Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?

Journal of Interactive Marketing 25 (2011) 95 – 109 www.elsevier.com/locate/intmar

Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions? Wenzel Drechsler ⁎ & Martin Natter Strothoff Chair of Retailing, University Frankfurt, Germany Available online 22 March 2011

Abstract Online price comparison sites (shopbots) like PriceGrabber.com are the most powerful tools for consumers to easily compare prices and find offers for desired products. Besides providing distributions of actual prices in price comparison tables, shopbots like NexTag.com have recently introduced price charts (line charts) displaying a product's full price history. Price charts should support consumers in forming expectations about future prices. Nevertheless, it is currently unclear how price charts influence consumer price expectations and purchase decisions. The results of this study show that the provision of past prices leads to strong adjustments of price expectations depending on price chart characteristics. In particular, the trend, variance and range of past prices in the chart strongly affect price expectations and purchase timing decisions. Furthermore, in the case of a strong downward trend and high variance in past prices, results show that nearly 50% of the total effect is caused by the visualization of the price history. © 2011 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved. Keywords: Price expectations; Purchase timing; Shopbots; Price comparison sites; Information visualization

Introduction Nielsen NetRatings (2007) shows that the ability to efficiently compare offers is one of the most popular reasons for consumers to shop on the Internet, as it is cited by 62% of those surveyed. Thus, online price comparison sites (shopbots) like PriceGrabber.com and YahooShopping.com are the most powerful tools for consumers to easily compare prices and find offers for desired products. As such, shopbots reduce the cost of search for information about products and facilitate better and more efficient purchase decisions (e.g., Häubl and Trifts 2000; Trifts and Häubl 2003). Besides providing distributions of actual prices for any product in the form of price comparison tables, shopbots like NexTag.com, PriceScan.com, and Skinflint.co.uk have recently introduced line charts displaying a product's full price history. NexTag.com, for instance, calls this feature “price history,” ⁎ Corresponding author at: Strothoff Chair of Retailing, Goethe University Frankfurt, Department of Marketing. Grueneburgplatz 1, 60323 Frankfurt, Germany. E-mail addresses: [email protected] (W. Drechsler), [email protected] (M. Natter).

while on pricescan.com, it is called “price trend graph.” Most frequently, such charts show the minimum prices for a product across different retailers over time (see Fig. 1). Information on a product's price history is a source of external reference prices and should therefore stimulate consumer behavior (Kopalle and Lindsey-Mullikin 2003). Consequently, price charts should support consumers when forming expectations about future prices. Research shows that price expectations and purchase timing are conceptually related (Danziger and Segev 2006). Price history information included in the price chart could therefore enforce strategic buying behavior with respect to buying now or later. Since studies in behavioral finance show that some investors base their buy or sell decisions depending on certain chart patterns of past stock prices (Park and Irwin 2007), one might also expect that visualization itself has an effect on consumer expectations and purchase timing decisions. Depending on price charts characteristics and, therefore, price history, consumers potentially form and/or adjust expectations about future prices, which in turn influence purchase timing decisions. Information on the most attractive product categories on shopbot sites reveals that consumers use shopbots especially for finding the best offers in the category of consumer durables,

1094-9968/$ - see front matter © 2011 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.intmar.2011.02.001

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Fig. 1. Examples of shopbots with price charts.

particularly consumer electronics such as notebooks, TVs or digicams. This is because purchase timing is a critical decision, especially for the most durable product purchases (Mazumdar, Raj, and Sinha 2005). Hence, consumers who use shopbot sites that provide information about a product's price history can gain easy access to information about a product's life cycle stage. Although consumers expect prices to decrease over a product's life cycle especially in high-tech markets (Bridges, Yim, and Briesch 1995), the price chart makes this information more apparent to consumers. Understanding the response of consumers to price chart information should be relevant for shopbots, retailers and manufacturers. When price charts are perceived as relevant information, shopbots could increase their popularity. Research shows that with respect to purchase timing, a change in price expectations has a strong influence on demand elasticities (Erdem et al. 2005). Hence, retailer and manufacturer sales and profits are potentially affected by consumer reactions to price charts. To the best of our knowledge, there is no study that analyzes the effect of price history charts on consumer decision-making. The introduction of a product's full price history visualized in a line chart introduces two types of information, namely, 1) historical information about prices and 2) a graphical display of this information; as such, it is currently unclear how price charts influence consumer expectations and purchase decisions. Based on the foregoing discussion, it is the aim of this study to explore the effects of price charts on consumer expectations regarding durables prices and purchase timing decisions. In particular, this study investigates whether the introduction of price charts (that is, price history) induces reference price effects in terms of adjusting a consumer's price expectation. Furthermore, this study analyzes the impact of price chart characteristics on consumer price expectations and purchase timing and disentangles the effects of reference price histories from effects due to their visualization. This study contributes to several fields of research. First, with regard to reference price research, this study extends the analysis of the impact of external reference prices at a certain point in time to an analysis of a whole series of external reference prices over time that are captured and ordered in a single source. Second, the

study enhances knowledge about reference price effects on durable goods purchase timing (Mazumdar, Raj, and Sinha 2005). Third, the results of this study update current knowledge concerning the relation between the presentation of information at shopbot sites and consumer price perceptions (Smith 2002). Finally, this study contributes to the field of research on visual representation and decision-making (Lurie and Mason 2007). To test our hypotheses, we conducted experiments in which participants were asked to state their price expectations and purchase timing decisions after viewing a particular price chart condition, which we had manipulated for our purposes. Further, we also tested the effects of the same price histories on price expectations and purchase timing when presented in a nongraphical manner to explore the effects of the visualization. The results show that in general, the price charts induce strong reference price effects. Further, the trend, variance/volatility and amount of price decline (i.e., range) shown in a chart exert a strong influence on price expectations. The trend and variance also exhibit a strong influence on purchase timing decisions. In general, results indicate that the strength of the impact of the chart characteristics increases with long-term price expectations. Finally, the results clearly demonstrate that the graphical representation itself enforces the impact of the price trend and variance on consumer price expectations and purchase timing decisions. The remainder of this article is organized as follows. First, we discuss the related literature and derive the underlying hypotheses. Second, we present the outline and results of Study 1, which analyzes the influence of price charts on price expectations and purchase timing. We then present Study 2, which assesses the effect of graphical representation. The last section summarizes the results, discusses managerial implications and presents avenues for further research. Related Literature Consumer Price Expectations for Durables Expected future prices are particularly important for all product categories that experience significant price changes

