• Journal of Distribution Science
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• v.13 no.11
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• pp.59-67
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• 2015

Purpose - The number of consumers adopting a lifestyle of health and sustainability has recently increased with the rise of trends in healthy living. The size of the organic agricultural product market has also increased given that these consumers prefer consuming environmentally friendly products that promote family health. However, awareness of organic agricultural products remains insufficient because of the characteristics of the Korean organic agriculture system, which only focuses on food safety inspection. The object of this research is to suggest a policy approach to increase understanding and to expand the purchasing of organic agricultural products by analyzing the influence of customer recognition of such products on their willingness to repurchase. Research design, data, and methodology - This study used binomial logistic regression analysis with the aim of explaining the effects of consumers' socio-demographic characteristics, their awareness of the equivalence arrangement for organic food and of the abolishment of low-pesticide agricultural product certification, and their viewing of negative broadcasts about organic agricultural products on their repurchase intention of such products. A questionnaire survey was conducted with 655 respondents who were in their 20s, lived either in Seoul or in its metropolitan area, and had purchased organic agricultural products. Result - From the results of the analysis, the majority of the respondents recognized organic agricultural products, but they found their prices to be expensive. The majority of the respondents were also aware of the certification system and the reliability of organic agricultural products. However, the results indicate that efforts need to be made to recover consumer trust as many respondents stated that their trust levels in these products were low. In general, those purchasing organic agricultural products were satisfied, but those answering "very satisfied" were not in the majority. Binomial logistic regression analysis results revealed that repurchase intention decreased as consumers viewed a greater number of negative broadcasts about these products. On the other hand, repurchase intention increased as they became more aware of the abolishment of low-pesticide certification. Repurchase intention also increased as income increased, as the number of family members decreased, and when a consumer was a member of a consumer organization. In addition, the older the consumers were who watched the TV programs, the smaller the number of family members that were aware of the abolishment of low-pesticide agricultural product certification and, the higher the income of the consumers aware of organic equivalence arrangement, the greater their repurchase intention. Conclusion - External stimuli, such as negative TV programs on organic agricultural products and the abolishment of the low-pesticide agricultural product certification, relevant social issues and systems, influence consumer repurchase intention. To that end, positive environmental and ecological broadcasting about organic agricultural products would contribute to an increase in purchasing. Additionally, this could be used for promotion and marketing plans as the results indicate that trust in organic agricultural products would cause a positive repurchasing effect.

• Knowledge Management Research
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• v.25 no.3
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• pp.231-251
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• 2024

For Airbnb, one of the most successful examples of the sharing economy, to continuously grow, it needs a steady supply of attractive listings. To this end, this study draws on existing research on Airbnb and signaling theory to examine the factors that influence the performance of Airbnb listings. From a dynamic perspective, we expect the importance of factors affecting listings' performance to differ between new and existing listings. Analyzing Airbnb data from the 10 most active US cities using negative binomial regression, we find that dynamic attributes that require a time investment have a stronger impact on existing listings, while static attributes that require less of a time investment have a similar effect on both types. The host's membership duration and number of listings were expected to have positive effects, but showed negative effects. While the instant booking policy increased users' convenience, some hosts were reluctant to use it. The results of the analysis suggest that the importance of the factors varies depending on the type of Airbnb listing (new vs. existing), and the need for differentiated policies and complementary measures based on the type of listing is necessary in a practical perspective.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.369-379
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• 2014

The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.331-344
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• 2014

This study attempts to explore the difference of innovation sources between high-tech industry and mid-tech industry through patent analysis. After extracting 119 corporates, commonly surveyed in 2007 HCCP(Human Capital Corporate Panel) and 2005~2006 Korea Innovation Survey, their patents applied for the Korean Intellectual Property Office in 2007~2012 are analysed mainly through negative binomial regression model. Analytical results shows that external information source could be opposite effects to technological innovation depending on technological level and industrial characteristics. The current results are still bounded in the statistical significance, mainly due to the limited observations and information.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.371-385
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• 1995

The yield of semiconductor chips is dependent not only on the average defect density but also on the distribution of defects over a wafer. The distribution of defects leads to consider a cluster index. This paper briefly reviews the existing yield prediction models ad proposes a new cluster index, which utilizes the information about the defect location on a wafer in terms of the coefficient of variation. An extensive simulation is performed under a variety of defect distributions and a yield prediction model is derived through the regression analysis to relate the yield with the proposed cluster index and the average number of defects per chip. The performance of the proposed simulation-based yield prediction model is compared with that of the well-known negative binomial model.

