• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.220-232
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• 2010

Companies have been difficult to capture the minds of consumers by staying in the sales phase in order to sell products. The marketing and brand management strategies of more understanding consumers and systematically managing for the perception of them are becoming important. Since launched, EXR has implemented a systematic brand management process from its early stage. Based on the idea of 'brand asset by customer' by Kevin Lane Keller that the total of consumer brand knowledge ultimately completes the company brand power, 'brand asset', to satisfy areas of 'functionality' and 'design', EXR pioneered its ultimate brand asset which is 'CAPORTS' and established its own identity called, 'PROGRESSIVE'. For the success of a brand management, there must be a clear vision supported by the determination of a specific brand identity. Also, it needs to be shared by members of the organization, and to maintain and manage it in the long run, an integrated management strategy is essential. On the basis of such identity, EXR was able to carry out the integrated management for its brand and the overall implementation of the marketing strategy. Therefore, In this study, we seek for a sustainable brand management strategy of fashion brand by looking into the process of the EXR's brand establishment.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.5-16
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• 2009

Corporate social responsibility (CSR) activities have been shown to be potential factors that can improve corporate image and increase the ability of corporations to compete. However, most previous studies related to CSR activities investigated how these activities influence product and corporate evaluation, as well as corporate image. In addition, some researchers treated consumers' perceptions of corporate motives as moderator variables in evaluating the relationship between corporate social responsibilities and consumer response. However, motive-based theories have some weaknesses. Corporate social responsibility activities cause two motives(egoistic vs. altruistic) for consumers, but recently, Vlachos et al. (2008) argued that these motives should be segmented. Thus, it is possible to transform the original theory into a modified theory model (persuasion knowledge model, PKM). Vlachos et al. (2008) segmented corporate social responsibility motives into four types and compared the effects of these motives on customer loyalty. Prior studies have proved that CSR activities with positive motives have positive influences on customer loyalty. However, the psychological reasons underlying this finding have not been determined empirically. Thus, the objectives of this research are twofold. First, we attempt to determine why most customers favor companies that they feel have positive motives for their corporate social responsibility activities. Second, we attempt to measure the effects of consumers' reciprocity when society benefits from corporate social responsibility activities. The following research hypotheses are constructed. H1: Values-driven motives for corporate social responsibility activities have a positive influence on the perceived reciprocity. H2: Stakeholder-driven motives for corporate social responsibility activities have a negative influence on the perceived reciprocity. H3: Egoistic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H4: Strategic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H5: Perceived reciprocity for corporate social responsibility activities has a positive influence on consumer loyalty. A single company is selected as a research subject to understand how the motives behind corporate social responsibility influence consumers' perceived reciprocity and customer loyalty. A total sample of 200 respondents was selected for a pilot test. In addition, to ensure a consistent response, we ensured that the respondents were older than 20 years of age. The surveys of 172 respondents (males-82, females-90) were analyzed after 28 invalid questionnaires were excluded. Based on our cutoff criteria, the model fit the data reasonably well. Values-driven motives for corporate social responsibility activities had a positive effect on perceived reciprocity (t = 6.75, p < .001), supporting H1. Morales (2005) also found that consumers appreciate a company's social responsibility efforts and the benefits provided by these efforts to society. Stakeholder-driven motives for corporate social responsibility activities did not affect perceived reciprocity (t = -.049, p > .05). Thus, H2 was rejected. Egoistic-driven motives (t = .3.11, p < .05) and strategic-driven (t = -4.65, p < .05) motives had a negative influence on perceived reciprocity, supporting H3 and H4, respectively. Furthermore, perceived reciprocity had a positive influence on consumer loyalty (t = 4.24, p < .05), supporting H5. Thus, compared with the general public, undergraduate students appear to be more influenced by egoistic-driven motives. We draw the following conclusions from our research findings. First, value-driven attributions have a positive influence on perceived reciprocity. However, stakeholder-driven attributions have no significant effects on perceived reciprocity. Moreover, both egoistic-driven attributions and strategic-driven attributions have a negative influence on perceived reciprocity. Second, when corporate social responsibility activities align with consumers' reciprocity, the efforts directed towards social responsibility activities have a positive influence on customer loyalty. In this study, we examine whether the type of motivation affects consumer responses to CSR, and in particular, we evaluate how CSR motives can influence a key internal factor (perceived reciprocity) and behavioral consumer outcome (customer loyalty). We demonstrate that perceived reciprocity plays a mediating role in the relationship between CSR motivation and customer loyalty. Our study extends the research on consumer CSR-inferred motivations, positing them as a direct indicator of consumer responses. Furthermore, we convincingly identify perceived reciprocity as a sub-process mediating the effect of CSR attributions on customer loyalty. Future research investigating the ultimate behavior and financial impact of CSR should consider that the impacts of CSR also stem from perceived reciprocity. The results of this study also have important managerial implications. First, the central role that reciprocity plays indicates that managers should routinely measure how much their socially responsible actions create perceived reciprocity. Second, understanding how consumers' perceptions of CSR corporate motives relate to perceived reciprocity and customer loyalty can help managers to monitor and enhance these consumer outcomes through marketing initiatives and management of CSR-induced attribution processes. The results of this study will help corporations to understand the relative importance of the four different motivations types in influencing perceived reciprocity.