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over time like consumer durables (Ofir and Winer 2002). Since price decreases over the product life cycle are fairly common among durable products, they are widely anticipated by purchasers of durable products (Balachander and Srinivasan 1998). Marketing literature suggests that consumers form expectations regarding a product's attributes (most notably price) based on historical patterns for the attributes of the product category; they then incorporate these expectations in their purchase decisions (Bridges, Yim, and Briesch 1995). There is evidence that consumers form forward-looking expectations that affect consumer durable purchases (Winer 1985). Consumers expect price declines due to experience curve effects and plan or delay their purchases accordingly (Doyle and Saunders 1985). This discussion suggests that due to prior experiences, consumers develop mental schemas or a set of expectations about a product category (Sujan, Bettman, and Sujan 1986). Erdem et al. (2005), for instance, find that in the case of PCs, consumers generally expect a steady-state rate of price decline. Further, they find that consumers seem to expect mean reversion in price declines; i.e., if the decline over the past few months was greater (or less) than normal, then consumers expect a lesser (greater) price decline over the next few months. However, since durables have longer interpurchase times than frequently purchased packaged goods (FPPG) consumers are normally less informed about the development and changes of attribute configuration, technology, and price level of a durable. The information acquired during prior purchase occasions is therefore less salient in the formation of price expectations for a durable product than it is for a FPPG (Mazumdar, Raj, and Sinha 2005). With the introduction of information about a product's price history displayed in a price chart, consumers no longer have to rely on likely obsolete price knowledge based on prior experience when forming price expectations for purchase timing of durables. Therefore, it is essential to investigate whether the introduction of price charts induces reference price effects in terms of adjusting prior price expectations and how different chart patterns affect price expectations and purchase timing. Price Chart Information and Consumer Purchase Decisions In the marketing literature, the influence of price history visualized in a line chart and chart pattern characteristics on consumer decisions is not discussed. Research on behavioral finance suggests that investors base their investment decisions on specific stock price chart characteristics. Standard economic theory, however, assigns little informative value to stock price charts due to the random walk assumption (Brealey, Myers, and Allen 2007). Accordingly, historical price movements shown in a chart should not predict how a stock will behave in the future. Nevertheless, research shows that some investors base their investment decisions on specific stock price-chart characteristics. Mussweiler and Schneller (2003) show that investors use salient standards in price charts such as salient highs or lows for their future stock price expectations and, thus, for investment decisions. Additionally, Benartzi and Thaler (1999) show that

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a varying time horizon for past performance charts induces a significant influence on a trader's investment decisions. Especially with regard to investment decisions to sell or buy, investors follow stock price trends typically visualized in price charts. Indeed, many investors practice technical analysis of price chart patterns (Leigh et al. 2002), which presumably identifies patterns in price charts that may offer an indication of whether a trend is likely to continue or terminate (Park and Irwin 2007). Of course, such a possibility is in principle ruled out by the random walk assumption, and so many researchers are quite skeptical of these ideas. However, practitioners use them routinely for trading (Caginalp and Balenovich 1996), and so the price chart can be regarded as standard information for investors. Typically, technical analysis is operationalized through trading rules of the following form: “If chart pattern X is identified then buy/sell within/after the next N trading days” (Leigh et al. 2002). This means that by using chart information, investors try to maximize their profits and hence the economic value of their transactions. Likewise, in the case of durable products, consumers also try to maximize the economic value of their transaction, since they typically make a trade-off between buying now or later when the price level has reached a certain level. In particular, they try to maximize transaction utility. This transaction utility increases when the price meets or falls below an expected price (Darke and Chung 2005). Hence, price charts on a product's price history should also support consumers when forming future price expectations and when making purchase decisions. However, there is no study available that provides insights on the influence of price charts on consumer price expectations and purchase decision for products that are priced according to their stage in the life cycle. Study 1 Expected Effects and Hypotheses Development Reference Prices — Anchoring and Adjustment According to adaptation-level and assimilation-contrast theories, price information (or external reference prices) affects consumer perceptions when it is judged acceptable or plausible relative to the internal price standards of consumers (Monroe 2003; Urbany, Bearden, and Weilbaker 1988). These processes also occur when consumers are exposed to a price that is outside the expected range but still plausible. Instead of rejecting this price information outright, consumers assimilate and reduce it to a level more reasonable for the product category (Urbany, Bearden, and Weilbaker 1988). Taken together, these points indicate that consumers who encounter new price information tend to update their prior (Yadav and Seiders 1998) price expectations. Hence, we expect that consumers use price charts showing the development of past prices to update (adjust) their prior price expectations. Influence of Chart Characteristics A key feature of high-tech durables is the tendency for prices to fall quickly over time, creating an incentive to delay

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purchases. The strength of this incentive depends on consumer forecasts of how quickly prices will drop (Erdem et al. 2005). In the formation of price expectations for durables, consumers especially use product histories if a trend exists (Mazumdar, Raj, and Sinha 2005). In general, consumer expected prices for time period t are then equal to the current price plus a fraction reflecting the difference between this period's price and last period's price (i.e., extrapolative expectations). This means that consumers update their price expectations by factoring in a price trend observed from prior prices (Mazumdar, Raj, and Sinha 2005). Therefore, we expect that consumers who systematically process chart information use trend characteristics (that is, weak vs. strong downward trends) as a salient standard (i.e., chart characteristic) to form their expectations and make their decisions. If consumers notice a strong decrease in price from one week to the next, they may expect even lower prices in the future and decide to acquire the product only if a certain price is reached (Kalyanaram and Little 1994). In the case of a weak decrease in which the trend of past prices levels off (i.e., longterm stationarity of price series), consumers instead may forecast no further price decreases such that postponing does not seem worthwhile. These points lead to the following hypothesis: H1. Price charts with a strong downward price trend compared to price charts with a weak downward trend lead to a) a downward shift in price expectations and b) a postponement of the purchase time. Besides trend characteristics, we expect that the variance in a chart (i.e., price fluctuations from one point to another) is another important chart characteristic. Research shows that consumers might use the variance of observed prices as a heuristic for price search behavior (Darke, Chaiken, and Freedman 1995). The underlying idea is that the more variance consumers observe in prices, the more likely they will be to continue to search. They defer their purchase decision in the belief that further searching will pay off. Kalwani and Yim (1992) also provide empirical evidence that consumers expect prices to strongly decrease in light of frequently-changing prices. This leads to the following hypothesis: H2. Price charts with a high variance in past prices compared to price charts with a low variance in past prices lead to a) a downward shift in price expectations and b) a postponement of the purchase time. Besides expecting direct effects of the trend and variance on price expectations and purchase timing, we further assume that these two chart characteristics are not perceived independently. That is due to the fact that determining a trend in a set of past prices requires a consumer to interpolate between a large number of data points (Vessey 1991). If, however, the variance of these data points increases, it becomes more difficult to interpolate in order to infer the underlying trend correctly. Put differently, the

