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Spurred by the globalization of competition, organizational learning and continuous improvement have attracted a great deal of research and managerial interest in recent years. Notwithstanding the growing literature on these topics, there is still considerable conceptual ambiguity about organizational learning and continuous improvement among researchers. The paper clarifies the underlying processes through which organizations “learn,” highlights the role of learning in continuous improvement programs, and shows how an organization may go about building a continuous improvement culture. Specific tools and techniques of organizational learning which may be used in continuous improvement programs are also discussed.

Continuous learning has been referred to as an integral phenomenon of learning organization. While a range of studies have associated continuous learning with employee performance; understanding of this relationship remains underdeveloped in the context of mergers and acquisitions. The present study investigates the relationship among continuous learning, change-efficacy and contextual performance of employees working with a recently merged bank and further explores the mediating role of employees' change-efficacy in relationship between continuous learning and contextual performance of employees.

A research model with underlying hypothesis is proposed by drawing upon the theory of transformative learning, social cognitive theory and theory of change management. Cross-sectional data was collected from bank employees undergoing integration in a post-merger phase.

The findings of the study suggest that continuous learning influences employees' change-efficacy and change-efficacy significantly mediates the relationship between continuous learning and contextual performance of employees. The empirical association drawn from the variables has been recognized by extant research as a brighter manifestation of learning organizations.

The research is undertaken in a large Indian bank and the analysis is based on cross-sectional data which may not be generalized across a broader range of sectors and international environment.

The findings of the study have a potential to help HR practitioners to utilize continuous learning in change management by enhancing contextual performance of employees post-mergers and acquisitions.

The study is one of its kinds in a post-merger setup which captures the outcomes of continuous learning by either confirming or extending the existing theories.

The purpose of this study is to explore the role of continuous learning and the mitigation or elimination of knowledge barriers affecting information technology (IT) assimilation in the health-care sector. Most of the problems with IT assimilations stem from a poor understanding of the nature of suitable information, the lack of trust, cultural differences, the lack of appropriate training and hierarchical bureaucratic structures and procedures. To overcome these barriers, this study provides evidence that a continuous learning process can play a part in overcoming some of the obstacles to the assimilation of IT.

This study investigates how a continuous learning environment can counteract the presence of knowledge barriers, and, along with such an environment, can, in turn, facilitate IT assimilation. The study uses ADANCO 2.0.1 Professional for Windows and involves the collection and analysis of data provided by 210 health-care end users.

The study provides evidence in support of the proposition that continuous learning may facilitate the assimilation of IT by health-care end users through the mitigation of knowledge barriers (e.g. lack of trust or resistance to change). The mitigation of these barriers requires the gathering and utilization of new knowledge and knowledge structures. The results support the hypothesis that one way in which this can be achieved is through continuous learning (i.e. through assessing the situation, consulting experts, seeking feedback and tracking progress).

A limitation of the study is the relatively simple statistical method that has been used for the analysis. However, the results provided here will serve as a preliminary basis for more sophisticated analysis which is currently underway.

The study provides useful insights into ways of using continuous learning to facilitate IT assimilation by end users in the health-care domain. This can be of use to hospitals seeking to implement end user IT technologies and, in particular, telemedicine technologies. It can also be used to develop awareness of knowledge barriers and possible approaches to mitigate the effects of such barriers. Such an awareness can assist hospital staff in finding creative solutions for using technology tools. This potentially augments the ability of hospital staff to work with patients and carers, encouraging them to take initiative (make choices and solve problems relevant to them). This, in turn, allows hospitals to avoid negative and thus de-motivating experiences involving themselves and their end users (patients) and improving IT assimilation. This is liable to lead to improved morale and improved assimilation of IT by end users (patients).

As ICT systems and services should entail participation of a wide range of users, developers and stakeholders, including medical doctors, nurses, social workers, patients and programmers and interaction designers, the study provides useful social implication for health management and people well-being.

The paper contributes to a better understanding of the nature and impacts of continuous learning. Although previous studies in the field of knowledge management have shown that knowledge management procedures and routines can provide support to IT assimilation, few studies, if any, have explored the relationship between continuous learning and IT assimilation with particular emphasis on knowledge barriers in the health-care domain.

Conceptual model in this paper combines existing scientific knowledge grounded in theories of planned behavior, diffusion of innovation and a unified theory of acceptance and use of technology, while aiming to identify relevant determinants of continuous use of e-learning by employees who used e-learning in the past year at their workplace.

The authors developed and empirically tested the positive impact of professional, personal, IT and environmental factors on the continued use of e-learning among 672 employees across different sectors using the structural equation modeling technique.

Research results suggest that the most powerful determinant of continuous use of e-learning are personal factors. Environmental influences and technological aspects also exhibit a positive and significant impact on the continuous use of e-learning. Research hypothesis related to the positive influence of professional factors on the continuous use of e-learning has not been empirically confirmed. Also, results demonstrated that continued use of e-learning contributes to better individual business performance.

The practical contribution is threefold: to companies, education institutions and human resource managers. For companies, identification of key determinants will lead to a better understanding of employees needs regarding continuous job improvements. The findings can be used by educational institutions to design e-learning programs according to results and real value to employees. On the other hand, human resource managers can benefit from this study in terms of getting concrete factors that motivate employees for continuous job improvement.

The research sheds light on the proposed integrated model that tests the post-adoption of the continuous use of e-learning within an organizational context.

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.

The firm’s structural inertia seems to be a crucial roadblock in continuous improvement efforts. The management paradigm shift required in the transition towards a continuous improvement culture is from individual‐based learning to system‐based learning. Explores the role of an organizational learning mechanism in overcoming the barriers for continuous improvement. Examines the implications of the creation of a parallel learning structure mechanism and its concomitant impact on continuous improvement in a paper mill firm over an eight‐year period. Concludes with the identification and discussion of some theoretical issues.

