The Role of Social Distance in the Collaborative Consumption

Kang, Juyoung(강주영),Kim, Sooyun(김수연),Kim, Heejin(김희진),Choi, Sunmee(최선미) 한국서비스경영학회 2019 서비스경영학회지 Vol.20 No.4

In the collaborative consumption context, peer service customers and peer service providers can both be from diverse backgrounds. When their backgrounds differ, the difference can lead to social distance between the two, which can influence their reactions to each other. Few studies have addressed this issue or how to resolve it. This study aims to be the first to examine this issue from the peer service customer’s perspective by examining the effect of a peer service customer’s perceived social distance on his/her response to the peer service provider and the platform providing firm, and the effect of the peer service provider’s communication style on reducing the negative effect of perceived social distance. Data was collected using a scenario-based online survey with a 2(levels of social distance: high vs. low) x 2(communication accommodation styles: convergence vs. maintenance) between-subject experimental design. The findings demonstrated the significant negative effect of social distance, and the use of convergence style as a viable option to effectively reduce the negative effect.

HOW TO IMPROVE P2P SHARING-SERVICE QUALITY: THE MODERATING ROLE OF THE FORM OF ADDRESS ON THE EFFECT OF INCONGRUENCE IN GENDER AND RACE ON PERCEIVED SOCIAL DISTANCE

Juyoung Kang,Sooyun Kim,Sunmee Choi,Heejin Kim 글로벌지식마케팅경영학회 2018 Global Marketing Conference Vol.2018 No.07

