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Working from home (WFH) was one of the major changes that occurred in many organizations during the COVID-19 pandemic. This also led to online training being conducted during this WFH period. The present study investigated the role of technology, manager support and peer support on self-efficacy and job outcomes (i.e. training transfer, work engagement and job satisfaction) of employees while WFH.

The study framework incorporated Bandura's self-efficacy theory. Data were collected from 852 employees in India, and structural equation modeling was used to analyze the data.

The study found positive relationships between ease of technology use, manager support and peer support on self-efficacy and a negative relationship between self-efficacy and technostress. The study also found significant positive relationships between self-efficacy and training transfer, work engagement and job satisfaction. Moreover, the study also identified the moderating effects of WFH and technical issues in the relationships of self-efficacy with training transfer, work engagement and job satisfaction.

The study is novel in that it extended self-efficacy theory regarding the WFH context with influencers such as technology, managers and peers as organizational factors. It also demonstrated the effectiveness of remote working and online training considering the potential antecedents while WFH. Moreover, the study highlighted the simultaneous role of technology and people (managers and peers) in enhancing job outcomes by increasing self-efficacy among employees.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

This study aims to investigate the influence of two perceived organizational ethics systems (perceived ethics training quality and integrity-based climate) on the ethical decision-making (EDM) of tax accountants in Ghana. The study also examines the moderating role of the decision-makers’ financial situation on the quality ethics training–EDM relationship.

Survey data from 356 tax accountants were analyzed using the partial least squares structural equation modeling technique.

The results show that the two ethics systems influence EDM, but their extent of influence varies across the stages of EDM. Specifically, quality ethics training is a better predictor of EDM at the ethical issue recognition stage, whereas integrity-based climate is a better predictor of EDM at the ethical intention stage. The study also found that decision-makers’ financial situation predicts the ethical recognition stage of EDM but does not moderate the quality ethics training–EDM relationship.

This study recommends the concurrent deployment of quality ethics training and an integrity-based work climate to improve ethical behavior. Policymakers should also emphasize a work climate that promotes honesty, conscientiousness and ethical principles (integrity-based climate) to improve ethical intentions.

This study applied the interactionist theory by capturing the relative effects of two organizational ethics systems and an individual-level situational factor in a single model. To the best of the authors’ knowledge, this is the first study that tests the moderation effect of decision-makers’ financial situation on the ethics training–EDM relationship in a developing country context.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

In the literature, evidence is to be found of the positive effect of high-performance work systems (HPWSs) on innovation in firms. However, innovation is enabled by not only human resources but also digital technology, and scholars have called for further investigation into the interplay between digital technology and HRM systems. Drawing on signalling theory and HPWSs research, the purpose of this study is to explore the moderating role of digital technologies in the relationship between HPWSs and innovation in the firm and consider employee participation as an additional conditioning factor.

This study uses data from the European Company Suvery 2019 administered in a sample of more than 20,000 European establishments and applies logistic regression with a three-way interaction.

HPWSs underpin product and process innovation. Moreover, this study shows that in firms with low levels of employee participation, digital technology enhances the effect of HPWSs on innovation, while in firms with high levels of employee participation, this effect is reduced.

This study enriches the scholarly discussion about the link between HPWSs and innovation in the firm, by investigating in theoretical and empirical terms the moderating effect of digital technology, underlining that either positive or negative synergistic effects are possible. By adding employee participation to the analysis, the authors cast light on an important boundary condition for understanding when the synergic effects become more prominent. This intends to respond to recent calls from scholars and practitioners for more insight into the precise nature of the synergies between HPWSs and digital technology on innovation in the firm, with important implications for management.

This study aims to explore how the three types of human resource (HR) practices, encapsulated in the ability, motivation and opportunity (AMO) model, foster a learning organizational culture (LOC). In doing so, the authors evaluate the centrality of knowledge sharing (KS) in mediating this relationship.

A quantitative survey is undertaken to collect data from managers working in organizations operating in the UK. The authors use several statistical techniques to assess the psychometric properties of the measures and test the hypotheses using multiple regression executed with Preacher and Hayes’ Process macro.

The findings show that the AMO HR practices significantly facilitate the development of a LOC in the workplace, and KS among organizational members amplifies the effects of these HR practices in the process.

A LOC functions as an important source of organizational performance and effectiveness. It enhances the absorptive capacity of the organization to capture, share and transfer knowledge to optimize work. Hence, developing a culture that nurtures organizational learning could be a priority for managing HR. This study, therefore, extends the understanding of the role of AMO HR practices in fostering a learning culture – thus, providing managers with the essential knowledge to improve performance. The study also enriches the literature on HR practices, KS and LOC by integrating these three variables into a unifying framework.

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

This case uses an array of carefully selected and excerpted revenue recognition related information contained in Salesforce.com's January 31, 2019, 10-K. Maria, the fictional protagonist, is seeking to understand those disclosures as part of her preparation for an upcoming job interview with the company. As such, she is relying on those disclosures to provide insights as to the company's main product/service lines, the events that signal when and how much revenue the company has earned (i.e., the essence of its business model), along with the related official generally accepted accounting principles (GAAP) criteria pertinent to the valuing and timing of recorded revenues.

This study aims to propose a research model that emphasizes the moderating influence of ethical leadership (ELS) and the link between socially responsible human resource management (SRHRM) and leaders’ eco-helping behavior (LEH) in the aviation industry.

The authors used a time-lag research design. Data were gathered from 397 respondents working for aviation companies in Vietnam and analyzed using SmartPLS 4.0.

The findings show that SRHRM has a beneficial effect on LEH through employees’ voluntary workplace green behavior (EVB). Based on social cognitive theory, this study developed a theoretical model of how SRHRM influences LEH through EVB. The authors discovered that SRHRM increased LEH and that EVB mediated this favorable relationship. Furthermore, the results demonstrate that ELS mitigates the indirect impact of SRHRM on LEH via EVB.

Future studies should assess constructs with numerous observations across time, with a larger sample size and in different industrial settings.

Volunteerism is one of the most important values in the aviation industry given that it is vulnerable to practices such as overbooking, delaying, postponing flights and pressure weather.

This study emphasized the impact of SRHRM and ELS on LEH while fulfilling their professional obligations. EVB may be best positioned to mediate the relationship between SRHRM and LEH.