trend becomes less obvious in a highly volatile price series. This leads to the following hypothesis: H3. A high variance in prices displayed in a price chart moderates the effect of the chart's trend on a) price expectations and b) purchase time. According to range and range–frequency theories (Parducci 1965; Volkmann 1951), consumers judge actual prices not only by their location within the distribution of other prices but also by the perceived range at the time of judgment. Range theory postulates that consumer price perceptions depend on a comparison of a market price to the endpoints of an evoked price range. Janiszewski and Lichtenstein (1999) show that in a high-range price situation in which the market price is nearer the lower endpoint, consumers judge the market price as more attractive as compared to a low-range situation. In the context of price charts, we accordingly expect to find an impact of the range of the historical prices on price expectations and purchase timing. In particular, we propose that the difference between the first and last price in the chart (that is, the range) affects price expectations because it establishes a benchmark for the evaluation of the actual price. If the range between the first and last prices in the price history increases, consumers should evaluate the last price (i.e., actual price) more favorably, as it indicates a higher gain within a certain time frame. Due to the assumption of extrapolative expectations, consumers should consequently also expect higher rates of price decline in the future. Thus, this leads to the following hypothesis: H4. Price charts with a high range of past prices compared to price charts with a low range of past prices lead to a) a downward shift in price expectations and b) a postponement of the purchase time. Methodology To test the hypotheses, we conducted experiments in which participants are asked to state their price expectations and purchase timing decisions after viewing a particular price chart condition that we manipulated. Study Design Manipulations As indicated in the introductory section, consumers use shopbots most frequently for gathering information about prices for durables, especially consumer electronics. In order to determine the appropriate product category for the experiment, we therefore conducted a pre-test with 63 participants. Given the overall goal of this study, the aim of the pre-test was to identify a durable product category for which purchase timing plays an important role. Therefore, the participants rated on a 7-point scale the importance of purchase timing for the three most popular electronic product categories on Internet shopbots, namely, notebooks, MP3-Player/IPods and digicams. Results show that differences in mean across all three categories are significant at p b .05, with the highest score for the notebook category

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(MNotebook = 6.06, SDNotebook = .83). Consequently, we use the notebook category in the main study. The time horizon of the price charts used in the experiment was set to three months, since a typical lifecycle of a notebook model is only about six months (Guide, Muyldermans, and Van Wassenhove 2005). The time horizon of three months seemed appropriate because it neither indicates that the notebook is at the end of its life cycle, nor that it was recently introduced, which could cause the majority of consumers to wait for better prices. The experimental design encompasses price chart manipulations in terms of the actual downward trend (strong vs. weak), variance (high vs. low) and price range (i.e., the rate of price decline (high vs. low)), resulting in a 2 × 2 × 2 between-subjects design. The trend manipulations are given by the two most representative chart patterns of notebook past prices on price comparison sites. The procedure to assign realistic price levels and price patterns to the different chart conditions was as follows. First, we inferred information about the most popular notebooks in the market and their prices from Amazon's top 100 selling notebooks. We calculated a mean sales price as a rank-weighted sales price of the top 100 notebooks. Calculation of the average notebook price delivers a mean price of MPrice = 828.66€ (SDPrice = 478.59€). Based on this result, we assigned 850€ as the actual lowest price to the notebook in the experiment. Second, we ensured that price declines follow realistic levels and patterns. Different shopbots show price declines for notebooks within a three-month period ranging from 10 to 30%. For the low-range condition, we therefore assigned 1000€ as a start price corresponding to a 15% price decline. Furthermore, 1150€ was the price assigned to the high-range condition corresponding to a 26% price decline. The intervals on the horizontal axis of the charts were adjusted accordingly, holding all else equal. Examples for this manipulation in the low-range condition are presented in Fig. 2.

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To create a realistic shopbot environment for all treatment groups with price charts and a control group (that is, without price chart), we further constructed a price comparison table with current prices for 10 competing retailers. In this price comparison table, the minimum price of 850€ corresponds to the last price in the chart. Compared to the treatment groups, the control group only received the price comparison table of actual prices without information on the notebook's past prices. Other potentially available information, such as shipping fees and retailer ratings, were kept constant. This latter information was only provided for creating a more realistic experimental environment. Hypothetical brands for the notebook, shopbot (namely, cheaper.com), and retailers (listed in the price comparison table) were used to minimize the potential participant's tendency to base their decisions on brand and retailer images or previous experiences (Kwon and Schumann 2001). Furthermore, the overall quality of the notebook and retailers were rated as very good to avoid the effect of quality uncertainties. Experimental Procedure Participants were asked to imagine that they have a desktop computer they bought several years ago that is currently working. They were further told that this computer is technically out of date and too inflexible and that they are thinking about buying a notebook in order to make their work more efficient. The aim of framing the story this way was to prevent participants from feeling pressure that an immediate purchase was necessary. In particular, the participants were told that they have decided to buy a specific notebook because of its convincing price performance ratio. After providing them with the average offline price of this notebook they were exposed to additional information about the desired notebook's current prices at a shopbot website and ask to make their final purchase timing

Fig. 2. Example charts.

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decision. To eliminate effects caused by financial constraints, participants were provided with a budget of 1000€, which exceeded the actual lowest price of the notebook (850 €). In total, 531 people participated in an online experiment and were randomly assigned to one of nine conditions, including eight treatment groups plus one control group. In particular, a 2 (trend) × 2(variance) × 2(range) between-subject design (N = 427) was employed such that each treatment was exposed to a price comparison table of current prices and one of the manipulated price charts with the notebook's price history. The control group (N = 104) only received the price comparison table of current prices. In the treatment groups, participants were asked twice to state their price expectations concerning the notebook's minimum price development at price points of 4, 8 and 12 weeks later. This question first appeared after participants saw the price comparison table alone; it appeared second after being exposed to the price comparison table and the price chart together. Finally, participants were told to think about the optimal time to purchase the notebook. In the control group, participants indicated their price expectations and planned purchase timing directly after seeing the price comparison table. After participating in the experiment, participants filled out an online questionnaire. In order to control for individual differences between participants, we measured important covariates that might affect price expectations and purchase timing. In particular, we controlled for participant usage and/or experience with shopbots and the information they use on shopbots (e.g., price chart used = 1, not used = 0). In total, 79% of participants indicated that they regularly use shopbots for finding offers, whereas 31% of these shopbot users also use price chart information. Regarding consumer psychographics (see Appendix), we control for deal proneness and notebook expertise as literature indicates that they are related to price expectations, timing decisions and information processing (Biswas and Sherrell, 1993; Lurie and Mason 2007; Martinez and Montaner, 2006; Rao and Sieben 1992). Finally, we control for the individual's evaluation of the current notebook price, as this might affect study results. In particular, participants should indicate the perceived expensiveness of the current notebook price. Key Measures As indicated in the previous section, the treatment groups, which are provided with price charts, were asked twice about their price expectations regarding the minimum price in the future. In particular, they were asked about the expected future minimum prices of the notebook in the upcoming 4, 8 and 12 weeks (PEt before). Updates of these three price estimates were obtained after participants received additional information about the notebook's price history (PEt after). Reference price effects are measured in terms of the adjustment of price expectations (ΔPEt) in response to the chart as follows:

