• 벤처혁신연구
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• 제4권1호
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• pp.51-65
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• 2021

공공부문의 인사 관련 정책은 폐쇄적이며 필기시험 위주로 운영이 되어왔으나, 2006년 고위공무원단 도입을 통하여 공무원의 승진과 선발제도에서 역량을 기반으로 하는 평가와 승진, 교육체계가 새로이 도입되었다. 특히 승진과 관련된 역량평가(Assessment Center)를 운영하여 연공서열 중심의 승진제도가 역량을 기준으로 평가받는 계기가 되었다. 역량평가는 현재까지 사용하는 평가방법들 중에서 신뢰성과 타당성이 가장 높은 평가방법으로 알려져 있으며 성과에 대한 예측 타당성도 높은 것으로 알려져 있다. 2001년 정부 표준역량 19개 역량모델을 설계하였으며, 2006년 고위공무원단제도의 시행과 함께 역량평가를 실행하였고, 2015년 중앙부처 과장급으로 확대 시행과 2012년 서울시를 시작으로 하여 광역지방자치단체도 간부급 공무원에 대한 역량평가를 도입, 시행하고 있다. 공공부문에서 역량평가의 활용 목적은 주로 3급은 선발, 4급은 배치(보직), 5급은 승진에 초점을 맞춰 운영하고 있다. 하지만 공공부문의 역량평가는 민간부문과 비교하면, 역량평가의 목적, 평가과정, 역량평가 프로그램의 측면에서 차이를 보이고 있다. 역량평가의 목적에서 공공부문은 후보자 선발 승진을 위한 것이며, 민간부문은 경력개발과 육성에 초점을 두고 있다. 따라서 민간부문보다 지속적인 역량개발을 하지 못하고 있으며, 직무를 수행하는 데 있어서 성과를 높이기 위한 목적으로 활용 되지 못하고 있다. 평가항목과 관련해서, 공공부문은 일반적으로 6개 역량에 대하여 5점 만점에 2.5점 이상이면 역량평가를 통과하는 방식으로 제도를 운영하며, 역량의 수준이 어느 정도이며, 부족한 부문은 무엇인지에 대한 피드백이 미흡한데 반하여, 민간부문은 탈락자를 선별보다는 우수자와 적임자를 선별하기 위하여 개인별 평균점수 뿐 아니라 각 개인의 역량별 점수를 중요시 하고 이를 활용하여 보직과 경력개발에 활용한다. 평가자 선발 및 운영과 관련해서는 공공부문은 평가에 있어 공정성에 중점을 두고, 민간부문은 활용성에 중점을 두어 공공부문은 역량평가를 통하여 역량을 개발하고 적재적소에 인적자원을 활용한다는 측면에는 부합하지 못하고 있다. 따라서 공공부문도 역량평가를 통하여 우수자를 파악하고 이들이 동기부여가 될 수 있도록 하는 방안으로 개선하고 정확한 보고서와 개인별 피드백을 통해서 더 우수한 관리자로 성장할 수 있도록 역량평가제도 운영에 변화를 기하여야 한다.

• Asia pacific journal of information systems
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• 제18권3호
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.