This thesis consists of three papers in optimization and revenue management over complex networks: Robust Linear Control in Transmission Systems, Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence, and Revenue Management with Complementarity Products. This thesis contributes to analytical methods for optimization problems in complex networks, namely, power network, social network and product network. In Chapter 2, we describe a robust multiperiod transmission planning model including renewables and batteries, where battery output is used to partly offset renewable output deviations from forecast. A central element is a nonconvex battery operation model plus a robust model of forecast errors and a linear control scheme. Even though the problem is nonconvex we provide an efficient and theoretically valid algorithm that effectively solves cases on large transmission systems. In Chapter 3, we propose a new class of Linear Threshold Model-based information-diffusion model that incorporates the formation and spread of negative attitude. We call such models negativity-aware. We show that in these models, the expected positive influence is a monotone sub-modular function of the seed set. Thus we can use a greedy algorithm to construct a solution with constant approximation guarantee when the objective is to select a seed set of fixed size to maximize positive influence. Our models are flexible enough to account for both the features of local users and the features of the information being propagated in the diffusion. We analyze an online-learning setting for a multi-round influence-maximization problem, where an agent is actively learning the diffusion parameters over time while trying to maximize total cumulative positive influence. We develop a class of online learning algorithms and provide the theoretical upper bound on the regret. In Chapter 4, we propose a tractable information-diffusion-based framework to capture complementary relationships among products. Using this framework, we investigate how various revenue-management decisions can be optimized. In particular, we prove that several fundamental problems involving complementary products, such as promotional pricing, product recommendation, and category planning, can be formulated as sub-modular maximization problems, and can be solved by tractable greedy algorithms with guarantees on the quality of the solutions. We validate our model using a dataset that contains product reviews and metadata from Amazon from May 1996 to July 2014. We also analyze an online-learning setting for revenue-maximization with complementary products. In this setting, we assume that the retailer has access only to sales observations. That is, she can only observe whether a product is purchased from her. This assumption leads to diffusion models with novel node-level feedback, in contrast to classical models that have edge-level feedback. We conduct confidence region analysis on the maximum likelihood estimator for our models, develop online-learning algorithms, and analyze their performance in both theoretical and practical perspectives.

From amusement parks to casinos, cruises to video games, large sectors of the economy market consumer fun. Yet surprisingly, little research has been devoted to understanding the consumer psychology of fun—both the experience and its main psychological drivers in marketplace settings. This dissertation aims to develop a psychological theory of consumer fun that can help inform how fun experiences are engineered and managed by both businesses and consumers. I use a multimethod approach combining in-depth interviews and narrative analyses with controlled experiments, structural equation modeling, and field data analysis of consumer selfies. Two psychological pillars of consumer fun are identified: (1) hedonic engagement and (2) a sense of liberation. Each pillar in turn rests on two sub-pillars: (1a) perception of novelty and (1b) connectedness, and (2a) a sense of spontaneity and (2b) impressions of boundedness. My dissertation research shows that fun is an experience of liberating engagement, a temporary release from psychological restriction via a hedonically engaging activity. Importantly, a digital ethnography of consumer selfies showed that compared to other positive experiences such as happiness, pride, or relaxation, fun is much more likely to be situated in commercial settings, thus substantiating the business relevance of fun. Through five experiments, I show that marketers can engineer fun by directly activating feelings of liberation through situational cues such as boundedness. Using a proprietary dataset by Brand Asset Valuator, I show that fun emerges as a major brand image attribute that is significantly related to brand preference and key financial outcomes such as revenue. Broadly, my dissertation reveals that fun leads to increased consumer well-being, independently from the meaningful, eudaimonic path toward happiness.

Reducing energy use is a critical near-term strategy to mitigate climate change. Energy savings behaviors provide multiple benefits to the consumer and to society in addition to reducing greenhouse gas emissions: financial savings from lower energy bills, improved home comfort, fossil fuel resource conservation, energy independence, and improved local and indoor air quality, among others. Yet many policies to encourage reductions in energy use continue to focus on motivating behavior change with financial benefits, and little behavioral research has explored how these multiple benefits influence energy use decisions. Given the continued need for decreased energy use, more research is needed on how to leverage both financial and nonfinancial motivations to encourage energy saving behaviors. This dissertation consists of three separate papers, each addressing different elements of how individuals integrate financial and nonfinancial benefits to make energy use decisions. It presents the results of eight online and field studies conducted with over 395,000 U.S. residents. Chapters 1 and 2 focus on the decision architecture of the presentation of multiple benefits. Chapter 1 develops an inconspicuous change in savings metric to gently nudge individuals to consider energy use in addition to financial savings. It shows that presenting energy savings as a percentage of end-use energy increases behavioral adoption compared to a standard presentation of dollars saved. Chapter 2 explicitly presents environmental benefits in different ways, examining whether message effectiveness differs according to participants’ political ideologies. It shows that presenting environmental benefits in addition to financial benefits can increase interest in a large energy efficiency investment. Furthermore, while environmental benefits framed as climate change are motivating only for liberals, environmental benefits framed as stewardship and energy independence are motivating for both liberals and conservatives. Chapter 3 develops a measurement scale for a potential mechanism explaining why environmental and financial benefit frames lead to different decision outcomes: decision modes, or the qualitatively different ways that people make decisions. It defines six decision modes: calculation, affect, social norms, identity, habitual, and moral. These papers contribute to the behavioral science literature, expanding our understanding of the ways that decision makers incorporate the financial and environmental benefits of energy saving behaviors when making energy savings choices. These papers also provide actionable insights for policy makers to decrease energy consumption by improving the presentation of energy saving decisions.

In this thesis, we consider improving supply chain and service systems through demand-side management. In Chapters 1 and 2, we focus on a new notion of flexibility that has emerged in e-commerce called consumer flexibility. Motivated by the fact that some customers may willingly provide flexibility on which product or service they receive in exchange for a reward, firms can design flexible options to leverage this consumer flexibility for significant benefit in their operations. In Chapter 1, we consider the context of online retailing where consumer flexibility can be realized through opaque selling, where some specific attributes of the products are not revealed to the customer until after purchase. In Chapter 2, we focus on the context of online booking systems for scheduled services where consumer flexibility can be realized through large time windows. The main findings are on the power of limited flexibility using simple flexible options with just a small fraction of customers willing to be flexible. In Chapter 3, we study the issue of congested elevator queuing systems due to the requirement of social distancing during a pandemic. We propose simple interventions for safely managing the elevator queues, which require no programming of the elevator system and only manage passenger behaviors. The key idea is to explicitly or implicitly group passengers going to the same or nearby floor into the same elevator as much as possible. Simulations and stability analysis show that our proposed interventions significantly reduce queue length and wait time.