• Asia pacific journal of information systems
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• 제18권4호
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• pp.27-55
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• 2008

Due to the increasing and intensifying competition in the Internet shopping market, it has been recognized as very important to develop an effective policy and strategy for acquiring loyal customers. For this reason, web site designers need to know if a new Internet shopping mall(ISM) will be accepted. Researchers have been working on identifying factors for explaining and predicting user acceptance of an ISM. Some studies, however, revealed inconsistent findings on the antecedents of user acceptance of a website. Lack of consideration for individual differences in user ability is believed to be one of the key reasons for the mixed findings. The elaboration likelihood model (ELM) and several studies have suggested that individual differences in ability plays an moderating role on the relationship between the antecedents and user acceptance. Despite the critical role of user ability, little research has examined the role of user ability in the Internet shopping mall context. The purpose of this study is to develop a user acceptance model that consider the moderating role of user ability in the context of Internet shopping. This study was initiated to see the ability of the technology acceptance model(TAM) to explain the acceptance of a specific ISM. According to TAM. which is one of the most influential models for explaining user acceptance of IT, an intention to use IT is determined by usefulness and ease of use. Given that interaction between user and website takes place through web interface, the decisions to accept and continue using an ISM depend on these beliefs. However, TAM neglects to consider the fact that many users would not stick to an ISM until they trust it although they may think it useful and easy to use. The importance of trust for user acceptance of ISM has been raised by the relational views. The relational view emphasizes the trust-building process between the user and ISM, and user's trust on the website is a major determinant of user acceptance. The proposed model extends and integrates the TAM and relational views on user acceptance of ISM by incorporating usefulness, ease of use, and trust. User acceptance is defined as a user's intention to reuse a specific ISM. And user ability is introduced into the model as moderating variable. Here, the user ability is defined as a degree of experiences, knowledge and skills regarding Internet shopping sites. The research model proposes that the ease of use, usefulness and trust of ISM are key determinants of user acceptance. In addition, this paper hypothesizes that the effects of the antecedents(i.e., ease of use, usefulness, and trust) on user acceptance may differ among users. In particular, this paper proposes a moderating effect of a user's ability on the relationship between antecedents with user's intention to reuse. The research model with eleven hypotheses was derived and tested through a survey that involved 470 university students. For each research variable, this paper used measurement items recognized for reliability and widely used in previous research. We slightly modified some items proper to the research context. The reliability and validity of the research variables were tested using the Crobnach's alpha and internal consistency reliability (ICR) values, standard factor loadings of the confirmative factor analysis, and average variance extracted (AVE) values. A LISREL method was used to test the suitability of the research model and its relating six hypotheses. Key findings of the results are summarized in the following. First, TAM's two constructs, ease of use and usefulness directly affect user acceptance. In addition, ease of use indirectly influences user acceptance by affecting trust. This implies that users tend to trust a shopping site and visit repeatedly when they perceive a specific ISM easy to use. Accordingly, designing a shopping site that allows users to navigate with heuristic and minimal clicks for finding information and products within the site is important for improving the site's trust and acceptance. Usefulness, however, was not found to influence trust. Second, among the three belief constructs(ease of use, usefulness, and trust), trust was empirically supported as the most important determinants of user acceptance. This implies that users require trustworthiness from an Internet shopping site to be repeat visitors of an ISM. Providing a sense of safety and eliminating the anxiety of online shoppers in relation to privacy, security, delivery, and product returns are critically important conditions for acquiring repeat visitors. Hence, in addition to usefulness and ease of use as in TAM, trust should be a fundamental determinants of user acceptance in the context of internet shopping. Third, the user's ability on using an Internet shopping site played a moderating role. For users with low ability, ease of use was found to be a more important factors in deciding to reuse the shopping mall, whereas usefulness and trust had more effects on users with high ability. Applying the EML theory to these findings, we can suggest that experienced and knowledgeable ISM users tend to elaborate on such usefulness aspects as efficient and effective shopping performance and trust factors as ability, benevolence, integrity, and predictability of a shopping site before they become repeat visitors of the site. In contrast, novice users tend to rely on the low elaborating features, such as the perceived ease of use. The existence of moderating effects suggests the fact that different individuals evaluate an ISM from different perspectives. The expert users are more interested in the outcome of the visit(usefulness) and trustworthiness(trust) than those novice visitors. The latter evaluate the ISM in a more superficial manner focusing on the novelty of the site and on other instrumental beliefs(ease of use). This is consistent with the insights proposed by the Heuristic-Systematic model. According to the Heuristic-Systematic model. a users act on the principle of minimum effort. Thus, the user considers an ISM heuristically, focusing on those aspects that are easy to process and evaluate(ease of use). When the user has sufficient experience and skills, the user will change to systematic processing, where they will evaluate more complex aspects of the site(its usefulness and trustworthiness). This implies that an ISM has to provide a minimum level of ease of use to make it possible for a user to evaluate its usefulness and trustworthiness. Ease of use is a necessary but not sufficient condition for the acceptance and use of an ISM. Overall, the empirical results generally support the proposed model and identify the moderating effect of the effects of user ability. More detailed interpretations and implications of the findings are discussed. The limitations of this study are also discussed to provide directions for future research.

