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Using bibliometric techniques, the author analyzes a dataset of 276 articles on cross-border mergers and acquisitions (CBMAs) published in 13 management and international business journals. The author assesses the scientific impact and visualizes the intellectual landscape of research on CBMAs by analyzing publication and citation data and interconnections between publications. First, the author assesses annual publication trends and identifies highly cited articles and productive journals in the dataset that have significantly contributed to our understanding of CBMAs. Second, the author identifies main themes in recent research on CBMAs by focusing on frequently used keywords in publications. Third, the author identifies clusters of related research and explores their interrelationships to outline emerging trends, new perspectives, and directions for future research on CBMAs. Overall, this chapter contributes to the understanding of CBMAs by documenting the progress made to date and providing important insights for future research.

The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs.

This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

The primary objectives of this article are to systematically explore whether and how certain WeChat use motives could lead to bridging social capital, bonding social capital and civic engagement among young people.

The data was collected from a large-scale online survey of 1208 young people in mainland China. Zero-order correlation analyses and structural equation modeling were carried out to examine the corresponding hypotheses.

Obtained findings show that WeChat use for informational and social motivations are positively associated with bonding and bridging social capital. Moreover, bonding social capital could mediate the relationship between WeChat usage for informational and relational motivations and civic engagement.

Theoretically, this article underlines the unique social and technological affordances of WeChat by exploring mobile social media use and how it would contribute to the quality of democracy by fostering young people's engagement in civic life. Practically, bridging and bonding social capital play significant roles in enhancing young people's civic engagement, which could be the meaningful resource for mobile social media designers, managers and government officials.

These obtained outcomes underlined the vital role of these newly emerging communication technologies in fostering democratic involvement and production of social capital in contemporary socially networked society.

Contemporary stochastic optimal control by synergy of the probability density evolution method (PDEM) and conventional optimal controller exhibits less capability to guarantee economical energy consumption versus control efficacy when non-stationary stochastic excitations drive hysteretic structures. In this regard, a novel multiscale stochastic optimal controller is invented based on the wavelet transform and the PDEM.

For a representative point, a conventional control law is decomposed into sub-control laws by deploying the multiresolution analysis. Then, the sub-control laws are classified into two generic control laws using resonant and non-resonant bands. Both frequency bands are established by employing actual natural frequency(ies) of structure, making computed efforts depend on actual structural properties and time-frequency effect of non-stationary stochastic excitations. Gain matrices in both bands are then acquired by a probabilistic criterion pertaining to system second-order statistics assessment. A multi-degree-of-freedom hysteretic structure driven by non-stationary and non-Gaussian stochastic ground accelerations is numerically studied, in which three distortion scenarios describing uncertainties in structural properties are considered.

Time-frequency-dependent gain matrices sophisticatedly address non-stationary stochastic excitations, providing efficient ways to independently suppress vibrations between resonant and non-resonant bands. Wavelet level, natural frequency(ies), and ratio of control forces in both bands influence the scheme’s outcomes. Presented approach outperforms existing approach in ensuring trade-off under uncertainty and randomness in system and excitations.

Presented control law generates control efforts relying upon resonant and non-resonant bands, and deploys actual structural properties. Cost-function weights and probabilistic criterion are promisingly developed, achieving cost-effectiveness of energy demand versus controlled structural performance.

As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.

Based on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.

First, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.

Studying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.

This paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.

This study aims to evaluate how the structure of medical compression stockings, including three compression levels and five cross-sections from the ankle to the thigh part, will be changed after washing in different conditions and further investigate the effect of the washing parameters on the medical compression stockings.

By washing medical compression stockings in different conditions and measuring the structures (including the density, the girth, the transversal and lengthwise dimension, the weight per unit area and the thickness) of medical compression stockings from the knee to the thigh part.

It was concluded that the density, the weight per unit and the thickness increase and the girth, the transversal and lengthwise dimension, the weight per unit and the thickness decrease. The change degree of Class one and Class two is greater than Class 3. Moreover, the washing temperature is the most significant factor affecting all the structures of medical compression stockings. Meanwhile, the mechanical actions of the washing machine, like drum speed and washing time, also influence different medical compression stockings structures to different degrees.

The washing parameter not only includes the temperature and washing cycles but also has other factors, such as the drum speed and washing time. In addition, different kinds of factors will be influenced by each other.

This study can provide consumers advices on the washing of medical compression stockings, and attribute to the optimization of materials and structures to maintain its properties for manufacturers.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This study broadens the understanding of how omnichannel service convenience, shopping value and channel congruence affect customer perceived trust and satisfaction and, in turn, affect selection intention after an omnichannel shopping experience.

Target participants were recruited based on previous purchases from the Japanese clothing brand Uniqlo. A questionnaire was distributed via social media. In total, 341 valid responses were collected for structural equation modelling (SEM).

The results revealed that in omnichannel shopping context, perceived trust and satisfaction are positively affected by service convenience and shopping value and are especially affected by channel congruence. Further analysis showed that perceived trust and satisfaction have a positive effect on omnichannel selection intention, with satisfaction playing a mediating role in the relationships of omnichannel service convenience, shopping value and channel congruence with omnichannel selection intention.

This study contributes to the literature on omnichannel customer behaviour by shedding light on the antecedents of intention to select omnichannel retailers from the customer’s perspective.

The purpose of this paper is to study the high temperature oxidation behavior of Ti and C-added FeCoCrNiMn high entropy alloys (HEAs).

Cyclic oxidation method was used to obtain the oxidation kinetic profile and oxidation rate. The microstructures of the surface and cross section of the samples after oxidation were characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM).

The results show that the microstructure of the alloy mainly consisted of FCC (Face-centered Cubic Structure) main phase and carbides (M₇C₃, M₂₃C₆ and TiC). With the increase of Ti and C content, the microhardness, strength and oxidation resistance of the alloy were effectively improved. After oxidation at a constant temperature of 800 °C for 100 h, the preferential oxidation of chromium in the chromium carbide determined the early formation of dense chromium oxide layers compared to the HEAs substrate, resulting in the optimal oxidation resistance of the TC30 alloy.

More precipitated CrC can preferentially oxidize and rapidly form a dense Cr₂O₃ layer early in the oxidation, which will slow down the further oxidation of the alloy.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.