Building on the aforementioned definition of ‘H’ and ‘T’ products, the research question is enounced in this chapter. How can e-tailers drive profitable growth through smart pricing? Then, how should e-tailers respectively price Head products (whose prices consumers tend to compare and remember) and Tail ones (whose prices are often neglected and with a few discoverable alternatives)?
In other words, as smart pricing means ‘starting with the customer’, the dependent variable studied is the purchase likelihood in responce to a price variation, which is supposed to be influenced by customer Price Awareness ( H p 1 ), and the presence (or absence) of Comparison Effect ( H p 2 ). As different degrees of Price Awareness (PA) and the Comparison Effec (CE) define Head (H) and Tail (T) products (as described in the previous section), they are hypothesized to be key proxies to identify the different sensitivity to price in each case. This findings could be eventually used to craft the most appropriate pricing approach to adopt over hits (H) and niches (T).
This section better identifies the study purposes. In the next parapraphs, the research question is addressed, and the related objectives are enounced. A better understanding of the study contributions and the research model is provided. In particular, the concepts of Price Awareness (PA), Comparison Effect (CE), and customer response to price (PS) are investigated, drawing fully from existing literature.
This premise will ease the explanation of:
- the relationships between the independent variables and the dependent one explored by the existing literature so far;
- the possible analogical extension of these relations to this thesis model ( H p 1 ,H p 2 ).
2.1 Research Question
This study precisely aims at building a bridge between Head and Tail Theory and Pricing Strategy, posing the following questions: how can e-tailers drive profitable growth through smart pricing? Then, how should e-tailers respectively price Head products (whose prices consumers tend to compare and remember) and Tail ones (whose prices are often neglected and with a few discoverable alternatives)? This question has been further framed in two hypotesis, in order to translate concepts into measurable variables.
The finishing line that this thesis wishes to cross is the identification of a balance between customer satisfaction and profitability preservation (see 4.4 Managerial Implications). Customer satisfaction – eventually translating into retention – has to be maintained meeting price expectations. Profitability preservation depends on how accurately items driving price perception are identified, and eventually attractively priced. To the research hypotesis, price sensitive behavior is more likely to take place when evaluating the purchase of a hit (Head product), while the contrary is expected for niches (Tail products). In other words, this study also wishes to make the extra-mile to translate these different approaches into a virtuous cycle, where:
- attractively priced Head items bring sales,
- positive price perception generates customer satisfaction,
- customer satisfaction evolves into retention,
- customer retention generates additional sales on Tail.
Please note that, naturally, this cycle can even start from the Tail.
As anticipated in the paragraph 1.3 How long is the Long Tail?, consumers may be attracted to an e-tailer because it is able to offer niche, hard-to-find products. The demand for niche products could have a spillover effect on the demand for hits, and vice-versa.
This research is indeed supported by a statistical analysis (Logistic regression), aiming at:
Figure 2.1: Translate Pricing into Long-term value: strike the balance between profitable growth and customer expectations
- diagnosing price-sensitive and non-price sensitive behaviours in different experimental scenarios, designed to fit the Head and Tail purchase cases;
- accordingly identify the most congruent pricing approach for each product-role,
- unlocking the aforementioned benefits (Figure 2.1).
Before moving to the analysis, a better understanding of the study contributions and the research model is needed. In particular, the concepts of Price Awareness (PA), Comparison Effect (CE), and Sensitivity to price (PS) are investigated, drawing fully from existing literature.
Contribution to existing background
As mentioned in section 1.3, in the context of the Long Tail Theory, the previous research mostly focused on the sale concentration changes. The Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels (Brynjolfsson et al., 2011). Netessine and Tan (2009) suggest that an accurate detection of the Tail thickness can better help e-tailers to evaluate how worth it is to carry its low-selling selection. Lee (2010) analyses Head and Tail roles to develop ad-hoc marketing strategies.
However, none of the previous studies struggle to find a pricing pattern to adopt.
In the context of the pricing literature, research has generally focused on routine decisions in response to price format (e.g., Dhar & Hoch, 1996), or price framing (e.g., Lichtenstein & Bearden, 1989). Although still in an embryonic phase, Rao (1984) was the first talking about ‘smart pricing’. Rao’s study generated a growing awareness of the need to increase focus on customer-related factors such as customer satisfaction (Oliver 1999), customer service (Parasuraman and Grewal 2000), customer loyalty (Kumar and Shah 2004; Reichheld 2001), and quality as perceived by the customer (Boulding et al. 1993; Rust, Moorman, and Dickson 2002). However, to my knowledge, none of these studies properly bridges the Long-Tail Theory with pricing literature.
