Do online shops support customers’ decision strategies by interactive information management tools? Results of an empirical analysis

Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media (such as the World Wide Web) for prepurchase information search and online shopping, very little is known about how consumers make purchase decisions in such settings. A unique characteristic of online shopping environments is that they allow vendors to create retail interfaces with highly interactive features. One desirable form of interactivity from a consumer perspective is the implementation of sophisticated tools to assist shoppers in their purchase decisions by customizing the electronic shopping environment to their individual preferences. The availability of such tools, which we refer to as interactive decision aids for consumers, may lead to a transformation of the way in which shoppers search for product information and make purchase decisions. The primary objective of this paper is to investigate the nature of the effects that interactive decision aids may have on consumer decision making in online shopping environments. While making purchase decisions, consumers are often unable to evaluate all available alternatives in great depth and, thus, tend to use two-stage processes to reach their decisions. At the first stage, consumers typically screen a large set of available products and identify a subset of the most promising alternatives. Subsequently, they evaluate the latter in more depth, perform relative comparisons across products on important attributes, and make a purchase decision. Given the different tasks to be performed in such a two-stage process, interactive tools that provide support to consumers in the following respects are particularly valuable: (1) the initial screening of available products to determine which ones are worth considering further, and (2) the in-depth comparison of selected products before making the actual purchase decision. This paper examines the effects of two decision aids, each designed to assist consumers in performing one of the above tasks, on purchase decision making in an online store. The first interactive tool, a recommendation agent (RA), allows consumers to more efficiently screen the (potentially very large) set of alternatives available in an online shopping environment. Based on self-explicated information about a consumer's own utility function (attribute importance weights and minimum acceptable attribute levels), the RA generates a personalized list of recommended alternatives. The second decision aid, a comparison matrix (CM), is designed to help consumers make in-depth comparisons among selected alternatives. The CM allows consumers to organize attribute information about multiple products in an alternatives × attributes matrix and to have alternatives sorted by any attribute. Based on theoretical and empirical work in marketing, judgment and decision making, psychology, and decision support systems, we develop a set of hypotheses pertaining to the effects of these two decision aids on various aspects of consumer decision making. In particular, we focus on how use of the RA and CM affects consumers' search for product information, the size and quality of their consideration sets, and the quality of their purchase decisions in an online shopping environment. A controlled experiment using a simulated online store was conducted to test the hypotheses. The results indicate that both interactive decision aids have a substantial impact on consumer decision making. As predicted, use of the RA reduces consumers' search effort for product information, decreases the size but increases the quality of their consideration sets, and improves the quality of their purchase decisions. Use of the CM also leads to a decrease in the size but an increase in the quality of consumers' consideration sets, and has a favorable effect on some indicators of decision quality. In sum, our findings suggest that interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality and the efficiency of purchase decisions—shoppers can make much better decisions while expending substantially less effort. This suggests that interactive decision aids have the potential to drastically transform the way in which consumers search for product information and make purchase decisions.

This paper explores consumer decision making, particularly focusing on the increasing prevalence of choices on the Internet such as online shopping. Examining the fundamental question of how individuals decide how to decide, our paper draws upon the effort–accuracy framework. This framework indicates that people typically consider both the cognitive effort associated with employing a specific decision strategy and the decision quality (i.e., accuracy) implied by using a particular strategy. However, decision strategies with high accuracy imply high effort. Empirical evidence shows that people often use decision strategies that require little effort. As a result, accuracy is often not high. Against this backdrop, this paper introduces a quantitative approach leveraging principal component analysis (PCA) as a decision support tool. Based on a simulation study, the approach demonstrates that it is possible to maintain high accuracy with significantly reduced effort in multi-attribute decision situations where attribute information is available in a quantitative format. This demonstration is based on the example of two decision strategies, which are both theoretically and practically highly relevant: the multi-attribute utility model (MAU) and the elimination-by-aspects strategy (EBA). By employing PCA for dimensionality reduction, the approach becomes particularly advantageous for online shops and online comparison portals, presenting users with concise yet accurate information. It is important to emphasize that our PCA approach is designed for data with a natural ordering, primarily focusing on quantitative variables. Consequently, decision situations where qualitative variables (e.g., product design or color) play a role in the decision-making process will need further exploration in future studies. However, we present a first solution to this problem so that our approach, based on this solution, can be implemented by online shops and online comparison portals in the near future.

Résumé Pour tester des initiatives marketing en magasin (packaging, prix, etc.), un magasin laboratoire virtuel (3D) offre une alternative attractive au magasin laboratoire réel sur le plan opérationnel. Par une comparaison sur des échantillons contrôlés, cette recherche montre que les mesures attitudinales sont identiques, mais aussi qu’il existe des différences significatives sur les comportements en magasin et sur l’attention au prix et que celles-ci dépendent de l’achat habituel de la marque.

