Determinants of the continuous use of mobile apps: The mediating role of users awareness and the moderating role of customer focus

This research empirically explored the factors influencing the continuous use of mobile Apps in Jordan. The research utilized the Theory of Planned Behavior, the Diffusion of Innovation Theory and the Social Cognitive Theory to build the conceptual foundation of the research model. Using a quantitative approach, the study utilized a questionnaire with a set of well-validated items for the purpose of collecting data. The study collected 524 usable surveys, and analyzed the data using structural equation modeling technique (PLS-SEM). Results indicated that the three constructs relevant to this study (perceived risk, mobile self-efficacy, and social influence) were significant in predicting a users' awareness and the continuous use of mobile Apps. Customer focus moderated the relationship between awareness and continuous intention. In addition, the findings also confirm that users’ awareness mediated the relationship between the three independent variables and the continuous use. The Detailed findings of this research are discussed, with conclusions and future research reported at the end

Examining social influence factors affecting consumer continuous usage intention for mobile social networking applications

Due to the rapid increase in the use of mobile devices, mobile socialnetworking applications (MSNAs) have proliferated during recent years. MSNAs can provide social groups with a means to communicate among group members. Although studies have shown that social influence is relevant to individual and group collective behaviour, few studies have investigated the predictive relationships between multiple integrated social influence factors and the behavioural intention for the use of MSNAs. Therefore, we examined the effects of social influence factors (injunctive norms, descriptive norms, social identity, and group norms) on the continued intentions to use MSNAs. Data collected though the website of an online survey company yielded 830 usable questionnaires. We used structural equation modelling (SEM) to test the hypothesised relationships. The results indicate that injunctive norms, descriptive norms, and social identity were positively related to continued usage intention, whereas group norms were unrelated to continued usage intention. Understanding consumer decisions regarding the repeated use of an MSNA is necessary for mobile application (M-app) developers to design programs that ensure user retention

사용자 니즈 기반 어플리케이션 선택 방법 :월경통과 월경전 증후군을 대상으로한 무작위 대조 시험

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 협동과정의료정보학전공, 2018. 2. 김정은.Although several mobile apps are available for health management, many healthcare providers are hesitant to utilize them, particularly given that there is no definite method of selecting the best app based on users needs. The aim of this study is to create a Method of App Selection based on the Users Needs (MASUN). For this study, women suffering from dysmenorrhea and premenstrual syndrome were the target users. First, related apps were searched on the Apple iTunes and Google Play stores. Brainstorming, mind mapping, and persona and scenario techniques were adopted to create a checklist of the relevant criteria, which was then used to score the apps. After app experts, clinical experts, and potential users evaluated the apps, an intervention aimed at verifying MASUN was designed. To be able to apply the MASUN to a clinical or research setting, this study evaluated an intervention based on the results of the MASUN with a randomized controlled trial (RCT). Of the 2,784 apps found, 369 were quantitatively analyzed. Of those, the top five candidate apps in terms of scoring (App A–E) were evaluated by three groups: app experts, clinical experts, and potential users. All three groups ranked App A the highesthowever, their opinions differed for the remaining ranks. The RCT was implemented using App A (the best app according to the MASUN), and App E (the app with the largest number of users worldwide). The intervention was performed over four months so that at least three menstrual cycles could be included. As a result, the 16-week intervention was completed by 61 app users. For personal outcomes, there were non-significant main effects of app on app self-efficacy. For the app quality rating and overall satisfaction, there was a significant main effect of app. There was also a significant main effect of app on app outcome expectancy, while there was a significant main effect of time on the number of menus used. Moreover, there were statistically significant differences in the number of days with records per month between the groups. As for environmental outcomes, there was a significant main effect of app on app social influences and intent to recommend. Regarding the dysmenorrhea and PMS-related outcomes, there were no statistically significant differences in dysmenorrhea and PMS score but there were significant differences in the likelihood of behavioral and cognitive changes in dysmenorrhea and PMS management. Furthermore, the number of dysmenorrhea relief methods used showed a significant increase in the experimental group. Our results indicate that the MASUN is useful, as it not only considers the needs of various users but also draws on the knowledge of app and clinical experts. In deriving a way to find and utilize the best app for a given purpose, this study highlights how healthcare providers can understand and combine the opinions of users and app and clinical experts.Chapter 1. INTRODUCTION 1 1.1. Background 1 1.2. Aims of Research 3 1.3. Research Hypothesis 4 1.4. Definition of terms 5 Chapter 2. RESEARCH FRAMEWORK 8 2.1. Intervention mapping approach 8 2.2. Social cognitive theory 9 Chapter 3. MATERIALS AND METHODS 13 3.1. Planning MASUN: Derivation of requirement 15 1) App searching and screening 15 2) Brainstorming and mind mapping 15 3) Persona and scenario techniques 16 4) Checklist review 16 3.2. Designing MASUN: Intervention design 17 1) Scoring the searched apps using checklist 17 2) Selecting the candidate apps 17 3) Evaluating the five candidate apps 18 3.3. Implementing MASUN: Intervention 21 1) App users 21 2) Study design 22 3) Hypothesis 25 4) Similar literatures 25 5) Realistic possibilities 26 3.4. Verifying MASUN: Evaluation 27 1) Education 27 2) Questionnaires 29 3) Screenshot 33 4) Statistical analyses 34 5) Ethical Considerations 34 Chapter 4. RESULTS 35 4.1. Planning MASUN: Derivation of requirement 35 1) App searching and screening 35 2) Brainstorming and mind mapping 36 3) Persona and scenario techniques 37 4) Checklist review 37 4.2. Designing MASUN: Intervention design 39 1) Scoring the searched apps using a checklist 39 2) Selecting the candidate apps 40 3) Evaluating the five candidate apps 41 4) Complete MASUN 46 4.3. Implementing MASUN: Intervention 51 1) Characteristics of app users 51 4.4. Verifying MASUN: Evaluation 56 1) Changes in personal outcomes 56 2) Changes in behavioral outcomes 58 3) Changes in environmental outcomes 59 4) Changes in dysmenorrhea and PMS-related outcomes 63 5) Correlation analysis of all variables 64 Chapter 5. DISCUSSION 68 5.1. Planning MASUN: Derivation of requirement 68 1) App searching and screening 68 2) Brainstorming and mind mapping 68 3) Persona and scenario techniques 69 4) Checklist review 69 5.2. Designing MASUN: Intervention design 70 1) Scoring the searched apps using a checklist 70 2) Selecting the candidate apps 72 3) Evaluating the five candidate apps 73 4) Complete MASUN 76 5.3. Implementing MASUN: Intervention 77 1) Characteristics of app users 77 2) Education 78 5.4. Verifying the MASUN: Evaluation 79 1) Changes in personal outcomes 79 2) Changes in behavioral outcomes 80 3) Changes in environmental outcomes 82 4) Changes in dysmenorrhea and PMS-related outcomes 82 5) Strengths of the MASUN 84 6) What can healthcare providers do? 85 Chapter 6. CONCLUSION 86 REFERENCE 87 ABSTRACT in KOREAN 96Docto