My research investigates how tech businesses alter society by engaging with their consumers in online and mobile spaces. In the first chapter, I examine the effect of a user deterrence policy - a policy that adds friction to information sharing and promotion to curb malicious activities of abnormal users - on regular users in an online platform. According to a quasi-experiment on an online news site, the deterrence policy increases regular users’ feedback activities by 0.11 percent by upvoting or downvoting others’ comments. To examine the underlying rationale for effect, I use the rational voting decisions framework and find that the deterrent policy enhances the subjective likelihood of affecting the popularity of comments among regular users by casting a decisive vote, due to the expected drop in abnormal user activity. Additionally, I observe that the deterrence policy strongly affects active users and political extremes. As disinformation poses a substantial threat to the sustainability of online platforms, this study provides valuable insights for businesses and policymakers as they assess the consequences of abnormal user deterrence policies. In the second chapter, I develop a conceptual model that shows how the efficacy of artificial intelligence positively affects firm performance, which is mediated by improved distributive equity. Using a dataset of 45 million users from 35 countries across 5 continents collected from a global artificial intelligence-powered education app, I find strong support for distributive equity as a mediator between the perceived efficacy of artificial intelligence and firm performance. I also find that the mediating effect of distributive equity is more pronounced in severe environmental conditions regarding political regimes, economic development, socio-cultural aspect, and technological resources, hence aiding in the actualization of artificial intelligence in such contexts. This research contributes to the growing popularity of the blended value proposition, such as the simultaneous pursuit of financial and social interests, by expanding a business model from a bottom line to a double bottom line approach. I discuss how this study extends the literature and provides managerial implications of artificial intelligence firms for global expansion, recruitment, and an inclusive information system.

In two independent and thematically relevant chapters, I empirically investigate consumers’ mobile channel usage behaviors. In the first chapter, I examine the impact of mobile use in online higher education. With the prevalence of affordable mobile devices, higher education institutions anticipate that learning facilitated through mobile access can make education more accessible and effective, while some critics of mobile learning worry about the efficacy of small screens and possible distraction factors. I analyze individual-level data from Massive Open Online Courses. To resolve self-selection issues in mobile use, I exploit changes in the number of mobile-friendly, short video lectures in one course (“non-focal course”) as an instrumental variable for a learner’s mobile intensity in the other course (“focal course”), and vice versa, among learners who have taken both courses during the same semester. Results indicate that high mobile intensity impedes, or at most does not improve course engagement due mainly to mobile distractions from doing activities unrelated to learning. Finally, I discuss practical implications for researchers and higher education institutions to improve the effectiveness of mobile learning. In the second chapter, I investigate the impact of mobile users’ popular app adoption on their app usage behaviors. The adoption of popular apps can serve as a barrier to the use of other apps given popular apps’ addictive nature and users’ limited time resources, while it can stimulate the exploration of other apps by inspiring interest in experimentation with similar technologies. I use individual-level app usage data and develop a joint model of the number of apps used and app usage duration. Results indicate that popular app adoption stimulates users to explore new apps at app stores and allocate more time to them such that it increases both the number of apps used and app usage duration for apps excluding the popular app. Such positive spillover effects are heterogeneous across app categories and user characteristics. I draw insights for app developers, app platforms, and media planners by determining which new apps to release in line with the launch of popular apps, when to release such apps, and to whom distribution should be targeted.

Total digital media advertising spending of $72.5 billion surpassed total television Ad spending of $71.3 billion for the first time ever in 2016. Approximately $39 billion, or 54% of the digital media advertising spend, involved pre-programmed software that purchased Ads on behalf of a buyer in Real-Time Bidding (RTB) settings. A major concern for Ad buyers is sub-optimal spending in RTB settings owing to biases in the attribution of customer conversions to Ad impressions. The purpose of this research is twofold. First, identify and propose a novel experimental design and analysis plan for to handling a previously unidentified and unaddressed source of endogeneity: count/quality simultaneity bias (CQB). Second, conduct a field study using data for Ad response rates, cost, and observed consumer behavior to solve for the profit maximizing daily Ad frequency per customer. One large online retailer provided data for Ad impressions, bid costs, response rates, revenue per visit, and operating costs for 153,561 unique users over 23 days. Unique visitors were randomly assigned to one of seven treatment groups with one, two, three, four, five, and six impressions per day limits as well as a final condition with no daily impression cap. Ordinary least square models (OLS) were fit to the data and a non-linear relationship between Ad impressions and site visits demonstrating declining marginal effect of Ad impression on site visits after an optimal point. The results of the field study confirmed the existence of negative CQB and demonstrated how my novel experimental design and analysis can reduce the negative bias in the estimate of impression quantity on customer response. Second, managers interested in improving the efficiency of advertising spend should restrict display advertising to only the highest quality inventory through specific site targeting and by leveraging direct buys and private marketplace deals. This strategy ensures that subsequent impressions are not of lower quality by restricting the pool of possible impressions from a homogenous set of high quality inventory.

