Academic journal article Journal of Electronic Commerce Research

An Experimental Investigation of Regulatory Orientation and Post-Choice Regret in Online Product Selection

Academic journal article Journal of Electronic Commerce Research

An Experimental Investigation of Regulatory Orientation and Post-Choice Regret in Online Product Selection

Article excerpt


Delivering product information effectively is fundamental to customer satisfaction and e-retailer success. In this study we examine the way in which the presentation of online customer reviews in peer endorsement systems (PES) impact perceptions of post-choice regret. The theory of Regulatory Orientation is used to account for individual differences in the way that online review content is processed. Results of a laboratory experiment comparing two peer endorsement system formats show that PES content presentation significantly impacts perceptions of post-choice regret. These perceptions are found to be strong influencers of user intention to use the PES. The study's findings provide theoretical insights into how individual orientation and PES technology influence online decision-making with regards to product selection. As a result, the study has important implications for managers looking to get the most from investment in PES systems deployment and online web retail space design.

Keywords: peer endorsement system (PES), product information uncertainty, post-choice regret, regulatory orientation

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1. Introduction

Websites today are crowded with product information and recommendations, company offers, and customer reviews, all of which vie for a customer's attention. It is important to make sure that each of these components adds value and improves the web shopping experience, without adding unnecessary "noise" to the ecommerce decision-making process.

Over the last five years, presentation of online customer reviews and opinions has occupied an increasingly large share of prominent website space. The emergence and growing popularity of systems for displaying these online customer reviews, or peer endorsement systems (PES) has created new opportunities for ecommerce companies to interact with and understand their customers. Today, these systems represent an important source of aggregate product information [Wang & Benbasat 2007], and play an integral part in conveying the product's strengths and weaknesses to potential customers [Mudambi & Schuff2010; Ghose & Han 2009]. On account of the unique and valuable capabilities offered by PES, the technology has diffused to such a degree as to be ubiquitous on nearly all ecommerce sites.

Despite the potential value of PES, reading online customer reviews places certain demands on the cognitive load of customers [Ghose & Ipeirotis 2006]. This can pose a problem for decision making, for as customers' become overloaded with information they struggle to discern the important from the mundane [Keller & Staelin, 1987; Maes 1994; Wan et al. 2009]. When this happens, information vital to making informed purchase may go overlooked. Worse, customers may begin to question the value or accuracy of PES information, and as a result feel uncertain about the entire transaction [Pavlou et al. 2007]. This uncertainty leads to poor decision-making, customers buying unsuitable products, higher returns, and hence reduced customer satisfaction [Kuksov & Villas-Boas 2010; Larson & Czerwinski 1998; Lowengart & Tractinsky 2001]

Additionally, customers who experience uncertainty when selecting products may be more likely to regret their selections, leading to a decline in repeat business [Zeelenberg 1999; Lu, et al. 2012]. When competitors are only a click away, e-Commerce organizations can ill-afford to lose these customers. For this reason, understanding the role of PES content in online decision making has been an important area of research over the last few years [Mudambi & Schuff2010]. Much of this research centers on how to effectively display or summarize online review content, so that customers are only asked to analyze useful information [Ghose & Ipeirotis 2007]. The research in this area can be divided into two groups. The first group considers online reviews in their entirety, and seeks to understand the characteristics of helpful reviews, with the goal of showing only the most helpful reviews to customers [Hu, Pavlou and Zhang 2006]. …

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