• Information Systems Review
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• v.14 no.3
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• pp.53-74
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• 2012

IT outsourcing (ITO) is an integration of two firms-external vendor(s) and a client firm-IT resources by contract. According to resource-based view(RBV), three different resources-ITO vendor's resource, client firm's resource, and the relationship resource between two firms- may have an impact on ITO performance. However, there have been few previous studies considering all three IT resources simultaneously. There have been also few empirical studies in ITO context, which test Bharadwaj (2000)'s findings: 1) IT resources can be divided into tangible IT asset and intangible IT capability, and 2) only IT capability has an impact on the IT performance. Therefore we examined whether, in ITO context, all three different resources have a significant impact on ITO performance. Adopting the findings of previous IT studies, we also divided IT resource into IT asset and IT capability. To achieve this research objective, we analyzed 62 ITO cases of 45 companies being listed in Korean top 100 companies for recent 3 years. Also, we analyzed the data with the Partial Least Squares method. The results of this research lead to the following conclusions: First, only when partnership is high, ITO vendors' resource can have an influence on ITO performance. Second, only client firm's IT capability, not IT asset, is directly related to the ITO success. Third, a firm's IT capability can increase the partnership. Therefore, we concluded that 1) RBV is also an useful theory in ITO context, 2) Bharadwaj(2000)'s suggestion is valid in ITO context as well, and 3) the relationship resource is also important in ITO.

• Nuclear Engineering and Technology
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• v.28 no.2
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• pp.118-128
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• 1996

The lattice parameters of stoichiometric $UO_2$ and $U_{1-y}Er_{y}O_2$ in the range of y=0.01 to y =0.33 were determined with use of X-ray diffraction data. Oxygen potentials have been measured by means of a thermogravimetric method in the range of 1200~$1500^{\circ}C$ and $10^{-14}$ $\leq$ $Po_2$ $\leq$ $10^{-3}$ for pure $UO_2$ and $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions with y=0.02, y=0.06 and y=0.20, respectively. Their oxygen partial pressures were maintained by controlling $CO_2$/CO mixture atmosphere, and the $Po_2$ values corresponding to x of $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions were measured with an electrolyte oxygen sensor. The lattice parameter decreases linearly with an increase in the erbium content. The change of the lattice parameter can be expressed in a linear equation of y as a($\AA$) =5.4695-0.220y for 0 $\leq$y$\leq$0.33. The experimental coefficient of y -0.220 in $U_{1-y}Er_{y}O_2$ was an intermediate value between the calculated values -0.273 and -0.156 in the case of $U^{5+}$ and $U^{6+}$, respectively. The (equation omitted) has been found to undergo abrupt increase in the range of -360 to -270 kJ/mole for y=0.06 and -320 to -220 H/mole for y=0.20, respectively, in the temperature range of 1200-$1500^{\circ}C$. (equation omitted) increases with erbium content, but the effect of the dopant for x =0.01 is less significant than that for stoichiometry. The oxygen potentials for $UO_2$ and $U_{0.98}Er_{0.02}O_{2+x}$ can be approximately represented by the $U^{5+}$/$U^{4+}$ model but those for y$\geq$ 0.06 in $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions cannot be interpreted by the mean uranium valence model.

• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Food Science and Technology
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• v.40 no.6
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• pp.603-610
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• 2008

The principal objective of current study was to evaluate the potential of near infrared reflectance spectroscopy (NIRS) as a non-destructive method for the prediction of the amylose and protein contents of un-hulled and brown rice in broad-based calibration models. The average amylose and protein content of 75 rice accessions were 20.3% and 7.1%, respectively. Additionally, the range of amylose and protein content were 16.6-24.5% and 3.8-9.3%, respectively. In total, 79 rice germplasms representing a wide range of chemical characteristics, variable physical properties, and origins were scanned via NIRS for calibration and validation equations. The un-hulled and brown rice samples evidenced distinctly different patterns in a wavelength range from 1,440 nm to 2,400 nm in the original NIR spectra. The optimal performance calibration model could be obtained by MPLS (modified partial least squares) using the first derivative method (1:4:4:1) for un-hulled rice and the second derivative method (2:4:4:1) for brown rice. The correlation coefficients $(r^2)$ and standard error of calibration (SEC) of protein and amylose contents for the un-hulled rice were 0.86, 2.48, and 0.84, 1.13, respectively. The $r^2$ and SEC of protein and amylose content for brown rice were 0.95, 1.09 and 0.94, 0.42, respectively. The results of this study suggest that the NIRS technique could be utilized as a routine procedure for the quantification of protein and amylose contents in large accessions of un-hulled rice germplasms.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.258-263
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• 2019

