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The Industry 4.0 (I4.0) revolution originated in developed countries and has now been promoted worldwide as a powerful tool for improving productivity. However, adopting I4.0 technologies poses significant challenges, especially for emerging economies that are far from the I4.0 frontier. In the context of emerging economies, this paper aims to explore the impact of adopting I4.0 technologies on firms’ total factor productivity (TFP) and its mediation channels.

Using panel data for 2,928 firms in China’s manufacturing industry during the period 2010–2022, this study adopts fixed effects regression model to test the theoretical hypotheses. Endogeneity issues are addressed by the instrumental variable approach and propensity score matching.

The results show that adopting I4.0 technologies can significantly improve emerging economy firms’ TFP, and this effect is achieved by promoting technological innovation and alleviating financial constraints. Furthermore, the findings indicate a heterogeneity in the effects of I4.0 technology adoption. When top managers are long-term oriented, firms are state-owned, industry competition pressure is low or regional manufacturing innovation capability is strong, the positive impact of I4.0 on TFP is weakened.

This paper is one of the first attempts to offer empirical evidence about whether and how the adoption of I4.0 technologies boosts TFP growth among firms in emerging economies. The study expands on the organizational performance consequences of I4.0 adoption and provides implications for decision-makers in developing countries in implementing I4.0.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

This study aims to provide a timely review of hospitality and tourism live streaming (HTLS) research, aiming to understand the intellectual foundation in the past and identify opportunities for future research.

This study collected 56 papers from journals in the Social Science Citation Index database or the Australian Business Deans Council journal quality list and adopted a hybrid systematic literature review combining bibliometric analysis and the theory-context-characteristics-method framework-based approach. Additionally, a cross-tabulation analysis was conducted to compare the research on two specific forms of HTLS – tourism e-commerce live streaming (TEcLS) and travel live streaming.

The study found that the current research lacks in-depth theoretical insights, focuses on China as the national context and viewers as the research subject, mainly uses online surveys and lacks first-order knowledge. Moreover, it identified five research themes, including the impact of HTLS on viewer behavior, purchase behavior in HTLS and introduction of HTLS, within which eight categories of research constructs, such as viewer-related, streamer-related and relationship-related, are applied.

In terms of research, the study reveals various gaps in the existing literature and provides potential research directions to advance the field. As for practice, it offers valuable strategies for practitioners to optimize their marketing efforts using HTLS.

This study provides an up-to-date review of the HTLS field through a systematic and reproducible method. It offers critical discussions and suggestions for academic research and industry practices.

This study examines how leaders’ narcissistic rivalry (LNR) affects the in-role performance (IRP) and proactive customer service performance (PCSP) of employees in the hospitality industry. Specifically, this study investigates the mediating role of psychological distress and the moderating role of locus of control (LOC) in the aforementioned relationships.

This study administered a multi-wave, multi-source questionnaire survey with 323 employees working in 11 full-service hotels in China. Statistical analyses were performed using the PROCESS macro in SPSS 26 software and structural equation modeling using Mplus 8.3 software.

The authors' results suggest that LNR can negatively affect hospitality employees’ IRP and PCSP and that these relationships are mediated by psychological distress. Additionally, the impact of LNR on psychological distress can be lessened by internal LOC.

This study contributes to the literature on leader narcissism by investigating how LNR affects IRP and PCSP among hospitality employees. Drawing on conservation of resources theory, this study also identifies a novel mediating mechanism (psychological distress) connecting LNR to hospitality employees’ service outcomes. Furthermore, this study reveals the moderating role of LOC in the relationship between LNR and psychological distress.