• Asian Journal of Innovation and Policy
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• v.1 no.1
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• pp.50-70
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• 2012

This paper analyzes the factors that determine technology innovation in Korean manufacturing firms, focusing on the role of intra-firm knowledge diffusion and market strategy in patent production. For empirical analysis, zero-inflated negative binomial (ZINB) regression is applied to the 2009 Human Capital Corporate Panel data. The empirical findings confirm the critical role of intra-firm knowledge-sharing processes in technology innovation; firms with a market-leading strategy oriented to new product development also tend to be prolific in patent production.

• 한국벤처창업학회:학술대회논문집
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• 2022.04a
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• pp.129-134
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• 2022

This research investigates the effect of SMEs' digital transformation (DX) efforts in O2O platforms on customer engagement. Among the three DX stages (i.e., digitization, digitalization, and DX), this study focuses on digitalization, a practically viable DX phase for SMEs using O2O platforms. This study categorizes the DX efforts of SMEs into three: information diversity, responsiveness to customers, and the degree of functional use. To analyze the impact of these efforts on customer engagement, we conduct the zero-inflated negative binomial regression using the dataset of Naver Smartplace, one of the representative O2O platforms in South Korea. The analysis result confirms that all three factors have positive impacts on customer engagement. Therefore, this study demonstrates that employing O2O platforms can be an effective strategy for SMEs lacking resources to achieve successful DX.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.580-600
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• 2022

The purpose of this study is to investigates the effect of SMEs' digital transformation efforts in O2O platforms on customer engagement. This study focuses on digitalization, which is a practically viable phase for SMEs using O2O platforms among the three digital transformation stages (digitization, digitalization, and digital transformation). This study specifically categorizes digital transformation efforts into three categories: information diversity, responsiveness to customers, and the degree of functional use. To analyze the impact of these efforts on customer engagement, we conducted a zero-inflated negative binomial regression using the dataset provided by Naver SmartPlace, a representative O2O platform in South Korea. The results present that the positive relationship between these aforementioned factors and customer engagement. Thus, this study demonstrates that utilizing O2O platforms can be an effective strategy for SMEs that lack the resources to achieve a successful digital transformation.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.13-19
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• 2011

The purpose of this study is to analyze the characteristics of traffic accidents and to develop the models by day and night-time in the case of the arterial link sections. In pursuing the above, this study uses the 224 accident data occurred at the 24 arterial link sections in Cheongju. The main results analyzed are as follows. First, it was analyzed that the number of accidents during day was more than night, but the accidents rate during night was higher than day. Second, four models which were all statistically significant were developed. Finally, the differences between the day and night models were comparatively analyzed using independent variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2609-2626
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• 2023

The hotel industry is an example of experiential services. As consumers cannot fully evaluate the online review content and quality of their services before booking, they must rely on several online reviews to reduce their perceived risks. However, individuals face information overload owing to the explosion of online reviews. Therefore, consumer cognitive fluency is an individual's subjective experience of the difficulty in processing information. Information complexity influences the receiver's attitude, behavior, and purchase decisions. Individuals who cannot process complex information rely on the peripheral route, whereas those who can process more information prefer the central route. This study further discusses the influence of the complexity of review information on hotel ratings using online attraction review data retrieved from TripAdvisor.com. This study conducts a two-level empirical analysis to explore the factors that affect review value. First, in the Peripheral Route model, we introduce a negative binomial regression model to examine the impact of intuitive and straightforward information on hotel ratings. In the Central Route model, we use a Tobit regression model with expert reviews as moderator variables to analyze the impact of complex information on hotel ratings. According to the analysis, five-star and budget hotels have different effects on hotel ratings. These findings have immediate implications for hotel managers in terms of better identifying potentially valuable reviews.