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The effect of raising searching obstacles on online purchasing behavior: proof from field experiment

par Boris Helios Zocete LOKONON KOUDOGBO
Taiyuan University of Technology - Master of Business Administration 2020

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2. Review of Related Literature

This article focuses on two areas of literature: marketing and economics. The first examines the influence of research costs on consumer decision-making, particularly in the context of online sales. The second is price discrimination at the second level, where monopolists determine their product lines so that higher-value consumers choose higher-quality, more expensive products themselves. We discuss each literature in turn.

Search Costs in Online Retail

Existing literature on the costs of research in online environments generally explores the


effect of reducing friction between information on consumer choice, well-being, and market structure. The first articles associated the decrease in research costs with an increase in competition, especially on prices (e.g. Bakos, 1997). Subsequent work has shown that online research may be more expensive than originally thought, perhaps because online shoppers have higher search costs than offline shoppers, and there may be considerable heterogeneity in search costs among online shoppers (Brynjolfsson and Smith 2000).

Lynch and Ariely (2000) found conditions in which increased transparency of online quality could reduce price competition, confirming that «in a competitive environment, the strategy of maintaining certain high search costs is likely to fail». Others have shown that some companies have found it advantageous to artificially increase research costs to promote price comparison. Ellison and Ellison (2009) show that firms in a market have an incentive to mask actual price and quality information sold online to reduce the tendency of search-sensitive consumers to comprehensively compare prices.

Ellison and Ellison (2005) show, as examples of obfuscation, how online retailers cost-effectively offer complex price menus, products that appear to be grouped but are no hidden prices, descriptions complex products, and other tactics designed «to allow the tendering process to take sufficient time». They argue that many advances in search engine technology that are supposed to facilitate the collection of information from consumers have subsequently been accompanied by business investment to hinder research.

Our work differs from these documents in that, rather than analyzing the competitive incentives that drive firms to impede research, we focus on the potential benefits of price discrimination within firms by increasing the costs of doing business. research. Although previous work has recognized the importance of heterogeneous research costs for consumers, none of our knowledge has examined how this heterogeneity can be exploited by a monopolistic enterprise in an online context.

Price Discrimination

Varian (1980) formulates a price discrimination plan that allows a company to extract surpluses of uninformed consumers through high prices and simultaneously sell to informed consumers through low prices using a mixed price balance strategy. Recent models with heterogeneous research also find equilibrium in which sellers adopt a mixed strategy


(Ratchford 2009). For example, Stahl (1996) finds a mixed strategic balance that, when implemented by two retailers, creates a market divide so that fully informed consumers always buy from the low-cost business while Uninformed consumers stop at less than a complete search and end up paying more.

Previous research has shown that differences in learning, provider switching costs, and risk perception when shopping online lead to heterogeneity in consumers' willingness to search (Ratchford 2009). These differences in consumer propensity to search allow retailers to selectively target price or product promotions with different margins among customers (Kopalle et al., 2009). The majority of research cost literature assumes that multiple firms have different levels of research costs (Ratchford, 2009) and thus separate the market based on the heterogeneity of consumers who are willing to pay for additional research costs. In this article, we focus on a single multi-product company that exploits this heterogeneity of search costs to separate the market to offer different segments to different products and prices.

We identify ways for a company to increase its profits by simultaneously offering high prices and low prices, the latter in the form of discounts, to attract informed and uninformed consumers by using search costs as a segmentation mechanism. As Ellison and Ellison (2009) predict about obfuscation, we also expect this approach to increase increments. But unlike their result, we expect the company to not only capture the fraction of customers who are willing to incur research costs but also to realize higher profits from customers who are unwilling to incur high search costs. Besides, there is no explicit or implicit intention to confuse customers as to the quality or actual price of an item in the online store. All items and prices are easily accessible to all consumers on the same website.

Ngwe (2016) shows that fashion stores use travel costs, away from shopping centers, as a way to encourage shoppers to choose to buy in stores - which leads to high travel costs to obtain low prices, or buying in regular downtown stores - resulting in lower travel costs but higher prices. This form and other forms of deliberate addition of research frictions as a tool of price discrimination are well known to brick and mortar store managers and can lead to concrete action. Unfortunately, online retailers cannot charge travel costs by physically placing their stores. The question is whether managers of online stores can add non-traditional and «virtual» search costs to the online shopping process of consumers to obtain the same benefit of price


discrimination. To make matters worse, online store managers often sell new items and liquidation items through the same channel. Also, it is not certain that the increase in search costs for a multi-brand non-proprietary online retailer may be a promising strategy when consumers can buy cheap substitutes from a competitor within a few clicks from distance only.