Voting power in crediting organisations plays a huge role in determining a country's fiscal power and access to capital, determining who has power and access to capital when it comes to crisis management. In this chapter, I explore the impact it played during the COVID-19 pandemic to highlight the need to democratize global capital when it comes to global crisis management. Parallels will be drawn between the pandemic and the climate emergency to demonstrate how the democratic deficit in global capital translates to disastrous policymaking in the medium and long term for all parties. In response to this, I argue for the democratisation of global capital and highlight how the New Development Bank can provide the necessary force for this essential shift. Pre-empting the common antagonism regarding pooling national sovereignty to supernational organisations and richer countries unilaterally surrendering their privileged position, I will also highlight how continued poor crisis management threatens all nations' national sovereignty in the long-term, given the existential threats of the climate emergency and future pandemics.

This study aims to consolidate existing research on digital technology in business education, focusing on adoption determinants, theoretical frameworks and implementation challenges.

A hybrid literature review was conducted. The first phase involved a keyword co-occurrence analysis of 254 Association of Business Schools-ranked journal articles from the Web of Science database to identify key research themes. To deepen the analysis, the second phase included a content analysis of the 24 most globally cited articles, each with a minimum of 65 total global citations.

The review reveals that studies primarily focus on factors influencing digital technology adoption, including institutional support, perceived usefulness and ease of use. Researchers frequently cite theoretical models such as the technology acceptance model and the unified theory of acceptance and use of technology. Identified challenges include concerns over technology effectiveness, resistance to change and the influence of cultural and generational differences on technology use.

The findings guide educators and policymakers in formulating strategies for effective technology adoption, emphasizing alignment with educational goals and addressing cultural barriers.

Understanding generational and cultural impacts enables the creation of inclusive digital education strategies that cater to diverse student needs.

This study synthesizes fragmented literature, offering new insights into the integration of digital technologies in business education.

Managers in China prioritize the cultivation of loyal employees, resulting in positive effects associated with leader-member exchange (LMX). However, fragmented evidence suggests that LMX also can trigger deviant behavior. LMX provides employees with access to resources, while it also harbors potential risks for deviant behaviors. Based on the cognitive-affective system theory of personality and resource-related theories, this study aims to explore the double-edged sword effects of LMX by examining how LMX influences interpersonal deviant behaviors through emotional and cognitive pathways, respectively.

This study involved three waves of paired data surveys that were conducted in China over one month, and a total of 117 leaders and 235 subordinates participated in this study.

Even though LMX as a job resource reduces workplace anxiety, LMX also generates work overload for employees. Workplace anxiety and work overload further result in interpersonal deviant behavior. Narcissistic admiration, as a personality trait, can weaken the mediating role of work overload but not that of workplace anxiety.

The finding can help managers pay attention to negative effect of LMX and provide suggestions for preventing employees’ workplace deviant behavior.

The findings revealed how LMX leads to negative outcomes in the workplace. In addition, the results demonstrated the buffering effect of narcissistic admiration on the negative effect of LMX.

The purpose of this paper is to examine the underlying mechanisms influencing the social entrepreneurial intentions of individuals in China, adopting social capital theory, bottom-up and people-based approach. The interrelated effects of intellectual capital (social community trust, social capital bonding and social participation) on social innovation tendency were measured.

We recruited 502 Chinese individuals by utilising a reliable survey platform in China. This study used structural equation modelling as an analytical approach to examine the influence of social capital on social innovation and social entrepreneurship intention.

The findings showed that social innovation tendencies mediate the relationship between social community trust, social capital bonding and social participation and the social entrepreneurial intentions of individuals. The findings support the tested hypotheses that social innovation tendencies are the key mechanism to translate into stronger social entrepreneurial intentions. An iterative framework emphasising transparency and open collaboration among stakeholders, which are vital for fostering social entrepreneurial intentions, was proposed.

This is a novel empirical study to apply social capital theory to the field of social enterprises in the Chinese context using structural equation modelling, bottom-up, people-based approach and iterative model. The findings offer valuable practical insights for entrepreneurial and SME practices to foster social entrepreneurship through a people-based approach, emphasising the importance of fostering trust, strengthening social bonds, and encouraging active social participation.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.