Although older adults’ adoption rates of smartphones have continued to increase in recent years, health-related apps are not widely accepted by older adults. Therefore, the primary aim of this study was to explore older adults’ adoption behavior of mobile health (mHealth) apps in the U.S. Specifically, this study applied protection motivation theory (PMT; Rogers, 1975) as the theoretical base to explain older adults’ adoption process of health apps to understand the influences of threat and coping appraisals on older adults’ adoption behavior of health apps. In addition, the current study incorporated the technology readiness theory (Parasuraman, 2000) to understand the influences of older adults’ positive and negative technological readiness (TR) on their PMT components. Data were collected from older adults (n = 600; 60% female) aged over 55 in the U.S. using online survey questionnaires. The data analysis was conducted using partial least square structural equation modeling (PLS-SEM). The findings suggest the impact of coping appraisals on older adults’ attitudes toward mHealth apps and their (continuance) intention to use mHealth apps. Especially among PMT’s four appraisal components, response efficacy was the most critical underlying mechanism driving mHealth app-related attitude and behavioral intention. In addition, older adults’ beliefs about using mHealth apps (i.e., TR) significantly influenced their coping appraisal processes, while positive TR was more critical in forecasting their coping appraisals. The results suggest a need to classify older consumers into different segments based on their TR to develop differentiated marketing strategies. The findings also suggest that marketers understand various protective motivation components concerning the use of mHealth apps among older adults.

Technology, for example, Personalized Technology Services (PTS), has groomed consumers to expect an integrated and personalized shopping experience regardless of the channels, such as websites, mobile apps, physical stores, etc. PTS refers to technologies that offer personalization functions to meet customer needs at the time of their shopping for a seamless experience. The purpose of this study is to investigate the role of retailer mobile apps’ PTS in consumers’ omnichannel shopping experiences by: (1) identifying PTS values specific to retail mobile apps for in-store shopping and (2) testing the PTS values – channel integration – consumer responses links based on Information Integration Theory (IIT). We first proposed that PTS via mobile apps holds various positive values. Second, we postulated four hypotheses: H1. PTS values enhance the integration of PTS values, H2. Integration of PTS values positively affects customer engagement, H3. Customer engagement positively affects customer satisfaction and H4. Customer engagement mediates the relationship between integration and customer satisfaction. Two web-based survey studies were employed with US consumers who had an experience with mobile app-mediated PTS offered by retailers. For study 1, a total of 239 US consumers participated in the survey. Study 1 identified five value dimensions of the app-mediated PTS: hedonic value, utilitarian value, self-efficacy, co-creation, and synchronicity. For study 2, a total of 373 US participants completed the survey. Study 2 confirmed the proposed structural model that PTS values positively affected channel integration which, in turn, positively influenced customer engagement and shopping satisfaction. Additionally, customer engagement partially mediated the effect of integration on shopping satisfaction. This study expanded the literature on omnichannel retailing by exploring consumer in-store shopping experience using retail mobile apps from PTS and channel integration perspectives. Practically, the study findings provided insights for marketers into how to design the retailers’ mobile apps to enhance the integrated shopping experience of consumers.

This study aims to address two important questions: will advertising on mobile short-form video apps jeopardize the value perception of luxury brands (RQ1), and if so, how will self-deprecating online reviews eliminate these negative effects (RQ2). An experimental design approach was employed to investigate the proposed research questions. Three experiments were conducted to test the hypotheses. SPSS was used for data analysis. The study 1 finds that compared with traditional media, advertising on mobile short-form video apps shortened the psychological distance between consumers and luxury, therefore has a more negative impact on consumers’ perception of luxury brands. The study 2 reveals that self-deprecating online reviews can eliminate the negative effects of advertising of luxury brands. On the basis of previous research, this paper proves the negative influence of social media on luxury brands in the scene of new social media-mobile short format video application. In addition, it also studies the moderating effect of online comments, especially self-deprecating comments, on consumers' perception of luxury brands. This study outlines theoretical contributions and practical implications for the luxury marketing management and made suggestions for future research in the field of luxury marketing in Social Media.

The Youtube mobile app can be seen as the Electronic Word of Mouth (E-Wom) platform since it could spread both in positive of negative review about some products. The Haul video on youtube is one of the most popular content in comparing some fashion items. This research aim is to elaborate the determinant of Information Acceptance on E-wom Youtube Mobile app. Interview and questionnaires were applied on gathering the data. The respondent number on questionnaires survey is 350 participants. The analysis used Multi regression analysis to find relationship among Information acceptance with argument quality, source attractiveness, source credibility and source style. The result said that all independent variable (argument quality, source attractiveness, source credibility and source style) have relationship to Information acceptance.