Surely, none of them drives related considerations about the relation of Price Sensitivity with H- and T- product features (Price Awareness, Comparison Effect). What’s more, none of the previous studies is based on a comprehensive data set collected through a realistic simulation, which could simultaneously capture the customer behavior naturalness and guarantee an optimum sample size in terms of statistical representation of the results.
It is worth to restate that the context the study refers to is the online environment, characterized by peculiar trends, other than those prevailing in the physical, largely studied brick-and-mortar retailing (as suggested in 1.2 Long Tail Theory at market level). Even though the recent literature counts of a not negligible number of studies about consumer habits and the Internet, none of them look at the topic from the managerial perspective of striking the balance between customer expectations and profitability preservation.
In detail, this study broadens and enriches the existing literature background because:
- it closes the gap between the stream of research of “Long-Tail Theory” and the established pricing literature;
- it embraces a cross-category view, resulting in a widely fitting approach for online retailers;
- it uses an ‘ad hoc’ data collection setting designed to maximize the realism of an online purchase experience (see 3 Methodology).
Before translating the above-mentioned point into structured hypothesis, the variables of interest – respectively, Price Sensitivity, Price Awareness and Comparison Effect – are debriefed. For each of them, a theoretical background is provided.
2.1.1 Customer Sensitivity to Price
According to previous discussion, Price Sensitivity (PS) has been identified as one of the key factors affecting company’s pricing choices as well as its ultimate profitability. It refers to the extent to which individuals perceive and respond to changes or differences in prices for products or services (Monroe, 1973). Price Sensitivity can also be interpreted as the reaction of the consumers to what they perceive about the cost within which they will buy a particular product or service. Each customer will have a certain price acceptability range and different customers have different limits in their perceptions of what price is within their ranges.
Generally, economists are familiar with the concept of price elasticity. Price Elasticity of demand is the is the percentage change in the quantity demanded of a good or service divided by the percentage change in the price (Marshall, 1890). If the changes in price have a proportionately greater impact on demand for a product, then it is known as elastic demand. On the other hand, inelastic demand narrates the situation where changes in price have a negligible effect on the demand.
Price Sensitivity has also been approached as willingness-to-buy in response to price changes (Gabor and Granger, 1966), with the focus shifting from the change in quantity purchased (as a reaction to price changes), to the measurement of an ideal price to attribute to a product (especially when introduced for the first time in a certain market). Then, price sensitivity can be tested subjecting the respondents to different proposed price increases for the same item. The respondent will then accept or not the increased price, according to its sensitivity. According to Gabor and Granger (1966), the results can be eventually used to produce a demand chart (where x-axis are the prices and y axis the percentage of people willing to pay that price) and a revenue curve(where y-axis is the percentage of optimal revenue and x-axis is still price). For the purpose of my study, the binary response provided by the customer in the experimental simulation (accepting or not the price increase) will be used to design a model predicting the purchase decision likelihood in response to a set of variables.
The marketing studies using Gabor and Granger (1966) approach are several. Goldsmith and Newell (1997) measured price sensitivity in relation to customers’ behavioural features. They found shopping innovators to be less price sensitive than laggards. However, it is generally true that customer’s evaluation of the value of a good or service is based on their perceptions that what they receive and what they expected of having it (Monroe 1990; Zeithaml 1998).
This means that, when making pricing decision, adopting the customer perspective as starting point allow to go beyond than just the product cost. Managing price perception, not just pricing structure and actual price points, thus has become a critical capability for e-tailers.
