273 research outputs found
Eliminating unobserved heterogeneity, using hierarchical models
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ฒฝ์๋ํ ๊ฒฝ์ํ๊ณผ, 2023. 2. ๊น์ค๋ฒ.Freemium strategies contain both free and premium' options, offering some products or services for free as a sample to encourage paid option sales and expand their user base (Kumar, 2014; Liu et al., 2014; Gu et al., 2018). Distributing basic app downloads for free as a sample and selling paid options, usually through in-app purchases (IAP), has become a prevalent freemium strategy among mobile apps.
This paper empirically analyzes the freemium mobile game users' reaction to the price of add-ons using mobile game transaction data provided by an app store. The observed add-on price in the data is constant over time. Since the add-on information is not included in the dataset (e.g., the characteristics of add-ons, or the quality level of add-ons), the categorical information is limited. There are insufficient game characteristics to capture all the game-level variations, and the add-on price is the only add-on-level variable. The freemium mobile game users can download and experience the games before they purchase add-on options and infer the quality of the games and add-ons. Therefore, it is crucial to include add-on level intercepts to separate the impact of game-level and add-on-level heterogeneity and correctly specify the impact of the add-on price.
First, this research aims to determine mobile game users reactions to the add-on price of apps that use freemium strategies. Second, this study aims to find a categorical intercept that efficiently captures the time-invariant bias. Since the add-on price is time-consistent, including add-on level intercepts in the linear demand models is impossible. This study includes profit-maximizing firm assumptions to obtain a 2-stage model with add-on level intercepts. The categorical heterogeneity can be captured by including fixed or random intercepts. Third, reflecting the multi-level structure of this data is the objective of this paper. The price coefficient can be specified at the genre level. Since the data used in this paper is hierarchical, the Bayesian inference method was implied to improve the understanding of the multi-level structure of the models.ํ๋ฆฌ๋ฏธ์(Freemium) ์ ๋ต์ ์๋น์ค๋ ์ ํ์ ์ผ๋ถ๋ฅผ ๋ฌด๋ฃ๋ก ์ ๊ณตํ๊ณ ์ ๋ฃ ์ต์
