In many countries, especially one such as Malaysia, tourism has become a key factor in economic development, and the industry heavily relies on feedback from local residents. It is essential to observe and examine the perceptions of residents towards tourists and tourism development for better planning in realizing successful and sustainable tourism development. Therefore, this research measured the relationship between residents' welcoming nature, emotional closeness, and sympathetic understanding (emotional solidarity) towards tourists and their respective attitudes towards supporting tourism development. To test the proposed research model, we collected data using a questionnaire survey from 333 residents in rural areas in Malaysia. We used the structural equation modelling technique (Amos) to evaluate the research model, and the results revealed that the residents' willingness (welcoming nature) to accept tourists is the strongest factor that effects the residents' attitudes towards supporting tourism development. However, there was no significant relationship between residents' emotional closeness and their sympathetic understanding towards tourists with their attitude and support towards tourism development. Welcoming nature, emotional closeness, and sympathetic understanding are able to predict 48% of residents' attitudes towards tourism development and 62% of their support towards tourism development.
The adoption of energy-efficient lighting systems such as light-emitting diode (LED) lights is an effective strategy to address global warming and climate change. The adoption of LED light usage is largely shaped by consumer behavior. Understanding the factors that influence consumers' awareness of the advantages of using LED lights and consumers' eventual buying behavior is critical to industry players and policy-makers. The objective of this study is to investigate the significant factors that affect consumer intention to use LED lights among Malaysian households. A survey questionnaire of 1,075 potential consumers was employed and the partial least squares technique was applied in data analysis. The results show that consumer awareness creates responsibility and influences consumers' personal norms and attitudes, and their ultimate intention to buy LED lights. Feelings of guilt about contributing to air pollution and an awareness of the advantages of using LED lights both activate individual personal norms and attitudes, and influence the intention to buy LED lights. Awareness of the advantages of using LED lights, and of the negative consequences of not using them, prompts consumers to have feelings of guilt. The findings point to the importance of creating awareness among consumers.
Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.