Introduction Recent years have witnessed a rapid growth in peer to peer (P2P) sharing-service businesses such as Uber and Airbnb. In P2P sharing-service businesses, goods or services are provided by customers (peers) rather than by service firms, who act simply as an intermediary between customers. One customer acts as a service-providing customer (SPC), and the other as a service-receiving customer (SRC). P2P sharing-service firms have no direct control over an SPC’s quality of service provided to an SRC. Further, both SPCs and SRCs are customers to the firm, and therefore firms are concerned with the quality of service provided not only to SRCs but also to SPCs. In the P2P sharing-service context, particularly in the case of services serving the needs of diverse travelers (e.g., Uber or Airbnb), SPCs and SRCs can be people of different genders and races, which can cause them to feel socially distant from each other. Since SPCs are not professional service employees, they may not know how to cope with the social distance, which can result in uncomfortable service experience for both SPCs and SRCs. The more similar to the SPC an SRC feels, the more comfortable the SRC is likely to feel, which can lead to higher service satisfaction. Yet, few studies examined how such social distance can be reduced. This study is intended to fill this gap in the research. Specifically, we propose that an SPC’s form of address for SRC can moderate the effect of the incongruence in gender and race on an SRC’s perceived social distance. Theoretical Development Social distance refers to the level of acceptance people have of others outside of their own social group or class (Bogardus, 1928). It is a measure of perceived difference (or distance) between groups. In the context of the P2P sharing service, SRCs and SPCs can be from diverse social groups. When an SRC encounters an SPC from a social group that is different from theirs, the SRC can feel socially distant from the SPC. Immediate differences an SRC can identify upon meeting an SPC is demographic such as gender, age and race. In this study, we first propose that the difference (incongruence) in gender, age and race makes an SRC feel socially distant from an SPC. Social distance is closely related with similarity (Osbeck & Moghaddam, 1997; Liviatan, Trope & Liberman, 2009). In the context of mentoring, the higher the perceived and actual similarity a portage feels with a mentor, the higher the level of the portage liking and satisfaction for the mentor and with the mentoring service (Ensher, 1997). Matching gender and race between a mentor and a portage positively influenced self-reported grade point average, efficacy and confidence of a portage (Blake-Beard et al. 2011). Race was well demonstrated to influence social distance (Triandis & Triandis, 1960). Taken together, we conjecture that the effect of the difference in gender and race on social distance will apply to the P2P sharing service context and propose the following hypothesis: H1: The incongruence in gender and race between an SRC and an SPC will make SRCs feel more socially distant from SPCs compared to the case of congruence. We propose in this study that the form of address for SRCs by SPCs can influence the level of social distance SRCs feel because of the incongruence in gender and race. The relationship between forms of address and social distance has been proposed (Brown, 1965). Intimate terms of address is associated with intimate relationship. Intimate terms of address is inversely proportional to social distance (Keshavarz, 2001). Calling someone by the first name is related with friendliness (Brown, 1961). The use of first name is positively associated with closeness in relationship (Brown, 1965). An empirical study in the healthcare context showed that most patients preferred to be addressed by the first name (as opposed to last name) (Gillette, Filak & Thorne, 1984). Taken together, we propose the following: H2: Intimate forms of address by the SPC for the SRC will reduce the level of social distance caused by the incongruence in gender and race. We propose that the level of perceived social distance influences the level of comfort SRCs feel with the SPC during the service delivery. The negative effect of social distance on the level of interaction comfort has been shown (Paswan & Ganesh, 2005). In the context of service encounters where customers feel cultural differences, psychological distance was shown to influence comfort negatively (Weiermair, 2000). Since social distance is a dimension of psychological distance (Trope & Liberman, 2011), we propose the effect of social distance on comfort as following: H3: Reduced social distance will lead to a higher level of comfort. The effect of social distance on comfort can vary by customers. In this study, we propose that the effect is moderated by the customer’s motive for the purchase of the P2P sharing service. There are largely four movies for customers who participate in collective consumptions (i.e., sharing service): economic motive, social motive, hedonic motive, and the motive to reduce risks and responsibilities (Benoit, Baker, Bolton, Gruner & Kandampully, 2017). Economic motives are associated with reducing expenses, and social motives are with meeting other people (e.g. more authentic travel) (Benoit et al., 2017). Hedonic motives are related with “accessing products that are exciting or normally out of reach” while motives to reduce risks and responsibilities are related with “no burdens of ownership, option to preview a product for potential purchase” (Benoit et al., 2017). According to a research in the context of P2P accommodations, cost saving, familiarity, trust, and utility are determinants of satisfaction with a sharing option. Thus, we propose that customer motives moderate the effect of social distance on comfort (M?hlmann, 2015). H4: The effect of social distance on comfort vary by customer motives for purchase. When feeling comfortable, people are more likely to trust, feel satisfied, and commit themselves, which can help improve relationship (Spake, Beatty, Brockman & Crutchfield. 2003). Comfort positively influences perceived service quality and satisfaction (Dabholkar, Shepherd & Thorpe, 2000). Comfort influences satisfaction positively (Paswan & Ganesh, 2005). In the P2P sharing-service context, SRCs’ evaluation of SPCs are carried out by reviews. Customer reviews of SPCs’ services are vitally important for both SRCs and SPCs. Reviews help other SRCs to identify desirable SPCs (Ert, Fleischer & Magen, 2016) and SPCs to receive feedback for their service quality improvement. Therefore, we propose the following: H5: Comfort leads to SRC’s intention to write good reviews. Data Collection Data will be collected from American consumers who have used Uber at least once in the past one year through an online scenario-based survey using a 2 (genders: male vs. female) x 3 (races: white vs. yellow vs. black) x 3 (forms of address: no address vs. first name vs. last name) between-subject experimental design. Hypotheses will be tested by an analysis of variance and a structural equation modeling analysis. In the analyses, the potential effects of trust, familiarity, community belonging, utility (M?hlmann, 2015) and age will be controlled. Implications Findings of this study will reveal the importance of reducing social distance that SRCs feel during encounters with SPCs. Of many possible ways to reduce social distance between SRCs and SPCs, the result of this study will show that SPCs’ use of appropriate form of address to SRCs is effective. Further, it will show that the effect of social distance on customer comfort can vary by the purpose of the use of the sharing service. These findings will offer P2P service firms insights on how to help SPCs offer more comfortable services to SRCs and as a result receive positive reviews from SRCs.

SOM 기반 시공간 데이터 마이닝 시스템

강주영 ( Juyoung Kang ),이봉재 ( Bongjae Lee ),송재주 ( Jaeju Song ),신진호 ( Jinho Shin ),용환승 ( Hwanseung Yong ) 한국정보처리학회 2004 한국정보처리학회 학술대회논문집 Vol.11 No.2

