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like PriceGrabber.com and YahooShopping.com are the most Consequently, price charts should support consumers when

Journal of Interactive Marketing 25 (2011) 95powerful tools for consumers to easily compare prices and findoffers for desired products. As such, shopbots reduce the cost ofsearch for information about products and facilitate better andmore efficient purchase decisions (e.g., Hubl and Trifts 2000;Trifts and Hubl 2003).

Besides providing distributions of actual prices for anyproduct in the form of price comparison tables, shopbots likeNexTag.com, PriceScan.com, and Skinflint.co.uk have recentlyintroduced line charts displaying a product's full price history.

forming expectations about future prices. Research shows thatprice expectations and purchase timing are conceptually related(Danziger and Segev 2006). Price history information includedin the price chart could therefore enforce strategic buyingbehavior with respect to buying now or later. Since studies inbehavioral finance show that some investors base their buy orsell decisions depending on certain chart patterns of past stockprices (Park and Irwin 2007), one might also expect thatvisualization itself has an effect on consumer expectations andthe provision of past prices leads to strong adjustments of price expectations depending on price chart characteristics. In particular, the trend, variance andrange of past prices in the chart strongly affect price expectations and purchase timing decisions. Furthermore, in the case of a strong downward trend andhigh 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 toefficiently compare offers is one of the most popular reasonsfor consumers to shop on the Internet, as it is cited by 62% ofthose surveyed. Thus, online price comparison sites (shopbots)

while on pricescan.com, it is called price trend graph. Mostfrequently, such charts show the minimum prices for a productacross different retailers over time (see Fig. 1).

Information on a product's price history is a source ofexternal reference prices and should therefore stimulateconsumer behavior (Kopalle and Lindsey-Mullikin 2003).Do Price Charts Provided by Onlineand Purchase T

Wenzel Drechsler

Strothoff Chair of Retailing,

Available onlin

Abstract

Online price comparison sites (shopbots) like PriceGrabber.com are thfor desired products. Besides providing distributions of actual prices in pprice charts (line charts) displaying a product's full price history. Price cNevertheless, it is currently unclear how price charts influence consumer pNexTag.com, for instance, calls this feature price history,

Corresponding author at: Strothoff Chair of Retailing, Goethe UniversityFrankfurt, Department of Marketing. Grueneburgplatz 1, 60323 Frankfurt,Germany.

E-mail addresses: wenzel.drechsler@wiwi.uni-frankfurt (W. Drechsler),natter@wiwi.uni-frankfurt.de (M. Natter).

1094-9968/$ - see front matter 2011 Direct Marketing Educational Foundation,doi:10.1016/j.intmar.2011.02.001

Downloaded from http://www.elearnica.iropbots Influence Price Expectationsing Decisions?

& Martin Natter

iversity Frankfurt, Germany

2 March 2011

ost powerful tools for consumers to easily compare prices and find offerscomparison tables, shopbots like NexTag.com have recently introduceds should support consumers in forming expectations about future prices.e expectations and purchase decisions. The results of this study show that

109www.elsevier.com/locate/intmarpurchase timing decisions. Depending on price charts char-acteristics and, therefore, price history, consumers potentiallyform and/or adjust expectations about future prices, which inturn influence purchase timing decisions.

Information on the most attractive product categories onshopbot sites reveals that consumers use shopbots especially forfinding the best offers in the category of consumer durables,

Inc. Published by Elsevier Inc. All rights reserved.

opb

Inteparticularly consumer electronics such as notebooks, TVs ordigicams. 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 sitesthat provide information about a product's price history cangain easy access to information about a product's life cyclestage. Although consumers expect prices to decrease over aproduct's life cycle especially in high-tech markets (Bridges,Yim, and Briesch 1995), the price chart makes this informationmore apparent to consumers.

Understanding the response of consumers to price chartinformation should be relevant for shopbots, retailers andmanufacturers. When price charts are perceived as relevantinformation, shopbots could increase their popularity. Researchshows that with respect to purchase timing, a change in priceexpectations has a strong influence on demand elasticities (Erdemet al. 2005). Hence, retailer and manufacturer sales and profits arepotentially affected by consumer reactions to price charts.

To the best of our knowledge, there is no study that analyzesthe effect of price history charts on consumer decision-making.

Fig. 1. Examples of sh

96 W. Drechsler, M. Natter / Journal ofThe introduction of a product's full price history visualized ina line chart introduces two types of information, namely,1) historical information about prices and 2) a graphical displayof this information; as such, it is currently unclear how price chartsinfluence consumer expectations and purchase decisions.

Based on the foregoing discussion, it is the aim of this studyto explore the effects of price charts on consumer expectationsregarding durables prices and purchase timing decisions. Inparticular, this study investigates whether the introduction ofprice charts (that is, price history) induces reference price effectsin terms of adjusting a consumer's price expectation. Further-more, this study analyzes the impact of price chart character-istics on consumer price expectations and purchase timing anddisentangles the effects of reference price histories from effectsdue to their visualization.

This study contributes to several fields of research. First, withregard to reference price research, this study extends the analysisof the impact of external reference prices at a certain point in timeto an analysis of a whole series of external reference prices overtime that are captured and ordered in a single source. Second, thestudy enhances knowledge about reference price effects ondurable goods purchase timing (Mazumdar, Raj, and Sinha 2005).Third, the results of this study update current knowledgeconcerning the relation between the presentation of informationat shopbot sites and consumer price perceptions (Smith 2002).Finally, this study contributes to the field of research on visualrepresentation and decision-making (Lurie and Mason 2007).

To test our hypotheses, we conducted experiments in whichparticipants were asked to state their price expectations andpurchase timing decisions after viewing a particular price chartcondition, which we had manipulated for our purposes. Further,we also tested the effects of the same price histories on priceexpectations and purchase timing when presented in a non-graphical manner to explore the effects of the visualization.

The results show that in general, the price charts induce strongreference price effects. Further, the trend, variance/volatility andamount of price decline (i.e., range) shown in a chart exert a stronginfluence on price expectations. The trend and variance alsoexhibit a strong influence on purchase timing decisions. In general,results indicate that the strength of the impact of the chart

ots with price charts.

ractive Marketing 25 (2011) 95109characteristics increases with long-term price expectations.Finally, the results clearly demonstrate that the graphicalrepresentation itself enforces the impact of the price trend andvariance on consumer price expectations and purchase timingdecisions.

The remainder of this article is organized as follows. First,we discuss the related literature and derive the underlyinghypotheses. Second, we present the outline and results of Study 1,which analyzes the influence of price charts on price expectationsand purchase timing.We then present Study 2, which assesses theeffect of graphical representation. The last section summarizes theresults, discusses managerial implications and presents avenuesfor further research.

