The Online Medium and Customer Price Sensitivity

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1 ebusness Research Center Workng Paper The Onlne Medum and Customer Prce Senstvty Venkatesh Shankar Arvnd Rangaswamy Mchael Pusater ebrc 117F Technology Center Buldng Research Park Unversty Park, PA Phone: (814) Fax: (814) A jont venture of Penn State s Smeal College of Busness Admnstraton and the School of Informaton Scences and Technology ebrc 1999

2 The Onlne Medum and Customer Prce Senstvty Venkatesh Shankar* Arvnd Rangaswamy** Mchael Pusater*** July 1999 *Assstant Professor of Marketng, Robert H. Smth School of Busness, Unversty of Maryland, College Park, MD Tel: (301) Fax: (301) E-mal: **Jonas H. Anchel Professor of Busness Admnstraton, The Smeal College of Busness Admnstraton, The Pennsylvana State Unversty, Unversty Park, PA Tel: (814) Fax: (814) E-mal: ***Senor Vce Presdent, Sales & Marketng, Proxcom, Sunrse Valley Drve, Reston, VA Tel: (703) Fax: E-mal: We thank Marrott Internatonal for provdng fnancal support and data and fne.com for assstance wth onlne data collecton. We also thank the partcpants at the Marketng Scence Conference on Internet at Boston for ther helpful comments.

3 The Onlne Medum and Customer Prce Senstvty Abstract Many managers fear that the Internet wll ncrease customer prce senstvty and ntensfy prce competton. There s, however, very lttle conceptual or emprcal research on ths topc. In ths paper, we use the nformaton search lterature to develop a conceptual framework and a set of hypotheses, comprsng factors characterzng the onlne medum, customers, and ntermedares, to explan the man and moderatng effects of the onlne medum on customer prce senstvty. We test the hypotheses usng two sets of data from the hosptalty ndustry: (1) separate samples (demographcally matched) of both onlne and offlne customers and (2) the same sample of customers, who purchased from both the onlne and the offlne meda. We examne two mportant aspects of prce senstvty: the weght a customer attaches to prce relatve to other attrbutes (prce mportance), and customer s proclvty to undertake a search for better prces (prce search). Our results from both sets of data ndcate that the onlne medum does not have a man effect on prce mportance, but t ncreases prce search. However, some aspects of the onlne medum and the web ste can actually dampen prce senstvty. Specfcally, web stes can reduce prce senstvty by provdng n-depth nformaton (both prce and non-prce) that s avalable through a hghly nteractve nterface. Interestngly, the perceved content of the ste (n terms of prce versus non-prce orentaton) does not nfluence prce senstvty. In addton to these man effects, the onlne medum also moderates the effects of several other factors, helpng to dampen prce senstvty. These moderatng effects are dfferent for the two aspects of prce senstvty. Compared to the offlne medum, the onlne medum: (1) accentuates the effects of the range of products and prces offered, and product/prce bundlng by an ntermedary n reducng prce mportance, and (2) accentuates the effects of brand loyalty n decreasng prce search and dampens the effects that ntermedares have n ncreasng prce search by facltatng prce comparsons. We outlne the mplcatons of our results for developng web ste desgn, targetng, dstrbuton and allance strateges.