์ ๊ตฌ๋งคํ๋๋ก ์ฅ๋ คํ์ฌ ์ ์ ๊ธฐ๋ฐ์ ํ์ฅ์ํค๋ ํ๋งค์ ๋ต์ผ๋ก, ๋ฌด๋ฃ์ ํ๋ฆฌ๋ฏธ์(premium)์ต์
์ ๋ชจ๋ ํฌํจํ๋ค (Kumar, 2014; Liu et al., 2014; Gu et al., 2018). ๊ธฐ๋ณธ ์ฑ์ ๋ฌด๋ฃ๋ก ๋ค์ด๋ก๋ ํ ์ ์๋๋ก ์ํ๋ก ์ ๊ณตํ๊ณ ์ฃผ๋ก ์ธ์ฑ ๊ฒฐ์ (In-App Purchase)๋ฅผ ํตํด ์ ๋ฃ ์ ๋์จ(add-on)์ ํ๋งคํ๋ ์ ๋ต์ ๋ชจ๋ฐ์ผ ์ฑ ์์ฅ์์ ๋๋ฆฌ ์ฌ์ฉ๋๊ณ ์๋ ํ๋ฆฌ๋ฏธ์ ์ ๋ต์ด๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ฑ์คํ ์ด์์ ์ ๊ณต๋ฐ์ ๋ชจ๋ฐ์ผ ๊ฒ์ ๋ค์ด๋ก๋ ๋ฐ ๊ตฌ๋งค ๋ด์ญ์ ๊ดํ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ์ ๋์จ(add-on) ๊ฐ๊ฒฉ์ ๋ํ ํ๋ฆฌ๋ฏธ์ ๋ชจ๋ฐ์ผ ๊ฒ์ ์ด์ฉ์๋ค์ ๋ฐ์์ ์ค์ฆ์ ์ผ๋ก ๋ถ์ํ๋ค. ๋ฐ์ดํฐ์ ํฌํจ๋ ์ ๋์จ ๊ฐ๊ฒฉ์ ๊ด์ฐฐ๋ ๊ธฐ๊ฐ๋์ ์ผ์ ํ์๋ค. ์ ๋์จ์ ํน์ฑ์ด๋ ํ์ง ๋ฑ ์ ๋์จ์ ๊ดํ ์ ๋ณด๊ฐ ๋ฐ์ดํฐ์ ํฌํจ๋์ด ์์ง ์์ผ๋ฏ๋ก, ์นดํ
๊ณ ๋ฆฌ ๋ณ ์ ๋ณด๋ ์ ํ ์ ์ด๋ค. ๋ํ, ๊ฒ์ ์์ค์์ ๋ฐ์ํ๋ ๋ฐ์ดํฐ์ ๋ณ๋์ ๋ชจ๋ ํต์ ํ๊ธฐ์๋ ํฌํจ๋์ด ์๋ ๊ฒ์ ํน์ฑ์ด ์ถฉ๋ถํ์ง ์์ผ๋ฉฐ, ์ ๋์จ ์์ค์ ๋ณ์๋ก๋ ์ ๋์จ ๊ฐ๊ฒฉ์ด ์ ์ผํ๋ค. ๋ฐ๋ผ์, ๊ฒ์ ์์ค๊ณผ ์ ๋์จ ์์ค์์ ๋ฐ์ํ๋ ์ด์ง์ฑ (heterogeneity)์ ์ํฅ์ ๋ด์ ์ ์๋ ๋ณ์๋ฅผ ํฌํจํ๋ ๊ฒ์ด ์ค์ํ๋ค.
์ฒซ์งธ, ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ํ๋ฆฌ๋ฏธ์ ์ ๋ต์ ์ฌ์ํ๋ ๋ฌด๋ฃ ์ฑ์ ์ ๋์จ ๊ฐ๊ฒฉ์ ๋ํ ๋ชจ๋ฐ์ผ ๊ฒ์ ์ด์ฉ์๋ค์ ๋ฐ์์ ํ์
ํ๋ ๊ฒ์ด๋ค. ๋์งธ, ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ์๊ฐ ๋ถ๋ณ์ ๊ฒ์ ๋ณ, ์ ๋์จ ๋ณ ์ด์ง์ฑ์ ํจ์จ์ ์ผ๋ก ํฌ์ฐฉํ ์ ์๋ ๋ฒ์ฃผํ ๋ณ์๋ฅผ ํฌํจํ๋ ๊ฒ์ด๋ค. ์ ๋์จ์ ๊ฐ๊ฒฉ์ ๊ด์ฐฐ๋ ๊ธฐ๊ฐ๋์ ์ผ์ ํ๊ธฐ ๋๋ฌธ์ ์ ํ ์์ ๋ชจํ์์ ์ ๋์จ ์์ค์ ๊ณ ์ ํจ๊ณผ(fixed-effect)๋ฅผ ๋ฐ์ํ๋ ๊ฒ์ ๋ถ๊ฐ๋ฅํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๊ธฐ์
์ด ์์ต์ ๊ทน๋ํ ํ๋ค๋ ๊ณต๊ธ ์ธก๋ฉด์ ๊ฐ์ ์ ๋์
ํ์ฌ ์ ๋์จ ์์ค์ ๊ณ ์ ํจ๊ณผ๋ฅผ ํฌํจํ๋ 2๋จ๊ณ ๋ชจํ์ ์ฌ์ฉํ์๋ค. ๊ฒ์ ๋๋ ์ ๋์จ ์์ค์ ์ด์ง์ฑ์ ๊ณ ์ ํจ๊ณผ๋ฅผ ํฌํจํ์ฌ ๊ด์ฐฐํ ์ ์๋ค. ์
์งธ, ์ด ๋ฐ์ดํฐ์ ์๊ณํ ๊ตฌ์กฐ๋ฅผ ๋ฐ์ํ๋ ๊ฒ์ด ๋ณธ ๋
ผ๋ฌธ์ ๋ชฉ์ ์ด๋ค. ๊ฐ๊ฒฉ ๊ณ์๋ ์ฅ๋ฅด ๋ณ๋ก ์ธก์ ๋ ์ ์์ผ๋ฉฐ, ๋ณธ ๋
ผ๋ฌธ์์ ์ฌ์ฉํ ๋ฐ์ดํฐ๋ ๊ณ์ธต์ ์ด๊ธฐ ๋๋ฌธ์ ๋ฒ ์ด์ง์ ์ถ๋ก ๋ฐฉ๋ฒ์ ์ฌ์ฉํ์ฌ ๊ณ์ธต์ ๊ตฌ์กฐ๋ฅผ ์ถ์ ํ์๋ค.I. Introduction 1
1.1. Study Background 1
1.2. Research Objectives 3
II. Literature review 5
2.1. Freemium Strategies in the Mobile Game Industry 5
2.2. Sampling Effect in Freemium Apps 8
III. Model 13
3.1. Demand Model 13
3.2. Supply-Side Assumptions 17
IV. Data and Variables 21
4.1. Mobile-Game App Store Data 21
4.2. Independent Variables 22
4.3. Summary Statistics of the Data 24
V. Empirical Analysis with Fixed Effects 28
5.1. Fixed effect Models with Promotion and Download Lag 28
5.2. Comparison of the Fixed Effect Models 33
