Essays On Consumer Learning And Its Impact On Firm Strategies

In this dissertation, I study how the existence of consumer learning in a digital goods environment influences the profitability of various firm strategies. I develop a structural model of consumer’s learning-by-using. Adopting a Bayesian learning framework, the model describes how product experience influences willingness to pay, and also allows identification of key factors behind learning and quantification of the trade-offs that firms face. I estimate the model using a novel data set of videogame users’ play record. Using the estimated parameters, I first consider the optimal design of free trials. Digital goods providers often offer a trial version of their product in order to familiarize consumers with the product. The trial configurations considered herein include limiting duration of free usage (i.e. ``time-locked trial\u27\u27) and limiting access to certain features (i.e. ``feature-limited trial\u27\u27). I find that time-locked trials outperform feature-limited trials, and the revenue implication depends on the rate of demand depreciation during the trial period. I then consider the optimal product unbundling strategy. As digital goods can be considered as a bundle of identical services to be consumed at different points in time, the firm can unbundle and sell each component separately over time. Offering the product in an unbundled manner allows consumers to adopt part of the product after the learning takes place, resulting in higher willingness to pay through the option value. I find that pay-per-use, an extreme form of product unbundling, outperforms traditional outright sale when there exists consumer learning, while it does not in the absence of learning. Hence the existence of consumer learning has a substantial impact on the firm’s optimal policy. In addition to empirically studying the implications of consumer learning, I also examine an econometric problem of identifying state dependence in consumer utility. Identifying state dependence is challenging when there exists consumer heterogeneity unobservable to a researcher. I show that if consumers make two decisions at each decision occasion, one being a discrete choice from multiple alternatives and the other being a consumption intensity of the selected option, then we can nonparametrically separate state dependence and unobserved heterogeneity under mild conditions. Understanding conditions for nonparametric identification helps empirical modelers in choosing their modeling assumptions

Pay-per-download or Freemium: Revenue Models in a Competitive Mobile App Market

This paper examines the revenue model selection of app developers in a duopoly setting. Two developers offering vertically-differentiated apps can adopt either a pay-per-download or a freemium strategy. Under the pay-per-download strategy, consumers pay a fee to acquire the app. Under the freemium strategy, consumers are offered with a free basic version and can choose to pay an additional fee for the full version. A game theoretical model is used to analyze the competition in the presence of network effect and learning effect. We find that when the quality difference is moderate, the pay-per-download strategy is optimal for the high-quality app if the quality of basic version is low, otherwise freemium strategy is optimal. Responding to the pay-per-download strategy of the high-quality app, adopting the pay-per-download strategy is optimal for the low-quality app if quality of basic version is high, otherwise freemium strategy is adopted

Machine Learning-Based Medical Devices: the FDA’s Regulation, Requirements, and Restrictions

The FDA should create functional regulations for the growing number of machine learning medical devices. The healthcare system is increasingly using these devices for diagnosis. Machine learning devices trained on biased data sets are susceptible to furthering certain types of bias and generating flawed outcomes. The FDA should require ML medical devices to include a label that describes the demographics of the tested population. If manufacturers fail to include this information, the FDA could determine the label false or misleading under §502 of the FD&C Act and stop sales of the device. After approval, the FDA should use §814.89(2) and §519 to require manufacturers to report and evaluate the real-world performance of their approved devices. These reviews should include studies for clinical validation or model evaluation and model testing. While addressing bias in diagnostic medical machine learning devices will take more than the FDA, the agency should continue to support efforts to find an effective way to mitigate and measure bias

TechNews digests: Jan - Nov 2009

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Exploration and exploitation in the presence of network externalities

This paper examines the conditions under which exploration of a new, incompatible technologyis conducive to firm growth in the presence of network externalities. In particular, this studyis motivated bythe divergent evolutions of the PC and the workstation markets in response to a new technology: reduced instruction set computing (RISC). In the PC market, Intel has developed new microprocessors bymaintaining compatibilitywith the established architecture, whereas it was radicallyr eplaced byRISC in the workstation market. History indicates that unlike the PC market, the workstation market consisted of a large number of power users, who are less sensitive to compatibilitythan ordinaryusers. Our numerical analysis indicates that the exploration of a new, incompatible technologyis more likelyto increase the chance of firm growth when there are a substantial number of power users or when a new technologyis introduced before an established technologytakes off. (; ; ;

Exploratory literature review on free products : a structural topic model approach

