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This paper aims to systematically review and synthesize existing research on learning-driven strategic renewal and examines the findings to elucidate the dimensions, antecedents, mechanisms and consequences associated with learning-driven strategic renewal, thereby addressing gaps in the existing literature.

This research covers learning-driven strategic renewal from 1992 to 2022, using hybrid snowball sampling techniques and Boolean searches on the Scopus and Web of Science databases to extract 49 papers.

This review proposes an organizing framework for learning-driven strategic renewal, building upon existing literature. The framework identifies various dimensions of the process, including antecedents, mechanisms and consequences. The antecedents are categorized into individual, organizational and external factors. The mechanisms for learning-driven strategic renewal were explored within the context of Crossan’s established 4I framework, which serves as a lens for emphasizing the balance between exploratory and exploitative learning. Within this framework, intuiting, interpreting, integrating and institutionalizing are the four “Is” that guide the renewal process. These mechanisms require a robust system to enforce the prescribed processes effectively, thereby contributing to long-term firm performance and sustainability.

Despite using search terms similar to those in existing literature on strategic renewal, the scope and depth of this study may be limited. Further research may benefit from bibliometric screening or more refined inclusion criteria.

While there has been extensive research into both organizational learning and strategic renewal, no coherent framework links them. This study fills this gap by building a framework that identifies connections between these two concepts, providing valuable insights that may be used to foster successful strategic renewal efforts. The review offers valuable knowledge and understanding of the subject matter, serving as useful guidance for effectively driving renewal initiatives within organizations.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

In the context of virtual brand communities (VBCs), based on self-determination theory and organizational binary learning (OBL) theory, this study investigates the direct and indirect effects of various dimensions of consumer participation (CP) on firm innovation performance (FIP) and the mediating role of OBL. By introducing the perspective of knowledge absorptive capacity (KAC), this study examines the moderating effect of KAC on the relationship between OBL and TIP.

In this study, 751 samples are collected from Chinese firms, and stratified adjusted regression analysis is used to conduct empirical tests.

Information provision or co-creation directly affects FIP; the latter is more significant than the former, while social interaction does not directly affect FIP. Exploitative or exploratory learning plays a mediating role in the relationship between information provision or co-creation and FIP. In contrast, exploratory learning plays a mediating role in the relationship between social interaction and FIP. KAC has a positive moderating effect on the relationship between exploitative learning or exploratory learning and FIP. When the firm has a low KAC, the improvement of FIP through exploratory learning is slight.

In VBCs, this study tries to divide the dimensions of CP. It also clarifies the mediating effect of OBL on CP and FIP. In addition, it explores the moderating effect of KAC on OBL and FIP. The conclusions of this study provide theoretical support and practical inspiration for firms to use VBCs to improve FIP.

Employee training and learning have transformed over the years. The movement from classroom training to the blended format represents the magnitude of this evolution. This has placed much attention on self-regulated learning. This study aimed to understand the individual and organizational mechanisms that sustain the formal learning process in organizations. It explored the goals the organizations and employees strive to achieve by investing in learning. Through this, the authors investigated how technology assistance makes learning more goal-oriented, despite the possibility of different goals for different stakeholders. They also examined how person-job fit can be achieved in employee training.

The study adopted a grounded theory-based inductive approach using a qualitative inquiry that used in-depth interviews of employees working in the Indian IT/ITES sector. This sector is knowledge-intensive and engages in constant skill development. A content analysis of the interview transcripts unraveled the most relevant themes from the participants' discussion.

Individual learners use dimensions of self-regulated learning to set and achieve goals such as better performance and career development. On the other hand, organizations use learning support mechanisms such as better access and flexibility to direct employee learning behavior to achieve organizational goals. Focusing on goal congruence leads to better achievement of results. Goal congruence also implies good person-organization fit.

This research established how aligning individual and organizational mechanisms can help achieve training goals that ultimately contribute to organizational performance. The study differentiated itself by investigating training goal setting and goal achievement at two levels – organizational and individual – using a qualitative approach. It also showed how goal congruence is vital in improving organizational performance and how technology-enabled training practices rely on self-regulated learning and help achieve goal congruence.