Related Literature

Consumer Price Expectations for Durables

Expected future prices are particularly important for allproduct categories that experience significant price changes

Inteover time like consumer durables (Ofir and Winer 2002). Sinceprice decreases over the product life cycle are fairly commonamong durable products, they are widely anticipated bypurchasers of durable products (Balachander and Srinivasan1998). Marketing literature suggests that consumers formexpectations regarding a product's attributes (most notablyprice) based on historical patterns for the attributes of theproduct category; they then incorporate these expectations intheir purchase decisions (Bridges, Yim, and Briesch 1995).

There is evidence that consumers form forward-lookingexpectations that affect consumer durable purchases (Winer1985). Consumers expect price declines due to experience curveeffects and plan or delay their purchases accordingly (Doyle andSaunders 1985). This discussion suggests that due to priorexperiences, consumers develop mental schemas or a set ofexpectations about a product category (Sujan, Bettman, andSujan 1986). Erdem et al. (2005), for instance, find that in thecase of PCs, consumers generally expect a steady-state rate ofprice decline. Further, they find that consumers seem to expectmean reversion in price declines; i.e., if the decline over the pastfew months was greater (or less) than normal, then consumersexpect a lesser (greater) price decline over the next few months.

However, since durables have longer interpurchase timesthan frequently purchased packaged goods (FPPG) consumersare normally less informed about the development and changesof attribute configuration, technology, and price level of adurable. The information acquired during prior purchaseoccasions is therefore less salient in the formation of priceexpectations for a durable product than it is for a FPPG(Mazumdar, Raj, and Sinha 2005). With the introduction ofinformation about a product's price history displayed in a pricechart, consumers no longer have to rely on likely obsolete priceknowledge based on prior experience when forming priceexpectations for purchase timing of durables. Therefore, it isessential to investigate whether the introduction of price chartsinduces reference price effects in terms of adjusting prior priceexpectations and how different chart patterns affect priceexpectations and purchase timing.

Price Chart Information and Consumer Purchase Decisions

In the marketing literature, the influence of price historyvisualized in a line chart and chart pattern characteristics onconsumer decisions is not discussed.

Research on behavioral finance suggests that investors basetheir investment decisions on specific stock price chartcharacteristics. Standard economic theory, however, assignslittle informative value to stock price charts due to the randomwalk assumption (Brealey, Myers, and Allen 2007). Accord-ingly, historical price movements shown in a chart should notpredict how a stock will behave in the future. Nevertheless,research shows that some investors base their investmentdecisions on specific stock price-chart characteristics. Muss-weiler and Schneller (2003) show that investors use salient

W. Drechsler, M. Natter / Journal ofstandards in price charts such as salient highs or lows for theirfuture stock price expectations and, thus, for investmentdecisions. Additionally, Benartzi and Thaler (1999) show thata varying time horizon for past performance charts induces asignificant 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 pricecharts. Indeed, many investors practice technical analysis ofprice chart patterns (Leigh et al. 2002), which presumablyidentifies patterns in price charts that may offer an indication ofwhether a trend is likely to continue or terminate (Park andIrwin 2007). Of course, such a possibility is in principle ruledout by the random walk assumption, and so many researchersare quite skeptical of these ideas. However, practitioners usethem routinely for trading (Caginalp and Balenovich 1996), andso the price chart can be regarded as standard information forinvestors. Typically, technical analysis is operationalizedthrough trading rules of the following form: If chart patternX is identified then buy/sell within/after the next N tradingdays (Leigh et al. 2002). This means that by using chartinformation, investors try to maximize their profits and hencethe economic value of their transactions.

Likewise, in the case of durable products, consumers also tryto maximize the economic value of their transaction, since theytypically make a trade-off between buying now or later whenthe price level has reached a certain level. In particular, they tryto maximize transaction utility. This transaction utility increaseswhen the price meets or falls below an expected price (Darkeand Chung 2005). Hence, price charts on a product's pricehistory should also support consumers when forming futureprice expectations and when making purchase decisions.

However, there is no study available that provides insightson the influence of price charts on consumer price expectationsand purchase decision for products that are priced according totheir stage in the life cycle.

Study 1

Expected Effects and Hypotheses Development

Reference Prices Anchoring and AdjustmentAccording to adaptation-level and assimilation-contrast

theories, price information (or external reference prices) affectsconsumer perceptions when it is judged acceptable or plausiblerelative to the internal price standards of consumers (Monroe2003; Urbany, Bearden, and Weilbaker 1988). These processesalso occur when consumers are exposed to a price that is outsidethe expected range but still plausible. Instead of rejecting thisprice information outright, consumers assimilate and reduce itto a level more reasonable for the product category (Urbany,Bearden, and Weilbaker 1988). Taken together, these pointsindicate that consumers who encounter new price informationtend to update their prior (Yadav and Seiders 1998) priceexpectations. Hence, we expect that consumers use price chartsshowing the development of past prices to update (adjust) theirprior price expectations.

97ractive Marketing 25 (2011) 95109Influence of Chart CharacteristicsA key feature of high-tech durables is the tendency for prices

to fall quickly over time, creating an incentive to delay

comparison of a market price to the endpoints of an evoked

timing. In particular, we propose that the difference between thefirst and last price in the chart (that is, the range) affects price

Intepurchases. The strength of this incentive depends on consumerforecasts of how quickly prices will drop (Erdem et al. 2005). Inthe formation of price expectations for durables, consumersespecially use product histories if a trend exists (Mazumdar,Raj, and Sinha 2005). In general, consumer expected prices fortime period t are then equal to the current price plus a fractionreflecting the difference between this period's price and lastperiod's price (i.e., extrapolative expectations). This means thatconsumers update their price expectations by factoring in a pricetrend observed from prior prices (Mazumdar, Raj, and Sinha2005).

Therefore, we expect that consumers who systematicallyprocess chart information use trend characteristics (that is, weakvs. strong downward trends) as a salient standard (i.e., chartcharacteristic) to form their expectations and make theirdecisions. If consumers notice a strong decrease in price fromone week to the next, they may expect even lower prices in thefuture and decide to acquire the product only if a certain price isreached (Kalyanaram and Little 1994). In the case of a weakdecrease in which the trend of past prices levels off (i.e., long-term stationarity of price series), consumers instead mayforecast no further price decreases such that postponing doesnot seem worthwhile. These points lead to the followinghypothesis:

H1. Price charts with a strong downward price trend comparedto price charts with a weak downward trend lead to a) adownward shift in price expectations and b) a postponement ofthe purchase time.

Besides trend characteristics, we expect that the variance in achart (i.e., price fluctuations from one point to another) isanother important chart characteristic. Research shows thatconsumers might use the variance of observed prices as aheuristic for price search behavior (Darke, Chaiken, andFreedman 1995). The underlying idea is that the more varianceconsumers observe in prices, the more likely they will be tocontinue to search. They defer their purchase decision in thebelief that further searching will pay off. Kalwani and Yim(1992) also provide empirical evidence that consumers expectprices to strongly decrease in light of frequently-changingprices. This leads to the following hypothesis:

H2. Price charts with a high variance in past prices compared toprice charts with a low variance in past prices lead to a) adownward shift in price expectations and b) a postponement ofthe purchase time.