ΔPEt =

PEt before−PEt after ; with t = 4; 8; 12 weeks PEt before

1Þ

To test the impact of chart characteristics on the level of price expectation and purchase timing, another measure was calculated that relates future price expectations to the current price of 850€:

PEt =

PEt after ; with t = 4; 8; 12 weeks 850

2Þ

Since our experimental framing indicates that the notebook has already been on the market 12 weeks, then 12 weeks into the future would typically correspond to the end of its life cycle (i.e., 6 months). We therefore denote price expectations over the next 12 weeks as long term for notebooks. Purchase timing was measured with the question “Given the information from cheaper.com, when would you most likely buy this notebook?” Responses were anchored at a weekly scale from 1 = “now” up to 14 = “after 12 weeks.” Results Manipulation Checks. To assess whether the levels of trend and variance in the charts were actually perceived as different, the participants in each treatment group were asked to rate the slope of the trend and the level of price fluctuations in the price chart on a 7-point scale from 1 = low to 7 = high. Results of a 2 × 2 analysis of variance indicate that participants significantly perceive differences in slope (Mstrong trend = 5.57 vs. Mweak trend = 4.61, F (1,426) = 131,9, p b .01) and variance (Mlow variance = 3.26 vs. Mhigh variance = 4.42, F(1,426) = 103,94, p b .01) as intended. Furthermore, we find no significant interaction between our independent variables on the perceived level of variance (F (1,427) = 2.58, p N .10). For the trend, however, results show a significant interaction (F(1,427) =16.56, p b .01), indicating that the level of trend is not perceived independently from the level of variance. To check the relevance of price charts for purchase timing decisions, participant perceptions of the relevancy of the shopbot information were assessed with three 7-point Likerttype scale statements (Mason et al. 2001). Higher scores indicate that participants perceived the information provided as more relevant information (coefficient alpha = .90). With respect to purchase timing, participants in the price chart condition perceived the information provided by the shopbot as more relevant (MChart = 4.49) as compared to the control group, who received the price comparison table only (MNo Chart = 3.83, t = 3.85, p b .01). This first result reveals that the price chart is a useful tool for consumers who are deciding when to buy a product. Price Expectations — Anchoring and Adjustment ANOVAs and pairwise comparisons between all eight treatment groups show no significant differences in price expectations measured prior to the exposition to the chart information (PEtbefore). Results also show no significant differences between the control group's price expectations

W. Drechsler, M. Natter / Journal of Interactive Marketing 25 (2011) 95–109 Table 1 A priori price expectations in euros (PEt before).

Mean Std.

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Table 2 Adjusted price expectations.

4 weeks

8 weeks

12 weeks

823 42

795 54

756 74

N = 427.

and the treatment groups' prior expectations. Hence, participants seem to have homogeneous expectations about the future development of notebook prices. In particular, participants seem to expect a monthly steady-state rate of price decline of 3.8%. Table 1 shows the mean expected prices for the next 4, 8 and 12 weeks. After the treatment groups were exposed to the price chart, nearly all groups adjusted their price expectations. Table 2 reports the percentage change in price expectations (ΔPEt) for the eight different chart version groups and shows whether this change significantly deviates from zero. Results clearly demonstrate that the different price charts lead to adjustments of price expectations in both directions. For instance, in the conditions including the strong downward trend (versions 1, 3, 5 and 7), participants significantly lower their price expectations for all three point estimates. The strongest downward adjustments take place for the long-term price expectations (i.e., 12 weeks) ranging from − 4.9% (version 3) to − 10.6% (version 5). In contrast, the low-trend condition leads to an adjustment in the other direction. In particular, participants of the chart versions 2, 4, and 6 raised their prices expectations, especially for weeks 8 and 12. The results clearly show that the availability of price charts induce reference price effects in terms of adjusting price expectations, which stresses the chart's relevance for purchase decisions. Additional analyses that compare the price expectations (PEt) of the different treatment groups with those of the control group point in the same direction. For instance, control group comparisons reveal the strongest differences for the longterm price expectations (i.e., 12 weeks). As compared to the control group, chart version groups 1, 3, 5, and 7 show significantly (p b .05) lower price expectations ranging from − 5.2% (version 3) to − 9.3% (version 3). Furthermore, participants of the chart version groups 2, 4 and 6 show higher price expectations (p b .10) than the control group. Hence, these results indicate that consumers adjust their prior price expectations depending on chart patterns. Influence of Price Chart Characteristics In this section we test hypotheses H1 to H4, i.e., whether different charts lead to different price expectations and purchase timing decisions. Therefore, we first analyze the influence of different chart characteristics on price expectations (PEt) for weeks 4, 8, and 12. In a second step, we test the effects of price charts characteristics on participant purchase timing decisions while accounting for price expectations. Effects of Price Chart Characteristics on Price Expectations. Since the three dependent variables, i.e., the three

Δ Price expectations (ΔPEt ) Chart version

N

Week 4

Week 8

Week 12

Range: low 1) ST_LV

52

2) WT_LV

51

3) ST_HV

50

4) WT_HV

50

−.038*** (−6.68) .005 (0.72) −.015** (−2.46) .014* (1.72)

−.073*** (− 7.11) .020** (2.06) −.032*** (− 3.28) .021* (1.70)

−.086*** (− 5.02) .048** (3.26) −.049*** (− 3.86) .048*** (2.79)

Range: high 5) ST_LV

56

6) WT_LV

61

7) ST_HV

55

8) WT_HV

52

−.037 (−5.45) .016 (1.42) −.026*** (−2.65) −.023*** (−3.12)

−.077*** (− 6.05) .022** (1.96) −.049*** (− 4.75) −.029*** (− 3.08)

−.106*** (− 6.78) .033*** (2.32) −.075*** (− 5.42) −.028** (− 2.21)