Given the availability of information for both customers and competitors, prices are typically lower online (Shankar et al., 1999). In spite of the dominance of very low prices online, even price sensitive customers do not always purchase at the lowest price on every single item (Smith and Brynjolfsson, 2001). In 1999, Lynch and Ariely had already conducted experiments in a simulated online wine store to test this hypothesis. They found that price sensitivity was lower under experimental conditions where buyers had information on both price and product quality as compared to conditions where they had information only on price. Researchers also found that – when evaluating partitioned prices – consumers focus more on the price of the component that constitutes a smaller proportion of the total price (Chakravarti et al. 2002). Also, customers become less sensitive for hedonic products or services consumed in hedonic occasion (Wakefield and Inman, 2003). Loyal customers are price insensitive to the price changes while non-loyal customers are sensitive in making decision about a brand (Yoon and Tran, 2011). A customer’s price sensitivity can be significantly explained by a customer’s price perception. Perception is the process by which people translate sensory impressions into a coherent and unified view of the world around them (Munnukka et al. 2014). How customers perceive the price is as important as the price itself. Even if customers fail to notice specific price moves in isolation, companies should make sure customers have a good sense of how the firm’s prices compare to those of competitors. According to a survey conducted by Bain & Company and ROI Consultancy Services (2016) on a sample of 2,200 consumers in Atlanta and Washington, DC, retailers can get either more or less credit for their pricing than actual shelf prices would suggest. The study involved eight retail chains carrying groceries. Already back in 1984, Kahneman and Tversky had shown that people perceive less positive utility with a gain of – say – 10€ than negative utility with a loss of the same amount. This general conclusion about loss aversion has many times over the years been shown to have a powerful impact on decisions made in diverse situations, also in retailing.
In their overview of empirical reference price research, Kalayanram and Winer (1995) also found support for demonstrating that consumers’ reaction is stronger to price increases than decreases. This notion is also known as ‘asymmetric consumer price response’. Consumers are thought to interpret price variations as gains or losses relative to their internal reference price.
Table 2.1 collects some key finding previously enounced in this paragraph body, before giving way to some literature review about the relation between customers’ responses to price fluctuations and related pricing approaches.
In order to provide sound managerial advices, buyers’ responses to prices have received a great deal of scholarly attention. As previously enounced, research has typically focused on routine decisions in response to changes in price (Bucklin, Gupta, & Han, 1995), price format (Dhar & Hoch, 1996), or price framing (Lichtenstein & Bearden, 1989). Liechty, Ramaswamy, and Cohen (2001) examine price sensitivity relative to light alternative information services offered on web-based menus. Steenburgh and Avery (2011) demonstrate that, generally, the higher the absolute value of the price, the more sensitive customers are to price changes. However, none of these studies investigates across the relation of price sensitivity and H/T roles.
Table 2.1: Customer sensitivity to price according to some research (1890 – today)
|Definition in Economics||Price sensitivity can be defined as the degree to which consumers’ behaviors are affected by the price of the product or service. Price sensitivity is also known as price elasticity of demand and this means the extent to which sale of a particular product or service is affected.||Marshall, 1890s|
|Price Sensitivity as Willingness-to-Buy||Price sensitivity can be tested subjecting the respondents to prices increases for the same item and monitoring whether they are still willing to buy (or not).||Gabor and Granger, 1966|
|Asymmetric consumer price response||Consumers’ reaction is stronger to price increases than decreases. This notion is also known as ‘asymmetric consumer price response’.||Kalayanram and Winer, 1995|
|Innovativeness and Price Sensitivity||Shopping innovators are less price sensitive than laggards.||Goldsmith and Newell, 1997|
|Perceived Benefits and Price Sensitivity||Consumers’ evaluation of the value of a good or service is based on their perceptions that what they actually receive and what they expected of having it.||Monroe, 1990;
|Online Customers and Price Sensitivity||Given the availability of information for both customers and competitors, prices are typically lower online. In spite of the existence of very low prices online, even price sensitive customers do not always purchase products from online vendors that offer the lowest price on every single item.||Shankar et al., 1999
Smith and Brynjolfsson, 2001
|Situational Price Sensitivity||Consumers becomes less sensitive for hedonic products or services that are consumed in hedonic occasion.||Wakefield and Inman, 2003|
|Loyalty and Price Sensitivity||Loyal customers are less sensitive to the price changes.||Yoon and Tran, 2011|
|Price Perception||A customer’s price sensitivity can be significantly explained by a customer’s price perception. Perception is the process by which people translate sensory impressions into a coherent and unified view of the world around them.||Munnukka et al., 2014|
Beyond the traditional definition holding in microeconomics and consistently with the purpose of this study, price sensitivity will adopt the meaning proposed by Gabor and and Grager (1966); this will allow to recognize different behaviours in response to the price changes. Next paragraphs better define the concept of Price Awareness and Comparison Effect whose role in the present research model will be better explained in the dedicated section (2.2Hypotesis).