Besides expecting direct effects of the trend and variance onprice expectations and purchase timing, we further assume thatthese two chart characteristics are not perceived independently.That is due to the fact that determining a trend in a set of pastprices requires a consumer to interpolate between a large number

98 W. Drechsler, M. Natter / Journal ofof data points (Vessey 1991). If, however, the variance of thesedata points increases, it becomes more difficult to interpolate inorder to infer the underlying trend correctly. Put differently, theexpectations because it establishes a benchmark for theevaluation of the actual price. If the range between the firstand last prices in the price history increases, consumers shouldevaluate the last price (i.e., actual price) more favorably, as itindicates a higher gain within a certain time frame. Due to theassumption of extrapolative expectations, consumers shouldconsequently also expect higher rates of price decline in thefuture. Thus, this leads to the following hypothesis:

H4. Price charts with a high range of past prices compared toprice charts with a low range of past prices lead to a) adownward shift in price expectations and b) a postponement ofthe purchase time.

Methodology

To test the hypotheses, we conducted experiments in whichparticipants are asked to state their price expectations andpurchase timing decisions after viewing a particular price chartcondition that we manipulated.

Study Design ManipulationsAs indicated in the introductory section, consumers use

shopbots most frequently for gathering information about pricesfor durables, especially consumer electronics. In order todetermine the appropriate product category for the experiment,we therefore conducted a pre-test with 63 participants. Given theoverall goal of this study, the aim of the pre-test was to identify adurable product category for which purchase timing plays animportant role. Therefore, the participants rated on a 7-point scalethe importance of purchase timing for the three most popularelectronic product categories on Internet shopbots, namely,price range. Janiszewski and Lichtenstein (1999) show that in ahigh-range price situation in which the market price is nearer thelower endpoint, consumers judge the market price as moreattractive as compared to a low-range situation. In the context ofprice charts, we accordingly expect to find an impact of therange of the historical prices on price expectations and purchasetrend becomes less obvious in a highly volatile price series. Thisleads to the following hypothesis:

H3. A high variance in prices displayed in a price chartmoderates the effect of the chart's trend on a) price expectationsand b) purchase time.

According to range and rangefrequency theories (Parducci1965; Volkmann 1951), consumers judge actual prices not onlyby their location within the distribution of other prices but alsoby the perceived range at the time of judgment. Range theorypostulates that consumer price perceptions depend on a

ractive Marketing 25 (2011) 95109notebooks, MP3-Player/IPods and digicams. Results show thatdifferences in mean across all three categories are significant atpb .05, with the highest score for the notebook category

(MNotebook=6.06, SDNotebook= .83). Consequently, we use thenotebook category in the main study.

The time horizon of the price charts used in the experimentwasset to three months, since a typical lifecycle of a notebook modelis only about six months (Guide, Muyldermans, and VanWassenhove 2005). The time horizon of three months seemedappropriate because it neither indicates that the notebook is at theend of its life cycle, nor that it was recently introduced, whichcould cause the majority of consumers to wait for better prices.

The experimental design encompasses price chart manipula-tions 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 222 between-subjects design. Thetrend manipulations are given by the two most representative chartpatterns of notebook past prices on price comparison sites.

The procedure to assign realistic price levels and price patternsto the different chart conditions was as follows. First, we inferredinformation about the most popular notebooks in the market andtheir prices from Amazon's top 100 selling notebooks. Wecalculated a mean sales price as a rank-weighted sales price of thetop 100 notebooks. Calculation of the average notebook price

To create a realistic shopbot environment for all treatmentgroups with price charts and a control group (that is, without pricechart), we further constructed a price comparison table withcurrent prices for 10 competing retailers. In this price comparisontable, the minimum price of 850 corresponds to the last price inthe chart. Compared to the treatment groups, the control grouponly received the price comparison table of actual prices withoutinformation on the notebook's past prices. Other potentiallyavailable information, such as shipping fees and retailer ratings,were kept constant. This latter information was only provided forcreating a more realistic experimental environment. Hypotheticalbrands for the notebook, shopbot (namely, cheaper.com), andretailers (listed in the price comparison table) were used tominimize the potential participant's tendency to base theirdecisions on brand and retailer images or previous experiences(Kwon and Schumann 2001). Furthermore, the overall quality ofthe notebook and retailers were rated as very good to avoid theeffect of quality uncertainties.

Experimental ProcedureParticipants were asked to imagine that they have a desktop

99W. Drechsler, M. Natter / Journal of Interactive Marketing 25 (2011) 95109delivers a mean price of MPrice=828.66 (SDPrice=478.59).Based on this result, we assigned 850 as the actual lowest priceto the notebook in the experiment. Second, we ensured that pricedeclines follow realistic levels and patterns. Different shopbotsshow price declines for notebooks within a three-month periodranging from 10 to 30%. For the low-range condition, wetherefore assigned 1000 as a start price corresponding to a 15%price decline. Furthermore, 1150 was the price assigned to thehigh-range condition corresponding to a 26% price decline. Theintervals on the horizontal axis of the charts were adjustedaccordingly, holding all else equal. Examples for this manipula-tion in the low-range condition are presented in Fig. 2.Fig. 2. Exampcomputer they bought several years ago that is currently working.Theywere further told that this computer is technically out of dateand too inflexible and that they are thinking about buying anotebook in order to make their work more efficient. The aim offraming the story thiswaywas to prevent participants from feelingpressure that an immediate purchase was necessary.

In particular, the participants were told that they have decidedto buy a specific notebook because of its convincing priceperformance ratio. After providing them with the average offlineprice of this notebook they were exposed to additionalinformation about the desired notebook's current prices at ashopbot website and ask to make their final purchase timingle charts.

price of 850:

the level of trend is not perceived independently from the level of

Intedecision. To eliminate effects caused by financial constraints,participants were provided with a budget of 1000, whichexceeded the actual lowest price of the notebook (850 ).

In total, 531 people participated in an online experiment andwere randomly assigned to one of nine conditions, includingeight 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 exposedto a price comparison table of current prices and one of themanipulated price charts with the notebook's price history. Thecontrol group (N=104) only received the price comparisontable of current prices.

In the treatment groups, participants were asked twice to statetheir price expectations concerning the notebook's minimumprice development at price points of 4, 8 and 12 weeks later. Thisquestion first appeared after participants saw the price comparisontable alone; it appeared second after being exposed to the pricecomparison table and the price chart together. Finally, participantswere told to think about the optimal time to purchase thenotebook. In the control group, participants indicated their priceexpectations and planned purchase timing directly after seeing theprice comparison table. After participating in the experiment,participants filled out an online questionnaire. In order to controlfor individual differences between participants, we measuredimportant covariates that might affect price expectations andpurchase timing.

