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Technology addiction is an increasingly severe problem. TikTok has become increasingly popular recently, and its addiction is also a major concern. This study aims to examine the antecedents and outcomes of TikTok addiction.

The authors collect 579 data from Chinese users using an online survey. The authors use structural equation modeling with partial least squares (PLS-SEM) to analyze data and test hypotheses.

The results illustrate that perceived enjoyment, social relationship, utilitarian need and social influence positively affect TikTok addiction. Both social anxiety and loneliness have positive effects on TikTok addiction. Moreover, parasocial relationships positively moderate the association between the antecedents of self-determination theory (SDT) (perceived enjoyment, social relationship, utilitarian needs, social influence, social anxiety and loneliness) and TikTok addiction. Meanwhile, TikTok addiction intensifies conflicts, including technology-family conflict, technology-person conflict and technology-work conflict. These conflicts reduce life satisfaction.

It offers practical implications for preventing and avoiding TikTok addiction to create a healthy environment.

This study is one of the few to provide a complete process of TikTok addiction. It systematically investigates the antecedents and outcomes of TikTok addiction.

This study aims to investigate the impact of funding liquidity on bank risk-taking, in the context of developing countries. While high liquidity (low liquidity risk) as measured by ratio of total deposits to total assets may improve bank stability, other strand of literature shows that low liquidity risk may increase bank risk-taking (as measured by Z-score) and lowering bank stability. This study also aims to investigate the moderating influence of the COVID-19 crisis and bank size on the relationship between funding liquidity and bank risk-taking.

The authors collect annual bank-level data from 86 commercial banks in Indonesia from 2014 to 2021, comprising 686 bank-year observations, on return on assets (ROA), equity-to-assets ratio, ROA standard deviation (to calculate Z-score, a proxy for bank risk-taking), ratio of total deposits to total assets (as a proxy for liquidity risk), bank size, bank equity, ratio of operating expenses to income, asset growth, ratio of cash to total assets and COVID-19 period. The authors collect data on gross domestic product growth and inflation. The authors perform system generalized method of moments to investigate the effect of liquidity risk on bank risk-taking, incorporating control variables.

The authors find negative impacts of funding liquidity on the Z-score. Lower liquidity risk tends to increase moral hazard and bank risk-taking. The authors also find that COVID-19 period increases the negative impact of liquidity on bank risk-taking. Thus, banks engage in higher bank risk-taking in COVID-19 period than in non-COVID-19 period. Bank size lower the negative impact of liquidity on bank risk-taking. Thus, larger banks tend to engage in less bank risk-taking than small banks when they face high liquidity (lower liquidity risk).

The results support the notion that liquidity risk has a negative impact on bank risk-taking, thus confirming a potential seed for bank instability. The COVID-19 period, which is characterized by liquidity abundance, tends to increase bank risk-taking when the banks face low liquidity risks. Size tends to lower bank risk-taking in the context of liquidity risk. Small sample size becomes the limitation of this study.

Bank regulatory oversight is imperative all times. Even in a seemingly low-risk situation (high liquidity funding), bank stability may be threatened. The COVID-19 period, which is characterized by liquidity abundance, tends to increase bank risk-taking in the context of liquidity risk. Regulators should monitor small banks more closely because small banks tend to exhibit higher risk-taking in the context of liquidity risk.

The authors investigate the impact of liquidity risk on bank risk-taking, taking COVID-19 period and size as moderating variables. COVID-19 period is characterized by abundant liquidity. However, banks tend to restrict their activities because the risks increase during crisis period.

To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.

The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.

The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.

This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.

User engagement is critical for online health Q&A communities. Financial incentives, which vary across different communities and reward schemes, are expected to motivate such contribution behaviors. Even though financial incentives have been extensively examined in prior studies, the impact of newly designed contingent financial incentives of a new pay-for-answer reward scheme has not been empirically examined in any online health Q&A community. Given this research gap, our study aims to perform an exploratory investigation of the effects of contingent financial incentives on user engagement in terms of knowledge contribution and social interactions.

