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The purpose of this paper is to investigate the mechanism of transmitting economic policy uncertainty (EPU) shocks to capital structure.

The authors adopt a novel approach that bridges the asset pricing implications of EPU and the debt-financing decisions of Chinese firms by introducing a variable “policy-risk-induced equity return” (PRER). PRER is the product of the EPU beta and the EPU shock. Differentiating firms as per the signs of the EPU beta helps to shed light on the deep questions of whether their respective leverage targets and speeds of adjustment are different and how the targets and speeds are determined.

The empirical evidence shows that it is the equity market that channels EPU shocks to capital structure through PRER in China. Firms with positive (negative) EPU betas have PRER impact negatively (positively) the leverage target, conforming to the market-timing theory. EPU and non-policy uncertainty shocks cause the speed of adjustment to change over time. Overall, the intertemporal relation between EPU and leverage is negative. These results are robust to alternative leverage measures and after controlling for non-policy uncertainty shocks and conventional firm characteristics and have implications for academic research, policymaking, market stability, and corporate financing.

This study is the first to probe for, and provide insights into, the underlying reason why EPU impacts capital structure by connecting asset pricing to corporate financing for a large sample of Chinese publicly traded firms.

Citizens often perceive surveillance by government authorities as oppressive and, hence, demonstrate reluctance in value co-creation from such services. This study aims to investigate the challenges and benefits of citizen empowerment through technology-driven surveillance or “smart surveillance.”

Guided by Dynamic Capability theory, the authors conduct in-depth interviews with officers in-charge of surveillance in smart cities. Given the contemporary advancements, this approach allows a retrospective and real-time understanding of interviewees’ experiences with smart surveillance.

The authors develop five propositions for citizen empowerment through smart surveillance to summarize the findings of this study.

This study advances the relevance of Dynamic Capability in public administration.

Smart city authorities and policymakers may leverage the insights provided in this study to design appropriate policies for smart surveillance.

The authors find that factors such as digital technology and infrastructure, information management, skill divide and perceived return on investment may influence citizen empowerment through smart surveillance.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

This research aims to explore how consumers' local brand choices differ between air-polluted days and clean days, and why the difference occurs.

Two studies were conducted. Study 1 used the longitudinal consumption data of various yogurt brands and daily air quality indexes in 2014 and 2015. Study 2 conducted three rounds of surveys on a clean day, a general air-polluted day and a seriously air-polluted day.

The findings indicate that consumers show less tendency of attribution and compensatory consumption during air-polluted days, which in turn decrease their willingness to choose local brands.

Implications are provided for future research and marketing practice, especially for local companies that rely heavily on local consumers, and retailers in heavy air-polluted areas.

This paper is the first to illustrate the influence of air pollution on consumers' local brand choices, and it extends current understanding on air pollution and consumer choices by discovering psychological process underneath to explain the effect.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

This study aims to explore the pandemic's opportunities for enhancing the environmental practices of the food and beverages green supply chains and its effect on the supply chains' viability by exploring the relationship between fear and uncertainty of COVID-19, food and beverages green supply chain management (F&B-GSCM) and supply chains’ viability based on the two dimensions (robustness and resilience) and examine the moderating effect of innovative technology adoption like big data analysis (BDA) capabilities and blockchain technologies (BCT) on this relationship.

This study adopted partial least squares structural equation modeling (PLS-SEM) on a sample of 362 F&B small and medium enterprises (SMEs)’ managers in the Egyptian market for data analysis and hypothesis testing.

The empirical results show that the fear and uncertainty of the pandemic have a significant positive effect on green supply chain management (GSCM). Also, BDA moderates the relationship between fear and uncertainty of COVID-19 and GSCM. However, BCT do not moderate that relationship. Similarly, GSCM positively affects supply chain viability dimensions (robustness and resilience). In addition, F&B-GSCM significantly mediates the relationship between fear and uncertainty of COVID-19 and supply chain viability dimensions (robustness and resilience).

Food and beverages (F&B) managers could develop a consistent strategy for applying BCT and BDA to provide clear information and focus on their procedures to meet their stakeholders' needs during COVID-19. Governments and managers should develop a consistent strategy to apply food and beverages supply chains (F&B SCs)' green practices to achieve F&B SCs' resilience and robustness, especially during the pandemic.

The Egyptian F&B SCs have been linked directly with many European countries as a main source of many basic food and agriculture products, which have been affected lately by the pandemic. Based on the “social-cognitive,” “stakeholder” and “resource-based view” theories, this study sheds light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of F&B-GSCM, SC resilience, SC robustness and innovative technologies back into the light, which helps in solving F&B SC issues and helps to achieve their viability.

This study attempts to fill the research gap by extending sustainability literature and providing empirical evidence that considers sustainability marketing commitment (SMC) as a fundamental attribute of effective marketing strategy that consequently improves tourism service quality, as represented by service attractiveness.

In the current study, data was collected from 313 tourism and hospitality firms. To test the model, this study applied structural equation modeling (SEM) to investigate the relationships among environmental strategy, SMC, supplier trust and service attractiveness in a mediation-moderation setting.

The results indicate that the multiple mediation effects of environmental strategy may indirectly influence tourist attractiveness through SMC and tourism services. The two-way moderating effects reveal that supplier trust and socialization strengthen the service attractiveness development process, while three-way interaction discovered that socialization and supplier trust positively moderate the relationships between tourism services and service attractiveness.

Sustainable strategy is a future trend for tourism business management; however, unknown to most is the role of marketing and environmental strategy in tourism business due to lack of integration with concepts in marketing strategy, with the multidimensionality of tourism services, and with the function of trust and socialization, critically undermining analyses of service attractiveness. This paper combines corporate sustainability and sustainability marketing methods to explore how an environmental strategy can improve tourism services and enhance a destination's attractiveness based on a mediation-moderation mechanism.

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.

This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot anthropomorphism and consumer usage intention and explores the mediating effect of perceived risk.

To examine the developed model, two complementary studies are designed. In Study 1, multi-time data of 511 participants show that service robot anthropomorphism inverts U-shaped (curvilinear) relationship on consumer usage intention and perceived risk mediates this curvilinear relationship. In Study 2, multi-source data of 460 volunteers are used to confirm the findings of Study 1 and examine that consumer empathy moderates the complex nonlinear effect of service robot anthropomorphism on perceived risk, and the indirect curvilinear effect of service robot anthropomorphism on consumer usage intention through perceived risk.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.