مکانیزم توصیه خرید آنلاین و نفوذ آن بر تصمیمات و رفتار مصرف کننده : رویکرد نقشه سببی

Purpose of this paper Online product recommendation mechanism (agents) are becoming increasingly available on websites to assist consumers with reducing information overload, provide advice in finding suitable products, and facilitate online consumer decision-making. Central of these services is consumers’ satisfaction with recommendation results. Traditional recommendation mechanism (TRM) is based content and/or collaborative filtering approach. However, the remaining problem concerning TRM is how to analyze the causal relationships between quantitative and qualitative factors, and investigate their impact on the central routes and peripheral routes through which both quantitative and qualitative factors can affect customer online shopping decisions. It is well known that qualitative factors are hard to codify yet they have a significant effect on a customer’s decision-making process in the form of causal relationships with quantitative factors. Thus, a new online recommendation mechanism is required that incorporates qualitative factors systematically with quantitative factors to analyze their combined influence on customers’ purchasing decision-making process. So, our study suggest that causal maps based recommendation mechanism (CMRM). Design/methodology/approach ELM was applied to build hypotheses concerning how consumers’ decision satisfaction and online shopping behavior are affected by CMRM. Specifically, the performance of the proposed CMRM is analyzed empirically by garnering the experiment data from 250 qualified respondents who were asked to refer to the proposed CMRM before making purchasing decisions on mobile phones. Findings Statistical results proved that the proposed CMRM could enhance consumers’ decision satisfaction, attitude towards the recommended products, as well as positive purchase intentions and actual purchase. Practical implications CMRM can be easily implemented on the web, allowing target consumers to experience a real recommendation process. And, a wide variety of qualitative factors that seem crucial to most consumers can be pre-defined through a survey, and incorporated into causal maps. Thus, such causal maps will improve the personalization effect on the target consumer’s purchase intentions.

Recently, information technology has been utilized to help companies maintain competitive advantage (Nissen & Sengupta, 2006). Data mining techniques with recommendation systems are a widely used information technology for extracting customer’s knowledge and further supporting marketing decisions (Balabanovic & Shoham, 1997). The buying patterns of individual customers and groups can be identified via analyzing customer data (Maes, Guttman, & Moukas, 1999), but also allows a company to develop one-to-one marketing strategies that provide individual marketing decisions for each customer (Lampel and Mintzberg, 1996 and Murthi and Sarkar, 2003). Recommendation systems are technologies that assist businesses to implement such strategies, and provide a type of mass customization that is becoming increasingly popular on the internet (Ansari et al., 2000 and Lee and Lee, 2005). They have emerged in e-commerce applications to support product recommendation. The recommendation systems use customer purchase history to determine preferences and identify products that a customer may wish to purchase. Schafer, Konstan, and Riedl (2001) presented a detailed taxonomy of recommendation systems in e-commerce, and determined how they can provide personalization to establish customer loyalty. Generally, recommendation systems increase the probability of cross-selling; establish customer loyalty; and fulfill customer needs by discovering products in which they may be interested. The traditional recommendation mechanism (TRM) is a web-based system designed to help customers sort through available products and/or services on the online shopping malls and advise customers about what products to buy, based on the needs expressed by the customers. Through a variety of tasks such as defining needs, forming consideration sets, making recommendations, and negotiating purchases (West et al., 1999), the TRM serves to potentially reduce the cost of thinking (Shugan, 1980), as well as the uncertainty surrounding an online shopping purchasing decision, and thus both reduce the difficulty of making a choice while increasing the confidence associated with it. Since the TRM is perceived by customers to be highly credible and to have particular expertise in the decision context, its positive impact is expected to intensify. However, the remaining problem concerning TRM is how to analyze the causal relationships between quantitative and qualitative factors, and investigate their impact on the central routes and peripheral routes through which both quantitative and qualitative factors can affect customer online shopping decisions. It is well known that qualitative factors are hard to codify yet they have a significant effect on a customer’s decision-making process in the form of causal relationships with quantitative factors. Thus, a new online recommendation mechanism is required that incorporates qualitative factors systematically with quantitative factors to analyze their combined influence on customers’ purchasing decision-making process. To accomplish this, the first objective of this study is to propose an alternative online recommendation mechanism where causal relationships, possibly existing among quantitative and qualitative factors that seem relevant to customers’ online shopping decisions. For this purpose, causal mapping is introduced to analyze causal relationships among quantitative factors and qualitative factors and help customers revise prior decisions that were based on quantitative factors alone. Quantitative factors in the case of mobile phones include such terms as handset price, folder type, rate of communication charge, brand, etc. In contrast, qualitative factors indicate customers’ tacit preferences towards body color, design, usefulness, etc., all of which reside mentally and have causal relationships with quantitative factors. Causal mapping is a widely accepted methodology for analyzing causal relationships rigorously (Eden, 1989 and Nelson et al., 2000). A causal map, therefore, looks at the pattern or structure of assertion of causal relationships among variables and not at how or why variables or their causal relationships come to be part of the map (Ford & Hegarty, 1984). Causal maps have been shown to be relatively stable and thus capable of providing the basis for prediction (Bonham & Shapiro, 1976). The first objective of this study then is to propose a causal map-driven online recommendation mechanism, named CMRM, to (1) analyze causal relationships among both quantitative factors as well as qualitative factors, (2) help customers revise their prior online shopping behaviors based on quantitative factors alone, and (3) prove its validity statistically with respect to customer decisions satisfaction and behaviors (i.e., attitude towards recommended products, purchase intention, actual purchase) in comparison with TRM.

This study proposed a new online recommendation mechanism using causal maps to enhance consumers’ online shopping decisions and behaviors by incorporating qualitative factors systematically into the recommendation process. Through the empirical analysis, we found that the proposed CMRM can enhance the four dependent variables. Moreover, the results revealed the importance of explicitly considering the ELM concept in designing the online recommendation mechanisms-related research model. However, the usefulness of causal maps should not be overemphasized when considering causal relationships among the quantitative and qualitative factors clearly and inducing revised recommendation results for the consumers. To summarize, the CMRM surpassed TRM on all four dependent variables. As an important future research topic, CMRM needs to be further tested empirically with various types of products and services. In particular, it seems useful to compare CMRM effects based on involvement level (high vs low).