In particular, we controlled for participant usage and/orexperience with shopbots and the information they use onshopbots (e.g., price chart used = 1, not used = 0). In total, 79% ofparticipants indicated that they regularly use shopbots for findingoffers, whereas 31% of these shopbot users also use price chartinformation.

Regarding consumer psychographics (see Appendix), wecontrol for deal proneness and notebook expertise as literatureindicates that they are related to price expectations, timingdecisions 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'sevaluation of the current notebook price, as this might affectstudy results. In particular, participants should indicate theperceived expensiveness of the current notebook price.

Key MeasuresAs indicated in the previous section, the treatment groups,

which are providedwith price charts, were asked twice about theirprice expectations regarding the minimum price in the future. Inparticular, they were asked about the expected future minimumprices of the notebook in the upcoming 4, 8 and 12 weeks(PEtbefore). Updates of these three price estimates were obtainedafter participants received additional information about thenotebook's price history (PEtafter). Reference price effects aremeasured in terms of the adjustment of price expectations (PEt)in response to the chart as follows:

100 W. Drechsler, M. Natter / Journal ofPEt =PEtbeforePEtafter

PEtbefore;with t = 4; 8; 12 weeks 1variance.To check the relevance of price charts for purchase timing

decisions, participant perceptions of the relevancy of theshopbot information were assessed with three 7-point Likert-type scale statements (Mason et al. 2001). Higher scoresindicate that participants perceived the information provided asmore relevant information (coefficient alpha= .90). With respectto purchase timing, participants in the price chart conditionperceived the information provided by the shopbot as morerelevant (MChart =4.49) as compared to the control group, whoreceived the price comparison table only (MNo Chart =3.83,t=3.85, pb .01). This first result reveals that the price chart is auseful tool for consumers who are deciding when to buy aproduct.

Price Expectations Anchoring and AdjustmentANOVAs and pairwise comparisons between all eight

treatment groups show no significant differences in pricePEt =PEtafter850

;with t = 4; 8; 12 weeks 2

Since our experimental framing indicates that the notebookhas already been on the market 12 weeks, then 12 weeks intothe future would typically correspond to the end of its life cycle(i.e., 6 months). We therefore denote price expectations over thenext 12 weeks as long term for notebooks.

Purchase timing was measured with the question Given theinformation from cheaper.com, when would you most likelybuy this notebook? Responses were anchored at a weekly scalefrom 1=now up to 14=after 12 weeks.

Results

Manipulation Checks. To assess whether the levels of trendand variance in the charts were actually perceived as different, theparticipants in each treatment groupwere asked to rate the slope ofthe trend and the level of price fluctuations in the price chart on a7-point scale from 1 = low to 7 = high. Results of a 22 analysisof variance indicate that participants significantly perceivedifferences in slope (Mstrong trend=5.57 vs. Mweak trend=4.61, F(1,426)=131,9, pb .01) and variance (Mlow variance=3.26 vs.Mhigh variance=4.42, F(1,426)=103,94, pb .01) as intended.Furthermore, we find no significant interaction between ourindependent variables on the perceived level of variance (F(1,427)=2.58, pN .10). For the trend, however, results show asignificant interaction (F(1,427)=16.56, pb .01), indicating thatTo test the impact of chart characteristics on the level of priceexpectation and purchase timing, another measure wascalculated that relates future price expectations to the current

ractive Marketing 25 (2011) 95109expectations measured prior to the exposition to the chartinformation (PEtbefore). Results also show no significantdifferences between the control group's price expectations

correlation coefficients (ranging from .79 to .93, pb .01), wehave to control for this correlations (Tabachnik and Fidell,2007). Hence, we analyse the data by MANCOVA through amultivariate generalized linear model (GLM).

In addition to the price chart characteristics, two covariates(namely, notebook expertise and price chart usage=1/0) areincluded in the model estimation of each price expectationmeasure. In particular, we use the following underlying modelstructure to estimate the GLM:

PEt = t0 + t1Range + t2Trend + t3Variance

+ t4Trend Variance

+ t5NotebookExpert + t6ChartUser + t;PEt

t = 4; 8; 12

1

Results of the MANCOVA part indicate significantmultivariate main effects for the range (Wilks' Lambda= .98,

Inteand the treatment groups' prior expectations. Hence, partici-pants seem to have homogeneous expectations about the futuredevelopment of notebook prices. In particular, participants seemto expect a monthly steady-state rate of price decline of 3.8%.Table 1 shows the mean expected prices for the next 4, 8 and12 weeks.

After the treatment groups were exposed to the price chart,nearly all groups adjusted their price expectations. Table 2reports the percentage change in price expectations (PEt) forthe eight different chart version groups and shows whether thischange significantly deviates from zero.

Results clearly demonstrate that the different price chartslead to adjustments of price expectations in both directions. Forinstance, in the conditions including the strong downward trend(versions 1, 3, 5 and 7), participants significantly lower theirprice expectations for all three point estimates. The strongestdownward adjustments take place for the long-term priceexpectations (i.e., 12 weeks) ranging from 4.9% (version 3) to10.6% (version 5). In contrast, the low-trend condition leadsto an adjustment in the other direction. In particular, participantsof 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 chartsinduce reference price effects in terms of adjusting priceexpectations, which stresses the chart's relevance for purchasedecisions. Additional analyses that compare the price expecta-tions (PEt) of the different treatment groups with those of thecontrol group point in the same direction. For instance, controlgroup comparisons reveal the strongest differences for the long-term price expectations (i.e., 12 weeks). As compared to thecontrol group, chart version groups 1, 3, 5, and 7 showsignificantly (pb .05) lower price expectations ranging from5.2% (version 3) to 9.3% (version 3). Furthermore,participants of the chart version groups 2, 4 and 6 show higherprice expectations (pb .10) than the control group. Hence, theseresults indicate that consumers adjust their prior price expecta-tions depending on chart patterns.

Table 1A priori price expectations in euros (PEtbefore).

4 weeks 8 weeks 12 weeks

Mean 823 795 756Std. 42 54 74

N=427.

W. Drechsler, M. Natter / Journal ofInfluence of Price Chart CharacteristicsIn this section we test hypotheses H1 to H4, i.e., whether

different charts lead to different price expectations and purchasetiming decisions. Therefore, we first analyze the influence ofdifferent chart characteristics on price expectations (PEt) forweeks 4, 8, and 12. In a second step, we test the effects of pricecharts characteristics on participant purchase timing decisionswhile accounting for price expectations.

Effects of Price Chart Characteristics on Price Expecta-tions. Since the three dependent variables, i.e., the threedifferent price expectation measures for the upcoming 4, 8 and12 weeks are related to each other as confirmed by the Pearson

Table 2Adjusted price expectations.

