• Asia Marketing Journal
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• v.13 no.3
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• pp.103-134
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• 2011

The recent economic recession and price rise reduces excessive consumption as a whole. So companies take more interest in and use discount coupons as a means of sales promotion to reinforce their competitiveness. The combination of Internet and mobile communication technology leads to an explosive increase in the use of mobile Internet service, which promotes commercialization of mobile coupons. Nevertheless, there are absolutely insufficient researches on mobile coupons than those on paper ones. In this context, this study tries to consider intention of accepting and using mobile coupons. The innovated Technology Acceptance Model (TAM) was used to see factors of using mobile coupons considered important by customers. Through the combination of characteristics of mobile coupon service and values obtained from mobile coupons, effects of variables to enhance intention of using mobile coupons were empirically analyzed. In particular, this study suggested importance of psychological as well as economic values of mobile coupons and emphasized good considerations of the psychological aspect, such as shame, stinginess, and reputation sensitivity, in using mobile coupons as an important factor for intention of using the coupons. Another empirical analysis was made of what moderating roles consumers' self-construalplayed in the effects of mobile coupon values perceived by consumers on intention of using coupons. As a result, immediate connectivity and situational provision among characteristics of mobile coupon service were found to affect ease and usability. It was also shown that perceived ease and usability had significant effects on both economic and psychological values, which then had significant effects on intention of using a mobile system. After testing moderating effects of self-construal, the degree of effects of perceived mobile coupon values on intention of using mobile coupons was greater among inter-dependent self-construal users than among independent ones. This study considered various schemes of improving intention to use mobile coupons and provided a foundation to help companies make a strategy for mobile coupons to be activated in the future.

• Korea Trade Review
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• v.48 no.3
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• pp.217-242
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• 2023

This study aims to analyze the factors influencing the logistics demand of inland ports along the Yangtze River and predict future port logistics demand based on these factors. The logistics demand prediction using system dynamics techniques was conducted for a total of six ports, including Chongqing and Yibin ports in the upper reaches, Jingzhou and Wuhan ports in the middle reaches, and Nanjing and Suzhou ports in the lower reaches of the Yangtze River. The logistics demand for all ports showed an increasing trend in the mid-term prediction until 2026. The logistics demand of Chongqing port was mainly influenced by the scale of the hinterland economy, while Yibin port appeared to heavily rely on the level of port automation. In the case of the upper and middle reach ports, logistics demand increased as the energy consumption of the hinterland increased and the air pollution situation worsened. The logistics demand of the middle reach ports was greatly influenced by the hinterland infrastructure, while the lower reach ports were sensitive to changes in the urban construction area. According to the sensitivity analysis, the logistics demand of ports relying on large cities was relatively stable against the increase and decrease of influential factors, while ports with smaller hinterland city scales reacted sensitively to changes in influential factors. Therefore, a strategy should be established to strengthen policy support for Chongqing port as the core port of the upper Yangtze River and have surrounding ports play a supporting role for Chongqing port. The upper reach ports need to play a supporting role for Chongqing port and consider measures to enhance connections with middle and lower reach ports and promote the port industry. The development strategy for inland ports along the Yangtze River suggests the establishment of direct routes and expansion of the transportation network for South Korean ports and stakeholders. It can suggest expanding the hinterland network and building an efficient transportation system linked with the logistics hub. Through cooperation, logistics efficiency can be enhanced in both regions, which will contribute to strengthening the international position and competitiveness of each port.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.109-115
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• 2022

As the 4th industrial revolution progresses, foreign countries are promoting smart manufacturing innovation through digital transformation as a priority task early on to secure a competitive edge in the manufacturing industry. In response, the Korean government is also promoting a policy to enhance the competitiveness of small and medium-sized manufacturing companies by promoting digital transformation in the corporate sector to meet the global trend of the 4th industrial revolution era. Manufacturing powerhouses such as Germany and Japan see manufacturing as a key sector in digital transformation and are leading related policies, while emerging countries such as China are also promoting manufacturing innovation strategies such as building digital infrastructure and creating a digital innovation ecosystem. Korea is promoting the 'Korean-style smart factory dissemination and expansion strategy' by transforming Germany's manufacturing innovation strategy for smart factory supply to suit the domestic situation. However, the policy to supply smart factories so far has been conducted with support from individual companies under the leadership of the government, and most of the smart factories are at the basic level, and it is evaluated that there are limitations such as the lack of manpower to operate smart factories. In addition, while the current policy focuses on expanding the supply of smart factories in SMEs, it is necessary to establish a smart manufacturing system through linkages between large and small businesses in order to achieve the original goal of establishing a smart manufacturing system. Therefore, it can be said that from the standpoint of small and medium-sized enterprises (SMEs), who are consumers of smart factories, it can be said that the digital transformation policy can achieve the expected results only when appropriate incentives are provided for the introduction of smart factories in a situation where management resources such as funds, technology, and human resources are lacking. In addition, it is judged that the uncertainty of the performance of digital investment always exists, and as long as large and small companies are maintained as an ecosystem of delivery and subcontracting, there is very little incentive for small and medium-sized manufacturing companies to voluntarily invest in or advance digital transformation. Therefore, the digital transformation policy of small and medium-sized manufacturing companies in the future has practical significance in that it suggests that there is a need to seek ways to attract SMEs' digital-related voluntary investment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.133-150
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• 2022

The purpose of this study was to develop a female entrepreneurial competency model. For the purpose, two Focus Group Interviews (FGI) were conducted with seven outstanding female entrepreneurs, and three expert reviews were conducted. In addition, in order to verify the validity of the provisional female entrepreneur competency model derived from the FGI and competency modeling expert review, the female entrepreneur competency model was finally confirmed through a survey of 442 female entrepreneurs. The results were as follows. First, a female entrepreneur competency model consisting of 6 competency groups and 25 competencies of entrepreneurship, emotion, business management, relationship management, strategic management, and multitasking, and 75 behavioral indicators describing each competency was developed. Second, sensibility and multitasking are competencies that reflect the characteristics of female entrepreneurs. In particular, 'social sense', which is the ability to be considerate of others in the emotional competency group and the ability to respond well to subtle nuances, and the multitasking competency group's unique strengths are women's ability to perform various tasks at the same time. The 'work-family control ability' of a female entrepreneur who maintains a balance between 'multi-tasking' and work and family is a representative competency of only female entrepreneurs. Third, the developed female entrepreneurship competency model is meaningful in that it not only increases female entrepreneurial competency so that prospective female entrepreneurs can successfully run a business through entrepreneurship education, but it also makes it easy for existing female entrepreneurs to reflect and improve their competencies. If we provide appropriate training programs to female entrepreneurs based on their competency, it will be possible to effectively enhance the entrepreneurial competency, which is the key to strengthening the competitiveness of female entrepreneurs. The female entrepreneur competency model developed through this study can provide a basis for future research on competency diagnosis and education needs analysis.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.