Search results

The purpose of this paper is to understand the impending relationship between the impact of the US–China trade war on Taiwanese firms' spending on R&D and their offshore investment in technologically advanced countries (TAC), the authors examined if changes in these firms' R&D ratios and the growing presence of skilled workers in Taiwan's labour market during the trade war have affected their offshore investments in TAC.

Using a model built on pooled cross-sectional time-series data from 2012–2019, the authors examined whether a change in R&D ratios of domestic firms in Taiwan and the growing presence of skilled workers in Taiwan's labour market have affected the offshore investment by these firms during the trade war. Using data from the Manpower Utilisation Survey, the authors applied differences–in–differences–in–differences and differences–in–differences–in–differences–in–differences estimation methods and found that the trade war indeed gave a boost to Taiwan's job market, particularly for skilled workers.

From the estimation results, the authors noticed a rise in employment opportunities alongside a decline in the earnings of skilled workers in industries where more firms have spent on R&D as well as invested in offshore operations. However, firms in Taiwan that had not heavily spent on R&D from industries where investment in foreign operations was otherwise high have also attracted skilled workers during the trade war.

An in-depth analysis of the impact of the trade war on domestic firms' spending on R&D and their investment in offshore operations in TAC should be helpful to policymakers interested in understanding the effects of the trade war and subsequent changes in firms' spending on R&D on labour market outcomes. If changes in the R&D ratios and a steady supply of skilled workers influenced the outflow of Foreign Direct Investment (FDI) to TAC, this insight could be helpful for those devising policies and measures to curb the impact of the trade war on domestic spending on R&D.

The study findings not only provide broad lessons to policymakers in Taiwan, but the country case study can guide growing economies that are equally careful while perceiving trade war as a significant deterrent to domestic R&D spending and the outflow of FDI.

This chapter examines the six smart city dimensions that serve as pillars in smart city projects. These dimensions are crucial in the development and evaluation of smart city initiatives, representing key areas for consideration. This chapter offers a detailed analysis of the smart city ecosystem, focusing on the governance, environment, people, living, mobility, and economy dimensions. It challenges the prevailing media portrayal of the smart city strategy and engages in the current academic debate surrounding these dimensions. This chapter defines, discusses, and explains each dimension, incorporating case studies from cities such as Copenhagen, San Francisco, Lisbon, and Barcelona. It also includes interviews and factual data to highlight the internal implementation and objectives of the smart city within each dimension. This chapter provides a comprehensive understanding of the smart city ecosystem, its implementation, and the potential benefits and challenges associated with each dimension.

Due to the close connection between urban cluster and carbon emissions (CEs) but a lack of study on it of the construction industry, this paper aims to explore the relationship between the polycentric spatial structure (PSS) of the urban clusters and CEs of the construction industry (CECI).

This research uses panel data of 10 Chinese urban clusters from 2006–2021, calculates their PSSs in the aspects of economy and employment and adopts a panel regression model to explore the effect of the spatiotemporal characteristics of the PSSs on the CECI.

First, the CECI in 10 Chinese urban clusters showed a rising trend in general, and the CECI in the Yangtze River Delta (YRD) was much higher than those in the rest of urban clusters. Second, both Shandong Peninsula (SP) and Guangdong-Fujian-Zhejiang (GFZ) exhibited high degrees of polycentric characteristics, while Beijing-Tianjin-Hebei (BTH) showed weaker degrees. Third, the results demonstrated that the polycentric development of urban clusters did not help reduce the CECI but rather promote the CE. The polycentric index, considering the linear distance from the main center to sub center, had a more significant impact on the CECI.

Previous studies have investigated the impact of urban spatial structure (USS) on CEs; however, few of them have studied in the field of construction industry. Moreover, most research of CEs have concentrated at the national and provincial levels, with fewer studies on urban clusters. This paper contributes to this knowledge by investigating how the PSS of urban cluster influence the CECI.

