No abstract

In the global competitive environment, how to establish and maintain the customer relationship is an important concept of the success. The connection and service of frontline employees to the consumer could play an important role and keep the long-term relationship. Therefore, managers need to find out the effective way in enhance the job performance and job satisfaction of frontline employees, help them provide prominent service, and keep the good long-term relationship with the customers. For the organization, there is a large body of literature that focuses on the variables of organization and how they effect on the frontline employees and job satisfaction. In contrast, little work has pay attention on the personal characteristics, such as the effect of self-efficacy and effort on job performance and job satisfaction. To shed light on these potentially complex relationships, this research was chosen on the basis of convenience sampling and was selected from automobile sales persons of Taipei, Taiwan. Among the total amount of 803 copies, a usable sample of 616 questionnaires was utilized in this study, yielding a response rate of 76.7%. We use the structural equation modeling (SEM) with LISREL to analyze and test the data. The results reveal that (1) Self-efficacy has a positive effect on job performance and job satisfaction; (2) effort has a positive effect on job performance and job satisfaction; (3) job satisfaction has a negative effect on turnover intention. These results increase understanding of the effect of personal characteristics on organization performance and helped organization to explore the management policies.

In this study, we conducted an empirical survey of the avoidance behaviors and risk perceptions of active and passive smoking pregnant smokers and recent quitters. We employed an online questionnaire survey by recruiting 166 voluntary participants from an online parenting community in Taiwan. The results of the empirical survey revealed that three-fourths of smokers quit smoking during pregnancy and one-fourth continued smoking. All pregnant women who continued smoking had partners or lived with relatives who smoked. Current smokers and quitters differed significantly in their risk perceptions and attitudes toward smoking during pregnancy. Most pregnant smokers and quitters adopted passive smoking avoidance behaviors at home and in public. Nevertheless, one-fifth of pregnant women chose not to avoid passive smoking. We concluded that most women stop smoking during pregnancy; however, most women continue to be exposed to passive-smoking environments. Perceived fetal health risks and attitudes toward smoking during pregnancy are critical predictors of the anti-smoking behaviors of pregnant women.

PurposeThe study discusses organic agricultural product persuasion using an empirical survey. This study argued that strong argument persuasive advertising message would trigger individuals' self-reference to the harm of pesticide residue in non-organic agricultural product, which would raise their purchase intention of organic agricultural product.Design/methodology/approachThe present study conducted an empirical investigation in Taiwan by recruiting 527 Taiwanese participants using the convenience sampling procedure. The current research performed structural equation modeling analysis and used LISREL software to report the analytical results.FindingsIndividuals with health consciousness may perceive a high-level risk of non-organic agricultural product, which would raise individuals' fear perception to the harm of pesticide residue. Fear perception will increase individual's purchase intention of organic agricultural product. Results can help industry practitioners benefit from the results by enabling them to develop their advertising strategy for organic food.Originality/valueResults can help industry practitioners benefit from the results by enabling them to develop their advertising strategy for organic food.

This study develops a new hybrid model by integrating empirical mode decomposition (EMD) and support vector regression (SVR) for tourist arrivals forecasting. The proposed approach first uses EMD, which can adaptively decompose the complicated raw data into a finite set of intrinsic mode functions (IMFs) and a residue. After identifying the IMF components and residue, they are then modeled and forecasted using SVR. The final forecasting value can be obtained by the sum of these prediction results. Real data sets of international tourist arrivals to Taiwan were used. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches.