Critical Factors Influencing Consumer Online Purchase Intention for Cosmetics and Personal Care Products in Vietnam
-
- The Journal of Asian Finance, Economics and Business
- /
- v.8 no.9
- /
- pp.131-141
- /
- 2021
With the rapid development of the Internet, online shopping has grown so fast that almost any good or service can be sold online today. The popularity and rapid growth of e-commerce signal a huge market opportunity for e-retailers. From the organizational perspective, it is necessary to evaluate and explore what drives customers to buy their products or to use their services. This study, therefore, aims to explain the online purchase intention and its determinants of Vietnamese customers for cosmetics and personal care products. Quantitative data was collected from an online survey conducted among university students, then was put into SPSS and AMOS for further analysis. Descriptive statistics, Cronbach's alpha test, exploratory factor analysis (EFA), regression analysis, and SEM were used to examine data from 434 valid answers. The research findings reveal that four factors positively affect purchase intention: Shopping enjoyment has the most significant impact, followed by trust, benefit, and website quality. On the other hand, perceived risk negatively influences purchase intention. While the cosmetics and personal care industry is thriving with a huge number of producers and consumers throughout the world, this study contributes to the existing literature in terms of capturing customers' needs and developing effective strategies to attract more online users.
With the proliferation of information technology communication and smart device, the environment where contents are produced and distributed is changing. People can use the contents quickly and easily, and the content industry is attracting attention and creating newly added value by converging with other industries. Accordingly, there is a need for content-related companies to understand the quality of content perceived by users in order to succeed in content, and to use it strategically. Therefore, this study aims to examine the relationship between content quality factors, user satisfaction, and recommendation intention through empirical analysis based on an IS success model. The analysis was conducted using smartPLS3.0 based on a total of 301 survey responses. As a result of the study, it was found that content usefulness, accessible system quality, convenient system quality, service provider trust, and interaction had a significant effect on user's satisfaction. Perceived privacy protection had a significant effect on user satisfaction and recommendation intention. Lastly, it was found that user satisfaction had a significant effect on recommendation intention. The results of this study are expected to provide useful information and therefore content companies can understand about the quality perceived by users.
This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.
As the popularity of logistics service outsourcing has been continuously growing, one of the most frequently addressed issues is how to estimate the effect of cost reduction for users and profit gains from outsourcing contract for providers. Many manufacturing companies agree that logistics outsourcing helps to reduce their operating costs, but some other companies still do not achieve the cost saving or do not trust logistics service providers, so they keep up in-house logistics operations. On the other hand, logistics service providers have low profitability from domestic business activities since they should meet the requirements for highly customized and diverse services from customers and unstable market situation. This study provided the status report dealing with logistics service contracts in Korea. From the focused group interview with logistics professionals, we found out that service scope, scale and cost structure are the most influential factors affecting logistics service contracts. Also, logistics service providers are more sensitive than users regarding value-added logistics service, contract duration and process. Moreover, this study also proposed the standard clauses for logistics service contract and types of logistics contract applied in current logistics fields. As a result, it is expected that these achievements from this study can be utilized to improve the satisfaction of logistics outsourcing in upgrading service quality and customer service level.
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as