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This study aims to extend ambidexterity theory from the perspective of organisational learning and examine how process ambidexterity, which comprises operational flexibility and operational routine, responds to environmental uncertainty and ultimately reduces organisational risks.

This study tests the hypotheses by analysing 464 annual reports of 115 listed companies in the Chinese agricultural and food industry using content and secondary data analyses. Four case studies are also provided.

The results show that (1) environmental uncertainty has a positive effect on either operational flexibility or operational routine; (2) both operational flexibility and operational routine have negative effects on organisational risks, supporting the view that process ambidexterity mediates the relationship between environmental uncertainty and organisational risks; and (3) organisational slack plays the role of “double-edged sword” by negatively moderating the effect of environmental uncertainty on operational flexibility and positively moderating the effect of environmental uncertainty on operational routine.

In an uncertain environment, companies are exposed to greater risk. This study contributes to risk management in three ways: first, it extends ambidexterity theory to process management and proposes how process ambidexterity balances operational flexibility and routines. Second, it distinguishes between the different conditions under which flexibility or routines are superior. Third, it explains the mechanisms related to how organisations can resolve environmental uncertainty into risk through process ambidexterity.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

This study aims to deliver a new perspective on how the interaction of independent and cooperative innovation affects firm digitization. Based on resource constraint theory, this study argues that the aforementioned interaction negatively affects firm digitization. The moderating role of managerial discretion is also discussed in light of the principles of the awareness-motivation-capability (AMC) framework.

The proposed hypotheses are empirically tested using a negative binomial modeling approach. The data used are from A-share listed companies in China’s Shanghai and Shenzhen stock markets from 2006 to 2020.

This study suggests that the interaction of independent innovation and cooperative innovation negatively impacts digitization. In addition, this study argues that environmental discretion and organizational discretion weaken the negative impact of the mentioned interaction on digitization. However, additional discretion in the Chinese context has no effect on above relationships.

This study explores the impact of the interaction of independent and cooperative innovation on digitization and incorporates managerial discretion into this framework based on the AMC framework.

The objective of this study is to systematically review the current body of literature in order to gain insights into the progress of research in accounting and auditing of cryptocurrencies, while also highlighting the associated risks and identifying gaps for future exploration.

To achieve this, a structured literature review was carried out, presenting a thorough and critical assessment of the available studies focused on cryptocurrencies within the accounting and auditing domain.

The analysis reveals that the majority of the research has concentrated on the reporting and measurement aspects of cryptocurrencies, neglecting the auditing aspect. Regarding the methodology, future investigations should incorporate both theoretical and empirical manners to address this gap. Various spheres require further exploration, as they have the potential to significantly impact practitioners and academics.

The significance of this paper lies in its comprehensive examination of the existing literature, synthesizing and organizing information pertaining to accounting and auditing considerations of crypto transactions. Moreover, it provides valuable insights into best practices and prompts identifying avenues for further research in this field.

This study aims to examine the full mediating role of supply chain flexibility (SCF) between lean production (LP) and business performance (BP) found in the previous literature. This effect negates the direct LP-BP effect (the so-called “total eclipse effect”). The authors analyze the individual contributions that the different SCF dimensions (sourcing flexibility; operating system flexibility, distribution flexibility and information system [IS] flexibility) make to the “total eclipse effect” between LP and BP produced by SCF. The relational resources-based view and resource orchestration theory are used to support the theoretical framework.

Covariance-based structural equations modeling (CB-SEM) is used to test the SCF LP-BP total eclipse hypothesis and four additional mediation hypotheses, one for each of the SCF dimensions. Data obtained via a questionnaire given to 260 companies are analyzed with CB-SEM, and SPSS Process is used to evaluate the mediation effect.

Research results indicate that only one of the dimensions (operating system flexibility) has a full mediation effect between LP and BP and is, therefore, the main contributor to the eclipse effect. Two other dimensions (sourcing flexibility and distribution flexibility) have partial mediation effects, so they also contribute to developing the eclipse effect, although to a lesser extent. Finally, IS flexibility is neither a full nor a partial mediation factor and does not contribute to the eclipse effect.