• Asia pacific journal of information systems
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• 제21권2호
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Asia pacific journal of information systems
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• 제21권4호
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• pp.63-104
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• 2011

The study examines the relationships among employee's goal orientation, IT personnel competency, personal effectiveness. The goal orientation includes learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Personal effectiveness consists of personal work satisfaction and personal work performance. In general, IT personnel competency refers to IT expert's skills, expertise, and knowledge required to perform IT activities in organizations. However, due to the advent of the internet and the generalization of IT, IT personnel competency turns out to be an important competency of technological experts as well as employees in organizations. While the competency of IT itself is important, the appropriate harmony between IT personnel's business capability and technological capability enhances the value of human resources and thus provides organizations with sustainable competitive advantages. The rapid pace of organization change places increased pressure on employees to continually update their skills and adapt their behavior to new organizational realities. This challenge raises a number of important questions concerning organizational behavior? Why do some employees display remarkable flexibility in their behavioral responses to changes in the organization, whereas others firmly resist change or experience great stress when faced with the need to alter behavior? Why do some employees continually strive to improve themselves over their life span, whereas others are content to forge through life using the same basic knowledge and skills? Why do some employees throw themselves enthusiastically into challenging tasks, whereas others avoid challenging tasks? The goal orientation proposed by organizational psychology provides at least a partial answer to these questions. Goal orientations refer to stable personally characteristics fostered by "self-theories" about the nature and development of attributes (such as intelligence, personality, abilities, and skills) people have. Self-theories are one's beliefs and goal orientations are achievement motivation revealed in seeking goals in accordance with one's beliefs. The goal orientations include learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Specifically, a learning goal orientation refers to a preference to develop the self by acquiring new skills, mastering new situations, and improving one's competence. A performance approach goal orientation refers to a preference to demonstrate and validate the adequacy of one's competence by seeking favorable judgments and avoiding negative judgments. A performance avoid goal orientation refers to a preference to avoid the disproving of one's competence and to avoid negative judgements about it, while focusing on performance. And the study also examines the moderating role of work career of employees to investigate the difference in the relationship between IT personnel competency and personal effectiveness. The study analyzes the collected data using PASW 18.0 and and PLS(Partial Least Square). The study also uses PLS bootstrapping algorithm (sample size: 500) to test research hypotheses. The result shows that the influences of both a learning goal orientation (${\beta}$ = 0.301, t = 3.822, P < 0.000) and a performance approach goal orientation (${\beta}$ = 0.224, t = 2.710, P < 0.01) on IT personnel competency are positively significant, while the influence of a performance avoid goal orientation(${\beta}$ = -0.142, t = 2.398, p < 0.05) on IT personnel competency is negatively significant. The result indicates that employees differ in their psychological and behavioral responses according to the goal orientation of employees. The result also shows that the impact of a IT personnel competency on both personal work satisfaction(${\beta}$ = 0.395, t = 4.897, P < 0.000) and personal work performance(${\beta}$ = 0.575, t = 12.800, P < 0.000) is positively significant. And the impact of personal work satisfaction(${\beta}$ = 0.148, t = 2.432, p < 0.05) on personal work performance is positively significant. Finally, the impacts of control variables (gender, age, type of industry, position, work career) on the relationships between IT personnel competency and personal effectiveness(personal work satisfaction work performance) are partly significant. In addition, the study uses PLS algorithm to find out a GoF(global criterion of goodness of fit) of the exploratory research model which includes a mediating variable, IT personnel competency. The result of analysis shows that the value of GoF is 0.45 above GoFlarge(0.36). Therefore, the research model turns out be good. In addition, the study performs a Sobel Test to find out the statistical significance of the mediating variable, IT personnel competency, which is already turned out to have the mediating effect in the research model using PLS. The result of a Sobel Test shows that the values of Z are all significant statistically (above 1.96 and below -1.96) and indicates that IT personnel competency plays a mediating role in the research model. At the present day, most employees are universally afraid of organizational changes and resistant to them in organizations in which the acceptance and learning of a new information technology or information system is particularly required. The problem is due' to increasing a feeling of uneasiness and uncertainty in improving past practices in accordance with new organizational changes. It is not always possible for employees with positive attitudes to perform their works suitable to organizational goals. Therefore, organizations need to identify what kinds of goal-oriented minds employees have, motivate them to do self-directed learning, and provide them with organizational environment to enhance positive aspects in their works. Thus, the study provides researchers and practitioners with a matter of primary interest in goal orientation and IT personnel competency, of which they have been unaware until very recently. Some academic and practical implications and limitations arisen in the course of the research, and suggestions for future research directions are also discussed.