5.3. Comparison of Models with Popular Games 37
VI. Bayesian Estimation 39
6.1. Bayesian Structure for Genre-Specific Price Coefficients 39
6.2. Sampling the Genre-Specific Price Coefficients 41
VII. Add-on Price Elasticities 42
VIII. Conclusion and Discussion 44
References 47
Abstract in Korean 52
Appendix A 54
Table Index
Table 1. Summary of Relevant Freemium Studies 11
Table 2. Summary Statistics of the Data 26
Table 3. Comparison of Different Fixed Effect Models Non-Purchased 30
Table 4. Fixed Effect Model Comparison Non-Purchase and OnlyPurchased 35
Table 5. Model Comparison with FOC j-level Top Selling Apps 37
Table 6. Posterior of alpha 42
Table 7. Estimated Price Elasticities of the Models 44
Figure Index
Figure 1. Scatter Plot of the Full Data, Including Non-Purchased Observations 27
Figure 2. Correlation Heat Map of the Download and Promotion Variables 33
Figure 3. Scatter Plot of Price and Sales Quantity of Add-ons by Genre 39
Figure 4. Bayesian Structure of Genre-Specific Price Coefficients in Stage 2. 40
Figure 5. Posterior Plot of the Inverse of Genre-specific Price Coefficients 41์
Motivations in the adoption and conversion of Music Freemium Services
Mestrado Bolonha em MarketingCom o recente avanรงo tecnolรณgico, รฉ possรญvel ouvir mรบsica de novas maneiras. Isto resultou no aumento do valor de mercado da mรบsica e no surgimento de diversos serviรงos de streaming on-demand com o modelo de negรณcio freemium. Estes serviรงos tรชm sucesso, especialmente, quando os seus utilizadores convertem a sua subscriรงรฃo de free para premium.
O presente trabalho propรตe-se a estudar quais as motivaรงรตes que levam os consumidores a adotar uma plataforma de streaming de mรบsica, e quais as motivaรงรตes e caracterรญsticas de utilizador que levam ร conversรฃo para o serviรงo premium.
Alguns estudos dedicaram-se a explicar o porquรช desta conversรฃo, mas pouco foi pesquisado no que toca ร s motivaรงรตes dos consumidores para distinguir entre diferentes plataformas. Para aprofundar estas questรตes, este estudo analisa um conjunto de motivaรงรตes e caraterรญsticas de utilizador como variรกveis explicativas em conjunto, de forma original, nรฃo encontrada na literatura. Deste modo, os dados foram obtidos atravรฉs de um inquรฉrito online, com uma amostra de 231 utilizadores portugueses de plataformas de streaming.