데이터 양이 급증함에 따라 축적된 데이터로부터 의미있는 지식을 추출해 내고자 하는 데이터 마이닝에 대한 연구가 활발하게 진행되어 왔다. 특히 최근, 환경이 이동 분산화 되어감에 따라 감시·모니터링 시스템, 기상 관측 시스템, GPS 시스템과 같은 다양한 응용 시스템으로부터 방대한 양의 시공간 데이터가 발생하게 되었고, 이를 효율적으로 분석하고자 하는 시공간 데이터 마이닝 연구에 대한 관심이 더욱 높아지고 있다. 기존의 데이터 마이닝 기법의 경우 문자나 숫자 데이터를 대상으로 최적화 되어있기 때문에 시·공간 속성을 동시에 가지는 데이터를 분석하기에는 한계가 있는 것이 사실이다. 본 논문에서는 SOM(Self-Organizing Map)을 적용하여 시공간 클러스터링 모듈을 개발하고, 개발된 모듈의 성능 및 클러스터링 정확성을 다른 세 가지 군집분석 알고리즘과 비교, 분석하였다. 또한 가시화 모듈을 개발하여 입력 데이터의 특성과 결과를 더욱 정확하게 분석할 수 있도록 하였다.

Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection

Park, Juyoung,Kang, Mingon,Gao, Jean,Kim, Younghoon,Kang, Kyungtae PLENUM PUBLISHING CORPORATION 2017 JOURNAL OF MEDICAL SYSTEMS Vol.41 No.1

엔진과 변속기간에 정격 출력 회전수 매칭을 위한 어댑터 개발

강주영(Juyoung Kang),배종원(Jongwon Bae),김종민(Jongmin Kim),최진구(Jingoo Choi),김주호(Jooho Kim),김바다(Bada Kim),이정호(Jeongho Lee),이충근(Chunggeun Lee),이대엽(Daeyup Lee) 한국자동차공학회 2017 한국자동차공학회 학술대회 및 전시회 Vol.2017 No.11

데이터 축소와 군집화를 사용하는 시공간 데이터의 이산화 기법

강주영(Juyoung Kang),용환승(Hwan-Seung Yong) 한국정보과학회 2008 한국정보과학회 학술발표논문집 Vol.35 No.1

협업 필터링 기반의 휴대폰 무선 서비스 추천을 위한 기지국 군집분석과 검증

강주영(Juyoung Kang),김현구(HyunKu Kim),박상언(Sangun Park) 한국전자거래학회 2010 한국전자거래학회지 Vol.15 No.2

이동통신 시장의 경쟁이 격화됨에 따라 이미 포화상태에 다다른 음성 서비스보다는 데이터 서비스를 증가시키기 위해 통신사들이 노력하고 있다. 그러나 단말기의 한계로 인해 자신이 원하는 서비스를 검색하는 것이 쉽지 않고, 시간 역시 많이 소모되는 문제점으로 인해 데이터 서비스 시장이 기대한 만큼 성장하지 않고 있는 상황이다. 본 논문에서는 이와 같은 문제점을 극복하기 위하여, 이동통신사에서 보유하고 있는 위치정보와 무선인터넷 이용로그를 기반으로 개인이 처한 상황에 맞게 무선 서비스를 추천하는 개인화 서비스 방안을 제시하고자 한다. 이를 위해 무선통신사의 실제 데이터를 기반으로 기지국의 위치정보와 해당 위치에서의 서비스 이용 정보를 이용하여 군집 분석을 실시하고, 이를 기반으로 협업 필터링를 이용한 무선 서비스의 개인화 서비스 방안을 제안한 후 이를 검증하였다. 또한 분류된 군집의 특성을 분석하고, 이를 기반으로 한 무선 서비스 추천 방안을 제시하였다. Mobile Communication Companies are trying to increase data services rather than telephone communication services that already became saturated as the competition of mobile communication market gets intensified. However, it is hard and time-consuming for customers to find desired mobile phone wireless services because of the limitation of screen and speed of the mobile phone. Therefore, the market does not grow rapidly as mobile communication companies expected. In our research, we suggest a personalized wireless service recommendation system that considers each individual context by using geographic information and wireless internet usage logs to overcome the mentioned problems. In order to design and implement the system, we conducted clustering analysis on base stations and real service usage logs of each base station, and suggested a personalized recommendation system based on collaborative filtering that uses the clustering results. Moreover, we verified the performances of our system with experiments.