Price expectations (PEt )

Chart version N Week 4 Week 8 Week 12

Range: low1) ST_LV 52 .038*** .073*** .086***

(6.68) (7.11) (5.02)2) WT_LV 51 .005 .020** .048**

(0.72) (2.06) (3.26)3) ST_HV 50 .015** .032*** .049***

(2.46) (3.28) (3.86)4) WT_HV 50 .014* .021* .048***

(1.72) (1.70) (2.79)

Range: high5) ST_LV 56 .037 .077*** .106***

(5.45) (6.05) (6.78)6) WT_LV 61 .016 .022** .033***

(1.42) (1.96) (2.32)7) ST_HV 55 .026*** .049*** .075***

(2.65) (4.75) (5.42)8) WT_HV 52 .023*** .029*** .028**

(3.12) (3.08) (2.21)

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

101ractive Marketing 25 (2011) 95109F(3)=2.92, pb .05), the trend (Wilks' Lambda= .83, F(3)=28.10, pb .01) and the interaction between trend and variance(Wilks' Lambda= .98, F(3)=2.90, pb .05) on expected prices.Further, the variable chart-user shows a significant multivariatemain effect (Wilks' Lambda= .98, F(3)=2.76, pb .05).

To get a more differentiated picture of the impact of differentchart characteristics on the three different price expectationmeasures we report the parameter estimates of the GLM inTable 3.

As expected, estimation results in Table 3 show that thedirect effects of a high range and a strong downward trend

chart characteristics and covariates according to (Bhattacharjeeet al. 2007)

= exp0 + 1Range + 2Trend + 3Variance + 4Trend Variance

+ 5PEt + 4

m=15+mCovariates +

0@

1A

Interactive Marketing 25 (2011) 95109significantly lower participants' price expectations (pb .01).Further, the negative effect of a high variance of past pricesseems to increase from week 4 (pb .10) and week 8 (pb .10) toweek 12 (pb .05). Hence, these results provide strong supportfor hypotheses H1a, H2a and H4a. Further, the results show astrong significant positive interaction (pb .01) between the trendand variance for price expectations in weeks 8 and 12 and amarginal effect for week 4 (pb .10). This finding supportshypothesis H3a, which states that the variance in a chartmoderates the influence between the trend and price expecta-tions. Overall, the results indicate that the magnitude of effectsincreases from week 4 to week 12. This means that the pricechart characteristics particularly influence long-term price

Table 3Influence of price chart characteristics on price expectations.

Price expectations (PEt)

Week 4 Week 8 Week 12

Coefficient St.-error

Coefficient St.-error

Coefficient St.-error

Chart characteristicsRange .01*** .005 .02*** .007 .03*** .009Trend .04*** .007 .08*** .009 .11*** .013Variance .01* .007 .02* .010 .03** .013Trendvariance .02* .010 .04*** .014 .05*** .018

CovariatesNotebookexpertise

.00 .002 .00 .002 .00 .003

Price chart user .00 .006 .01 .008 .02* .011Constant .07** .008 .11*** .012 .18*** .016R2 (Adj.) .11(.10) .18(.17) .22(.21)

***pb .01, **pb .05, *pb .1.

102 W. Drechsler, M. Natter / Journal ofexpectations.

Effects of Price Chart Characteristics on Purchase Timing.The response in the questionnaire concerning the independentvariable purchase timing allowed for the option of not buyingthe notebook within the upcoming twelve weeks. Indeed, 45(10.5%) participants stated they would buy the notebook afterthis time frame. Hence, our dependent variable is rightcensored, which implies the need for an event history modelingapproach to assess the influence of price chart characteristics onpurchase timing (Helsen and Schmittlein 1993).

Following Bayus (1998), who analyzes purchase behaviorfor personal computers, we use a parametric survival model. Inparticular, an accelerated failure time (AFT) model is used toestimate the effects of chart characteristics on purchase time t.Generally, we model the effects of the different chartcharacteristics and covariates on purchase time t as

S t = S0 t 2where the survivor function S(t) gives the probability that aparticipant would buy the notebook after some specifiedpurchase time t. S0(t) is the baseline survivor function and is termed acceleration factor which depends on the different3This model allows us to test hypotheses H1b to H4b, which

propose direct effects of price chart characteristics on the timeof purchase. Prior research indicates that price expectations andpurchase timing are conceptually related (Kalwani et al. 1990).Therefore, we control for price expectations by estimating themodel for each of the three different price expectation measuresPEt (t=4, 8, 12 weeks). In addition to the covariates included inthe price expectations models, we control for general dealproneness and the perceived expensiveness of the notebook'scurrent price.

In line with previous research on purchase timing decisions,we assume that the baseline survivor function S0(t) follows aWeibull distribution (Helsen and Schmittlein 1993; Seetharamanand Chintagunta 2003).1 For the estimation, the AFT model inEq. (2) is put into the log-linear formwith respect to purchase timet (Bradburn et al. 2003). The estimation results are presented inTable 4.

A positive coefficient indicates that increasing values of therespective independent variable lead to a delay of the purchase.By exponentiation of the coefficients, we further obtain the timeratio which can be interpreted as a deferral factor (DF) in ourcontext (Bradburn et al. 2003). DF indicates whether the chartcharacteristics lead to a delay of the purchase (DFN1) ascompared to the respective reference group. Results for Model 1with price expectations for four weeks as a control variablereveal strong significant effects for the trend and variance(pb .01), showing that a strong downward trend and a highvariance of price charts increase the probability that participantswould defer their purchase. In particular, the probability to deferincreases by 32% (DF=1.32) in response to a strong trend andby 34% (DF=1.34) in response to a high variance. Hence,hypotheses H1b and H2b are supported. These findings alsolargely hold for Models 2 and 3 (with price expectations for8 and 12 weeks, respectively). However, the effect of the trendseems to decrease fromModel 1(pb .01) to Model 3 (pb .10). Atthe same time, the effect of the price expectations increases.This result indicates that the variance in the chart is the maindriver of purchase timing decisions, whereas the direct effectof trend weakens when long-term price expectations areconsidered for purchase timing. In particular, higher long-term 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 Bayesianinformation criterion (BIC) confirm that the Weibull distribution best describesthe underlying purchase timing process. Further, the shape parameter of theWeibull distribution is highly significant (pb .01) and larger than 1 in all

models, indicating that the Weibull distribution is more appropriate to describethe underlying purchase timing process as compared to, for instance, anexponential distribution.

ode

oeff

.032**9**.20

.85*

.07*7**0**.24*.67.57*503

Intepurchases (pb .05). This finding indicates that participants donot expect strong price decreases in the future, which impliesthat longer waiting is not worthwhile (Kalyanaram and Little1994).

Model 1 shows a marginal significant negative effect(pb .10) of the interaction term between trend and variance on

Table 4Influence of price chart characteristics on purchase timing.

