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There is a dearth of knowledge regarding how user dependency offers valuable resources to develop the intellectual capital of social streaming apps (SSAs) companies. This study aims to integrate major conceptual components of the UandD model, identify contextualized goal-oriented SSA dependency and empirically evaluate their interrelated user-dependency relationships in the SSA context.

A mixed-methods approach was utilized in this study. First, user gratifications were elicited through a qualitative approach, considering the exploratory stage of the SSA phenomenon. Second, statistical methods were applied to investigate and extract the sub-dimensions of SSA dependency. At last, a research model was developed grounded on the UandD model and empirically validated using the quantitative approach.

The results validated the gratification-dependency-attitude-behavior relationships hypothesized by the UandD framework in SSA. The role of user-SSA dependency in enhancing intellectual capital in the social media industry has been highlighted in this study.

This research not only provides an opportunity for the UandD model to realize its theoretical potential as envisioned by scholars but also contributes to the scholarship on social streaming apps and media dependency theory by conceptualizing goal-oriented dependency in SSAs.

The research results will guide digital media practitioners to a more nuanced understanding of the relationships between their users and modern digital media apps and thus empower the practitioners to better manage their intellectual capital based on the facilitation of their users’ dependency.

This work is one of the pioneers in contextualizing the UandD model in the SSA field, refining and measuring the SSA dependency and its distinct subdimensions and employing mixed-methods to offer a comprehensive understanding of how user dependency boosts intellectual capital in the SSA industry.

This study embraces the call for exploring the determinants of continuous intention in TikTok. Taking the perspective of social influence, this study not only tries to explore the contextual sources of two types of social influence but also aims to unveil the influence mechanism of how social influence affects TikTok viewers’ continuous intention.

This study empirically analyzes how TikToker attractiveness, co-viewer participation, platform reputation and content appeal affect informative and normative social influence and then lead to the continuous intention of TikTok. Based on 547 valid survey data, this study adopts a mixed analytical approach for data analysis by integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

SEM results unveil that content appeal is the most critical antecedent of informational social influence, while the TikToker attractiveness and platform reputation have no effect on it. Differently, all four external sources positively lead to normative social influence. Among them, content appeal and co-viewer participation influence the most. The influences of both two types of social influence on continuous intention are demonstrated. FsQCA results reveal seven alternative configurations that are sufficient for influencing continuance intention and further complement and reinforce the SEM findings.

Addressing the critical contextual elements of TikTok, this study explores and confirms the sources which may engender social influence. The authors also demonstrate the critical role of social influence in affecting TikTok viewers’ continuous intentions by the hybrid analytical approach, which contributes to existing academic literature and practitioners.

The purpose of this paper revolves around the informal coming together of various doctoral students in the area of qualitative accounting and management research and the attempt to learn from their respective experiences. Together, the authors share personal reflections and valuable insights in revealing their vulnerabilities, aspirations and how they make sense of the PhD journey and their becoming as academics.

This paper builds on an open discussion and written reflections among the authors, who represent a diverse set of both doctoral students at various levels and recent graduates from different countries, schools and backgrounds.

The discussion highlights the struggles the authors experience as doctoral students, how they learn to cope with them as well as how they are socialized throughout their PhD journey. This allows them to take a critical stance towards increased productivity demands in academia and to embrace doctoral students as a powerful collective, whose aspirations may inspire a change of academic reality for the better.

While guidance on how to succeed as doctoral students is common, we seldom hear about doctoral students as particularly “fragile selves” (Knights and Clarke, 2014) who, as opposed to more established scholars, are more actively experiencing difficulties with finding their ways in academia. The authors are thus motivated to create a rare common voice of a group of doctoral students here by providing a more intimate account of the PhD journey.

This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.

The study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed analytic method that incorporates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to provide a complete picture of individual information process mechanisms in AR retailing under the tenet of ELM.

The SEM analysis results confirm the relationships between the central and peripheral route factors, information processing outcomes and eventual behavioral intentions. Moreover, all configurations revealed by the fsQCA include both central and peripheral factors. Hence, the dual routes proposed in the ELM are verified by using two distinct analytical approaches.

This study is pioneering in validating and contextualizing ELM theory in AR retailing. In addition, this study offers a methodological paradigm by demonstrating the application of multi-analysis in exploring consumers’ information process mechanisms in AR retailing, which offers a holistic and comprehensive view to understand consumers’ decision-making mechanisms.

Combining the coping theory and social support theory, this study aims to reveal users' coping strategies for mobile fitness app (MFA) engagement and fitness intentions with a rigorous and comprehensive hybrid research approach.