To practice price discrimination, companies must first be able to identify and distinguish sensitive customers or prospects from the high prices of low-price customers or prospects. The literature has generally defined three sets of characteristics associated with price sensitivity: the characteristics of a person, e.g., demographic characteristics, current behavioral characteristics, e.g., shopping characteristics, and past purchase characteristics. Besides, previous literature has shown that demographic characteristics are primitives of behavioral and purchasing characteristics (Kim, Srinivasan, and Wilcox 1999). From the variations in each of these characteristics, companies have tried to offer different prices to different price-sensitive customers.

Discrimination of prices by demographics: Previous research has linked demographic variables such as age, sex, income and geographic location, and consumer price sensitivity (Kim, Srinivasan and Wilcox, 1999). Traditional retailers and other businesses have used this knowledge to offer different prices based on demographic profiles. Supermarkets offer special discounts to retirees, cinemas offer students lower prices, grocery stores offer discounts depending on location, and educational institutions tend to offer scholarships and discounts to low-income families. And because many companies believe that men are more price-sensitive than women in certain categories, market comparisons have revealed considerable price differences between virtually identical products for men and women.

The use of price discrimination based on online demographics, on the other hand, has had mixed success. One obvious reason is that once consumers know who benefits from the rebate and who does not, they are incited to misrepresent or hide their identities. As a result, it becomes difficult for online businesses to distinguish between different demographic characteristics without requiring proof of identity, which would hinder many clients.

Price discrimination through buying behavior: Monitoring current buying behavior has also been a ubiquitous tool for brick and mortar retailers to assess a customer's price sensitivity. The way consumers browse, search, compare options, and negotiate prices has been widely


observed and used by sellers of expensive products that require more thought. These include consumer electronics, automobiles, and homes.

The use of behavioral variables observed during online shopping has had mixed results. On the one hand, and unlike demographic variables, search, click, and online navigation patterns are easily observable without the client having to reveal themselves. Hannak et al. (2014) showed that among 16 retailers and online travel providers, half of them showed that they offer different products and/or prices to online shoppers based on the users' cookies, clicks recorded, whether they have logged in or not, and the use of the mobile phone or PC to search for products and services. Yet, online, there are some disadvantages of behavior-based price discrimination if consumers discover the rules and can emulate the behavior that will allow them to get the lowest price. Amazon discovered it in 2000 when it offered the same DVD for fewer buyers who did not have a cookie in their browser, to offer new customers a cheaper price (CNN 2005). This encouraged Amazon's customers to delete their cookies or not to log in to the website to obtain lower prices. In summary, while behavioral price discrimination may be more observable than demographics or personal characteristics, it is also «playable» because clients are encouraged to learn to behave in a way that will allow them to get low prices.

Price discrimination based on purchase history: The third category of variables that have been associated with price sensitivity is related to previous purchases. Looking at five categories of groceries, Kim, Srinivasan, and Wilcox (1999) found that the characteristics of a person's purchase history, such as previous purchase frequency, quantity purchased, loyalty, and incidence cheaper items, are strongly associated with individual price sensitivity, far more than demographic characteristics. Offline grocery retailers and non-food retailers have successfully used this method by tracking past consumer purchases using loyalty cards (Lal and Bell 2003) and using this information to selectively target coupons, the prototype price discrimination method used. Online, pre-purchase couponing is also ubiquitous. It is observable and difficult to be playable because it is unlikely that consumers will buy items that are not their best options at the moment just so they can get future discounts.

The practice of price discrimination by the history of online shopping poses two major challenges. First, online retailers that tend not to have frequent or repeat purchases, such as auto, furniture, or apparel resellers, do not have good data to reliably estimate the sensitivity


of new customers to price. Second, while many industries such as groceries and fast food tend to offer fidelity-based discounts, the more you buy, the less expensive, some do the opposite. It has been shown that the insurance industry generally charges higher prices, not lower, for customer loyalty (Hughes 2008). The reasoning is that people who tend to stay with their insurance company for many years are opposed to the stress and hassle of shopping for better insurance quotes and are therefore less price sensitive. Insurance companies take advantage of this to increase loyalty customers' premiums year by year, a «loyalty tax» by charging more than new customers (Bankrate 2016). In the latter case, higher prices for loyalty, consumers are encouraged to move from one company to another. The reason most consumers do not do this is probably because of the considerable effort they put into doing it. In these cases, purchases that are infrequent or discouraging redemption, price discrimination from past purchases can have serious negative effects on firms.

In summary, there are three generalizable categories of variables associated with an individual's price sensitivity, according to previous literature. Furthermore, no variable is perfect for price discrimination in the sense that there are tradeoffs inherent in observability and «playability». In particular, some variables are better than others for the implementation of price discrimination policies, depending on the category of product or industry. Therefore, a good model for estimating price sensitivity should incorporate as many variables as possible of the three types, demographic, behavioral, and past purchases. In section 5, we try to estimate a price sensitivity model specific to each person in our context, taking into account the constraints linked to the data available to the company. Previously, we develop the research plan used in this paper.

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