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Servitization is a business transformation that increases service provision in manufacturers. This study aims to empirically examine how a manufacturer's global supply chain dependence and its power positions affect its servitization output.

This study employs secondary longitudinal datasets and econometric specifications to test the relationship between global supply chain dependence and servitization. It further examines the moderating roles of the firm's market power and the degree of being principal customers and principal suppliers. Heterogeneity analyses are performed to verify the robustness of the results.

The findings indicate that fewer global suppliers and more global customers contribute to a higher level of servitization. The negative effect of global supplier dependence is mitigated when manufacturers have less market power and are the principal customers for most of their suppliers. The positive effect of global customer dependence is stronger when manufacturers have less market power and their customers are less dependent on the manufacturers.

Data mixing manufacturing and service inputs and data on public US manufacturers may restrict the generalizability of the findings. Nonetheless, the study urges future research to focus more on other countries/markets.

This study encourages manufacturers who servitize their businesses to connect with more global customers and fewer global suppliers and manage powers among stakeholders. Other recommendations for policymakers and industry associations are also proposed.

This study is the first to explore the impacts of the global supply chain dependence on servitization. Multiple-level findings offer important implications for researchers and practitioners.

The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.

An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.

Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.

This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.

This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.

The purpose of this paper is to provide further insight into Chinese wine consumers’ preference, grasp the regional sensory preference differences of China and build up a predictive model for wine consumers’ sensory preferences.

The study involved 3,421 Chinese wine consumers in the survey. Classified statistics were conducted to excavate regional differences of wine consumers’ sensory preferences. By analyzing influencing factors, prediction models for consumers’ sensory attribute preferences were constructed on the basis of multivariate disorder logistic regression method.

Empirical research showed that the wine with the following sensory attributes was the most preferred by Chinese consumers: dry red, refreshing and soft taste, still type, moderate aroma degree and mellow aroma, and sweet wine was also popular. Consumers’ preference varied from region to region. The proposed predicting method of the study realized more than 70 percent accuracy when conducting prediction for color, sweetness, aroma type and flavor preferences.

By shedding light on the latest sensory attribute preferences of Chinese wine consumers, this study will help wine industry participants conduct market segmentation based on the diversification of consumers’ preferences. The wine enterprises can gain guidance from the results to conduct market positioning, adjust strategies and provide specific production for target wine consumers.

Based on the actual situation of Chinese wine market, this study defines sensory attribute indexes of wine from the perspective of wine consumers and presents the most recent comprehensive research on the sensory preferences of Chinese wine consumers through a nationwide survey.