Purpose: This study investigates how Danmu (danmaku) technological features (DTFs) of recommendation vlogs (rec-vlogs) impact consumer experiences and decisions.Design/methodology/approach: The authors adopt the partial least squares structural equation modeling (PLS-SEM) technique with a sample of 422 viewers of Danmu-enabled rec-vlogs to examine the proposed model.Findings: The DTFs positively affect consumer loyalty intentions through the mediation of social presence and immersion. Also, vlogger-product congruency significantly moderates the relationship between social presence and immersion.Originality/value: With the increasing popularity of vlogs, retailers have adopted the vlog as a new marketing channel to connect with consumers. Although a growing body of studies focuses on this phenomenon, little is known about how DTFs help to increase consumer loyalty toward using rec-vlogs as product information sources. Anchored on the stimulus–organism–response framework, this study investigates how pseudo-synchronicity, Danmu-content congruency and parallelism, three DTFs used in rec-vlogs, impact consumer experiences of social presence and immersion, eventually influence the consumers' loyalty intentions toward rec-vlogs. The authors also examine whether the effect of social presence on immersion is contingent on vlogger-product congruency. These findings have implications for both researchers and practitioners to understand the effect of DTFs in the context of rec-vlogs.

In exploring the business operation of Internet companies, few researchers have used data envelopment analysis (DEA) to evaluate their performance. Since the Internet companies have a two-stage production process: marketability and profitability, this study employs a relational two-stage DEA model to assess the efficiency of the 40 dot com firms. The results show that our model performs better in measuring efficiency, and is able to discriminate the causes of inefficiency, thus helping business management to be more effective through providing more guidance to business performance improvement.

With the rapid economic development, the transportation sector becomes one of the high‐energy‐consumption and high‐CO₂‐emissions sectors in China. In order to ensure efficient use of energy and to reduce CO₂ pollution, it is important to gain the best performance standards in China’s transportation sector. Data envelopment analysis (DEA) has been accepted as a popular tool of efficiency measurement. However, previous studies based on DEA are mainly restricted to the radial expansions of outputs or radial contractions of inputs. In this paper, we present a nonradial DEA model with multidirectional efficiency analysis (MEA) involving undesirable outputs for the measurement of regional energy and environmental efficiency of China’s transportation sector during the period 2006–2010. We not only evaluate the energy and environmental efficiency level and trend of China’s transportation sector but also investigate the efficiency patterns of 30 regions and three major areas of China. Additionally, we identify the energy saving potential and CO₂ emissions reduction potential for each province and area in China in this study.