Recommender systems based on Collaborative Filtering (CF) algorithms have established an extensive means for retailers to suggest personalized item lists that will maximize each consumer’s utility. Nevertheless, in the mobile game industry, which characterized by the intense competition from the avalanche of other game options and fast-changing demands of game users, there has been no marked success with recommender systems. Instead, app stores merely show summaries of general market trends without any individual-level information, fail to suggest personalized lists based on preferences of the future. For modeling dynamics of game usage, we assume that an individual’s preferences on games can be represented as the proportion of each game’s running time, which can be calculated in daily basis by the individual’s usage time for each game apps divided by the individual’s total capacity. Then, we construct a tensor filled with the induced preferences. For the next step, we apply Bayesian probabilistic tensor factorization (BPTF), an extension of Singular Value Decomposition (SVD) to consider dynamic pattern, to restore all the empty entries of the tensor. Each restored component becomes an estimate of each user’s preference on each game at certain period. For empirical analysis, we use mobile log data in app-level for total 1,000 panels over 2 years. Top 100 mobile games in cumulative usage time are treated as focal apps in this study, making the dimension of the tensor by 1000 (users) * 100 (focal games) * 730 (2 years). We compare the model performance by the root-mean-squared error (RMSE) with that of baseline model, the static counterpart in collaborative filtering algorithm (Salakhutdinov, and Mnih, 2008). The results showed that our model (BPTF) defeats the baseline throughout overall user-game pairs, especially outperforming under the conditions that there are severe fluctuations in daily usage pattern and when the life span of newly adopted apps are relatively short. Furthermore, we compose personalized suggestions, which consists of the top-10 highly likable lists according to the predicted usage patterns for each individual, and compare the performance with that of the established general recommender system in app stores. For that matter, our suggestion also outweighed the existing recommender system by the typical performance metrics that commonly used in the mobile game industry.

This study aimed to investigate the influence of consumers' innovativeness, perceived benefits of home shopping mobile apps, and satisfaction with telephone orders on switching to and satisfaction with such apps (for those who have used these apps). This study also investigated the influence of the aforementioned factors on the intention to switch to home shopping mobile apps (for those who have not used these apps). Data were collected from 546 customers ranging in age from 18 to 59 with experience purchasing fashion goods from home shopping networks, and 502 of the questionnaires were used in the statistical analyses. Structure equation models were employed using AMOS 23.0. The results were as follows: for consumers experienced with home shopping mobile apps, their innovativeness and perceived benefits of such apps (convenience, usefulness) influenced their switching behavior. Additionally, consumers' innovativeness and perceived benefits of these apps (usefulness, accessibility, interactiveness) affected their satisfaction with the apps. For those not experienced with home shopping mobile apps, consumers' innovativeness and perceived benefits of the apps (interactiveness, security, enjoyment) influenced their intention to switch to these apps. These results indicate that different strategies should be developed for home shopping consumers experienced and inexperienced with these apps for the improvement of app satisfaction.

Personalization and privacy issues are the most important factors in the online context. To reveal how these factors affect on users’ acceptance of mobile apps, we performed structural equation modeling using 337 respondents in Japan. Our findings suggested that adaption behaviour of mobile apps can be explained by privacy paradox.

The purpose of this paper is to empirically examine factors affecting customers’ intention to buy apparels through mobile apps. The proposed model was evaluated using survey data collected from 304 mobile app users and assessed by structural equation modeling. Our study contributes to a theoretical understanding of mobile apps usage.

Purpose : The purpose of this study is to examine the effect of the attributes of mobile shopping apps on the purchase intention of fashion products in the steadily growing mobile commerce market and analyze the mediation effect of shopping flow. Research design, data and methodology : In this study, a survey was conducted on users in their 20s to 50s who had experience of fashion products via mobile shopping apps. The questionnaire was analyzed on the final 507. The research issues were verified using Frequency analysis, Exploratory factor analysis, Reliability analysis, Confirmatory factor analysis, and Structural equation model. Results : Among the attributes of mobile shopping apps, reliability, enjoyment and ease of use were found to have a significant impact on shopping flow and fashion product purchase intention, while shopping flow had a significant impact on fashion product purchase intention. Product diversity and usefulness of shopping apps didn’t show a significant effect. After examining the mediation effect, reliability, enjoyment, and ease of use were shown to have indirect effects by showing partial mediation effects. Conclusions : Studies show that consumers are not putting much emphasis on how diverse a product line is available and how useful a mobile shopping app is when they shop for fashion products on mobile devices. Factors that affect consumers’ purchase intention are reliability, enjoyment and ease of use of shopping apps. These results shows that it is important to provide reliable information about fashion products, provide reliability to customers by setting up means for safe transactions, and provide a wider variety of services and information to make using the mobile shopping app enjoyable. In addition, there is a need to make it easy to find a fashion product that consumers like and make it easy to purchase when consumers find out fashion products that they like, and to configure how to use the app easily. Consequently, consumers become immersed in shopping which is also able to improve consumers’ purchase intention on fashion products when a reliable, enjoyable and easy to use mobile shopping app is provided.