4 1 1. Introducton In many product categores, onlne shoppng on the Internet s becomng ncreasngly more convenent than offlne shoppng. Several authors have noted the potental transformatons of compettve marketng actvtes n the onlne envronment (Glazer 1991; Rangaswamy and Wnd 1994; Hoffman and Novak 1996). The onlne shoppng ndustry, currently domnated by travel servces, fnancal servces, books and CD's, and computer products, s growng fast as the number of onlne sellers and the number of ndvduals and companes wth access to the Internet contnues to ncrease at a rapd pace. Accordng to Forrester Research, the onlne shoppng ndustry, estmated at $48 bllon n 1998, could grow to a $1.3 trllon ndustry by the year Internet marketng can sgnfcantly mpact the nature and degree of customer prce senstvty. Prcng decsons, whch are central to a brand s marketng strategy, depend on prce senstvty toward that brand. In some cases, marketng on the Internet can ncrease prce senstvty, leadng to more ntense prce competton. Bakos (1997) argues that because onlne markets reduce customer search costs even for dfferentated products, they are lkely to ncrease competton between sellers and lead to lower prces. Ths seems to be case n the market for books. Amazon.com has spurred sgnfcant prce competton n both the Internet and conventonal channels (Wall Street Journal, January 28, 1997). Brynjolfsson and Smth (1999) fnd that onlne book and CD retalers realze prces about 9-16% lower on average than those of offlne retalers (however, the actual dsperson of prces were hgher onlne). As another example, accordng to a study by JD Power and Assocates n 1998, less than ten percent of those who bought a car or truck onlne pad stcker prce about half as many as walk-n buyers. Surprsngly, t appears that the onlne medum could also dampen prce senstvty by enablng customers to fnd products that best ft ther needs. Alba et al. (1997) propose that when qualty-related nformaton s mportant to customers and brands are dfferentated, nteractve retalng could lead to lower prce senstvty. Lynch and Arely (1999) conducted experments n a smulated onlne wne store to test ths hypothess. They report that prce elastcty was lower under expermental condtons where shoppers had nformaton on both prce and product qualty as compared to condtons where shoppers had nformaton only on prce. Degeratu, Rangaswamy, and Wu (1999) used choce modelng on panel data to assess how prce response n an onlne subscrpton grocery servce dffered from prce response n tradtonal grocery stores. They found lower prce senstvty onlne for some product categores. Lkewse, Choudhury et al. (1998) fnd that whle n some cases, electronc markets help buyers fnd

5 2 better prces, n other cases, they can help supplers extract prce premums. Lee (1998) reports that an onlne auto aucton servce n Japan has realzed hgher prces consstently over a long perod of tme (snce 1985) than conventonal auto auctons. Cortese and Stepaneck (1998) also pont to several onlne markets that realze hgher prces, on the average, than equvalent offlne prce determnng mechansms (e.g., AucNet, Easy Sabre). Clemens et al. (1999) report that there s wde prce dsperson not just lower prces--n an emprcal analyss of the onlne travel agent market. Gven these conflctng fndngs, we need a better understandng of the effects of the onlne medum on customer prce senstvty and the condtons under whch t ncreases or decreases prce senstvty. Ths s an mportant ssue from both theoretcal and practcal perspectves. From a theoretcal perspectve, we need to go beyond the extant studes of prce senstvty that have focused on the relatonshp between advertsng n tradtonal mass meda and prce senstvty (see Kaul and Wttnk 1995; Shankar and Krshnamurth (1996) for revews). Our prmary objectve for ths research s to artculate how the onlne medum dfferentally mpacts prce senstvty, as compared to the offlne medum. To do ths, we: (1) develop a conceptual framework to delneate the factors (n partcular those unque to the onlne medum) that nfluence prce senstvty, (2) dentfy the condtons that ncrease or decrease onlne prce senstvty relatve to offlne prce senstvty, and (3) artculate Internet marketng strateges that could lower onlne prce senstvty, thereby reducng the potental for prce competton. There are at least three ways to assess prce senstvty: (1) Econometrc analyss of prce elastcty/senstvty based on actual prces, brand choces, and quanttes purchased by ndvduals, or by a market as a whole; (2) Surveys of customers to determne ther prce-related atttudes (e.g., ntent to purchase, prce search propensty), ncludng such methods as Conjont Analyss; (3) Expermental evaluaton of customer reactons/purchases to varous prces that are manpulated under controlled condtons. Our nterest s n studyng the relatonshp between the onlne medum and prce senstvty n a settng where customers choose from dfferentated offerngs (ether from the same or dfferent supplers), rather than a stuaton where customers choose the same tem from dfferent supplers. In ths context, the customer survey approach s the most effectve n allowng us to apply a unform method n both the onlne and offlne meda to study the effects of the medum on prce senstvty. We explore several theoretcal ssues of nterest. What are the determnants of onlne prce senstvty? How do these determnants dfferentally mpact onlne prce senstvty as compared to offlne prce senstvty? There has been lttle research on these ssues. Another mportant ssue s that