2.1.2 Price Awareness
Price Awareness (PA) as a construct has been one of the top behavioural pricing themes in the last fifty years. It refers to the ability of buyers to keep prices in mind and eventually use them in later purchase decisions. Many theories of consumer price information processing hinge upon the premise that consumers encode, evaluate and integrate price information into memory (Dickson & Sawyer, 1990; Grewal & Compeau, 2007).
The consumer’s reference price is the standard against which an encountered price is measured (Del Vecchio and Craig, 2008) or, in other words, what Janiszewski and Lichtenstein (1999) call the “dominant, going price”. Thus, consumers may consider frequently occurring reference prices to be more diagnostic of whether a subsequent price offer is a good deal or not.
According to the definition, to quantify the degree of Price Awareness, most studies examine the extent by which the price elicited from the customer deviates from the actual price. The percentage deviation between the recalled price and the actual price is a common measure_PercentDeviation= |actualprice-recalledprice| actualprice
This measure has been used extensively by previous researchers (e.g., Dickson and Sawyer 1990; Mazumdar and Monroe 1992; Wakefield and Inman 1993; Zeithaml 1982). To express the overall level of Price Awareness in a study, the average deviation across all respondents referred to as percent average deviation (PAD) is often reported. This is simply the average of the percentage deviations in the price recall of all respondents in a study and has an inverse relationship with consumer price recall accuracy, as higher PAD levels indicate lower levels of price recall accuracy. Additional details about this scale are reported in the section 3.3Scales of Measurement.
As anticipated, there has been considerable research investigating customer Price Awareness. Monroe and Lee (1999) cite over sixteen previous studies, most of which focus on measuring customers’ short or medium-term price knowledge of consumer packaged-goods. In a typical study, customers are interviewed either at the point-of-purchase or in their home and asked to recall the price of a product, or alternatively, to recall the price they last paid for an item. In one of the earliest studies, Gabor and Granger (1961) conducted in-home interviews with hundreds of housewives in Nottingham, England. Price awareness was tested on products previously bought by the interviewed customers. They found that consumers were able to provide price estimates for 82% of the products in their study. Thus, 18% of customers were not able to recall the price of an item. In addition, only 65% of customers were able to recall a price within 5% of the actual price. These findings have been replicated in later studies, which generally reveal that only half of the customers can accurately recall prices (Allen, Harrell and Hutt 1976; Conover 1986). In perhaps the most frequently cited study, Dickson and Sawyer (1990) asked supermarket shoppers to recall the price of an item shortly after they placed it into their shopping cart. Surprisingly, fewer than 50% of consumers accurately recalled the price. With the spread of the internet, price recall has gradually improved (Del Vecchio and Craig, 2008) as consumer are more exposed to prices. In every moment and everywhere, they can get to know how much a certain item is priced. None of these studies, however, properly took into consideration the distinguishing between Head (H) and Tail (T) products. Although there is large consensus that H-type product prices are better recalled and recognized if compared to T-type products, product-families have mostly been considered as ‘self-contained compartments’, leaving the big picture partially incomplete.
In section 2.2Hypotesis, the relation between Price Awareness and Price Sensitivity is dampened, and an example is provided in order to ease the hypothesis formulation.
2.1.3 Comparison Effect
Broadly speaking, Comparison Effect (CE) – meaning the possibility to compare a target product with similar alternatives – is the result of lower search costs. Search costs became an economic topic since the seminal work of Stigler (1961). For the Internet it emerges also as an important topic because search costs – as a mix of time spent and monetary effort – is assumed to decrease with the adoption of the Internet. It is true that electronic shopping may reduce the cost of search in ways that enlarge consumers’ consideration sets and that make price comparisons easier. Additionally, the Internet encourages consumers to undertake unimpeded search across stores (Alba et al. 1997). Lower search costs also found to result in increased competition and thereby in reduced prices (Bakos 2001). However, lower search costs also allows firms to better monitor their competitors. The Internet enables even profiling and monitoring back such strategies for evaluation (Bakos 2001, Lee and Gosain 2002).
From a customer’s perspective, the presence of Comparison Effect means that, when evaluation a purchase, he or she may compare – with little or no effort – a target product with plausible alternative products. As demonstrated by Grewal & Compeau (2007) external comparisons could contribute to creating feelings of paying a fair price. Nowadays, customers are not that blind to buy a product without searching information about a product or service. Inofrmation is accessible with a few effortless keystrokes. Comparisons reduce the risk of a bad deal for customers and narrow price variation among the online vendors, ultimately increasing the competition (Bakos, 1997). Further, increased competition makes it harder for firms to generate profits (Liebowitz 2002).
