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C ollaborative logistics, also known as shipper collaboration, is a method of reducing the freight logistics cost of firms that produce and/or distribute tangible goods (shippers), which seeks to improve capacity utilizations of trucks. This study looks at this shipper collaboration problem in the U.S. truckload (TL) industry and proposes a new approach. Unlike other studies, which focused on reducing TL costs by utilizing economies of density, we present an approach that utilizes specific TL economies gained by mixing multiple products with different weight-to-volume ratios, which we call economies of product diversity. Using theoretical and empirical evidence, we show that the performance of shipper collaboration can be enhanced notably using this concept, economies of product diversity, which is currently overlooked in the literature.

Purpose Content analysis is a methodology that has been used in many academic disciplines as a means to extract quantitative measures from textual information. The purpose of this paper is to document the use of content analysis in the supply chain literature. The authors also discuss opportunities for future research. Design/methodology/approach The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis. Findings The findings suggest that content analysis for quantitative studies and hypothesis testing purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully realize the potential of content analysis, future content analysis research should conduct more hypothesis testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage multi-method research designs. Originality/value The current research synthesizes the use of content analysis methods in the supply chain domain and promotes the need to capitalize on the advantages offered by this research methodology. The paper also presents several topics for future research that can benefit from the content analysis method.

This paper examines the environmental Kuznets curve (EKC) in Vietnam between 1977 and 2019. Using the autoregressive distributed lag (ARDL) approach, we find an inverted N-shaped relation between economic growth and carbon dioxide emissions in both the long- and short-run. The econometric results also reveal that energy consumption and urbanization statistically positively impact pollution. The long-run Granger causality test shows a unidirectional causality from energy consumption and economic growth to pollution while there is no causal relationship between energy consumption and economic growth. These suggest some crucial policies for curtailing emissions without harming economic development. In the second step, we also employed the back-propagation neural networks (BPN) to compare the work of econometrics in carbon dioxide emissions forecasting. A 5-4-1 multi-layer perceptron with BPN and learning rate was set at 0.1, which outperforms the ARDL’s outputs. Our findings suggest the potential application of machine learning to notably improve the econometric method’s forecasting results in the literature.

Background The COVID-19 pandemic is still undergoing complicated developments in Vietnam and around the world. There is a lot of information about the COVID-19 pandemic, especially on the internet where people can create and share information quickly. This can lead to an infodemic, which is a challenge every government might face in the fight against pandemics. Objective This study aims to understand public attention toward the pandemic (from December 2019 to November 2020) through 7 types of sources: Facebook, Instagram, YouTube, blogs, news sites, forums, and e-commerce sites. Methods We collected and analyzed nearly 38 million pieces of text data from the aforementioned sources via SocialHeat, a social listening (infoveillance) platform developed by YouNet Group. We described not only public attention volume trends, discussion sentiments, top sources, top posts that gained the most public attention, and hot keyword frequency but also hot keywords’ co-occurrence as visualized by the VOSviewer software tool. Results In this study, we reached four main conclusions. First, based on changing discussion trends regarding the COVID-19 subject, 7 periods were identified based on events that can be aggregated into two pandemic waves in Vietnam. Second, community pages on Facebook were the source of the most engagement from the public. However, the sources with the highest average interaction efficiency per article were government sources. Third, people’s attitudes when discussing the pandemic have changed from negative to positive emotions. Fourth, the type of content that attracts the most interactions from people varies from time to time. Besides that, the issue-attention cycle theory occurred not only once but four times during the COVID-19 pandemic in Vietnam. Conclusions Our study shows that online resources can help the government quickly identify public attention to public health messages during times of crisis. We also determined the hot spots that most interested the public and public attention communication patterns, which can help the government get practical information to make more effective policy reactions to help prevent the spread of the pandemic.