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This study sets out to empirically test the research framework and identify the relationship between green supply chain management (GSCM) practices and technological innovation (TI) in manufacturing firms. It is one of the first studies that experimentally validate the relationship between GSCM practices and TI from the perspective of a developing nation. In this study, 133 usable sets of data were collected from manufacturing firms in Malaysia. Results obtained from the partial least square-structural equation modeling (PLS-SEM) approach supported the significant positive relationship between three GSCM practices (i.e. internal environmental management, eco-design and investment recovery) and TI, but the study found that green purchasing and cooperation with customers do not have a significant positive correlation with TI. The importance of GSCM practices has been proven to enhance firms' TI, in addition to improving the environment, bringing about a positive impact on the manufacturing establishment. Therefore, this study has relevance to all manufactures seeking to enhance their TI through the effective use of GSCM practices.

This research examines the predictors of open interorganizational systems (IOS) adoption by using RosettaNet as a case study. The model used in this research derived its theoretical supports from literature related to interorganizational relationships and knowledge management studies. A sequential, multi-method approach integrating both structural equation modeling (SEM) and neural network analysis was employed in this research. Data was collected from 136 small and medium sized enterprises (SME). Our result showed that interorganizational relationships such as communication, collaboration and information sharing play an important role in SMEs' RosettaNet adoption decisions. Knowledge management practices such as knowledge application, knowledge acquisition and knowledge dissemination also influenced SMEs' decision to adopt RosettaNet. The findings are useful for decision makers to understand how they can improve the adoption of RosettaNet in their organizations. Unlike previous studies, this research provided additional insights into what influence the adoption of RosettaNet by examining variables beyond the traditional technological attributes which have been studied quite extensively. By integrating SEM with artificial intelligence techniques such as neural network, this study also examined the non-linear and non-compensatory relationships involved in the adoption of RosettaNet.

This paper presents a stochastic model that identifies network effect and customer orientation empowerment as strong antecedents of trustworthiness and trust in social media network environment. As Trust is emerging as a critical influencer of consumer behavior in organizations, social media has become the facilitator or the enabler of this influence. This paper aims to explore and examine the relationships amongst the antecedents of trust in the context of the social media network environment via Bayesian estimation and testing of the structural equation model. In facilitating the estimation of the stochastic model, a survey was conducted on 255 social media users. A series of statistical analyses were conducted prior to the estimation using Bayesian Markov Chain Monte Carlo (MCMC). Results showed that network effect and customer orientation empowerment were significant predictors of trustworthiness and trust. Bayesian MCMC results confirmed that the findings were consistent with the literature.

The advancement in wireless and mobile technologies has presented tremendous business opportunity for mobile-commerce (m-commerce). This research aims to examine the factors that influence consumers' m-commerce adoption intention. Variables such as perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, network influence, and variety of services were used to examine the adoption intentions of consumers. Data was collected from 376 m-commerce users. A multi-analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model to predict m-commerce adoption. The result showed that perceived usefulness, perceived enjoyment, trust, cost, network influence, and trust have significant influence on consumers' m-commerce adoption intentions. However, the neural network model developed in this research showed that the best predictors of m-commerce adoption are network influence, trust, perceived usefulness, variety of service, and perceived enjoyment. This research proposed an innovative new approach to understand m-commerce adoption, and the result for this study will be useful for telecommunication and m-commerce companies in formulating strategies to attract more consumers.

M-commerce has continued to grow at an explosive rate. This purpose of this paper is to examine the predictors of m-commerce adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extended model incorporates additional constructs such as perceived value, trust, perceived enjoyment and personal innovativeness. A non-linear, non-compensatory model is developed to understand the predictors of m-commerce adoptions. Online survey was used to collect data from 140 Chinese users. Neural network analysis was used to predict m-commerce adoption, and the model was compared with the results from regression analysis. The neural network model outperformed the regression model in adoption prediction, and captured the non-linear relationships between predictors such as perceived value, trust, perceived enjoyment, personal innovativeness, users demographic profiles (e.g. age, gender and educational level), effort expectancy, performance expectancy, social influence and facilitating conditions with m-commerce adoption. This study applied neural network to provide further understanding of m-commerce adoption decisions based on a non-linear, non-compensatory model. The UTAUT model was also extended to examine consumer information systems such as m-commerce. The m-commerce study conducted in this research is in China, one of the fastest growing m-commerce markets in the world.