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The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

The use of “homework”, activities outside of the classroom or session, is widely applied in a range of disciplines including teaching, therapy and training. The argument advanced by advocates is that it provides an opportunity to consolidate knowledge learnt in the classroom and develop mastery in an applied environment. However, the use of homework has not been widely discussed or researched within business coaching, which is a form of personal development. This exploratory study aims to examine whether homework, as a coaching intervention, may enhance the clients' learning experience.

Data were collected from eight early career coaches and eight coaching clients. Not all clients were related to the coaches. Each client had experienced a minimum of three coaching sessions. Interviews were recorded and analysed using thematic analysis. The study explored the use of (1) client-led, (2) coach-led and (3) collaboratively developed homework during the engagements.

The findings indicated that homework is widely used and was perceived to have mixed effects. The positioning of the homework by the coach, including the terminology used to describe the activity, and the type of work can affect the level of engagement and thus the perceived value generated.

This is the first study to explore the nature of “homework” in coaching. More work is needed to better inform the use of “homework” in coaching practice, including the type of work and how this is agreed with different types of clients, for example, should homework be coach, collaborative or client led?

Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies.

The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education.

The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies.

The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

With the rise of the gig economy, management positions are increasingly staffed with flexible labor, so-called interim managers. They plunge into organizations for a limited period, operating in a liminal position as partly insider, partly outsider. Although several contributions to their client organizations are acknowledged, it is unknown how the interim manager’s knowledge from previous assignments is made useful in the new context under these particular working conditions. Therefore, the purpose of this paper is to increase the understanding of how the interim manager’s knowledge is transferred to the client organization while operating from a liminal position.

This paper presents an interview-based multiple case study of six interim assignments where knowledge transfer is considered a social and context-dependent process.

The findings unveil the multifaceted nature of the liminal position, which consists of task orientation, time limitation, political detachment and cultural distance. These facets contribute to knowledge transfer in terms of new shared understandings and joint interests, which in turn might create new practices that augment continuous knowledge-sharing patterns.

The results contribute to the research on flexible work arrangements by shedding light on how the liminal position, predominantly depicted as an obstacle for the individual, might facilitate knowledge transfer. Through the process of knowledge generation, it is shown how a short-term engagement might enable the organization to increase its knowledge over time.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The aim of this paper is to analyse the role of communities of practice (CoP) as knowledge-sharing tools in family small and medium-sized enterprises (SMEs). In this context, CoPs that jointly involve family and non-family members are expected to act as knowledge-sharing tools.

This paper employs a multiple case study methodology, analysing the cases of six small companies in different sectors and countries over a period of 8 years. Both primary and secondary data are used.

The results show the role CoPs play in involving family and non-family members in empowering knowledge-sharing initiatives. A CoP's role in knowledge sharing depends on the presence (or lack) of a family leader, the leadership approach, the degree of cohesion around shared approaches and values within the CoP, and the presence of multiple generations at work.

This paper contributes to the literature on knowledge sharing in family businesses, by exploring for the first time the role of the CoP as a knowledge-sharing tool, depending on families' involvement in the CoP.

The purpose of this study is to empirically test the intelligent career framework in a public sector setting in a country with a clientelistic culture to inform human resource management strategies.

Based on a qualitative methodology and an interpretivist paradigm, 33 in-depth interviews were conducted with Greek civil servants before the COVID-19 pandemic. The interview recordings were subsequently transcribed and coded via a blend of inductive and deductive approaches.

Outcomes of the study indicate that in a public sector setting in a country with a clientelistic culture, the three dimensions of knowing-whom, knowing-how and knowing-why are less balanced than those reported by findings from private sector settings in countries with an individualistic culture. Instead, knowing-whom is a critical dimension and a necessary condition for career development that affects knowing-how and knowing-why.

The theoretical contribution comes from providing evidence of the dark side of careers and how imbalances between the three dimensions of the intelligent career framework reduce work satisfaction, hinder career success and affect organisational performance. The practical contribution offers recommendations for human resource management practices in the public sector, including training, mentoring, transparency in performance evaluations and fostering trust.

This study relied on the job demands and resource model to understand employees’ turnover intentions. Recent studies have consistently lent support for the significant association between role ambiguity and turnover intentions; however, only a handful of studies focused on examining the potential mediators in this association. The authors argued that role ambiguity positively influences turnover intentions through affective mechanisms: job involvement and satisfaction.

To test the model, a large sample of working adults participated (N = 505).

Structural equation modeling results showed that role ambiguity, job involvement and job satisfaction were significantly associated with turnover intentions. Moreover, a serial mediation was found among the variables: employees with low levels of role ambiguity tended to report higher job involvement, which further increased their satisfaction with the job and subsequently decreased their turnover intentions.

The cross-sectional design is a limitation.

Practical suggestions regarding how organizations can reduce employee turnover are discussed.

The findings provide support for theory-driven interventions to address developing the intention to stay at work among working adults.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.