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Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.

Delving into family business heterogeneity, this study applies fuzzy-set qualitative comparative analyses (fsQCA) to explain overlooked differences in the international performance of born global family firms (BGFFs) and non-born global family firms (n-BGFFs); through the lens of assemblage theory of family business internationalization, the study develops distinctive configurations of international entrepreneurial culture (IEC) for BGFFs and n-BGFFs.

This study compares the theoretical tenets of IEC among 167 BGFFs versus 192 n-BGFFs in Malaysia using fsQCA – a configurational method. The study further deploys necessity analysis of fsQCA (NCA) to determine the necessity conditions within the identified configurations.

BGFFs manifest elevated levels of international entrepreneurial orientation, international motivation and international non-competitor network orientation. In contrast, n-BGFFs rely on international markets, learning and competitor network orientations to secure international performance. Furthermore, necessary condition analysis (NCA) reveals that international entrepreneurial orientation and international motivation are the necessity conditions for BGFFs. In contrast, international market, learning and competitor network orientation are all required for n-BGFFs’ international performance.

This study is timely and contributes to advancing the international business theory of family firm internationalization. It also offers better theorizing for family firms’ heterogeneity, locating the source of that heterogeneity not just in the speed of internationalization but also in the composition of their different IECs.

The purpose of this paper is to explore the determinants of entrepreneurs’ subjective social status perception (SSP) on firm international behaviors based on the upper echelons theory and social class theory.

To test the hypotheses, the authors studied a large sample of 10,823 small- and medium-sized private Chinese enterprises from 2006 to 2014.

The results showed that entrepreneurs with higher status perception prefer international activity and firms have higher export intensity and intention. In addition, the social capital of entrepreneurs and institutional environment amplifies the positive relationship between SSP and international behavior.

This paper contributes to research on the upper echelon of management and extends our understanding of how managerial social characteristics influence international strategic decision-making. Besides, it also contributes to the emerging stream of social status research in international expansion studies and expand researchers’ limited understanding of the effects of social status in business settings.

This study aims to investigate Australian civil tribunal decisions to ascertain compliance with decisional quality standards in Australian law, with a particular focus on strata and community title cases.

An orthodox doctrinal legal analysis and assessment of cases and tribunal policies was adopted. All Australian jurisdictions were surveyed, including federal, state and territory jurisdictions. The case law in each jurisdiction was screened to identify whether the principles applicable to decisional quality were engaged and then analysed as to the extent of that engagement.

Where a party presents a substantial, clearly particularised argument relying upon established facts, tribunals are obliged to address those facts and the arguments by way of an active intellectual process. However, appellate decisions disclose a degree of deference not often accorded to judicial officers, and there is a need for a more disciplined approach to ascertain whether any errors have been made by a tribunal lie on the critical path to the decision. As strata and community title disputes become more complex, the importance of decisional quality standards can only increase.

Up to date as of 1 March 2023.

The present position would appear to be that where a party presents a substantial, clearly particularised argument relying upon established facts, a tribunal must address its mind to those facts and the arguments by way of an active intellectual process. The requirement is limited to circumstances prescribed by a statute and factual and legal issues which are necessary to be determined in order for the tribunal to be satisfied as to circumstances prescribed by a statute. However, where the errors are not gross and plainly obvious, appeals from defective tribunal decisions are unlikely to succeed. There is a degree of deference not often accorded to judicial officers. That deference is unfortunate when tribunals are allocated jurisdiction over what quite often are significant property disputes.

The impact on community living of uncorrected poor quality tribunal decisions can be immense, depending on the degree of error. For example, water ingress into people’s homes might remain unremedied for many years, as, for example, occurred in the Marinko case.

The research and analysis is entirely original. A search of journals and textbooks did not identify any prior analysis, at least in the Australian context, relating to decisional quality standards of tribunals.

The adoption of emerging information and communication technologies in construction project teams has engendered numerous virtual spaces, characterized by communication visibility and content persistence. As a result, the knowledge exchanged in these virtual spaces serves as a team’s digital resources. However, the extant literature mostly takes a process-based approach to examine the impact of knowledge sharing, thus failing to fully comprehend the process of converting digital resources into performance, resulting in a gap in the literature.

This study employs team resource-based theory to construct a theoretical model and develop hypotheses. Specifically, knowledge integration capability and team efficacy are hypothesized as two types of critical capabilities that mediate the links between knowledge sharing (quantity and quality) in virtual spaces and management performance. Data from 128 middle and senior construction project managers were collected to test the proposed theoretical model.

The results suggest that relationships between knowledge sharing (quantity and quality) and project management performance are both mediated by knowledge integration capability. Moreover, team efficacy could only partially translate knowledge sharing quantity into performance and couldn’t transform knowledge sharing quality into performance. Besides, knowledge integration is found to strengthen the link between knowledge sharing quantity and performance but weaken the relationship between knowledge sharing quality and performance.

This study explores how knowledge shared in virtual spaces could be leveraged for improving management performance in construction project teams. The findings in this study enhance the understanding of knowledge sharing in digital environments and afford important insights into transforming digital resources into performance within construction project teams.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.

Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.

The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.

This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.

This paper aims to examine the optimal sourcing strategies and pricing decisions of competing toy manufacturers and to discuss how manufacturers’ decisions are impacted by competition.

The authors consider a single-period model to characterise the competition between two competing toy manufacturers. Both of them are free to choose between virgin material and recycled material. The authors consider two types of consumers: sensitive consumers who are concerned about product safety and prefer the toy made of virgin material and insensitive consumers who do not care what material is used in the toy. The competing manufacturers play a Cournot competition.

The results reveal a special case of a win-win situation for both the manufacturer and the consumer. In addition, an increasing number of sensitive consumers does not always raise the price of virgin-material toys.

The authors derive the manufacturer’s equilibrium sourcing strategies, corresponding market-clearing prices and profits obtained.

The paper investigates how toy manufacturers’ optimal sourcing strategies are impacted by competition, considering market segments.

Grounded in self-congruency theory, this study aims to investigate the impact of different types of congruence in social media influencer marketing on consumers’ online impulse buying intentions under the moderating influence of wishful identification.

This study collects survey responses from an online sample of 232 social media users and analyses them using partial least squares structural equation modelling.

This study delineates two distinct pathways influencing online impulse buying intentions within influencer marketing: direct consumer–product congruence and the conditional role of consumer–influencer congruence. Particularly, the alignment between a consumer’s self-image and the product’s attributes independently drives online impulse buying intentions. Conversely, consumer–influencer congruence, despite high alignment, fails to spur online impulse buying intentions unless amplified by wishful identification – the consumer’s aspirational desire to emulate the influencer. This finding underscores the complexity of impulsive consumer behaviours in the digital marketplace, highlighting the pivotal role of product appeal and the conditional influence of influencer relationships on spontaneous purchasing decisions.

This study pioneers by elucidating the congruence interplay between consumers, influencers and products in online impulse buying, emphasising wishful identification as a critical moderating factor. Theoretically, it expands self-congruency theory by detailing the distinct roles of congruence types on impulsive behaviours, notably underlining the essential role of wishful identification for the effect of consumer–influencer congruence. Practically, the insights equip brands with a deeper understanding of the key drivers behind impulsive purchases in an influencer-centric digital marketplace, offering strategic guidance for optimising influencer collaborations and product presentations to enhance consumer engagement and sales.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.