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Rosewood, as the most internationally traded endangered species, is subject to a series of restrictive trade policies globally. China has historically been the largest importer of rosewood in the world. The fluctuation of China’s rosewood import prices will have a profound impact on the global rosewood trade pattern. This study, therefore, assessed the impact of restrictive trade policies on China’s rosewood import prices to explore the fluctuation rule of rosewood trade prices under restrictive policies.

The study built a partial equilibrium framework about the formation mechanism of rosewood import price bubbles under supply constraints. On this basis, with China’s daily import prices of major rosewood species, the generalized supremum augmented Dickey–Fuller (GSADF) and backward supremum augmented Dickey–Fuller (BSADF) tests were applied to explore the effect of restrictive trade policies on China’s rosewood import prices.

The empirical analysis revealed that there were multiple price bubbles for five of the seven rosewood species. The largest bubbles were always created before and after the deployment of supply constraints. The empirical results for the counterfactual examples implied that price bubbles would not have occurred if restrictive rosewood trade policies had not been implemented. The above findings indicated that these measures tended to trigger significant price bubbles in China’s rosewood imports.

The effect of restrictive rosewood trade policies on rosewood trade prices had not yet been explored in previous research studies. This study empirically analyzed the effect of restrictive trade policies on China’s rosewood import prices using econometric models.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

The aim of this article is to highlight the major part played by executives in the escalation of corporate social irresponsibility (CSI). Based on the upper echelons theory, the authors developed a model which shows the essential role of CEOs in explaining CSI. The authors proposed that the key personality traits of CEOs—narcissism—, as well as their power, could explain the degree of CSI.

Due to the significant methodological challenges when investigating CSI, the authors explored a novel method for measuring CSI in order to assess the degree of irresponsible behaviors. The authors build a CSI scale based on the perceptions of key informants, i.e. experts with diverse professional backgrounds. The authors apply CSI scale in a sample of 84 Spanish companies that were involved in CSI.

The results of the authors’ empirical study show the positive and significant influence of CEO narcissism and CEO power on the degree of CSI.

On the one hand, corporate irresponsibility scandals have relevant social consequences and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

The authors’ findings may help CEOs, TMTs and corporate boards to acknowledge potential sources of CSI decreasing its likelihood through counterbalancing CEO's power and considering the dark side of narcissism.

On the one hand, corporate scandals have relevant social and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

In this paper, the authors highlight the role of CEOs characteristics and their firms as the key actors for explaining and understanding the degree of CSI.

本研究擬強調行政主管在加劇企業無社會責任感上所扮演的主要角色。我們根據高層梯隊理論, 開發了一個模型, 來顯示執行長如何在解釋企業無社會責任感上所起的關鍵作用。我們提出一個見解, 就是: 執行長的主要人格特質-自戀和其權力-或許能解釋企業無社會責任感的程度。

由於面對在關於企業無社會責任感的研究法上的重大挑戰, 我們探索了一個估量企業無社會責任感的新穎方法, 以能對企業無社會責任感行為的程度進行評估。我們根據主要的資訊提供者的觀念和看法, 制定了一個企業無社會責任感的等級 (這些主要的資訊提供者均為具有不同專業背景的專家), 我們在84間西班牙公司的樣本裡, 使用了這企業無社會責任感等級, 而這些公司均涉及企業無社會責任感的。

我們這實證研究的結果顯示, 執行長的自戀和其權力, 對企業無社會責任感的程度, 起著正面和顯著的影響。

在本學術論文裡, 我們強調了執行長的特徵和他們的公司, 是瞭解和解釋企業無社會責任感的程度的關鍵行為者。

一方面, 企業無社會責任感的醜聞會帶來相關的社會後果和實務方面的影響;而另一方面, 自戀是高層主管的自然特徵, 而這些特徵, 在我們的社會裡不斷增強。本研究的結果, 或許可幫助執行長、高層管理團隊和公司董事會去承認企業無社會責任感的潛在原因, 並以透過平衡執行長的權力和考慮自戀的陰暗面而減低企業無社會責任感產生的可能。

With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Online food delivery applications (OFDAs) provide an expedient platform, and consumers’ access to food has been drastically altered, especially during and after the COVID-19 pandemic. This study aimed to completely explore the attributes that influence consumers' purchase intention and how an app's aesthetics can evoke feelings that predict continuous usage intentions for OFDAs. The food industry, especially restaurants, heavily relies on mobile technology to facilitate critical online food delivery during the pandemic crisis.

The data for this study are gathered from 477 food consumers located in the federal capital territory (FCT) of Islamabad, Pakistan, through convenient sampling by developing a self-administrated online survey. SmartPLS is used for structural equation modeling to test the proposed research model and perform bootstrapping and algorithmic analysis.

Our findings revealed that perceived value positively predicted consumers’ purchase intentions. Moreover, perceived value mediates the association of information quality, familiarity, time-saving, usability and reputation with purchase intentions and fear of COVID-19 moderates the relationship between perceived value and purchase intention.

This research work has significant implications for researchers, web developers, app designers, delivery services, restaurants and other enterprises as it demonstrates the importance of aesthetically pleasing OFDAs in eliciting positive emotions and bolstering consumers’ intentions to continue using the app for efficient food delivery services.

This study expanded the application of the technology acceptance model (TAM) and attention, interest, desire and action (AIDA) by examining consumers’ purchase intentions in the context of OFDAs. Further, the successful utilization of TAM enhanced the understanding of consumer perceptions and behavioral intentions about the usage of OFDAs.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The purpose of this study is to analyze the influence of the firms' external environment on their export intensity. More specifically, it assesses whether domestic market characteristics such as domestic demand and general export environment related to tradability across borders affect firms' export intensity.

The authors use a sample of 29,266 firms from nine European countries, for the period of 2010–2016, and test several estimation methods (random effects models, Tobit models, and Heckman's selection models).

Results show that external factors such as domestic demand and ease of trade across borders are important determinants of firms' export intensity. Moreover, results reveal that firm's internal characteristics such as age, size and productivity also play an import role.

Studies about the influence of the firms' external environment on firms' export intensity are scarce because most of them are confined to a single country context. In this way, the present study contributes to the body of knowledge on the influence that external factors can have on firms' export performance by analyzing firms from nine European countries, which has important policy implications.