• Journal of Digital Convergence
• /
• v.19 no.1
• /
• pp.355-360
• /
• 2021

In this study, the factors of confirmation bias that may occur in the initial user experience stage were analyzed using a honeycomb model by deriving user experience factors for each factor. In the initial user experience stage, confirmation bias occurs in the impression stage. At the processing stage of memory, sensory memory, working memory, and long-term memory, which stores and retrieves selective memory, were closely related. Confirmation bias was classified into visibility, correlation, memory, clarity, and universality in the usability part, and satisfaction, joy, and dissatisfaction were derived as emotional factors. As a result of the analysis with the honeycomb model, visuality, clarity, universality in the usability factor, and joy in the emotional factor had little effect on the confirmation bias, and satisfaction and dissatisfaction were analyzed as the main factors of the confirmation bias in the correlation, memory and emotional factors. This study is meaningful in that it can be usefully used as a reference material for companies that customize design patterns for the factor of confirmation bias.

• The Journal of Information Systems
• /
• v.23 no.3
• /
• pp.143-164
• /
• 2014

This study draws on status quo bias theoretic perspective as a meta-theoretic lens to explain why individuals have resists to adopt smart devices for their tasks. More specifically, we attempted to examine the relationships among user's resist, perceived usefulness and individual productivity in the smart work context. By employing the status quo bias theoretic perspective, we develop and test our research model by using a survey data from 235 individual users. We demonstrate that satisfaction on the current state influence users' resist, and also the users' resist is mediated by perceived usefulness on individual productivity. From the status quo bias view, this study presents an alternative meta-theoretical lens in order to understand individuals' resist in the smartwork context.

• The Journal of Information Systems
• /
• v.27 no.1
• /
• pp.133-151
• /
• 2018

Purpose Global fintech industry is proliferating. Although domestic investment in fintech service is also increasing fast, user acceptance of fintech payment service seems slower than expected. Previous fintech literature mainly focuses on overall characteristics and technical aspects of fintech including security issues and explores factors affecting the service adoption. This study aims to examine crucial factors affecting user's resistance intention to fintech payment service adoption. The research model was formulated based on innovation diffusion theory and status quo bias theory and validated empirically. Design/methodology/approach The proposed research model was empirically validated with 149 responses from college students who have used fintech payment service. The component-based SEM was employed for data analysis. Findings The significant findings are as follow. First, compatibility has significant negative effect and complexity, and perceived risk has a positive effect on user resistance. Second, institutional trust does not show a significant effect on user resistance but has an indirect effect through the mediation of trust in service provider. Finally, trust in service provider shows the significant negative effect on user resistance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3798-3814
• /
• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• 한국경영정보학회:학술대회논문집
• /
• 2007.06a
• /
• pp.539-544
• /
• 2007

People tend to resist changing their software even alternatives are better then the current one. This study examines the resistance to change in the use of software from the switching costs perspective based on status quo bias theory. For this study, we select Web Browsers as software. Based on the classification of switching costs into three groups (psychological, procedural, and loss), this study identifies six types of switching costs (uncertainty, commitment, learning, setup, lost performance, and sunk costs). This study tests the effects of six switching costs on user resistance to change based on the survey of 204 web browser users. The results indicate that lost performance costs and emotional costs have significant effects on user resistance to change. This research contributes towards understanding of switching costs and the effects on user resistance to change. This study also offers suggestions to software vendors for retaining their users and to organizations for managing user resistance in switching and adopting software.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.3
• /
• pp.109-122
• /
• 2023

As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users' abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.161-161
• /
• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.2
• /
• pp.415-421
• /
• 2009

TOA(time-of-arrival) and TDOA(time-difference-of-arrival) positioning techniques are commonly used in many radio-navigation systems. From the literature, it is known that the position estimate and error covariance matrix of TDOA obtained by GN(Gauss-Newton) method is exactly the same as that of TOA when the error source of the range measurement is only an IID white Gaussian noise. In case of geo-location and indoor positioning, however, multi-path or NLOS(non-line-of-sight) error is frequently appeared in range measurements. Though its occurrence is random, the multipath acts like a bias for a stationary user if it occurs. This paper presents the comparisons of error characteristics between TOA and TDOA positioning in presence of multi-path or NLOS error. It is analytically shown that the position estimate of TDOA is exactly the same as that of TOA even when bias errors are included in range measurements with different magnitudes. By computer simulation, position estimation error and error distribution are analyzed in presence of range bias errors.

• The Journal of Industrial Distribution & Business
• /
• v.13 no.4
• /
• pp.35-49
• /
• 2022

Purpose - According to the recently changing consumer smart environment and consumer decision-making process, this study investigates the structural relationship between electronic(online) WOM information characteristics and brand community experience value types on specific brand reliability and brand transformation. In particular, the characteristics of word of mouth information and the experience value of brand community users were divided into detailed fac tors and approached. Methodology - In order to proceed with this study, we review previous studies and setting hypotheses. The hypothesis was verified through a survey that was conducted for the consumers with online consumption activities in less than six months. With reference to previous studies, operational definition was made for the questionnaire design. In order to verify the hypothesis, 282 people were statistically analyzed through the survey This data were used for AMOS for confirm hypothesis established. Results - eWOM information characteristics were classified into usefulness, timeliness and un-bias, and online community experience values were classified into interaction, playfulness, and virtuality. In addition, it is to investigate the relationship between the brand reliability and user's experience value in brad community. The main results are as follows. The first result was that usefulness and un-bias, which are the eWOM information characteristics had a positive effect on forming brand reliability. However, the factor of timeliness did not affect brand reliability. Second, in terms of user experience value and brand reliability in the brand community. It was fo und that experience values such as interaction, playfulness, and vituality all had a positive influence on brand reliability. Third, it was found that brand reliability has a positive influence on the on-line conversion activity of users. Conclusions - Through this study, the field of online consumer behavior research is expanding, and this study suggested that careful management is necessary according to the type or characteristics of eWOM information. Additionally, it presents the importance of the user's empirical value in the brand community influencing brand attitude and reliability. In practice, the implementation of the marketing communication mix in digital marketing has recently been underway to enhance the conversion behavior of users. At this level, it also reveals the preceding factors that increase user conversion behavior.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.125-134
• /
• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.