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Anonymity and its psychological implications in consumer behaviour
The dissertation sheds light on the construct anonymity and its applications in consumption contexts. The rapid growth of media platforms and online technology has transformed the way we communicate and exchange information. Simultaneously, concerns for online privacy protection have considerably increased. Under the domain of privacy, anonymity is often regarded as an online tool to protect personal information. This understanding, however, narrows the scope of the construct and hinders its research in the consumer behaviour domain. As one of the first studies to explore the psychological implications of anonymity, the dissertation endeavours to lift the "online label" of the construct and position it more properly in consumer research. To achieve this purpose, I develop a scale of consumer attitudes towards anonymity in Essay 1 and use this scale to explore the manifestations of anonymity in everyday behaviour in Essay 2. The findings make significant contributions to the literature and marketing practices.
Tutumlu tuketici ve 3R tuketim modeli
In the face of unsustainable rate of waste generation, the 3R (reduce, reuse, recycle) model of consumption are getting attention from companies, public policy makers and academics. Despite the growing interest at sustainability, there is still little evidence on it from the perspective of consumers. Overall, this dissertation consists of two essays which contribute to the understanding of sustainable consumer behavior. First essay explores the relationship between frugality and the 3R model of consumption. Previous literature conceptualizes frugality as a unidimensional consumer trait, and different operationalizations exist. This essay distinguishes frugality from conceptually related constructs such as thriftiness, price consciousness, value consciousness, voluntary simplicity, and environmental consciousness. Bringing diverse conceptualizations and operationalizations of frugality together, then, it argues that frugality consists of two distinct dimensions: spending-related frugality and consumption-related frugality. Through a disaggregated perspective, the relationship between the distinct dimensions of frugality and the 3R model of consumption is investigated. Findings show that consumption-related frugality is positively correlated with the 3R strategies of consumer behavior while spending-related frugality has no correlation with these. Essay 1 sheds light on the contradictory findings in previous research, and suggests that branding initiatives and public policies on sustainability can be more powerful if they appeal to consumption-related frugality as a consumer trait. It is co-authored by Zeynep Gurhan-Canli and Ceren Hayran Sanli. Second essay explores the preowned luxury market. The market for luxury products has been changing due to economic conditions and digital transformation. One of the luxury branding strategies for enhanced sustainability and waste reduction is the preowned luxury, yet, such a strategy also allows access to high–end luxury products. This essay provides insights into the spillover effects of the preowned market on luxury customers' brand attachment. Findings show that the preowned market does not decrease existing customers' brand attachment and this is due to the type of the products available in the preowned market. Customers' brand attachment decreases when recent (vs. older) season products are available in the preowned market. Findings also suggest that the preowned market can actually benefit luxury brands. Customers' brand attachment increases with a sustainability (vs. affordability) appeal. The effect mediated by customers' ideal self concept connection. The essay is co-authored by Vanitha Swaminathan and Zeynep Gurhan-Canli.