Based on expectancy-value theory and equity theory, a research model was developed to reflect the influences of contingent financial incentives on user engagement. A unique dataset was gathered from a large online health Q&A community utilizing this contingent financial incentive reward structure, and the Heckman selection model was applied using a two-step procedure to test these hypotheses. Possible endogeneity issues were also addressed in the robustness check.

Our results demonstrate that the effect of contingent financial incentives on answer quantity and quality is quadratic. Additionally, our study reveals that this contingent financial incentive enhances both comment and emotional interactions among users.

Our study enriches the literature on financial incentives, knowledge contribution and user engagement by revealing the nuanced effects of financial incentives within a novel pay-for-answer scheme. This study also offers significant implications for practitioners involved in online community incentive design.

Digitalization in hospitality and tourism (DHT) is undergoing a profound revolution, yet its actual effect remains overlooked. This paper aims to delve into the actual effect of DHT and uncover the underlying mechanisms that trigger its subtle success.

Triangulation was used by leveraging complementary multisource data and mixed methods to explore DHT’s challenges. First, regression and statistical analysis revealed intricate outcome of DHT at the regional level. Then, DistilBERT and nonnegative matrix factorization were used to cluster the tourists’ complaints about DHT. Finally, based on a modified diffusion innovation system and stakeholder theory, this study proposed the Integrated Framework for Digitalization Mismatch in Hospitality and Tourism (IFDMHT).

The quantitative techniques identified the subtle contributions of digitalization on demand. The qualitative technique explored the strategy–practice and demand–supply mismatches as the core of the failure.

This study synthesizes macroregional and micropsychological perspectives to examine the actual effect of DHT. An IFDMHT model is established to investigate the underlying factors influencing the subtle successes of DHT. The findings provide targeted recommendations to assist stakeholders in developing sustainable DHT.

酒店与旅游业数字化（DHT）正经历深刻变革, 但其实际影响常被忽视。本研究旨在深入探讨DHT的实际效果, 揭示其微妙成功的潜在机制。

本研究采用三角验证方法, 通过整合多源数据与混合方法探讨DHT所面临的挑战。首先, 回归分析和统计分析揭示DHT在区域层面的复杂结果; 其次, 利用DistilBERT与非负矩阵分解对游客关于DHT的投诉进行聚类分析; 最后, 基于修订的扩散创新系统与利益相关者理论, 提出了“酒店与旅游业数字化错配综合框架”（IFDMHT）。

定量方法识别了数字化对需求的微妙贡献, 定性技术则深入探讨了战略与实践、需求与供给之间的不匹配是数字化失败的核心原因。

本研究从宏观区域和微观心理学视角出发, 综合审视DHT的实际效果, 并构建了IFDMHT模型, 以探讨影响DHT微妙成功的潜在因素。研究结果为利益相关者提供了具体建议, 以推动可持续的DHT发展。

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This paper aims to investigate the non-overlapped winding dual stator electrically excited flux switching machine (NOW-DSEEFSM) for wind energy applications. To reduce the cost of conventional design, several rotor pole topologies of low cost have been examined, and finally, an optimum design is compared with conventional design.

In this paper, NOW-DSEEFSM is designed, optimized, evaluated and compared with different rotor poles. Initially, the electromagnetic performance of the proposed machine is investigated on the 8, 10, 14, 20 and 22 rotor poles based on the finite element analysis by using JMAG software. From the initial results, 8-, 10- and 14-pole machines are further selected for parametric optimization to enhance the electromagnetic performance. After optimization, the result indicates that the machine with 14 poles can be considered as the overall most appropriate design for the proposed wind generator. Furthermore, analysis has been used on different armature and field current densities to study the effect on electromagnetic performances. Finally, a comparison is performed between the proposed machine and the conventional machine.

In the proposed machine to reduce the copper losses and overhang effect, the NOW topology is used against the well-known overlap winding and provides excellent flux regulation capability due to the existence of field winding, and to obtain high torque and power densities, the presented machine has two stator units and a single robust rotor.

