• Journal of Digital Convergence
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• v.22 no.3
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• pp.1-7
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• 2024

This study provides an in-depth analysis of the impact of the deepening reform and opening-up policies announced at the 20th CPC Central Committee's Plenary Session, particularly focusing on the <Foreign Investment Law> and its effects on the tax decisions and organizational restructuring of Korean companies operating in China. Using a comprehensive literature review and policy analysis, the study compares the dual legal structure and tax differences before and after the implementation of the law, assessing how legal unification has influenced the organizational forms and tax strategies of Korean companies. The findings indicate that the <Foreign Investment Law> has played a crucial role in enhancing legal consistency and tax equity between foreign-invested enterprises and domestic enterprises, thereby enabling Korean companies to manage their operations in the Chinese market more stably and efficiently. Additionally, in the context of the ongoing U.S.-China trade conflict, the law's provision of national treatment and tax benefits has proven to be a significant factor in the survival strategy of Korean companies in China. Future research should focus on empirically examining the long-term effects of this law and its impact on actual corporate performance.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Journal of Korean Obstetrics and Gynecology
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• v.26 no.4
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• pp.30-47
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• 2013

Objectives: The purpose of the present study is to investigate anti-depressive effects of Samul-tanggahyangbuja (SGH) on ovariectomized and chronic mild stress (CMS) induced rats. Methods: Ovariectomized rats were exposed to CMS for 4 weeks. Changes of depression behavior were tested by using sucrose intake test (SIT), elevated plus maze (EPM), forced swimming test (FST) and Morris water maze test (MWMT) in rats until being orally medicated with SGH (100 or 400 mg/kg/day). In addition, the serum levels of corticosterone (CORT), IL-4, IL-$1{\beta}$ and changes of 5-HT in the brain were measured. Results: 1. SGH 400 mg/kg treated group (SGH 400) significantly increased amount of sucrose intake compared with the control group (p<0.05). 2. SGH 100 mg/kg treated group (SGH 100) and SGH 400 significantly increased the time spent in the open arms of the EPM compared with the control group (p<0.01). SGH 400 also significantly increased the number of crossing of the open and closed arms compared with the control group (p<0.05). 3. SGH significantly shortened the immobility time in FST compared with the control group (SGH 100 p<0.05, SGH 400 p<0.01). 4. SGH significantly increased performance of acquisition trials compared with the control group (p<0.05, on day 4, 5 of SGH 100 and 400). SGH 400 also significantly increased performance of retention trials compared with the control group (p<0.05). 5. The serum levels of corticosterone and IL-4 were not significantly different among the groups. There were no changes on the serum levels of corticosterone, IL-$1{\beta}$ and IL-4 after administration with SGH. 6. SGH 400 significantly increased the level of 5-HT in the hippocampus compared with the control group (p<0.05). SGH significantly increased the levels of 5-HT in the hypothalamus compared with the control group (SGH 100 p<0.05, SGH 400 p<0.01). Conclusions: These results suggest that SGH has the anti-depressive effect on ovariectomized rat and affect 5-HT system rather than hypothalamic-pituitary-adrenal (HPA) axis and immune system.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.581-596
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• 2010

The purposes of this study are to investigate and analyze the level of information and the state of digital divide of the disabled by surveying the demand for information and communication assistive devices, and to provide basic data for plans on the development and utilization of information and communication assistive devices. In order to understand the actual condition and the state of digital divide of persons with disabilities, the differences of possession and accessibility of information technology devices, usage ability, and utilization were analyzed according to the disability profile by using the T-test. The results show that there are significant differences (T=-2.510*) of possession and accessibility of information technology devices with respect to the disability profile, and that the disabled have lower possession and accessibility of devices than the non-disabled. Result of this study's demand forecast shows that about 28% of total respondents are currently using information and communication assistive devices, and a majority (67%) of them answered that the use of assistive devices lend great help to their lives. The proportion of those who have been supported by the government or related organizations with information and communication assistive devices were 36% of the total respondents, and those satisfied with the performance of the devices were 46% of the total responses. Meanwhile, only 36% of total users answered that the operation and use of functions of the devices was easy and convenient, responding that the difficulty of operating assistive devices was the greatest inconvenience. Moreover, the general requests of respondents in regards to the devices were stabilization of device performance, miniaturization of size, simplification of buttons, and reduction of weight.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.229-241
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• 2006

