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This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

Continuous fiber-reinforced thermoplastic composites are a class of materials highly valuable for structural applications and modeling of heat transfer within them is critical to the design of their processing methods. However, the fiber reinforcement leads to highly anisotropic thermal conduction. Among a variety of methods to account for anisotropic thermal conductivity, continuum models with effective media approximation thermal conductivity are computationally efficient and require minimal data to begin modeling a specific composite material. The purpose of this study is to evalute the utility of these models.

In this work, six potential effective media approximation models are evaluated against experimental heating data. Thick (>25 mm) glass fiber-reinforced polyethylene terephthalate glycol (PET-G) specimens with 40% fiber volume fraction were heated with embedded resistance heating to produce validation and testing data sets. A two-dimensional finite-difference solver was implemented using each of the six effective media approximation models. The accuracy of each model is compared.

The model developed by Cheng and Vachon was found to predict the experimental results most accurately. Fit statistics were similar in the testing and validation data sets. This model is recommended for simulation of transient heating in continuous fiber-reinforced thermoplastic composites with low-to-moderate fiber volume fractions.

There are a wide variety of mathematical models for effective media approximation thermal conductivity, though very few have been applied to continuous fiber-reinforced thermoplastic composites. This work shows that the simplest methods based on rules of mixtures are well outperformed by more modern and complex models, and should be incorporated for accurate prediction of heating during thermal processing of fiber-reinforced thermoplastic composites.

The authors investigate the relationship between instant messenger (IM) use and work performance, mediated by interruptions and two key indicators of the stress associated with technology use: overload and complexity.

The authors validate this research model using partial least squares structural equation modelling (PLS-SEM) with data collected through a survey of 416 working professionals.

The data reveal that while IM use contributes minimally to work interruptions and to a greater extent to technological complexity, these two constructs fully mediate the direct influence of IM use at work on technology overload, and meanwhile significantly and directly contribute to work performance.

This research provides theoretical insights into the deployment of IM and its actual impacts in the workplace. To improve the generalisation of the findings, the authors call for more IM-related research in other countries, with more native theories and various methodologies in this domain.

The level of stress generated through IM use is moderate, considering IM is not a significant contributor to work interruptions. Thus, despite the potential negative effects of IM communication, the positive effects of using IM at work prevail. As a result, the technology can be promoted as long as employees, their managers and the organisation as a whole are well prepared. Employees can transfer skills and behaviour from the personal setting to their work environment and thus may find an intrinsic motivation to make better use of the IM technology at work.

The authors argue that this research model is novel for its perspective on evaluating the actual impacts of IM use at work instead of the reasons of using it. The authors conceptualise the process to explain how IM contributes to interruptions and other technostress indicators in the working context, and the impact on performance. Contrary to some prior research, the authors find that overall IM applications do not have a negative impact on work performance, and instead may enhance it.

Social networking sites (SNS) have become popular mediums for individuals to interact with others. However, despite the positive impact of SNS on people’s lives, cyberbullying has become prevalent. Due to this prevalence, substantial research has examined cyberbullying from the perspectives of perpetrators, bystanders, and victims, but little is known about SNS users’ confrontations with cyberbullying. The objectives of this study are to examine confrontation as a victim’s coping response, the effect of blockability affordance on victims’ protection motivation, the impact of a victim’s experiences with cyberbullying perpetration, and social desirability (SD) bias in the context of cyberbullying victimization.

This study examines the effect of blockability affordance on SNS users’ protection motivation. It also investigates the relationships among perceived threat, perceived coping efficacy, and use of confrontation. Furthermore, this investigation analyzes the effect of SNS users’ experiences as perpetrators on their decision to confront cyberbullies. Finally, this study assesses and controls SD bias in SNS users’ confrontation behavior. To test the research model, we used an online vignette study to collect 314 data points.

Blockability affordance, perceived threat, perceived coping efficacy, and cyberbullying perpetration experiences are essential factors in explaining use of confrontation. This study also finds SD bias in the context of cyberbullying victimization.

This is one of the first studies in information systems research to empirically examine the effect of blockability affordance in the context of cyberbullying.

