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Organizations spend considerable time and money educating individuals on Six Sigma; however, existing literature does not examine Six Sigma adoption at the individual level or the factors that impact individual Six Sigma adoption. The purpose of this paper is to increase the understanding of individual adoption of Six Sigma tools and methodology.

This paper used a single-site field study in a manufacturing organization to empirically test and refine a theory of the factors impacting Six Sigma adoption at the individual level.

Reaction to training, project management and project infrastructure were found to be significant input factors for individual Six Sigma adoption with an R² of 0.482, which indicates that about 48 per cent of the variation in Six Sigma adoption is explained by the input factors. All of the identified input factors were found to have a positive relationship with individual Six Sigma adoption, as well as positive correlations with each other.

This paper was not a controlled experiment or a longitudinal study, so it is not possible from the results of this research to prove causal relationships, although the literature supports a causal relationship between the input factors and outcome.

The findings of this paper will be useful to practicing organizations which seek to improve individual Six Sigma adoptions, as well as inform future Six Sigma adoption research.

Six Sigma adoption at the organizational level has been well documented in the existing literature. The successful adoption of Six Sigma in an organization is dependent, at least in part, to adoption Six Sigma at the individual level. A review of the existing literature indicates that there has been no research into individual adoption of Six Sigma tools and methodology.

This paper sets out to apply practically the constructs of the entrepreneurial performance training model to three different training interventions, known as the business start‐up, basic entrepreneurship, and advanced entrepreneurship programmes. Furthermore, the paper aims to measure the business performance indicators and skills transfer that took place after the training interventions.

Quantitative research was conducted, using three validated research questionnaires. The research design consists of a pre‐test, post‐test and post‐post test (ten weeks after the training interventions took place). Factor analysis was done, descriptive statistics arising from opinions and expressions are presented and statistical tests such as the Chi‐square test and ANOVA provide inferential statistics.

The business performance indicators improved for all three training groups after they attended the training interventions. Furthermore, it was proved that skills transfer took place after the respondents attended the training interventions.

The training groups can be measured again after 18 months of three years to really determine the impact of the training interventions. The results of the three training programmes can be compared to see whether the basic entrepreneurship groups gained more skills and their business performance indicators increased more than the business start‐up or advanced entrepreneurship programmes.

The outcomes and implications of this research paper emphasise that it is imperative to design training programmes based on training models that have been tested. This paper highlights some aspects of how constructs used within the training models can be tested.

The entrepreneurial performance‐training model was practically applied and provides a set of expectations for other entrepreneurship models as well as presenting a benchmark against which programme performance can be measured. A unique teaching methodology is portrayed that contributes to the overall effectiveness of the training model.

Previous research on the use of behaviour modelling techniques for training in industry have shown it to be generally effective. Further, more specific work has suggested that effectiveness might be improved by the use of techniques (symbolic coding and rehearsal) designed to improve trainees’ retention processes. This study examined the use of symbolic coding (learning points) and rehearsal techniques in behaviour modelling training. The data were derived from a field experiment conducted in a UK financial services organisation. Although, as expected, the behaviour modelling approach did produce effective learning the results showed that, contrary to hypotheses, variations in symbolic coding (different learning points conditions) and rehearsal did not influence training outcomes.

Morgan State University (Morgan) is a leading undergraduate institution for black science and engineering doctoral degree recipients. Morgan also is a leader in the production of black engineering degree recipients in the United States. This chapter provides a historic overview of the major programs with a tie to the impact on the institutional metrics, a discussion of the process for developing researchers in science and engineering, and alumni perspectives. The undergraduate research development models used in engineering at Morgan are compared and contrasted with the life sciences and physical sciences. The programs focus on developing communities of engineering practice and communities of science, thereby enhancing students’ self-efficacy and resilience, shaping disciplinary identity, and creating learning communities. These approaches are critical for the success of minority students and are supported by the social science literature. Best practices have been adopted at varying levels by the School of Engineering, the School of Computer Mathematics and Natural Science and the Behavioral Science departments that have netted these Ph.D. outcomes including multiyear mentored research, research training courses, and participation in professional meetings. Multiple approaches to student development, when matched with the disciplinary culture, are shown to result in national impact.