About the Authors

About Our Authors

Online stores offer an increasingly large set of products. Interactive decision aids are becoming indispensable tools assisting users as they search for an ideal product to purchase. For an e-commerce website, adopting the correct tools can affect its survival: effective product recommender tools are increasingly recognized by online stores as effective means to sell more products; on the other hand, sites that do not employ intelligent tools will not only see poor purchase volumes but also experience less traffic because consumers are more likely to return to a site employing recommender systems.This paper presents ongoing research in understanding the impact of various decision aids on users' interaction behaviors and their subjective perceptions of these aids. In the current experiment, we employed an eye tracker in an in-depth user study to understand the influence of recommenders on how users select items for the basket set. We collected more than 20,300 fixation data points in 3,648 areas of interest. Our studies show that while users still rely on product filtering tools, the use of recommenders is becoming more prominent in helping them construct the basket set and is monotonically increasing as time goes on.

About the Authors

About Our Authors

About the Authors

Purpose – This study aims to theorize and empirically examine the relationship between “purchase intention and conversion rate”, “website satisfaction and conversion rate” and “purchase intention and conversion rate”. E-Commerce conversion rate represents the percentage of visits to an e-tailer’s website that includes a purchase transaction. Despite the importance of conversion rates for e-tailers, prior research predominantly used purchase intention and website satisfaction as main dependent variables and implicitly assumed that these variables will influence the actual purchase. Design/methodology/approach – Data on 85 US retail websites were used to test the hypotheses. The unit of the analysis is the online retail website. Regression analysis was used to perform the data analysis. Findings – The results indicate that both purchase intention and website satisfaction positively influence conversion rates. It was also found that website satisfaction positively influences purchase intention. Research limitations/implications – Only data from 85 US e-tailers from the top-100 US online retailers are used to test the hypotheses. Also, conversion rate is only one of the several important success metrics used by e-tailers. Originality/value – This study not only examines antecedents of e-commerce conversion rates, but also theorizes and tests if there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”. This is because, although previous studies used purchase intention and website satisfaction as main dependent variables and proxies for actual purchase behavior, they did not validate this relationship. This study shows that: there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”, there is also a statistically significant relationship between “website satisfaction and purchase intention” and this study used firm-level data to theorize, measure and analyze the data, whereas prior literature used only individual-level data.

This article presents a classification method that makes possible the detection of important decision strategies that people use in nonrisky multiattribute decision tasks. The classification method incorporates a multimethod approach that combines elements of both structural modeling and process tracing. We begin with a discussion of nine common characteristics of decision strategies and then classify 13 decision strategies on the basis of these characteristics. Next, we demonstrate that six of the characteristics can be used to develop one outcome-based and three process-based metrics. In addition, we present an algorithm that shows how to use the metrics. We demonstrate that using our metrics in conjunction with a measure for direction of search allows identification of most of the 13 decision strategies. Finally, we outline the potential of our research for the design of decision support systems such as online shops.

About the Authors

Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In “Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail,” Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.

This study examines the influences of entertainment, informativeness, and Web irritation on attitude toward online shopping and satisfaction in online shopping. Particularly, satisfaction in online shopping is explored as the consequences of attitude toward online shopping, while informativeness, entertainment, and irritation are the antecedents of attitude toward online shopping. Data were collected through a quantitative study applying an online questionnaire carried out in 2020, using a representative sample. Findings showed that attitude toward online shopping is determined by entertainment, while informativeness does not significantly affect consumers’ attitude toward online shopping. Web irritation was also found to be of equal importance in influencing the formation of consumers’ attitudes toward online shopping but in a negative manner. This suggests that Web irritation on online shopping sites has a significant influence on consumers. If online shopping sites are irritating, then consumers form negative attitudes toward online shopping. This study also found that consumers’ satisfaction to shop online is determined by their attitude toward online shopping. The findings contribute to the evolution of the Uses and Gratification Theory, particularly in online shopping in developing countries like Indonesia. This study provides implications to e-business, marketers, and website system developers of online shopping sites dealing with products.

This study endeavours to understand customer satisfaction in online shopping while investigating the major reasons that motivated customers' decision-making processes as well as inhibitions of online shopping. The Kotler and Killers (2009) Five Stage Buying Process Model was chosen as the basis of framework of this study to explain customer satisfaction through their motivations to buy products online. The existing literature was reviewed to discover reasons that would influence customers positively or negatively towards shopping online. Surveys were conducted by distributing questionnaires in the Wrexham area (North Wales) to gather data for this research. SPSS software package was used to present research data graphically and to test research hypothesis. From the findings, it was discovered that respondents use internet to purchase products through online because they believe it is convenience to them and the term convenient includes elements such as time saving, information availability, opening time, ease of use, websites navigation, less shopping stress, less expensive and shopping fun. In contrast, along with respondents' mind-sets, online payment security, personal privacy and trust, unclear warranties and returns policies and lack of personal customer service are the foremost barriers of online shopping. Furthermore, the result of hypotheses established that even though online shopping is convenient to all consumers, online payment system and privacy or security anxieties have significant impact on online shopping. Finally, some recommendations have been offered for online retailers to take initiatives for making online shopping more admired and trustworthy.