• Asian Journal of Business Environment
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• v.12 no.4
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• pp.13-24
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• 2022

Purpose: Motivated by an expedited growth and distribution of Online Food-Delivery (OFD) services, especially during the recent Covid-19 pandemic, this research aims to explore 1) how consumers' sustainability considerations are associated with satisfaction with the services via opt-out cutlery options and 2) the role of the pandemic in the relationships between sustainability considerations, attitudes toward opt-out cutlery options, and satisfaction with the OFD services. Data and Methodology: An analysis of survey data using 434 consumers in the United States recruited from Amazon M-Turk was conducted using structural equation modeling. Results: Findings suggest that consumers' environmental, health, and ethical considerations are positively related to their attitudes toward opt-out cutlery options. Furthermore, attitudes toward opt-out cutlery options are positively related to satisfaction with the OFD services only when they feel connected with the environment, driven by perceived threats of an infectious disease (i.e. Covid-19). Conclusion: The study findings provide new insights to managers in the OFD service industry on how to promote sustainable consumption during the pandemic.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.167-175
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• 2020

The purpose of this paper is to provide directional and policy references to develop a higher level of service quality and consumer-oriented e-commerce platform. This paper has established a model of consumer behavior of Chinese fresh agricultural e-commerce using customer satisfaction theory and cognitive value theory, and used survey and SPS23.0 to verify hypothesis. Studies have shown that when consumers consume fresh agricultural products, product quality, logistics and distribution service quality, interactive quality of e-commerce platform, and product price and cognitive value have a positive effect on consumer behavior. This study is meaningful in the study of consumer behavior of fresh agricultural e-commerce, and in the case of fresh agricultural e-commerce companies, consumer behavior can be understood. In the model constructed in this paper, the relationship between each influencing factor and consumer behavior is considered comprehensively, but the possible relationship between fine molecular factors has not been studied and analyzed. In the future learning process, it is necessary to make clear the characteristics and particularity of the industry, think about its influencing factors comprehensively and make in-depth analysis.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.287-295
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• 2022

With the dramatic increase in mobile usage, more and more businesses see the potential of m-commerce. This study focuses on a subcategory of m-commerce, a mobile shopping application. To understand the purchase intention via m-commerce applications, this study is aimed to identify the main factors that are related to the applications and explore the influence of these factors on consumers' mobile shopping intention. This study uses quantitative research methods and selects Vietnam as its case study. The survey responses of 450 Vietnamese mobile shoppers were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicated that online reviews, e-service quality, and information quality are significant predictors of behavior intention, and perceived risk negatively influences consumer online purchase intention via the applications. The content enriches the combined research of detailed and possible models with quality dimensions and risk perception. Practitioners such as e-retailers and developers can enhance the quality of applications and determine strategies to reach potential users and maximize revenue. M-commerce providers should pay adequate attention to credible and influential online reviews since mobile shoppers heavily rely on reading reviews before buying a product.

• Journal of Distribution Science
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• v.21 no.12
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• pp.91-102
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• 2023

Purpose: This study analyzed the structural relationship between interaction quality and relationship satisfaction, towards providing managerial implications for effective relationship management in the B2B market. Research design, data and methodology: The following survey was conducted only if respondents had used the airline's B2C more than twice. A total of 398 copies were collected and empirical analysis was conducted using AMOS 18.0 and PASW 18.0. Results: The flexibility, quickness, and fairness that make up the interaction quality in airline B2C have been shown to have a significant impact on trust, relationship performance and relationship satisfaction. Conclusions: Usefulness, quickness, and fairness, which are sub-variables of airline B2C mutual quality, have a positive effect on trust. In addition, trust was found to have a positive effect on relationship performance and relationship satisfaction. We draw implications for the importance of interaction quality in order to strengthen and sustain relationships with users in the airline B2C distribution market. In addition, in order to build meaningful relationship performance and relationship satisfaction, interaction quality and trust level should be examined first, and interaction quality improvement should be the top goal.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.265-268
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• 2001

As growing the electronic commerce there are significant changes in the products/services catalog into the on-line environment. Advertent of e-catalog business opportunity for their own product/services enlarges the market volume and there are diverse methods for the presentation of its product/services. A method for the presentation of product/services features one uses identification and classification system. This study constructs a classification system and database layout for the product/services classification system as a part of e-catalog system. We consider the specific method for the GDAS-based dataset and UNSPSC classification system in the distribution industry.

