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소비자의 기질적 욕심이 무행동 관성에 미치는 영향: 예상되는 무행동 후회와 극대화 성향의 조절된 매개효과
구현진 ( Koo Hyunjin ),손영우 ( Sohn Young Woo ),임혜빈 ( Rim Hye Bin ) 한국소비자학회 2016 소비자학연구 Vol.27 No.5
Inaction inertia refers to a phenomenon that after having bypassed an initial attractive opportunity, people are less likely to act on subsequent similar opportunities. This phenomenon is considered as one of the negative side effects of sales marketing, and is a nuisance for retailers and marketers. Previous studies on inaction inertia have focused on the preconditions and contexts pertaining to the occurrence of the phenomenon. However, only very little attention has been paid to the effects of individual differences of the customers on inaction inertia, such as personal characteristics or decision-making styles. To address the issue, we aimed to examine the influence of consumers` personal characteristics on inaction inertia. Specifically, we explored consumers` level of dispositional greed on inaction inertia. Dispositional greed refers to an individual tendency to never being satisfied with what one currently has, and always craving for more. That is to say, people with high levels of dispositional greed would always want more than what they possess at present. Therefore, we hypothesized that greedier people would show higher purchase intention even in the face of less attractive and inferior opportunity because they would primarily focus on acquiring more products. It implies that greedier people might be less affected by the inaction inertia. Furthermore, we assumed that anticipated inaction regret would mediate the relationship between greed and purchase intention. According to previous research, regret is a negative emotion which is closely related to inaction inertia. Anticipated inaction regret is a type of regret that a person experiences when facing the bargains, anticipating how one might feel when foregoing the upcoming opportunity. According to previous studies, anticipating higher inaction regret leads to higher purchase intention under the inaction inertia contexts. As greedier people tend to want more, we presumed that they would report higher anticipated inaction regret. Taken together, we hypothesized that anticipated inaction regret would positively mediate the relationship between greed and purchase intention. In addition to the mediation model of greed, anticipated inaction regret and purchase intention, we hypothesized that consumers` level of maximizing tendency would affect the relationship between greed and anticipated inaction regret. Maximization refers to a style of decision-making, which is characterized by pursuing the best option through exhaustively searching and comparing the alternatives. Since maximizers want the best option, they are prone to feeling regrets, both before and after the decision-making process. Hence, we assumed that people with high maximization tendency would anticipate higher inaction regret, thus positively moderating the positive relationship between greed and anticipated inaction regret. In sum, our study aimed to explore the influence of consumers` dispositional greed on inaction inertia, by testing a moderated mediation model of anticipated inaction regret and maximization. We hypothesized that anticipated inaction regret would positively mediate the relationship between greed and purchase intention, and individuals` maximization tendencies would moderate the mediating effect of anticipated inaction regret. To confirm our hypotheses, we conducted an online survey via Amazon Mechanical Turk (M = 245). Using SPSS PROCESS macro, the moderated mediation model was tested. The results supported our hypotheses. Participants with stronger dispositional greed anticipated higher levels of inaction regret, and reported higher purchase intention. In addition, the mediating effect of anticipated inaction regret was greater for participants with stronger maximization tendencies. This research shed light on the psychological mechanism of inaction inertia by examining the impacts of consumers` individual difference variables. Additionally, this research is meaningful in terms of expanding previous studies of the impacts of affect on consumer behaviors by focusing on greed which was gained little attention to. Further implications, limitations and possible future studies were also discussed.
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김나연(Nayeon Kim),신윤희(Yunhee Shin),김수정(Soojeong Kim),김지인(Jeein Kim),정갑주(Karpjoo Jeong),구현진(Hyunjin Koo),김은이(Eunyi Kim) 한국정보과학회 2007 정보과학회논문지 : 소프트웨어 및 응용 Vol.34 No.9
본 논문에서는 신경망을 이용하여 텍스타일 영상으로부터 인간의 감성을 인식할 수 있는 시스템을 제안한다. 자동감성인식 시스템의 구현을 위해 220장의 텍스타일 영상을 수집한 후, 일반인 20명을 대상으로 설문조사를 실시하였다. 이를 통해 텍스타일 영상에서의 패턴과 감성간의 상관관계, 즉 특정 패턴이 사람의 감성에 영향을 준다는 것을 발견하였다. 따라서 본 연구에서는 텍스타일 영상에 포함된 패턴의 인식을 위해 신경망을 이용하였으며, 이때 패턴 정보의 추출을 위해 두 가지 특징 추출 방법을 사용한다. 첫 번째는 auto-regressive method를 이용한 raw-pixel data extraction scheme(RDES)을 사용하는 것이고, 두 번째는 wavelet transformed data extraction scheme(WTDES)을 사용하는 것이다. 제안된 시스템의 유용성을 증명하기 위해서 실제 100장의 텍스타일 영상을 감성을 인식하는데 사용했다. 그 결과 RDES와 WTDES를 사용했을 때 각각 71%와 90%의 인식률로, WTDES를 사용했을 때가 RDES를 사용했을 때보다 더 좋은 성능을 보였다. 데이타 추출방법에 따라 다소 차이가 있었지만 전체적으로 약 81%의 정확도를 보였다. 이러한 실험 결과는 제안된 방법이 감성인식 기반으로 텍스타일 데이타를 검색 할 수 있는 시스템 및 다양한 산업 분야에 응용 가능함을 보여주었다. This paper proposes a neural network based approach for automatic human emotion recognition in textile images. To investigate the correlation between the emotion and the pattern, the survey is conducted on 20 peoples, which shows that a emotion is deeply affected by a pattern. Accordingly, a neural network based classifier is used for recognizing the pattern included in textiles. In our system, two schemes are used for describing the pattern; raw-pixel data extraction scheme using auto-regressive method (RDES) and wavelet transformed data extraction scheme (WTDES). To assess the validity of the proposed method, it was applied to recognize the human emotions in 100 textiles, and the results shows that using WTDES guarantees better performance than using RDES. The former produced the accuracy of 71%, while the latter produced the accuracy of 90%. Although there are some differences according to the data extraction scheme, the proposed method shows the accuracy of 80% on average. This result confirmed that our system has the potential to be applied for various application such as textile industry and e-business.
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