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The purpose of this paper is to show how accelerated technology innovations lead to shorter product lifecycles, and consumers often face the dilemma of choosing between keeping the existing product and upgrading to a new version. They may enact certain coping strategies to deal with the stress and uncertainty. Based on the work of Mick and Fournier, this study aims to propose a set of coping strategies, which include refusal, delay, extended decision‐making, and pretest.

Based on a survey of consumers regarding the 3G mobile phones, the authors test the effects of coping strategies within the framework of the technology acceptance model.

The results of canonical analyses suggest that coping strategies have significant influence on consumers' product beliefs, which in turn mediate the effects of coping strategies on consumers' attitude toward adoption and their purchase intention.

Coping strategies help better understand consumers' adoption of new technology products and furnish meaningful implications for marketing technology products to today's tech‐savvy consumers.

This study develops measures of coping strategies and provides an empirical test of their effect on product beliefs and behavioral intentions with respect to consumers' decision whether to upgrade to a new technology product.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

The paper's purpose is to investigate the effects of digital finance on the risk of stock price crashes and the underlying transmission mechanisms, and to provide suggestions to inhibit the stock crash risk (CR).

This paper selects all companies that were listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2011 to 2020. It then uses the two-way fixed effect model and the intermediary effect model to verify such effects.

The overall outcomes demonstrate such a result that the CR of listed companies in China can be significantly reduced by the development of digital finance, and the overall transparency of business financial information and the equity pledge of controlling shareholders are the two underlying transmission mechanisms that digital finance can cause effects on the CR of stocks.

The main limitations are that there may exist some problems in the method for evaluating the CR of stocks. And there may be a problem of endogeneity caused by the empirical model cannot control all correlation variables.

This paper would provide policy implications, for different roles, to inhibit the stock CR and to make the development of the economy more stabilize.

Digital finance can promote economic development while restraining financial risks at the same time. Therefore, although this study is based on the relevant data from China, it can also provide a reference for other economies with different basic conditions from China, to promote the overall development of the world economy.

The current academic research on digital finance or stock price CR has been relatively sufficient, but there are few papers that combined both. By combining digital finance with stock CR, this paper researches the influence of digital finance on the CR of stocks through empirical analysis. So, this paper would provide new research ideas and evidence for potential influence factors of the CR of stocks, fill the gap in this research field and provide certain help for subsequent scholars to conduct relevant research.

The mobile medical consultation (MMC) service is growing rapidly, but not all consumers are always willing to actively engage with it. To address this issue, based on IT identity theory, this study explores the underlying mechanism of how two types of platform-related consumer experience influence MMC platform identity, in turn, result in consumer negatively-valenced engagement in MMC.

The data was collected from 400 consumers with the experience of MMC and analyzed by the partial least square (PLS) method.

The findings unfold that these two distinct consumer experience, servicescape experience (i.e. perceived telepresence and perceived platform surveillance) and service search experience (i.e. perceived diagnosticity and perceived serendipity), are associated with MMC platform identity and consumer negatively valenced engagement with MMC.

Research on consumer negatively-valenced engagement in the field of MMC is still in a nascent stage. The study identifies consumer experience in accordance with the unique context of the MMC platform and fills the research gap on the role of IT identity in consumer negatively valenced engagement.

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

The entrepreneurial process often cannot be explained by a single entrepreneurial theory. Instead, it is more likely the result of the interaction between various entrepreneurial behavior patterns and different environmental conditions. However, existing research has frequently overlooked the complexity inherent in the entrepreneurial phenomenon. Building on a configurational perspective, this study aims to examine how new ventures can use different behavioral configurations to achieve high performance amid various uncertain environments.

Based on the survey data from 143 new start-ups in China’s software industry, this study uses fuzzy-set qualitative comparative analysis (fsQCA).

This study jointly considers multiple entrepreneurial behaviors − causation, effectuation and entrepreneurial bricolage and different types of environmental uncertainty − state uncertainty, effect uncertainty and response uncertainty. The findings reveal three behavioral configurations for high/nonhigh new venture performance.

This study expands previous insights into the relationship between entrepreneurial behaviors and new venture performance from the perspective of configurational theory. Moreover, it offers new insights into the types of uncertainty, further refining our understanding of the uncertainties inherent in entrepreneurial activities.

As the new frontier in online healthcare innovation, mobile health consultation (MHC) is transforming how traditional healthcare is delivered. Despite being known on a large scale for its benefits, MHC still faces consumer resistance. MHC is a technology-enabled service, so an in-depth analysis of consumer resistance from the perspective of technology is crucial to enhance service adoption. This study sought to determine the mechanism by which two information technology (IT)-specific traits – IT affordance and IT identity – influence consumer resistance to MHC during consumer–platform interactions.

We used the Credamo platform to gather data from 786 users with medical consulting experience to validate the resulting relationships.

Based on partial least squares structural equation modeling, three of the six IT affordances (visibility, searching and guidance shopping) exerted a significant positive influence on IT identity, while trigger attending and association affordance had no significant effect on IT identity. Persistence affordance was negatively associated with IT identity, and IT identity negatively influenced consumer resistance to MHC.

Academically, this empirical paper primarily contributes to the MHC literature and the theory of IT affordance and IT identity. Practically, several valuable guidance for MHC platforms is provided.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood.

To explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide.

The study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn.

This paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.

Severe airborne particulate pollution frequently occurs over the North China Plain (NCP) region in recent years. To better understand the characteristics of carbonaceous components in particulate matter (PM) over the NCP region.

PM samples were collected at a typical area affected by industrial emissions in Handan, in January 2016. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM of different size ranges (i.e. PM_2.5, PM₁₀ and TSP) were measured. The concentrations of secondary organic carbon (SOC) were estimated by the EC tracer method.

The results show that the concentration of OC ranged from 14.9 μg m⁻³ to 108.4 μg m⁻³, and that of EC ranged from 4.0 μg m⁻³ to 19.4μg m⁻³, when PM_2.5 changed from 58.0μg m⁻³ to 251.1μg m⁻³ during haze days, and the carbonaceous aerosols most distributed in PM_2.5 rather than large fraction. The concentrations of OC and EC PM_2.5 correlated better (r = 0.7) than in PM_2.5−10 and PM_>10, implying that primary emissions were dominant sources of OC and EC in PM_2.5. The mean ratios of OC/EC in PM_2.5, PM_2.5–10 and PM_>10 were 4.4 ± 2.1, 3.6 ± 0.9 and 1.9 ± 0.7, respectively. Based on estimation, SOC accounted for 16.3%, 22.0% and 9.1% in PM_2.5, PM_2.5–10 and PM_>10 respectively.

The ratio of SOC/OC (48.2%) in PM_2.5 was higher in Handan than those (28%–32%) in other megacities, e.g. Beijing, Tianjin and Shijiazhuang in the NCP, suggesting that the formation of SOC contributed significantly to OC. The mean mass absorption efficiencies of EC (MACEC) in PM₁₀ and TSP were 3.4 m² g⁻¹ (1.9–6.6 m² g⁻¹) and 2.9 m² g⁻¹ (1.6–5.6 m² g⁻¹), respectively, both of which had similar variation patterns to those of OC/EC and SOC/OC.