Model 1 M

Coefficient St.-error DFa C

Chart characteristicsRange .01 .068 0.99 Trend .27*** .095 1.32 .2Variance .29*** .095 1.34 .2Trendvariance .23* .134 0.79

Price expectationPE4 .12 .651 0.89 PE8 PE12

CovariatesNotebook expertise .07*** .025 0.93 Deal proneness .07*** .022 1.07 .0Perceived expensiveness .10*** .026 1.10 .1Price chart user .25*** .079 0.78 Constant 1.70*** .183 5.45 1 (shape parameter) 1.56*** .068 1LL (2) 502.26 (58.23***)

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

W. Drechsler, M. Natter / Journal ofpurchase timing. Hence, a high variance of past prices reducesthe strong direct effect of the trend, which increases theprobability 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 byincluding the reference group without price charts. In particular,we entered dummy variables for each of the chart versiongroups, while the control group served as the referencecategory. Estimation results show that all price chart groups,except for those exposed to chart version 2 and chart version 5with low trend low variance, respectively, would significantlydefer their purchase time as compared to the control group(pb .05). This additional analysis underlines the effects ofstrong decreasing trends and high variance of past prices onpurchase timing decisions.

Summary Study 1Results of Study 1 clearly show that a price chart of a

product's price history is perceived as relevant information forpurchase timing decisions. Further, this information inducesstrong reference price effects in terms of adjusting consumerprice expectations. Participants lower or raise price expectationsdepending on chart characteristics.

In particular, a strong downward trend, high variance and ahigh range of past prices lead to lower price expectations with astrongest impact on long-term price expectations. Further, thetrend and variance cause participants to postpone their purchase;put differently, they potentially trigger strategic buying behavior.Comparisons of the treatment groups and control groups confirmsignificant differences in price expectations and purchase timing.

Study 2

l 2 Model 3

icient St.-error DFa Coefficient St.-error DFa

.068 0.97 .04 .068 0.96

.098 1.24 .18* .099 1.19* .095 1.33 .27*** .094 1.32

.134 0.82 .18 .134 0.83

.483 0.43 .94** .369 0.39

** .024 0.93 .08*** .024 0.93* .022 1.07 .07*** .022 1.07* .026 1.10 .09*** .026 1.10** .079 0.79 .22*** .079 0.80

.182 5.31 1.65*** .182 5.19** .068 1.57*** .068.86 (55.04***) 500.42 (61.92***)

103ractive Marketing 25 (2011) 95109The Impact of VisualizationAs discussed in the introductory section, the chart itself not

only includes price information but also represents thisinformation in a graphical manner. Studies in behavioralfinance show that the visualization of past prices itself leadsto a change in investment decisions. Hence, it is necessary toassess the impact of the graphical representation of pricehistories on price expectations and purchase timing decisions tofully understand the effects of price charts provided byshopbots. The aim of Study 2 is therefore to disentangle theeffects caused by reference price histories from effects causedby their visualization.

In general, charts are spatial problem representations, sincethey present spatially-related information. Charts are expectedto be both faster and more accurate decision sources than, forinstance, tables (Vessey 1991). According to Larkin and Simon(1987), charts preserve explicit information about the topolog-ical and geometric relations among the components of theproblem; that is, they emphasize information about relation-ships in data. Seminal research reports empirical evidence insupport of the notion that line charts especially facilitate therecall of trends (Vessey 1991; Washburne 1927). The tabulatedrepresentation of data, in contrast, facilitates the recall ofspecific amounts. Therefore, line charts are preferred wheneverit is important for consumers and/or investors to quickly andeasily recognize characteristics of data such as trends, volatilityand functional relations. Thus, we expect that in particular,

trend and variance in a price history are more visible in a line-chart than in a table presentation. Accordingly, we expect thattrend and variance exert stronger effects on price expectationsand purchase timing when visualized in a price chart. Theinfluence of the price range, however, should not be affected byits presentation format since the start- and endpoints are easilyobservable independent of the presentation format. Overall, thisdiscussion leads to the following hypothesis:

104 W. Drechsler, M. Natter / Journal of InteH5. The visualization of the price trend and price variance in aline chart as compared to the presentation in a table leads to a) astronger downward shift in price expectations and b) apostponement of the purchase time.

MethodologyTo assess the influence of the visualization of a product's

past prices in a line chart, we gathered additional data in anonline experiment from N=399 people who were exposed to theexperimental setting and questionnaire used in Study 1. Themajor difference is that the notebook's price history waspresented in a table instead of a chart. Similar to Study 1, 81%of the participants indicated that they regularly use shopbots forsearching for offers. In addition, 38% of these shopbot users useprice chart information provided by these shopbots.

To guarantee fair comparison between both studies, bi-weeklypriceswere shown in the table. This intervalwas chosen because itcorresponds to the intervals on the horizontal axis in the chartconditions from Study 1. Everything else (i.e., dependent andindependent variables) were measured in the same way as inStudy 1. Fig. 3 shows two exampleswith the prices correspondingto the chart versions 1 (that is, low range, strong trend and lowvariance) and chart version 2 (that is, low range, weak trend andlow variance).

ResultsIn this section we first report the empirical results of table

conditions. Second, we explicitly test hypothesis H5, i.e., weshow how the visualization itself (chart vs. table) affects priceexpectations and purchase timing.

The Influence of Tabulated Price Histories on PriceExpectations and Purchase Timing

As in Study 1, participants show no significant differences intheir a priori price expectations (PEtbefore). ANOVA and pairFig. 3. Example tables.wise comparisons also show no significant differences betweenthe treatment (tables, charts) and control (no price history)groups. Hence, participants again express homogeneous priceexpectations before they are exposed to the price history.

Corresponding to Study 1, we estimateMANCOVA through amultivariate generalized linear model (GLM) to investigate theinfluence of the price history characteristics on price expectations(after respondent's were exposed to the table conditions). Theresults show multivariate main effects for for the range (Wilks'Lambda=.97, F(3)=4.26, pb .01) and the trend (Wilks' Lamb-da= .93, F(3)=10.01, pb .01). Furthermore, the parameterestimates of the GLM confirm that a strong downward trendand a high range of past prices (pb .01) shift down all three priceexpectation measures (weeks 4, 8, and 12). However, comparedto the chart condition in Study 1, the results show no significanteffects of the variance and the interaction effect between varianceand trend on price expectations. Furthermore, estimating thecorresponding survival models for purchase timing reveals onlymarginal negative directs effects (pb .10) for the range inModel 2andModel 3. Together, these results indicate that the visualizationitself might have an influence on the strength of the effects inStudy 1. This seems particularly plausible because no directeffects of the price history characteristics on purchase timing areapparent in the table condition, whereas the trend and range stillhave an influence on price expectations. Further, the results showno significant effect of the variance on price expectationsindicating that this characteristic is difficult to recognize in atable. Accordingly, we explicitly test hypothesis H5, i.e., theeffect of the graphical representation (visualization) on priceexpectations and purchase timing in the next section.

