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The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

Food SMEs is the backbone of local and world economy. Even while food SMEs are aware of the potential advantages of implementing supply chain analytics (SCA), only a small number of companies use data-based decision-making. This is because of technophobia. In light of this, the purpose of this study is to investigate the factors that have an impact on SCA adoption which in turn influence the sustainable performance of firms.

The data were collected from 221 managers working in food-related SMEs in China by using a questionnaire-based survey. The framework of this study was validated using a rigorous statistical procedure using the technique, namely, partial least squares structural equation modelling.

The findings of this study suggest that all modified UTAUT components (i.e. performance expectancy, effort expectancy, social influence, facilitating conditions and technophobia) significantly influence SCA adoption. Moreover, the existing study highlights and confirms the significance of adopting SCA to improve sustainable performance.

This research is novel, as it extends and investigates the theoretical framework based on UTAUT theory in SCA context and its impact on sustainable organizational performance. In addition, the factor of technophobia is tested in SCA context. This study has several contributory managerial implications for food SMEs.

China's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.

This study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.

The results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.

The research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.

This study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

The purpose of this study is to comprehensively explore the password manager adoption landscape, delving into crucial factors such as performance, trust, social influence, self-efficacy, risk perception, security concerns, enjoyment and facilitating conditions. It also aims to contribute meaningful insights to security product research and practice.

A survey was used to investigate the characteristics of adoption intention for password managers. In total, 156 participants from a public university located in the Midwest region of the USA voluntarily completed the survey. Partial least squares structural equation modeling was used to estimate and validate causal relationships and the proposed research model.

Through empirical validation, this study demonstrates that constructs such as social influence, web-specific self-efficacy and perceived risk directly impact trust in password managers. Facilitating conditions and perceived security controls are identified as direct influencers on performance expectancy, deviating from the pathways of the traditional framework. Moreover, the model introduces novel elements crucial for comprehending password manager adoption, including “web-specific self-efficacy” and “perceived security control.”

The paper systematically reviews existing research on password managers, shedding light on crucial factors significantly influencing adoption behavior. By introducing deviations from conventional frameworks and theories, the study emphasizes the innovative nature of its model. It also formulates strategies to catalyze wider adoption and promote effective design of password managers, increasing user engagement rates.

The purpose of this study is to prolong the service life of the Al–Si alloy cylinder and achieve the objective of energy saving and emission reduction by the composite treatments.

Chemical etching + laser texturing + filled MoS₂ composite treatment was applied to the friction surface of aluminum–silicon (Al–Si) alloy cylinder. The friction coefficient and wear loss were measured to characterize the tribology property of cylinders.

The composite-treated Al–Si alloy cylinder had the lowest friction coefficient and weight loss. The friction coefficient and weight loss of the composite treatment were approximately 27.08% and 54.17% lower than those of the untreated sample, respectively. The laser micro-textures control the release of solid lubricant to the interface of friction pairs slowly, which prolongs the service life of cylinders.

The synergistic effect of the chemical etching + laser texturing + filled MoS2 modified the tribology properties of Al–Si alloy cylinder. The chemical etching raised the silicon particles to bear the load, and laser micro-textures control the release of solid lubricant to improve the lubrication property.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

This study aims to improve the control accuracy and antidisturbance performance of the manipulator with the flexible link, a combined controller, which combines the novel backstepping sliding mode controller based on the extended state observer (ESO) and super-twisting sliding mode controller.

First, the dynamic of the system is constructed by Lagrange method and assumed mode method, and then the dynamic is decoupled by the singular perturbation theory to obtain the slow-varying subsystem and fast-varying subsystem. For the slow-varying subsystem, the novel backstepping sliding mode controller based on ESO is used to achieve joint tracking. For the fast-varying subsystem, the super-twisting sliding mode controller is used for vibration suppression. At the same time, to suppress chattering, the tanh function is used to replace the sign function in the reaching law.

The simulation results show that the combined control has better trajectory tracking performance, antiinterference performance and vibration suppression performance than traditional sliding mode control (SMC).

A novel backstepping sliding mode controller based on ESO is designed to guarantee the performance of the tracking trajectory. The new controller improves the converge rate. A super-twisting sliding mode controller, which can stabilize the fast-varying subsystem, is used to suppress the vibration of flexible link.

Despite the importance of top management team (TMT) gender diversity in a firm's strategic decisions and the high degree of innovation activities that several firms have experienced in recent years, little or no research has examined how TMT gender diversity affects a firm's open innovation decision. The authors examine how TMT gender diversity impacts firms' open innovation activities. The authors further examine how this impact is affected by women executives' personal attributes and institutional conditions.

The sample comprised of 62,745 firm-year observations (9,831 firms) from 25 countries from 1990 to 2010. The authors employed the system generalized method of moments (GMM) estimation technique to estimate the results.

Employing novel panel data on co-owned patents across 25 economies, the authors find that proportion of women in TMTs has a positive impact on open innovation activities. Moreover, the authors find that women managers' power and institutional gender parity strengthen the association between gender diversity and open innovation.

The findings of this study indicate that firms committed to optimizing their open innovation policies and practices should include women in TMTs and create such conditions that are supportive for women executives to effectively express their innate inclinations. Importantly, our study supports the business case for gender diversity in top leadership positions by providing a compelling evidence for the positive impact of TMT gender diversity on open innovation.

This study contributes to the gender diversity literature by showing how women leaders' values and character become embedded in their companies' strategy and present empirical evidence that having women in TMTs increase the likelihood of conducting open innovation. Further, the authors show how women executives' power and institutional level gender parity provide boundary conditions that moderate the relationship between TMT gender diversity and open innovation.