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    (The) Effect of Users’ Social Interaction Activity on Users’ App Usage Frequency and Profitability

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    https://www.riss.kr/link?id=T14816590

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Advances in information and mobile technologies enable digital service firms to connect their services to social media. Users of the services are able to interact with one another using social features (e.g., sending messages, inviting friends, etc.). In addition, the firms now provide certain types of rewards per each social interaction (e.g., discounts, bonus points, coupons, etc.) to promote communication among users. Under such conditions, the effect of users’ social interaction behavior on service usage has not been studied. We examine the effect of users’ social interaction activity on users’ app usage frequency and profitability. This paper focuses on social interaction behaviors in a mobile service where users can earn in-app reward per their interaction. Using control function approach on a large dataset of 390,009 users, we find that the marginal effect of the increase by one percent in sending social messages on app usage frequency to be 8% and on purchase amounts to be 19%. In addition, we find that a unit increase in the intensity of sending social messages positively effects only when the intensity is less than 40 and 50 in each model. Under the topic of social network, and interactions in it, our findings can help decision makers to set proper encouragement to promote interactions which is very effective to increase more revenue.
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    Advances in information and mobile technologies enable digital service firms to connect their services to social media. Users of the services are able to interact with one another using social features (e.g., sending messages, inviting friends, etc.)....

    Advances in information and mobile technologies enable digital service firms to connect their services to social media. Users of the services are able to interact with one another using social features (e.g., sending messages, inviting friends, etc.). In addition, the firms now provide certain types of rewards per each social interaction (e.g., discounts, bonus points, coupons, etc.) to promote communication among users. Under such conditions, the effect of users’ social interaction behavior on service usage has not been studied. We examine the effect of users’ social interaction activity on users’ app usage frequency and profitability. This paper focuses on social interaction behaviors in a mobile service where users can earn in-app reward per their interaction. Using control function approach on a large dataset of 390,009 users, we find that the marginal effect of the increase by one percent in sending social messages on app usage frequency to be 8% and on purchase amounts to be 19%. In addition, we find that a unit increase in the intensity of sending social messages positively effects only when the intensity is less than 40 and 50 in each model. Under the topic of social network, and interactions in it, our findings can help decision makers to set proper encouragement to promote interactions which is very effective to increase more revenue.

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    목차 (Table of Contents)

    • 1. INTRODUCTION. 1
    • 2. LITERATURE REVIEW 2
    • 3. THEORETICAL BACKGROUND & HYPOTHESES 6
    • 3.1. Social Capital Attainment. 6
    • 3.2. Social Interactions 7
    • 1. INTRODUCTION. 1
    • 2. LITERATURE REVIEW 2
    • 3. THEORETICAL BACKGROUND & HYPOTHESES 6
    • 3.1. Social Capital Attainment. 6
    • 3.2. Social Interactions 7
    • 3.3. Tie Strengths. 8
    • 4. DATA. 9
    • 4.1. Data Collection. 9
    • 4.2. Social Interaction Participation in for Focal App . 9
    • 4.3. Variable Operationalization 11
    • 5. ECONOMETRIC SPECIFICATION 13
    • 5.1. Empirical Model Specification. 13
    • 5.2. Endogeneity Issue. 14
    • 6. RESULTS 16
    • 7. ROBUSTNESS CHECKS. 19
    • 8. SUMMARY AND CONCLUSION. 23
    • REFERENCES 25
    • APPENDIX 1: Scatter Plot of sendcount vs. playcount 32
    • APPENDIX 2: Scatter Plot of sendcount vs. purchaseamt . 33
    • APPENDIX 3: Scatter Plot of receivecount vs. playcount 34
    • APPENDIX 4: Scatter Plot of receivecount vs. playcount 35
    • APPENDIX 5: Multicollinearity Check 36
    • 국문초록 . 37
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