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Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.

The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor.

The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm.

The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S.

Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

The recent proliferation of social media platforms has witnessed a growth in social commerce by using social media to facilitate interactivity between customers and vendors. While emergent studies on social commerce are growing, their focus tends to be on millennials and cross-age groups. Given the growth of digital natives in shaping the online shopping experience of the future, we deemed an application to Generation Z necessary and overdue.

We draw on the existing literature and develop a framework to understand social commerce dynamics for digital natives. We employ PLS and CB-SEM to test our proposed model.

Our findings demonstrate the importance of social commerce information sharing activities in facilitating social support, a sense of warmth and belongingness, and online trust for Generation Z platform users. We also investigate the roles of online trust and perceived risk on intention to purchase and find support for both relationships. Finally, we discuss the findings in terms of theoretical and managerial contributions and conclude the study with limitations and future research directions.

This research is unique by using social commerce theory to explore Gen Z platform users. The finding will contribute to information system literature by expanding the social commerce research stream.

This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.

The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.

This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.

This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.

To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.

The aim was not to perform a systematic review but firstly to search in PubMed, Science Direct, Scopus and Web of Science databases on the papers published in the last five years using tools for reviewing the statement of preferred information item for systematic reviews without focusing on a randomized analysis and secondly to perform a bibliometric analysis on the properties of films and coatings added of tocopherol for food packaging.

On January 24, 2022, information was sought on the properties of films and coatings added of tocopherol for use as food packaging published in PubMed, Science Direct, Scopus and Web of Science databases. Further analysis was performed using bibliometric indicators with the VOSviewer tool.

The searches returned 33 studies concerning the properties of films and coatings added of tocopherol for food packaging, which were analyzed together for a better understanding of the results. Data analysis using the VOSviewer tool allowed a better visualization and exploration of these words and the development of maps that showed the main links between the publications.

In the area of food science and technology, the development of polymers capable of promoting the extension of the shelf life of food products is sought, so the knowledge of the properties is vital for this research area since combining a biodegradable polymeric material with a natural antioxidant active is of great interest for modern society since they associate environmental preservation with food preservation.

There is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.

A survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.

A total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.

The study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.

The aim of this research was to investigate fans’ responses toward a sponsor that has had a long-standing sponsorship deal with a club and decided also to sponsor the club’s rival.

A long-term sponsorship deal between a retsina wine company and a popular football club and a newly established deal between the company and the main rival club were selected as the research setting. Data were collected from a total sample of 302 participants, fans of the two teams, using an online survey and PLS-SEM was employed to test the relationships of the proposed structural model.

The results provided evidence for the importance of the inclusion of perceptions of fit for both teams to the model as it impacted the responses in the joint sponsorship. Team identification emerged significant for improving fans perceptions of fit between the sponsor and their favorite club but also led fans of the long-term sponsored club to feel betrayed from the sponsor. The sense of betrayal impacted the level of fit, the rejection of sponsorship but did not emerge significant for driving negative responses toward the sponsor’s brand. The same held for the rejection of the joint sponsorship.

This is the very first study that incorporated the effects of the perceptions of fit of two rival clubs to test the effect of sponsorship for a sponsor brand of a deal that includes a longtime sponsored football club and its rival as a newly sponsored one. It is also one of the first attempts that explores relationships between perceptions of fit, sense of betrayal and rejection of a joint sport sponsorship in a rivalry context, highlighting the importance of preventing fans' betrayal.

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.

First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.

Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.

To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

大多数先前预测评论有用性的研究忽视了嵌入在评论文本中的深层特征的重要性, 而主要依赖手工制作的特征。手工制作和深层特征具有高解释性和预测准确性的优势。本研究提出了一种新颖的评论有用性预测模型, 利用深度学习技术来考虑手工制作特征和深层特征之间的互补性。

首先, 采用先进的卷积神经网络从非结构化的评论文本中提取深层特征。其次, 本研究利用先前研究中提取的手工制作特征, 这些特征影响了评论的有用性并增强了其解释性。第三, 本研究将深层特征和手工制作特征结合到一个评论有用性预测模型中, 并使用Yelp.com数据集对其性能进行评估。为了衡量所提出模型的性能, 本研究使用了2,417,796条餐厅评论。

广泛的实验验证了所提出的方法优于传统的机器学习方法。此外, 通过实证分析, 本研究证实了结合手工制作和深层特征可以展现出更好的预测性能。

据我们所知, 这是首个在餐厅评论预测中应用深度学习技术, 并结合了结构化和非结构化数据来预测评论有用性的研究之一。此外, 本研究采用了先进的特征融合方法, 更好地利用了提取的特征信息, 并识别了特征之间的互补性。

To delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.

Prior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.

The proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.

The case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.

The framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.