• 마케팅과학연구
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• 제20권3호
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• pp.239-248
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• 2010

本文阐述了产业和学术之间的合作项目. 这个合作项目关注美国东北部的一家大型地区连锁百货商店的两个自有品牌服装的营销和产品开发战略发展. 这个项目的目标是通过和学生的想法的合作来振兴产品线. 从而给学生提供真实产业环境中的实践经验. 这个项目中有很多关键者. 在美国东北部的一家私有连锁百货商店为已有的两个自有服装品牌寻求一个学术伙伴. 他们的目标客户是追求休闲, 适中价格的中年消费者. 这个公司想要改变包装和展示的方向, 甚至是产品的设计. 公司的品牌和产品开发部门联系东北一个州立大学的学术部门的教授. 有两位教授认为这个项目非常适合他们的课程-一个是初级的媒介品牌管理课程; 一个是高级的时装产品开发课程. 这些教授认为通过合作项目, 学生在安全的学术学习环境中能进入一个真实的工作场景中在一个多学科协作团队, 提供超出一个学生的能力, 经验和资源优势, 并增加了解决问题的过程中的 "智囊" (Lowman 2000). 这种提高学生的能力目标的方向让每班教师去组织品牌和产品开发类的跨学科团队. 此外, 许多大学都聘请科研和教学的产业伙伴关系, 协作的时间(学期)和环境(教室/实验室)的约束有助于提高学生的知识和对现实世界的经验. 在田纳西大学, 产业服务中心和UT-Knoxville's 工学院和一家公司合作来发展它们美国公司的的设计进步. 本研究中, 因为是和一个自有商标零售品牌, Wickett, Gaskill 和Damhorst's (1999) 指出产品开发和品牌管理团队使用的零售服装产品开发模型. 之所以选择这个框架是因为它从零售这个角度强调了服饰产品开发. 两个班级参与了这个项目: 一个初级品牌管理班级和一个高级时装产品开发班级. 7个团队包括四名学习品牌管理的学生和两名学习产品开发的学生. 这两个课程在同一个学期但是不同的时间. 在学期开始的时候, 每个班级都被介绍给了产业合作伙伴并接受了问题. 一半的团队指定为男士品牌, 另一半是女士品牌. 这些小组负责制定解决问题的方法, 制定自己的工作时间表, 在与业界代表保持接触, 并确保每个小组成员以积极的方式负责任. 这些小组的目标是通过用销售规划进程来计划, 发展和展示一条产品线(遵循Wickett, Gaskill和Damhorst 模型) 并为这条产品线发展新的品牌战略. 这些小组展示了趋势, 色彩, 面料和目标市场调查; 制定一个产品线的草图;编辑了草图, 介绍他们的执行计划书写说明书, 配上合适的模型并最终开发生产样品. 品牌班的学生完成了SWOT分析, 品牌测量研究报告, 品牌心智图和完整综合的营销报告. 这些报告在介绍新产品线时同时发表. 将来如果有更多这样的协作机会而且公司希望同时考虑品牌和产品开发战略, 那么课程应该定在相同的时间, 这样学生有更多的时间在一起讨论时间表和被分配的任务. 像上面的任务, 学生不得不每堂课之外的时间见面. 这使得团队工作变得具有挑战性(Pfaff和Huddleston, 2003). 虽然这项工作的后勤是费时设立和管理, 但教授认为对学生的好处是多种多样的. 根据两堂课的学生的回复, 最重要的好处是和产业专业人士一起工作的机会, 跟进他们的进程, 并看到公司里做决定级别的高层对他们作品的评估. 教员们都感激有一个 "真实的世界" 的案例. 制定的创意和战略扩大和加强了品牌和产品开发两个部门的联系. 通过和来自不同知识领域的学生一起工作并且和产业伙伴联系, 遵守产业活动的框架和时间表, 学生小组在新的环境中完成优秀创新的作品是具有挑战性的. 在产品开发和为 "现实生活" 品牌的品牌工作, 这些品牌都在努力给学生一个机会, 看看他们的课程是如何紧密的与现实世界联系, 以及公司运营中设计和商业方面如何需要创造性, 协作和灵活性. 行业人员对(a)学生的知识水平和深度以及执行力, (b)品牌的新思路的创造性产生了深刻的印象.

