• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.524-539
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• 2022

With the advent of generative artificial intelligence technology, it became possible to create a virtual human, and produce a lecture video only with textual information. It is expected that the virtual human will enhance the efficient production of educational contents and the student's entertainment experience and satisfaction. However, there have been still few studies that have demonstrated the process of how virtual human technology reaches students' satisfaction. Therefore, the purpose of this study is to empirically examine whether the human likeness, which is the main characteristic of a virtual human based on Uncanny Valley theory, affects human experience and satisfaction. In particular, human likeness of the Uncanny Valley theory was subdivided into human likeness in the visual and verbal dimensions, and the process of reaching satisfaction was understood based on the experience economy model. In particular, human similarity in Uncanny Valley theory was classified as similarity in the visual and language levels, and the process of reaching satisfaction based on the experiential economic model was analyzed with a partial least squares structure model equation (PLS-SEM). The survey was conducted online for a panel of office workers at a specialized research institution in China. The results indicate that both the visual and verbal human likeness had a positive effect on experience economy factors (education, entertainment, esthetic, escape), and then these experiential factors had a significant effect on satisfaction. The results also provide some suggestions to consider when designing educational contents by virtual human.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.17-39
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• 2024

The purpose of this study was to analyze the effect of the government's R&D support and the use of stock options by venture companies on the innovation of venture companies, that is, innovation capabilities and innovation performance. An empirical analysis was conducted using the partial least squares structural equation modeling (PLS-SEM) method using the data from the detailed survey of venture companies conducted on domestic venture confirmation companies. As a result of the analysis, it was found that the benefit of government R&D support had a positive (+) effect on strengthening the innovation capabilities of venture companies, and R&D support also had a positive (+) effect on the innovation performance of venture companies. Next, it was found that the use of stock options by venture companies had a positive (+) effect on the reinforcement of the innovation capabilities of companies and a positive (+) effect on the innovation performance of venture companies. In addition, it was found that the innovation capabilities of venture companies significantly mediate between the government's R&D support and the use of stock options by venture companies and the innovation performance of companies. These analysis results show that the government's R&D support and the use of stock option systems can play a meaningful role in the innovation of venture companies, and also show that the innovation capabilities of venture companies have an important meaning in the process of innovation. Therefore, it is necessary to continue the stance of R&D support for ventures and at the same time to introduce multi-faceted policy measures to support corporate capacity building, and legal and institutional maintenance and policy support to revitalize the stock option system need to be continuously provided.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.33-59
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• 2008

This paper aims to analyse the preference for German farm tourism among the German population. For this reason, we conducted an empirical study in Germany during summer 2007 and we applieda structural equation model based on partial leasts quares(PLS) to analyse the data. In the following chapters we will introduce the literature review and our conceptual frame work. We will then outline the procedures we adopted and the results of the empirical analysis. In the final part so me conclusions will be presented and a discussion will follow in order to draw the future directions of our research. According to our hypotheses, the possibility that agri-tourism enters in the evoked set of an individual is higher: H1: The higher the information degree about it. H2: The lower the influence of the social stimuli. H3: The higher the physical exposure to it (experience). H4: The higher the wellness image of agri-tourism. H5: The higher the traditional image of agri-tourism. H6: The higher the exciting image of agri-tourism. H7: The higher the perceived value for money. Our further hypotheses affirm that the possibility that agri-tourism enters in the evoked set of an individual is higher: H8: The lower the perceived risk. H9: The higher the motive to enjoy a holiday in the nature. H10: The higher the motive to enjoy a sport holiday. H11: The lower the motive to have an organized holiday. H12: The lower the motive to have a holiday abroad. H13: The lower the motive of action and night life. H14: The higher the motive to spend a holiday with the family. H15: The lower the motive to spend a city holiday. Finally, our model has some socio-demographics data. As we mentioned before, German agri-tourism has traditionally been the travel destination of large-size families, with low-to-middle income. For that reason, our final hypothesises are the following: the possibility that agri-tourism enters in the evoked-set of an individual is higher: H16: The higher the number of family members. H17: The lower the family income. Since in this study we use a path model with a PLS approach, we are able to state some interrelations among the exogenous latent variables: H18: The motive of sport holiday has a positive influence towards nature motives. H19: The physical exposition to agri-tourism has a positive influence toward information. H20: The motive of family holiday has a negative influence toward the motive of action and night life. H21: Social stimuli have a positive influence towards individuals risk perceptions. H22: Social stimuli have negative influence towards experience. Data for this study were gathered via administrated questionnaires during the summer 2007 within the frame of an academic "marketing research" course. The corresponding t-values are assessed using the bootstrapping method with 500 re-samples. In our model 61% of the degree of appreciation of German agri-tourism (evoked set) is explained by five independent variables: value for money ($0.335^{{\ast}{\ast}{\ast}}$) (H7) experience ($0.267^{{\ast}{\ast}}$) (H3), exciting image ($0.204^{\ast}$) (H6) organisation ($-0.162^{\ast}$) (H11) and holiday abroad ($-0.156^{\ast}$) (H12). The variance explained ($R^2$) for the other endogenous variables are the following: nature 24.3%, information 14.1%, action holiday 13.8%, risk perception 5.8% and experience 2.4%. An overview can be inferred from table 5. The results also allow us to test each of the proposed hypotheses. With exception of organization and abroad, none of the others travel style factors (H9 to H15) seem to have any significant impact towards evoked set which leads to the rejection of the respective hypotheses. As expected, social stimuli have a significant influence on individuals' risk perception (H21 accepted), however neither the former nor the latter have a valuable impact on evoked set (rejection of H2 and H8). Besides, since the influence of social stimuli towards experience is not significant, also H22 has to be rejected. Experience influences information (H19 accepted) but the latter does not affect significantly the evoked set (H1 rejected). Both H4 as well as H5, referring respectively to the perceived images of German agri-tourism as a wellness destination and the traditional image of the German farm tourism have to be rejected. Finally, none of the demographic data included in the model explains significantly the variance of the factor evoked set. Therefore neither H16 nor H17 has been accepted. As far as the interrelation between sport and nature (H18) and family and action (H20) are concerned, the stated relationship among these variables has been statistically confirmed. Our path model based on partial least squares shows the factors influencing the preference for farm tourism in Germany. Among others value for money and experience are the most significant ones. Practical implications are discussed.