***p b .01, **p b .05, *p b .1, t-values in parantheses. Notes: ST (WT) = strong (weak) downward trend. HV (LV) = high (low) variance.

different price expectation measures for the upcoming 4, 8 and 12 weeks are related to each other as confirmed by the Pearson correlation coefficients (ranging from .79 to .93, p b .01), we have to control for this correlations (Tabachnik and Fidell, 2007). Hence, we analyse the data by MANCOVA through a multivariate generalized linear model (GLM). In addition to the price chart characteristics, two covariates (namely, notebook expertise and price chart usage = 1/0) are included in the model estimation of each price expectation measure. In particular, we use the following underlying model structure to estimate the GLM: PEt = αt0 + αt1 Range + αt2 Trend + αt3 Variance + αt4 Trend × Variance

ðt = 4; 8; 12Þ

+ αt5 NotebookExpert + αt6 ChartUser + εt;PEt ð1Þ Results of the MANCOVA part indicate significant multivariate main effects for the range (Wilks' Lambda = .98, F(3) = 2.92, p b .05), the trend (Wilks' Lambda = .83, F(3) = 28.10, p b .01) and the interaction between trend and variance (Wilks' Lambda = .98, F(3) = 2.90, p b .05) on expected prices. Further, the variable chart-user shows a significant multivariate main effect (Wilks' Lambda = .98, F(3) = 2.76, p b .05). To get a more differentiated picture of the impact of different chart characteristics on the three different price expectation measures we report the parameter estimates of the GLM in Table 3. As expected, estimation results in Table 3 show that the direct effects of a high range and a strong downward trend

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Table 3 Influence of price chart characteristics on price expectations.

chart characteristics and covariates according to (Bhattacharjee et al. 2007)

Price expectations (PEt) Week 8

Week 12

Coefficient St.error

Coefficient St.error

Coefficient St.error

.005 .007 .007 .010

−.02*** −.08*** −.02* .04***

.007 .009 .010 .014

−.03*** −.11*** −.03** .05***

.009 .013 .013 .018

.00

.002

.00

.002

.00

.003

.00 −.07** .11(.10)

.006 .008

.01 −.11*** .18(.17)

.008 .012

.02* −.18*** .22(.21)

.011 .016

Chart characteristics Range −.01*** Trend −.04*** Variance −.01* Trend ∗ variance .02* Covariates Notebook expertise Price chart user Constant R2 (Adj.)

0

Week 4

ϕ = exp@

***p b .01, **p b .05, *p b .1.

significantly lower participants' price expectations (p b .01). Further, the negative effect of a high variance of past prices seems to increase from week 4 (p b .10) and week 8 (p b .10) to week 12 (p b .05). Hence, these results provide strong support for hypotheses H1a, H2a and H4a. Further, the results show a strong significant positive interaction (p b .01) between the trend and variance for price expectations in weeks 8 and 12 and a marginal effect for week 4 (p b .10). This finding supports hypothesis H3a, which states that the variance in a chart moderates the influence between the trend and price expectations. Overall, the results indicate that the magnitude of effects increases from week 4 to week 12. This means that the price chart characteristics particularly influence long-term price expectations. Effects of Price Chart Characteristics on Purchase Timing. The response in the questionnaire concerning the independent variable purchase timing allowed for the option of not buying the notebook within the upcoming twelve weeks. Indeed, 45 (10.5%) participants stated they would buy the notebook after this time frame. Hence, our dependent variable is right censored, which implies the need for an event history modeling approach to assess the influence of price chart characteristics on purchase timing (Helsen and Schmittlein 1993). Following Bayus (1998), who analyzes purchase behavior for personal computers, we use a parametric survival model. In particular, an accelerated failure time (AFT) model is used to estimate the effects of chart characteristics on purchase time t. Generally, we model the effects of the different chart characteristics and covariates on purchase time t as S ðt Þ = S0 ðϕt Þ

ð2Þ

where the survivor function S(t) gives the probability that a participant would buy the notebook after some specified purchase time t. S0(t) is the baseline survivor function and ϕ is termed acceleration factor which depends on the different

β0 + β1 Range + β2 Trend + β3 Variance + β4 Trend × Variance 4

+ β5 PEt + ∑ β5 + m Covariates + ε

1 A

m=1

ð3Þ This model allows us to test hypotheses H1b to H4b, which propose direct effects of price chart characteristics on the time of purchase. Prior research indicates that price expectations and purchase timing are conceptually related (Kalwani et al. 1990). Therefore, we control for price expectations by estimating the model for each of the three different price expectation measures PEt (t = 4, 8, 12 weeks). In addition to the covariates included in the price expectations models, we control for general deal proneness and the perceived expensiveness of the notebook's current price. In line with previous research on purchase timing decisions, we assume that the baseline survivor function S0(t) follows a Weibull distribution (Helsen and Schmittlein 1993; Seetharaman and Chintagunta 2003).1 For the estimation, the AFT model in Eq. (2) is put into the log-linear form with respect to purchase time t (Bradburn et al. 2003). The estimation results are presented in Table 4. A positive coefficient indicates that increasing values of the respective independent variable lead to a delay of the purchase. By exponentiation of the coefficients, we further obtain the time ratio which can be interpreted as a deferral factor (DF) in our context (Bradburn et al. 2003). DF indicates whether the chart characteristics lead to a delay of the purchase (DF N 1) as compared to the respective reference group. Results for Model 1 with price expectations for four weeks as a control variable reveal strong significant effects for the trend and variance (p b .01), showing that a strong downward trend and a high variance of price charts increase the probability that participants would defer their purchase. In particular, the probability to defer increases by 32% (DF = 1.32) in response to a strong trend and by 34% (DF = 1.34) in response to a high variance. Hence, hypotheses H1b and H2b are supported. These findings also largely hold for Models 2 and 3 (with price expectations for 8 and 12 weeks, respectively). However, the effect of the trend seems to decrease from Model 1(p b .01) to Model 3 (p b .10). At the same time, the effect of the price expectations increases. This result indicates that the variance in the chart is the main driver of purchase timing decisions, whereas the direct effect of trend weakens when long-term price expectations are considered for purchase timing. In particular, higher longterm price expectations for week 12 favor earlier notebook 1 We also tested other distributions to specify the baseline survivor function (e.g., exponential and log-logistics). Likelihood-ratio tests and the Bayesian information criterion (BIC) confirm that the Weibull distribution best describes the underlying purchase timing process. Further, the shape parameter θ of the Weibull distribution is highly significant (p b .01) and larger than 1 in all models, indicating that the Weibull distribution is more appropriate to describe the underlying purchase timing process as compared to, for instance, an exponential distribution.