Free products and services have increased tremendously in the last decade as a market ing technique and business strategy. We can observe this trend in the academic literature as well. In this exploratory literature review, we are interested in the different themes of this research area and how they have changed through time. We create a structural topic model (STM) to identify the underlying themes. An STM allows us to incorporate metadata into the model. This way, we can observe how the topics evolve depending on the publication year of the article. The study includes 279 academic papers from 1976 until 2022. We survey an increasing trend of themes situated in a digital world, especially for freemium products, and research concerning online consumer behavior. We identify two categories to classify free products; free products used as a marketing technique and free products as part of a business strategy.Os produtos e serviços gratuitos aumentaram tremendamente na última década como técnica de marketing e estratégia comercial. Podemos observar esta tendência também na literatura académica. Nesta revisão exploratória da literatura, estamos interessados nos di ferentes temas desta área de investigação e em como eles mudaram ao longo do tempo. Criamos um modelo temático estrutural (STM) para identificar os temas subjacentes. Um STM permite-nos incorporar metadados no modelo. Desta forma, podemos observar como os temas evoluem em função do ano de publicação do artigo. O estudo inclui 279 artigos académicos desde 1976 até 2022. Inquirimos uma tendência crescente de temas situa dos num mundo digital, especialmente para produtos "freemium", e investigação sobre o comportamento dos consumidores em linha. Identificamos duas categorias para classifi car produtos gratuitos; produtos gratuitos utilizados como técnica de marketing e produtos gratuitos como parte de uma estratégia empresarial

Using Adobe Flash Lite on mobile phones for psychological research: reaction time measurement reliability and inter-device variability

Mobile telephones have significant potential for use in psychological research, possessing unique characteristics—not least their ubiquity—that may make them useful tools for psychologists. We examined whether it is possible to measure reaction times (RTs) accurately using Adobe Flash Lite on mobile phones. We ran simple and choice RT experiments on two widely available mobile phones, a Nokia 6110 Navigator and a Sony Ericsson W810i, using a wireless application protocol (WAP) connection to access the Internet from the devices. RTs were compared within subjects with those obtained using a Linux-based millisecond-accurate measurement system. Results show that measured RTs were significantly longer on mobile devices, and that overall RTs and distribution of RTs varied across device

Celebrities and Shoes on the Female Brain: The Neural Correlates of Product Evaluation in the Context of Fame

Celebrity endorsement is omnipresent. However, despite its prevalence, it is unclear why celebrities are more persuasive than (equally attractive) non-famous endorsers. The present study investigates which processes underlie the effect of fame on product memory and purchase intention by the use of functional magnetic resonance imaging methods. We find an increase in activity in the medial orbitofrontal cortex (mOFC) underlying the processing of celebrity-product pairings. This finding suggests that the effectiveness of celebrities stems from a transfer of positive affect from celebrity to product. Additional neuroimaging results indicate that this positive affect is elicited by the spontaneous retrieval of explicit memories associated with the celebrity endorser. Also, we demonstrate that neither the activation of implicit memories of earlier exposures nor an increase in attentional processing is essential for a celebrity advertisement to be effective. By explaining the neural mechanism of fame, our results illustrate how neuroscience may contribute to a better understanding of consumer behavior

Medical Device Artificial Intelligence: The New Tort Frontier

The medical device industry and new technology start-ups have dramatically increased investment in artificial intelligence (AI) applications, including diagnostic tools and AI-enabled devices. These technologies have been positioned to reduce climbing health costs while simultaneously improving health outcomes. Technologies like AI-enabled surgical robots, AI-enabled insulin pumps, and cancer detection applications hold tremendous promise, yet without appropriate oversight, they will likely pose major safety issues. While preventative safety measures may reduce risk to patients using these technologies, effective regulatory-tort regimes also permit recovery when preventative solutions are insufficient. The Food and Drug Administration (FDA), the administrative agency responsible for overseeing the safety and efficacy of medical devices, has not effectively addressed AI system safety issues for its clearance processes. If the FDA cannot reasonably reduce the risk of injury for AI-enabled medical devices, injured patients should be able to rely on ex post recovery options, as in products liability cases. However, the Medical Device Amendments Act (MDA) of 1976 introduced an express preemption clause that the U.S. Supreme Court has interpreted to nearly foreclose liability claims, based almost completely on the comprehensiveness of FDA clearance review processes. At its inception, MDA preemption aimed to balance consumer interests in safe medical devices with efficient, consistent regulation to promote innovation and reduce costs. Although preemption remains an important mechanism for balancing injury risks with device availability, the introduction of AI software dramatically changes the risk profile for medical devices. Due to the inherent opacity and changeability of AI algorithms powering AI machines, it is nearly impossible to predict all potential safety hazards a faulty AI system might pose to patients. This Article identifies key preemption issues for AI machines as they affect ex ante and ex post regulatory-tort allocation, including actual FDA review for parallel claims, bifurcation of software and device reviews, and dynamics of the technology itself that may enable plaintiffs to avoid preemption. This Author then recommends an alternative conception of the regulatory-tort allocation for AI machines that will create a more comprehensive and complementary safety and compensatory model