• 마케팅과학연구
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• 제20권3호
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• pp.262-268
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• 2010

虚拟社区(virtual community, VC)今年来发展迅速, 越来越多的人参与到虚拟社区中交换信息和分享观点. 虚拟社区是通过计算机布告板和网络进行非面对面的知识和语言交流的一种大众集合体. B2C电子商务网站虚拟社区则是商业性的虚拟社区, 通过培养信任环境来促进消费者在该网站的购买行为. B2CVC通过信息交流, 如推荐, 评论, 买者与卖者评级等, 来建立社会性的氛围. 目前, 虽然学术界已经认识到B2CVC的重要性, 但是关于社区成员的口碑传播行为的研究还不充分. 本研究提出了一个理论模型, 探讨在B2C网站社区中参与度, 满意度, 信任度, 粘度和口碑传播之间的关系. 本研究的目的有三个: 1, 通过整合信念, 态度和行为的测量来实证检验B2C网站社区模型; 2, 更好地理解各因素对口碑传播的影响关系; 3, 更好地理解B2C网站社区黏度在CRM营销中的作用. 研究模型包含以下要素: 1, 社区成员的信念变量, 通过参与度来测量; 2, 社区成员的态度变量, 通过满意度和信任度来测量; 以及3, 社区成员的行为变量, 通过网站黏度和口播传播意愿来测量. 参与度是消费者在虚拟社区的参与动机. 对于社区成员来说, 信息的查找和发布是他们参与到社区的主要目的. 满意度是成员对社区整体评价的重要指标, 反映了成员与社区的交互程度. 虚拟社区的形成与发展依靠成员分享信息和服务的自愿程度. 研究者已经发现信任是促进匿名交互的关键, 因此构建信任被看作是虚拟社区的重要研究课题. 此外, 虚拟社区的成功依靠成员的粘度来提高购买潜力. 社区成员间的观点交流和信息交换代表一种 "写作式" 的口碑传播. 因此口碑传播是推动B2C虚拟社区在互联网上扩散的主要因素之一. 研究模型及假设如图一所示. 本研究通过实证调查中国携程旅游网虚拟社区成员来验证模型. 数据收集过程中共发放243份问卷, 其中有效问卷204份. 通过实证数据验证了参与度, 满意度和信任度影响粘度和口碑传播之间的假设关系. 结构方程模型(SEM)方法用来进行数据分析. 模型的拟合指数结果为χ2/df 是2.76, NFI是 .904, IFI是 .931, CFI是 .930, 以及RMSEA是 .017. 结果表明, 参与度对满意度具有显著的影响(p<0.001, ${\beta}$=0.809). 参与度可以解释满意度的方差比例超过50%, 调整R2为0.654. 参与度对信任度具有显著影响(p<0.001, ${\beta}$=0.751), 解释率为57%, 调整R2为0.563. 此外, 满意度对黏度的影响显著(${\beta}$=0.514), 但是信任度对黏度的影响并不显著(p=0.231, t=1.197). 黏度对口碑传播的影响显著, 且解释率超过80%, 调整R2为 0.846. 总之, 研究结果支持了大部分的研究假设, 但是信任度显著影响粘度的假设没有得到支持. 本研究丰富了电子商务网站虚拟社区的学术研究成果, 深入探讨了在B2C电子商务环境下的用户信念, 态度和行为等因素. 研究成果有助于实践者进行更有针对性的资源开发和市场开拓. 网络营销人员可以针对B2C网站社区来有针对性地制定营销策略, 如对于国际旅游业务, 营销人员可以针对中国的B2C网站社区用户开展营销活动, 如为活跃的用户提供特殊折扣以及为早期参与者提高社区黏度定制营销计划等. 未来的研究应该拓展社区成员行为的研究, 并在不同的行业, 社区和文化背景下开展研究.

