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Shared leadership is an effective mechanism for managing project teams. Its performance-enhancing benefits have been demonstrated in many studies. Nonetheless, there is an obvious silence about how to promote shared leadership in Lean Six Sigma (LSS) project teams. To address this deficit, the purposes of this study are to investigate the influence of shared leadership on LSS project success and to explore how team psychological safety, project task complexity and project task interdependence influence shared leadership.

A multi-source, time-lagged survey design with a four-month interval was conducted. To do this, the authors collected data from 71 project teams (comprising 71 project managers and 352 project members) using LSS approaches in the manufacturing and service industries.

The findings show that shared leadership positively influences LSS project success. The authors also found that team psychological safety fosters the development of shared leadership and, more importantly, these effects are stronger when the tasks are more complex and more interdependent.

These findings advance our understanding of the factors that enable shared leadership and equip LSS project managers with practical techniques to improve shared leadership for the success of their projects.

This study extends the theory of shared leadership to the context of LSS project management and is among the first, to the best of the authors’ knowledge, to theoretically propose and empirically validate how to promote shared leadership in LSS project teams.

Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.

The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.

Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.

The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.

The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.

The literature on help-seeking at work has experienced significant growth in the past decades. However, our knowledge about this research domain remains fragmented and lacks sufficient theoretical integration. Therefore, this paper aims to comprehensively integrate the extant literature on help-seeking behavior at work and propose an overarching, organized framework to propel this field forward.

A state-of-the-art review and theoretical development on help-seeking at work are conducted.

First, the authors provide the conceptual clarity of its definitions, key characteristics, types and measurement techniques. Second, the authors develop a fine-grained and integrative process-based framework consisting of antecedents, proximal psychological mechanisms, subsequent influencing processes and distal outcomes to advance our understanding of seeking help in the workplace. Third, the authors offer a detailed agenda for future research to target opportunities within the field.

The current study is comprehensive in surveying the full body of knowledge on help-seeking at work. It uniquely provides a coherent overarching framework that organizes prior findings and channels future research. Additionally, this review paints a complete picture of what has been done and what needs to be done in the field. More research can be spurred based on our conceptual framework.

This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.

We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.

According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.

Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.

This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.

This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.

The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.

This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

本研究通过分析酒店评论文本和图像之间情感特征的不一致性来检测消费者的讽刺。

本文提出了一种基于多模态深度学习的讽刺检测模型, 使用从两个旅行平台收集的三个酒店品牌的评论, 该模型能够识别模态内部和模态之间的情感不一致性。利用图神经网络（GNN）探索文本-图像交互信息, 以检测讽刺情感中的关键线索。

研究结果显示, 多模态深度学习模型优于其他基线模型, 这有助于理解酒店服务评估, 并为酒店经理提供决策建议。

该研究可以在两方面帮助酒店业者：检测服务质量和制定策略。通过选择参考酒店品牌, 酒店业者可以更好地评估其服务质量水平（随之而来的是最佳资源分配）, 因此, 讽刺检测研究不仅有助于寻求提高服务质量的酒店经理。本研究介绍的多模态深度学习方法可以在其他行业复制, 帮助旅行平台优化其产品和服务。

The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This study aims to develop a fast and simple method to distinguish natural indigo from synthetic one.

A static headspace gas chromatography-mass spectrometry (GC-MS) method was developed for identification of natural and synthetic indigo samples. Natural indigo samples prepared from three different plants and synthetic indigo samples from three famous manufacturers in China, were involved in this study, along with some nonindigo blue samples (such as direct blue, active blue and neutral blue). The yarns and fabrics dyed with natural and synthetic indigo were also analyzed by the GC-MS method.

High levels of aniline (21.87%–71.59%) or N-methylaniline (25.26%–38.73%) were detected only in synthetic indigo samples (1 g) using the static headspace GC-MS method. The yarns and fabrics dyed with the synthetic indigo were also detected with residual aniline (0.47%–14.86%) or N-methylaniline (6.59%–40.93%).

The results clearly demonstrated that aniline or N-methylaniline can be used a diagnostic marker for distinguishing natural indigo from synthetic indigo. The proposed static headspace GC-MS method is a rapid, simple and convenient approach for differentiation of natural and synthetic indigo, as well as for the yarns and fabrics dyed with synthetic indigo.

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.

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.

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.

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.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

This study aims to assess the oxidative stability of avocado oil (AO) at various temperatures, using butylated hydroxytoluene (BHT) as an artificial antioxidant and different concentrations of ultrasonic extract of Chlorella vulgaris.

Extracts of C. vulgaris were obtained using four solvents: water, acetone, ethanol and 80% ethanol-aqueous. Standard techniques were used to conduct qualitative phytochemical screening of the extracts. The extracted samples were analyzed for total phenolics, total flavonoids, antioxidant activity and phenolic compound fractionation. Some physicochemical parameters of AO treated with various concentrations of C. vulgaris ultrasonic extract compared to a 200 ppm BHT and exposed to different temperatures were measured.

The highest phenolic, flavonoids content and antioxidant activity was achieved by 80% ethanolic extract of C. vulgaris . The results showed that exposure of AO to high temperatures led to significant changes in the oil's physicochemical properties. These changes increased as the temperature increased. On the other hand, adding 80% ethanolic extract of C. vulgaris into AO reduced the effect of heat treatment on the change in physicochemical properties.

Adding 80% ethanolic extract of C. vulgaris into AO can potentially reduce the impact of heat treatment on the alteration of physicochemical properties.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.