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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.

This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM (p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.

This study aims to examine the impact of website design, reliability and perceived ease of use as an engagement motivational factors on customer e-trust and e-retention in online shopping.

By using deductive approach, quantitative methods and purposive sampling technique, this study has collected the data from 295 young online customers to enhance an understanding of website design, reliability and perceived ease of use in an online shopping context.

The findings revealed interesting insights where reliability is the most significant predictor of customer e-trust in online shopping, followed by perceived ease of use and website design. In addition, a significant mediating effect of e-trust is found between customer e-retention, website design, reliability and perceived ease of use.

Future research is recommended to predict the antecedents of online engagement motivational factors with value co-creation and co-creation experience in online shopping context.

This study offers fresh insights about driving elements and impediments of online customer retention. Customer engagement comprising of website design, reliability and perceived ease of use appear to influence the online customer retention through direct and indirect effect.

Although the sharing economy improves comfort and convenience, it is yet unclear how it affects subjective well-being. This study aims to offer a conceptual model for understanding the linkages between the antecedents and consequences of subjective well-being in ridehailing services.

Using a non-probabilistic sampling method and a pre-tested survey instrument, 450 responses were collected from January to March 2020. The data were analysed using structural equation modelling.

Experience quality and perceived convenience are correlated with subjective well-being. Perceived value and personal innovativeness were not correlated with subjective well-being, as the former does not contribute to the latter’s development. Continuous usage intention significantly correlated with subjective well-being, followed by customer relationship proneness and advocacy. Regarding gender and age differences, men place higher value on customer relationship proneness than women, while women place higher value on subjective well-being than men. Older users value perceived convenience and customer relationship proneness in ridehailing services more than younger users.

Understanding key factors contributing to user well-being in ridehailing would promote a more affordable mobility sector globally. This understanding would enable ridehailing businesses to create more effective business and marketing plans while prioritising user well-being, thus enhancing user happiness and reducing turnover rates.

This research demonstrates how crucial it is for users’ well-being to have a positive experience and find the service convenient. It also highlights the importance of building strong customer relationships and examines how gender and age influence people’s adoption and use of these services.

Aunque la economía colaborativa mejora la comodidad y conveniencia, aún no está claro cómo afecta al bienestar subjetivo. Ofrecemos un modelo conceptual para comprender las conexiones entre los antecedentes y consecuencias del bienestar subjetivo en los servicios de transporte compartido.

Utilizando un método de muestreo no probabilístico y un instrumento de encuesta previamente probado, se recopilaron 450 respuestas entre enero y marzo de 2020. Los datos fueron analizados utilizando un modelo de ecuaciones estructurales.

La calidad de la experiencia y la percepción de conveniencia están correlacionadas con el bienestar subjetivo. El valor percibido y la innovación personal no se correlacionaron con el bienestar subjetivo, ya que el primero no contribuye al desarrollo del último. La intención de uso continuo se correlacionó significativamente con el bienestar subjetivo, seguida por la propensión a las relaciones con los clientes y la defensa de estos servicios. En cuanto a las diferencias de género y edad, los hombres valoran más la propensión a las relaciones con los clientes que las mujeres, mientras que las mujeres valoran más el bienestar subjetivo que los hombres. Los usuarios mayores valoran más la percepción de conveniencia y la propensión a las relaciones con los clientes en los servicios de transporte compartido que los usuarios más jóvenes.

Esta investigación demuestra lo crucial que es para el bienestar de los usuarios tener una experiencia positiva y encontrar el servicio conveniente. También resalta la importancia de construir relaciones sólidas con los clientes y examina cómo el género y la edad influyen en la adopción y uso de estos servicios.

Comprender los factores clave que contribuyen al bienestar de los usuarios en los servicios de transporte compartido promovería un sector de movilidad más asequible a nivel global. Esta comprensión permitiría a las empresas de transporte compartido crear planes de negocios y marketing más efectivos, priorizando el bienestar de los usuarios y mejorando así su felicidad y reduciendo las tasas de rotación.

尽管共享经济提高了舒适度和便利性, 但它如何影响主观幸福感尚不清楚。我们提供了一个概念模型, 用于理解乘车服务中主观幸福感的前因后果之间的联系。

采用非概率抽样方法和预先测试的调查工具, 在 2020 年 1 月至 3 月期间收集了 450 份回复。数据采用结构方程模型进行分析。

体验质量和感知便利性与主观幸福感相关。感知价值和个人创新性与主观幸福感不相关, 因为前者无助于后者的发展。持续使用意愿与主观幸福感密切相关, 其次是客户关系倾向和拥护。在性别和年龄差异方面, 男性比女性更重视客户关系倾向, 而女性比男性更重视主观幸福感。老年用户比年轻用户更重视乘车服务中的便利感和客户关系代言。

这项研究表明, 用户获得积极的体验和便捷的服务对他们的福祉至关重要。研究还强调了建立牢固的客户关系的重要性, 并探讨了性别和年龄如何影响人们采用和使用这些服务。

了解有助于提高乘车旅行用户幸福感的关键因素, 将在全球范围内推动建立一个更加经济实惠的移动出行行业。这种理解将使打车企业能够制定更有效的业务和营销计划, 同时优先考虑用户福祉, 从而提高用户幸福感并降低流失率。

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.

Research about tourism impacts is abundant. However, existing studies often investigate tourism impacts from residents’ and tourists’ perspectives. The study targeting students is absent. The students’ perception and evaluation of tourism impact must be investigated independently to have more insights into improving the current education curriculum, which covertly addresses the impacts. Therefore, this study aims to examine university students’ perception of tourism impacts as young tourists to provide implications for better teaching of such content in the future.

Vietnam was chosen as the context, and a mixed method was employed. First, free elicitation with 118 university students was carried out to identify the students’ perceived impacts (109 items, four categories). Second, structured surveys using the previously generated content with 243 and 224 university students were implemented to identify the latent structures of the impacts (3-4 factors per impact category).

Tourism impacts can be dually perceived. In other words, they can come in positive or negative forms or are directed toward the destinations or the residents. However, being positive or negative, or affecting the place or the people, tourism impacts have some general and universal elements concerning the economic, sociocultural and environmental ones. In addition, they have some specific and contextual aspects, such as cross-border marriages, child labor and economic autonomy loss.

Tourism impacts seen from the perspective of university students are relatable and valuable for tourism education. Educators in Vietnam should consider revising the current curriculum to address all the tourism impacts overtly.

This paper extends entrepreneurial ecosystems theory by testing how aspects of the local business environment affect individual entrepreneurs' ability to translate their personal resources into firm performance.

Data were collected from 223 business owners across Australia. Moderation hypotheses were tested using multiple hierarchical regression and confirmed with the Preacher and Hayes (2004) bootstrapping method.

The results show that business owners' psychological capital, social capital and entrepreneurial education directly affect their individual firm performance. These positive relations are moderated by specific aspects of the business environment, such that they are stronger when the environment is more favorable.

This study puts individual business owners back into entrepreneurial ecosystems theory and explains how they can make the most of their personal resources, suggesting a complex interplay where one size does not fit all. Far-reaching practical implications for policymakers are discussed.

This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers.

This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative.

The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach.

This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.