데이타 축소와 군집화를 사용하는 시공간 데이타의 이산화 기법

강주영(Juyoung Kang),용환승(Hwan-Seung Yong) 한국정보과학회 2009 정보과학회 컴퓨팅의 실제 논문지 Vol.15 No.1

To increase the efficiency of mining process and derive accurate spatio-temporal patterns, continuous values of attributes should be discretized prior to mining process. In this paper, we propose a discretization method which improves the mining efficiency by reducing the data size without losing the correlations in the data. The proposed method first abstracts original trajectories into approximations using line simplification and then groups them into similar clusters. Our experiments show that the proposed approach improves the mining efficiency as well as extracts more intuitive patterns compared to existing discretization methods. 항목 기반의 순차 패턴 마이닝 기법들을 시공간 데이타에 적용하기 위해서는 시공간 속성 값에 대한 적절한 이산화가 필수적이다. 본 논문에서는 입력 데이타의 시공간적 상관 정보를 유지함과 동시에 데이타 수를 축소시킴으로써 마이닝 프로세스의 효율성을 높이는 이산화 기법을 제안한다. 제안된 기법은 선 단순화를 사용하여 궤적에 대한 근사치를 구함으로써 마이닝 단계에서 처리할 데이타 크기를 축소시킨다. 또한 단순화 된 궤적을 유사한 시공간적 특성을 가지는 논리적 그룹으로 군집화하여 데이타의 분포를 고려한 이산화를 수행한다. 실험을 통해 제안된 기법이 마이닝 프로세스의 효율성을 높일 뿐 아니라 보다 직관적이고 해석이 용이한 패턴을 도출하는 것을 보였다.

OLAP 시스템에서 희박 데이터의 패턴 분류 및 성능 평가

강주영(Juyoung Kang),이봉재(Bongjae Lee),송재주(Jaeju Song),신진호(Jinho Shin),용환승(Hwanseung Yong) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.2Ⅱ

OLAP(On-Line Analytical Processing)은 데이터 웨어하우스 내의 방대한 양의 데이터에 대해 사용자와의 상호 작용이 가능하도록 질의에 대하여 빠른 응답성능을 보장해야 한다. 이를 위해 OLAP 시스템은 데이터에 대한 다량의 다차원 집계 연산을 수행해야 하기 때문에, 일반적으로 사전 연산 결과를 저장하여 직접적인 집계 연산을 줄임으로써 응답 성능을 높이는 방법을 사용하고 있다. OLAP 다차원 데이터의 희박성은 이러한 사전 연산 시 데이터 폭발 현상을 일으켜 도리어 성능을 저하시키는 요인으로 작용할 수 있다. 본 논문에서는 데이터의 희박성과 성능 문제에 대해 고찰하고 OLAP 응용에서 발생할 수 있는 다차원 데이터의 희박성 패턴에 대해 정의하였다. 또한 정의된 패턴에 따라 희박 데이터를 생성하는 데이터 생성기를 구현하고 이를 이용하여 생성된 데이터를 기반으로 MS SQL Server Analysis Services와 Pilot DSS 두 OLAP 제품의 성능을 평가하고 결과를 비교하였다.

시공간 데이터를 위한 클러스터링 기법의 성능 비교

강주영(Juyoung Kang),이봉재(Bongjae Lee),송재주(Jaeju Song),신진호(Jinho Shin),용환승(Hwanseung Yong) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.2Ⅱ

최근 GPS 시스템, 기상 관측 시스템과 같은 다양한 응용 시스템으로부터 수집된 시공간 속성을 가진 데이터를 분석하고자 하는 시공간 데이터 마이닝에 대한 관심이 더욱 높아지고 있다. 기존의 시공간 데이터 마이닝에 대한 연구는 문자·숫자 데이터를 기반의 마이닝 기법을 그대로 적용하고 있기 때문에 데이터의 시공간 속성을 충분히 고려한 분석으로는 한계가 많은 것이 사실이다. 본 논문에서는 패턴 인식과 클러스터링 능력이 뛰어나다고 알려진 SOM을 기반으로 한계가 많은 것이 사실이다. 본 논문에서는 패턴 인식과 클러스링 정확성에 대하여 K-means,응집 계층 알고리즘(Average Linkage, Ward)과 비교함으로써 시공간 데이터 마이닝을 위한 각 알고리즘들의 성능을 분석하였다. 또한 입력 데이터의 특성과 클러스터링 결과를 더욱 정확하게 나타내어 가시적인 분석을 도울 수 있도록 시공간 데이터 클러스터링을 위한 가시화 모듈을 개발하였다.