Search results

This study aims to understand how different levels of streamer channels influence luxury brand sales in live streaming commerce. This study also seeks to understand the conditions under which luxury brands may benefit more from different level streamer channels.

Panel data were collected from 17 international luxury brands on the Douyin live streaming platform in an 18 week period from August to December 2020 and analyzed by using a two-way fixed effects model.

The authors compared different mega-, macro- and micro-streamer channels within live streaming commerce and found that the densities of mega- and macro-streamer channels had significant positive impacts on luxury brand sales in live streaming commerce. Moreover, the effects of the density of streamer channel on luxury brand sales were moderated by such variables as product line breadth, product line depth, product type (star/non-star) and product price (high/low). The authors found that product line breadth and depth could reduce the positive impact of the densities of mega- and macro-streamer channels on luxury brand sales. For star products and high-priced products, the relationship between the density of mega-streamer channel and luxury brand sales was more likely to be observed than for non-star products and low-priced products. The relationship between the density of macro-streamer channel and luxury brand sales was more likely to be observed in low-priced products than in high-priced products.

The findings make important contributions to the literature in that the authors expand the influencer-brand fit theory by proposing a new model of effects of the densities of mega-, macro- and micro-streamer channels on sales performance across different luxury products to improve our understanding of the fit among influencers, brands and products. This helps luxury brands make basic decisions of “who sells” and “sells what” when engaging in live streaming commerce.

The purpose of this paper is to identify and examine two contrasting mechanisms of information asymmetry for cross-listed firms with respect to the information environment and its impact on earnings response.

The study empirically assesses two mechanisms of information asymmetry (“seeing” and/or “believing”) by looking at abnormal returns and volume reactions to international firms’ earnings announcements pre- and post-listing in the USA from 1990 to 2012.

The authors’ findings indicate that investors “seeing” more (media and analyst coverage) decrease the earnings response; however, “believing” more or gaining more credibility has the opposite effects. Based on the results, both mechanisms of information asymmetry can take effect simultaneously.

The study sheds light on the multi-dimensional impact of the improved information environment that non-US firms face when they list their securities on US exchanges.

This study identifies and reconciles these two mechanisms of information asymmetry (visibility and credibility) under one setting and estimates the magnitude of each effect empirically.

The purpose of this paper is to develop and redefine the “classic” roles of shop floor management and quality control circles (QCCs) in Kaizen. In specific, it aims to examine the linkage between shop floor management and QCCs, and test the relationships among shop floor management, QCCs and long-term Kaizen improvement outcomes.

This study employs qualitative method by using a questionnaire to obtain data from 371 respondents in nine Sino-Japanese automotive joint-ventures. The data are analysed with the method of canonical correlation approach.

The study identifies important factors to assist the adoption of shop floor management and QCCs for Kaizen. The analysis on the survey indicates that not all the shop floor management tools could help to identify improvement opportunities. QCCs are effective in addressing large problems and challenging current policies in companies, however, they have low impacts on individual learning.

The data of this study come from nine Sino-Japanese automotive joint ventures. Therefore, the sample selection is limited to these companies. The findings are able to be applied for improving the similar problems which were identified in this study.

The study has the following practical implications, first is small shop floor problems can be identified and solved rapidly and continuously at source by shop floor management. The second one is QCCs, or other similar group-based improvement approaches take long to be fully addressed and implemented. Third, practical solutions can be achieved from small and gradual changes, and they can prevent the results backsliding to the pre-improvement stage. Finally, QCCs are hardly to achieve a better improvement alone. It requires other Kaizen approaches to support.

This study is probably the first to explore and investigate the implementation of the four building block tools of shop floor management in real business practise, and more specific the first to discuss the relationship among shop floor management, QCCs and long-term improvement outcomes based on empirical data from Sino-Japanese automotive joint-ventures.

The purpose of this paper is to obtain the matching relationship between spiral groove, equalizing groove and operation parameters through the biparametric analysis for the bidirectional pumping hydrodynamic-static hybrid dry gas seal (BP-HHDGS).

The large eddy simulation (LES) model in Fluent is used to simulate the flow field of BP-HHDGS, and the biparameter variables method is chosen to analyze the effects of different parameters on the performance of BP-HHDGS.

BP-HHDGS has a greater opening force than hydrostatic dry gas seal (HDGS); the vortex is formed after lubricating gas is exhausted from the throttle. Increasing the depth of the equalizing groove and spiral groove has a synergistic enhancement effect on the opening force and leakage of BP-HHDGS. There is a matching relationship between spiral angle and rotational speed. The preferred parameter ranges in current conditions are found as follows: spiral angle αa = 15°–24°; groove-dam ratio λ = 0.4–0.7; equalizing groove depth h_j > 35 µm; spiral groove depth h_g = 5-10 µm.

The high starting capacity of HDGS is given to the hydrodynamic type seal, and thus the application promotion of HDGS in high-speed working condition is realized at the same time. This work also provides precise and quick theoretical guidance for the selection and design of hydrodynamic-static dry gas seal and further promotion.

The high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.

The Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.

Through empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.

TFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.

As an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

The purpose of this paper is to investigate the mediating effect of online reviewers' affect (ORA) on the relationship between weather and online review ratings (ORR).

The consumers' online review data were collected from the third-party restaurant website, and the weather data were obtained from the weather part of Chinese e-government website. SnowNLP was utilized to analyze sentiment and further extract ORA. Furthermore, the mediating effects of ORA on temperature and ORR, rain and ORR were explored separately using PROCESS 3 Macro Model 4, and the interaction effect of temperature and rain was tested through PROCESS 3 Macro Model 7.

The findings of this work demonstrate that ORA mediates the relationship between temperature and ORR and the relationship between rain and ORR. Besides directly leading to higher ORR, a higher temperature can bring about higher ORR by elevating ORA. On the other hand, little rain and heavy rain have a direct negative influence on ORR, and they can also lead people into a bad mood state, thus leading to lower ORR. Furthermore, temperature moderates the effect of rain on ORA. When the temperature is higher, the differences of ORA are larger between different types of rain than that of lower temperature.

This study appears to be the first to investigate the relationship among weather, ORA and ORR using online data. The results could help managers understand when consumers are more likely to provide negative eWOM under corresponding weather conditions and adopt appropriate strategies to improve ORR.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

This study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.

The research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.

This study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.

This research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

This paper aims to prove the validity and necessity of knowledge stickiness and knowledge investment level.

Empirical study method is taken in this investigation which focuses on knowledge‐related industries' workers and proves the validity and necessity of knowledge stickiness and knowledge investment level with SPSS13.0 software.

The authors confirm the positive correlation between knowledge contribution and sharing residual claims based on management, and also confirm the positive correlation between knowledge stickiness, knowledge investment level and sharing residual claims based on technology. However, a negative correlation on management is also confirmed.

After an analysis on the incentive distortion caused by the information asymmetry between the principal and agent in the traditional incentive mode, a residual claims sharing structure containing knowledge contract is put forward.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.