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The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com . Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Driven by the internet-based advanced information technologies and logistics channel improvement, the cross-border e-commerce industry keeps an increasing trend in Chinese industrial market. Blockchain, as an empowered technology, contributes to the management innovations for industrial sectors. The blockchain technology, due to its transparency, visibility, and dis-intermediation characteristics, helps to improve operations management of cross-border e-commerce supply chain by innovative industrial applications. However, practical applications of the blockchain technique-enabled cross-border e-commerce sector are still in their infancy and still at the proof-of-concept stage. This paper presents a systematic review on blockchain-enabled cross-border e-commerce supply chain management by employing a bibliometric data-driven analysis. All relevant publications from the Web of Science database from 2013 to 2021 were collected as the research samples. Besides, the VosViewer is adopted to conduct the network and co-word study by visualizing collaborative relationships of sampled literatures. Results show that the blockchain technique has substantial applications in the field of cross-border e-commerce supply chain, whose contributions mainly focus on cross-border e-commerce platform, supply chain operations, and data governance and information management. Academic researchers and industrial managers can promote innovative management practices in cross-border e-commerce supply chain by adopting blockchain. Moreover, we hope this study serves as a future direction for both researchers and engineers on leveraging blockchain to improve the supply chain management performance of the cross-border e-commerce.

The coupling of logistics and economy is of great significance for regional development. To promote the regional development of urban agglomeration in Central Plains of China, this paper attempts to study the spatial divergence of coupling development and the influential factors. The 30 urban cities in Central Plains of China have been regarded as the research objects. We develop an integrated framework to derive the coupling degree between logistics and economy in this region including linear weighting method, coupling coordination degree model, and exploratory spatial data analysis. The spatial pattern of the coupling coordination degree between logistics and economy is studied by the visualization evolution analysis. In addition, the GWR model is formulated to study the influential factors of regional coupling development. The results show that (1) the integrated development level of logistics industry and economy in the Central Plains City Cluster is low, and the development difference between regions is significant; (2) the overall coupling and coordination level of logistics industry and economy in the Central Plains City Cluster is not high and is at the stage of imminent disorder; in space, it presents a spatial pattern of “high in the center and low around”; (3) the coupling coordination degree around each region and city is a strong positive spatial correlation and agglomeration situation significantly; (4) the ranking of the influence degree of each driving factor from high to low is urbanization rate; science and technology level; education level; and population density.

The sustainable development of the Yellow River Basin (YRB) is regarded as a national strategy for China. Previous literature has focused on the green efficiency measurement of YRB, ignoring its evolution process and influential mechanism. This paper tries to disclose the spatial-temporal evolution of green efficiency and its influential mechanism of the YRB region by proposing a novel integrated DEA-Tobit model to fill the gap. Based on the development path of the YRB region, the multi-period two-stage DEA model is adopted to evaluate the green development efficiency (GDE) from provincial and urban dimensions. In addition, the panel Tobit model is developed to investigate the influential factors of the GDE for the YRB region. The GDE in the YRB region shows an unbalanced state where the downstream is best, followed by the middle and upstream. The unbalanced development also exists within the province. Both Henan and Shandong Province achieved the optimal value, while cities in these two provinces show lower green efficiency. The results also show that economic development, technological innovation and foreign capital utilization obviously affect the GDE of the YRB region positively, while industrial structure, urbanization levels and environmental regulation have negative effects.

With the increasing demand of individual customption and awareness of cost reduction in express delivery organizations, the Chinese express industry faced with serious challenges especially under the background of government’s strict restrictions on environment and transportation. Therefore, a new service mode called joint distruction (JD) is being tried by the logistics industry, which is expected to address the challenges on online shopping. However, the insufficient understanding of JD adoption factors and their complicated interactions blocks the effectively implementation of the joint distribution. This study aims at identifying potential factors for JD adoption and promoting an effective joint distribution by discovering the interactive relationships among addressed factors. Firstly, potential ingredients for the adoption and implementation of JD are summarized from the literature and industrial interviews. Then, 23 variables are selected and classified into as objectives, drivers, barriers and affected operations. The Interpretive Structural Modeling (ISM) approach is then employed to analyze the crucial factors and the mutual influences amongst 23 variables. Finally, a case study is performed to construct the hierarchical structure of factors toward joint distribution adoption using the proposed ISM-modeling steps. The perplex hierarchical co-relationships are also identified by categorizing the driving variables and dependent variables. Results can assist express enterprises to promote the novel joint distribution mode and acheive higher efficiency of logistics operation by better understanding on crucial factors of JD adoption and implementation.

Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

Here we use an analytical method to determine δ^13C in local plants and organic matter in the soils above Furong cave, Chong- qing, China. We also monitored d13C in dissolved inorganic carbon （DIC） of drip water, δ^13C of active deposits under the drip waters, and the concentration of air CO2 （PCO2）. Based on these, we preliminarily studied the transportation characteristics of stable carbon isotope （^13C） in cave system of the subtropical karst area. The average δ^13C value of 27 local plant samples, which belong to 16 families, was -32% and the weighted δ^13C for surface dry biomass was -33%0. We found that for 54 soil samples collected from 5 soil profiles, δ^13C of soil organic matters was -22%o, which could be attributed to the different trans- portation rates of stable carbon isotopes during the decomposition of plants and organic matters in soils. The relatively lighter 12C tended to transfer into gaseous CO2, which made the relatively heavier ^13C concentrated in the soils. On the basis of moni- toring of DIC- δ^13C in drip waters from July 2009 to June 2010, we found that values in winter months were heavier and values in summer months were lighter in general, the reason of which was that in summer months, both the temperature and the hu- midity were comparatively higher, resulted in more CO2 with lighter δ^13C generated from organic matters decomposition and plants respiration. The average DIC- δ^13C value was -11%o, about 11%o heavier than the δ^13C of organic matters in soils, which proved that part of DIC in cave drip water was sourced from dissolution of inorganic carbonate （host rock, with heavier δ^13C. As for the δ^13C of active deposits at five drip water sites in Furong cave, they had almost the same variation with relatively light values. In other words, these active speleothems were deposited at equilibrium conditions for isotopic fractionation. These results suggest that the carbon isotopic information of speleothems could be used to track the evolution of local vegetation in certain situations.

In this study, the relationship between circumglobal teleconnection (CGT) and East Asian summer monsoon rainfall was analyzed by data diagnoses and numerical experiments. It is found that the CGT has high spatial and temporal similarities with the teleconnection pattern incurred by the variation of the South Asian high (SAH), which is collaboratively influenced by both Indian and East Asian summer monsoon rainfall. These two teleconnections have similar spatial distributions, and their indices are strongly correlated in temporal variations. Using the partial correlation method, it is revealed that SAH plays a significant role on the propagation of CGT, especially to the east of 90°E. The numerical experiments indicate that the latent heat release from East Asian summer monsoon rainfall stimulates an upper-tropospheric teleconnection, which shows the same spatial structure with CGT. This study demonstrates that the generation of CGT is not only associated with the Indian summer monsoon rainfall, but also closely with the East Asian summer monsoon rainfall. The CGT is maintained by the latent heat released from the rainfall of both monsoons.

In flexible manufacturing system, the estimation of man-hour is a difficult problem because of its production-diversity. To explore a more effective method, this paper tried to estimate man-hour from the perspective of operation`s character by establishing standard operation unit (SOU) in this paper. A method of parameterizing the SOU is proposed, and a new man-hour estimation model is established on the basis of SOU. At last, this paper verified the effectiveness of this method by the operation of large-scale welding parts.

With the rapid development of digital technology, the smart sensor-based container equipment and intelligent logistics operations contribute to achieving the efficiency improvement and sustainability achievement of container supply chain under the IoT-based logistics 4.0 scenarios. This paper tries to study the state-of-the-art knowledge of the container logistics supply chain management motivated by digital techniques. Through data-driven analysis this review is performed to assist researchers and practitioners to better understand the container logistics management. The integrated research framework is designed by developing a bibliometric analysis study to address the research themes of the container logistics era. The related publications from the Web of Science database from 2003 to 2022 were indexed and 2897 reference samples are collected as the research data. In addition, the VosViewer is adopted to portray the network, co-occurrence, and co-word analysis by visualizing the collaborative relationships of collected samples. The results show that digital technology has been widely applied in container logistics supply chain management practices, contributing to resilience and sustainability improvement by intelligent operations. These research findings are also helpful for researchers by providing a deep penetrating insight into research opportunities and great potentials of container logistics supply chain by innovative digital technology-enabled practices.