eccentricity and introduces a criterion for identifying leakage gaps, providing valuable theoretical guidance for the design and optimization of radial oil seals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0192/.

The study attempts to examine the impact of the US-China trade war on Vietnamese exports to the United States, which has consistently served as a key market for Vietnamese goods and services in recent decades. The heterogeneous effects of the trade war on different export sectors are also evaluated.

The secondary data on Vietnamese exports to the US at a 6-digit level is collected from UN Comtrade. Besides, the difference-in-differences (DiD) method is employed to analyze the impact of the trade war on exports from Vietnam to the United States.

The findings revealed a 14% increase in total Vietnamese exports to the United States due to the trade war. Examining heterogeneous effects, certain industries, such as plastics, iron or steel articles, textiles and garments, and machinery and mechanical appliances, experience significant benefits. However, the study did not identify statistically significant effects on other sectors, such as electrical machinery products, agricultural and forestry, and furniture.

The paper is one among limited studies considering the causal effects of the trade war on a developing country, accounting for the heterogeneous effects on different export sectors.

This study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience.

Thematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts.

Digital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms.

Our first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets.

The novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

The adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment.

The novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

This study addresses a critical gap in understanding consumer behavior toward indigenous chicken, investigating the interaction between cognitive factors and purchase decisions. The current research offers a valuable contribution to the field of sustainable food marketing by shedding light on these dynamics.

The research employed a quantitative survey method to gather data from consumers of indigenous chicken in Malaysia and analyzed it using structural equation modeling.

Health and price consciousness, along with effort expectancy, significantly influence consumer attitudes toward indigenous chicken. However, environmental consciousness and availability do not directly impact attitude. Similarly, attitude mediates the relationship between health consciousness, price consciousness, effort expectancy and purchase behavior, while attitude does not mediate the relationship between environmental consciousness, availability and purchase behavior of indigenous chicken.

This study is one of the pioneering works to apply the cognitive affect behavior (CAB) model to examine the factors influencing consumer attitudes and purchase behavior toward indigenous chicken. It investigates how constructs such as health consciousness, environmental consciousness, price consciousness, effort expectancy and availability affect these attitudes and behaviors, offering novel insights into the purchase intentions of younger and educated individuals.

This paper aims to examine the role of local/provincial government in influencing corporate social and environmental reporting (CSER) in China, and more specifically, how the underlying economic and political factors associated with local government have influenced the quality of CSER.

The authors used 234 environmentally sensitive companies listed on the Shanghai and Shenzhen Stock Exchanges during 2013 and 2015 as the research sample to test the relationship between CSER and local government’s political connection and economic prioritisation and the potential mediating effect of local economic prioritisation.

The analysis provides evidence that local/provincial government’s political geographical connectedness with the central government has directly and positively influenced the level of CSER, while local prioritisation of economic development has a direct but negative effect on CSER in China. In addition, local/provincial prioritisation of economic development has mediated the relationship between local–central political geographical connectedness and CSER.

While local/provincial governments are heavily influenced by the coercive pressure from the central government, they also act in their own political and economic interests in overseeing CSER at the local level. This study raises the question about the effectiveness of the top-down approach to improving CSER in China and suggests that the central government may need to focus more on coordinating and harmonising different local/provincial governments’ interests to enable achieving a common sustainability goal.

The authors provide evidence revealing how the economic and political contexts of local government have played a significant role in shaping CSER in China. More specifically, this paper addresses a gap in the literature by highlighting the importance of local government oversight power for CSER development and how such oversight is determined by local prioritisation of economic development and political geographical connectedness of local and central governments.