Os resultados principais apontam que a satisfaรงรฃo, valor percebido e ubiquidade sรฃo motivaรงรตes estatisticamente significativas que influenciam positivamente a escolha de diferentes plataformas. Para alรฉm disto, as mesmas motivaรงรตes, bem como a idade e ocupaรงรฃo (caracterรญsticas de utilizador) mostraram-se impactantes no que diz respeito ร conversรฃo, sendo relevante do ponto de vista teรณrico e do ponto de vista prรกtico. No entanto, os resultados destacam a influรชncia negativa da satisfaรงรฃo e idade nesta compra. Isto significa que um utilizador altamente satisfeito nรฃo se converte e de modo semelhante, quanto mais velho for o utilizador, menos provรกvel รฉ que a compra ocorra. Nรฃo hรก evidรชncia estatรญstica que as motivaรงรตes de descoberta, exclusividade, social e personalizaรงรฃo e as restantes caracterรญsticas de utilizador influenciem a conversรฃo de utilizadores free em utilizadores premium.With the recent technological advancement, music is being experienced in new ways. This resulted in the rising value of the music market and the surge of diverse on-demand streaming services with the freemium business model. These services thrive especially when its users convert their subscription from free to premium.
The current dissertation aims to study what motivations drive consumers to adopt different music streaming platforms and what motivations and user characteristics leads them to convert to the premium service.
Several studies endeavoured on explaining this phenomenon, but little research was dedicated on what are the motivations for consumers to distinguish between different platforms. To enhance comprehension in this matter, this study analysis a group of motivations and user characteristics as explanatory variables together as a set, in a original way, not found on the literature. Thus, data was obtained via an online questionnaire, with a sample of 231 Portuguese users of streaming platforms.
The main results suggest that satisfaction, perceived value and ubiquity are statistically significant motivations that positively influence choosing a different platform. Regarding subscribing to the premium service, the same motivations, as well as age and occupation (user characteristics) present influential results, which poses relevancy from a theorical point of view and managerial point of view. However, the findings highlight satisfaction and age as negative influences for this purchase. This means that highly satisfied free users donโt convert and similarly, the older the consumer, the less likely the conversion happens. No statistical evidence was found in discovery, exclusivity, social and personalization motivations alongside the remaining user characteristics for the conversion of free users into premium users.info:eu-repo/semantics/publishedVersio
Exploring social gambling: scoping, classification and evidence review
The aim of this report is to speculate on the level of concern we might have regarding consumer risk in relation to โsocial gambling.โ In doing so, this report is intended to help form the basis to initiate debate around a new and under-researched social issue; assist in setting a scientific research agenda; and, where appropriate, highlight concerns about any potential areas that need to be considered in terms of precautionary regulation. This report does not present a set of empirical research findings regarding โsocial gamblingโ but rather gathers information to improve stakeholder understanding
