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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.

The purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.

Using Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.

The findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.

These results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.

The negative cultural bias vis-à-vis international business and cross-cultural management has been duly acknowledged, necessitating recommendations towards investigating its positive effects. Methodologically, quantitative research clearly predominates, and there have been calls for alternative approaches. Thus, this conceptual paper addresses the research gap (methodological and thematic) by investigating if multicultural teams can be an essential part of the global workforce and whether positive effects exist regarding dynamic capabilities, learning and knowledge transfer.

The underlying ethnographic research design enabled exploring within the embedded single case study from an emic perspective, including qualitative observation and semi-structured expert interviews, and provided detailed insights into the company’s multicultural work environment.

The results reveal that applying a qualitative design allowed the needed exploration and show that multicultural, geographically dispersed teams are positively experienced and considered necessary in today’s globalised world. They are likely to increase in the future. Moreover, dynamic capabilities (multicultural competencies) are indispensable for multicultural teamwork. Regarding learning opportunities, different viewpoints for discussion and the ability to reflect on these offer valuable insights. In line with theory, multiculturality is considered a “two-edged sword”, providing simultaneous benefits and challenges. Contrary to the theory, even highly important information transfers can occur virtually, although occasional physical contact is essential for trust building.

The multinational family business offers a unique example of a positive relationship between multiculturalism and organisational excellence and demonstrates how the application of a qualitative methodology can support theory building by delivering a revised model of dynamic capabilities in multicultural environments with geographical dispersion.

The emergence of the COVID-19 pandemic changed the way in which education was delivered in early 2020, and the impacts of these changes continue to be questionable. The aims of this study were to evaluate: (1) the results obtained by students of the Dentistry course in the progress test carried out both before and after the pandemic, (2) the results obtained by a specific group of students who took the test in 2019–2022, and compare their results and (3) subjects that showed a reduction in the percentage of correct answers when the two tests were compared.

The progress test consisting of 100 multiple choice questions was applied before and after the pandemic to all students in the Dentistry course. The analyses were performed using the IBM SPSS for Statistics v.26 software program. The level of significance of 5% was adopted (<0.05).

The test was applied to 320 students in 2019 and to 272 in 2022, of whom the sample of this study was composed. The mean score values in 2019 (M = 49.10; SD = 12.03) were significantly (p = 0.026) higher than those in the year 2022 (M = 46.97; SD = 12.15), with the disciplines in the area of specific knowledge showing a greater drop in the percentage of correct answers by students. This study showed that the emergency remote education had a negative effect on the academic performance of students, based on the progress testing as an evaluation method.

Many studies that assessed the impacts of the pandemic on teaching were focused on the opinions of students. However, the great advantage of our study was the use of a theoretical assessment tool to verify student performance. The post-pandemic landscape beckons for comprehensive inquiries into these domains. This type of research would be valuable for gathering evidence relative to the performance of students after the emergency remote education.

In this study, a dynamic contracting model is developed between a venture capitalist (VC) and an entrepreneur (EN) to explore the influence of asymmetric beliefs regarding output-relevant parameters, agency conflicts and complementarity on the VC's posterior beliefs through the EN's unobservable effort choices to influence the optimal dynamic contract.

The authors construct the contracting model by incorporating the VC's effort, which is ignored in most studies. Using backward induction and a discrete-time approximation approach, the authors solve the continuous-time contract design problem, which evolves into a nonlinear ordinary differential equation (ODE).

The optimal equity share that the VC provides to the EN decreases over time. In accordance with the empirical evidence, the EN's optimistic beliefs regarding the project's profitability positively affect its equity share. However, the interactions between the optimal equity share, project risk and both partners' degrees of risk aversion are not monotonic. Moreover, the authors find that the optimal equity share increases with the degree of complementarity, which indicates that the EN is willing to cooperate with the VC. This study’s results also show that the optimal equity shares at each time are interdependent if the VC is risk-averse and independent if the VC is risk-neutral.

In conclusion, the authors highlight two potential directions for future research. First, the authors only considered a single VC, whereas in practice, a risk project may be carried out by multiple VCs, and it is interesting to discuss how the degree of complementarity affects the number of VCs that ENs contract. Second, the authors may introduce jumps and consider more general multivariate stochastic volatility models for output dynamics and analyze the characteristics of the optimal contracts. Third, further research can deal with other forms of discretionary output functions concerning complementarity, such as Cobb–Douglas and constant elasticity of substitution (See Varian, 1992).

The results of this study have several implications. First, it offers a novel approach to designing dynamic contracts that are specific and easy to operate. To improve the complicated venture investment situation and abate conflict between contractual parties, this study plays a good reference role. Second, the synergy effect proposed in this study provides a theoretical explanation for the executive compensation puzzle in economics, in which managers are often “rewarded for luck” (Bertrand and Mullainathan, 2001; Wu et al., 2018). This result indicates a realistic perspective on financing and establishing cooperative relationships, which enhances the efficiency of venture investment. Third, from an empirical standpoint, one can apply this framework to study research and development (R&D) problems.

First, the authors introduce asymmetric beliefs and Bayesian learning to study the dynamic contract design problem and discuss their effects on equity share. Second, the authors incorporate the VC's effort into the contracting problem, and analyze the synergistic effect of effort complementarity on the optimal dynamic contract.

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.

In today’s world, empowering individuals, promoting social cohesion and advancing economic development all hinge on access to high-quality education, prioritizing diversity, inclusion and equality. Rethinking current educational strategies using cyber-physical learning assets is necessary to accommodate the learning inclusivity and equity and escalating demands of a globalized world. There is a pressing demand for evidence to support the efficacy of collaborative learning in transforming curriculum and fostering learner inclusion. However, it is recognized as a pedagogical technique within the quality education domain. This study aims to address this knowledge gap by investigating how hybridized cybergogy paradigms facilitate collaborative learning, focusing on diversity, equity and inclusion, to improve educational quality in higher education.

This study used a qualitative approach with an exploratory design guided by an interpretive philosophical perspective. The data was gathered from 60 prospective teachers from the public sector university of Sindh, Pakistan. Semi-structured interviews were conducted with participants. They were then analyzed using theme analysis to understand their views on the potential of hybridized cybergogy paradigms for collaborative learning to improve the quality of education provided at institutions.

The study results confirm that learners benefit from increased access to learning resources, improved critical thinking and problem-solving skills and a more diverse and inclusive classroom working together in a collaborative hybridized cybergogy setting. By fostering SDG 4 (Quality Education) and the 21st-century skills necessary for global marketplace engagement and competing in progressive environments, this creative method equips learners with the capabilities to face modern global challenges.

The study offers valuable practical suggestions to stakeholders in higher education, including faculty, policymakers and teacher education programs, for integrating hybridized cybergogy and collaborative learning to align curricula with sustainable development goals. Additionally, it bridges a significant gap in the existing literature, which will aid future researchers interested in exploring this area.

This study stands out as it explores an underexamined area while providing novel educational insights.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.