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R²) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R² and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.217-223
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• 2008

Amylose and protein contents are important traits determining the edible quality of rice, especially in East Asian countries. Near-Infrared Reflectance Spectroscopy (NIRS) has become a powerful tool for rapid and nondestructive quantification of natural compounds in agricultural products. To test the practically of using NIRS for estimation of brown rice amylose and protein contents, the spectral reflectances ($400{\sim}2500\;nm$) of total 9,483 accessions of rice germplasm in Rural development Administration (RDA) Genebank ere obtained and compared to chemically determined amylose and protein content. The protein content of tested 119 accessions ranged from 6.5 to 8.0% and 25 accessions exhibited protein contents between 8.5 to 9.5%. In case of amylose content, all tested accessions ranged from 18.1 to 21.7% and the grade from 18.1 to 19.9% includes most number of accessions as 152 and 4 accessions exhibited amylose content between 20.5 to 21.7%. The optimal performance calibration model could be obtained from original spectra of brown rice using MPLS (Modified Partial Least Squares) with the correlation coefficients ($r_2$) for amylose and protein content were 0.865 and 0.786, respectively. The standard errors of calibration (SEC) exhibited good statistic values: 2.078 and 0.442 for amylose and protein contents, respectively. All these results suggest that NIR spectroscopy may serve as reputable and rapid method for quantification of brown rice protein and amylose contents in large numbers of rice germplasm.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.3
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• pp.301-308
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.73-91
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• 2013

Advertising market has been facing new challenges due to dramatic change in advertising channels and the advent of innovative media such as mobile devices. Recent research related to mobile devices is mainly focused on the fact that mobile devices could identify users'physical location in real-time, and this sheds light on how location-based technology is utilized to achieve competitive advantage in advertising market. With the introduction of smartphone, the functionality of smartphone has become much more diverse and context awareness is one of the areas that require further study. This work analyses the influence of context awareness value resulted from the transformation of advertising channel in mobile communication market, and our research result reflects recent trend in advertising market environment which is not considered in previous studies. Many constructs has intensively been studied in the context of advertising channel in traditional marketing environment, and entertainment, irritation and information are considered to be the most widely accepted variables that has positive relationship with advertising value. Also, in smartphone advertisement, four main dimensions of context awareness value are recognized: identification, activity, timing and location. In this study, we assume that these four constructs has positive relationship with context awareness value. Finally, we propose that advertising value and context awareness value positively influence smartphone advertising attitude. Partial Least Squares (PLS) structural model is used in our theoretical research model to test proposed hypotheses. A well designed survey is conducted for college students in Korea, and reliability, convergent validity and discriminant validity of constructs and measurement indicators are carefully evaluated and the results show that reliability and validity are confirmed according to predefined statistical criteria. Goodness-of-fit of our research model is also supported. In summary, the results collectively suggest good measurement properties for the proposed research model. The research outcomes are as follows. First, information has positive impact on advertising value while entertainment and irritation have no significant impact. Information, entertainment and irritation together account for 38.8% of advertising value. Second, along with the change in advertising market due to the advent of smartphone, activity, timing and location have positive impact on context awareness value while identification has no significant impact. In addition, identification, activity, location and time together account for 46.3% of context awareness value. Third, advertising value and context awareness value both positively influence smartphone advertising attitude, and these two constructs explain 31.7% of the variability of smartphone advertising attitude. The theoretical implication of our research is as follows. First, the influence of entertainment and irritation is reduced which are known to be crucial factors according to previous studies related to advertising value, while the influence of information is increased. It indicates that smartphone users are not likely interested in entertaining effect of smartphone advertisement, and are insensitive to the inconvenience due to smartphone advertisement. Second, in today' ubiquitous computing environment, it is effective to provide differentiated advertising service by utilizing smartphone users'context awareness values such as identification, activity, timing and location in order to achieve competitive business advantage in advertising market. For practical implications, enterprises should provide valuable and useful information that might attract smartphone users by adopting differentiation strategy as smartphone users are sensitive to the information provided via smartphone. Also enterprises not only provide useful information but also recognize and utilize smarphone users' unique characteristics and behaviors by increasing context awareness values. In summary, our result implies that smartphone advertisement should be optimized by considering the needed information of smartphone users in order to maximize advertisement effect.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.1-28
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• 2013