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Table 4 Influence of price chart characteristics on purchase timing. Model 1

Model 2

Model 3

Coefficient

St.-error

DFa

Coefficient

St.-error

DFa

Coefficient

St.-error

DFa

Chart characteristics Range Trend Variance Trend ∗ variance

−.01 .27*** .29*** −.23*

.068 .095 .095 .134

0.99 1.32 1.34 0.79

−.03 .22** .29*** −.20

.068 .098 .095 .134

0.97 1.24 1.33 0.82

−.04 .18* .27*** −.18

.068 .099 .094 .134

0.96 1.19 1.32 0.83

Price expectation PE4 PE8 PE12

−.12 – –

.651 – –

0.89 – –

– −.85* –

– .483 –

– 0.43 –

– – −.94**

– – .369

– – 0.39

Covariates Notebook expertise Deal proneness Perceived expensiveness Price chart user Constant θ (shape parameter) LL (χ2)

−.07*** .025 .07*** .022 .10*** .026 −.25*** .079 1.70*** .183 1.56*** .068 − 502.26 (58.23***)

0.93 1.07 1.10 0.78 5.45

−.07*** .024 .07*** .022 .10*** .026 −.24*** .079 1.67 .182 1.57*** .068 − 503.86 (55.04***)

0.93 1.07 1.10 0.79 5.31

−.08*** .024 .07*** .022 .09*** .026 −.22*** .079 1.65*** .182 1.57*** .068 − 500.42 (61.92***)

0.93 1.07 1.10 0.80 5.19

***p b .01, **p b .05, *p b .1. a = Deferral factor = exp(coeff.).

purchases (p b .05). This finding indicates that participants do not expect strong price decreases in the future, which implies that longer waiting is not worthwhile (Kalyanaram and Little 1994). Model 1 shows a marginal significant negative effect (p b .10) of the interaction term between trend and variance on purchase timing. Hence, a high variance of past prices reduces the strong direct effect of the trend, which increases the probability to buy earlier (hypothesis H3b). To obtain a complete picture of the effects of the price charts, we re-estimated the above described survival models by including the reference group without price charts. In particular, we entered dummy variables for each of the chart version groups, while the control group served as the reference category. Estimation results show that all price chart groups, except for those exposed to chart version 2 and chart version 5 with low trend low variance, respectively, would significantly defer their purchase time as compared to the control group (p b .05). This additional analysis underlines the effects of strong decreasing trends and high variance of past prices on purchase timing decisions. Summary Study 1 Results of Study 1 clearly show that a price chart of a product's price history is perceived as relevant information for purchase timing decisions. Further, this information induces strong reference price effects in terms of adjusting consumer price expectations. Participants lower or raise price expectations depending on chart characteristics. In particular, a strong downward trend, high variance and a high range of past prices lead to lower price expectations with a strongest impact on long-term price expectations. Further, the trend and variance cause participants to postpone their purchase;

put differently, they potentially trigger strategic buying behavior. Comparisons of the treatment groups and control groups confirm significant differences in price expectations and purchase timing. Study 2 The Impact of Visualization As discussed in the introductory section, the chart itself not only includes price information but also represents this information in a graphical manner. Studies in behavioral finance show that the visualization of past prices itself leads to a change in investment decisions. Hence, it is necessary to assess the impact of the graphical representation of price histories on price expectations and purchase timing decisions to fully understand the effects of price charts provided by shopbots. The aim of Study 2 is therefore to disentangle the effects caused by reference price histories from effects caused by their visualization. In general, charts are spatial problem representations, since they present spatially-related information. Charts are expected to be both faster and more accurate decision sources than, for instance, tables (Vessey 1991). According to Larkin and Simon (1987), charts preserve explicit information about the topological and geometric relations among the components of the problem; that is, they emphasize information about relationships in data. Seminal research reports empirical evidence in support of the notion that line charts especially facilitate the recall of trends (Vessey 1991; Washburne 1927). The tabulated representation of data, in contrast, facilitates the recall of specific amounts. Therefore, line charts are preferred whenever it is important for consumers and/or investors to quickly and easily recognize characteristics of data such as trends, volatility and functional relations. Thus, we expect that in particular,

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trend and variance in a price history are more visible in a linechart than in a table presentation. Accordingly, we expect that trend and variance exert stronger effects on price expectations and purchase timing when visualized in a price chart. The influence of the price range, however, should not be affected by its presentation format since the start- and endpoints are easily observable independent of the presentation format. Overall, this discussion leads to the following hypothesis: H5. The visualization of the price trend and price variance in a line chart as compared to the presentation in a table leads to a) a stronger downward shift in price expectations and b) a postponement of the purchase time.

Methodology To assess the influence of the visualization of a product's past prices in a line chart, we gathered additional data in an online experiment from N = 399 people who were exposed to the experimental setting and questionnaire used in Study 1. The major difference is that the notebook's price history was presented in a table instead of a chart. Similar to Study 1, 81% of the participants indicated that they regularly use shopbots for searching for offers. In addition, 38% of these shopbot users use price chart information provided by these shopbots. To guarantee fair comparison between both studies, bi-weekly prices were shown in the table. This interval was chosen because it corresponds to the intervals on the horizontal axis in the chart conditions from Study 1. Everything else (i.e., dependent and independent variables) were measured in the same way as in Study 1. Fig. 3 shows two examples with the prices corresponding to the chart versions 1 (that is, low range, strong trend and low variance) and chart version 2 (that is, low range, weak trend and low variance). Results In this section we first report the empirical results of table conditions. Second, we explicitly test hypothesis H5, i.e., we show how the visualization itself (chart vs. table) affects price expectations and purchase timing. The Influence of Tabulated Price Histories on Price Expectations and Purchase Timing As in Study 1, participants show no significant differences in their a priori price expectations (PEtbefore). ANOVA and pair

Fig. 3. Example tables.

