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Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

With recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.

Two studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.

Findings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.

Findings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.

The findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

Thermal characterization of power switching devices is critical for the performance and reliability of power electronic applications. This paper aims to characterize the junction-case thermal properties of insulated gate bipolar transistor (IGBT) modules by fractional calculus-related techniques.

A fractional-order thermal impedance model is proposed for IGBT modules. Riemann–Liouville definition of fractional calculus and Laplace transformations are used to describe the thermal impedance characteristics of IGBT modules, thus, a fractional-order equivalent model is derived. Furthermore, the task of identifying model parameters is transformed into the problem of finding the global optimal solution for the fractional-order model, where the Harris Hawks optimization algorithm is used.

The thermal impedance curves of IGBT modules generally exhibit a “long-tail distribution” over time. This work demonstrates that, there is a potential relationship between this distribution law and fractional calculus operation. Therefore, fractional-order equivalent models can be used to describe the thermal impedance characteristics of IGBT modules in effective and concise way.

This work proposes for the first time a fractional-order model for the junction-to-case thermal impedance characteristics of IGBT modules. Using the proposed fractional-order model and parameter identification scheme, the maximum relative error remains below 8% within a time range of 0.001–100 s and a power range of 14.5 W–273 W, demonstrating high accuracy across diverse operating conditions. This model’s advantages in accuracy and complexity provide theoretical support for the manufacturing and thermal characteristics analysis of IGBT modules, confirming the widespread presence of fractional-order properties in the physical world.

To remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.

Based on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.

According to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.

The paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance?

The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data.

The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance.

This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices.

This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context.

This study aims to explore the determinants of green electronics purchase intention in Malaysia by extending existing knowledge on green consumer behavior and contributing to the field of sustainable consumption.

A quantitative research approach was adopted, with data collected from 250 Malaysian consumers. The proposed model was tested using partial least squares structural equation modeling to assess the relationships between various determinants and green purchase intention.

The results demonstrate that perceived consumer effectiveness, green advertising and monetary cost positively affect environmental attitudes, which subsequently influence green purchase intention. The study also identifies that brand image and information quality significantly enhance green brand trust (GBT), leading to stronger intentions to engage in green purchasing. Additionally, it finds that environmental knowledge and environmental concern shape perceived behavioral control, which further impacts green purchasing intention.

The study focuses on Malaysian consumers, which may limit the generalizability of the findings to other cultural contexts. Future research could expand the scope to include cross-cultural comparisons to validate the model in different settings.

By providing insights into the key factors driving consumers’ intention to purchase green electronics, the study offers valuable guidance for marketers and manufacturers to develop targeted strategies that promote sustainable consumption and capitalize on the growing demand for green products in Malaysia.

This study is unique in measuring the influence of green attitude, GBT and perceived behavioral control on green purchase intention specifically within the electronics sector, offering a novel contribution to the literature on sustainable consumer behavior.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.

The advent of digital technologies has ushered in a new era of quality management (QM) known as Quality 4.0 (Q4.0). The successful implementation of Q4.0 requires the involvement of both leaders and employees. Leadership plays a critical role in motivating employees involved in quality practices. However, the mechanisms by which leadership inspires quality professionals to engage in continuous learning and remain vigorous in their roles are not well understood. This study aims to determine the impact of Q4.0 leadership on thriving at work (TAW) among quality professionals and to identify the underlying mechanisms.

Utilising the identity theory and social identity theory, a multilevel TAW model was developed. This study surveyed 63 team leaders and 243 subordinates, who are quality professionals working for companies implementing Q4.0. Multilevel structural equation modelling (MSEM) was applied to assess the hypotheses.

The study finds that Q4.0 leadership enhances TAW among quality professionals. The linkage between Q4.0 leadership and TAW is mediated by work group identification (WGI) at the group level and job identification (JI) at the individual level.

Insights from this study will enable organisations to make informed decisions regarding the leadership styles that best support TAW among quality professionals. By understanding the mechanisms linking Q4.0 leadership to TAW, organisations can foster both WGI and JI, ultimately enhancing engagement and performance in quality initiatives.

This study offers a novel contribution to the QM field by examining the role of Q4.0 leadership in motivating and sustaining the engagement of quality professionals. Exploring the relationships between Q4.0 leadership, WGI, JI and TAW helps to deepen our understanding of how Q4.0 leadership can enhance TAW among quality professionals.