In this study, we aim to investigate how two different types of online recommender systems affect the cross-selling of a retailer on a website using the online recommender systems. Furthermore, this will give us the chance to study the direct effect of targeting on cross-selling. Different from the previous studies, this study will mainly focus on two aspects.
On the one hand, the online recommender systems will be divided into two types, the targeting recommender systems, and popular recommender systems. The first one is the personalized form through which we can give the targeted consumers the specific products recommenders based on the consumers’ purchase history. This type has been fully discussed in previous studies. The second type of a recommender system is the public form giving the recommendations based on the hot products, which is a common phenomenon in e-commerce platforms. In other words, recommended products or services come from common preferences of all consumers. Research on the relationship between cross-selling and this type of recommender system is still relatively lacking.
On the other hand, we will discuss the different moderating effects between two different types of the recommended products — search-type and experience-type products — as well as the influence caused by product familiarity on the relationship between the online recommender systems and cross-selling. These two types of products have been fully discussed in the area about the helpfulness of online reviews. In addition, familiar and unfamiliar products differ in terms of the knowledge regarding the products that a consumer has stored in memory. This will affect how consumers search and process the online recommender systems and co-purchase information. Therefore, we will consider these in the process of data collecting.
Through examining those previously described causal effects, we can make the following two contributions. Firstly, we can make further suggestions about how the choice for an online recommender system can influence cross-selling and thereby further contribute to the discussion about recommender systems in the e-commerce ecosystem. Secondly, we can classify the cross-influence from the product types and product familiarity in the above-stated relationship between online recommender systems and cross-selling.
Table of Contents
1 Introduction
2 Literature Review
2.1 Cross-Selling
2.2 Recommender Systems
2.3 Product categories and product familiarity
3 Hypothesis
3.1 Research Model and Hypothesis Development
3.2 The effect between different types of recommender systems and cross-selling
3.3 The moderation effect of product categories and product familiarity
4 Research Method
4.1 Experimental Design
4.2 Experimental Task and Procedures
4.3 Measurement of Dependent Variable
Objectives and Research Focus
This paper investigates the impact of two distinct types of online recommender systems (targeting vs. popular) on the cross-selling performance of e-commerce retailers, while considering the moderating roles of product categories and consumer product familiarity.
- Analysis of targeting recommender systems versus popular recommender systems.
- Evaluation of how search-type and experience-type products influence cross-selling.
- Examination of product familiarity as a boundary condition in consumer decision-making.
- Development of a comprehensive research model and empirical testing of six hypotheses.
- Application of a 2x2x2 factorial experimental design involving 200 participants.
Excerpt from the Book
The effect between different types of recommender systems and cross-selling
Recommender systems play an important role in the way how customers seek information in the e-commerce ecosystem. Recommender systems can give online customers appropriate suggestions on what to buy by guiding them through the potentially overwhelming set of choices of different products. Therefore, those systems can improve the quality of the decision making by customers (Xiao and Benbasat, 2007; Grenci and Todd, 2002). Since the product recommendation lowers the search cost of the consumer, this may cause the consumer to buy more or stop continuing the search, and thus may have an influence on the cross-selling (Schafer et al., 1999). In the following we will examine the different influences of the two different types of recommender systems, the popular recommender system and the targeting recommender system, on cross-selling.
The popular recommender system collects data from all the participants of the e-commerce ecosystem like users’ preferences, product characteristics, searching behavior, trending products, recommendations of opinion leaders and new product releases to recommend products to a customer. In other words, recommended products or services come from common preferences of all consumers, which rely on the collected data from the whole e-commerce ecosystem. Therefore, these recommended products, can heavily differ in the characteristics from products that can appear as suggested items. Moreover, it will reduce the search cost for the consumer when they receive these popular products recommendation, and can therefore foster the sales (Schafer et al., 1999). Therefore, we can argue that the popular recommender system increases cross-selling.
Summary of Chapters
1 Introduction: Provides an overview of the competitive e-commerce landscape and explains how recommender systems help reduce consumer search costs.
2 Literature Review: Synthesizes existing research on cross-selling definitions, the functionality of various recommender systems, and the categorization of products based on consumer familiarity.
3 Hypothesis: Develops a formal research model and proposes six testable hypotheses regarding the interaction between recommendation types, product categories, and familiarity.
4 Research Method: Details the experimental setup, specifically the 2x2x2 between-subject factorial design and the data collection procedures used to validate the hypotheses.
Keywords
e-commerce, cross-selling, recommender systems, targeting, popular products, search-type products, experience-type products, product familiarity, search costs, consumer behavior, online marketplace, sales diversity, information search, decision-making, digital marketing
Frequently Asked Questions
What is the core focus of this research paper?
The paper explores how different configurations of online recommender systems influence the success of cross-selling strategies in e-commerce, specifically looking at how product type and consumer knowledge affect these outcomes.
What are the primary themes addressed in the study?
The themes include the mechanisms of cross-selling, the distinction between popular and targeting-based recommendation algorithms, the classification of products (search vs. experience goods), and the psychological impact of product familiarity.
What is the central research question or goal?
The goal is to determine how targeting and popular recommender systems differentially affect cross-selling, and how this relationship is moderated by the specific product category and the consumer's familiarity with the product.
Which scientific methodology is employed?
The researchers utilized an empirical quantitative approach, conducting a 2x2x2 between-subject factorial experiment with approximately 200 participants to test their hypotheses.
What does the main body of the paper cover?
The main body reviews relevant literature to build a theoretical foundation, defines the research model, derives testable hypotheses, and describes the experimental procedures used to measure dependent variables like sales data.
Which keywords define this work?
Key terms include e-commerce, cross-selling, recommender systems, search costs, and product familiarity.
How do "popular" recommender systems differ from "targeting" ones according to the text?
Popular systems offer recommendations based on aggregate trends and common preferences across the entire user base, whereas targeting systems are personalized, relying strictly on the individual user's specific purchase and browsing history.
Why is the distinction between "search-type" and "experience-type" products important?
The text explains that consumers evaluate these product types differently; search-type products are evaluated through cognitive, goal-oriented search, while experience-type products rely more on subjective experience, which in turn changes how they respond to automated recommendations.
- Arbeit zitieren
- Anonym (Autor:in), 2018, Product Recommendations and Cross-Selling. Recommendation Forms, Product Categories and Product Familiarity, München, GRIN Verlag, https://www.grin.com/document/469311