The Impact of Price History Visualization on PriceExpectations and Purchase Timing

Since we employed a between subjects design in Study 1 andStudy 2 we are able to directly assess the influence of thevisualization effect itself. In particular, we re-estimate themodels presented in Study 1 by incorporating participants fromboth studies (N=826). Hence, we introduce the visualization asan additional factor (1 = chart, 0 = table) into the analysis.Examining the interaction effects between price historycharacteristics and whether they are presented in a chart or atable should reveal the effect of the visualization. With respectto the chart characteristics the MANCOVA part of theestimation procedure shows multivariate effects only for therange (Wilks' Lambda= .98, F(3)=4.32, pb .01) and the trend(Wilks' Lambda= .96, F(3)=10.40, pb .01). In addition, thecovariate chart-user exhibits a multivariate main effect (Wilks'Lambda= .98, F(3)=5.75, pb .01).

However, results of the GLM part in (Table 5) reveal deeperinsights into the effect of the visualzation on the three priceexpectation measures (PEt).

Estimation results show that the visualization of price historycharacteristics as a chart increases the effects of a strongdownward trend for PE8 and PE12 (ChartTrend; pb .05). This

ractive Marketing 25 (2011) 95109effect 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

effect. As expected, the effect of the price range (pb .05) isindependent of visualization. Similarly, the results show nosignificant direct and indirect effects of the variance on priceexpectations, indicating that this effect vanishes under the non-

on purchase timing decisions estimated by use of a survivalmodel, as in Study 1.

The survival models in Table 6 show that the direct effects oftrend and variance are not significant, which indicates that they

Table 5Influence 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 characteristicsRange .01** .005 .02*** .007 .03*** .009Trend .02*** .008 .05*** .010 .07*** .013Variance .00 .008 .00 .010 .01 .013Trendvariance .00 .011 .01 .014 .02 .019

VisualizationChart vs. table .01 .008 .01 .011 .02 .015Chart range .00 .007 .00 .010 .01 .013Chart trend .02 .010 .03** .014 .04** .018Chartvariance .02 .010 .02 .014 .02 .018Chart trendvariance .02 .015 .03 .020 .03 .026

CovariatesNotebook expertise .00 .001 .00 .002 .00 .002Price chart user .00 .004 .01* .006 .02*** .007Constant .03*** .008 .06*** .011 .09*** .000R2(Adj.) .08(.07) .15(.13) .15(.17)

***pb .01, **pb .05, *pb .1.

ode

105W. Drechsler, M. Natter / Journal of Interactive Marketing 25 (2011) 95109graphical condition. Table 6 shows the effects of visualization

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

Model 1 MCoefficient St.-error DFa Coeff

Price history characteristicsRange .11 .072 .89 .13*Trend .06 .101 .94 .08Variance .08 .101 .93 .07Trendvariance .10 .142 1.10 .08

VisualizationChart vs. table .29*** .112 0.75 .28*Chart range .11 .099 1.11 .12Chart trend .34** .139 1.40 .31**Chartvariance .38*** .141 1.46 .37**Chart trendvariance .33* .199 .72 .29

Price expectationPE4 .21 .455 .81 PE8 .89*PE12

CovariatesNotebook expertise .08*** .017 .93 .08*Deal proneness .06*** .017 1.07 .07**Perceived expensiveness .12*** .019 1.13 .12**Price chart user .17*** .057 .84 .17*Constant 1.89*** .141 6.65 1.85* (shape parameter) 1.50*** .047 1.51*LL (2) 1006.86 (100.03***) 100

***pb .01, **pb .05, *pb .1.a = Deferral factor = exp(coeff.).are mainly driven by the way they are presented to participants.

l 2 Model 3icient St.-error DFa Coefficient St.-error DFa

.072 .87 .15** .072 .86

.101 .92 .10 .101 .91

.101 0.93 .07 .100 .93

.142 1.09 .09 .141 1.09

* .112 .75 .27** .112 .76.099 1.12 .11 .099 1.12.139 1.36 .29** .139 1.33

* .140 1.44 .36** .140 1.43.199 .75 .27 .199 .76

* .362 .41

.92*** .278 .40

** .017 .92 .08*** .017 .92* .017 1.07 .06*** .017 1.07* .019 1.12 .12*** .019 1.12** .057 .85 .15*** .057 .86** .141 6.35 1.83*** .141 6.22** .047 1.51*** .0473.83 (106.35***) 1001.21 (111.58***)

Indeed, these results confirm that in all three survival models, thevisualization of the trend (strongly decreasing) and variance(high) as a chart increases the tendency to defer the purchase(interaction terms; pb .05). Hence, hypothesis H5b is supported.Similar to Study 1, we again find that the variance exerts thestrongest impact on purchase timing decisions.

Fig. 4 shows that in the case of a price chart with a strongdownward trend, the survival model (model 3)predicts thatparticipants would in general defer their purchase by 41.1 % ascompared to the control group without a price history. Further, thepredictions show that 16.9 percentage points can be attributed to the

i.e., a large amount of price decline, leads to a downward shift inprice expectations. Further, the influence of price trends ismoderated by the variance of the past price series. Overall, theresults indicate that the strength of the impact of chartcharacteristics increases with long-term price expectations.

Study 2 shows that the previously discussed effects can partly beattributed to the visualization of price history. The visualizationespecially exhibits strong effects on consumers' purchase timingdecisions.

In particular, for a strong downward trend, 16.9 percentagepoints of the total effect of the price history is caused by

106 W. Drechsler, M. Natter / Journal of Interactive Marketing 25 (2011) 95109visualization. In the case of a price chart with a high variance, thiseffect becomesmuch stronger. In particular, 20.3 percentage pointsof the total effect can be attributed to the visualization, which isnearly the half of the total effect. Hence, 50% of the total effect iscaused by the price history information and 50% by thevisualization.

Summary Study 2Study 2 reveals that the negative impact of the strong

downward trend of a notebook's past prices on price expectationscan partly be attributed to its visualization. Furthermore, thevisualization of the trend and variance exhibits a strong influenceon a consumer's purchase timing decisions. This means that notonly the information about past prices influence consumerdecision-making but also the graphical presentation itself.

Discussion

General Findings

This research investigates whether price charts regarding aproduct's price history induce reference price effects and howchart characteristics affect consumer price expectations andpurchase timing decisions. The results of Study 1 show that theprice chart is perceived as highly relevant information withwhich to form price expectations and to plan purchase time.

