본 연구는 스포츠 휴먼브랜드의 굿즈상품(Goods) 가치, 감정반응(PAD), 소비 행동 간 일어나는 현상을 분석하기 위해 2021년 10월 18일~2022년 1월 12일까지 온라인 서베이(URL), DM, E-mail 등을 활용하여 371명을 편의 표준추출법으로 표집하였으며, 불성실한 응답자 51명을 제외한 유효 표본 320명을 인과 관계(SEM)를 적용하여 분석하였다. 첫째, 휴먼 브랜드의 굿즈 가치는 감정반응에 정(+)의 영향으로 가설이 채택되었다. 둘째, 감정반응은 소비 행동에 정(+)의 영향을 미치는 것으로 가설이 채택되었다. 셋 째, 휴먼 브랜드의 굿즈 가치는 소비 행동에 정(+)의 영향을 미치며 부분적으로 가설이 채택되었다. 마지막 으로 굿즈상품(goods) 가치와 소비 행동의 관계에서 감정반응의 간접효과는 통계적으로 유의 의미한 것으 로 나타났다.

Customers’ final purchase decisions for electronic products are understandably influenced by previous experiences, marketing messages such as price and promotion, and opinions from other consumers (Simonson and Rosen 2014). In particular, millions of product reviews are posted daily on online review boards or social media represent aggregate consumer preference data (Decker and Trusov 2010). Past studies analyzing online reviews or word-of-mouth (WOM) have focused more on the quantitative dimension of volume of WOM (or “how much people say”), but less on qualitative dimension of valence of WOM (or “what people say”) (Gopinath, Thomas, and Krishnamurthi 2014). However, recent studies have analyzed disaggregate-level UGC by performing text mining in addition to a general analysis of volume and valence of OUGC. Onishi and Manchanda (2012) investigate the relationship between movie sales and both TV advertising and blogs. Although the authors find that the volume and the valence of OUGC (i.e., blogs) are predictive of market outcomes, they retain only certain words (i.e., advertising, award, interesting, and viewed) that consumers would find useful, therefore having general predictive power for market outcomes. Gopinath, Thomas, and Krishnamurthi (2014) address the relationship between the content of online WOM, advertising, and brand performance of cell phones and find that the volume of OUGC does not have significant impact on sales, but only the valence of recommendation UGC has a direct impact. Liu, Singh, and Srinivasan (2015) find that both the volume and sentiments of Tweets do not outperform the information content of Tweets in predicting TV series ratings. Although these three papers have investigated the importance of qualitative UGC through text mining techniques, such studies have not accounted for the detailed dimensions of specific contents. For example, Onishi and Manchanda (2012) use only 4 words out of top 30 frequently cited words for their analysis, and Gopinath, Thomas, and Krishnamurthi (2014) classify the OUGC into three disaggregated dimensions (i.e., attribute, emotion, and recommendation) without further classifications of subcategories and valence of positivity and negativity. Liu, Singh, and Srinivasan (2015) mainly focus on positive and negative Tweet contents about TV shows, lacking further classification of functional and emotional dimensions. In contrast to these studies, this study aims to examine in-depth multidimensional aspects of the content of online reviews, i.e., qualitative UGC, and their impacts on product sales. In this process, we develop defensible measurements of UGC by executing a comprehensive empirical text analysis and evaluate the impact of measures of qualitative UGC relative to volume measure of quantitative UGC. Specifically, we analyze a large data set of UGC on the 350 most talked-about smartphone games from seven different genres (e.g., action, arcade, shooting, puzzle, role playing, simulation, and sports) over a 30 month period, August 2010 to February 2013. We utilize a theoretical framework that classifies qualitative UGC into two major perceptions of functional and emotional dimensions. Prior studies show that perceptions of both functional (cognitive) and emotional (affective) dimensions should be considered to investigate their effects on perceived user satisfaction (Coursaris and van Osch 2015) and online shopping behavior (Van der Heijden 2004). It is evident that both functional and emotional UGC influence consumers to purchase a focal product (Lovett, Peres, and Shachar 2013). The functional UGC relates to the positive and negative attributes and beliefs about a product, and the emotional UGC pertains to the feelings and emotions in response to product experience. As an example, consider one innovative car-racing mobile game which, although expensive, has 3D graphics and high level of complexity. After playing this game, consumers may express their feedback on this game online by describing it as well-made, unique, but sometimes fearful (because a high bill charge is expected from excessive playing time), and addictive (because they like the game too much to stop playing it). This type of online reviews contains different types of UGC: functional (e.g., quality, innovativeness) and emotional (e.g., fear). Another layer of our analysis involves the heterogeneity of impact on product sales across different qualitative UGCs. Specifically, we consider the effects of functional UGC on product sales across emotional contexts such as anger and happiness, in other words, a simultaneous association between functional UGC and emotional UGC. For example, although a consumer may be attracted by some reviews on the high quality graphics of a mobile game (functional UGC), she may hesitate to purchase this product because other reviews express their fear about high cost of purchasing virtual goods (emotional UGC). Accordingly, we expect the functional UGC’s effects on sales to be moderated (amplified or reduced) by emotional UGC. We accommodate such interaction effects in both aggregate and disaggregate models. To the best of our knowledge, this study is the first to empirically identify two dimensions of qualitative UGC (functional and emotional), and shed light on the effects of multidimensional UGC categories on sales. Our findings on the influence of qualitative UGC on product sales are quite different from the prevailing view that firms should pay attention more to the volume of UGC (Chevalier and Mayzlin 2006; Liu 2006) but little to the valence of UGC (Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Liu 2006). Rather, our research is in line with recent three papers (Gopinath, Thomas, and Krishnamurthi 2014; Liu, Singh, and Srinivasan 2015; Onishi and Manchanda 2012) in terms of the importance of considering specific contents from a vast amount of text data. However, our paper provides two key contributions. First, we show that specific categories of qualitative online UGC such as functional and emotional variables can be used to predict product sales; this result will be of a high managerial relevance. Especially, traditional methods that use simple metrics such as volume and valence of UGC are less accurate than our method that employs a sophisticated, multidimensional content analysis. Second, the results offer guidance to firms in determining which specific UGC (quantitative or qualitative; functional or emotional; under what contexts) they should focus on for increasing the efficiency of their online marketing activities. Utilizing a large dataset of online reviews on 350 mobile games consisting of four million postings generated for thirty months, the authors identified 76 representative words to describe the functional and emotional UGC using text analysis and word classification. We combined the resulting UGC volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. We find that functional UGC includes 54 representative words to describe various levels of product quality, product innovativeness, price acceptability, and product simplicity, and emotional UGC includes 22 words to express anger, fear, shame, love, contentment, and happiness. The results show that the volume and valence of aggregated functional UGC and the share of aggregated emotional UGC have the positive effects on sales. The volume and valence of functional UGC subcategories have mixed effects on sales and the link is moderated by the share of emotional UGC subcategories. These results are in contrast to those in the literature. Further, a sales forecasting model which includes 13 variables of UGC subcategories shows the best predictive validity. The authors discuss the implications of these results for online marketers.