Global air temperature has risen by $0.6^{\circ}C$ over the last one hundred years due to increased atmospheric greenhouse gases. Moreover, this global warming trend is projected to continue in the future. This study was carried out to evaluate spatial variations in rice production areas by simulating rice-growth and development with projected high resolution climate data in Korea far 2011-2100, which was geospatially interpolated from the 25 km gridded data based on the IPCC SRES A2 emission scenario. Satellite remote sensing data were used to pinpoint the rice-growing areas, and corresponding climate data were aggregated to represent the official 'crop reporting county'. For the simulation experiment, we used a CERES-Rice model modified by introducing two equations to calculate the leaf appearance rate based on the effective temperature and existing leaf number and the final number of leaves based on day-length in the photoperiod sensitive phase of rice. We tested the performance of this model using data-sets obtained from transplanting dates and nitrogen fertilization rates experiments over three years (2002 to 2004). The simulation results showed a good performance of this model in heading date prediction [$R^2$=0.9586 for early (Odaebyeo), $R^2$=0.9681 for medium (Hwasungbyeo), and $R^2$=0.9477 for late (Dongjinbyeo) maturity cultivars]. A modified version of CERES-Rice was used to simulate the growth and development of three Japonica varieties, representing early, medium, and late maturity classes, to project crop status for climatological normal years between 2011 and 2100. In order to compare the temporal changes, three sets of data representing 3 climatological years (2011-2040, 2041-2070, and 2071-2100) were successively used to run the model. Simulated growth and yield data of the three Japonica cultivars under the observed climate for 1971-2000 was set as a reference. Compared with the current normal, heading date was accelerated by 7 days for 2011-2040 and 20 days for 2071-2100. Physiological maturity was accelerated by 15 days for 2011-2040 and 30 days for 2071-2100. Rice yield was in general reduced by 6-25%, 3-26%, and 3-25% per 10a in early, medium, and late maturity classes, respectively. However, mid to late maturing varieties showed an increased yield in northern Gyeonggi Province and in most of Kwangwon Province in 2071-2100.

• Journal of Distribution Research
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• v.15 no.2
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• pp.71-99
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• 2010

Most of studies on patriotism in the marketing area have focused on ethnocentric tendencies observed in consumption behaviors. On the contrary, there have been few empirical studies on how patriotism in the general sense, indicating affection for, attachment to, and pride in the country, influences consumers' evaluation of domestic and foreign products. Given the current situation that marketing activities appealing to people's patriotism is increasing, this is somewhat surprising. Thus, this study examined empirically how patriotism influences people's evaluation of domestic and foreign products. In addition, we tested whether consumer ethnocentrism works as an intervening variable in the relation between patriotism and product evaluation. The empirical analysis was conducted through a questionnaire survey of undergraduate and graduate students at universities in Seoul. The survey asked about the respondents' patriotism, consumer ethnocentrism, domestic product evaluation, foreign product evaluation, and demographical characteristics. In foreign product evaluation, the respondents were requested to evaluate Chinese and Japanese products. Email was used to send and recover the questionnaires, and 135 replies were used in the analysis. Major findings from the empirical analysis are as follows. First, a significant relationship was observed between patriotism and domestic product evaluation. That is, patriotic participants evaluated domestic products more favorably. On the other hand, no significant relationship was observed between patriotism and foreign product evaluation(See Table 1-1 and 1-2). Next, the effect of patriotism on domestic product evaluation was mediated by consumer ethnocentrism. However, whether the effect of patriotism on domestic product evaluation is mediated by consumer ethnocentrism partially or fully was different according to product(See Table 2-1 and 2-2). Lastly, we tried to analyze the relation between consumer ethnocentrism and product evaluation and comparing the results with findings of previous researches. According to the results, a significant relationship was observed between consumer ethnocentrism and domestic product evaluation but not between consumer ethnocentrism and foreign product evaluation. The meanings of this study are as follows. First, there have been few marketing studies that investigated the relation between patriotism and product evaluation. Thus, this study is meaningful in that it supplemented the limitation of previous research. Second, consumer ethnocentrism was found to mediate the relation between patriotism and domestic product evaluation. Considering the absence of previous research that examined the role of consumer ethnocentrism as an intervening variable, this study is significant in that it expanded the scope of research on consumer ethnocentrism. Third, from the practical aspect, the results of this study suggest that marketing appealing to patriotism is effective in stimulating consumers' purchase and consumption of domestic products. Accordingly, such a marketing strategy is expected to be effective in protecting domestic markets from imported goods and overseas brands and to increase demands for domestic products and brands. However, there is the question of whether the effect of patriotism based marketing strategies in promoting demand for domestic products would persist. That is, this study could not find a significant relation between patriotism and foreign product evaluation, and this means that the increase in patriotism for the home country does not damage people's view to the quality of foreign products negatively. Accordingly, without change in people's perception of foreign products, it is highly likely that the increase in demand for domestic products or brands induced by patriotism elevated at a specific time or situation may not last long. Fourth, the results of this study suggest that the patriotism level may influence consumers' choice behavior toward retailers strongly connected to a specific country or region. That is, consumers with high level patriotism may hesitate or avoid using a retailer associated with some foreign country. Fifth, according to the results of this study, when people's patriotism is stimulated by a specific social situation or event, it can be an opportunity for domestic franchise brands to increase their market performance such as sales and market share and, at the same time, for foreign franchise brands to experience adversities. Therefore, during a period like the Olympic Games or the World Cup when people's sense of belonging or attachment to their country is heightened, domestic franchise brands need to make marketing activities that may lead market opportunities to substantial results and foreign franchise brands to cope with such adversities. Sixth, consumers' brand choice is often made in retail stores. It has been demonstrated by numerous studies that in store stimuli such as point of purchase display can affect consumers' behavior. Considering this, domestic brands facing competition with foreign brands should make continuous efforts to enhance the market performance of their products through developing in store stimuli that can stimulate consumers' patriotism. Finally, based on the major findings of this study, both academic and practical issues were discussed. Suggestions for future studies were provided.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.125-150
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• 2009