• Journal of East Asia Management
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• v.1 no.1
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• pp.29-49
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• 2020

In an intensively competitive global market, small-and medium-sized firms are puzzled about how to develop sustainable competitive advantages against global rivalries, thus leading satisfactory economic performance. However, despite the roles and contributions of such small-and medium-sized firms in the local community and national economies in Japan, little guidance has been offered to the practical issues related to their strategic behaviors toward global management. To fill this notable knowledge gap, this study aims to investigate the conditions in which how Japanese small-and medium-sized could dominates global market, which is one of key challenges in the literature of small business and entrepreneurship. To obtain better insights to this research area, this study undertakes an in-depth interview survey with I.S.T (Industrial Summit Technology) Corporation that shows off the highest global market share (40 per cent) with seamless polyimide tube product widely used in office automation equipment (e.g., copiers and printers). This method of survey is designed to deeply understand historical considerations about how I.S.T Corporation could dominate in the global market of such seamless polyimide tube product. Based on findings drawn from an interview, this study identifies five major factors enabling I.S.T Corporation to be a competitive global hidden company: vision sharing through founder's entrepreneurship, core competence, strategic network, risk management, and employee engagement. Specifically, to become a global hidden champion, sharing the vision motivating employees to partake in shaping company's future will be the first step on the road to global success through founder's entrepreneurship. However, in order to achieve such a vision, the importance of company's core competence cannot be overemphasized, which differentiates your customer solution with those of competitors. As such, a group of experts will be naturally formed and demonstrates your expertise in the global market, thereby building sustainable competitiveness. On the other hand, to maintain sustainable competitiveness, it is necessary to make up for the weaknesses small-and medium-sized firms suffer from competitive resources while strengthening their own strengths through strategic networks with external organizations. Here, every company has to understand the critical role of risk management, which is essential in this process of being global company so as not to lose your own strengths. Last but not least, do not forget the significant effects of employee engagement in firm performance. To enhance employees' engagement, a company has to create an ideal organization culture which fits into company's history and personality. In doing so, such organization culture can allow the vision and strategy to be implemented into detailed business tactics while facilitating employees to challenge the status quo by experimenting with creative ideas.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Knowledge Management Research
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• v.24 no.1
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• pp.73-97
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• 2023

Recently, the MyData market has been growing as the importance of data and issues related to personal information protection have drawn much attention together. MyData refers to the concept of guaranteeing an individual's right to personal information and providing and utilizing one's data according to individual consent. MyData service providers can combine and analyze customer information to provide personalized services. In the early days, the MyData business was activated mainly by private companies and the financial industry, but recently, public institutions are also actively taking advantage of MyData. Meanwhile, the importance of an individual's intention to provide MyData for the success of MyData businesses continues to increase, but research related to this is lacking. Moreover, existing studies have been mainly conducted on individual benefits of MyData; there are not enough studies in which both public benefit and perceived risk factors are considered at the same time. In this regard, this study intends to derive factors affecting the intention to provide MyData based on the privacy calculus model, examine their influencing mechanism, and further verify the moderating effects of individual capabilities and institutional type. This study can find academic significance in that it expanded and demonstrated the privacy calculus model in the context of MyData providing intention. In addition, the results of this study are expected to offer practical guidelines for developing and managing new services in MyData businesses.