Passive, Active, or Co-Active? The Link Between Synchronous User Participation and Willingness to Pay for Premium Options
Social media-enabled business models have transformed the content industry. To increase usersโ willingness to pay (WTP), many of todayโs content providers have changed from mere content provision towards offering social content experiences. Recent research has confirmed that usersโ participation activities, e.g. commenting on content, increase the WTP for social content servicesโ premium options. So far, social content has been available predominantly on-demand, only allowing asynchronous user participation. Recently, social live content services emerged, which facilitate synchronous user participation and enable so-called co-active behavior. With this study, we conceptualize co-active behavior as the interplay between users while co-experiencing content together, and empirically show that co-active behavior has a stronger effect on WTP for premium options than the classic forms of passive and active behavior. Our work provides theoretical contributions on the WTP for social content as well as implications for the management of social content services
Strategies for Achieving Profitability in the Music Streaming Service Business Model
Although the rapid growth of the music streaming industry has led to record levels of global music consumption, many leaders in the music streaming industry have not developed a financially sustainable business model for music streaming. This descriptive single case study focused on strategies that some global music streaming service leaders used to generate sustainable profits through their business models. Christensen\u27s theory of disruptive innovation served as the conceptual framework for this study. Semistructured interviews with the chief executive officer and 4 senior managers of a leading music streaming service in southeastern Asia were analyzed to identify themes. Secondary data collected for this research included practitioner reports, government reports, company documentation, and peer-reviewed journal articles. During data analysis, I used method triangulation to generate insights regarding the key themes identified in the literature review. Analysis of the data revealed strategies that global music streaming leaders used to generate profits: (a) optimization of the firm\u27s dynamic capabilities, (b) optimization of the subscription and freemium business models, and (c) a deliberate focus on the niche of local music. The findings of this study could be useful to music streaming service leaders who need to generate sustainable revenues and lack the strategies to do so on their own as well as to music streaming leaders who want their service to implement a disruptive innovation strategy. Additionally, the findings of this study might promote social change by generating awareness of proven strategies leading to sustainable profits for music streaming services and job security for artists who contribute to sustaining or increasing local economies cash flows and taxable incomes
