• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.117-128
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• 2021

In order for the users (shipping firms and shippers) and suppliers (stevedoring firms) in the container terminal industry to win-win, it is necessary to have some appropriate diverse market conditions for the industry. This study analyses the basic conditions and demand and supply characteristics of the industry and investigates the market performance of Busan container ports. First, this article analyses the basic characteristics of demand and supply. As the demand characteristics, there are five ones such as 1) exogeneity of demand, 2) function as export/import transportation and hub for transshipment, 3) increase of users' bargaining power, 4) high substituting elasticity, 5) reduction of volume growth. As the supply characteristics, there are seven ones such as 1) inelasticity of supply, 2) homogeneity of stevedoring services, 3) over-supply, 4) adoption of cutting-edge stevedoring technology, 5) scale economy and impossibility of storage, 6) labor market rigidity, 7) enhancing port's role in SCM. In addition, this study conducts the so-called structure-conduct-performance analysis. For the structure analysis, 1) lacks of scale economy in stevedoring companies, 2) high entry barrier, 3) strengthening of shipping firms' bargaining power, 4) transitory permission scheme for tariff are analyzed. For the conduct analysis, 1) price discrimination between export/import and transshipment, 2) mid-term length of terminal use contract, 3) continuous investment in equipment, 4) low level of cooperation among terminal operating firms are derived. For the performance analysis, 1) inequality in profitability, 2) reduction of export/import cost, 3) delay in adopting cutting-edge technology, 4) idle equipment are analyzed. Following this logical flow, the hypothesis that the market structure influences the market conduct is tested based on the actual dataset. As a future agenda in the conclusion, this article recommends the so-called port industrial policy.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.147-154
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• 2023

This study aims to determine the necessity, role, utilization, and operation and management plan in relation to the underwater space platform where humans can newly reside. It provides a comprehensive opinion on the need for creating undersea space and operation plans based on opinions of industry-university-affiliated organizations involved in the R&D project of the Ministry of Maritime Affairs and Fisheries for the utilization of undersea space and external experts participating in marine technology development. In this study, a survey was conducted on researchers participating in the construction of a Korean submarine space platform. FGI was conducted on marine technology development experts. Results were then derived. As a result of the analysis, the need for subsea space construction was found to be high. As for the role of subsea space, the most common opinion was to develop technology for utilizing subsea space and to secure marine science research functions. It was found that the creation of subsea space would have a positive impact on the domestic industry, especially the deep-sea development industry and the shipbuilding/offshore structure industry. In terms of utilization, after the end of the seabed space test bed, the response to utilization as a marine observation base and marine ecosystem research had the highest proportion. As for expected inconvenience, discomfort in the psychological environment was the highest. Experts suggest that securing a continuous budget is most important for stable operation in the future and that securing a manpower budget is essential for itemized budgets. In addition, it was judged that it would be appropriate to establish a prior agreement from the time of the prior agreement and prepare a countermeasure before proceeding with the project in order to ensure ownership issues, consignment management issues, and cost issues when using the project after the end of the project.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Distribution Research
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• v.16 no.4
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• pp.65-93
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• 2011

As relationship between buyer and seller has been brought closer and long-term relationship has been more important in B2B markets, the importance of service and service convenience increases as well as product. In homogeneous markets, where service offerings are similar and therefore not key competitive differentiator, providing greater convenience may enable a competitive advantage. Service convenience, as conceptualized by Berry et al. (2002), is defined as the consumers' time and effort perceptions related to buying or using a service. For this reason, B2B customers are interested in how fast the service is provided and how much save non-monetary cost like time or effort by the service convenience along with service quality. Therefore, this study attempts to investigate the impact of service convenience on relationship factors such as relationship satisfaction, relationship commitment, and relationship performance. The purpose of this study is to find out whether service convenience can be a new antecedent of relationship quality and relationship performance. In addition, this study tries to examine how five-dimensional service convenience constructs (decision convenience, access convenience, transaction convenience, benefit convenience, post-benefit convenience) affect customers' relationship satisfaction, relationship commitment, and relationship performance. The service convenience comprises five fundamental components - decision convenience (the perceived time and effort costs associated with service purchase or use decisions), access convenience(the perceived time and effort costs associated with initiating service delivery), transaction convenience(the perceived time and effort costs associated with finalizing the transaction), benefit convenience(the perceived time and effort costs associated with experiencing the core benefits of the offering) and post-benefit convenience (the perceived time and effort costs associated with reestablishing subsequent contact with the firm). Earlier studies of perceived service convenience in the industrial market are none. The conventional studies that have dealt with service convenience have usually been made in the consumer market, or they have dealt with convenience aspects in the service process. This service convenience measure for consumer market can be useful tool to estimate service quality in B2B market. The conceptualization developed by Berry et al. (2002) reflects a multistage, experiential consumption process in which evaluations of convenience vary at each stage. For this reason, the service convenience measure is good for B2B service environment which has complex processes and various types. Especially when categorizing B2B service as sequential stage of service delivery like Kumar and Kumar (2004), the Berry's service convenience measure which reflect sequential flow of service deliveries suitable to establish B2B service convenience. For this study, data were gathered from respondents who often buy business service and analyzed by structural equation modeling. The sample size in the present study is 119. Composite reliability values and average variance extracted values were examined for each variable to have reliability. We determine whether the measurement model supports the convergent validity by CFA, and discriminant validity was assessed by examining the correlation matrix of the constructs. For each pair of constructs, the square root of the average variance extracted exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the Smart PLS 2.0 and we calculated the PLS path values and followed with a bootstrap re-sampling method to test the hypotheses. Among the five dimensional service convenience constructs, four constructs (decision convenience, transaction convenience, benefit convenience, post-benefit convenience) affected customers' positive relationship satisfaction, relationship commitment, and relationship performance. This result means that service convenience is important cue to improve relationship between buyer and seller. One of the five service convenience dimensions, access convenience, does not affect relationship quality and performance, which implies that the dimension of service convenience is not important factor of cumulative satisfaction. The Cumulative satisfaction can be distinguished from transaction-specific customer satisfaction, which is an immediate post-purchase evaluative judgment or an affective reaction to the most recent transactional experience with the firm. Because access convenience minimizes the physical effort associated with initiating an exchange, the effect on relationship satisfaction similar to cumulative satisfaction may be relatively low in terms of importance than transaction-specific customer satisfaction. Also, B2B firms focus on service quality, price, benefit, follow-up service and so on than convenience of time or place in service because it is relatively difficult to change existing transaction partners in B2B market compared to consumer market. In addition, this study using partial least squares methods reveals that customers' satisfaction and commitment toward relationship has mediating role between the service convenience and relationship performance. The result shows that management and investment to improve service convenience make customers' positive relationship satisfaction, and then the positive relationship satisfaction can enhance the relationship commitment and relationship performance. And to conclude, service convenience management is an important part of successful relationship performance management, and the service convenience is an important antecedent of relationship between buyer and seller such as the relationship commitment and relationship performance. Therefore, it has more important to improve relationship performance that service providers enhance service convenience although competitive service development or service quality improvement is important. Given the pressure to provide increased convenience, it is not surprising that organizations have made significant investments in enhancing the convenience aspect of their product and service offering.