We find that charts illustrating a product's price history inducestrong reference price effects. This means that consumers adjusttheir prior price expectations according to chart characteristics.Like investors, consumers especially follow price trends inpredicting prices and planning purchase timing. Besides a strongdownward trend and high variance, a high range of past prices,Fig. 4. The impact of price history vvisualization, which corresponds to 41.1% of the total effect.Under the condition of high variance, this effect is much strongerand reaches 20.3 percentage points, which is nearly 50% of thetotal effect. Hence, the trend and variance of the price charts arethe most important chart characteristics.

Implications

From a consumer's perspective, the results of this study showthat price chart information is especially valuable in situations inwhich the price history indicates a stronger future decrease andhigh variance of prices than consumers would have expected.When charts indicate that prices will further decline, thisinformation is incorporated into consumer expectations andhelps them to maximize the transaction utility, i.e., to predictprices and to purchase when the price meets or falls below thefuture expected price. Hence, from a consumer's perspectiveprice, charts provided by shopbots are valuable information thatserves as an anchor to support consumer purchase decisions. Forthis reason, the price chart feature is positively evaluated by, forinstance, the press when comparing and recommending differentshopbot sites (SmartMoney 2010). Based on the above discus-sion, it is obvious that from a shopbot's perspective, the provisionof price charts is worthwhile because it is perceived as containingvaluable information from a consumer's and a public policymaker's perspective. Hence, we recommend that shopbotsprovide 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 seriousimpact on sales and profits. This is because consumers adjust theirreference prices depending on the price chart pattern. On the onehand, price charts can help to uphold price expectation levels, butisualization on purchase timing.

transaction data in a real-world setting. Such a setting wouldallow researchers to measure the actual effects of purchasepostponement on conversion or click rates. Third, it would beinteresting to incorporate the effect of price charts in demandmodels in order to derive optimal pricing decisions. Whenmodeling reference prices through first-order exponentialsmoothing, a systematic error occurs when a trend is present.According to our findings concerning the relevance of pricetrends, it would be interesting to model reference prices throughsecond-order exponential smoothing. Fourth, since we focus onthe most popular product category on shopbot sites that normallyexperiences significant price decreases over time, we only useddeclining price charts in the experiments. However, futureresearch could extend our research by using categories withprice increases aswell as decreases (e.g., airline tickets). Finally, itwould be interesting to investigate the effect of the price chartinformation on consumer replacement decisions, especially intechnology markets (Gordon 2009).

Acknowledgements

The authors gratefully acknowledge many helpful commentsfrom the editors and the two anonymous reviewers.

Deal I wait until there is an Roy (1994) .73 .71

107Interactive Marketing 25 (2011) 95109on the other hand, they can accelerate anticipated price declines.This problem is especially relevant for durable products withtypical life cycle pricing patterns, since price expectations exhibit astrong influence on price elasticities. Reference price effectstriggered by price chart information should therefore affect priceelasticities in the market, which subsequently affect retailer andmanufacturer sales. The reason behind this rationale is that pricechart information is especially valuable for imitators and followersin the adoption process of durables, since they normally buy at alater stage and at a lower price level in a product life cycle. Hence,durables face increasing absolute price elasticities until the end oftheir product life cycle (Parker and Neelamegham 1997). Whenprice chart information leads to a change in price expectations, thesedynamics affect when manufacturers break even because chartinformation can lead to a shift in periodic sales due to increasedstrategic planning of the more price sensitive segment of followersin the market. Since this segment is normally responsible for thebulk of the purchases over the whole life cycle of a product, thisproblem also affects retailer margins because they often acquireproducts at later times for a reduced price from the manufacturer.

Nevertheless, it is important to note that retailer competition ina shopbot setting especially drives price chart patterns. This is dueto the fact that retailers mostly compete on their rank in the pricecomparison table of actual prices since it is possible to gain salesby implementing a small price decrease (Iyer and Pazgal 2003;Smith 2002). Hence, we recommend retailers to balance the effectof constantly reducing prices at present with the long-term effectcaused by this strategy on sales and profit development.

In summary, the results of the study show that the initial priceexpectations of participants are homogeneous and that dependingon chart characteristics, participants adjust their price expectations.Manufacturers and retailers should infer from shopbot sites howmarket price expectations develop over time and should incorporatethis information in their dynamic pricing strategy.However, as longas not all consumers use price history information about a product'sprice history, there is still some uncertainty about the market priceexpectations. Hence, it would be useful to make price historyinformation available to all consumers in the market.

Limitations and Future Research

In this study, we find significant effects of price charts onpurchase timing and expected future prices. To isolate the charteffects, 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 firstdirection for future research would therefore be to assess theinfluence of this additional information on consumer behaviorand on the profitability of shopbots, retailers andmanufacturers. Itwould also be worthwhile to identify product categories that linkthe strengths of these effects. Further, one could extend futureresearch to another setting in which the supply of the productcategory is limited. For instance, Microsoft's Bing travel website(http://www.bing.com/travel/) provides graphs of historical prices

W. Drechsler, M. Natter / Journal offor flight tickets. The goal of this service is to predict air travelprices and offer the consumer advice about when to purchase aticket. Second, it would be useful to analyze a shopbot'sproneness advertised sale beforegoing to shop at a mall.I hunt around until I find areal bargain.

Notebookexpertise

I regularly use notebooks. Roehm andSternthal(2001)

.73 .77I am very familiar withnotebooks.I would call myself a notebookexpert.

Shopbotinformationrelevancy

The information provided bythe shopbot was relevant forthe purchase timing task.

Mason et al.(2001)

.90 .90

The information that wasprovided by the shopbot wouldhelp me in making purchasetiming decisions.The information providedby the shopbot aided mein completing thepurchase timing task.

Perceivedexpensiveness

The actual priceof the notebook is high.

Yoo,Donthu, and

.85 .80Coefficientalpha

Construct Scale items Source Study1

Study2AppendixLee (2000)The actual price of thenotebook is expensive.

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

Kalyanaram, Gurumurthy and John D.C. Little (1994), An Empirical Analysis

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109W. Drechsler, M. Natter / Journal of Interactive Marketing 25 (2011) 95109

Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?IntroductionRelated LiteratureConsumer Price Expectations for DurablesPrice Chart Information and Consumer Purchase Decisions

Study 1Expected Effects and Hypotheses DevelopmentReference Prices Anchoring and AdjustmentInfluence of Chart Characteristics

MethodologyStudy Design ManipulationsExperimental ProcedureKey MeasuresResultsManipulation Checks

Price Expectations Anchoring and AdjustmentInfluence of Price Chart CharacteristicsEffects of Price Chart Characteristics on Price ExpectationsEffects of Price Chart Characteristics on Purchase Timing

Summary Study 1

Study 2The Impact of VisualizationMethodologyResultsThe Influence of Tabulated Price Histories on Price Expectations and Purchase TimingThe Impact of Price History Visualization on Price Expectations and Purchase TimingSummary Study 2

DiscussionGeneral FindingsImplicationsLimitations and Future Research

AcknowledgementsAppendixReferences