This thesis is broken down into two sections. The first being market research and the second being the creation of the financial guide. The first section includes our primary and secondary data. In this section, we discover whether or not there is an actual need for a financial guide among college students, as well as the relationships among different variables that we included in our survey. The second section comes in the format of a financial guide that we have created. It includes topics that that our survey respondents feel most pertinent to them. It also uses the data we collected to emphasize certain topics over others in order to educate our readers and capture their attention as much as possible.

In two independent and thematically connected chapters, I investigate consumers' willingness to pay a price premium in response to product development that entails prosocial attributes (PATs), those that allude to the reduction of negative externalities to benefit society, and to an innovative participatory pricing design called 'Pay-What-You-Want' (PWYW) pricing, a mechanism that relinquishes the determination of payments in exchange for private goods to the consumers themselves partly relying on their prosocial preferences to drive positive payments. First, I propose a novel statistical approach built on the choice based contingent valuation technique to estimate incremental willingness to pay (IWTP) for PATs that accounts for consumer heterogeneity, dependence in the decision making processes, and incentive compatibility. I validate the approach by estimating IWTP for a variety of PATs and contrast the theoretical and managerial benefits of using the proposed approach over extant techniques used in the literature for this purpose. Second, I propose a general and flexible statistical modeling framework for estimating PWYW payments that exceed zero. It relies on the joint estimation of three types of consumer decision processes namely, the consumer propensity to default to an explicit price recommendation, the propensity to pay a least legitimate price, and the payment of a freely-chosen non-zero payment. Of particular interest is the model's ability to account for a wide variety of design constraints such as the setting of price bounds, explicit price recommendations, and the provision of a menu of discrete prices to choose from. I validate the approach by estimating PWYW payments for a variety of products such as music licenses, snacks, and sports tickets. I specifically examine and report the differential impact of three managerially controllable variables namely, 'payment anonymity', 'information on payment recipients' and 'information of product value/quality'.

Mobile applications (Apps) markets with App stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. Several distinctive features of mobile App store markets including – (a) highly heterogeneous consumer preferences and values, (b) high consumer cognitive burden of searching a large selection of similar Apps, and (c) continuously updateable product features and price – present a unique opportunity for IS researchers to investigate theoretically motivated research questions in this area. The aim of this dissertation research is to investigate the key determinants of mobile Apps success in App store markets. The dissertation is organized into three distinct and related studies. First, using the key tenets of product portfolio management theory and theory of economies of scope, this study empirically investigates how sellers’ App portfolio strategies are associated with sales performance over time. Second, the sale performance impacts of App product cues, generated from App product descriptions and offered from market formats, are examined using the theories of market signaling and cue utilization. Third, the role of App updates in stimulating consumer demands in the presence of strong ranking effects is appraised. The findings of this dissertation work highlight the impacts of sellers’ App assortment, strategic product description formulation, and long-term App management with price/feature updates on success in App market. The dissertation studies make key contributions to the IS literature by highlighting three key managerially and theoretically important findings related to mobile Apps: (1) diversification across selling categories is a key driver of high survival probability in the top charts, (2) product cues strategically presented in the descriptions have complementary relationships with market cues in influencing App sales, and (3) continuous quality improvements have long-term effects on App success in the presence of strong ranking effects.

It is well understood that decisions made under uncertainty differ from those made without risk in important and significant ways. Yet, there is very little research into how uncertainty manifests itself in the most ubiquitous of decision-making environments: Consumers' day-to-day decisions over where to shop, and what to buy for their daily grocery needs. Facing a choice between stores that either offer relatively stable "everyday low prices" (EDLP) or variable prices that reflect aggressive promotion strategies (HILO), consumers have to choose stores under price-uncertainty. I find that consumers' attitudes toward risk are critically important in determining store-choice, and that heterogeneity in risk attitudes explains the co-existence of EDLP and HILO stores - an equilibrium that was previously explained in somewhat unsatisfying ways. After choosing a store, consumers face another source of risk. While knowing the quality or taste of established brands, consumers have very little information about new products. Consequently, consumers tend to choose smaller package sizes for new products, which limits their exposure to the risk that the product does not meet their prior expectations. While the observation that consumers purchase small amounts of new products is not new, I show how this practice is fully consistent with optimal purchase decision-making by utility-maximizing consumers. I then use this insight to explain how manufacturers of consumer packaged goods (CPGs) respond to higher production costs. Because consumers base their purchase decisions in part on package size, manufacturers can use package size as a competitive tool in order to raise margins in the face of higher production costs. While others have argued that manufacturers reduce package sizes as a means of raising unit-prices (prices per unit of volume) in a hidden way, I show that the more important effect is a competitive one: Changes in package size can soften price competition, so manufacturers need not rely on fooling consumers in order to pass-through cost increases through changes in package size. The broader implications of consumer behavior under risk are dramatic. First, risk perceptions affect consumers' store choice and product choice patterns in ways that can be exploited by both retailers and manufacturers. Second, strategic considerations prevent manufacturers from manipulating package size in ways that seem designed to trick consumers. Third, many services are also offered as packages, and also involve uncertainty, so the effects identified here are likely to be pervasive throughout the consumer economy.