• 지능정보연구
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• 제20권2호
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• pp.137-148
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• 2014

상품 검색시간의 단축과 쇼핑에 투입되는 노력의 감소 등, 온라인 쇼핑이 주는 장점에 대한 긍정적인 인식이 확산되면서 전자상거래(e-commerce)의 중요성이 부각되는 추세이다. 전자상거래 기업들은 고객확보를 위해 다양한 인터넷 고객관계 관리(eCRM) 활동을 전개하고 있는데, 개인화된 추천 서비스의 제공은 그 중 하나이다. 정확한 추천 시스템의 구축은 전자상거래 기업의 성과를 좌우하는 중요한 요소이기 때문에, 추천 서비스의 정확도를 높이기 위한 다양한 알고리즘들이 연구되어 왔다. 특히 협업필터링(collaborative filtering: CF)은 가장 성공적인 추천기법으로 알려져 있다. 그러나 고객이 상품을 구매한 과거의 전자상거래 기록을 바탕으로 미래의 추천을 하기 때문에 많은 단점들이 존재한다. 신규 고객의 경우 유사한 구매 성향을 가진 고객들을 찾기 어렵고 (Cold-Start problem), 상품 수에 비해 구매기록이 부족할 경우 상관관계를 도출할 데이터가 희박하게 되어(Sparsity) 추천성능이 떨어지게 된다. 취향이 독특한 사용자를 뜻하는 'Gray Sheep'에 의한 추천성능의 저하도 그 중 하나이다. 이러한 문제인식을 토대로, 본 연구에서는 소셜 네트워크 분석기법 (Social Network Analysis: SNA)과 협업필터링을 결합하여 데이터셋의 특이 취향 사용자 (Gray Sheep) 문제를 해소하는 방법을 제시한다. 취향이 독특한 고객들의 구매데이터를 소셜 네트워크 분석지표를 활용하여 전체 데이터에서 분리해낸다. 그리고 분리한 데이터와 나머지 데이터인 두 가지 데이터셋에 대하여 각기 다른 유사도 기법과 트레이닝 셋을 적용한다. 이러한 방법을 사용한 추천성능의 향상을 검증하기 위하여 미국 미네소타 대학 GroupLens 연구팀에 의해 수집된 무비렌즈 데이터(http://movielens.org)를 활용하였다. 검증결과, 일반적인 협업필터링 추천시스템에 비하여 이 기법을 활용한 협업필터링의 추천성능이 향상됨을 확인하였다.

• 지능정보연구
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• 제23권3호
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• pp.29-44
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• 2017