A three-stage hybrid research design was employed in this study. In the first stage, this study utilized structural equation modeling (SEM) to investigate the associations between coping resources and coping outcomes. A post hoc analysis was conducted in the second stage to unveil the reasons behind the insignificant or weak linkages. In the third stage, the fuzzy-set qualitative comparative analysis (fsQCA) technique was applied to explore the various configurations of coping resources that lead to the coping outcomes.

The results in the three stages verify and compensate each other. The SEM results confirm the presence of two coping strategies in MFA, highlighting the importance of the intertwining of the strategies, and the post hoc analysis unveils the mediating role of positive affect. Moreover, the fsQCA results reinforce and complement the SEM findings by revealing eight alternative configurations that are sufficient for leading to users' MFA engagement and fitness intention.

This study offers a prominent methodological paradigm by demonstrating the application of multi-analysis in exploring users' coping strategies. In addition, the study also advances the understanding of the complexity of the mechanism that determines users' behavioral decisions by presenting a comprehensive interpretation.

The purpose of this paper is to model the effect of alternative products in motivating consumers’ e-service switching behavior in the context of the social network game (SNG) industry. In particular, the effects of both alternative attractiveness and change experience on switching behavior are quantified.

With the aid of a leading e-service provider in China, 220,000 questionnaires were distributed to the players of a SNG. Valid responses from potential switching users are included in the data analysis. Structural equation modeling technique is utilized to test the research framework.

The study found that alternative attractiveness negatively affects both the perceived service quality and individual users’ satisfaction with their current SNG. Additionally, alternative attractiveness has a strong and positive impact on both switching intention and behavior. The results show that users’ satisfaction and perceptions on service quality deteriorate significantly when faced with the presence of attractive SNG alternatives. The effect is stronger for the customers used to switching.

The study is among the first to introduce cognitive dissonance theory to explain e-service switching behavior. A number of new hypotheses are proposed, tested and supported. The results of the study illustrate the use of cognitive dissonance as an alternative perspective of understanding users’ switching behavior in a real-world free-choice situation.

This study aims to present the critical role of knowledge management in digital entrepreneurship by reviewing the literature and proposing future research directions for digital entrepreneurship and knowledge management through an interdisciplinary framework.

This study uses the Derwent Data Analyzer to identify and visualise the extant studies on digital entrepreneurship. This study qualitatively analyses the hot topics and trends in digital entrepreneurship research to understand digital entrepreneurship from the knowledge management perspective.

The authors found two dominant trends in existing research: logical and development trend exploration at the theoretical background and empirical research at the practical dimension. To understand digital entrepreneurship from a knowledge management perspective, the authors summarised the theoretical logic and internal and external reasons why knowledge management is required in digital entrepreneurship. Moreover, the authors analysed the new features of digital entrepreneurship under five aspects: management concept, object, content, scope and focus. The authors concluded that existing research on integrating knowledge management and digital entrepreneurship is primarily conducted from three perspectives: technology, platform and ecosystem.

This study provides an in-depth analysis of digital entrepreneurship from a knowledge management perspective. The findings can further promote the theoretical research and practical development of digital entrepreneurship and knowledge management. This approach provides a new direction for interdisciplinary study and enriches entrepreneurship research. In addition, this study proposes a knowledge management framework for digital entrepreneurship research. The findings contribute to understanding the role and function of knowledge management in digital entrepreneurship.

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

本研究使用集成的四层神经网络构建面向方面的情感分类模型：BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

The existing Nonlinear Dynamic Vibration Absorbers (NLDVAs) have the disadvantages of complex structure, high cost, high installation space requirements and difficulty in miniaturization. And most of the NLDVAs have not been applied to reality. To address the above issues, a novel Triple-magnet Magnetic Dynamic Vibration Absorber (TMDVA) with tunable stiffness, only composed of triple cylindrical permanent magnets and an acrylic tube, is designed, modeled and tested in this paper.

(1) A novel TMDVA is designed. (2) Theoretical and experimental methods. (3) Equivalent dynamics model.

It is found that adjusting the magnet distance can effectively optimize the vibration reduction effect of the TMDVA under different resonance conditions. When the resonance frequency of the cantilever changes, the magnet distance of the TMDVA with a high vibration reduction effect shows an approximately linear relationship with the resonance frequency of the cantilever which is convenient for the design optimization of the TMDVA.

Both the simulation and experimental results prove that the TMDVA can effectively reduce the vibration of the cantilever even if the resonance frequency of the cantilever changes, which shows the strong robustness of the TMDVA. Given all that, the TMDVA has potential application value in the passive vibration reduction of engineering structures.