• Horticultural Science & Technology
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• v.33 no.3
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• pp.403-408
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• 2015

Color is one of the quality determining factors for pepper powder. To measure the color of pepper powder, several methods including high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), and ASTA-20 have been used. Among the methods, the ASTA-20 method is most widely used for color measurement of a large number of samples because of its simplicity and accuracy. However it requires time consuming preprocessing steps and generates chemical waste containing acetone. As an alternative, we developed a fast and simple method based on a visible/near infrared (Vis/NIR) hyperspectral method to measure the color of pepper powder. To evaluate correlation between the ASTA-20 and the visible/near infrared (Vis/NIR) hyperspectral methods, we first measured the color of a total of 488 pepper powder samples using the two methods. Then, a partial least squares (PLS) model was postulated using the color values of randomly selected 3 66 samples to predict ASTA values of unknown samples. When the ASTA values predicted by the PLS model were compared with those of the ASTA-20 method for 122 samples not used for model development, there was very high correlation between two methods ($R^2=0.88$) demonstrating reliability of Vis/NIR hyperspectral method. We believe that this simple and fast method is suitable for highthroughput screening of a large number of samples because this method does not require preprocessing steps required for the ASTA-20 method, and takes less than 30 min to measure the color of pepper powder.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.

• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.137-160
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• 2016

This study assesses the structure relationships between resource, product and process innovation capability, technology commercialization competence and performance of firms in Daedeok Innopolis. In particular, this study attempts to analyze technology commercialization competence that may be influenced by product and process capabilities. The development of the research model is based on the literature of resource-based view and the empirical studies of technology commercialization competence. The survey of 109 firms was conducted from January 5, 2015 through February 4, 2015. We investigate the role of a firm's technology commercialization competence in determining its performance by performing Partial Least Squares analysis. The results indicate that a firm's human resources and intangible resources lead to a higher level of its product and process innovation capabilities. This study, however, finds that a firm's tangible resources do not have significant effects on its product and process innovation capabilities. And the study finds evidence that firm's product and process capabilities have positive effects on its technology commercialization competence. The study also finds that a firm's technology commercialization competence is a driving force behind its performance, showing that its technology commercialization competence positively involves its performance. In addition the study finds that technology commercialization competence mediates the relationship between a firm's innovation capability and performance, indicating that the technology commercialization used as mediating variable positively affects its innovation performance.

• Journal of Distribution Science
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• v.14 no.2
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• pp.135-144
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• 2016

Purpose - The purpose of this paper is to empirically investigate the moderating effects of social capital on antecedents factors path of export performance in the materials and components SMEs(Small and Medium-sized Enterprises) of Busan and Kyungnam region. In case of materials and components SMEs, they are always trying to achieve business performance including export sales and market share, but it is difficult for them to increase performance due to the limitation of inner & tangible resources. Therefore intangible asset such as technology capability and its antecedents factors which are technology innovation and learning orientation are getting more important to SMEs. In addition, it is supposed that social capital such as local network including distribution channel in overseas market plays an essential role to enhance export performance. Accordingly, the main goal of this study is to find out the relationship between export performance and antecedents factors and the validity of social capital as a moderating valuable. Research design, data, and methodology - Technology innovation, learning orientation and technology capability have been used as antecedents factors for export performance and social capital such as network diversity and intensity has been used for moderating effects analysis. In order to select these valuables mentioned above, this study examined the existing researches on a basis of Resources Based View, Organizational Learning Theory and Social Capital theory. To achieve the objective of this paper, 7 hypotheses including the moderating effects have been proposed with 6 potential variables measured by 24 questions. The survey was carried out from December 2014 to March 2015 and 137 samples out of total 175 were selected for the analysis. PLS(Partial Least Squares) has been used for the methodology of empirical analysis for both antecedents factors path and moderating effects. Results - Research findings are as follows. First, technology innovation has a significant impact on learning orientation, learning orientation has a positive effect on the technology capability and technology capability also has a significant impact on export performance. Therefore 3 valuables are proved as antecedents factors of export performance. Second, the social capital(both network diversity and intensity) plays a moderating role with learning orientation to technology capability. However, there is no moderating effects between both of social capital and technology capability regarding export performance. Conclusions - According to path analysis results, it is suggested that the materials and components SMEs should raise technology innovation and learning orientation in order to improve technology capability and export performance. Meantime, the moderating effect analysis shows that SMEs should consider local network diversity and intensity along with learning orientation to add up technology capability. But local network diversity and intensity does not work systematically with technology capability for export performance and it means that SMEs should find the appropriate local partners for the purpose of establishing concrete distribution channels based on marketing perspective, not for improving technology capability.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1527-1527
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• 2001

