Search results

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

This study investigates the quantitative aspect of the various strains of operational excellence (OE) and competitive-potential (CP) in the SME sector. It has five steps, i.e., identifying the key performance constructs of OE and their hypothesized relationship pattern from literature, validating these constructs through factor analysis, formalizing their empirical relationships by structural-equation-modeling (SEM), path analysis of performance constructs with the empirical results, and lastly proposing a framework for OE deployment in SMEs.

Data for the deployment scores of operational excellence procedures (OEPs) were collected through a structured questionnaire survey. Nine hundred participants from a stratified random sample were approached for the survey, and 473 responses were received. Sample stratification was based on Gender, Education, Experience, Position, Department and Industry. Respondents had 5–30 years of experience managing manufacturing operations, holding the manager position and above.

The path analysis of the structural model provides unique insights into OE's practical aspects in SMEs (small and medium enterprises). For example, Contractual-conformance and Process-efficiency play pivotal roles as both have a significant positive impact on CP. Supplier efficacy, Consistency and Product-excellence do not improve CP unless mediated by Contractual-conformance or Process-efficiency.

The study provides important implications for academia, policymakers and managers. The study identifies and validates the operational excellence key performance practices and proposes a framework for manufacturing organizations. SME managers can follow the framework to develop effective operational excellence strategies to help them achieve their organizational goals. Additionally, the study emphasizes the need for continuous culture in SMEs, which will help to support operational excellence deployment. Overall, the implications presented in the study will help SMEs to enhance their competitiveness and operational performance.

The study explores the empirical investigation of the operational excellence deployment in SMEs. The study uses a mixed method approach for research design, including qualitative and quantitative approaches, and uses SEM to test the proposed framework. Validation of OE's six key performance constructs and establishing their empirical relation is an attempt to advance the Operations excellence theory. Unlike large enterprises, SMEs demonstrate an incohesive response to the practices pertaining to Supplier efficacy, Consistency and Product-excellence. This unique response pattern requires special treatment, which is incorporated into the proposed framework.

This 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.

This 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.

The 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.

Prior 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.

The 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.

Cryptocurrencies 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.

Given 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.