wise comparisons also show no significant differences between the treatment (tables, charts) and control (no price history) groups. Hence, participants again express homogeneous price expectations before they are exposed to the price history. Corresponding to Study 1, we estimate MANCOVA through a multivariate generalized linear model (GLM) to investigate the influence of the price history characteristics on price expectations (after respondent's were exposed to the table conditions). The results show multivariate main effects for for the range (Wilks' Lambda = .97, F(3) = 4.26, p b .01) and the trend (Wilks' Lambda = .93, F(3) = 10.01, p b .01). Furthermore, the parameter estimates of the GLM confirm that a strong downward trend and a high range of past prices (p b .01) shift down all three price expectation measures (weeks 4, 8, and 12). However, compared to the chart condition in Study 1, the results show no significant effects of the variance and the interaction effect between variance and trend on price expectations. Furthermore, estimating the corresponding survival models for purchase timing reveals only marginal negative directs effects (p b .10) for the range in Model 2 and Model 3. Together, these results indicate that the visualization itself might have an influence on the strength of the effects in Study 1. This seems particularly plausible because no direct effects of the price history characteristics on purchase timing are apparent in the table condition, whereas the trend and range still have an influence on price expectations. Further, the results show no significant effect of the variance on price expectations indicating that this characteristic is difficult to recognize in a table. Accordingly, we explicitly test hypothesis H5, i.e., the effect of the graphical representation (visualization) on price expectations and purchase timing in the next section. The Impact of Price History Visualization on Price Expectations and Purchase Timing Since we employed a between subjects design in Study 1 and Study 2 we are able to directly assess the influence of the visualization effect itself. In particular, we re-estimate the models presented in Study 1 by incorporating participants from both studies (N = 826). Hence, we introduce the visualization as an additional factor (1 = chart, 0 = table) into the analysis. Examining the interaction effects between price history characteristics and whether they are presented in a chart or a table should reveal the effect of the visualization. With respect to the chart characteristics the MANCOVA part of the estimation procedure shows multivariate effects only for the range (Wilks' Lambda = .98, F(3) = 4.32, p b .01) and the trend (Wilks' Lambda = .96, F(3) =10.40, p b .01). In addition, the covariate chart-user exhibits a multivariate main effect (Wilks' Lambda = .98, F(3) = 5.75, p b .01). However, results of the GLM part in (Table 5) reveal deeper insights into the effect of the visualzation on the three price expectation measures (PEt). Estimation results show that the visualization of price history characteristics as a chart increases the effects of a strong downward trend for PE8 and PE12 (Chart ∗ Trend; p b .05). This effect is stronger for long-term price expectations (week 12) than for short-term expectations (week 4 and week 8). Thus, hypothesis H5a is in generally supported with regard to the trend

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Table 5 Influence of the visualization of past prices on price expectations. Price Expectations (PEt) Week 4

Week 8

Week 12

Coefficient

St.-Error

Coefficient

St.-Error

Coefficient

St.-Error

Price history characteristics Range Trend Variance Trend ∗ variance

−.01** −.02*** .00 .00

.005 .008 .008 .011

−.02*** −.05*** .00 .01

.007 .010 .010 .014

−.03*** −.07*** −.01 .02

.009 .013 .013 .019

Visualization Chart vs. table Chart ∗ range Chart ∗ trend Chart ∗ variance Chart ∗ trend ∗ variance

.01 .00 −.02 −.02 .02

.008 .007 .010 .010 .015

.01 .00 −.03** −.02 .03

.011 .010 .014 .014 .020

.02 .01 −.04** −.02 .03

.015 .013 .018 .018 .026

Covariates Notebook expertise Price chart user Constant R2(Adj.)

.00 .00 −.03*** .08(.07)

.001 .004 .008

.00 .01* −.06*** .15(.13)

.002 .006 .011

.00 .02*** −.09*** .15(.17)

.002 .007 .000

***p b .01, **p b .05, *p b .1.

effect. As expected, the effect of the price range (p b .05) is independent of visualization. Similarly, the results show no significant direct and indirect effects of the variance on price expectations, indicating that this effect vanishes under the nongraphical condition. Table 6 shows the effects of visualization

on purchase timing decisions estimated by use of a survival model, as in Study 1. The survival models in Table 6 show that the direct effects of trend and variance are not significant, which indicates that they are mainly driven by the way they are presented to participants.

Table 6 Influence of the visualization of past prices on purchase timing. Model 1

Model 2

Model 3

Coefficient

St.-error

DFa

Coefficient

St.-error

DFa

Coefficient

St.-error

DFa

Price history characteristics Range Trend Variance Trend ∗ variance

−.11 −.06 −.08 .10

.072 .101 .101 .142

.89 .94 .93 1.10

−.13* −.08 −.07 .08

.072 .101 .101 .142

.87 .92 0.93 1.09

−.15** −.10 −.07 .09

.072 .101 .100 .141

.86 .91 .93 1.09

Visualization Chart vs. table Chart ∗ range Chart ∗ trend Chart ∗ variance Chart ∗ trend ∗ variance

−.29*** .11 .34** .38*** −.33*

.112 .099 .139 .141 .199

0.75 1.11 1.40 1.46 .72

−.28** .12 .31** .37*** −.29

.112 .099 .139 .140 .199

.75 1.12 1.36 1.44 .75

−.27** .11 .29** .36** −.27

.112 .099 .139 .140 .199

.76 1.12 1.33 1.43 .76

Price expectation PE4 PE8 PE12

−.21 – –

.455 – –

.81 – –

– −.89** –

– .362 –

– .41 –

– – −.92***

– – .278

– – .40

Covariates Notebook expertise Deal proneness Perceived expensiveness Price chart user Constant θ (shape parameter) LL (χ2)

−.08*** .017 .06*** .017 .12*** .019 −.17*** .057 1.89*** .141 1.50*** .047 − 1006.86 (100.03***)

.93 1.07 1.13 .84 6.65

−.08*** .017 .07*** .017 .12*** .019 −.17*** .057 1.85*** .141 1.51*** .047 − 1003.83 (106.35***)

.92 1.07 1.12 .85 6.35

−.08*** .017 .06*** .017 .12*** .019 −.15*** .057 1.83*** .141 1.51*** .047 − 1001.21 (111.58***)

.92 1.07 1.12 .86 6.22

***p b .01, **p b .05, *p b .1. a = Deferral factor = exp(coeff.).

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Indeed, these results confirm that in all three survival models, the visualization of the trend (strongly decreasing) and variance (high) as a chart increases the tendency to defer the purchase (interaction terms; p b .05). Hence, hypothesis H5b is supported. Similar to Study 1, we again find that the variance exerts the strongest impact on purchase timing decisions. Fig. 4 shows that in the case of a price chart with a strong downward trend, the survival model (model 3)predicts that participants would in general defer their purchase by 41.1 % as compared to the control group without a price history. Further, the predictions show that 16.9 percentage points can be attributed to the visualization. In the case of a price chart with a high variance, this effect becomes much stronger. In particular, 20.3 percentage points of the total effect can be attributed to the visualization, which is nearly the half of the total effect. Hence, 50% of the total effect is caused by the price history information and 50% by the visualization.