Product ranking, pricing, and recommendation for e-commerce retailers and platforms
Product ranking and pricing in an online store or marketplace plays a crucial role in customer engagement and subsequently in sales and total revenue. An online retailer or marketplace operator must decide on the set of the products and their positions and prices at various points, such as campaigns, first page, landing pages, side-bar recommendations, or search query lists. In this dissertation, we investigate the optimal ranking and pricing in centralized and decentralized environments at online e-commerce platforms. In the first part, we propose a new personalized content-based method specially designed for online retailers. The focus of the study is on customers' activities in an online store, such as clicks, and purchases, more than the feedback such as ratings or comments. This property is useful for online retailers where there is limited rating feedback, but there are several search sessions including clicks and purchases. We test the method using data provided by an apparel retailer. Our method outperforms benchmark methods (Collaborative filtering, Popular products, and MNL choice model) and it has a strictly better performance in recommending new products. Also, our method outperforms benchmark methods in recommending products to customers who are generally interested in less popular (fringe) products. The main practical contribution of this study for online retailers is a novel personalized recommending system that is build-up based on panel data (instead of rating data) that causes better use of limited detail provided in the dataset and improved the quality of recommendation. In the second part, we consider a decentralized pricing and ranking problem in an online marketplace where sellers decide on their prices simultaneously and ranking takes place based on a pre-defined rule by the marketplace operator (MO). Empirical studies show that the ranking of sellers in an online marketplace is highly correlated to prices. We model the price competition as a full information game where all players (sellers) know the price-based ranking rule defined by the marketplace operator and the demand function of each seller. We obtain the Nash equilibrium of each game under binary and probabilistic ranking policies and show how ranking policy and commission rate affect the pricing strategies and equilibrium. Then, we investigate the profit of MO and third-party sellers dependent on the ranking rules and market structure. From a practical and implementation point of view, this study sheds light on the optimal pricing and ranking strategies in an online marketplace with three main contributions. First, we find the optimal ranking rule for MO is dependent on the sale margin and cost of the products. Further, we show that the MO may deviate from the optimal ranking to either increase the profit of each supply-side party by implementing a less competitive ranking rule and higher commission rate or enhance the consumers' experience quality such as demand satisfaction rate and demand surplus by implementing a competitive ranking rule and lower commission rate. Finally, in cases that MO participates as a seller, s/he may rank himself higher than other sellers. This strategy increases the profit of MO in any case, however, surprisingly it increases both total supply-side profit and customers demand surplus if the demand functions are asymmetric. In the third part, we study product ranking and pricing at e-commerce platforms, where customers are generally window shoppers. Window shoppers are a type of customer that browse online stores as a form of leisure or external search behavior without a clear intent to purchase a specific product. We develop an optimization model with deterministic demand and vertically differentiated positions (ranking) where products that are placed in earlier positions are more likely to be considered and, therefore, purchased. We show that revenue-ordered ranking is optimal when there is infinite inventory for products. However, when there is a finite inventory case, despite the deterministic and invertible structure of demand, we prove that the optimal price would result in product stock-out during the campaign in some cases. Also, we show that solving the problem with a set of predefined prices (and not joint pricing ranking) would result in insignificant profit loss if the diversity of the product's demand parameters is limited. The precise optimization model in the infinite inventory case is intractable due to the existence of integer variables and the complexity of the constraints. Thus, we propose two heuristic methods to solve the problem and provide an upper bound by relaxing the integer variables. We show that the performance of the heuristic methods is dependent on the customer's search behavior. Selecting the appropriate heuristic method, we provide a near-optimal solution in all cases. Finally, numerical analyses show that trivial solutions such as sorting by price or sorting by inventory would result in significant profit loss.
Perceptual inaccuracies in marketing relationships
This dissertation sheds light on the perceptual inaccuracies in marketing relationships, a recently flourishing research stream. While perceptual inaccuracies have been investigated in a wide area of research such as social psychology, behavioral decision and organizational behavior, marketing literature's interest in the topic is relatively new. Despite being few in number, studies in this domain demonstrate significant financial and relational consequences. The dissertation consists of two essays that delve into this open-ended and interesting area of perceptual inaccuracies in marketing. Essay 1 consists of an integrative and critical review of the current knowledge about perceptual inaccuracies in marketing relationships. We analyze antecedents of these inaccuracies, identify misperceived constructs and related downstream consequences and moderators. With a focus on salesperson-customer dyads, we provide a synthesis of existing research, develop an emergent conceptual framework and identify several research gaps. Drawing from various theories such as relationship lifecycle, social perception, and the behavioral decision theory, we suggest ways of reconciling inconsistent findings and develop propositions that could guide future perceptual inaccuracy research. Essay 2 investigates perceptual differences between consumers and subsidiary managers regarding a global brand's standardization level in a subsidiary market. Results reveal that emerging market consumers perceive global brands as less standardized than they are. A perceived standardization gap exists concerning the product characteristics and positioning. Antecedents and consequences of the "standardization gap" are analyzed using triadic data obtained from managers, consumers, and advertisements in an emerging market. Perceived credibility, quality, and prestige of the brand are related to the size of standardization gaps. Importantly, positioning standardization gaps decrease emerging market consumers' attitudinal brand equity, their willingness to pay, and the brand's market share in the subsidiary market. Overall the dissertation advances and enriches the understanding of perceptual inaccuracies in marketing by focusing on two different domains: salesperson- customer and international marketing. It particularly suggests that both researchers and practitioners should take into account perceptual inaccuracies, their antecedents and consequences as they may have remarkable outcomes for marketing relationships.