This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimator clearly has better finite sample properties compared to a frequently used Discrete Choice (DC) estimator. Second, the CCB estimator has better estimation efficiency when data size is relatively small and it still retains the advantage of the CC estimator over the DC estimator. The second chapter estimates baseball's managerial efficiency using a stochastic frontier function with the Bayesian approach. When I apply a stochastic frontier model to baseball panel data, the difficult part is that dataset often has a small number of periods, which result in large estimation variance. To overcome this problem, I apply the Bayesian approach to a stochastic frontier analysis. I compare the confidence interval of efficiencies from the Bayesian estimator with the classical frequentist confidence interval. Simulation results show that when I use the Bayesian approach, I achieve smaller estimation variance while I do not lose any reliability in a point estimation. Then, I apply the Bayesian stochastic frontier analysis to answer some interesting questions in baseball.

Identifying factors associated with service infusion success has become an important issue in theory and practice, as manufacturers turn to services to advance performance. The goals of this dissertation are to identify the key factors associated with service infusion success and develop an integrative framework and associated research propositions to isolate the underlying determinants of successful hybrid solution strategies for business customers. This dissertation is comprised of two phases. The first phase taps into the experience and learning gained by executives from Fortune-100 manufacturing firms who are managing the transition from goods to hybrid offerings for their customers. A discovery-oriented, theory-in-use approach is adopted to glean insights concerning the factors that facilitate and hinder those service transition strategies. Twenty-eight interviews were conducted with key executives, transcripts were analyzed and key themes were identified with special attention directed to the particular capabilities that managers consider crucial for successful service-growth strategies. One such capability centers on the ability of a firm to successfully transfer newly-developed hybrid solutions from one customer engagement to another. Building on this foundation, phase two involves a case study that provides an in-depth examination of the hybrid offering replication process in a business-to-business firm attempting to replicate four strategic hybrid offerings. Emergent themes, based on 13 manager interviews, reveal factors that promote or impede successful hybrid offering transfer. Among the factors that underlie successful hybrid offering transfers across customer engagements are close customer relationships, a clear value proposition embraced by organizational numbers, an accurate forecast of market potential, and collaborative working relationships across units. The findings from the field studies provided a catalyst for a deeper examination of existing literature and formed the building blocks for the conceptual model and several key research propositions related to the successful transfer of hybrid offerings. The model isolates five sets of factors that influence the hybrid offering transfer process, including the characteristics of (1) the source project team, (2) the seeking project team, (3) the hybrid offering, (4) the relationship exchange, and (5) the customer. The conceptualization isolates the critical role that the customer assumes in service infusion strategy implementation.

Consumers search before making virtually any purchase. The notion that consumers engage in costly search is well-understood to have deep implications for market performance. However to date, no theoretical model allows for the observation that consumers often purchase more than a single product in an individual shopping occasion. Clothing, food, books, and music are but four important examples of goods that are purchased many items at a time. I develop a modeling approach that accounts for multi-purchase occasions in a structural way. My model shows that as preference for variety increases, so does the size of the consideration set. Search models that ignore preference for variety are, therefore, likely to under-predict the number of products searched. It is generally thought that lower search costs increase retail competition which pushes prices and assortments down. However, I show that there is an optimal number of products to offer depending on the intensity of consumer search costs. Consumers with high search costs prefer to shop at a store with a large assortment of goods and purchase multiple products, even if the prices that firm charges is higher than competing firms' prices. On the other hand, consumers with low search costs tend to purchase fewer goods and shop at the stores that have lower prices, as long as the store has a reasonable assortment offering. The implications for market performance are dramatic and pervasive. In particular, the misspecification of demand model in which search is important and/or multiple discreteness is observed will produce biased parameter estimates leading to erroneous managerial conclusions.