Effect of timing and source of online product recommendations: An eye-tracking study

Online retail business has become an emerging market for almost all business owners. Online recommender systems provide better services to the consumers as well as assist consumers with their decision making processes. In this study, a controlled lab experiment was conducted to assess the effect of recommendation timing (early, mid, and late) and recommendation source (expert reviews vs. consumer reviews) on e-commerce users\u27 interest and attention. Eye-tracking data was extracted from the experiment and analyzed. The results suggest that users show more interest in recommendation based on consumer reviews than recommendation based on expert reviews. Earlier recommendations do not receive greater user attention than later recommendations --Abstract, page iii

Taste and the algorithm

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”

A video-based automated recommender (VAR) system for garments

In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0984.The authors thank the participants in presentations given by the authors in College of Business at City Univeristy of HongKong and Cambridge Judge Business School for their feedback, as well as the Editor, the Area Editor, and two anonymous Marketing Science reviewers for their insightful comments. This research was supported by two National Natural Science Foundation of China Fund (Grants 71232008 & 71502039), and the Institute for Sustainable Innovation and Growth (iSIG) at School of Management, Fudan University

Neuromarketing: a review of research and implications for marketing

In this research, we reviewed existing studies which used neuromarketing techniques in various fields of research. The results revealed that most attempts in neuromarketing have been made for business research. This research provides important results on the use of neuromarketing techniques, their limitations and implications for marketing research. We hope that this research will provide useful information about the neuromarketing techniques, their applications and help the researchers in conducting the research on neuromarketing with insight into the state-of-the-art of development methods

Visual Representation of Explainable Artificial Intelligence Methods: Design and Empirical Studies

Explainability is increasingly considered a critical component of artificial intelligence (AI) systems, especially in high-stake domains where AI systems’ decisions can significantly impact individuals. As a result, there has been a surge of interest in explainable artificial intelligence (XAI) to increase the transparency of AI systems by explaining their decisions to end-users. In particular, extensive research has focused on developing “local model-agnostic” explainable methods that generate explanations of individual predictions for any predictive model. While these explanations can support end-users in the use of AI systems through increased transparency, three significant challenges have hindered their design, implementation, and large-scale adoption in real applications. First, there is a lack of understanding of how end-users evaluate explanations. There are many critiques that explanations are based on researchers’ intuition instead of end-users’ needs. Furthermore, there is insufficient evidence on whether end-users understand these explanations or trust XAI systems. Second, it is unclear which effect explanations have on trust when they disclose different biases on AI systems’ decisions. Prior research investigating biased decisions has found conflicting evidence on explanations’ effects. Explanations can either increase trust through perceived transparency or decrease trust as end-users perceive the system as biased. Moreover, it is unclear how contingency factors influence these opposing effects. Third, most XAI methods deliver static explanations that offer end-users limited information, resulting in an insufficient understanding of how AI systems make decisions and, in turn, lower trust. Furthermore, research has found that end-users perceive static explanations as not transparent enough, as these do not allow them to investigate the factors that influence a given decision. This dissertation addresses these challenges across three studies by focusing on the overarching research question of how to design visual representations of local model-agnostic XAI methods to increase end-users’ understanding and trust. The first challenge is addressed through an iterative design process that refines the representations of explanations from four well-established model-agnostic XAI methods and a subsequent evaluation with end-users using eye-tracking technology and interviews. Afterward, a research study that takes a psychological contract violation (PCV) theory and social identity theory perspective to investigate the contingency factors of the opposing effects of explanations on end-users’ trust addresses the second challenge. Specifically, this study investigates how end-users evaluate explanations of a gender-biased AI system while controlling for their awareness of gender discrimination in society. Finally, the third challenge is addressed through a design science research project to design an interactive XAI system for end-users to increase their understanding and trust. This dissertation makes several contributions to the ongoing research on improving the transparency of AI systems by explicitly emphasizing the end-user perspective on XAI. First, it contributes to practice by providing insights that help to improve the design of explanations of AI systems’ decisions. Additionally, this dissertation provides significant theoretical contributions by contextualizing the PCV theory to gender-biased XAI systems and the contingency factors that determine whether end-users experience a PCV. Moreover, it provides insights into how end-users cognitively evaluate explanations and extends the current understanding of the impact of explanations on trust. Finally, this dissertation contributes to the design knowledge of XAI systems by proposing guidelines for designing interactive XAI systems that give end-users more control over the information they receive to help them better understand how AI systems make decisions

Designing Social Nudges for Enterprise Recommendation Agents: An Investigation in the Business Intelligence Systems Context

According to behavioral economists, a “nudge” is an attempt to steer individuals toward making desirable choices without affecting their range of choices. We draw on this concept, and design and examine nudges that exploit social influence’s effects to control individuals’ choices. Although recommendation agent research provides numerous insights into extending information systems and assisting end consumers, it lacks insights into extending enterprise information systems to assist organizations’ internal employees. We address this gap by demonstrating how enterprise recommendation agents (ERAs) and social nudges can be used to tackle a common challenge that enterprise information systems face. That is, we use an ERA to facilitate information (i.e., reports) retrieval in a business intelligence system. In addition, we use social nudges to steer users toward reusing specific recommended reports rather than choosing between recommended reports randomly. To test the effects of the ERA and the four social nudges, we conduct a within-subject lab experiment using 187 participants. We also conduct gaze analysis (“eye tracking”) to examine the impact of participants’ elaboration. The results of our logistic mixed-effects model show that the ERA and the proposed social nudges steer individuals toward certain choices. Specifically, the ERA steers users toward reusing certain reports. These theoretical findings also have high practical relevance and applicability: In an enterprise setting, the ERA allows employees to reuse existing resources (such as existing reports) more effectively across their organizations because employees can more easily find the reports they actually need. This, in turn, prevents the development of duplicate reports

Measurement in marketing

We distinguish three senses of the concept of measurement (measurement as the selection of observable indicators of theoretical concepts, measurement as the collection of data from respondents, and measurement as the formulation of measurement models linking observable indicators to latent factors representing the theoretical concepts), and we review important issues related to measurement in each of these senses. With regard to measurement in the first sense, we distinguish the steps of construct definition and item generation, and we review scale development efforts reported in three major marketing journals since 2000 to illustrate these steps and derive practical guidelines. With regard to measurement in the second sense, we look at the survey process from the respondent's perspective and discuss the goals that may guide participants' behavior during a survey, the cognitive resources that respondents devote to answering survey questions, and the problems that may occur at the various steps of the survey process. Finally, with regard to measurement in the third sense, we cover both reflective and formative measurement models, and we explain how researchers can assess the quality of measurement in both types of measurement models and how they can ascertain the comparability of measurements across different populations of respondents or conditions of measurement. We also provide a detailed empirical example of measurement analysis for reflective measurement models

Neuroeconomics: Using Neuroscience to Make Economic Predictions

Neuroeconomics seeks to ground economic theory in detailed neural mechanisms which are expressed mathematically and make behavioural predictions. One finding is that simple kinds of economising for life-and-death decisions (food, sex and danger) do occur in the brain as rational theories assume. Another set of findings appears to support the neural basis of constructs posited in behavioural economics, such as a preference for immediacy and nonlinear weighting of small and large probabilities. A third direction shows how understanding neural circuitry permits predictions and causal experiments which show state-dependence of revealed preference – except that states are biological and neural variables