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This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness mediate the relationship between learning intent and generative learning of international joint ventures (IJVs) in emerging markets. We also decipher the degree of necessity of these factors for generative learning and of generative learning for marketing innovation.

A sample of 181 marketing managers of IJVs in Vietnam, an emerging market, was surveyed to collect data. Partial least squares structural equation modeling (PLS-SEM) was employed to test the net effect, and necessary condition analysis (NCA) was used to decipher the degree of necessity.

The PLS-SEM results demonstrate that the effect of learning intent on generative learning is fully mediated by organizational control and relationship openness, which in turn leads to marketing innovation. The NCA findings reveal that all three factors, namely learning intent, organizational control and relationship openness, serve as necessary conditions for generative learning. However, generative learning does not play the role of a necessary condition for marketing innovation.

The study findings suggest that IJVs in emerging markets should pay attention not only to the net effects of those factors but also to their degrees of necessity for generative learning in order to achieve marketing innovation.

The study contributes to the literature by confirming the mediating roles of organizational control and relationship openness in the relationship between learning intent and generative learning. Furthermore, it is among the first to decipher the degrees of necessity of these factors for generative learning and of generative learning for the marketing innovation of IJVs in emerging markets.

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.

This largely conceptual study aims to draw from the author’s experience of conversations with Svalbard’s educators, lessons for international higher education institutions’ engagement with climate change education and thinking for non-specialists.

In situ discussions with Svalbard’s educators informed the theoretical work of the author towards the development of conceptual conclusions. The theoretical frame used – “Red Biocentrism” – draws on both radical left and green thought to posit an emplaced, materialist understanding of author’s, participants’ and place’s intra-related contributions.

That, insofar as universities represent nodes in an ethical ecology, they have a capacity to realise that which is obvious in Svalbard – their role as embassies for their learning places, generative of spokespeople or ambassadors.

There is sparse published research into the work of Svalbard’s climate educators, as a pedagogical project undertaken under such extreme and rapidly changing environmental conditions. This study represents the first to reflect on what can be learnt from the educators of Svalbard by Universities elsewhere.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

E-learning has become a polarizing issue. Some say that it enhances accessibility to education and some say that it hinders it. While the literature on the subject underscores the effectiveness of the pedagogical frameworks, strategies and distance learning technologies, the firsthand accounts of students, parents and practitioners challenge the validity of experts’ assessments. There is a gap between theory and practice and between the perceptions of providers and consumers of online learning. Following a period of lockdowns and a transition to online learning during the recent pandemic, the prevailing sentiment toward a distance mode of instruction became one of strong skepticism and negative bias. The aim of the study was to examine why e-learning has struggled to meet stakeholder expectations. Specifically, the study posed two research questions: 1. What are the reasons for dissatisfaction with online learning? 2. What are the implications for future research and practice?

The study used a mixed methods approach to examine the reasons behind negative perceptions of online learning by comparing the firsthand accounts posted on social media with the literature. To this end, n = 62,874 social media comments of secondary and postsecondary students, as well as parents, teachings staff and working professionals, covering the span of over 14 years (2008–2022), were collected and analyzed.

The study identified 28 themes that explain the stakeholder’s discontent with the online learning process and highlighted the importance of user-centric design. The analysis revealed that the perceived ineffectiveness of distance education stems from the failure to identify and address stakeholders’ needs and, more particularly, from the incongruence of instructional strategies, blindness to the cost of decisions related to instructional design, technology selection and insufficient levels of support. The findings also highlight the importance of user-centric design.

To address dissatisfaction with e-learning, it is imperative to remove barriers to learning and ensure alignment between technology and learners’ needs. In other words, the learning experience should be personalized to account for individual differences. Despite its cost-effectiveness, the one-size-fits-all approach hinders the learning process and experience and is likely to be met with resistance.

Drawing from the extensive literature, the study offers an explanation for stakeholders’ discontent with e-learning. Unlike survey research that is prone to social desirability bias, the sample provides a rare opportunity to observe and measure the visceral reactions that provide a more authentic sense of stakeholders’ perceptions toward online learning. The authors offer recommendations and identify areas for future research.

In recent decades, emerging market multinational enterprises (EMNEs) have predominantly adopted a big step internationalization strategy to expand their business overseas. This study aims to examine the effect of big step internationalization on the speed of subsequent foreign direct investment (FDI) expansion for EMNEs. The authors also investigate the potential boundary conditions.

The authors use the random effects generalized least squares (GLS) regression following a hierarchical approach to analyze the panel data set conducted by a sample of publicly listed Chinese firms from 2001 to 2012.

The findings indicate that implementing big step internationalization in the initial stages accelerates the speed of subsequent FDI expansion. Notably, the authors find that this effect is more pronounced for firms that opt for acquisitions as the entry mode in their first big step internationalization and possess a board of directors with strong political connections to their home country’s government. In contrast, the board of director’s international experience negatively moderates this effect.

This study provides insights into our scholarly and practical understanding of EMNEs’ big step internationalization and subsequent FDI expansion speed, which offers important implications for firms’ decision-makers and policymakers.

This study extends the internationalization theory, broadens the international business literature on the consequences of big step internationalization and deepens the theoretical and practical understanding of foreign expansion strategies in EMNEs.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

The outbreak of COVID-19 significantly disrupted educational activities and forced universities to rapidly transition from the traditional face-to-face (F2F) environment to online learning formats. The purpose of this paper is to examine the effects of self-directed learning (SDL) on three instructional modalities (F2F, online and HyFlex) among emerging adults. The authors propose that class interaction enjoyment serves as a channel to understand how SDL relates to students’ satisfaction and stress reduction.

An online survey was distributed to the emerging adults, aged 18–25, at six universities across five different US states. Construct validity and reliability were tested by using confirmatory factor analysis. The moderated mediation relationship was examined by calculating the indirect effects of each course delivery format.

The results show that the positive indirect effect of SDL on stress reduction via interaction enjoyment was stronger for F2F classes. In addition, the positive indirect effect of SDL on class satisfaction via interaction enjoyment was stronger for HyFlex classes.

This literature has shown contradictory results: the effects of SDL on student satisfaction and stress reduction prove to be sometimes positive, sometimes non-significant. To better understand this relationship, the authors aim at a mediating variable – enjoyment of class interaction – as a mechanism, and a moderating variable – the instructional modality – as a boundary condition. This research contributes to emerging adults learning literature by involving the interplay among SDL, enjoyment of class interaction and the instructional modality.

Based on the conservation of resource theory and the affective events theory, the study aims to explore the role of workplace incivility in predicting work engagement through emotional exhaustion and how psychological capital moderates this relationship.

Using the questionnaire survey with a sample of 278 restaurant employees in Ghana and through process macro analysis, the hypotheses were tested.

The results depict the mediating role of emotional exhaustion on the workplace incivility–engagement relationship. Also, the level of an individual’s psychological capital buffers the impact of workplace incivility on engagement through emotional exhaustion. When psychological capital is high, the negative effect of workplace incivility on work engagement through emotional exhaustion weakens.

The findings suggest that organizations, particularly those in developing economies in Africa, can derive immense benefit from giving psychological capital training to employees to help buffer the effects of incivility on engagement through emotional exhaustion.

With a focus on a developing economy in Africa, to the best of the author’s knowledge, this study is novel in exploring the mediating and moderating mechanisms of the incivility–engagement relationship.