The Participatory Theater Framework of Leadership
The traditional conceptualization of leadership can be understood as the leader occupying centerstage whilst the audience of 'followers' remains largely in the dark. Furthermore, as an actor on stage would perceive a booing member of the audience as malevolent or ignorant, the extant literature mainly depicts non-followers as obstacles, inefficiencies, or irritants that should be corrected so that they can become productive members of the group (Ford & Harding, 2018). Yet, leadership does not only revolve around the leader and his/her ability to create 'good followers' (Collinson, 2006). It is the compilation of cooperating and clashing individual agencies of the group members that results in more than mere aggregation. Through their support, opposition, and even apathy, every group member is a participant of the leadership phenomenon and they individually and collectively influence the group goal and to what extent it is actualized. As such, this dissertation is dedicated to understanding who the constituents of leadership really are and how leadership as a multilevel social phenomenon that is co-created by all group members actually works. In this dissertation, I introduce the participatory theater framework (PTF) of leadership as a novel and holistic theoretical approach that can enable us to answer the questions above. The participatory theater framework recognizes everyone in the group, including the leader and all forms of followers and non-followers, individually and collectively, as agentic performers of leadership. Deriving its foundations from various research areas such as social and cognitive psychology, moral philosophy, and game theory, this dissertation (i) establishes the theoretical foundation of the PTF and proposes a typology of roles people adopt in leadership situations, (ii) demonstrates that even those who are most commonly thought to be passive (i.e., devoted followers of toxic leaders; frequently called 'sheep' ) are indeed willful co-creators of the leadership phenomenon, and (iii) presents empirical evidence for the existence of the proposed typology of roles.
Emotions in social networks
In this dissertation, I explore emotional dynamics in a social network and how brands can leverage emotional state in a network to create engagement. Essay 1 focuses on emotional dynamics, specifically how long certain emotions last, and how emotions follow each other in short- and long-term time frames, using emotional dimensions (valence-arousal- dominance) as predictors, analyzing millions of tweets retrieved from Twitter and processed with NLP techniques. Understanding how long emotions last and which emotions follow each other can act as a social appraisal mechanism that allows policy makers, politicians and marketing professionals be better prepared for emotional fluctuations in a society. Essay 2 focuses on the role of consumers' emotional state in receiving consumer engagement for the brand-generated social media content. Second essay highlights the importance of context for receiving consumer engagement and offers insights into the congruency between the emotional characteristics of the brand-generated content and consumers' states.
An exploration of consumer experiences in the age of artificial intelligence: Perception of being observed
This dissertation explores consumers' experiences in the age of artificial intelligence and identifies a phenomenon, the perception of being observed, which evolved with the rapid transformation of the social environment with the technological revolution. I use the term "being observed" to refer to the instances where consumers think that they are being recorded, watched, or tracked by other parties that can be people or companies. Consisting of three essays, this dissertation aims to provide an understanding of what it means "to be observed" in the digital age, identify antecedents and consequences that are important for consumers' wellbeing and companies' success. Essay 1 provides an understanding of the perception of being observed. Identifying the technology anxiety, self-consciousness and privacy concerns as antecedents, this research develops a scale to measure the extent to which individuals think that they are being observed. Further, it demonstrates that people who think that they are being observed are more sensitive to the data collection practices and protective of their data through limiting their information disclosure. Essay 2 explores an important outcome of being observed by companies for building successful relationships with consumers. The findings of this research show that being observed by companies decrease consumers' willingness to engage with the company, and use their resources such as time, money, and data in their interactions. Being observed by companies implicates that one's data, which is a resource, is being utilized by companies without reciprocity. This, in turn, makes consumers be more protective of their available resources. Making the interaction more reciprocal can mitigate the effect. Essay 3 focuses on the consequences of the use of privacy notices, which are integrated into consumers' lives with the General Data Protection Regulations in 2018. This research shows that privacy notices decrease consumers' willingness to use websites and applications, and purchase products. The underlying mechanism is that privacy notices make it salient to the consumers that they are being observed by companies, which prompts people to calculate the benefits and costs of these observations for both parties. This calculus results in the idea that companies benefit more than consumers, which, in turn, decreases perceived customer orientation of the company, reducing usage intentions. Overall, this dissertation contributes to the consumer behavior literature through enhancing our understanding of consumers' experiences evolving with the advancements in technology and providing important managerial and public policy implications.
