The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.

Purpose: The development of digital technology has provided technical support to various industries. Specifically, Internet-based freight platforms can ensure the high-quality development of the logistics industry. Online freight platforms can use cargo transportation insurance to improve their service capabilities, promote their differentiated development, create products with platform characteristics and increase their core competitiveness.Design/methodology/approach: This study uses a generalised linear model to fit the claim probability and claim intensity data and analyses freight insurance pricing based on the freight insurance claim data of a freight platform in China.Findings: Considering traditional pricing risk factors, this study adds two risk factors to fit the claim probability data, that is, the purchase behaviour of freight insurance customers and road density. The two variables can significantly influence the claim probability, and the model fitting outcomes obtained with the logit connection function are excellent. In addition, this study examines the model results under various distribution types for the fitting of the claim intensity data. The fitting outcomes under a gamma distribution are superior to those under the other distribution types, as measured by the Akaike information criterion.Originality/value: With actual data from an online freight platform in China, this study empirically proves that a generalised linear model is superior to traditional pricing methods for freight insurance. This study constructs a generalised linear pricing model considering the unique features of the freight industry and determines that the transportation distance, cargo weight and road density have a significant influence on the claim probability and claim intensity.

This study discusses the cooperation model between the small and major scenic spots in peak seasons of tourism. First, we create a tourism supply chain that contains only one major and one small scenic spots to divert some tourists to a nearby scenic spot, reduce large scenic spot pressure, increase the benefits of small scenic spots, and promote the healthy development of the tourism supply chain. Second, we build two cooperation models between the major and the small scenic spots: the revenue-sharing model and the cost-sharing model. Based on a numerical analysis, we give a more clear comparison about the revenue changes and the overall efficiency changes of the tourism supply chain in these two models. Our results confirm that the cost-sharing model is an ideal cooperation model. In the cost-sharing model, the two parties’ revenues in the tourism supply chain are better than those in the revenue-sharing model, and the entire supply chain efficiency could be improved.

Traditional manufacturers can take part in the sharing economy by renting products to consumers through sharing platforms. We develop an analytical framework consisting of two manufacturers and a sharing platform to study the effect of product sharing on competing manufacturers’ entry and pricing strategies. On the one hand, when the high-quality manufacturer works with the sharing platform, if the perceived quality of renting the high-quality product is larger than that of purchasing the low-quality product, it shows that the high-quality manufacturer will benefit and should enter the sharing market when the rental price is moderate. However, if the perceived quality of renting a high-quality product is smaller than that of purchasing a low-quality product, both manufacturers will always suffer losses; thus, the high-quality manufacturer should not provide sharing. Consequently, when the high-quality manufacturer chooses to share, the quality advantage should be maintained. On the other hand, when the low-quality manufacturer works with the sharing platform, it also finds that the low-quality manufacturer will always be better off from a moderate rental price. This implies that the low-quality OEM has more interest in offering product sharing if the perceived quality of renting high-quality product is smaller than that of purchasing low-quality product.