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This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

Building upon a competence-based employability model and a social exchange and proactive perspective, the purpose of this paper is to investigate the relationship between learning value of the job and employability among academic staff employees. Moreover, this study also examined whether this relationship was moderated by leader–member exchange (LMX) and a proactive coping style.

An online self-report questionnaire with thoroughly validated measures was distributed among academic staff employees (n=139).

The results partially supported the specific study assumptions. Concrete, learning value of the job was positively related to anticipation and optimization, corporate sense and balance. LMX moderated the relationship between learning value of the job, on the one hand, and all employability dimensions, on the other hand. However, proactive coping only moderated the relationship with anticipation and optimization, flexibility and balance. In all cases, under the condition of high moderator variable levels, the relationship became stronger.

This study extends past employability research by applying an interactionist perspective (person: proactive coping style, context: LMX and learning value of the job) approach for explaining employability enhancement. The results of this scholarly work provide useful insights for stimulating future career development and growth, which is of upmost importance in nowadays’ labor markets.

Different studies have analyzed the relationship between organizational learning and value creation. However, the question of how crowdsourcing affects the relationships between organizational learning and value creation remains unexplored. This paper aims to explore the mediating role of crowdsourcing in the relationship between organizational learning and value creation in local governance.

The hypotheses were tested based on data collected from 205 local governance units in Poland using crowdsourcing. Data collection was carried out by using a set of standardized questionnaires. Correlation analyses were used to specify the strength of the relationships between the variables. To test the hypotheses, multiple regression analysis was used.

The results have shown that in the local governance organizational learning is related to crowdsourcing, while organizational learning is not related to value creation. Crowdsourcing does not play a mediating role in explaining the relationship between organizational learning and value creation.

A research model was developed based on the relevant literature in the field of organizational learning, value creation and crowdsourcing. This study urges researchers to explore the relationship between organizational learning and value creation in other public organizations using crowdsourcing.

This is the first study on the intermediate role of crowdsourcing in the relationship between organizational learning and value creation in local governance. The proposed model enriches the existing literature and allows better understanding of how crowdsourcing acts as an intermediary in the organizational learning-value creation relationship.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

The purpose of this study is to explore Swedish police education teachers’ informal workplace learning and its perceived value for their professional development. Two categories of teachers, police teachers and university teachers, with different professional knowledge and experience, work together at the police education unit.

The method used was in-depth interviews with teachers working at a Swedish police education unit.

Informal workplace learning was perceived by both teacher groups to be of great value for gaining knowledge about the local practice and for their professional development. Their learning emerged in discussions, observations and practically oriented activities in their daily work. Four conclusions: firstly, the teachers’ informal workplace learning was socially and practice-oriented and learning emerged in a collaborative, reciprocal and active process. Secondly, the embodied nature of the learning is evident in the teachers’ joint activities in the teaching practice. Thirdly, it takes time and active involvement in the local practice to become a professional teacher in this kind of education. Fourthly, an educational structure where academic knowledge and experience can be integrated with police knowledge and experience constitutes an important basis for teachers’ professional development in police education and training.

The study’s focus on police education and the professional development of teachers in this specific practice contributes to increased knowledge of the social, practice-oriented and embodied nature of informal workplace learning.

This study aims to assess the socio-cognitive transformation and subject knowledge development of international students studying in China's universities with diverse learning environment.

The data was collected through on-line survey and quantitative approach was adopted on a Likert scale to assess students' cognitive and social development as a responsible mind-set, ethical awareness, understanding cultural diversity and subject knowledge development. The assessment scale consisted of 25 statements to cover the foundational indicators that represent socio-cognitive transformation. The questionnaire was pilot tested for internal consistency by calculating Cronbach’s alpha. Furthermore, exploratory factor analysis was applied to ensure the traits of the construct intended to measure. A total of 316 participants responded to the survey. With descriptive statistics, X² of association and Friedman’s two-way analysis of variance by rank was applied to test the hypothesis.

This study argues that diverse learning environment has positive impact on learners’ socio-cognitive transformation. It enhances students' capability to understand cultural values to accept diversity and awareness about global community issues and also subject knowledge skill development.

In the current century, study abroad programs have increased the mobility of international students, and the role of higher education institutions has become immense, multifaceted and dynamic. Universities are taking a position to play a considerable role in creating learning opportunities for awareness of societal issues and transform social behavior. Studying abroad programs is a growing concept in response to learners' cogitative and socialized transformation. There is an absence of research conducted to explore the impact of a diverse cultural environment on the socio-cognitive transformation of international students. Therefore, the current study focused on exploring the cognitive and societal development of international students studying in China.

Organisational responses to the United Nations Sustainable Development Goals depend on the competency and mindset of business leaders to lead responsibly. This study is informed and underpinned by the Principles of Responsible Management Education. This study aims to examine how embedding the “sustainability mindset principles” within a university programme can contribute to responsible management education and, by extension, leadership development.

An illustrative case study using 84 students was applied, including undergraduate, postgraduate and executive Master of Business Administration students. An exploratory, qualitative design was followed, primarily adopting focus groups.

Evidenced learning gains in connecting sustainability knowledge with personal beliefs and behaviours, provide a compelling basis for educational and business practitioners to focus on the sustainability mindset principles (SMPs). Mapping of mindset against leading global competency frameworks provides important theoretical insight. Learning is illustrated through multiple dimensions (i.e. cognitive, behavioural and affective) to inform leadership development approaches.

The mapping of sustainability competency frameworks against the SMP, alongside qualitative research insights, provides a compelling basis for further research into the learning gains from embedding the mindset principles. The situated nature of the study and the lack of longitudinal measurement of what students take forward into their lives and workplaces is a limiting factor to be considered.

This study evidences the value of “whole-person” learning for responsible management, which can helpfully inform the design of both educational and workplace leadership development programmes.

This study is original in the pedagogic examination of the learning dimensions of the SMPs in a Business and Management programme. It also offers new insights in terms of the implications for leadership development.

This study aims to explore co-learning classes, a novel approach to leveraging universities’ capacity to contribute to the local sustainable development agenda whilst enhancing students’ learning. These participatory classes were piloted within a UK university masters’ module focussed on action for sustainability. The classes sought to combine knowledge exchange, reflection and social network development by bringing together students and community stakeholders.

The classes were run as a series of five free events, each focussed on sustainability issues relevant for local practitioners. These were either regular timetabled sessions opened up to the public or additional on-campus public events. Attendance was either face-to-face or online. Evaluation was based upon participation data, written feedback and module leader’s post-event reflections.

The classes successfully secured participation from diverse community members, including local government staff, voluntary sector workers and interested individuals. Both students and community stakeholders valued the participatory format, linkages of theoretical and practical knowledge and diversity of attendees.

Findings are based upon a small-scale pilot study. Further research using a wider range of contexts is required to enhance understanding of the co-learning approach.

This paper highlights some key practical issues to consider if employing co-learning approaches in other contexts, including using inclusive language, aligning with students’ motivations and choosing appropriate focal event topics.

Opening up participatory university classes for the public to attend as co-learners is a rarely used approach and has little coverage in academic literature. This small-scale study therefore has value by highlighting some of the potential impacts, strengths and limitations of this approach.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.