모바일 기반의 기기(스마트폰, 스마트 패드, 태블릿PC 등)들을 이용한 학습 앱 사용이 늘어나면서 많은 학습 앱 콘텐츠가 모바일 환경(앱 스토어나 구글 플레이)에서 구현되고 있다. 그러나 스마트폰 시장의 성장이 다양한 모바일 앱을 사용할 수 있는 밑거름이 되었지만 한편으로는 무분별한 유포가 앱의 품질 저하로 이어져 앱 사용자경험의 만족 및 품질에 대한 관심과 평가가 주목받게 되었다. 본 연구는 모바일 기반의 학습 앱의 사용자경험과 품질이 사용자만족과의 구조 및 영향관계를 파악하고자 먼저, 기존 문헌과 선행연구를 통해 이론적 고찰을 하고 모바일 기반의 학습 앱 경험이 있는 남녀 직장인 및 대학생 100명을 대상으로 사용자경험, 품질, 만족에 관한 설문지를 진행하였다. 그리고 각각 측정 변수들의 요인 분석 및 신뢰도 분석, 다중회귀분석, 매개분석을 통해 사용자경험과 품질이 만족에 미치는 영향을 검증하였다. 그 결과, 모바일 기반의 학습 앱 사용자경험이 만족에 정(+)의 영향을 미치는 것으로 나타났으며, 사용자경험이 만족에 정(+)의 영향을 미치는데 있어 시스템 품질이 매개역할을 하는 것으로 나타났다.

최근 몇 년간 미디어를 통해 국내외 여행 프로그램 콘텐츠에 대한 관심이 많아지고 함께 삶을 질적으로 향상시킬 수 있는 여가활동에 대한 관심도 높아지고 있다. 아울러 여가생활의 증가로 여행에 대한 관심과 소비가 급증함에 국내 저비용항공사(LCC)도 증가하고 있는 경향을 바탕으로 모바일 앱 UI(User Interface)의 현황을 파악하여 사용성 평가 및 개선방안에 대해 도출하고자 하였다. 현재 에어부산, 에어서울, 이스타항공, 제주항공, 진에어, 티웨이항공 총 6개의 저비용항공사들은 치열한 이용경쟁에서 살아남기 위해 각각의 자사의 브랜드 정체성을 확고부동하기 위한 노력을 아끼지 않고 있다. 특히 모바일로 홍보하고 있는 앱은 타사와는 차별화된 다른 서비스와 기능을 제공하고 있다. 이에 현재 각각의 저비용항공사 앱 UI와 콘텐츠의 사용성에 대해 비교분석 후 평가하여 사용자들에게 좀 더 간편하게 앱을 사용할 수 있는 UI를 제시하는 것이 목적이다. 연구의 범위는 국내 저비용항공사 6곳을 선정하여 항공사 앱 UI를 살펴보았다. 연구 방법으로는 항공사에 대한 학위논문, 간행물, 웹사이트, 선행연구 자료 및 국내외 항공 정보 D/B를 참고하였다. 사용성 평가 방법으로는 HCI 및 뉴미디어 전문가 분야 5명을 선정하여, 대체적으로 사용자가 항공사 앱 UI에서 자주 사용하는 콘텐츠 항목을 선택하여 접근성과 사용성에 대한 휴리스틱(Heuristic) 평가법을 진행하였다. 이러한 사용성 평가를 통하여 사용자의 니즈를 충족시키기에는 모바일 앱이 부족함이 있어 원이에 대한 개선방안을 제시하였다. 이 연구를 시작으로 소비자 성향에 맞는 저비용항 공사의 모바일 앱 UI의 연구와 실제적인 개선이 지속적으로 이루어지기를 기대한다.

Purpose - This paper aims to identify factors that influence the users' word-of-mouth intention (WOMI) regarding mobile apps, focussing on the impacts of technology acceptance model (TAM) and social network theory. Research design, data and methodology - Based on TAM, this study integrates social network theory into the research model. The 317 sets of data collected in a survey were tested against the model using SmartPLS. Results - Our findings suggest the following: 1) Personal innovativeness positively influences perceived usefulness (PU), perceived ease of use (PEU) and perceived enjoyment (PE); 2) PEU affects PU and PE; 3) Both PU and Satisfaction are directly correlated with WOMI. Although PEU and PE has no direct impact on WOMI, they may indirectly affect WOMI via Satisfaction, as PU, PEU and PE all positively influence satisfaction; 4) Network density and network centrality both play a mediating role in the relation between PEU and WOMI. Referral Reward Program have a positive moderating effect on the relation between PU and WOMI. Conclusions - The findings of this study illustrate the traits of Apps that can promote users’ WOMI, as well as the characteristics of people who are more likely to participate in the word-of-mouth process. The findings provide a theoretical basis for app developers to make word-of-mouth a marketing strategy.