• Journal of Distribution Research
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• v.10 no.2
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• pp.73-97
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• 2005

The importance of emotional factors like as product aesthetics, branding strategies and service quality is increasing in today's marketplace. At the point of fashion product purchases, emotional factor is mainly related to personal selling. Rasearches related to job aptitudity of fashion salesperson are necessary as their role becoming more serious. This study was designed (1) to determine factors of job aptitudity of fashion salesperson; and (2) to examine relations between job aptitudity of fashion salesperson and other variables: demographics, job statistics, fashion involvement fashion innovativeness, ongoing information searching and job satisfaction. As a result, job aptitudty of fashion salesperson was determined into six dimensions by factor analysis: product knowledge, professionalism, involvement to the present profession, marketing cooperator, customer orientation and self management. Marketing cooperator and product knowledge was relatively !ow compared to other aptitudity factors. This finding suggests that the special education programs including textile/fashion product knowledge and value of field information for fashion salesperson is needed to enhance the integrated fashion marketing. The job aptitudity of salesperson was correlated with fashion involvement, fashion innovativeness, ongoing information searching, and job satisfaction. The job aptitudity-related scales which showed significant relation to job aptitudity in this study could be used as the index to examine the job aptitudity of potential employees of fashion retail company.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.137-146
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• 2012

In SCM(Supply Chain Management), a management paradigm where the customer satisfaction is to be achieved by minimizing the cost, reducing the uncertainty, and obtaining the overall optimization. As it performs the integrated operation of the paths of information, assets, and knowledge from the raw material providers to the retailers, the adoption of RFID(Radio Frequency Identification) in SCM could be expected to magnify the effectiveness of the system. However, there is a huge risk by deciding whether or not RFID system is adopted without the objective analysis under the uncertain circumstances. This research paper presents the statistical analysis methodologies for the comparison of RFID with Barcode on the aspect of utility and the statistical analysis tool, RUSAT, which was programmed for nonstatisticians' convenience. Assuming a pharmaceutical industry, this paper illustrates how the data were entered and analyzed in RUSAT. The results of this research are expected to be used not only for the pharmaceutical related company but also for the manufacturer, the whole-saler, and the retailer in the other logistic industries.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• CRM연구
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• v.2 no.2
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• pp.53-70
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• 2009

Understanding consumer behavior based on the analysis of the customer data is one essential part of analytic CRM. To do this, the analytic skills for data extraction and data processing are required to users. As a user has various kinds of questions for the consumer data analysis, the user should use database language such as SQL. However, for the firm's user, to generate SQL statements is not easy because the accuracy of the query result is hugely influenced by the knowledge of work-site operation and the firm's database. This paper proposes a natural language based database search framework finding relevant database elements. Specifically, we describe how our TableRank method can understand the user's natural query language and provide proper relations and attributes of data records to the user. Through several experiments, it is supported that the TableRank provides accurate database elements related to the user's natural query. We also show that the close distance among relations in the database represents the high data connectivity which guarantees matching with a search query from a user.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.319-326
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• 2014

According to the increasing mobile security users who have experienced authentication failure by forgetting passwords, user names, or a response to a knowledge-based question have preference for biological information such as hand geometry, fingerprints, voice in personal identification and authentication. Therefore biometric verification of personal identification and authentication for mobile security provides assurance to both the customer and the seller in the internet. Our study focuses on human hand biometric information recognition system for personal identification and personal Authentication, including its shape, palm features and the lengths and widths of the fingers taken from mobile phone photographs such as iPhone4 and galaxy s2. Our hand biometric information recognition system consists of six steps processing: image acquisition, preprocessing, removing noises, extracting standard hand feature extraction, individual feature pattern extraction, hand biometric information recognition for personal identification and authentication from input images. The validity of the proposed system from mobile phone image is demonstrated through 93.5% of the sucessful recognition rate for 250 experimental data of hand shape images and palm information images from 50 subjects.