The chapter is about the revamping of the hospitality and hotel industry in the South East Asian Region after the COVID-19 pandemic. The hotel industry contributes largely to the GDP of the economy. Therefore, the success of this industry is mandatory for the development of a country. The reconstruction of this sector depends upon the advanced techniques and practices to boost up this sector after crisis. Therefore, to implement such practices highly depends upon the highly educated and aware managers. This chapter focuses on how crisis management plan revamps this industry after the greatest destruction.

This chapter aims to provide a comprehensive understanding of the urban outcomes of smart city projects, focusing on their primary objectives. The first objective is to facilitate the management and flow of information, data, and resources to enhance resource efficiency, sustainability, and the quality of life for citizens and stakeholders. This chapter offers insights into the urban objectives of smart city projects within the local ecosystem, with a specific emphasis on digital and key urban outcomes. It provides an overview of the digital outcomes, including the advancement of digital systems for safety and urban monitoring, the provision of customized digital services, and the promotion of citizen engagement through digital platforms. This chapter also evaluates the environmental outcomes of smart city projects, such as improved quality of life, increased urban efficiency, and contributions to a sustainable environment. To provide a well-rounded understanding, interviews with policymakers and city managers, as well as case studies from cities like London, Medellin, Helsinki, Singapore, Girona, and San Diego, are incorporated. Furthermore, this chapter incorporates data and findings from top-tier international journals to provide a clear understanding of the impact of smart cities on the local ecosystem.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This chapter examines the impact of smart city projects on the economic and business environment, with a specific focus on their potential to foster innovation and entrepreneurship. It highlights how smart cities create an enabling environment for new businesses, start-ups, and promotes gender and green entrepreneurship, while enhancing urban properties and contributing to the overall entrepreneurial ecosystem. The exponential growth of smart city projects worldwide has underscored the critical relationship between smart cities and the entrepreneurial ecosystem for city prosperity and competitiveness. This chapter explores the interplay between smart cities and the economic environment, emphasizing their role as hubs for innovation and business creation. It analyzes the influence of smart city ecosystems and economic factors on local growth and economic outcomes, drawing on international research and factual data. This chapter evaluates the economic outcomes of smart cities, including their impact on the economic environment, business creation, start-ups, innovative and green companies, as well as gender entrepreneurship. This chapter emphasizes the significance of smart city implementation in driving economic advancements within both the urban and economic landscapes. Finally, this chapter concludes by presenting a conceptual framework that synthesizes the insights gained, providing a comprehensive understanding of the economic impact of smart cities and offering a roadmap for further exploration of this topic.

Although many studies have sought to address the topic of continuance intention among Fintech customers, the reported findings are fragmented. Therefore, the present study proposes a research model that integrates the main constructs involved in Fintech continuance intention.

The current study uses a meta-analytic-based correlation analysis of effect sizes, meta-regression analysis and meta-analytic structural equation modeling, with 247 effect sizes in 69 studies involving 26,140 respondents.

The results reveal continuance intention is driven by satisfaction and trust, with ease of use and usefulness being antecedents of satisfaction and trust. The authors also found evidence to show satisfaction partially mediates the relationship between ease of use and continuance intention and that trust fully mediates the relationship between ease of use and continuance intention and partially mediates the relationship between usefulness and continuance intention. In addition, the authors found that in Western countries, with higher Human Development Index levels and greater of use of electronic payment, satisfaction has more impact on continuance intention.

From a theoretical standpoint, this meta-analytic study has implications for the literature on Fintech by offering an empirical generalization on the strength of the antecedents of Fintech continuance intentions and by testing possible moderators in a wide range of countries and studies. In other words, this study’s goal is to broaden the scope of the research. Regarding managerial implications, it is important to listen to user opinions regarding the positive and negative points of their experience with these technologies and take them into consideration when planning improvements. Additionally, the analysis shows the importance of using data from user interaction with technology, obtained, for example, through big data analytics, whereby companies can see how users behave, how much time they spend accessing certain functions and which technological features they use most, and thus seek to improve whatever is needed.

This meta-analytic study advances the understanding of Fintech continuance intentions. Using the proposed approach, it is possible to generate accurate estimates of the effect size of each analyzed antecedent as the meta-analytic method jointly evaluates the results produced by a wide variety of studies performed in different contexts, allowing more accurate conclusions to be drawn.