๊ณ ๊ฐ์ ๋ฌด๋ฃ ์ํ ๋ธ๋๋ ์ ํ์ ์ํฅ์ ๋ฏธ์น๋ ๋ณ์ ์๋ณ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ฒฝ์๋ํ ๊ฒฝ์ํ๊ณผ, 2023. 2. ๋ฐ์ฑํธ.Numerous companies employ free sample promotion strategies to increase sales through risen customer experiences. Prior researches mainly focused on investigating the psychological mechanism of free sample
promotion: risk aversion, experience effect, reciprocity. Nowadays, companies are introducing new types
of free sample promotion strategies which allow customers autonomy when they are selecting sampled products. Therefore, identifying the variables that may affect customers choice of free sample would be new and meaningful to the field. Based on Korean leading cosmetic companys free sample promotion result data, current research builds a random utility model for individual customer. Then the research conducts multinomial logit to explore significant variables that may explain customers choice considering the brand level. For the results, customers preferred to choose the brand that the original price is higher due to price-quality inference. Also, customers presented inertia behavior rather than variety-seeking that past purchase amount and frequency for a specific brand played a significant role in selecting the free sample. In terms of
heterogeneity, deal-prone customers showed higher sensitivity for price compared to those who were relatively insensitive to discount. Customers who have experienced various brands before participating the free sample promotion did not show preferences for a specific brand compared to customers who were loyal to a certain brand.๊ธฐ์
์ ๋ฌด๋ฃ ์ํ ํ๋ก๋ชจ์
์ ๋ต์ ํตํด ๊ณ ๊ฐ ๊ฒฝํ์ ์ ๊ณตํจ์ผ๋ก์จ ๋งค์ถ ์ฆ์ง์ ๊พํ๊ณค ํ๋ค. ์ํ ํ๋ก๋ชจ์
์ ๋ต๊ณผ ๊ด๋ จํ ์ ํ ์ฐ๊ตฌ๋ ์ฃผ๋ก ์ํ ํํผ, ๊ฒฝํ ํจ๊ณผ, ํธํ์ ๊ฐ์ ๊ณ ๊ฐ์ ์ฐธ์ฌ ๋์ธ์ ํ์ํ๋๋ฐ ์ฃผ๋ ฅํ๋ค. ์ต๊ทผ, ๊ธฐ์
์ ๊ณ ๊ฐ์ ์์จ์ฑ์ ํ์ฉํ๋ ๋ฐฉ์์ ์๋ก์ด ๋ฌด๋ฃ ์ํ ํ๋ก๋ชจ์
์ ๋ต์ ์๋ํ๋ค. ๋ฐ๋ผ์ ์ด๋ฌํ ํ๋ก๋ชจ์
์ ์ฐธ์ฌํ๋ ๊ณ ๊ฐ์๊ฒ ์ํฅ์ ๋ฏธ์น ์ ์๋ ๋ณ์๋ฅผ ์๋ณํ๋ ์ฐ๊ตฌ๊ฐ ์๋ฏธ ์๊ฒ ๋ค. ๋ณธ ์ฐ๊ตฌ๋ ์
๊ณ๋ฅผ ์ ๋ํ๋ ํ๊ตญ ํ์ฅํ ๊ธฐ์
์ ๋ฌด๋ฃ ์ํ ํ๋ก๋ชจ์
๊ฒฐ๊ณผ ๋ฐ์ดํฐ๋ฅผ ํ ๋๋ก ๊ฐ๋ณ ๊ณ ๊ฐ์ ํจ์ฉ์ ์ค๋ช
ํ ์ ์๋ ๋ชจํ์ ์๋ฆฝํ๋ค. ์ดํ ๋คํญ ๋ก์ง ๋ชจํ์ ํตํด ๋ธ๋๋ ๋จ๊ณ์์ ๊ณ ๊ฐ์ ์ ํ์ ์ํฅ์ ๋ฏธ์น ์ ์๋ ๋ณ์๋ฅผ ํ์ํ๋ค. ์ฐ๊ตฌ ๊ฒฐ๊ณผ, ๊ณ ๊ฐ์ ๊ฐ๊ฒฉ-ํ์ง ์ถ๋ก ์ฑํฅ์ ์ํด ๋ณธ ์ ํ์ ํ๊ท ์ ์๊ฐ๊ฒฉ์ด ๋์ ์ํ ๋ธ๋๋๋ฅผ ์ ํธํ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ ๊ณผ๊ฑฐ ๊ตฌ๋งค ๊ธ์ก์ด๋ ๋น๋๊ฐ ๋์ ๋ธ๋๋์ ์ํ์ ์ ํํ ํ๋ฅ ์ด ๋์ ์๋ก์ด ์ ํ์ ๋ชจํ์ ์ผ๋ก ์๋ํ๊ธฐ๋ณด๋ค ๊ด์ฑ์ ์ธ ๊ฒฝํฅ์ ๋ณด์๋ค. ๊ฐ๋ณ ๊ณ ๊ฐ์ ์ฌ๋ ๊น๊ฒ ์ดํดํ๊ธฐ ์ํ ์ด์ง์ฑ ๋ถ์์ ๋ฐ๋ฅด๋ฉด, ํ ์ธ์ ์ ํธํ๋ ์ฑํฅ์ด ๊ฐํ ๊ณ ๊ฐ์ผ์๋ก ๊ทธ๋ ์ง ์์ ๊ณ ๊ฐ์ ๋นํด ๋ณธ ์ ํ ํ๊ท ์ ์๊ฐ๊ฒฉ์ ๋์ฑ ๋ฏผ๊ฐํ๋ค. ๋ ์ํ ์ ํ ์ด์ ๊น์ง ๋ค์ํ ๋ธ๋๋๋ฅผ ์ฌ์ฉํด๋ณธ ๊ณ ๊ฐ์ผ์๋ก ๊ทธ๋ ์ง ์์ ๊ณ ๊ฐ์ ๋นํด ํน์ ๋ธ๋๋์ ๋ํ ์ ํธ ํน์ ์ถฉ์ฑ๋๊ฐ ๋จ์ด์ง๋ค๋ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ์ธํ ์ ์์๋ค.1. Introduction 1
2. Literature Review 3
2.1. Traditional Free Sample Promotion 4
2.2. Mechanism of Free Sample Promotion 6
2.3. Digital Free Sample Promotion 7
3. Hypothesis Development 8
3.1. Price-Quality Inference 8
3.2. Monetary and Frequency 9
3.3. Deal-Proneness 10
3.4. Variety-Seeking 11
4. Data 11
4.1. Data Period 12
4.2. Initial Choice 13
4.3. Brand Categorization 14
4.4. Manual Selection 15
5. Model 17
6. Results 18
6.1. Main Results 18
6.2. Heterogeneity Results 21
7. Discussion 23
7.1. Summary 23
7.2. Implication 24
7.3. Limitation and Future Research 25
References 27
๊ตญ๋ฌธ์ด๋ก 34์
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