Given that smartphone-based social network services (SNS), such as KakaoStory is now being widely used as a way for people to connect and communicate with each other, this study examines key factors leading to the continued use of SNS. People have been using PC-based SNS, such as Cyworld, for years are now using smartphone-based SNS, such as KakaoStory. KakaoStory developed by KakaoTalk has rapidly grown up as the largest smartphone-based SNS in Korea as smartphone penetration increases. It is more difficult for firms to maintain their current users over time in that alternative SNSs satisfying people's new needs are constantly emerging and evolving. In this sense, one of the most challenging issues for SNS firms is how to retain their current users in the long run. However, there are few empirical studies on this issue. Applying the IS continuance model proposed by Bhattacherjee [2001], this study explores key determinants of users' smartphone-based SNS continuance intention. The model suggests that perceived usefulness and user satisfaction are the key determinants of IS continuance intention. However, the model includes only the utilitarian value that users can obtain through the use of smartphone-based SNS, by considering perceived usefulness. Therefore, the study attempts to extend the IS continuance model by considering hedonic and social values simultaneously. More specifically, we consider subjective norms as social value that are proposed by the theory of reasoned action and the theory of planned behavior. We also consider perceived enjoyment as hedonic value that is emphasized as a key factor influencing users' behavior intention and actual behavior, particularly in the context of hedonic IS use. By considering the three values in our model simultaneously, we could offer a deeper understanding of smartphone-based SNS continuance. That is, this study could offer an explanation of how each value is associated with user satisfaction and SNS continuance intention. The customer value-satisfaction-loyalty model can strengthen the assertion that smartphone-based SNS continuance intention is determined by various different types of customer values, such as utilitarian, hedonic, and social ones. Moreover, the model provides a theoretical basis for the assertion that customer values lead to increased loyalty via customer satisfaction. In this regard, we theorize that SNS continuance intention is influenced by users' various values, namely perceived usefulness, perceived enjoyment, and subjective norms, via user satisfaction. To test the proposed research model and hypotheses, we conducted a partial least squares analysis using a total of 253 data collected on the users of smartphone-based SNS (i.e., KakaoStory). The key findings are as follows: First, it has been found that SNS continuance intention considerably depends on user satisfaction. Second, user satisfaction is determined by confirmation, perceived usefulness, and perceived enjoyment. Third, concerning the effects of the three values on SNS continuance intention, only perceived enjoyment regarded as hedonic value was statistically significant. That is, perceived usefulness considered as utilitarian value and subjective norms considered as social value had no effect on SNS continuance intention. Finally, our results indicate that confirmation increases perceived usefulness, perceived enjoyment, and user satisfaction. The results reconfirm the effectiveness of IS continuance model in predicting smartphone-based SNS continuance intention. Moreover, the results demonstrate that perceived enjoyment reflecting hedonic value is the most important predictor of SNS continuance intention. Therefore, it is imperative for firms to meet SNS users' hedonic value to retain them in the long run. Meanwhile, we could not find any empirical evidence to support the assertion that subjective norms are associated with user satisfaction and SNS continuance intention. The results lead us to conclude that when users have enough direct experience in SNS use, subjective norms have no effect on SNS continuance intention. Discussions and implications of the results are provided.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.1
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• pp.50-57
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• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.