Customers’ susceptibility to the influence of externally provided information has drawn the attention of both researchers and non. There is a spread conviction that externally supplied prices are often subconsciously integrated by customers to form “internal standards of comparison” that are then used in subsequent price judgments (Adaval and Wyer, 2011).
Also, it is largely recognized that the willingness to pay for a certain product may depends not only on the reference price associated to that specific item, but also on comparable product prices (Krishna et al. 2006; Lichtenstein and Bearden 1989; Urbany, Bearden, and Weilbaker 1988).
In section 2.2.2, the relation between Comparison Effect and Price Sensitivity is investigated, and a simplified anecdote contemplating the impact of the comparison effect (low Search Complexity) is provided, in order to ease the hypothesis formulation.
The following section is dedicated to explaining the Research Model (Figure 2.2) used to approach the Research Question. Please note that the figure shows only the most relevant independent variables (those on which the hypotesis are elaborated). At first, the variables of interest, their established bonds, and their hypothesized relations are introduced. For the sake of simplicity two plausible scenarios are then used to contextualize each hypothesis.
Figure 2.2: Research ModelThe figure above represents the research model guiding the research design.
2.2.1 Price Awareness and Sensitivity to Price
Generally, there is a shared consensus about the fact that consumer awareness of prices plays a key role in price management since it not only determines how prices are perceived and valued, but also influences consumers’ purchase decisions (Binkley and Bejnarowicz, 2003; Mesak and Clelland, 1979; Monroe, 1973; Shapiro, 1968; Simon, 1989; Turley and Cabaniss, 1995; Vanhuele and Drèze, 2002). Most of these studies have found that those consumers who perceive prices more accurately are the ones who place a higher degree of importance on them (Hirn, 1986; Kujala and Johnson, 1993; McGoldrick and Marks, 1987).
Several studies on Price Awareness investigated about how customers become price aware. Janiszewski and Lichtenstein (1999) argued that a phenomenon is judged based on where in its current range it is positioned, in this case a range of prices. Upshaw (1969) and Helson (1964) suggest that over time, the remembered values of the individual stimuli gradually become assimilated to the ‘adaptation level
’. Nevertheless, the perspective formed on the basis of the original context stimuli (which determines the range of values considered relevant) persists. As a result, it may continue to influence judgments of both the original stimuli and new ones. Consumer’s perceived lower and upper prices define the boundaries of the acceptance range, eventually influencing final purchasing decisions. This is in line with what shown by Strack and Mussweiler (1997), who describe the anchoring effect as the human tendency to rely on previous pieces of information offered (the “anchor”) when making decisions at a later time.
In their work “Conscious and Nonconscious Comparisons with Price anchors”, Adaval et al. (2011) explicitly transfer these concepts to the purchasing decision field. They show that reference prices – consciously or subconsciously memorized by customers – contribute to shape their Price Awareness, which gets decisive during the purchase finalization. This confirms some previous findings about the fact that price evaluations and customers’ willingness to pay depend on reference prices (Mazumdar and Papatla 2000). Then, an increase in price will be less tolerated by price aware consumers, who typically stick with their “anchor price”: the more confident in their reference prices customers are (and, then, the more price aware), the more sensitive to price changes they will be (María Rosa-Díaz , 2004; Brown, 1971; Hirn, 1986; Kujala and Johnson, 1993; McGoldrick and Marks, 1987).
Trying to extremely simplify this concept, suppose a customer – Sally – to visit an e-store and to click on a product (say, Product 1) priced 105€, now on sale at 95€. Now suppose that a friend of hers, few days ago, told Sally to have bought that product at 90€. Just that morning, Sally heard on the radio that the recommended price of that product was 100€. However, the Facebook group she recently joined (“Best Deals in Town”) sponsored the same product at the incredible price of 85€.
To Sally, the attractiveness of the price of Product1 will be then judged according to where it falls within the range at a certain moment ‘t’. The set of prices (€85 – €105) may provide the consumer with a range of plausible values the product is likely to be priced at (Ostrom and Upshaw 1968; Parducci 1965).
The positions of these prices are replicated in a simplified version in figure 2.3.a. If the five (€85, €90, €95, €100, €105) prices to which the customer is exposed represent the totality of its experience with this product and no additional contextual information is present, she will consider as too high those prices beyond the expected one, and a good deal those lower than the expected one. When making her purchase decision, Sally – who will have anchored her expectation to a reference price – will tolerate price increases much less than what she would have done without any points of reference.