• Journal of Distribution Research
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• v.6 no.2
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• pp.91-108
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• 2002

While a lot of research has been done in the area of the B2C e-commerce, heavily on the Internet shopping, a study on the B2B e-commerce has not been improved enough to be competitive for its importance and practical use. We have studied to build an effective database, attempting to complement and further replace in the long term the off-line distribution channel which has yet to be fully evolve with respect to effectiveness and efficiency. For the building of the effective database, we have gathered information from the related institutions and characterized a fishery channel, and surveyed existing literatures and consumers for deriving factors affecting their purchase intentions and satisfactions. Based upon this survey we constructed the entity-relationship diagram(ERD) which possibly provided some perspectives for the future application. This study focuses on the improvement in the distributional efficiency not only minimizing the intra-organizational conflict and resistance but also maximizing the role function of each party of the organization.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.19-28
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• 2021

The present study aims to evolve the value cycle of e-business through value co-creation during the Coronavirus pandemic. The population of the study is experts consisting of university professors in the fields of marketing management, e-commerce, and managers of organizations and companies in Iran. Using the snowball sampling method, 50 of them were selected as the sample. This study employs the factor analysis method and structural equation modeling (SEM) approach for identification of the factors. The findings of this study reveal that 10 factors affect the evolution of the value chain into the value cycle, including customer relationship management, e-literacy, value co-creation, e-readiness, and integrated value creation, the logic of service dominance, shared value creation, virtual culture, e-trust, and network economics. Despite the difficulties that COVID-19 has created for businesses worldwide, the evolution of the e-business value cycle through value co-creation in the Coronavirus pandemic can be considered as a positive aspect of the pandemic. In fact, with more pandemics and more customers turning to e-businesses due to the co-creation of customer value, e-businesses can cover their weaknesses and improve their strengths by engaging customers and receiving their feedback, thus transforming their value chain into the value cycle.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.261-270
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• 2019

The study examines the relationship between perceived mental benefits, online trust, and personal information disclosure when shopping online in Vietnam. The e-commerce market has been booming in Vietnam since 2015. The number of online transactions and e-commerce sites has increased steadily in recent years. However, the number of online sales in Vietnam is still not high, and consumers are still limited in buying from websites when they have to provide too much information during and after the shopping process. The mix-method is used to ensure the scientific nature of the study. Qualitative research method (phenomenological research) along with the quantitative research method (survey) are applied to meet the research objectives. The data in the study was collected through the group discussion with eight experts and the survey with 917 respondents. Data processing result via SmartPLS software indicate the positive relationships between the factors in the research. The perceived mental benefits have the most potent influence on the online trust of Vietnamese customers; at the same time, both the perceived mental benefits and online trust affect customers personal information disclosure in electronic commerce. Some managerial implications relating increasing the perceived mental benefits, and customers' online trust are proposed for online businesses.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.131-151
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• 2009

Natural interface is important to select and provide the services in ubiquitous smart space such as u-plant, u-distribution. Augmented Reality(AR) has recently begun to receive attention as a realization tool for natural interface. AR provides virtual object on real environment and it differs from virtual reality. When AR is used, it has advantage to provide information intuitively and collaboratively. However AR is rarely used in e-commerce domain of ubiquitous smart space, and it has limitation which predefined information and services provide in a static manner. Hence, the purpose of this paper is to propose a methodology of AR based e-commerce which provides personalized association service by considering user's dynamic context. To do so, association algorithm is developed based on Rescorla-Wagner model and Galton's free association test.