Platforms that provide product recommendations to consumers, such as marketplaces like Amazon or online travel agencies like Expedia, govern a substantial part of transactions in many markets. In addition to selling via platforms, most firms, however, also operate a direct channel. This paper...

Recommender systems are indispensable in e-business due to the extensive product range and the large number of niche articles. Collaborative filtering (CF) algorithms play a central role in generating personalised recommendations. There are numerous CF approaches in the literature that have...

This paper investigates the impact of social media algorithms on mental health outcomes. I exploit a quasi-experimental setting combining data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) coupled with the introduction of the algorithmic feed on Instagram in 2016. I...

Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals,...

We examine the strategic considerations and effects of product placement services in digital content aggregators, focusing in particular on Spotify’s “Discovery Mode”. Discovery Mode introduces bias in users’ consumption bundle that content providers pay through discounted royalties, and...

Purpose This study investigates the effects of trust, satisfaction, and loyalty on AI‑driven e‑commerce, with a particular focus on how personalized recommendations moderate these relationships. It aims to explore how personalized AI features reshape consumer perceptions and decision-making....

The integration of large language models (LLMs) into recommender systems (RS) has given rise to a new generation of Conversational RS (CRS). This study asks how CRS systems shape consumer behavior, and, in particular, their spending. Despite the rapid proliferation of such systems, including...

Purpose: This article aims to reveal how online reviews in virtual communities affect the reputation of platform sellers. Through research, consumers are encouraged to make effective use of online reviews, and it also provides enlightenment for the marketing management of cross-border e-commerce...

Food-related sustainability and health issues have led to calls for improving consumer food choices. As food purchase decisions predominantly occur in supermarkets and increasingly in online supermarkets, online interventions to stimulate healthy and sustainable grocery choices have gained...

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical...

Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging. We build a discrete choice model that embeds recommendation-induced utility, low-rank heterogeneity, and flexible...

Operational alignment, defined as the alignment between business processes (BPs) and information systems (ISs), is essential for ensuring that IS capabilities effectively support organizational operations. Despite extensive efforts, existing approaches to operational alignment remain constrained...

Advances in data collection enable firms to use consumer information for personalized pricing. In Clavor'a Braulin's (2023) symmetric two-dimensional model, this reduces prices and profits, while partial privacy yields the highest profits. Extending the model to asymmetric firms and vertically...

Background: The study aims to examine the role of artificial intelligence-oriented personalisation in customer interaction of social commerce platforms in the developing market, such as Kosovo. Objectives: A quantitative design was used, and 312 active users were sampled, with surveys offered to...

Algorithm- and data-based recommendation systems (DARS) have become a central component of the digital economy, shaping how users access, evaluate, and consume information and goods. These systems encompass both search rankings tailored to estimated user preferences and direct recommendations...

The integration of large language models (LLMs) into recommender systems (RS) has given rise to a new generation of Conversational RS (CRS). This study asks how CRS systems shape consumer behavior, and, in particular, their spending. Despite the rapid proliferation of such systems, including...

Current studies primarily focus on the recommender systems (RSs) through the algorithmic accuracy, and which insufficient to fully measure accurately the practical effectiveness of the RSs and understand user experience. There is an urge examine the dynamic interactions on the critical user...

Purpose: The product recommendation type is attracting attention from both the retail and academic communities. However, there is no consensus on the effectiveness of set recommendation versus separate recommendation. This research investigates the interaction effect of product recommendation...

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products,...

In recent years, online shopping is one of the routine parts in people’s life. It is convenient and takes less effort to purchase it. Regarding the increasing revolution of e-commerce businesses, recommendation engine plays a crucial role in them. Recommendation engines are very popular and...

This paper investigates the value of recommendations for disseminating economic information, with a focus on frictions resulting from preference heterogeneity. We consider Bayesian expected-payoff maximizers who receive non-strategic recommendations by other consumers. The paper provides...

This paper investigates the value of recommendations for disseminating economic information, with a focus on frictions resulting from preference heterogeneity. We consider Bayesian expected-payoff maximizers who receive non-strategic recommendations by other consumers. The paper provides...

Recommendation-based communication plays a pivotal role in consumer choices. From human sources to electronic word of mouth or different types of recommender systems, recommendations help consumers adopt or reject leads, and can be highly beneficial for organizations. In view of its relevance...