This research aims to analyze the literature on Quality 4.0 and pinpoint the essential factors contributing to its success. Additionally, the research aims to develop a framework that can be used to create a capable workforce necessary for the successful implementation of Quality 4.0.

By following a systematic approach, the authors could ensure that their literature review was comprehensive and unbiased. Using a set of pre-determined inclusion and exclusion criteria, the authors screened 90 research articles to obtain the most relevant and reliable information for their study.

The authors' review identified essential findings, including the evolution of literature in the field of Quality 4.0 and the systematization of previous literature reviews focusing on training and development. The authors also identified several training barriers to implementing Quality 4.0 and proposed a model for building a competent workforce using Kolb's experiential learning model.

The authors' research offers insights into the training barriers that must be considered when building a competent workforce. Using the framework proposed in the authors' research, consultants and managers can better integrate Quality 4.0 into their organizations.

The adoption of Quality 4.0 has significant social implications and is essential for advancing sustainability. It can improve efficiency, reduce waste, minimize environmental impacts and better meet the needs and expectations of stakeholders.

The authors' study stands out as one of the earliest reviews of the literature on Quality 4.0 to incorporate the theory-context-method (TCM) framework, allowing to provide unique insights into future research directions that had not been previously explored.

Aims to commend SEM (structural equation modeling) that excels beyond multiple regression, which is a popular statistical technique to test the relationships of independent and dependent variables, in expanding the explanatory ability and statistical efficiency for parsimonious model testing with a single comprehensive method. SEM is employed to find the real “best fitting” model. This article also presents an incremental approach to SEM, which is a procedural design and sounds workable for testing simple models and presents an example to test a parsimonious model of MBA knowledge and skills transfer using SEM and multiple regression. The results indicate that only one significant relationship can be justified by multiple regression. SEM, on the other hand, has helped to develop new relationships based on the modification indexes, which are also theoretically accepted. Finally, three relationships are shown to be significant and the “best fitting” structural model has been established.

Focusing on management training, the purpose of this paper is to establish whether identical elements in a training program (i.e. aspects resembling participants' work situation) can improve training transfer and whether they do so beyond the contribution of two well‐established predictors – motivation to learn and expected utility. In an effort to establish mechanisms connecting identical elements with training transfer, the authors aim to propose and test motivation to transfer as a mediator.

Data were collected online from 595 managers who participated in a management training program. Structural equation modeling was used to test the model.

Identical elements, expected utility and motivation to learn, each had a unique contribution to the prediction of training transfer. Whereas motivation to learn partially mediated these relationships, identical elements and expected utility also showed direct associations with training transfer.

Identical elements represent a relevant predictor of training transfer. In future research, a longitudinal analysis from different perspectives would be useful to better understand the process of training transfer.

Participants may profit more from management training programs when the training better resembles participants' work situation. Organisations and trainers should therefore apply the concept of identical elements in their training, to increase its value and impact.

This study contributes to the training literature by showing the relevance of identical elements for transfer, over and above established predictors.

The focus of this chapter is to describe the methods and results of ASCEND, an innovative program that empowers undergraduate students to lead research projects. ASCEND, which stands for “A Student-Centered Entrepreneurship Development Training Model to Increase Diversity in the Biomedical Research Workforce,” is funded by the National Institutes of Health and is being implemented at Morgan State University, a historically black university in Baltimore, Maryland. The results are thus far very promising and show that placing undergraduate students in leading research positions and surrounding them with like-minded peers enhances their sense of science identity, leadership, peer support, and research capabilities. It is hoped that students who participate in ASCEND will pursue graduate training and become future successful biomedical researchers.

Information technology (IT) has often been cited as being able to create competitive advantage. However, the degree of leverage is often dependent upon several factors, not least the type and level of IT training provided, resources available, management commitment, and prevailing level of corporate culture. This paper introduces the generic processes involved in developing an IT training framework in order to support and deliver the business strategy, and presents findings in the form of a generic IT training model. This model identifies the sequential stages needed to commission and deploy IT training in a construction environment in the form of an implementation roadmap. This model was developped with two leading UK construction organizations. Findings have identified that seven core process phases should be considered before committing resources to training. Recommendations include identifying the core business benefits and matching these to the training outcomes, albeit cognisant of barriers such as lack of empowerment, organizational culture, resource limitations, and so on.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.