In a price-sensitive market, quality improvement framework also needs to incorporate cost factor. Past research on Lean Six Sigma gives limited insight on any framework catering to quality and cost together. This study aims to contribute in this niche by illustrating a hybrid framework, DMAIoC (define, measure, analyse, improve, optimise and control) to attain desired quality at minimum investment cost by integrating simplex method of optimisation in conventional DMAIC (define, measure, analyse, improve and control) framework. A case study is presented highlighting a field quality rejection problem faced by a manufacturing organisation of consumer goods. Sustainable drop height of a finished good is identified as a response variable to improve the quality. Proposed framework has been used to arrive at a statistical model to define relationship between response and input variables. Investment cost involved with change in input variables has been formulated as objective function. Constrains of objective functions were derived by extendable limits of input variables and by statistical model generated for sustainable drop height. Several feasible solutions to the objective function were identified using simplex method in optimise phase and the most economic was recommended for implementation to meet quality requirement at minimum investment. Suggested framework has significant practical implication in price–quality sensitive markets where manufacturers seek low cost process improvement solutions.
PurposeThis paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.Design/methodology/approachThis study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.FindingsThe findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.Research limitations/implicationsPrior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.Practical implicationsThe research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.Social implicationsCryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.Originality/valueGiven the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.