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The escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance levels. This paper aims to delve into academic performance within the context of the ChatGPT era by exploring the influence of several pivotal predictors, such as academic integrity, academic competence, personal best goals and perceived stress, as well as the moderating effect of ChatGPT adoption on academic performance.

This study uses a quantitative method to investigate the impact of essential variables on academic integrity, academic competence, perceived stress and personal best goals by analysing 402 responses gathered from ResearchGate and Academia.edu sites.

While affirming the established direct positive relationship between academic integrity and performance since adopting AI tools, this research revealed a significant moderating role of ChatGPT adoption on this relationship. Additionally, the authors shed light on the positive relationship between academic competence and performance in the ChatGPT era and the ChatGPT adoption-moderated interaction of competence and performance. Surprisingly, a negative association emerges between personal best goals and academic performance within ChatGPT-assisted environments. Notably, the study underscores a significant relationship between heightened performance through ChatGPT and increased perceived stress among academicians.

The research advocates formulating clear ethical guidelines, robust support mechanisms and stress-management interventions to maintain academic integrity, enhance competence and prioritise academic professionals’ well-being in navigating the integration of AI tools in modern academia.

This research stands out for its timeliness and the apparent gaps in current literature. There is notably little research on the use of ChatGPT in academic settings, making this investigation among the first to delve into how faculty and researchers in education use OpenAI.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

This study aims to examine various factors influencing the inclination of students toward Green Entrepreneurial Intention (GEI), including University Support (USP), Family Support (FSP), Religiosity (REL), Commitment to Environment (CEN) and Green Entrepreneurial Motivation (GEM), as well as Attitude towards Green Entrepreneurship (AGM).

Data were collected through an online survey of Muslim students at Indonesian Islamic universities. A five-point Likert scale was used in the online questionnaire, with 419 processed data. Partial least squares structural equation modeling was used to analyze the data and test the relationship between the variables.

The results showed that AGM, CEN and REL impacted GEM. AGM was influenced by FSP but not by USP while GEI was significantly influenced by AGM, FSP and USP.

The limitation of the study is the composition of the sample, consisting solely of Islamic university students. Another limitation is the variables used. Future studies should analyze other factors, such as role models, green knowledge or family background.

This study provided fresh perspectives by empirically establishing a framework for assessing GEI, considering REL variables, an unexplored area conceptually. Practically, it helped to advance sustainable entrepreneurship education, particularly in Islamic universities. Accordingly, it provided several practical contributions for universities to develop curricula that better support green entrepreneurship among students.

This study represented the first investigation into the influence of REL on GEI, specifically among university students. Furthermore, Stimuli, Organism and Response theory was used as a foundation for the development of the diverse variables under investigation.

This paper aims to elucidate the relationship between entrepreneurial orientation, reverse innovation and international performance of emerging economy multinational enterprises (EMNEs).

The authors analyze archival data of Chinese limited companies between 2010 and 2016, including 11,230 firm-year observations about 1708 firms. In order to test the study’s mediation hypotheses, the authors apply an ordinary least square (OLS) regression.

The authors find evidence that the entrepreneurial orientation of EMNEs has a positive effect on reverse innovations. Furthermore, the authors find positive effects of reverse innovation on the international performance of EMNEs. This pattern of results suggests that the relationship between entrepreneurial orientation and international performance is partially mediated by reverse innovation.

The study’s findings help managers in EMNEs to promote reverse innovation by building and using their entrepreneurial orientation. It also helps them to set out and gauge the chances of success of their internationalization strategies. The findings also hold relevance for firms in developed economies as well, as they may understand which emerging economy competitors stand to threaten their positions.

The strategic role of reverse innovations – i.e. clean slate, super value and technologically advanced products originating from emerging markets – has generated considerable research attention. It is clear that reverse innovations impact the international performance of EMNEs. Yet how entrepreneurial orientation influences international performance is still underexplored. Thus, the current study clarifies the mechanism by examining and testing the mediating role of reverse innovation among the entrepreneurial orientation–international performance link.

The study aims to investigate the relationship between internal knowledge dissemination and employee-based brand equity (EBBE) through the lens of inclusive marketing among university faculty members. The study also examines the role of employee absorptive capacity and brand knowledge as mediators.

A sample of 362 faculty members from Pakistani universities was considered for analysis using a quantitative study design. A questionnaire was used to measure the variables under study, and structural equation modeling was used to examine the direct and indirect relationships.

There exists a positive and significant relationship between internal knowledge dissemination and EBBE among faculty members. Moreover, it is noteworthy to highlight that employee absorptive capacity and brand knowledge play pivotal roles as mediators.

The research findings have significant implications for the universities. Universities can strengthen their EBBE by properly disseminating knowledge among faculty members, which in turn fosters a sense of belongingness toward them. By improving the absorptive capacity of faculty members, universities can better prepare them to contribute successfully to the university’s brand and image. Developing brand knowledge among faculty members can help in fostering a unified and coherent brand image that deeply resonates with stakeholders such as colleagues, students and the academic community as a whole. Furthermore, promoting an inclusive culture within the organization will emphasize diversity and equity in internal knowledge dissemination practices, thereby further enhancing EBBE.

This study contributes to the prevailing knowledge-base by exploring the role of internal knowledge dissemination in developing EBBE among university faculty members. The research not only enriches the understanding of brand management in universities but also provides practical guidelines for the expansion of effective branding initiatives. Moreover, this study adds value by examining the association between internal knowledge dissemination and EBBE from the perspective of inclusive marketing strategies. It highlights the significance of encouraging a culture of diversity, inclusion and equity within organizations, leading toward significant outcomes in terms of enhanced brand equity among employees.

This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving tribological properties.

Pored PTFE (PPTFE) was prepared by mixing powder PTFE and citric acid and experienced a cold-press sintering molding process. Subsequently, textured surfaces were obtained with using a tattoo strategy. Surface-textured PPTFE was thus impregnated with polyethylene glycol 200, yielding oil-impregnated and pore-connected PPTFE.

This study found that oil-impregnated and surface-textured PPTFE exhibited excellent tribological performances with an 82% reduction in coefficient of friction and a 72.5% lowering in wear rate comparing to PPTFE.

This study shows an efficient strategy to improve the tribological property of PTFE using a tattoo-inspired surface texturing method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0378/

Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.

Underpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.

Our results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.

These findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.

The purpose of the study was to investigate both the positive and negative effects of workplace loneliness on innovative behavior. By applying the unified theory on contingencies of self-worth, the study aimed to integrate these effects into a single framework, thereby confirming the presence of the double-edged sword effect of workplace loneliness on innovative behavior.

A survey was conducted among enterprises across China, involving 246 employees. Hierarchical regression analysis was utilized to test the moderating hypotheses. Additionally, the mediating effects and the moderated mediation effects were further explored using the bootstrapping method.

The results indicated that workplace loneliness positively influenced innovative behavior through the desire to prove ability, with the promotion regulatory focus enhancing this relationship. Conversely, workplace loneliness negatively influenced innovative behavior through self-handicapping, with the prevention regulatory focus intensifying this relationship.

The findings revealed that workplace loneliness exerts a double-edged effect on innovative behavior. Lonely employees can enhance their sense of self-worth by engaging in domain switching, thereby alleviating feelings of loneliness.

The research confirmed a novel perspective: workplace loneliness can promote innovative behavior by influencing employees’ desire to prove ability. It also revealed the double-edged sword effect of workplace loneliness on innovative behavior. Based on these findings, employees experiencing loneliness can enhance their self-worth and alleviate feelings of loneliness through domain switching.