i.e., a large amount of price decline, leads to a downward shift in price expectations. Further, the influence of price trends is moderated by the variance of the past price series. Overall, the results indicate that the strength of the impact of chart characteristics increases with long-term price expectations. Study 2 shows that the previously discussed effects can partly be attributed to the visualization of price history. The visualization especially exhibits strong effects on consumers' purchase timing decisions. In particular, for a strong downward trend, 16.9 percentage points of the total effect of the price history is caused by visualization, which corresponds to 41.1% of the total effect. Under the condition of high variance, this effect is much stronger and reaches 20.3 percentage points, which is nearly 50% of the total effect. Hence, the trend and variance of the price charts are the most important chart characteristics. Implications

Summary Study 2 Study 2 reveals that the negative impact of the strong downward trend of a notebook's past prices on price expectations can partly be attributed to its visualization. Furthermore, the visualization of the trend and variance exhibits a strong influence on a consumer's purchase timing decisions. This means that not only the information about past prices influence consumer decision-making but also the graphical presentation itself. Discussion General Findings This research investigates whether price charts regarding a product's price history induce reference price effects and how chart characteristics affect consumer price expectations and purchase timing decisions. The results of Study 1 show that the price chart is perceived as highly relevant information with which to form price expectations and to plan purchase time. We find that charts illustrating a product's price history induce strong reference price effects. This means that consumers adjust their prior price expectations according to chart characteristics. Like investors, consumers especially follow price trends in predicting prices and planning purchase timing. Besides a strong downward trend and high variance, a high range of past prices,

From a consumer's perspective, the results of this study show that price chart information is especially valuable in situations in which the price history indicates a stronger future decrease and high variance of prices than consumers would have expected. When charts indicate that prices will further decline, this information is incorporated into consumer expectations and helps them to maximize the transaction utility, i.e., to predict prices and to purchase when the price meets or falls below the future expected price. Hence, from a consumer's perspective price, charts provided by shopbots are valuable information that serves as an anchor to support consumer purchase decisions. For this reason, the price chart feature is positively evaluated by, for instance, the press when comparing and recommending different shopbot sites (SmartMoney 2010). Based on the above discussion, it is obvious that from a shopbot's perspective, the provision of price charts is worthwhile because it is perceived as containing valuable information from a consumer's and a public policy maker's perspective. Hence, we recommend that shopbots provide price charts in order to increase their popularity. From a manufacturer's and retailer's perspective, however, consumer reactions to the price chart information can have a serious impact on sales and profits. This is because consumers adjust their reference prices depending on the price chart pattern. On the one hand, price charts can help to uphold price expectation levels, but

Fig. 4. The impact of price history visualization on purchase timing.

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on the other hand, they can accelerate anticipated price declines. This problem is especially relevant for durable products with typical life cycle pricing patterns, since price expectations exhibit a strong influence on price elasticities. Reference price effects triggered by price chart information should therefore affect price elasticities in the market, which subsequently affect retailer and manufacturer sales. The reason behind this rationale is that price chart information is especially valuable for imitators and followers in the adoption process of durables, since they normally buy at a later stage and at a lower price level in a product life cycle. Hence, durables face increasing absolute price elasticities until the end of their product life cycle (Parker and Neelamegham 1997). When price chart information leads to a change in price expectations, these dynamics affect when manufacturers break even because chart information can lead to a shift in periodic sales due to increased strategic planning of the more price sensitive segment of followers in the market. Since this segment is normally responsible for the bulk of the purchases over the whole life cycle of a product, this problem also affects retailer margins because they often acquire products at later times for a reduced price from the manufacturer. Nevertheless, it is important to note that retailer competition in a shopbot setting especially drives price chart patterns. This is due to the fact that retailers mostly compete on their rank in the price comparison table of actual prices since it is possible to gain sales by implementing a small price decrease (Iyer and Pazgal 2003; Smith 2002). Hence, we recommend retailers to balance the effect of constantly reducing prices at present with the long-term effect caused by this strategy on sales and profit development. In summary, the results of the study show that the initial price expectations of participants are homogeneous and that depending on chart characteristics, participants adjust their price expectations. Manufacturers and retailers should infer from shopbot sites how market price expectations develop over time and should incorporate this information in their dynamic pricing strategy. However, as long as not all consumers use price history information about a product's price history, there is still some uncertainty about the market price expectations. Hence, it would be useful to make price history information available to all consumers in the market. Limitations and Future Research In this study, we find significant effects of price charts on purchase timing and expected future prices. To isolate the chart effects, we eliminated dynamic effects such as consumer learning, brand choice, brand switching and shopbot and retailer switching, which could also be triggered by viewing the price chart. A first direction for future research would therefore be to assess the influence of this additional information on consumer behavior and on the profitability of shopbots, retailers and manufacturers. It would also be worthwhile to identify product categories that link the strengths of these effects. Further, one could extend future research to another setting in which the supply of the product category is limited. For instance, Microsoft's Bing travel website (http://www.bing.com/travel/) provides graphs of historical prices for flight tickets. The goal of this service is to predict air travel prices and offer the consumer advice about when to purchase a ticket. Second, it would be useful to analyze a shopbot's

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transaction data in a real-world setting. Such a setting would allow researchers to measure the actual effects of purchase postponement on conversion or click rates. Third, it would be interesting to incorporate the effect of price charts in demand models in order to derive optimal pricing decisions. When modeling reference prices through first-order exponential smoothing, a systematic error occurs when a trend is present. According to our findings concerning the relevance of price trends, it would be interesting to model reference prices through second-order exponential smoothing. Fourth, since we focus on the most popular product category on shopbot sites that normally experiences significant price decreases over time, we only used declining price charts in the experiments. However, future research could extend our research by using categories with price increases as well as decreases (e.g., airline tickets). Finally, it would be interesting to investigate the effect of the price chart information on consumer replacement decisions, especially in technology markets (Gordon 2009).

Acknowledgements The authors gratefully acknowledge many helpful comments from the editors and the two anonymous reviewers. Appendix

Coefficient alpha Construct Deal proneness

Scale items

I wait until there is an advertised sale before going to shop at a mall. I hunt around until I find a real bargain. Notebook I regularly use notebooks. expertise I am very familiar with notebooks. I would call myself a notebook expert. Shopbot The information provided by information the shopbot was relevant for relevancy the purchase timing task. The information that was provided by the shopbot would help me in making purchase timing decisions. The information provided by the shopbot aided me in completing the purchase timing task. Perceived The actual price expensiveness of the notebook is high. The actual price of the notebook is expensive.

Source

Study Study 1 2

Roy (1994)

.73

.71

Roehm and Sternthal (2001)

.73

.77

Mason et al. .90 (2001)

.90

Yoo, .85 Donthu, and Lee (2000)

.80

Note: All scales ranged from 1 = “Totally disagree” to 7 = “Totally agree”.

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