RFID(Radio Frequency IDentification) is a wireless frequency of recognition technology that can be used to recognize, trace, and identify people, things, and animals using radio frequency(RF). RFID will bring about many changes in manufacturing and distributions, among other areas. In accordance with the increasing importance of RFID techniques, great advancement has been made in RFID studies. Initially, the RFID research started as a research literature or case study. Recently, empirical research has floated on the surface for announcement. But most of the existing researches on RFID adoption have been restricted to a dichotomous measure of 'adoption vs. non-adoption' or adoption intention. In short, RFID research is still at an initial stage, mainly focusing on the research of the RFID performance, integration, and its usage has been considered dismissive. The purpose of this study is to investigate which factors are important for the RFID adoption and implementation with organizational resources. In this study, the organizational resources are classified into either finance resources or IT knowledge resources. A research model and four hypotheses are set up to identify the relationships among these variables based on the investigations of such theories as technological innovations, adoption stage, and organizational resources. In order to conduct this study, a survey was carried out from September 27, 2008 until October 23, 2008. The questionnaire was completed by 143 managers and workers from physical distribution and manufacturing companies related to the RFID in South Korea. 37 out of 180 surveys, which turned out unfit for the study, were discarded and the remaining 143(adoption stage 89, implementation stage 54) were used for the empirical study. The statistics were analyzed using Excel 2003 and SPSS 12.0. The results of the analysis are as follows. First, the adoption stage shows that perceived benefits, standardization, perceived cost savings, environmental uncertainty, and pressures from rival firms have significant effects on the intent of the RFID adoption. Further, the implementation stage shows that perceived benefits, standardization, environmental uncertainty, pressures from rival firms, inter-organizational cooperation, and inter-organizational trust have significant effects on the extent of the RFID use. In contrast, inter-organizational cooperation and inter-organizational trust did not show much impact on the intent of RFID adoption while perceived cost savings did not significantly affect the extent of RFID use. Second, in the adoption stage, financial issues had adverse effect on both inter-organizational cooperation and the intent against the RFID adoption. IT knowledge resources also had a deterring effect on both perceived cost savings and the extent of the RFID adoption. Third, in the implementation stage, finance resources had a moderate effect on environmental uncertainty and extent of RFID use while IT knowledge resources had also a moderate effect on perceived cost savings and the extent of the RFID use. Limitations and future research issues can be summarized as follows. First, it is difficult to say that the sample is large enough to be representative of the population. Second, because the sample of this study was conducted among manufacturers only, it may be limited in analyzing fully the effect on the industry as a whole. Third, in consideration of the fact that the organizational resources in the RFID study require a great deal of researches, this research may deem insufficient to fulfill the purpose that it initially set out to achieve. Future studies using performance research are, therefore, needed to help better understand the organizational level of the RFID adoption and implementation.