273 research outputs found

    Eliminating unobserved heterogeneity, using hierarchical models

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2023. 2. ๊น€์ค€๋ฒ”.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) ์ „๋žต์€ ์„œ๋น„์Šค๋‚˜ ์ œํ’ˆ์˜ ์ผ๋ถ€๋ฅผ ๋ฌด๋ฃŒ๋กœ ์ œ๊ณตํ•˜๊ณ  ์œ ๋ฃŒ ์˜ต์…˜์„ ๊ตฌ๋งคํ•˜๋„๋ก ์žฅ๋ คํ•˜์—ฌ ์œ ์ € ๊ธฐ๋ฐ˜์„ ํ™•์žฅ์‹œํ‚ค๋Š” ํŒ๋งค์ „๋žต์œผ๋กœ, ๋ฌด๋ฃŒ์™€ ํ”„๋ฆฌ๋ฏธ์—„(premium)์˜ต์…˜์„ ๋ชจ๋‘ ํฌํ•จํ•œ๋‹ค (Kumar, 2014; Liu et al., 2014; Gu et al., 2018). ๊ธฐ๋ณธ ์•ฑ์„ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋กœ๋“œ ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ƒ˜ํ”Œ๋กœ ์ œ๊ณตํ•˜๊ณ  ์ฃผ๋กœ ์ธ์•ฑ ๊ฒฐ์ œ(In-App Purchase)๋ฅผ ํ†ตํ•ด ์œ ๋ฃŒ ์• ๋“œ์˜จ(add-on)์„ ํŒ๋งคํ•˜๋Š” ์ „๋žต์€ ๋ชจ๋ฐ”์ผ ์•ฑ ์‹œ์žฅ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ํ”„๋ฆฌ๋ฏธ์—„ ์ „๋žต์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์•ฑ์Šคํ† ์–ด์—์„œ ์ œ๊ณต๋ฐ›์€ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ๋‹ค์šด๋กœ๋“œ ๋ฐ ๊ตฌ๋งค ๋‚ด์—ญ์— ๊ด€ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์• ๋“œ์˜จ(add-on) ๊ฐ€๊ฒฉ์— ๋Œ€ํ•œ ํ”„๋ฆฌ๋ฏธ์—„ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ์ด์šฉ์ž๋“ค์˜ ๋ฐ˜์‘์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•œ๋‹ค. ๋ฐ์ดํ„ฐ์— ํฌํ•จ๋œ ์• ๋“œ์˜จ ๊ฐ€๊ฒฉ์€ ๊ด€์ฐฐ๋œ ๊ธฐ๊ฐ„๋™์•ˆ ์ผ์ •ํ•˜์˜€๋‹ค. ์• ๋“œ์˜จ์˜ ํŠน์„ฑ์ด๋‚˜ ํ’ˆ์งˆ ๋“ฑ ์• ๋“œ์˜จ์— ๊ด€ํ•œ ์ •๋ณด๊ฐ€ ๋ฐ์ดํ„ฐ์— ํฌํ•จ๋˜์–ด ์žˆ์ง€ ์•Š์œผ๋ฏ€๋กœ, ์นดํ…Œ๊ณ ๋ฆฌ ๋ณ„ ์ •๋ณด๋Š” ์ œํ•œ ์ ์ด๋‹ค. ๋˜ํ•œ, ๊ฒŒ์ž„ ์ˆ˜์ค€์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฐ์ดํ„ฐ์˜ ๋ณ€๋™์„ ๋ชจ๋‘ ํ†ต์ œํ•˜๊ธฐ์—๋Š” ํฌํ•จ๋˜์–ด ์žˆ๋Š” ๊ฒŒ์ž„ ํŠน์„ฑ์ด ์ถฉ๋ถ„ํ•˜์ง€ ์•Š์œผ๋ฉฐ, ์• ๋“œ์˜จ ์ˆ˜์ค€์˜ ๋ณ€์ˆ˜๋กœ๋Š” ์• ๋“œ์˜จ ๊ฐ€๊ฒฉ์ด ์œ ์ผํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, ๊ฒŒ์ž„ ์ˆ˜์ค€๊ณผ ์• ๋“œ์˜จ ์ˆ˜์ค€์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ด์งˆ์„ฑ (heterogeneity)์˜ ์˜ํ–ฅ์„ ๋‹ด์„ ์ˆ˜ ์žˆ๋Š” ๋ณ€์ˆ˜๋ฅผ ํฌํ•จํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํ”„๋ฆฌ๋ฏธ์—„ ์ „๋žต์„ ์‚ฌ์š”ํ•˜๋Š” ๋ฌด๋ฃŒ ์•ฑ์˜ ์• ๋“œ์˜จ ๊ฐ€๊ฒฉ์— ๋Œ€ํ•œ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ์ด์šฉ์ž๋“ค์˜ ๋ฐ˜์‘์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์‹œ๊ฐ„ ๋ถˆ๋ณ€์˜ ๊ฒŒ์ž„ ๋ณ„, ์• ๋“œ์˜จ ๋ณ„ ์ด์งˆ์„ฑ์„ ํšจ์œจ์ ์œผ๋กœ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฒ”์ฃผํ˜• ๋ณ€์ˆ˜๋ฅผ ํฌํ•จํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์• ๋“œ์˜จ์˜ ๊ฐ€๊ฒฉ์€ ๊ด€์ฐฐ๋œ ๊ธฐ๊ฐ„๋™์•ˆ ์ผ์ •ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์„ ํ˜• ์ˆ˜์š” ๋ชจํ˜•์—์„œ ์• ๋“œ์˜จ ์ˆ˜์ค€์˜ ๊ณ ์ • ํšจ๊ณผ(fixed-effect)๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์—…์ด ์ˆ˜์ต์„ ๊ทน๋Œ€ํ™” ํ•œ๋‹ค๋Š” ๊ณต๊ธ‰ ์ธก๋ฉด์˜ ๊ฐ€์ •์„ ๋„์ž…ํ•˜์—ฌ ์• ๋“œ์˜จ ์ˆ˜์ค€์˜ ๊ณ ์ • ํšจ๊ณผ๋ฅผ ํฌํ•จํ•˜๋Š” 2๋‹จ๊ณ„ ๋ชจํ˜•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ฒŒ์ž„ ๋˜๋Š” ์• ๋“œ์˜จ ์ˆ˜์ค€์˜ ์ด์งˆ์„ฑ์€ ๊ณ ์ • ํšจ๊ณผ๋ฅผ ํฌํ•จํ•˜์—ฌ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์ด ๋ฐ์ดํ„ฐ์˜ ์œ„๊ณ„ํ˜• ๊ตฌ์กฐ๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ๋…ผ๋ฌธ์˜ ๋ชฉ์ ์ด๋‹ค. ๊ฐ€๊ฒฉ ๊ณ„์ˆ˜๋Š” ์žฅ๋ฅด ๋ณ„๋กœ ์ธก์ •๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉํ•œ ๋ฐ์ดํ„ฐ๋Š” ๊ณ„์ธต์ ์ด๊ธฐ ๋•Œ๋ฌธ์— ๋ฒ ์ด์ง€์•ˆ ์ถ”๋ก  ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ณ„์ธต์  ๊ตฌ์กฐ๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹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