Drawing from organizational learning theory, social exchange theory and positive psychology approach, this study aims to examine the relationship between commitment to learning and prosocial silence, as well as the mediating role of perceived organizational support (POS) in this relationship.

The authors used path analysis to examine the relationships between research variables. Data were collected from 275 employees of private sector companies in Turkey through an online survey platform. To test the proposed hypotheses, the authors conducted regression and mediation analyses using the bootstrapping method.

The results indicate that the organization’s commitment to learning positively and significantly impacts employee prosocial silence, and POS partially mediates this relationship.

Managers who aim to promote other-oriented and helping behavior in the organization should understand how prosocial silence can be golden. They should cultivate and model a learning mindset by focusing on strengths instead of weaknesses, reward experimentation and provide employees with timely feedback allowing them to think and reflect on their failures.

Although the dominant position of previous studies endorses the detrimental sides of organizational silence, less research has focused on employees’ prosocial silence behavior and the underlying mechanisms that may explain employees’ tendency to remain silent with helpful intent, a gap this research attempts to fill.

Drawing on the framework of the trickle-down effect and social learning theory, this study aims to examine how and when leaders' voluntary green behavior (VGB) stimulates that of employees.

This study conducted a time-lagged multisource field survey. The final sample consisted of 417 employees matched to 67 leaders. The unconflated multilevel modeling (MLM) approach was employed.

A social learning mechanism underlies the trickle-down effect of leaders' VGB, which involves observation and imitation. The green role model influence serves as a mediator of these two processes. Moreover, leader-member exchange (LMX) moderates the strength of the social learning mechanism.

Leaders can gain useful insights of how to promote employees' VGB and are further inspired to reflect on the managerial philosophy of leading by example.

This study contributes to workplace green behavior literature by examining the trickle-down effect of leader VGB and uncovering a social learning mechanism. This study also offers promising directions for leadership research concerning about role modeling.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

As online learning is the embryonic form of education in the metaverse, it is extremely important to explore the behavioral preferences of users. The aim is to explore the impact of interactive features on continuous use in online learning and to further explore what kind of interaction mode should be constructed for different types of students to obtain the best educational experience.

The study developed an empirical model and used a real-world dataset to test hypotheses. Specifically, the interaction in online learning is analyzed from different dimensions, including the interaction intensity of multiple subjects, the immersion of interactive technology, the timeliness of interactive feedback, and the fun in interaction.

The authors found that the intensity of interaction, immersion, timeliness of feedback and fun in the interaction all had significant positive effects on continuous use. Among them, the most important is the interaction between teachers and students. With the growth of user grades, the role of parents in the interaction is getting smaller and smaller, and the fun in the interaction is gradually becoming unnecessary. For high school students, gamified interactions can even have a negative impact. In addition, from the perspective of gender, males prefer immersive interaction, while females pay more attention to themselves and have negative feedback on fees.

The authors deepened the interaction and summarized the impact of different interactive features on continuous use in online learning platforms. The authors focused on the impact of the immersive experience brought by the application of interactive technology, which can confirm the user behavior preferences of online learning in the context of the metaverse. The research also provides a reference for online learning institutions to set up course interaction modes and targeted marketing programs.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

The lack of physical contact and the absence of nonverbal clues could make some learners uncomfortable interacting with others via online learning platforms. Hence, understanding the determinants of students' motivation and engagement in online learning platforms is crucial in harnessing digital technology as an enabler of unrestricted and quality learning experiences.

Drawing on the self-determination theory (SDT), this study investigates the factors associated with student’s motivation to learn (MOL) and their influence on online learning engagement (OLE). Data were collected from 228 university students from the Klang Valley region of Malaysia using the online survey method.

The results of data analysis using the partial least squares structural equation modeling indicate that self-directed learning, computer and Internet self-efficacy and online communication self-efficacy significantly influence MOL. Besides, these factors indirectly influence OLE through MOL.

This study adds to the SDT framework by demonstrating how students' perceptions of autonomy, competence and relatedness through online interaction relate to MOL and OLE.