Search results

This study aimed to investigate whether information from reliable news sources such as medical experts and government officials, along with governmental and individual risk responses, influences consumers’ perceptions of news and intention to seek more information. Additionally, it aimed to explore the relationships between these perceptions and consumers’ intentions to seek information in a food safety risk event.

A survey design methodology was employed. A quasi-experimental approach divided 470 Taiwanese participants into three groups, each exposed to varying online news content about food safety news, designed according to the Internalization-Distribution-Explanation-Action (IDEA) model. This involved different combinations of reliable sources and risk response advice to examine the impact on news comprehension and behaviour intentions.

The results indicated that consumers perceived the news as highly credible when they read it with reliable news sources or risk response advice. Governmental and individual risk response advice significantly impacted consumers’ understanding of news. In addition, perceptions of news credibility and understanding of news can increase individuals’ information-seeking intentions to protect themselves from food safety risks.

This study introduced novel insights into the application of the source credibility theory (SCT) model within Taiwanese food safety incidents, identifying key factors that motivate consumer information-seeking behaviour. It marks an initial attempt to incorporate the IDEA model-based risk communication content into research design, aligning with existing literature while highlighting the critical role of reliable sources in enhancing news credibility and consumer response.

This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.

Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.

The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.

This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

This paper aims to systematically study the effects of reflow temperature and SAC0307 (SAC) content on the micromorphology and mechanical properties of Sn58Bi-xSAC0307 composite solder joints to meet the requirements of high integration and low-temperature packaging of devices and provide references for the application of composite solder joints.

Sn58Bi and SAC0307 solder paste was mechanically mixed in different proportions to prepare Sn58Bi-xSAC0307/ENIG solder joints. The thermal properties, microstructure and mechanical properties of the composite solder joints were studied.

As SAC content in the solder increases, the balling temperature of SnBi-SAC solder gradually increases. The addition of SAC alloy reduces the grain size of large Bi-rich phase, and there are small-sized dispersed Bi and Ag3Sn particles in the bulk solder. The intermetallic compounds composition of the SnBi-xSAC/ENIG solder joint changes from Ni3Sn4 to (Ni, Cu)3Sn4 and (Cu, Ni)6Sn5 with SAC increasing. As the soldering temperature increases, the strength of all solder joints shows a rising trend. Among them, the shear strength of SnBi-20SAC solder joints at a reflow temperature of 150°C is approximately 37 MPa. As the reflow temperature increases to 250°C, the shear strength of solder joints increases to approximately 67 MPa.

This study provides a reference for the optimization of low-temperature solder composition and soldering process under different package designs.

This study explored how COVID-19 moderated the relationship between organizational learning capabilities (OLCs), technological innovation (TI), supply chain management (SMC) processes and enterprise performance (EP). It aimed to give ideas on how organizations could change and do well during big disruptions.

Design: A structured questionnaire served as the data collection tool, employing a stratified sampling technique. Partial least squares (PLS) was utilized for data processing. Information was gathered from the automobile industry in Xian, China, providing an in-depth understanding of how COVID-19 moderated the variables under examination.

The study discovered that COVID-19 changed how organizational learning, TI, SCM and EP interacted. Some organizations had trouble keeping up with learning and innovation, but others used them to make their SCM stronger, leading to better performance. Also, different effects of COVID-19 were seen in various industries and organizations.

This study provided practical implications for managers, policymakers and practitioners. It emphasized fostering OLCs and TI as crucial for resilience during disruptions like COVID-19. Strategic investments in SCM were highlighted to mitigate disruptions and seize opportunities. Additionally, context-specific approaches were underscored for navigating pandemic-induced challenges.

This study enhanced existing literature by analyzing how COVID-19 moderated the link between organizational learning, TI, SCM and EP. Through diverse methodologies and organizational contexts, it offered fresh insights into dynamic organizational responses to disruptions, advancing both theoretical understanding and practical knowledge in the field.

This paper aims to provide a new method to study and improve the dynamic characteristics of the four-column resistance strain force sensor through the elastomer structure design and optimization.

Based on the mechanism analysis method, the authors first present a dynamic characteristic model of the four-column resistance strain force sensors’ elastomer. Then, the authors verified and modified the model according to the Solidworks finite element simulation results. Finally, the authors designed and optimized two types of four-column elastomers based on the dynamic characteristic model and verified the improvement of sensor dynamic performance through a hammer knock dynamic experiment.

The Solidworks finite element simulation and hammer knock dynamic experiment results show that the relative error of the model is less than 10%, which confirms the accuracy of the model. The dynamic performance of the sensors based on the model can be improved by more than 30%, which is a great improvement in sensor dynamic performance.

The authors first present a dynamic characteristic model of the four-column elastomer and optimize the four-column sensors successfully based on the mechanism analysis method. And a new method to study and improve the dynamic characteristics of the resistance is provided.

Given the current friction and wear challenges faced by automobile parts and bearings, this study aims to identify a novel texture for creating anti-friction and wear-resistant surfaces. This includes detailing the preparation process with the objective of mitigating friction and wear in working conditions.

Femtosecond laser technology was used to create a mango-shaped texture on the surface of GCr15 bearing steel. The optimized processing technology of the texture surface was obtained through adjusting the laser scanning speed. The tribological behavior of the laser-textured surface was investigated using a reciprocating tribometer.

The friction coefficient of the mango-shaped texture surface is 25% lower than that of the conventional surface, this can be attributed to the reduced contact area between the friction ball and the micro-textured surface, leading to stress concentration at the extrusion edge and a larger stress distribution area on the contact part of the ball and disk compared to the conventional surface and the function of the micro-texture in storing wear chips during the sliding process, thereby reducing secondary wear.

The mango-shaped textured surface in this study demonstrates effective solutions for some of the friction and wear issues, offering significant benefits for equipment operation under light load conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0127/

This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This study aims to develop an associative text analyzer (ATA) to support users in quickly grasping and interpreting the content of large amounts of text through text association recommendations, facilitating the identification of the contextual relationships between people, events, organization and locations for digital humanities. Additionally, by providing text summaries, the tool allows users to link between distant and close readings, thereby enabling more efficient exploration of related texts.

To verify the effectiveness of this tool in supporting exploration of historical texts, this study uses a counterbalanced design to compare the use of the digital humanities platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW) with and without the ATA to assist in exploring different aspects of text. The study investigated whether there were significant differences in effectiveness for exploring textual contexts and technological acceptance as well as used semi-structured in-depth interviews to understand the research participants’ viewpoints and experiences with the ATA.

The results of the experiment revealed that the effectiveness of text exploration using the DHP-LCLW with and without the ATA varied significantly depending on the topic of the text being explored. The DHP-LCLW with the ATA was found to be more suitable for exploring historical texts, while the DHP-LCLW without the ATA was more suitable for exploring educational texts. The DHP-LCLW with the DHP-LCLW was found to be significantly more useful in terms of perceived usefulness than the DHP-LCLW without the ATA, indicating that the research participants believed the ATA was more effective in helping them efficiently grasp the related texts and topics during text exploration.

The study’s practical implications lie in the development of an ATA for digital humanities, offering a valuable tool for efficiently exploring historical texts. The ATA enhances users’ ability to grasp and interpret large volumes of text, facilitating contextual relationship identification. Its practical utility is evident in the improved effectiveness of text exploration, particularly for historical content, as indicated by users’ perceived usefulness.

This study proposes an ATA for digital humanities, enhancing text exploration by offering association recommendations and efficient linking between distant and close readings. The study contributes by providing a specialized tool and demonstrating its perceived usefulness in facilitating efficient exploration of related texts in digital humanities.