As anticipated, Strack and Mussweiler (1997) describe the anchoring effect as the human tendency to rely on previous pieces of information offered (the “anchor”) when making decisions at a later time. This effect applies to prices. In their purchase decision, consumers evaluate a price attractiveness according to the information they have been exposed to. The more distant the proposed price is from the expected one (the “anchor”), the less likely the finalization of the purchase will be.
In other words, our customer Sally will be more price sensitive than a comparable customer with no Price Awareness.
The case described fits the typical scenario of ‘H’ products, whose prices frequently occur and are better recalled at a later time. Del Vecchio and Craig (2008) show that frequently occurring reference prices are better recalled. Popular products price ranges are better framed, as the exposition to their price is higher. A higher exposure to prices, as in the case of H-type products, drives consumers’ price recognition and their overall price awareness. In general, for H-type products, consumers should be able to recall and recognize product prices with a relatively high accuracy.
Figure 2.3.b shows, on the contrary, the typical situation in which a ‘T’ product is encountered. In the case of Tail products (b), where Price Awareness is typically weaker, the price increase could be accepted, as no real reference prices have been set. Indeed, the customer was scarcely or not at all exposed to that product price.
Tversky and Kahneman (1975) assume that when people are asked to judge a stimulus along an attribute dimension and do not have a specific a priori value in mind, they first select a subjective extreme value along this dimension and then adjust either upward or downward from this anchor until they arrive at a value they consider plausible (anchoring and adjustment process). This value typically lies at the boundary of a range that they consider viable (Quattrone et al. 1984). Still, the ‘anchor’ is now strictly subjective and does not come from a previous exposure to price information.
In the case of T products, the customer has a few or none reference prices, and its price awareness will be negligible. As the expected price won’t be based on previous price experience, the customer could be likely to accept prices falling even beyond the subjective expectation. Being not able to quantify how much a price could be higher (or lower) than what expected (as it is just a subjective point), in this case the customer will be less price sensitive, tolerating even wider price variations (Brown, 1971; Hirn, 1986; Kujala and Johnson, 1993; McGoldrick and Marks, 1987). Then, when price increases, the customer could still likely buy the item of interest.
Hence, to sum up, the previous literature contributes to identify a solid relation between Price Awareness and Price Sensitivity (María Rosa-Díaz , 2004; Brown, 1971; Hirn, 1986; Kujala and Johnson, 1993; McGoldrick and Marks, 1987), but it has been mostly applied to the brick-and mortar retailing. This study wishes to “step outside” of the physical shops with the purpose of extending the relation between Price Awareness and Price Sensitivity to the online retailing. This would then allow further considerations based on the distinction of Head and Tail.
Table 2.2 summarizes those literature sources which open up the pathway towards H p 1 .
The aforementioned effect of Price Awareness on Price Sensitivity in the online environment is captured by the following hypothesis:
Table 2.2: Pathway towards H p 1
|Consumers who perceive prices more accurately are the ones who place higher importance on them.||Hirn (1986); McGoldrick and Marks (1987);
Kujala and Johnson (1993)
|Reference prices (anchors) – consciously or subconsciously memorized by customers – contribute to shape their Price Awareness.||Strack and Mussweiler (1997);
Adval et al. (2011)
|The more confident in their reference prices customers are, the more sensitive to price changes they will be.||Brown (1971); Hirn (1986);
McGoldrick and Marks (1987);
Kujala and Johnson (1993); María Rosa-Díaz (2004)
H p 1 : Ceteris paribus, the more price aware e-customers are, the higher is the likelihood that, in response to a price increase, they will not finalize the purchase.
In other words, when e-customers can accurately attribute the correct price to a product, a price sensitive behavior is more likely to take place.
2.2.2 Comparison Effect and Sensitivity to Price
Previous research shows the role of Price Comparison in alimenting (when high) or reducing (when low) the importance of price and price changes (Alba et al., 1997; Bakos, 1997). This finding has been better framed by Lynch and Ariely (2010), who carried out an in-depth study on field with a research design similar to the one adopted in this research. In their study, data were adopted through a simulation generated ad hoc for the data collection purpose. However, their study focuses on wine retailing, leaving outside other categories. On the contrary, as already mentioned, in this research the approach is cross-category.