인터넷 상거래에서, 소비자들은 기존에 제품을 구매한 다른 사용자들이 작성한 상품평에 많은 영향을 받는다. 그러나, 상품평이 점차 축적되어감에 따라, 소비자들이 방대한 상품평을 일일이 확인하는데 많은 시간과 노력이 소요되고, 또한 무성의하게 작성된 상품평들은 오히려 소비자들의 불편을 초래하기도 한다. 이에, 본 연구는 온라인 상품평의 유용성에 영향을 미치는 요인들을 분석하여, 소비자들에게 실제로 도움이 될 수 있는 상품평을 선별적으로 제공하는 예측모형을 도출하는 것을 목적으로 한다. 이를 위해, 텍스트마이닝 기법을 사용하여, 상품평에 포함되어있는 다양한 언어적, 심리적, 지각적 요소들을 추출하였으며, 이러한 요소들 중에서 상품평의 유용성에 영향을 미치는 결정요인이 무엇인지 파악하였다. 특히, 경험재인 의류군과 탐색재인 전자제품군에 대한 상품평의 특성 및 유용성 결정요인이 상이할 수 있음을 고려하여, 제품군별로 상품평의 특성을 비교하고, 각각의 결정요인을 도출하였다. 본 연구에는 아마존닷컴(Amazon.com)의 의류군 상품평 7,498건과 전자제품군 상품평 106,962건이 사용되었다. 또한, 언어분석 소프트웨어인 LIWC(Linguistic Inquiry and Word Count)를 활용하여 상품평에 포함된 특징들을 추출하였고, 이후, 데이터마이닝 소프트웨어인 RapidMiner를 사용하여, 회귀분석을 통한, 결정요인 분석을 수행하였다. 본 연구결과, 제품에 대한 리뷰어의 평가가 높고, 상품평에 포함된 전체 단어 수가 많으며, 상품평의 내용에 지각적 과정이 많이 포함되어 있는 반면, 부정적 감정은 적게 포함된 상품평들이 두 제품 모두에서 유용하다고 인식되는 것을 알 수 있었다. 그 외, 의류군의 경우, 비교급 표현이 많고, 전문성 지수는 낮으며, 한 문장에 포함된 단어 수가 적은 간결한 상품평이 유용하다고 인식되고 있었으며, 전자제품의 경우, 전문성 지수가 높고, 분석적이며, 진솔한 표현이 많고, 인지적 과정과 긍정적 감정(PosEmo)이 많이 포함된 상품평이 유용하게 인식되고 있었다. 이러한 연구결과는 향후, 소비자들이 효과적으로 유용한 상품평들을 확인하는데 도움이 될 것으로 기대된다.

• 지능정보연구
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• 제25권4호
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• pp.123-139
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• 2019

주요 구인구직사이트의 직무분류체계가 사이트마다 상이하고 SW분야에서 제안한 'SQF(Sectoral Qualifications Framework)'의 직무분류체계와도 달라 SW산업에서 SW기업, SW구직자, 구인구직사이트가 모두 납득할 수 있는 새로운 직무분류체계가 필요하다. 본 연구의 목적은 주요 구인구직사이트의 구인정보와 'NCS(National Competaency Standars)'에 기반을 둔 SQF를 분석하여 시장 수요를 반영한 표준 직무분류체계를 구축하는 것이다. 이를 위해 주요 구인구직사이트의 직종 간 연관분석과 SQF와 직종 간 연관분석을 실시하여 직종 간 연관규칙을 도출하고자 한다. 이 연관규칙을 이용하여 주요 구인구직사이트의 직무분류체계를 맵핑하고 SQF와 직무 분류체계를 맵핑함으로써 데이터 기반의 지능형 직무분류체계를 제안하였다. 연구 결과 국내 주요 구인구직사이트인 '워크넷,' '잡코리아,' '사람인'에서 3만여 건의 구인정보를 open API를 이용하여 XML 형태로 수집하여 데이터베이스에 저장했다. 이 중 복수의 구인구직사이트에 동시 게시된 구인정보 900여 건을 필터링한 후 빈발 패턴 마이닝(frequent pattern mining)인 Apriori 알고리즘을 적용하여 800여 개의 연관규칙을 도출하였다. 800여 개의 연관규칙을 바탕으로 워크넷, 잡코리아, 사람인의 직무분류체계와 SQF의 직무분류체계를 맵핑하여 1~4차로 분류하되 분류의 단계가 유연한 표준 직무분류체계를 새롭게 구축했다. 본 연구는 일부 전문가의 직관이 아닌 직종 간 연관분석을 통해 데이터를 기반으로 직종 간 맵핑을 시도함으로써 시장 수요를 반영하는 새로운 직무분류체계를 제안했다는데 의의가 있다. 다만 본 연구는 데이터 수집 시점이 일시적이기 때문에 시간의 흐름에 따라 변화하는 시장의 수요를 충분히 반영하지 못하는 한계가 있다. 계절적 요인과 주요 공채 시기 등 시간에 따라 시장의 요구하는 변해갈 것이기에 더욱 정확한 매칭을 얻기 위해서는 지속적인 데이터 모니터링과 반복적인 실험이 필요하다. 본 연구 결과는 향후 SW산업 분야에서 SQF의 개선방향을 제시하는데 활용될 수 있고, SW산업 분야에서 성공을 경험삼아 타 산업으로 확장 이전될 수 있을 것으로 기대한다.

