Electronic Recommendation Agents On The Web – Hyung Jun Ahn School of Business, Hongik University, Seoul, Korea Sangmoon Park Department of Business Administration, Kangwon National University, Chuncheon City, Gangwon-do, Korea
With the widespread acceptance of online shopping, many Internet businesses have developed and used customer information support tools often called recommendation agents (“agents”). Agents are software programs that are often embedded in online stores to help customers search for and find products. Due to the abundance of options on the Internet and the fact that real-time human support is not usually available in online stores, agents are now considered an essential tool to make the buying process efficient and effective (Adomavicius and Tuzhilin 2005; Ahn 2006; Burke; 2002; Kwon et al 2009; Wang and Doong 2010; Xiao and Benbasat 2007).
Electronic Recommendation Agents On The Web
Most agents have two main components: obtaining user preferences and matching preferences with product profiles. For example, in a recommendation system used on Amazon.com, user preferences are implicitly collected every time a user clicks or buys a product (Linden et al. 2003). This information is passed to an internal mechanism that finds other products that the user prefers and buys. Users can explicitly enter preferences, for example, about desired price ranges, authors or keywords.
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Much research has been done on agents from various perspectives. Among them, technical studies have developed algorithms or recommendation mechanisms that can more accurately reflect user preferences (Ahn 2008; Adomavicius and Tuzhilin 2005; Ahn 2006; Burke 2002). On the other hand, behavioral researchers in information systems or marketing fields have studied the factors that influence the reception or effectiveness of recommendation agents. This type of research often conducts empirical research by conducting experiments with real or virtual Internet stores and questionnaires (Cohen and Fan 2000; Greco et al. 2004; Wang and Benbasat 2005; Xiao and Benbasat 2007). The current study falls into the second category.
Although previous research has found some common factors that influence the usefulness or acceptance of agents, relatively little has been done on how the relationships may be moderated by user characteristics. The purpose of this study is to investigate the relationship between the user’s evaluation of agents and its antecedents, focusing in particular on the moderating effect of product expertise of the customer. This is believed to be an important issue, since the existence of this moderating effect may require online stores to develop and apply different personalization approaches for different types of customers. In other words, the result of this research can help companies better personalize agents or use different agents for heterogeneous types of customers to obtain more effective information recommendation. For research, an experimental shopping center for digital cameras was built and used for shopping experiments and data collection. The collected data were analyzed using the partial least squares method.
The paper is organized as follows. The next section explains the theoretical basis of the research and explains the hypotheses. The third section presents the experiments and data collection. The fourth section presents the results of the analysis. The final section concludes with a summary, implications for companies and limitations of the study.
Similar to the definitions of recommendation agents found in the literature (Adomavicius and Tuzhilin 2005; Burke 2002; Xiao and Benbasat 2007), this article defines an agent as a software program embedded in an Internet store to help -customers find products and/or provide guidance. . recommend products. In this sense, previous studies have considered agents as decision support systems for customers (Grenci and Todd 2002; Haubl and Trifts 2000; Kim et al. 2010).
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Many studies have shown that agents help customers make better and more efficient purchasing decisions (Hostler et al. 2004; Pedersen 2000). Agents can significantly improve the decision process and decision outcome, especially when the choice set is large and customers have limited time and capacity to evaluate all alternatives. For these reasons, agents are also thought to help businesses by enabling up-selling and cross-selling of products. This is why agents are increasingly considered an essential tool by many online stores.
There were many different classifications of agents. The most common classification is whether the recommendation is made using content similarity (content-based filtering) or user similarities (Cohen and Fan 2000; Greco et al. 2004; Kim and Kim 2001; Li et al. 2005; Li et al. 2005; ). . There are many variations on these recommendation methods, such as object-based collaborative filtering, where object similarity is used instead of user similarity. Another popular classification scheme is based on whether the inputs to an agent are at the trait level or at the needs level (Felix et al. 2001; Komiak and Benbasat 2006; Stolze and Nart 2004). Feature-based agents require customers to select product features to generate recommendations; The needs-based agents make recommendations based on the specific types of needs that a user chooses, such as a family camera or a sports camera. This article takes this last typology and offers two types of agents for experimental purchases. There are still more classifications of agents in the literature (Adomavicius and Tuzhilin 2005; Burke 2002; Xiao and Benbasat 2007).
Despite the abundant literature on the factors that determine user acceptance or agent evaluation, there has not been much research investigating the moderating role of personal characteristics, especially product experience of the client. Based on the literature review, this subsection develops hypotheses for our research objective. We present the antecedents, moderator variables, and dependent variables for the user agent evaluation model. The hypotheses are largely based on empirical findings from related research, as well as theories such as task technology adaptation, the technology acceptance model, and decision making of consumer.
Some studies show the effect of agent type on agent efficiency or user evaluation (Felix et al. 2001; Komiak and Benbasat 2006; Stolze and Nart 2004). The general conclusion of the studies is that some agents are preferred or users find them more effective. For example, Felix’s empirical study
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(2001) shows that needs-based agents are rated as more effective by participants in a digital camera purchase experiment. Similarly, in the review carried out by Xiao and Benbasat (2007), several proposals were presented that support the superiority of one type of agent in terms of decision quality and decision effort of -users.
However, the preference for a specific agent type may change depending on the user’s intent. For example, a user with a very specific purchase intent may know the desired attributes of the target product very well, in which case a feature-based agent may be more appropriate. The user can enter values or select options for features to easily generate recommendations. On the other hand, if the intention of a user is ambiguous or the user is not sure of the specific characteristics of the desired product, a needs-based agent may be more useful. The technology adaptation theory of the task also supports this idea and many studies have tested the theory in the field of information systems (Gebauer and Tang 2008; Klopping and McKinney 2004). For example, in Klopping and McKinney’s (2004) study, theory was found to have a significant effect on the perceived usability of an e-commerce system. Therefore, the following is proposed:
H1: A higher adjustment between a user’s purchase intention and the type of agent has a positive effect on the evaluation of the agent.
In addition to the above, only the specificity of the user’s intent can affect the relationship. When users have a specific goal and know what kind of products to look for, they can simply browse the categories of the online store to find the products. For example, most online digital camera stores offer browsing categories for different pixel counts, zoom capabilities, brands, and price ranges, along with some sort of functionality for each category, making it easier for users to find products once they know what to look for. Empirical studies on the acceptance of electronic commerce or Internet services support this as well. For example, in the research done by Kim
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(2008) show that user acceptance of e-commerce is influenced by the uniqueness of online tasks. In Jarvelainen’s (2007) work, it was found that task ambiguity had a negative effect on passenger acceptance of online booking for cruise services. In the experiment of and Tam
H2: Higher precision of a user’s purchase intention has a negative effect on the evaluation of the agent.
They also included variables that often had an impact in the literature on agents: the frequency of the user’s online shopping experience (Castaneda et al. 2007; Jarvelainen 2007; Kim et al. 2008; Klopping and McKinney 2004; Stern et al. 2008; ) and, in general, the user’s familiarity with agents (Komiak and Benbasat 2006; Stern et al. 2008).
H3: The higher frequency of a user of online shopping has a positive effect on the evaluation of the agent. H4: Greater user familiarity with agents in general has a positive effect on agent evaluation. Product experience as a moderator
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Conducted customer product impact studies