A Mobile Platform's In‐App Advertising Contract Under Agency Pricing for App Sales

Lin, Hongwei; Guo, Hong; Easley, Robert F.

doi:10.1111/poms.12647

Cited by 104 publications

(67 citation statements)

References 27 publications

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“…Then the number of premium content acquired through viewing ads is

rn

and the corresponding nuisance cost is cn (Anderson and Coate , Anderson and Gabszewicz , Hao et al. ), where

c = c_{L} or c_{H}

. Suppose the consumer also buys q units of premium content.…”

Section: Modeling Frameworkmentioning

confidence: 99%

“…Concerning the latter issue, Gabszewicz et al (2005) showed that a monopolistic media firm would lower its newspapers' price when it starts to profit from advertising spaces. Hao et al (2017) showed that a mobile app developer would reduce the app price to enlarge its app user base if she chooses to become an in-app ad publisher. Sun and Zhu (2013) empirically found that content providers would tailor their content as well as improve their content quality once they start to earn ad profit from the traffic to their content.…”

Section: Literature Reviewmentioning

confidence: 99%

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Economic Analysis of Reward Advertising

Guo

Zhao

Lin

et al. 2019

Production and Operations Management

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Reward advertising is an emerging monetization mechanism for app developers in which consumers choose to view ads in exchange for apps’ premium content. We provide the first economic analysis of reward advertising by studying the implications of offering reward ads, either by itself, or in conjunction with direct selling of premium content. We find that the condition for offering reward ads is surprisingly simple, and it is often optimal to offer reward ads jointly with direct selling of premium content. Interestingly, a high reward rate could decrease the number of reward ads viewed because of accelerated satiation for premium content; thus, developers need to balance the need to incentivize ad viewing and to prevent excessive accelerated satiation. The need for limiting the number of reward ads per consumer only arises when the marginal revenue of reward ads diminishes quickly. Such limit is only effective when the base ad revenue rate is not too high and when ad viewers have relatively homogenous nuisance costs. Finally, reward ads may increase or decrease consumer surplus.

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“…Then the number of premium content acquired through viewing ads is

rn

and the corresponding nuisance cost is cn (Anderson and Coate , Anderson and Gabszewicz , Hao et al. ), where

c = c_{L} or c_{H}

. Suppose the consumer also buys q units of premium content.…”

Section: Modeling Frameworkmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Economic Analysis of Reward Advertising

Guo

Zhao

Lin

et al. 2019

Production and Operations Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the 'sojourn' and 'exposure' effects, Sun et al study the ad-sequencing problem of fading ads shown to App users, without considering the role of the platform [45]. Hao et al discuss paid Apps and the revenue-sharing policy of the platform under in-App advertising mode [1]. Chen et al extend the model to discuss the strategic choice between paid Apps and free Apps with in-advertising mode when App developers face multiple platforms [46].…”

Section: Online Advertisingmentioning

confidence: 99%

“…In contrast, the revenue source of an App is more from in-App advertising than from sales. In-App advertising is a mode in which an App developer embeds advertisers' ads into App, and when the users click or watch the ads, the advertiser will pay App developer by a certain billing way [1]. However, even if in-App advertising brings huge advertising revenue to the developers, it also carries an immeasurable risk of damaging the user experience and causing annoyance to users, which is the great dilemma brought by this mode to the App developers [2].…”

Section: Introductionmentioning

confidence: 99%

Considering In-App Advertising Mode, Platform-App Channel Coordination by a Sustainable Cooperative Advertising Mechanism

Liu

2019

Sustainability

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Regardless of the platform or Apps the number of users is a prerequisite for monetization. Considering in-App advertising, through the optimal control theory, this paper establishes a dynamic advertising strategy model of one platform and n Apps under the decentralized and integrated systems. For each system, the model is constrained by the states dynamics of the number of the platform’s users and App’s users. Our research has obtained some management insights and findings as follows. Firstly, Apps don’t have to worry about the negative effects of in-App advertising caused by other Apps when making advertising decisions. Interestingly, the platform will use the advertising subsidy policy to limit the delivery density of in-App advertising. Specifically, the higher the negative impact of in-App advertising caused by App i, the lower the advertising subsidies provided to App i. Additionally, when determining the advertising subsidy rate, the platform will also comprehensively consider the profitability of Apps and the costs and benefits of in-App advertising. Secondly, our proposed sustainable cooperative advertising mechanism can simultaneously coordinate the Platform-App channel and implement the optimal integrated objective. Finally, we theoretically prove that under some mild conditions, the mechanism can both improve the profits of the platform and n Apps.

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“…, Hao et al. ). We make contributions to the literature gap of advertising platforms’ revenue maximization using offline operations by empirically studying a large classified ads website.…”

Section: Introductionmentioning

confidence: 99%

How Much is Too Much? The Effect of Offline Call Intensity on Online Purchase of Digital Services

Zhang

Yao

2020

Production and Operations Management

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Use a unique dataset collected from a large classified ads website, we empirically examine the effect of the offline call intensity on the online consumer purchase probability of digital services and the carryover effect of the call intensity. We find that the online consumer purchase probability is increasing in the call intensity but at a decreasing rate. We further demonstrate that the decreasing rate is sizable enough that the relationship between the online consumer purchase probability and the call intensity is an inverted U‐shaped curve. In addition, there exists a strong carryover effect where the online call intensity in the past 4 weeks does not fade away but has a positive effect on recent consumer purchases. Our estimations show that both too much and too little call intensity will result in considerably worse outcomes. As compared with the call intensity at the optimal level, too much call intensity potentially reduces the online consumer purchase probability by 29.11%, and too little call intensity potentially reduces the online consumer purchase probability by 54.90%. Furthermore, making calls every week for 4 weeks can increase consumer purchase probability by 10.69 times as compared with just initiating calls with the consumers.

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A Mobile Platform's In‐App Advertising Contract Under Agency Pricing for App Sales

Cited by 104 publications

References 27 publications

Economic Analysis of Reward Advertising

Economic Analysis of Reward Advertising

Considering In-App Advertising Mode, Platform-App Channel Coordination by a Sustainable Cooperative Advertising Mechanism

How Much is Too Much? The Effect of Offline Call Intensity on Online Purchase of Digital Services

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