Search results

This study longitudinally investigated the predictors and mediators of adolescent smartphone addiction by examining the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3, as well as the separate and sequential role of adolescent self-esteem and depression at T2 as mediating factors.

This study used a hierarchical regression and the PROCESS macro (Model 6) to investigate research model by collecting 3,904 parent-adolescent pairs. Panel data were collected from three waves of the Korean Children and Youth Panel Survey (KCYPS).

First, the result showed that parental smartphone addiction at T1 significantly and positively predicted adolescent smartphone addiction at T3. Second, the serial mediation analysis revealed that the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3 was mediated by adolescent self-esteem and depression at T2 independently and serially.

The findings enhance our comprehension of the impact of parental smartphone addiction, adolescent self-esteem and depression, on adolescent smartphone addiction.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

The connection between critical thinking and students' reliance on Artificial Intelligence (AI) has been examined in this chapter. While there are many educational advantages to AI technology, it has been demonstrated that a heavy dependence on it may impair autonomous problem-solving and critical thinking skills. The research highlights the necessity of incorporating a critical thinking approach into educational programs to assist students in scrutinizing, analyzing, and assessing AI technologies. For data analysis descriptive and correlational analysis were conducted to test the research hypothesis. Furthermore, by honing their critical thinking skills, students will be able to question data supplied by AI, recognize potential biases, assess the validity of sources, and make defensible conclusions. Additionally, the findings show a positive relationship between using AI and critical thinking toward transformation of innovative learning. This study highlights the importance of critical thinking abilities in navigating an AI-driven society by demonstrating a link between higher critical thinking capabilities and a stronger desire to employ AI technology. It is advised that lawmakers and educational institutions give the integration of critical thinking abilities in curricula top priority considering these findings. Furthermore, future studies should focus on the difficulties in integrating AI into classroom environments and the long-term impacts of a critical thinking approach on AI dependence. This chapter's main contention is that critical thinking is necessary for the ethical use of AI, autonomous reasoning, and independent decision-making.

The purpose of this paper is to optimize a suitable electrochemical method in evaluating the corrosion rate of structural materials of 20# carbon steel, P280GH carbon steel, 17-4PH stainless steel, 304 stainless steel and Alloy 690TT in high-temperature and high-pressure (HTHP) water of pressurized water reactor secondary circuit system.

Weight-loss method has been used to obtain the corrosion rate value of each structural material in simulated HTHP water. Besides, linear polarization method and weak polarization curve-based three-point method and four-point method have been compared in obtaining a sound corrosion rate value from the potentiodynamic polarization curve. Scanning electron microscopy (SEM) and atomic force microscope have been used to characterize the microstructure and corrosion morphology of each structural material.

Although there is deviation in gaining the corrosion rate value compared to weight-loss test, the weak polarization curve-based four-point method has been found to be a suitable electrochemical method in gaining corrosion rate value of structural materials in HTHP waters.

This paper proposes a suitable and reliable electrochemical method in gaining the corrosion rate value of structural materials in HTHP waters. The proposed weak polarization curve-based four-point method provides a timesaving and high-efficient way in corrosion rate evaluation of secondary circuit structural materials and thus has a potential application in nuclear power plants.

This study aims to explore the development and significance of sustainable finance instruments, mainly sustainable bonds. The purpose is to provide policymakers, regulators and researchers with insights into the current state of sustainable finance research and also provide future research directions.

This study used Scientific Procedures and Rationales for Systematic Literature Reviews as a review protocol and addressed four research questions concerning publication and citation trends, major themes and future research directions in sustainable bonds.

This study indicated growing attention in sustainable bond research, with increasing publication and citation trends. Along with identifying research themes, the findings include future direction on pricing and risk assessment, market dynamics and growth potential, policy and regulatory environments and global perspectives with local context.

Although this study provides a robust analysis of the current literature, it relies on existing publications and may not capture the latest developments in sustainable bond research. However, policymakers can benefit from insights into the growth and dynamics of sustainable bonds, enabling them to implement effective policies and regulations. Investors and businesses can use this research to inform their environmental, social and governance investment strategies and decision-making processes.

This paper suggests a comprehensive overview of the state of research in sustainable bonds, highlighting the emerging trends and research priorities. It also underlines the significance of sustainable finance in achieving sustainability goals and provides a roadmap for future research.

Organizations increasingly use knowledge arbitrage to stimulate innovation and achieve competitive advantage. However, in knowledge management its use in startups is yet unexplored. This study aims to examine the utilization of knowledge arbitrage by startups, specifically during COVID-19.

This study employed an open-ended essay methodology to explore the drivers and barriers that startups face in utilizing knowledge arbitrage. We collected data from 40 participants to understand the role of knowledge arbitrage in startups’ knowledge management practices.