Getting back on the pathway carrying to the second hypothesis formalization, there are some theoretical contributions worth to be mentioned. Search costs have been found to be negatively related to Price Sensitivity (Popkowski-Leszczyc and Rao 1990; Boulding et al. 1994). Press analysts and marketing scholars have predicted that lowered search costs through the internet should lead to intensified competition and greater Price Sensitivity (e.g., Anders 1998; Gove 1999; Kuttner 1998; Quelch and Klein 1996). Research also found that it is true that online shopping may reduce the cost of search in ways that enlarge consumers’ consideration sets and that make price comparisons easier. Indeed, ceteris paribus, if electronic shopping lowers the cost of acquiring and processing price information, it should increase price sensitivity, just as is the case for price advertising (Popkowski-Leszczyc and Rao 1990, Boulding et al. 1994). A recent study by Ellison G. and Fisher Ellison S. (2009) confirms that the easy search (alimenting the Comparison Effect) makes customers more price-sensitive in the online environment.
Trying to extremely simplify this concept, it is possible to propose a practical example (as done for Price Awareness). Remember Sally? Now, suppose that she wants to further explore the Product1best offer before actually buying it. She decides to visit www.e-tailer1.com, that offers relatively high-priced products. She finds Product2 and Product3 (equivalent to Product1) priced at €90 and €110, respectively.
However, Sally is a very careful buyer and decides to also have a look to www.e-tailer2.com, that offers relatively low-priced products, and she finds Product4 and Product5 (again, equivalent to Product1) priced at €75 and €100, respectively. Now, the customer will also:
judge the target product price based on similar product prices
offered by e-tailer1 and e-tailer2 (figure 2.4);
develop an impression (store price image
) of all the three competitors, eventually using the information collected during this shopping experience to decide to what e-store getting back at a later time
For a specific and exhaustive explanation of point (II), please see section 4.4 Managerial implications. For the moment, the discussion will focus on point (I), which is actually the one needed to carry out a second hypothesis. Figure 2.4 simplifies even more the situation described.
Knowing to have attractive alternatives and being informed, our customer is now more price sensitive, as she knows that if Product1 price increase even further, she could opt for other retailers or substitute products. It is largely recognized that the willingness to pay for a certain product may depends not only on the reference price associated to that specific item, but also on inputs related to comparable product (Krishna et al. 2006; Lichtenstein and Bearden 1989; Urbany, Bearden, and Weilbaker 1988).
The above-described is the typical case of H-products, whose information are easy to access, and comparison is immediate.
What if Sally could not benchmark the target product? Let’s try to imagine that Sally does not found any relevant comparable products. She would not have had any additional information which could help her to better identify if Product1 price at time ‘t’ is too high or not.
This is typically the case of Tail (T) products, difficult to compare and with a few equivalent products available. Technically, we say that for T products the Comparison Effect is relatively low, whereas it is high for H products.
As above mentioned, previous research show the role of Search Complexity in alimenting (when low) or reducing (when high) the importance of price and price changes (Alba et al. 1997; Bakos, 1997). This finding has been better framed by Lynch and Ariely (2000), who carried out an in-depth study on field. They actually used an “inter-store comparison” to collect data, finding that in the wine retailing this general rule applies. Accordingly, other industry-specific studies have been carried out. However, as above mentioned, this thesis aims at studying the negative relation between Comparison Effect and Price Sensitivity to a wide set of categories. Table 2.3 summarizes those literature sources which open up the pathway towards H p 2 .
Table 2.3: Pathway towards H p 2
|Lowered search costs through the Internet lead to intensified competition and greater price sensitivity.||Quelch and Klein (1996);
Anders (1998); Kuttner (1998);
|A higher product comparability increaseas the importance of price and price changes during the purchase decision.||Alba et al. (1997)|
|Willingness to buy a certain product may depends not only on the reference price associated to that specific item, but also on comparable product inputs.||Lichtenstein and Bearden (1989);
Urbany, Bearden, and Weilbaker (1988);
Krishna et al. (2006)
The aforementioned impact of Comparison Effect on Price Sensitivity is captured by the following hypothesis:
H p 2 : Ceteris paribus, the easier e-customers can compare a target product with similar alternatives, the higher is the likelihood that they will not finalize the purchase in response to a price increase.
In other words, when e-customers are exposed to plausible purchase alternatives, a price sensitive behavior is more likely to take place.
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