There has been much concern that social media, in particular YouTube, may facilitate radicalisation and polarisation of online audiences. This systematic review aimed to determine whether the YouTube recommender system facilitates pathways to problematic content such as extremist or radicalising...

We study a form of threshold matching in fundraising where donations above a certain threshold are topped up with a fixed amount. We show theoretically that threshold matching can induce crowding in if appropriately personalized. In a field experiment, we explore how thresholds should be chosen...

Based on consumers' preferences, AI (artificial intelligence) recommendation automatically filters information, which provokes scholars' debate. Supporters believe that by analyzing the consumers' preferences, AI recommendation enables consumers to choose products more quickly and at a lower...

This paper explores which variables are more significant in municipal executive recommendation by citizens. We estimated the influence of public dimensions, such as municipe loyalty, municipe satisfaction, and municipe perceived value in municipal executive recommendation by citizens. Then, we...

We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations...

Social media are at the center of countless debates on polarization, misinformation, and even the state of democracy in various parts of the world. An essential feature of social media is the ranking algorithm that determines how content is presented to the users. This paper studies the dynamic...

Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly...

Scholars exploring transitions to personalist rule focus on coercive power transfer to personalist rulers and argue that forming viable political coalitions, undermining power-sharing agreements, and mobilizing non-democratic institutions play a crucial role in transferring coercive power....

We study a form of threshold matching in fundraising where donations above a certain threshold are topped up with a fixed amount. We show theoretically that threshold matching can induce crowding in if appropriately personalized. In a field experiment, we explore how thresholds should be chosen...

An increasing number of consumption decisions happen on two (or more) sided platforms that carry goods and services from a large number of suppliers. In principle, such easy access to a multiplicity of products should lower consumers' search costs and thereby enhance their decision-making. In...

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully Bayesian approach into the traditional dynamic collaborative filtering and hence captures the change of customer preferences in the mobile game industry with...

I study a cheap talk model between two players: a buyer/decision maker and a seller/expert. There is two-sided private information and I allow for sequential, twoway communication. The buyer faces a decision about purchasing one of a number of goods, and the buyer’s valuation depends on: her...

Purpose - In user-oriented websites, e.g. in news websites or in seller websites, it is important to take the user's preferences into account when deciding which items to place in higher-exposure locations. The traditional approach to solving this problem, based on maximizing the average user...

Open innovation allows the hotel industry to create platforms and channels for collaboration with its customers, which results in a better understanding of their tastes and demands. In the current environment, which is full of uncertainty, many companies have seen their profits decrease, and...

This article argues that the European regulatory approach to disinformation online is stymied by inappropriate regulatory theories. On that basis, this article seeks to advance an alternative theoretical approach, inspired by the contemporary European paradigm of financial services regulation....

Policymakers have recently expressed concerns over the role of recommendation algorithms and their role in forming "filter bubbles". This is a particularly prescient concern in the context of extremist content online; these algorithms may promote extremist content at the expense of more moderate...

The use of personalization mechanisms should allow the insurance distributor to reduce exploration costs and adjust the offered insurance product to the needs, features, and situation of each individual client. This study seeks to examine how liability should be allocated when the process of the...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

Customer Management Softwares have become a critical strategy in business, since it became prominent that they improve a company's probability to achieve superior business results. Yet, for a company to be able to deliver superior portfolio management solutions, Technology, Data Analysis and...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

The use of personalization mechanisms should allow the insurance distributor to reduce exploration costs and adjust the offered insurance product to the needs, features, and situation of each individual client. This study seeks to examine how liability should be allocated when the process of the...

Recommender systems (RSs) are widely utilised across industries as a tool to provide users with recommendations based on their preferences. This paper reports on an examination of the influence of trusting belief on behavioural intentions to reuse RSs, emphasising the effects of trust...

The digital revolution has allowed sellers to make large assortments of products available to consumers. Recommendation systems have played a central role in this dynamic. At the core of these systems is the use of data and sophisticated algorithms to predict match values between products and...

Online media platforms such as Spotify, YouTube Music, and NetEase Cloud Music rely on long-term user engagement for revenue generation. The primary operational level under their control is content recommendation (i.e., what content to recommend to various users), where the right recommendation...

Many online marketplaces (e.g., Amazon) use recommendation systems to personalize product recommendations for each customer. In practice, many recommendation systems are “profit-based,” recommending the product that maximizes the marketplaces’ expected profit from each consumer. We study...

In this note we provide a method for converting sales rank data into sales quantity. We show how sales quantity data can be extracted from product inventories on Amazon for a subset of products. We collect a large dataset using this method and then generalize results from Chevalier & Goolsbee...