디자인에 대한 관심이 증대되면서 ‘시각적 미를 추구하는 행위’로 디자인의 의미에 대해 생각하기 시작하였다. 출판물 구매에 있어서도 인터넷과 정보통신의 발달로 블로그나 SNS 등 온라인 매체를 통해 검색하고 주문한다. 책 표지 디자인에 있어서 표제는 책이 담고 있는 내용과 성격을 드러내는 얼굴과 광고 역할을 하고 있다. 또한 출판사의 판매증진의 수단으로 이미지 형성을 하는 데 중요한 역할을 한다. 감성 손 글씨는 책 표제(북 커버), 예술 작품, 생활 소품, 광고 등 여러 분야에서 디자인과 접목되어 활용되고 있다. 이에 본 연구에서는 첫째, 책 표지 디자인에 있어서 표제의 중요성과 가치를 알아보고 둘째, 책표지 디자인의 제목 서체에서 감성 손 글씨의 제시와 독자들에게 구매의욕의 영향을 미치는지에 대해 독자의 선호도를 제시하고자 한다. 대상은 한국지역출판문화잡지연대(이하 ‘한지연’) 회원사의 발행 잡지와 출판물 그리고 수원에서 발행되고 있는 지역공동체 잡지 <사이다>를 중심으로 하였다. 설문조사를 통해 책 표제 글씨에 있어서 활자본 16.1%에 비해 감성 손 글씨에 대한 선호도가 83.9%로 높은 차이가 조사되었다. 또한, 책 구매 시 미치는 영향으로, 커버 디자인이 65%로 그 중에 제목이 51%, 레이아웃과 이미지가 31%, 색채, 출판사 순으로 나타났다. 이 결과를 통해, 책 커버 디자인의 중요성과 그에 적합한 폰트 및 감성 손 글씨의 활용의 중요성을 알 수 있었다. 이에, 대중의 선호도와 시대 트렌드를 통한 차별화된 디자인의 역할 또한 중요하며, 서체 개발 및 관련 연구를 통해 책 커버 서제의 독창적인 조형 예술이 구축되기를 기대한다.

본 연구는 성취정서 통제-가치이론(Pekrun, 2000, 2006)을 적용하여 체육 성취상황(수업, 학습, 시험)에 따라 학생들의 다양한 정서경험을 유발시키는 선행요소를 탐색하였다. 남녀 중학생 270명(남학생 156명, 여학생 114명)이 개방형 질문지에 응답하였으며, 자료를 위해 귀납적 내용분석이 실시되었다. 그 결과 9개의 개별 정서들(정적 활성화 정서=즐거움, 희망, 자부심; 정적 비활성화 정서=안도감; 부적 활성화 정서=화, 불안, 수치심; 부적 비활성화 정서=절망감, 지루함)을 유발시키는 주요 선행요소들이 각 성취상황의 세 가지 시간적 영역별(체육수업 이전, 중간, 이후)로 나뉘어 확인되었다. 특히, 각 성취상황에서 경험하는 학생들의 정서반응은 시간적 영역에 따라 차이가 있지만, 그 선행요소들은 전반적으로 정서적, 인지적, 생리적, 동기적 측면들과 관련이 있었다. 본 결과는 체육수업 상황에서 학생들의 정서경험을 결정짓는 주요 선행요소들과 성취정서의 역할, 그 교육적 시사점과 관련하여 논의되었다.