• Journal of Distribution Science
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• v.14 no.6
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• pp.53-63
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• 2016

Purpose - This study examines the effects of the influence strategies on sales representative and headquarter trust, and investigates how sales representative trust plays a mediating role in the relationship between influence strategies and headquarter trust. For these purposes, a structural model which consists of several constructs was developed. In this model, influence strategies that consist of relational influence strategies (information exchange, recommend, promise) and mandatory influence strategies (legal plea, request, threat) were proposed to affect the sales representative trust and in turn, increase the headquarter trust. Thus, this study proposed that sale representative trust plays a core mediating role in the relationship between relational and mandatory influence strategies and headquarter trust in B2B food materials distribution context. Research design, data, and methodology - For these purposes, the authors collected the data from 208 B2B specialized complex agents. We used the 2,200 B2B specialized complex agents which trade with CJ, Ottogi, and Daesang firms and supply food materials to restaurant, school cafeteria, supermarket and traditional market as a sample frame. Once we identified 330 B2B specialized complex agent owners, CEOs, and/or Directors who had agreed to participate in this study, we dropped off a questionnaire at each B2B specialized complex agent and explained the purpose of this study. The survey was conducted from October 1, 2015 to December 15, 2015. A total of 230 questionnaires were collected. Of these collected questionnaires, 28 questionnaires excluded since they had not been fully completed. The data were analyzed using frequency test, reliability test, measurement model analysis, and structural equation modeling with SPSS and SmartPLS 2. Results - First, information exchange, recommendation, and promise of relational influence strategies had positive effects on sales representative trust. The threat of mandatory influence strategies had a negative effect on sales representative trust, but legal plea and request did not have a significant effect on sales representative trust. Second, information exchange and recommendation of relational influence strategies had positive effects on headquarter trust, but promise did not. Also, legal plea, request, and threat of mandatory influence strategies did not have a significant effect on headquarter trust. Third, this findings show that sales representative trust plays a partial mediator between information exchange and headquarter trust, and threat and headquarter trust, and a full mediator between promise and headquarter trust, and recommendation and headquarter trust. Conclusions - The aim of this study was to examine the effects how diverse dimensions of relational and mandatory influence strategies relate to sales representative trust and headquarter trust. To do so, we integrated the influence strategies and the trust transfer theory to hypothesize that various influence strategies increase sales representative and headquarter trust. The findings of this study suggest that headquarter firms should establish and enforce proper influence strategies guidelines to make clear what proper actions sales representatives should implement in relationship with B2B specialized complex agents. Also, relational and mandatory influence strategies must be regarded as a long-term and ongoing strategy that eventually build a long-term orientation with B2B specialized complex agents and guarantee a company's sustainable growth and success.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.