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Based on the computers as social actors (CASA) theory, this study aims to explore the impact of three characteristics of virtual influencers (conversational tone, autonomy and responsiveness) on social presence, telepresence and customer engagement.

Using the purposive sampling technique, online survey was administered to Chinese Gen-Z consumers engaging with virtual influencers. Subsequently, 357 respondents were sampled. SPSS 29.0 and Smart PLS 4.0 were used to perform the analyses.

The results show that conversational tone and responsiveness have significant positive effects on both social presence and telepresence. Autonomy has a positive effect on telepresence, but not social presence. Social presence and telepresence have a positive impact on customer engagement.

As a burgeoning field, there is still uncertainty among both practitioners and researchers about the methods that virtual influencers engage their users in the context of social media. Limited research has focused on presence (social presence and telepresence) due to virtual influencers. Therefore, the CASA theory offers valuable insights into how virtual influencers’ characteristics contribute to the presence and customer engagement and provides practical guidance for the design of virtual influencers.

Despite widespread use of virtual streamers, academic research on this subject remains limited. This study aims to explore the mechanisms by which consumer perceptions of virtual streamers influence consumer purchase intentions.

We used partial least squares structural equation modeling to analyze validated online survey data from 414 consumers watching virtual streamers.

Consumer perceptions of virtual streamers (perceived competence, perceived interaction quality and perceived warmth) promote the establishment of transactional psychological contract and relational psychological contract between consumers and virtual streamers, which further affects consumers’ purchase intention.

This study enriches the research on virtual streamers, facilitates their adoption and introduces the psychological contract into a new research context by revealing the formation of the psychological contract from the perspective of virtual streamers. Moreover, this study provides a new understanding of the relationship between disembodied artificial intelligence and consumers.

This study aims to identify a value co-creation framework for live streaming through tourism scenes (LStTS). It also clarifies the value attributes of LStTS and makes an empirical test.

The study used a mixed-method approach. In Study 1, a total of 12,216 pieces of viewers’ comments and ten web news reports were coded and analyzed employing a grounded theory approach. In Study 2, data were collected from 587 Douyin e-commerce users. Exploratory factor analysis and partial least squares structural equation modeling were used to test the value co-creation framework of LStTS.

In Study 1, six value attributes in three categories were identified based on a content analysis of viewers’ comments. In Study 2, a three-order factorial model of value co-creation in LStTS was identified and tested.

Our study is limited by the preponderance of female respondents in the sample and the unique nature of the research context.

Merchants and streamers should consider whether there is a fit between the merchandise and the tourism scene when selecting the tourism scene for live streaming marketing; they can select novel and beautiful natural tourism scenes to attract viewers. Detailed and comprehensive product information should be provided in the process of live streaming marketing and sharing with consumers.

The novelty of our study lies in the provision of a new value co-creation framework in LStTS, which offers a theoretical basis for analyzing the value of the tourism scene in live streaming marketing.

This study aims to comprehensively examine the transitions in household livelihood strategies within rural China, including the underlying processes, drivers and outcomes.

This study uses two waves (2010 and 2018) of longitudinal data from the China Family Panel Studies (CFPS), employing latent cluster analysis, regression models and cumulative distribution function within a dynamic household livelihood strategy framework.

The results show that (1) households’ livelihood strategies can be categorized into four distinct types, i.e. agricultural dominated, agricultural dominated with non-agricultural supplementation, non-agricultural dominated with agricultural supplementation and employment oriented. (2) During 2010–2018, approximately 60% of households underwent transitions in their livelihoods, encompassing both upward and downward trajectories, with a prevalence of upward transitions. (3) Various forms of livelihood capital significantly contribute to upward transitions, while the availability of land resources and exposure to shocks impede the potential for upward mobility. (4) The transition towards non-agricultural livelihood strategies by households leads to a notable enhancement in their livelihood welfare.

In the context of urbanization, industrialization and globalization, rural areas in China are undergoing a gradual socioeconomic transformation, which has also led to changes in rural households’ livelihood strategies. Nevertheless, a dearth of empirical investigation exists regarding the dynamics of rural households’ livelihood strategies, the determinants behind such transitions and the resulting outcomes on their livelihoods. A comprehensive understanding of livelihood transitions can provide valuable insights for policymakers in their endeavors to promote rural revitalization in China.

Based on the nationwide representative datasets in China, it examines the micro-level livelihood transitions of rural households within the broader context of socioeconomic transformation that presents both opportunities and challenges, as well as vulnerable contexts, shaped by various government policies. This exploration would offer valuable theoretical and empirical evidence to advance our understanding of the process, driver and outcome of rural households’ livelihood transition in developing countries.