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    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 donโ€™t 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

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    The aim of this report is to speculate on the level of concern we might have regarding consumer risk in relation to โ€˜social 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 โ€˜social 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

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    Social media-enabled business models have transformed the content industry. To increase usersโ€™ willingness to pay (WTP), many of todayโ€™s 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

    Freemium in the Mobile Applications Market - A Competitive Strategy or Marketing Tool?

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    Startup dilemmas - Strategic problems of early-stage platforms on the internet

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    siirretty Doriast

    Strategies for Achieving Profitability in the Music Streaming Service Business Model

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    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 ๊ฐ์˜ ๋ฌด๋ฃŒ ์ƒ˜ํ”Œ ๋ธŒ๋žœ๋“œ ์„ ํƒ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณ€์ˆ˜ ์‹๋ณ„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2023. 2. ๋ฐ•์„ฑํ˜ธ.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—…์€ ๋ฌด๋ฃŒ ์ƒ˜ํ”Œ ํ”„๋กœ๋ชจ์…˜ ์ „๋žต์„ ํ†ตํ•ด ๊ณ ๊ฐ ๊ฒฝํ—˜์„ ์ œ๊ณตํ•จ์œผ๋กœ์จ ๋งค์ถœ ์ฆ์ง„์„ ๊พ€ํ•˜๊ณค ํ•œ๋‹ค. ์ƒ˜ํ”Œ ํ”„๋กœ๋ชจ์…˜ ์ „๋žต๊ณผ ๊ด€๋ จํ•œ ์„ ํ–‰ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์œ„ํ—˜ ํšŒํ”ผ, ๊ฒฝํ—˜ ํšจ๊ณผ, ํ˜ธํ˜œ์™€ ๊ฐ™์€ ๊ณ ๊ฐ์˜ ์ฐธ์—ฌ ๋™์ธ์„ ํƒ์ƒ‰ํ•˜๋Š”๋ฐ ์ฃผ๋ ฅํ–ˆ๋‹ค. ์ตœ๊ทผ, ๊ธฐ์—…์€ ๊ณ ๊ฐ์˜ ์ž์œจ์„ฑ์„ ํ—ˆ์šฉํ•˜๋Š” ๋ฐฉ์‹์˜ ์ƒˆ๋กœ์šด ๋ฌด๋ฃŒ ์ƒ˜ํ”Œ ํ”„๋กœ๋ชจ์…˜ ์ „๋žต์„ ์‹œ๋„ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ํ”„๋กœ๋ชจ์…˜์— ์ฐธ์—ฌํ•˜๋Š” ๊ณ ๊ฐ์—๊ฒŒ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋ณ€์ˆ˜๋ฅผ ์‹๋ณ„ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ์˜๋ฏธ ์žˆ๊ฒ ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์—…๊ณ„๋ฅผ ์„ ๋„ํ•˜๋Š” ํ•œ๊ตญ ํ™”์žฅํ’ˆ ๊ธฐ์—…์˜ ๋ฌด๋ฃŒ ์ƒ˜ํ”Œ ํ”„๋กœ๋ชจ์…˜ ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐ๋ฅผ ํ† ๋Œ€๋กœ ๊ฐœ๋ณ„ ๊ณ ๊ฐ์˜ ํšจ์šฉ์„ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจํ˜•์„ ์ˆ˜๋ฆฝํ•œ๋‹ค. ์ดํ›„ ๋‹คํ•ญ ๋กœ์ง“ ๋ชจํ˜•์„ ํ†ตํ•ด ๋ธŒ๋žœ๋“œ ๋‹จ๊ณ„์—์„œ ๊ณ ๊ฐ์˜ ์„ ํƒ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋ณ€์ˆ˜๋ฅผ ํƒ์ƒ‰ํ•œ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๊ณ ๊ฐ์€ ๊ฐ€๊ฒฉ-ํ’ˆ์งˆ ์ถ”๋ก  ์„ฑํ–ฅ์— ์˜ํ•ด ๋ณธ ์ œํ’ˆ์˜ ํ‰๊ท  ์ •์ƒ๊ฐ€๊ฒฉ์ด ๋†’์€ ์ƒ˜ํ”Œ ๋ธŒ๋žœ๋“œ๋ฅผ ์„ ํ˜ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ ๊ณผ๊ฑฐ ๊ตฌ๋งค ๊ธˆ์•ก์ด๋‚˜ ๋นˆ๋„๊ฐ€ ๋†’์€ ๋ธŒ๋žœ๋“œ์˜ ์ƒ˜ํ”Œ์„ ์„ ํƒํ•  ํ™•๋ฅ ์ด ๋†’์•„ ์ƒˆ๋กœ์šด ์ œํ’ˆ์„ ๋ชจํ—˜์ ์œผ๋กœ ์‹œ๋„ํ•˜๊ธฐ๋ณด๋‹ค ๊ด€์„ฑ์ ์ธ ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๊ฐœ๋ณ„ ๊ณ ๊ฐ์„ ์‹ฌ๋„ ๊นŠ๊ฒŒ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•œ ์ด์งˆ์„ฑ ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด, ํ• ์ธ์„ ์„ ํ˜ธํ•˜๋Š” ์„ฑํ–ฅ์ด ๊ฐ•ํ•œ ๊ณ ๊ฐ์ผ์ˆ˜๋ก ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ณ ๊ฐ์— ๋น„ํ•ด ๋ณธ ์ œํ’ˆ ํ‰๊ท  ์ •์ƒ๊ฐ€๊ฒฉ์— ๋”์šฑ ๋ฏผ๊ฐํ–ˆ๋‹ค. ๋˜ ์ƒ˜ํ”Œ ์„ ํƒ ์ด์ „๊นŒ์ง€ ๋‹ค์–‘ํ•œ ๋ธŒ๋žœ๋“œ๋ฅผ ์‚ฌ์šฉํ•ด๋ณธ ๊ณ ๊ฐ์ผ์ˆ˜๋ก ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ณ ๊ฐ์— ๋น„ํ•ด ํŠน์ • ๋ธŒ๋žœ๋“œ์— ๋Œ€ํ•œ ์„ ํ˜ธ ํ˜น์€ ์ถฉ์„ฑ๋„๊ฐ€ ๋–จ์–ด์ง„๋‹ค๋Š” ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹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 ๊ตญ๋ฌธ์ดˆ๋ก 34์„
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