• 지능정보연구
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• 제26권2호
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• pp.131-145
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• 2020

산업혁신의 흐름에 발맞추어 다양한 분야에서 활용되고 있는 IoT 기술은 빅데이터의 접목을 통한 새로운 비즈니스 모델의 창출 및 사용자 친화적 서비스 제공의 핵심적인 요소로 부각되고 있다. 사물인터넷이 적용된 디바이스에서 누적된 데이터는 사용자 환경 및 패턴 분석을 통해 맞춤형 지능 시스템을 제공해줄 수 있어 편의 기반 스마트 시스템 구축에 다방면으로 활용되고 있다. 최근에는 이를 공공영역 혁신에 확대 적용하여 CCTV를 활용한 교통 범죄 문제 해결 등 스마트시티, 스마트 교통 등에 활용하고 있다. 그러나 이미지 데이터를 활용하는 기존 연구에서는 개인에 대한 사생활 침해 문제 및 비(非)일반적 상황에서 객체 감지 성능이 저하되는 한계가 있다. 본 연구에 활용된 IoT 디바이스 기반의 센서 데이터는 개인에 대한 식별이 불필요해 사생활 이슈로부터 자유로운 데이터로, 불특정 다수를 위한 지능형 공공서비스 구축에 효과적으로 활용될 수 있다. 대다수의 국민들이 일상적으로 활용하는 도시철도에서의 지능형 보행자 트래킹 시스템에 IoT 기반의 적외선 센서 디바이스를 활용하고자 하였으며 센서로부터 측정된 온도 데이터를 실시간 송출하고, CNN-LSTM(Convolutional Neural Network-Long Short Term Memory) 알고리즘을 활용하여 구간 내 보행 인원의 수를 예측하고자 하였다. 실험 결과 MLP(Multi-Layer Perceptron) 및 LSTM(Long Short-Term Memory), RNN-LSTM(Recurrent Neural Network-Long Short Term Memory)에 비해 제안한 CNN-LSTM 하이브리드 모형이 가장 우수한 예측성능을 보임을 확인하였다. 본 논문에서 제안한 디바이스 및 모델을 활용하여 그간 개인정보와 관련된 법적 문제로 인해 서비스 제공이 미흡했던 대중교통 내 실시간 모니터링 및 혼잡도 기반의 위기상황 대응 서비스 등 종합적 메트로 서비스를 제공할 수 있을 것으로 기대된다.

• 지능정보연구
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• 제24권4호
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• pp.137-154
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• 2018

조류인플루엔자와 구제역 같은 동물감염병은 거의 매년 발생하며 국가에 막대한 경제적 사회적 손실을 일으키고 있다. 이를 예방하기 위해서 그간 방역당국은 다양한 인적, 물적 노력을 기울였지만 감염병은 지속적으로 발생해 왔다. 최근 빅데이터와 딥러닝 기술을 활용하여 감염병의 예측모델을 개발하고자 하는 시도가 시작되고 있지만, 실제로 활용가능한 모델구축 연구와 사례보고는 활발히 진행되고 있지 않은 실정이다. KT와 과학기술정보통신부는 2014년부터 국가 R&D사업의 일환으로 축산관련 차량의 이동경로를 분석하여 예측하는 빅데이터 사업을 수행하고 있다. 동물감염병 예방을 위하여 연구진은 최초에는 차량이동 데이터를 활용한 회귀분석모델을 기반으로 한 예측모델을 개발하였다. 이후에는 기계학습을 활용하여 좀 더 정확한 예측 모델을 구성하였다. 특히, 2017년 예측모델에서는 시설물에 대한 확산 위험도를 추가하였고 모델링의 하이퍼 파라미터를 다양하게 고려하여 모델의 성능을 높였다. 정오분류표와 ROC 커브를 확인한 결과, 기계 학습 모델보다 2017년 구성된 모형이 우수함을 확인 할 수 있었다. 또한 2017에는 결과에 대한 설명을 추가하여 방역당국의 의사결정을 돕고 이해관계자를 설득할 수 있는 근거를 확보하였다. 본 연구는 빅데이터를 활용하여 동물감염병예방시스템을 구축한 사례연구로 모델주요변수값, 이에따른 실제예측성능결과, 그리고 상세하게 기술된 시스템구축 프로세스는 향후 감염병예방 영역의 지속적인 빅데이터활용 및 분석 모델 개발에 기여할 수 있을 것이다. 또한 본 연구에서 구축한 시스템을 통해 보다 사전적이고 효과적인 방역을 할 수 있을 것으로 기대한다.