By integrating organizational support theory (OST) and social cognitive theory, this study investigates types of managers' coaching behavior as experienced by the employees. Furthermore, the study examines whether employees would exhibit greater task and contextual performance when organizational learning is blended with a specific coaching behavior of their manager.

Using primary data from 298 software engineers working in select information technology companies across India, the current study attempts to assess moderating effect of managers' coaching behavior in two relationships, including continuous learning and employees' task performance (CL-TP) and continuous learning and employees' contextual performance (CL-CP).

Result of exploratory factor analysis suggests that managers of select organizations exhibit two major types of coaching behavior: inspiration-based coaching behavior and facilitation-based coaching behavior. On the moderating role of coaching behavior, it is documented that facilitation-based coaching behavior significantly positively moderates both stated (CL-TP and CL-CP) relationships, whereas inspiration-based coaching behavior of supervisors has positive significant effect on CL-TP relationship but negatively moderates the CL-CP relationship.

The extent to which the findings of this study can be generalized is constrained by the limited sample and organizational context.

The most important managerial implication for all learning organizations is that both kinds of coaching behaviors help improving the task performance of the employees, but managers should prefer facilitation-based coaching style in order to generate higher contextual performance of employees.

This study contributes to practitioners and existing literature by explaining how individual performance of employees is affected by the investment made by organizations in facilitating continuous learning.

The purpose of this paper is to identify and explain the role of individual learning and development in acquiring tacit knowledge in the context of the inexorable and intense continuous change (technological and otherwise) that characterizes our society today, and also to investigate the software (SW) sector, which is at the core of contemporary continuous change and is a paradigm of effective and intrinsic knowledge sharing (KS). This makes the SW sector unique and different from others where KS is so hard to implement.

The study employed an inductive qualitative approach based on a multi-case study approach, composed of three successful SW companies in China. These companies are representative of the fabric of the sector, namely a small- and medium-sized enterprise, a large private company and a large state-owned enterprise. The fieldwork included 44 participants who were interviewed using a semi-structured script. The interview data were coded and interpreted following the Straussian grounded theory pattern of open coding, axial coding and selective coding. The process of interviewing was stopped when theoretical saturation was achieved after a careful process of theoretical sampling.

The findings of this research suggest that individual learning and development are deemed to be the fundamental feature for professional success and survival in the continuously changing environment of the SW industry today. However, individual learning was described by the participants as much more than a mere individual process. It involves a collective and participatory effort within the organization and the sector as a whole, and a KS process that transcends organizational, cultural and national borders. Individuals in particular are mostly motivated by the pressing need to face and adapt to the dynamic and changeable environments of today’s digital society that is led by the sector. Software practitioners are continuously in need of learning, refreshing and accumulating tacit knowledge, partly because it is required by their companies, but also due to a sound awareness of continuous technical and technological changes that seem only to increase with the advances of information technology. This led to a clear theoretical understanding that the continuous change that faces the sector has led to individual acquisition of culture and somatic knowledge that in turn lay the foundation for not only the awareness of the need for continuous individual professional development but also for the creation of habitus related to KS and continuous learning.

The study reported in this paper shows that there is a theoretical link between the existence of conducive organizational and sector-wide somatic and cultural knowledge, and the success of KS practices that lead to individual learning and development. Therefore, the theory proposed suggests that somatic and cultural knowledge are crucial drivers for the creation of habitus of individual tacit knowledge acquisition. The paper further proposes a habitus-driven individual development (HDID) Theoretical Model that can be of use to both academics and practitioners interested in fostering and developing processes of KS and individual development in knowledge-intensive organizations.

The purpose of this paper is to examine the dynamics of organizational learning and continuous improvement (CI) in the context six sigma implementation in business organizations operating in multicultural environments.

A specific research question is: does learning mechanisms and continuous improvement practices support each other and how, and what type of learning can be identified in the improvement of business processes. The question is linked to one of the fundamental issues currently discussed in the field of organizational learning; how do organizations get “from here to there”, in other words, what is the dynamics of the processes of learning and how progressive learning is achieved. A case study of a few Finnish companies is made and a procedural implementation model is applied.

The findings suggest that the learning process is characterized by measurement, detection and correction of errors, and cost reduction. In six sigma implementation, learning is a single‐loop type of learning. It is an incremental change process which reminds a technical variant of the learning organization. Continuous improvement occurs through procedural practices (the DMAIC‐cycle) which forms a structure for sustaining learning.

In this study, an essential question remains still unanswered: what type of learning is appropriate when organizational performance is enhanced by process improvements in production, delivery processes, etc. and what kind of learning mechanisms are the most supportive to continuous improvement practices. Further research is needed to find out how (if at all) the technical (single‐loop) approach develops into social (cultural and political) type of learning enabling sustainable capability development. For researching this a longitudinal case study setting would be required. As this paper has reported on the authors' first exploration, further research is needed to increase understanding of learning mechanisms that support CI practices. In further studies it is necessary to “dig” in real life practices of six sigma implementation more deeply.

Management should invest in, and allocate resources to staff training in order to promote learning and CI. On the level of operational leadership, the role of the leaders needs to be more clearly defined and leaders should be empowered. Managerial implication is that the development of information systems is a necessity for supporting CI and progressive learning in six sigma implementation.

Explains the dynamics of continuous improvement and learning process. Presents findings from a case study in three Finnish multinational companies. Presents a few key success factors for progressive organizational learning in conclusion.

The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives.

Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models.

From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification.

This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.