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The rapid development of Intelligent and Connected Vehicles (ICVs) has boomed a new round of global technological and industrial revolution in recent decades. The Technology Roadmap of Intelligent and Connected Vehicles (2020) comprehensively analyzes the technical architecture, research status and future trends of ICVs. The methodology that supports the roadmap should get studied.

This paper interprets the roadmap from the aspects of strategic significance, technical content and characteristics of the roadmap, and evaluates the impact of the roadmap on researchers, industries and international strategies.

The technical architecture of ICVs as the “three rows and two columns” structure is studied, the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed.

This paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Employee turnover expenses can cost businesses more than 100 per cent of a single employee’s annual wages and negatively affection an organization’s production and profits. High employee turnover also could affect community tax collections, social programs and physical and mental health issues. Therefore, understanding contributors to higher employee turnover remains essential for organizational managers from both a corporate and societal standpoint. This paper aims to provide an analysis of how job satisfaction and job embeddedness could predict employee turnover intent.

A randomly selected survey which consisted of Andrews and Withey’s (1976) job satisfaction questionnaire, a global job embeddedness scale (Crossley et al., 2007) and a three-item turnover intent questionnaire derived from a survey created by Mobley et al. (1978) using a Likert-type measurement to survey randomly selected individuals used within manufacturing plants located in the Southeastern USA.

The results of the multiple regression analysis showed a significant relationship between job satisfaction, job embeddedness and turnover intent; and that satisfied and committed employees are less likely to plan to leave their employment.

Limited current information is available on how job satisfaction and job embeddedness predict turnover intentions in US Southeast manufacturing. This study includes information that shows the importance of job satisfaction and job embeddedness on retaining employees in this region and industry. Given the importance of employee retention on corporate productivity, morale and profits along with the ability to improve the organization’s positive contribution to society, it is important for managers to understand these factors and their effect on employee turnover intent.

The purpose of this paper is to investigate whether management strategies implemented by non-commercial traders may be identified as a key factor in affecting oil price paths in the conventional pre- and post-financialization periods.

By using a vector autoregressive approach the dynamic analysis of the daily stock indexes for some of the most important world economies and the oil prices is conducted starting from 1992 to the end of 2020.

The findings do not support the idea that the financial markets act as a privileged conduit in transmitting the shocks to the oil spot quotations.

Such a direct assessment has not been previously proposed in literature wherein – under a financial perspective – the returns are generally taken into consideration.

Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy.

This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness.

Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy.

To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.

This study aims to examine how characteristics of an online review and a consumer reading the review influence the probability that the consumer will assess the review as authentic (real) or inauthentic (fake). This study further examines the specific factors that increase or decrease a consumer’s ability to detect a review’s authenticity and reasons a consumer makes these authenticity assessments.

Hypothesized relationships were tested using an online experiment of over 400 respondents who collectively provided 3,224 authenticity assessments along with 3,181 written self-report reasons for assessing a review as authentic or inauthentic.

The findings indicate that specific combinations of factors including review valence, length, readability, type of content and consumer personality traits and demographics lead to systematic bias in assessing review authenticity. Using qualitative analysis, this paper provided further insight into why consumers are deceived.

This research showed there are important differences in the way the authenticity assessment process works for positive versus negative reviews and identified factors that can make a fake review hard to spot or a real review hard to believe.

This research has implications for both consumers and businesses by emphasizing areas of vulnerability for fake information and providing guidance for how to design review systems for improved veracity.

This research is one of the few works that explicates how people assess information authenticity and their consequent assessment accuracy in the context of online reviews.

The purpose of this paper is to adopt the uses and gratifications theory to tourism.

This study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.

A research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.

This study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.

This research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.

This study aims to present the Chinese entrepreneurial environment and explore Chinese tertiary students’ online-startup motivation on live streaming platforms. Based on the COM-B behaviour changing theory, this paper discovers various influencing factors from environmental opportunity and personal capability aspects. It analyses their effects under the cooperative system established among official departments, industries and universities. Meanwhile, considering social and cultural control, it also refers to the uncertainty-avoidance dimension from the Hofstede cultural theory and re-evaluates its influence on Chinese tertiary students’ online-startup motivation.

The authors analyse 474 responses from online questionnaires through partial least squares path modelling and variance-based structural equation modelling. The paper claims that environmental opportunity and personal capability factors positively affect students’ online-startup motivation, but uncertainty-avoidance thinking plays a negative role. The study also measures the importance-performance map analysis to explore additional findings and discuss managerial implications.

Both platform support and official department support positively impact Chinese tertiary students’ online-startup motivation and entrepreneurial skills learned from universities are beneficial for them to build online-startup confidence. Meanwhile, influenced by the cooperative system implemented among official departments, industries and universities, official department support positively affects platform support and entrepreneurial skills. Conversely, influenced by Chinese traditional Confucian culture, uncertainty-avoidance thinking negatively affects tertiary students’ online-startup motivation.

This paper demonstrates the analysis of Chinese tertiary students’ online-startup motivation drawing on the COM-B behaviour changing and Hofstede cultural theories. Specifically, this study divides influencing factors into three specific aspects as follows: environmental opportunity, personal capability and social and cultural control. Unlike existing research applying traditional research models, the combination of the COM-B behaviour changing theory and the Hofstede cultural theory could be conducive to making the research model reflect influencing factors and present their different relationships.