Evaluating the Impact of Knowledge Management Factors on M-Learning Adoption: A Deep Learning-Based Hybrid SEM-ANN Approach

Linking green supply chain management practices and environmental performance in the manufacturing industry: a hybrid SEM-ANN approach

This research determines the influence of green supply chain management practices (GSCM) on environmental performance. It also investigates the moderating role of supply chain environmental cooperation on GSCM practices and environmental performance relationships. A total of 370 employees of several Bangladeshi manufacturing companies were conveniently chosen as respondents. To verify the data validity and reliability and to test the hypotheses, we used SmartPLS. Finally, we employed an artificial neural network (ANN) to examine the relationship. Green design and green manufacturing have significant positive impacts on environmental performance, while green procurement and green distribution do not. Moreover, environmental cooperation moderates the relationships of green design and green distribution with environmental performance. The moderating effect of supply chain environmental cooperation in the relationship between GSCM practices and environmental performance in the manufacturing industry adds knowledge to the existing literature by incorporating a hybrid model combining PLS-SEM and ANN. Our study adds to the current body of knowledge by delving into the literature on GSCM from the perspective of Bangladesh's industrial sector. This study fills a knowledge gap by shedding light on the interactions of GSCM and environmental performance. Indeed, this study represents a step forward from classic linear regression-based models to an ANN-based nonlinear model. It also demonstrates new contributions to the literature on green supply chain management and environmental performance.

Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs' environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

SEM-ANN-based approach to understanding students' academic-performance adoption of YouTube for learning during Covid

A hybrid analysis of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), through SmartPLS and SPSS software, as well as the importance-performance map analysis (IPMA) were used to examine the impact of YouTube videos content on Jordanian university students' behavioral intention regarding eLearning acceptance, in Jordan. According to the evaluation of both ANN and IPMA, performance expectancy was the most important and, theoretically, several explanations were provided by the suggested model regarding the impact of intention to adopt eLearning from Internet service determinants at a personal level. The findings coincide greatly with prior research indicating that users' behavioral intention to adopt eLearning is significantly affected by their performance expectancy and effort expectancy. The paper contributed to technology adoption e.g., YouTube in academia, especially in Jordan. Respondents showed a willingness to employ and adopt the new technology in their education. Finally, the findings were presented and discussed through the UTAUT and TAM frameworks.