Switching to green energy is a crucial step in achieving carbon neutrality. This study aims to explore what motivates people to use green energy and how much more people are willing to pay for green energy.

Grounded on the value–attitude–intention hierarchy, this study proposes that environmental consciousness as a human value influences attitudes including attitude toward environmental issues and attitude toward ecosocial benefits while attitudes, information and knowledge about green energy and quality and price of green energy influence people’s intention to use green energy. Data were collected from 342 Chinese adults.

Results showed that environmental consciousness significantly and positively influenced attitudes while attitude toward environmental issues had the greatest effect on people’s intention to use green energy, followed by quality and price of green energy. About 44% respondents were willing to pay 2.5% to <5% more money for green energy.

This study extended the use of value–attitude–intention hierarchy to investigate what motivates people to use green energy. Specifically, this study demonstrated that quality and price, and knowledge and information also significantly shaped people’s intention to use green energy significantly.

As artificial intelligence technology empowers service robots, they increasingly communicate with consumers in a human-like manner. This study aims to investigate the effect of service robots’ different conversational styles (competent conversational style vs. cute conversational style) on consumer service acceptance and demonstrate the moderating role of consumers’ technology anxiety.

Based on anthropomorphism theory and social presence theory, the authors conducted two scenario-based experiments (restaurant scenario and hotel scenario) to investigate this issue.

The results indicate that service robots’ conversational styles impact consumers’ willingness to accept the use of service robots through perceived social presence and positive emotion. Moreover, consumers perceived social presence and positive emotion play a serial mechanism. In addition, the effect of competent conversational style on consumers perceived social presence is less effective than that of cute conversational style. Finally, the authors demonstrate the moderating role of consumer technology anxiety in the relationship between conversational styles and perceived social presence.

To provide consumers with a positive human–robot interaction experience at the service front line, managers need to make better use of the conversational styles of service robots by comprehensively considering the characteristics of consumer technology anxiety.

This research extends the literature on service robots by integrating consumer characteristics and robots’ conversational styles. These findings highlight the effectiveness of cute conversational style in alleviating consumer technology anxiety.

This paper aims to explore the factors that contribute to inclusive leadership (IL) through the lens of leaders and further segregate these factors into four levels to highlight the attributes required at each level in an inclusive leader’s growth journey.

This study adopts a qualitative research method comprising 31 in-depth interviews with managers across various domains to comprehend their views on traits of an inclusive leader.

This paper provides valuable insights into the vital characteristics of an inclusive leader. The study demonstrates that an inclusive leader’s journey moves from tolerance to acceptance, value, and finally celebration. Leaders need to continuously work on their awareness, efforts, accommodation, openness and cultural intelligence to become truly inclusive. Leaders who appreciate diversity and embrace inclusion create a workplace that impacts employees positively and subsequently influences engagement, performance and productivity of the workforce.

This study will promote awareness and understanding amongst practitioners about critical attributes of inclusive leaders and how organizations can facilitate leaders’ journey in becoming truly inclusive leaders.

To the best of the authors’ knowledge, this study is the first attempt to explore the conceptualization of IL through various levels of inclusion (tolerance, acceptance, value and celebration) leading to maturity and growth as an inclusive leader.

As the risk of uncertain quality of used products potentially hinders remanufacturing, this study aims to examine the impact of risk aversion under quality uncertainty of used products in a remanufacturing supply chain (RSC) consisting of a manufacturer and an independent remanufacturer.

We develop an RSC model where the manufacturer produces new products, outsources remanufacturing to the independent remanufacturer and sells both new and remanufactured products to end consumers. Using a manufacturer-led Stackelberg game framework, we derive the equilibrium solutions under risk-neutral and risk-averse scenarios. Additionally, we design a two-part tariff contract to achieve coordination.

We show that while risk aversion leads the manufacturer to raise the outsourcing fee, which in turn reduces both the remanufactured quantity and the collection rate of used products. Consequently, consumer surplus and social welfare decline, while environmental impacts rise. The proposed two-part tariff contract can improve the collection rate and social welfare. We also explore two extensions: an authorization remanufacturing scenario and a two-period scenario. We find that risk aversion has no impact on the selection of remanufacturing mode and the equilibria in the first period. Our findings provide timely managerial insights for RSC management.

One of the main risks deterring remanufacturing is the quality uncertainty of used products. However, the risk aversion arising from this uncertainty and its effects have rarely been studied within a game-theoretic framework. This paper fills this gap by analyzing the remanufacturer’s risk aversion under quality uncertainty and investigating its impacts.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.