This study’s findings highlight the significance of knowledge arbitrage for startups. The benefits identified include organizational benefits such as building networks, innovating new products and achieving competitive advantage and financial benefits such as cost reduction and sales growth. The study also identifies several technological and organizational drivers and barriers that startups confront during knowledge arbitrage.

This study contributes to the existing literature on knowledge management by extending our understanding of knowledge arbitrage’s role in startups. Additionally, it sheds light on the importance of knowledge arbitrage for startups and the challenges they face, particularly in a disrupted environment reared by COVID-19. The study provides insights for the scholars and practitioners interested in effective knowledge management in startups.

This study aims to explore the emerging trend of business-wide Sustainability Third-Party Labels (STPLs), exemplified by entities like B-Corp. These labels are awarded to organizations committed to a distinctive approach to business, typically embracing the triple-bottom-line (TBL) framework, prioritizing not only financial performance but also social and environmental impact. The research investigates whether these labels enhance trust and influence perceptions of sustainability information quality among young consumers in Spain.

A factorial experiment has been conducted among a convenience sample of individuals belonging to the Z-generation (n = 126). The experiment involved randomly exposing the participants to different versions of an informational brochure from a fictional company in the agricultural sector (with and without label). Following the experiment, a focus group with 15 participants was conducted to assist in interpreting the results.

The results of this study suggest that the use of a nonsector specific label across various sectors with distinct sustainability challenges can lead to confusion among Z-generation consumers. Especially within sectors grappling with environmental concerns, such labels may be susceptible to being perceived as manifestations of greenwashing. Additionally, the study adds supporting evidence to the existing body of literature asserting gender differences in the interpretation of sustainability signals, including labels.

As far as this research is concerned, to the best of the authors’ knowledge, this is the first research that studies the perception of Z-generation members regarding business-wide STPLs. Focusing on studying, the attitudes toward sustainability of younger generations and how they respond to signals like business-wide STPLs are relevant, as they not only possess the longevity to drive substantial change but are also more susceptible to behavioral shifts, thereby holding significant potential in shaping a sustainable future. The study combines both qualitative and quantitative perspective and provides critical insights, relevant to stakeholders within business-wide STPL ecosystems, emphasizing the need for strategic coherence and transparency in label implementation.

Additive manufacturing offers the ability to produce complex, flexible structures from materials like thermoplastic polyurethane (TPU) for energy-absorption applications. However, selecting optimal structural parameters to achieve desired mechanical responses remains a challenge. This study aims to investigate the influence of key structural characteristics on the energy absorption and dissipation behavior and the deformation process of 3D-printed flexible TPU line-oriented structures.

Samples with varying line orientations and infill densities were fabricated using material extrusion and subjected to quasi-static compression tests. The design of experiments methodology explored the significance of design variables and their interaction effects on energy absorption and dissipation.

The results revealed a statistically significant interaction between infill density and orientation, highlighting their combined influence; however, the effect was less pronounced compared to infill density alone. For low-density structures, changing the orientation from 0°/90° to 45°/−45° and increasing infill density enhanced energy absorption and dissipation, while high-density structures exhibited unique energy absorption behavior influenced by deformation patterns and heterogeneity levels. This study facilitates the prediction of mechanical responses and selection of suitable TPU line-oriented printed parts for energy absorbing applications.

To the best of the authors’ knowledge, the present work have investigated for the first time the energy-related responses of flexible line-oriented TPU structures highlighting the distinction between the low and high density structures.

This paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?

Com_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.

Experiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.

Com_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.

Com_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

This study aims to provide a new perspective on the factors determining a country’s tourism performance, understand the interrelationships among these factors and explore their implications for the future of tourism in high-income countries.

The study employs a fuzzy-set qualitative comparative analysis (fsQCA) using five variables from the World Economic Forum’s Travel and Tourism Development Index (TTDI). The focus is on identifying seven configurations of antecedents of Travel and Tourism Industry Gross Domestic Product (T&T Industry GDP).

The study identifies seven configurations of antecedents influencing T&T Industry GDP, revealing how these factors operate in different scenarios, specifically in countries with high and low T&T GDP. These configurations offer insights into potential future pathways for tourism development.

The study implies that tourism is a complex phenomenon influenced by multiple interacting factors. It provides a framework for understanding how different combinations of factors can lead to high or low tourism performance, offering valuable insights for anticipating and shaping the future of tourism.

This study adds value by providing a more nuanced understanding of the tourism industry, challenging the notion of singular effects of variables and highlighting the importance of analyzing multiple, interacting factors in understanding and predicting tourism performance. It contributes to the field of futures studies by offering a tool for anticipating potential future scenarios and their impact on the tourism industry.