The dual stator flux switching machine with NOW is designed, evaluated and compared to get high torque and power densities.

In this work, we proposed a smart-analysis system of sound events for smart/intelligent environments based on an autonomic cycle of data analysis tasks.

We propose an autonomic cycle of data analysis tasks. An autonomic cycle of data analysis tasks is a set of data analysis tasks that supervise and control a process anonymously, which are based on knowledge models (of prediction, recognition, etc.), interacting with each other to reach a common goal. Each task has a different function in the cycle: observation of the process, or its analysis, or decision-making.

This work presents the autonomic cycle. With its components, this autonomic cycle detects sound information using a taxonomic model of the sound events to analyze them and give a recommendation about the context. The taxonomic model is a hierarchical pattern that considers different aspects to recognize the sound events. This work defines the architecture of this autonomic cycle, specifies its machine-learning-based analysis tasks and evaluates its capabilities of reasoning, adaptation and communication in case studies.

It is important to work in the future on the improvement of the accuracy of the system by implementing neural networks or more sophisticated techniques. To take the implemented autonomic cycle to a higher level, it could use parallel function management. The automation also needs improvement. In addition to that, future works are going to be directed not only to sound events but also to include emotion recognition and its relation with sound events happening simultaneously.

The main contributions of this paper are as follows: the detailed description of the intelligent sound analysis (ISA) autonomic cycle for the smart sound analysis of sound events (SAS-SE) in an intelligent environment (IE); the specification of the machine-learning-based analysis tasks of ISA for the smart sound analysis and the development of a case study that settles the use of the system in different IEs.

This study aims to investigate the causal complexity of ECF investors’ peer effect through two different paths of structural social influence.

Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, we employ 157 samples from a Chinese ECF source to explore how peer-effect are caused by both informational and normative mechanisms.

The findings suggests that there are multiple configurations could lead to ECF investors’ high level peer-effect through both informational and normative mechanisms, and the informational mechanism' role depends on the normative mechanism, while the normative mechanism could lead to peer-effect independently.

The findings enrich the literature on ECF investors’ behaviors by revealing the diverse configurations resulting in investors’ peer-effect and shedding new light on investigating the decision-making driven by information asymmetry and relationship settings for individuals at a disadvantage.

This is the first study that investigates the multiple-driven of ECF investors’ decision-making and the importance of mutual norms in individuals' decision-making by complex network analysis approach and qualitative comparative analysis from the perspective of complexity. The results reveal the complexity of investors’ decision-making in ECF.

The purpose of this study is to determine the strategy adopted by small- and medium-scale enterprise (SME) owners in setting up internal control systems, examine the relationship between the numbers of employees’ fraud and strategy adopted in setting up internal control and evaluate the difference between proactive and reactive strategies in employee’s fraud prevention among SMEs.

A survey research approach was adopted to determine whether proactive or reactive strategies were adopted by the SME owners. Specifically, a survey questionnaire was used to gather primary data from selected respondents in Lagos and Oyo States. Descriptive statistics, Spearman correlation and the Mann–Whitney test were used to analyse the primary data.

The study found that most of the SME owners used reactive internal control strategies in setting up their internal control systems after they suffered financial losses occasioned by employees’ fraud. Also, the study found a positive relationship between the number of employees’ fraud and reactive strategy. Finally, the study found a significant difference in the number of employees’ fraud occurrences between proactive and reactive internal control strategies in SMEs.

The study provides further confirmation that where internal control is properly set up and strengthened, a lower number of employee frauds will occur. Thus, giving credence to the fraud theory. The study was, however, conducted in six selected local government areas in two states.

The study provides recommendations on the adoption of a proactive strategy for curbing employees’ fraud at the onset of business operations and not until devastating events of employees’ fraud become a reality.

The study is original, as it focuses on the strategy adopted by SME owners in setting up internal control systems, which is rare in fraud empirical studies, particularly for studies conducted in emerging markets like Nigeria. It provides the need for the sustainability of SMEs as engine of growth and employment through the adoption of appropriate strategies in setting up internal control systems.