These findings have some important implications. For academia, they generate new knowledge of the role that each of the SCF dimensions or components plays in the LP-BP relationship. For company management, the findings offer supply chain managers specific information on the individual effects that the different types of SCF flexibility have between LP and BP. This will allow companies to target their efforts to develop certain types of flexibility in LP contexts depending on the outcomes that senior managers want to achieve with their SCs.

This research aims to build on the pre-existing corpus of literature through the integration of the technology acceptance model (TAM) and usage habit to more accurately capture the determinants associated with social media addiction among university students. This study seeks to delineate how usage habit and TAM may be used as predictors for addiction potential, as well as provide greater insight into current trends in social media usage across this population demographic.

A cross-sectional research design was employed to investigate the determinants of social media addiction among university students in Malaysia at the onset of their tertiary education. A self-administered survey, adapted from prior studies, was administered to a sample of 217 respondents. The hypotheses on social media addiction were subsequently tested using a partial least squares structural equation modeling (PLS-SEM) approach.

Usage habit was found to be a direct and strong predictor of this type of addiction, as well as all TAM variables considered in the research. Additionally, by integrating TAM with usage habit, the study revealed a comprehensive and multi-faceted understanding of social media addiction, providing an important insight into its complexity in the Malaysian context. Although several other factors have been identified as potential contributors to social media reliance and addictive behavior, it appears that usage habit is paramount in driving these addictive tendencies among university students.

This expanded model holds significant implications for the development of interventions and policies that aim to mitigate the adverse effects of social media addiction on students' educational and psychological well-being. The study illustrates the applicability of the TAM in examining addictive behaviors within emerging contexts such as the Malaysian higher education sector, thus contributing to the extant literature on the subject.

The integrated TAM and habit model is an effective predictor of social media addiction among young adults in developing countries like Malaysia. This highlights the importance of actively monitoring and controlling users' interactions with technology and media platforms, while promoting responsible usage habits. Educators can use these findings to create tailored educational programs to educate students on how to use technology responsibly and reduce their risk of becoming addicted to social media.

This study provides a unique perspective on social media addiction among university students. The combination of TAM and usage habit has the potential to shed significant light on how variables such as perceived usefulness (PU) and perceived ease of use (PEOU) may be associated with addictive behaviors. Additionally, by considering usage habit as an explanatory factor, this research offers a novel approach to understanding how addictions form over time.

The study examines a mediated, moderated process of students' intellectual engagement from optimism, academic self-efficacy and academic burnout.

Five hundred and twenty-seven participants who completed a self-reported questionnaire were selected using a convenient sampling technique. PLSc was used to test the proposed hypotheses.

Results showed that optimism positively affects students' intellectual engagement and academic self-efficacy. Additionally, academic self-efficacy correlates positively with students' intellectual engagement and further mediates the relationship between optimism and intellectual engagement. Finally, the moderation effect of academic burnout was positive and non-significant.

This paper is among the first to have tested a model including optimism, academic self-efficacy, intellectual engagement and academic burnout in a university setup from a developing country perspective.

This study aims to examine how business executives' political connections are associated with government subsidies and strategic change, and how they, in turn, influence firm performance, measured by return on assets (ROA) and market share.

Hypotheses were tested using the large firm-level dataset provided by the National Bureau of Statistics (NBS) of China for the period 2003–2013. This is one of the most comprehensive datasets of Chinese manufacturing companies and includes 321,722 firms on average per year, which spans over 37 industries.

The authors found that political connections, measured by senior executives' membership in the National People's Congress of China (NPC), were positively associated with government subsidies but were not associated with strategic change. Also, government subsidies, as the underlying mechanism, mediated the relationships between NPC membership and firm performance but strategic change did not.

By examining the possible mediators between corporate political strategies and firm performance, the authors confirmed the thought that the impact of political connections on firm performance is a complex phenomenon and goes beyond a simple direct effect. However, future research could explore other mediators in this relationship.

While the direct relationship between political connections and firm performance has been examined in management literature, the results are mixed. For the first time, the authors addressed the gap and opened the “black box” – the underlying mechanisms of this relationship. This study's findings contribute to the literature on corporate political activity, strategic change, and their influences on firm performance.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.