Glucose, fructose and sucrose being the main sugars that can be found in natural fruit juice. Many instrumental methods, such as GC, LC, electrochemical or spectrometric methods provide information about both the total content of sugars and the specific concentration of each carbohydrate[1]. The simplicity of sample handling and measurement in the near IR(NIR) wavelength region, which allows the use of long pathlength, optical glass cells and optical fibers, makes NIR a good alternative for sugar determination [2]. In the present study, six varieties of persian grapes were harvested at intervals through august to october and analysed for sugars by NIR. The results were processed by principal component regression (PCR) and partial least squares (PLS) analysis. Sample juice was prepared by squeezing through gauze from crashed grape. This solution was treated by zinc ferrocyanide prior to analysis in order to eliminate colored compounds and all optically active nonsugar substances. For glucose and fructose the most characteristic wavelengths were 1456nm corresponding to the first harmonic O-H stretching and the second at 2062nm corresponding to O-H stretching and deformation; secondary characteristic combination bands were also seen at 2265 nm (O-H and C-C stretching) and at 2240 nm (C-H and C-C stretching). However these spectra were taken over a wavelength range from 1100-2500nm at room temperature of 25-$30^{\circ}C$. To test the accuracy of the described procedure, samples of six varieties of grape were analysed by the proposed NIR and a standard method[2]. Good agreement were found between these two sets of the results. To perform the recovery studies , samples of grape juices previously analysed by the proposed method, were spiked with known amounts of each individual sugars and then analysed again. Relative standard deviations varied from 1.4 to 1.8% for six independent measurements of individual and total sugar concentration. In the analysis of real and synthetic samples, precise and accurate results were obtained , providing accuracy errors lower than 1.9% in all cases. Average recoveries of ${97}{\pm}{4%}$ for total sugar and between ${95}{\pm}{5%}$ and ${99}{\pm}{2%}$ for sing1e sugars demonstrate the applicability of the methodology developed to the direct analysis of grape Juice.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1121-1121
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• 2001

A previous study (Berzaghi et al., 2001) evaluated the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural networks (ANN) on the prediction of the chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with reference values for moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected using 10 different Foss NIR Systems instruments, only some of which had been standardized to one master instrument. The aim of the present study was to evaluate the behaviour of these different calibration methods when predicting the same samples measured on different instruments. Twenty-two sealed samples of different kind of forages were measured in duplicate on seven instruments (one master and six slaves). Three sets of near infrared spectra (1100 to 2500nm) were created. The first set consisted of the spectra in their original form (unstandardized); the second set was created using a single sample standardization (Clone1); the third was created using a multiple sample procedure (Clone6). WinISI software (Infrasoft International Inc., Port Mathilda, PA, USA) was used to perform both types of standardization, Clone1 is just a photometric offset between a “master” instrument and the “slave” instrument. Clone6 modifies both the X-axis through a wavelength adjustment and the Y-axis through a simple regression wavelength by wavelength. The Clone1 procedure used one sample spectrally close to the centre of the population. The six samples used in Clone 6 were selected to cover the range of spectral variation in the sample set. The remaining fifteen samples were used to evaluate the performances of the different models. The predicted values for dry matter, protein and neutral detergent fibre from the master Instrument were considered as “reference Y values” when computing the statistics RMSEP, SEPC, R, Bias, Slope, mean GH (global Mahalanobis distance) and mean NH (neighbourhood Mahalanobis distance) for the 6 slave instruments. From the results we conclude that i) all the calibration techniques gave satisfactory results after standardization. Without standardization the predicted data from the slaves would have required slope and bias correction to produce acceptable statistics. ii) Standardization reduced the errors for all calibration methods and parameters tested, reducing not only systematic biases but also random errors. iii) Standardization removed slope effects that were significantly different from 1.0 in most of the cases. iv) Clone1 and Clone6 gave similar results except for NDF where Clone6 gave better RMSEP values than Clone1. v) GH and NH were reduced by half even with very large data sets including unstandardized spectra.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1510-1510
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• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.