This study aims to identify critical determinants of sovereign credit risk by examining the influence of oil prices, gold prices, geopolitical risk, market volatility, exchange rates, inflation and non-performing loans on Türkiye’s credit default swap (CDS) spreads. This analysis provides a comprehensive understanding of how economic uncertainty and political risk impact Türkiye’s financial stability, as reflected in its CDS market. This study investigates the importance of ex ante proxies in explaining changes in CDS spread by financial and economic indicators in Türkiye.

This research explores the connections between critical financial and economic indicators and the credit risk of Türkiye between 2009 and 2022 by using advanced econometric techniques such as ARDL bound tests, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), Johansen co-integration tests and VECM Granger causality analyses.

ARDL bound test results reveal significant negative impacts of BIST and non-performing loans on CDS, and positive associations with inflation, VIX and geopolitical risk on CDSs. The short-term results show that BIST, INFL, NPL, USD, VIX and GPRT have negative coefficients. Johansen co-integration, FMOLS and DOLS results reinforce the ARDL findings. Moreover, BIST is a significant Granger cause of CDS.

This study is significant, as it jointly considers economic and political risk factors, thereby integrating multiple econometric models to provide more robust, meaningful and comparable results. By examining these factors together, the analysis offers a more comprehensive understanding of risk dynamics, yielding insights relevant to Türkiye. Although the findings are specific to Türkiye, they have broader implications, enriching the understanding of emerging economies. Türkiye’s status as a key representative of emerging markets strengthens the study’s value, as the results can serve as a reference point for other countries with similar economic structures. The importance of this study is also underscored by its potential to inform risk management strategies, guide policy decisions and offer insights to investors and financial analysts. By elucidating the intricate relationships among a broad spectrum of macroeconomic variables, this research contributes to a more comprehensive risk assessment framework. It equips stakeholders with a more informed perspective on the factors influencing credit risk in Türkiye’s economic landscape.

The principal purposes of the research are to empirically investigate three forms of perceived overload on social media and shed light on their associations with users’ passive usage intention by contemplating the mediating influence of social network exhaustion and discontented feelings.

This study employed a cross-sectional methodology to collect statistical data (N = 679) from WeChat users in mainland China. Primitive analysis, confirmatory factor analysis and structural equation modeling were employed to test the corresponding hypotheses.

The findings reveal that three dimensions of perceived overload influence social network exhaustion positively. In addition, communication overload and system feature overload exert positive impacts on the discontented feeling. Furthermore, it is uncovered that social network exhaustion and discontented feeling are related to passive usage intention positively.

Theoretically, this paper offers a conceptual framework to explicate passive usage intention through elucidating social network exhaustion and the discontented feeling that arises from perceived overload in contemporary social media-mediated environments. Practically, the current research has certain realistic implications for WeChat users and SNS operators.

Probing what triggers people’s passive usage intention of social media has been an emerging theme in recent years, yet there is a dearth of discourse that delves into the antecedents of WeChat users’ passive usage intention. The results obtained from the study have enhanced the understanding of the adverse consequences associated with the utilization of social media in mainland China.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

While the phenomenon of technostress has received significant attention from researchers in recent years, empirical findings concerning the consequences of specific forms of techno-stressors have remained scattered and contradictory. The authors aim to integrate the conclusions of previous studies to understand the effects of specific techno-stressors on strain and job performance.

This study employs meta-analytic techniques to calibrate the findings of 67 studies investigating more than 63,100 employees.

In general, not all techno-stressors have adverse effects. In particular, techno-uncertainty does not impact job performance. In addition, relative weight analyses reveal the relative importance of techno-complexity and techno-insecurity as predictors of both strain and job performance. Finally, this study finds that the effects of specific techno-stressors on job performance vary depending on research participants' gender, educational attainment and employment status.

First, this study provides a more nuanced view of the effects of specific techno-stressors. Second, this research clarifies the relative importance of specific techno-stressors as predictors of strain and job performance. Finally, this study reveals the moderating effects of demographic variables on the relationships between specific techno-stressors and job performance.

This paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.

Panel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.

ADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.

The sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.

Based on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.

This paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.

This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP).

Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators.

The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders.

Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries.

The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.