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Most previous research assumes that the outward foreign direct investment (OFDI) decisions of multinational corporations (MNCs) are made independently of the actions or characteristics of their peers. Therefore, the important influence of peer effects on the OFDI strategy is often neglected. The purpose of this paper is to identify two broad categories of peer effects, i.e. learning-based and profit-driven imitations and examine the important influence of peer effects on MNCs’ internationalization strategy.

Using Chinese manufacturing firms as the empirical sample, the authors employ an econometric method (logit regression) to test the relationship between peer effects and an internationalization strategy.

Learning-based and profit-driven imitations are positively associated with a focal MNC’s OFDI decision. Policy uncertainty also positively moderates the relationship between peer effects and the OFDI strategy. Moreover, both peer effects are amplified when a firm is equipped with a dense export network.

The study offers researchers and practitioners a detailed view of interorganizational imitation behavior in terms of an internationalization strategy.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Peer imitation is becoming clear as a way for organizations to successfully embark on OFDI. Whilst it doesn’t come without risks, the benefits to peer imitation can lead to significant gains of competitive advantage.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator.

The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations.

To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators.

The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.

The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.

The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.

The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

The purpose of this paper is to contextually theorise the different patterns of emerging multinational companies’ (EMNCs’) learning processes for innovation and the different influences of their technology-driven FDIs (TFDIs) on the processes.

A comparative case study method and process tracing technique are employed to investigate how and why firms’ learning processes for innovation took place, how and why the TFDIs emerged and influenced the firms’ learning processes in different ways.

The paper identifies two different patterns of learning process for innovation (Glider model vs Helicopter model) and two different roles of the case firms’ TFDIs (accelerator vs starter) in the different contexts of their learning processes. It is found that the capability building of the domestic wind energy industry has an important influence on the case of EMNCs’ learning processes and thus on the roles of their TFDIs.

The limitation of the paper lies in its small number of cases in a specific industry of a specific country. The two contextually identified learning models and roles of TFDIs may not be applied to other industries or other countries. Future research should investigate more cases in broader sectoral and geographic scope to test the models and also to identify new models.

For EMNCs, who wants to use the Helicopter model to rapidly gain production and innovation capability, cross-cultural management and integration management are crucial to practitioners. For emerging countries with ambitions to explore the global knowledge and technology pool, besides of the EMNC’s capability building, the capability building in the domestic industries should not be overlooked by policy makers.

The paper develops a dynamic and contextual analytical framework which helps to answer the important questions about how and under what context a TFDI emerges and influences firm’s learning process for innovation. It theorises the EMNCs’ learning process and TFDIs in the context of the development of the domestic industry. It strengthens the explanatory power of the learning-based view and adds new knowledge to the current FSA/CSA discourse in the international business literature.

This paper aims to present a hybrid control approach that combines learning-based reactive control and affordance-based deliberate control for autonomous mobile robot navigation. Unlike many current navigation approaches which only use learning-based paradigms, the authors focus on how to utilize the machine learning methods for reactive control together with the affordance knowledge that is simultaneously inherent in natural environments to gain advantages from both local and global optimization.

The idea is to decompose the complex and large-scale robot navigation task into multiple sub-tasks and use the hierarchical reinforcement learning (HRL) algorithm, which is well-studied in the learning and control algorithm domains, to decompose the overall task into sub-tasks and learn a grid-topological map of the environment. An affordance-based deliberate controller is used to inspect the affordance knowledge of the obstacles in the environment. The hybrid control architecture is then designed to integrate the learning-based reactive control and affordance-based deliberate control based on the grid-topological and affordance knowledge.

Experiments with computer simulation and an actual humanoid NAO robot have demonstrated the effectiveness of the proposed hybrid approach for mobile robot navigation.

The main contributions of this paper are a new robot navigation framework that decomposes a complex navigation task into multiple sub-tasks using the HRL approach, and hybrid control architecture development that integrates learning-based and affordance-based paradigms for autonomous mobile robot navigation.

This study investigates lecturers’ challenges in guiding students’ final scientific work online during the COVID-19 pandemic. It explores the impact of lecturers’ digital technology proficiency on the students’ ability to compile their thoughts and produce scientific work independently.

The study involved 45 lecturers and 140 students. Data was collected through online surveys using the Google Forms application and focus group discussions. The data were analysed qualitatively and interpretively based on the surveys and interviews.

The findings reveal three modes of mentoring: online, mixed, and offline. Many lecturers’ reluctance to use digital technology for mentoring stems from their lack of proficiency, resulting in mixed mentoring methods. This digital inadequacy affects students’ ability to write scientific work independently, as they are not accustomed to self-directed learning. The pandemic has necessitated more independent work from students, with limited physical guidance from lecturers, leading to a decline in the quality of scientific writing.

This paper contains the latest information related to students' scientific writing activities. Student scientific writing activities are disrupted because supervisors do not have the skills to use technology in the remote student mentoring process. Lecturers are not skilled at using technology in carrying out online tutoring assignments.

Intelligent control for unidentified systems with unstable equilibriums is not always a proper control strategy, which results in inferior performance in many cases. Because of the existing trial and error manner of the procedure in former duration of learning, this exploration for finding the appropriate control signals can lead to instability. However, the recent proposed emotional controllers are capable of learning swiftly; the use of these controllers is not an efficient solution for the mentioned instability problems. Therefore, a solution is needed to evade the instability in preliminary phase of learning. The purpose of this paper is to propose a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers.

An existing controller (model‐based controller) with limited performance is used as a mentor for the emotional learning controller in the first step. This learning phase prepares the controller to control the plant as well as mentor, while it prevents any instability. When the emotional controller can imitate the behavior of model based one properly, the employed controller is gently switched from model based one to an emotional controller using a fuzzy inference system (FIS). Also, the emotional stress is softly switched from the mentor‐imitator output difference to the combination of the objectives. In this paper, the emotional stresses are generated once by using a nonlinear combination of objectives and once by employing different stresses to a FIS which attentionally modulated the stresses, and makes a subset of these objectives salient regarding the contemporary situation.

The proposed model free controller is employed to control an inverted pendulum system and an oscillator with unstable equilibrium. It is noticeable that the proposed controller is a model free one, and does not use any knowledge about the plant. The experimental results on two benchmarks show the superiority of proposed imitative and emotional controller with fuzzy stress generation mechanism in comparison with model based originally supplied controllers and emotional controller with nonlinear stress generation unit – in control of pendulum system – in all operating conditions.

There are two test beds for evaluating the proposed model free controller performance which are discussed in this paper: a laboratorial inverted pendulum system, which is a well‐known system with unstable equilibrium, and Chua's circuit, which is an oscillator with two stable and one unstable equilibrium point. The results show that the proposed controller with the mentioned strategy can control the systems with satisfactory performance.

In this paper, a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers is proposed. This approach is based on imitative learning in preliminary phase of learning and soft switching to an interactive emotional learning. Moreover, FISs are used to model the linguistic knowledge of the ascendancy and situated importance of the objectives. These FISs are used to attentionally modulate the stress signals for the emotional controller. The results of proposed strategy on two benchmarks reveal the efficacy of this strategy of model free control.