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Prior research from transaction costs economics argued that central firms perform better because they have superior access to information to discipline their alliance partners. Central firms may also, however, face higher costs and risks of unintentional learning and weaken their competence through structural inertia. We propose that these costs and risks are influenced by the learning capacities of the firms in the network and can explain different outcomes for focal firm performance.

To test our predictions, we use instrumental variable–generalized method of moments estimation techniques on 15,517 firm-year observations from equity alliance portfolios in the global food industry across a 21-year window.

We find support for our predictions and show that the relationship between network degree centrality and firm performance is negatively influenced by partners’ learning capacity and positively influenced by focal firms’ learning capacity, while firms with low network degree centrality benefit less from their learning capacity.

Future developments in transaction cost economics may consider partner and focal firms’ learning capacity as moderators of the network degree centrality – firm performance relationship.

In alliance decisions, managers must consider that the combination of high network degree centrality and partners’ learning capacity can lead to high costs, risks of unintentional learning, and structural inertia, all of which have negative consequences for performance. In concentrated industries where network positions are controlled by a few large firms, policymakers must acknowledge that firms may face substantial barriers to collaboration with learning-intensive firms.

This study is the first to develop and test a comprehensive transaction cost analysis of the central firm’s unintended knowledge flows and structural inertia in alliance networks. It is also the first to incorporate theoretically and empirically the hazards of complex and unintended information flows on the relationship of network degree centrality to performance in equity alliance portfolios.

Professional learning networks (PLNs) involve teachers’ collaboration with others outside of their school to improve teaching and learning. PLNs can facilitate teachers’ professional growth and school improvement. This study aims to explore the drivers for participation within PLNs, the enactment process and the impact of PLN participation on teachers, students and schools in Greece.

A descriptive phenomenological study was conducted to explore the lived experience of primary school teachers participating in PLNs.

The findings showed that individuals who were open to change were driving innovation to address a need or a lack in their daily practice that was not satisfied within their usual community of practice. The key element of the participation was peer collaboration with openness of communication without attendant accountability pressures. The change was mainly identified in teacher skills and the school climate. An individual could bring change only if the school is already open to change. In some cases, resistance to change in schools was identified before enactment or during enactment. The transformation of teachers’ and leaders’ stances is discussed, enabling the opportunity to maximise school improvement.

The study examines PLNs as European Union-funded initiatives that are developed by teachers in centralised education systems under the phenomenological research paradigm. It explores the PLNs in a different setting compared to the existing conceptual theory of change for PLNs.

In view of the expanding global interest in leadership learning and development programmes for centre leaders, this study aims to investigate how an early childhood education leadership programme can enhance Norwegian centre leaders’ learning and development as a network professional learning community (PLC) by way of Schön’s reflective model, the hall of mirrors.

In this qualitative case study, we interviewed four centre leaders, the owner of the centres and a facilitator and/or coach from the local work and competence centre for inclusive work who was leading the leadership programme.

The findings demonstrate three aspects of the hall of mirrors that enhanced the centre leaders as a network PLC: engaging in collective inquiry towards shared visions and values, enhancing professionalism through distributed leadership and cultivating a trusting learning climate.

Data were collected in a single smaller municipality in Norway and therefore may not be generalisable to other areas.

The findings can be used to further discuss how early childhood education and care (ECEC) leadership development programmes can be employed to establish and sustain professional leadership teams and professional learning communities.

As there are parallels between the leadership programme and the workplace and the programme promoted a network PLC, this study contributes to existing knowledge by offering a transformative reflective model for leadership learning and change through the reconceptualisation of the hall of mirrors.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

This study aims to examine the underlying process through which learning organization culture positively influences knowledge sharing. It specifically explored the mediating role of social capital, underscoring its critical impact on enhancing both knowledge sharing and fostering learning organization culture.

To test the proposed hypotheses, structural equation modeling (SEM) analysis was conducted with a sample of 231 employees from a manufacturing firm in South Korea.

The results of this study indicate significant direct effects of learning organization culture on social capital. Also, social capital indicates a positive effect on knowledge sharing. Although learning organization culture had no direct effect on knowledge sharing, it indirectly affected learning organization culture and knowledge sharing by mediating social capital.

This study proposes that a learning organization culture will be interconnected with social capital and knowledge sharing. Organizations that can effectively harness the wealth of knowledge unlocked by social capital, and subsequently integrate this knowledge into their activities, are poised for competitive advantage.

First, this study places a special emphasis on the mediating role of social capital between learning organization culture and knowledge sharing. Despite extensive research exploring diverse knowledge-sharing factors (Wang and Noe, 2010), it is plausible that examining social capital as a mediator could offer insights for facilitating knowledge sharing through its structural, relational and cognitive dimensions. Second, while a plethora of literature examines knowledge sharing, this study also seeks to unravel the multifaceted pathways through which the learning organization culture influences knowledge sharing and how these processes could be optimized in organizations.

School leaders can significantly influence the conditions that affect teacher retention. Yet, leaders in rural and high-poverty schools often face limited opportunities to develop their abilities to enhance these conditions. In this case study, we examine how participating in a professional community supported school leaders' efforts to increase teacher retention and student learning outcomes.

We used case study methodology to study 14 leaders from rural, high-poverty or underperforming schools with greater-than-average levels of teacher turnover. The leaders were participating in a three-year research-practice partnership intended to assist them in using improvement science to address problems of practice related to teacher retention and student learning outcomes in their schools. We collected and analyzed data from interviews, exit surveys, artifacts and participant observations over a one-year period.

Participating in this professional community helped the leaders create the conditions for increased teacher retention and student learning outcomes by providing them with opportunities to collaborate with their peers, receive leadership coaching, exchange ideas and learn in a safe space.

These findings confirm and extend extant school leadership development research. A particularly interesting finding was the role of the professional community in reducing the leaders' feelings of isolation while providing them a safe space to learn. The findings also illustrate how universities and school districts can partner to provide professional learning opportunities that enhance school leaders' professional knowledge, leadership practices and well-being.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

This paper aims to examine employees’ online learning continuation intentions (OLCI), exploring factors such as performance expectancy (PE), effort expectancy (EE), social influence (SI), perceived benefits (PB) and management support (MS) influencing their commitment to online learning engagement.

The Unified Theory of Acceptance and Use of Technology (UTAUT) model was expanded to include PB and MS constructs. This study used a quantitative research approach using purposive sampling techniques. Three hundred and eighty-six responses from Indian information technology (IT) professionals at various levels were analysed using Statistical Package for the Social Sciences-Analysis of Moments Structures tool.

This study found a strong positive influence of PE, EE, PB and MS on OLCI in the context of post-pandemic. Workplace learning rapidly generates outcomes if employees associate it with their career growth. However, the authors found that SI does not significantly affect OLCI.

This research is unique work in the area of workplace learning by evaluating the OLCI of IT professionals using the extended UTAUT model in a new normal. Moreover, this study contributes to online learning literature with a combined study of technology usage, continuance intention and organization learning and development.