Big data and machine learning for behavioral analytics and inference: Cases in sports and education
This thesis focuses on the use of big data and machine learning methods in behavioral analytics and causal inference. The main motivation of the thesis is to illustrate how the researchers working with traditional econometric methods can benefit from big data and causal ML methods. In the absence of well-established literature, finding the right regression specification is a challenging task, especially when working with high dimensional data set. In this study, I have combined causal ML techniques with explainable AI methods and provided guidelines on how to measure heterogeneous treatment effects with the right regression specification (i.e. which main effects and interactions to be used, what control variables to be included). To empirically test these guidelines, I have curated a large data set in football including detailed variables about interim feedback, match-specific conditions, team features, and most importantly manager characteristics. Empirical evidence contributes to the sports analytics literature suggesting when and how risk-taking behavior of football managers pays off in light of interim and ex-ante information revealed to the manager (i.e. the decision maker). Moreover, this thesis contributes to the causal ML literature by evaluating the performances of two well-known causal ML techniques (a recently popular matching algorithm focusing on finding average treatment effects (FLAME) and Causal Forest that directly aims to estimate heterogeneous treatment effects) are evaluated by using synthetic data generated with known heterogeneous treatment effects. In addition to sports analytics, I have also worked with education data and demonstrated how grit, a non-cognitive skill, predicts academic achievement for students. I used a unique dataset from a digital learning platform to construct a behavioral measure of grit and showed that behavioral grit is a better predictor of student performance compared to survey grit that has been traditionally used by the researchers. I have also found that machine learning algorithms perform well in predicting academic resilience even without constructing any structural model or regression specification, thanks to the power of big data. I believe that my findings from cases in sports and education put forward the benefits of using Machine Learning and big data for researchers working with traditional and theory-based models for causal inference.
The impact of asset ratio policy on Turkish Banking Market Index
This study focuses on the effect of asset ratio policy on the return of Turkish banking market index (XBANK), which is value-weighted banking stock index in Borsa Istanbul. Initially, we employ parametric t-test and then event study regression analysis including event shock dummies with three different models, which are simple market, capital asset pricing and Fama French three factor models. According to parametric t-test and regression results, we do not observe any significant effect of asset ratio policy on the daily return of XBANK. In addition, we realize that daily trading volume of XBANK was not significantly reacted to policy revisions. Also, we do not capture any meaningful response in the performance of Turkish banking index after the repeal of asset ratio policy, according to the post-event analysis.
Pricing ambiguity and ambiguity aversion in the cross-section of stocks
Based on a theoretical model that relies on the theory of smooth ambiguity aver- sion, this thesis empirically investigates whether ambiguity and aversion towards it is priced in the cross-section of stocks and tests whether the model has explanatory power for resolving some of the cross sectional stock anomalies. The literature inves- tigating whether ambiguity is priced in stock returns typically studies the research question at the aggregate market level. The main finding in the thesis is that am- biguity is priced in the cross section of stocks. Abnormal excess returns of stocks (i.e. CAPM alphas) in the cross section are shown to increase with stocks' ambi- guity exposure which is defined as the difference between the ambiguity beta of a stock and its CAPM beta, where ambiguity beta is the stock's exposure to market portfolio ambiguity. In order to estimate stocks' ambiguity betas, macroeconomic uncertainty index of Jurado, Ludvigson and Ng (2015) is employed as a proxy for market portfolio ambiguity. The thesis shows when stocks are ranked into quintile portfolios according to their ambiguity exposure and ambiguity exposure is controlled for in the CAPM regression as a separate factor, alpha of the high-minus-low quintile as well as the alphas of the individual quintiles disappear. The thesis also shows that ambiguity and ambiguity aversion fully explain the beta anomaly, while providing partial explanations to operating profitability and momentum anomalies.