Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

Modelling the Consumption Behaviour of Heterogeneous Consumers: A Duty-Free Shop Case Simulation Analysis

Duty-free shops, which have emerged in major airports, first-tier cities, free trade zones and other places, have become ideal places for not only facilitating people to buy goods but also promoting the development of the local economy, which makes the study of the heterogeneous consumer purchase behavior in duty-free stores of great importance and great practical significance. Based on this, the agent model is used to study the purchase behavior of heterogeneous consumers in duty-free stores, the structure of the agent model is proposed, the consumer submodel and situation submodel are designed, and a service recommendation is made. On this basis, the consumer behavior is simulated and analyzed both with and without considering situational factors. The following conclusions are drawn: (1) The display, the month, holidays, and other factors have an important impact on the heterogeneous consumers of duty-free stores and affect consumers\u27 consumption behavior. (2) Salespeople\u27s recommendation rules and consumers\u27 purchase preferences affect consumers\u27 purchase behavior, which has an important impact on the types and quantity of goods consumers buy

Exploratory analysis of Internet of Things (IoT): revolutionizing the grocery retail industry

This dissertation has investigated the consequences of implementing Internet of Things (IoT) technologies in grocery retailing by analyzing customers' perceptions of eight prominent technologies. The objective was to investigate and explore to what degree implementing these technologies would impact the customer experience. Based on secondary research, this thesis focuses on eight prominent technologies that presumably will encounter an increasing utilization in the visible future; Self-Scanning, Smart Robots, Smart Shelves, Smart Shopping Cart, Smart Fridge, Just Walk Out, Personalized Promotion/Pricing, and Mobile Apps. The technology distribution varies across different stages in the customer journey, and research indicates that IoT has the most significant impact in the pre-purchase stage. A comprehensive exploratory survey was conducted through Amazon mTurk with a wide range of respondents (n=204), giving valuable insight into demographic differences' influence on each technology perception. The investigation uncovered vast differences in several areas such as age, attitude, and privacy. Among other findings, the age segment 35-44 is more confident towards IoT technology than the age segment 55+, and shoppers with a positive attitude towards grocery shopping have higher confidence towards the technologies than shoppers with a negative attitude. On a widespread basis, the findings revealed that all eight technologies would positively affect customer experience to a certain level. Keywords: Internet of Things, Grocery Retailing, Customer Journey, Customer Experience, Autonomous Retail

Towards building a review recommendation system that trains novices by leveraging the actions of experts

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find helpful information about a product (Porter, 2010). Thus, there is a need to train reviewers to write better reviews, which could in turn better serve customers, vendors, and online e-stores. In this Thesis, we propose a review recommendation framework to train reviewers to better write about their experiences with a product by leveraging the behaviors of expert reviewers who are good at writing helpful reviews. First, we use clustering to model reviewers into different classes that reflect different skill levels to write a quality review such as expert, novice, etc. Through temporal analysis of reviewer behavior, we have found that reviewers evolve over time, with their reviews becoming better or worse in quality and more or less in quantity. We also investigate how reviews are valued differently across different product categories. Through machine learning-based classification techniques, we have found that, for products associated with prevention consumption goal, longer reviews are perceived to be more helpful; and, for products associated with promotion consumption goal, positive reviews are more helpful than negative ones. In this Thesis, our proposed review recommendation framework is aimed to help a novice or conscientious reviewer become an expert reviewer. Our assumption is that a reviewer will reach the highest level of expertise by learning from the experiences of his or her closest experts who have a similar evolutionary pattern to that of the reviewer who is being trained. In order to provide assistance with intermediate steps for the reviewer to grow from his or her current state to the highest level of expertise, we want to recommend the positive actions—that are not too far out of reach of the reviewer—and discourage the negative actions—that are within reach of the reviewer—of the reviewer’s closest experts. Recommendations are personalized to fit the expertise level of reviewers, their evolution trend, and product category. Using the proposed review recommendation system framework we have found that for a random reviewer, at least 80% of the reviews posted by closest experts were of higher quality than that of the novice reviewer. This is verified in a dataset of 2.3 million reviewers, whose reviews cover products from nine different product categories such as Books, Electronics, Cellphones and accessories, Grocery and gourmet food, Office product, Health and personal care, Baby, Beauty, and Pet supplies. Advisor: Leen-Kiat So

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Data (r)evolution – the economics of algorithmic search and recommender services

The paper analyses the economics behind algorithmic search and recommender services, based upon personalized user data. Such services play a paramount role for online services such as marketplaces (e.g. Amazon), audio streaming (e.g. Spotify), video streaming (e.g. Netflix, YouTube), app stores, social networks (e.g. Instagram, Tik Tok, Facebook, Twitter) and many more. We start with a systematic analysis of search and recommendation services as a commercial good, highlighting the changes to these services by the systematic use of algorithms. Then we discuss benefits and risk for welfare arising from the widespread employment of algorithmic search and recommendation systems. In doing so, we summarize the existing economics literature and go beyond its insights, including highlighting further research desires. Eventually, we derive regulatory and managerial implications drawing on the current state of academic knowledge