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Competence development is a prerequisite for successfully meeting existing and future challenges in the retail sector. However, current human resource development practices are often limited in their scope and offerings. This study sets out to investigate and bring to light informal learning among retail professionals.

This research consists of two exploratory studies focused on store managers and procurement officers, respectively. Based on a typology of learning mechanisms, the authors conducted eleven in-depth interviews, generating a rich body of empirical data centred on how informal learning takes place at work.

The research points out key learning mechanisms for each occupation and offers detailed narratives of how they are embedded in everyday learning. In addition, the findings provide a synthesis of the complex nature of learning mechanisms and their variations in form and orientation.

By generating tangible and explicit knowledge on the topic of informal learning, the results contribute to both professionals, who may actively engage in useful learning mechanisms, and managers, who may develop processes and structures based on key learning mechanisms.

This paper helps to demystify the tacit and often unnoticed learning that takes place at work. In doing so, it broadens the perspective on competence development and facilitates future discussions of human resource development practices in the retail industry and the research community.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

Digital servitization concerns how manufacturers utilize digital technologies to enhance their provision of services. Although digital servitization requires that manufacturers possess new capabilities, in contrast to strategic (or dynamic) capabilities, little is known about how they develop the required operational capabilities. The paper investigates the mechanisms for developing operational capabilities in digital servitization.

This paper presents an exploratory study based on 15 large manufacturers operating in Europe engaged in digital servitization.

Three operational capability development mechanisms are set out that manufacturers use to facilitate digital servitization: learning (developing capabilities in-house), building (bringing the requisite capabilities into the manufacturer), and acquiring (utilizing the capabilities of other actors). These mechanisms emphasize exploitation and exploration efforts within manufacturers and in collaborations with upstream and downstream partners. The findings demonstrate the need to combine these mechanisms for digital servitization according to combinations that match each manufacturer’s traditional servitization phase: (1) initial phase - building and acquiring, (2) middle phase - learning, building and acquiring, and (3) advanced phase - learning and building.

This study reveals three operational capability development mechanisms, highlighting the parallel use of these mechanisms for digital servitization. It provides a holistic understanding of operational capability development mechanisms used by manufacturers by combining three theoretical perspectives (organizational learning, absorptive capacity, and network perspectives). The paper demonstrates that digital servitization requires the significant application of building and acquiring mechanisms to develop the requisite operational capabilities.

This paper investigates the relationship between past performance and the development of operational capabilities in manufacturing firms, focusing on the role of intra- and inter-organisational learning mechanisms.

This study is based on a survey database collected in 208 manufacturing plants in 15 countries from three industries: electronics, machinery and transport components. The authors developed a model and tested the study hypotheses using the structural equation modelling technique with two-stage analytical procedures.

In the analysis of the overall sample, the study findings support prior literature by suggesting that firms with successful experiences may become complacent and less motivated to engage in learning, leading to a decline in performance. However, high-performance firms overcome the “success trap” by engaging supply chain partners. In contrast, low-performance firms exhibit limited learning from past poor performance, leading to organisational inertia and further declines in their current performance.

This research provides practical guidance for managers in developing operational capabilities, highlighting collaboration with suppliers as an essential element for high-performance firms.

This study focuses on the little-researched topic of how past performance influences the development of operational capabilities in manufacturing firms. The authors highlight the path for developing capabilities in high- and low-performance firms based on intra- and inter-organisational learning mechanisms.

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 aim of this paper is to examine the usefulness of the stimulus-organism-response (S-O-R) model for systematizing and further exploring the knowledge of the role of entrepreneurship education (EE) in fostering students' entrepreneurial mindset (EM). Current research studying the EM in an educational setting often fails to conceptualize this mindset and its attributes rigorously and to include epistemological considerations regarding how the authors can know whether a person has developed an EM or not.

The authors test the S-O-R model by combining it with a critical realist approach to develop a conceptual model for understanding the EM and its development in an educational setting.

The authors identify eight psychological constructs that represent markers of the EM. The authors further surface the multi-causal characteristic of learning in EE. The authors' model cannot be applied to detect causal relationships, but it is useful in exploring potential causal tendencies and plausible explanations of the mechanisms and circumstances of EE, which may, in concert, potentially foster the psychological attributes associated with an EM.

The paper addresses the often-overlooked epistemological considerations related to how the authors can know anything about psychological and theoretical constructs, such as the EM. A conscious discussion about how the authors can develop knowledge and insight about the EM and how individuals develop it is critical to justify a continued focus on the EM in EE.

The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.

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.

Frontline employees’ proactive behaviors (i.e. job crafting) are critical to satisfying customers’ idiosyncratic needs. If the supervisors practice job crafting, their subordinates are more likely to mimic such behaviors. However, there has been limited research on how leaders’ job crafting can influence subordinates’ job crafting. This study aims to examine the cross-level mechanisms (i.e. trickle-down effects) of supervisors’ job crafting on the subordinates’ attitudes and performance. Specifically, such trickle-down effects can be explained via two cross-level mechanisms of the supervisors’ job crafting on the subordinates’ work engagement and performance: social learning mechanism and job demands-resources mechanism.

A three-wave cross-lagged study was conducted in two-week intervals. The valid responses from 67 supervisors and their 201 subordinates were collected. The data set was analyzed using multilevel Structural Equation Modeling.

The results demonstrated that the social learning and job demands-resources mechanisms are not independent. The supervisor’s job crafting improves employment relationships, subsequently encourages subordinates’ job crafting and ultimately enhances work engagement and work performance.

The findings suggested that hospitality organizations should encourage job crafting among supervisors and managers. A proactive hotel manager can establish strong employment relationships, motivate subordinates to work proactively and obtain positive work outcomes.

The findings enrich the knowledge about the trickle-down effects of supervisors on subordinates in terms of job-crafting behaviors. In particular, this study found a new theoretical perspective that the job demands-resources and social learning mechanisms may not be independent, and the subordinates’ perception of the employment relationship plays a critical role.