6 3 there are two dfferent atttudnal dmensons to prce senstvty: (1) Prce mportance - the relatve mportance of prce n nfluencng purchase decsons, and (2) Prce search - customers proclvty to undertake a prce search to fnd better prces. Although both aspects consttute prce senstvty, they are conceptually dfferent. In a smlar sprt, Kalra and Goodsten (1998) use two dmensons of prce senstvty: prce mportance and wllngness to pay. It s possble that customers who perceve prce to be an mportant attrbute wll stll not spend the tme and effort to fnd the lowest prce, f the observed prces are less than ther reservaton prces. By the same token, customers who do not attach much mportance to prce n ther choce decsons, may actually undertake an extensve search f the observed prces are hgher than ther reference prces (Wner 1986). From a practcal perspectve, managers are anxous to learn how the onlne medum and Internet marketng mpact prce competton n ther markets. Many managers fear that the onlne medum wll ncrease prce senstvty and ntensfy prce competton, but there s not much emprcal research to ndcate the condtons under whch ths s lkely to happen. Is customer s prce mportance hgher onlne? Does a customer undertake a more extensve search for better prces on the Internet among dfferentated products? How do web ste characterstcs such as nteractvty and depth of nformaton nfluence customer prce senstvty? What customers should we target onlne to ensure low prce senstvty? What should be our onlne dstrbuton and allance strateges? The answers to these questons have actonable mplcatons for web ste desgn, targetng, and dstrbuton and allance strateges of onlne marketers who wsh to have lower prce senstvty for ther products. Further, the dfferences between prce mportance and prce search onlne can have mportant manageral mplcatons. For example, f we fnd that prce s more mportant relatve to other attrbutes onlne, then the frm may need to enhance the value of non-prce attrbutes. On the other hand, f onlne customers are nclned to search more for better prces, t s mportant to smplfy prce search and at the same tme enhance customer search for more non-prce nformaton. It may also be mportant n ths stuaton to offer a wde range of dfferentated products and prces (e.g., some arlnes and hotels create hghly dfferentated products by varyng the restrctons assocated wth varous prces). Our research bulds on prevous research on ths topc. Burke et al. (1992) and Lynch and Arely (1999) use controlled experments to assess onlne prce senstvty of ther subjects. We measure the prce senstvty of people who make real purchases of the product category of nterest n a real store both onlne and offlne. A comparatve assessment s lkely to be far more useful to managers n frms

7 4 that have a strong offlne presence, but are contemplatng makng major nvestments n onlne markets. Degeratu et al. (1999) also compare shoppng behavor of n real onlne and offlne stores. However, ther study s based on behavoral data and the onlne store they used (Peapod) caters only to members who subscrbe to the servce. In contrast, we use atttudnal data, and our settng s more representatve of the shoppng mall envronment that characterzes much of the onlne transactons between frms and customers. We begn by developng a conceptual framework derved from the nformaton search lterature. Usng ths framework, we formulate a seres of hypotheses. To test these hypotheses, we develop a measurement model and estmate t usng two sets of data from the hosptalty ndustry: (1) separate samples (demographcally matched) of both onlne and offlne customers and (2) the same sample of customers, who purchased from both the onlne and the offlne meda. Fnally, we summarze and nterpret our results, explore manageral mplcatons, and artculate the contrbutons and lmtatons of the study. 2. Conceptual Framework and Hypotheses We derve our conceptual framework from the nformaton search lterature. The orgnal model of optmal search behavor proposed by Stgler (1961) has been extended n many ways (e.g., Meyer 1982; Ratchford 1982; Urbany et al. 1996). Customer prce senstvty depends on the benefts and costs of nformaton search (Srnvasan and Ratchford 1991). The benefts of nformaton search nclude economc benefts (Stgler 1961) as well as noneconomc benefts such as shoppng enjoyment (Marmorsten, Grewal, and Fshe 1992). The costs of nformaton search nclude the cost of searchng for prce nformaton and the cost of searchng for nformaton on non-prce attrbutes. Accordng to nformaton search theory, the hgher the expected benefts of nformaton search, the lower s the focus on prces, and therefore the lower s the prce senstvty; the hgher the cost of searchng for prce nformaton, the lower s prce senstvty; and the hgher the cost of searchng for non-prce nformaton, the hgher s the prce senstvty. The overall prce senstvty s the net effect of the benefts and costs of nformaton (prce and non-prce) search on prce senstvty. We use ths basc prncple to develop the hypotheses on the proposed moderatng effects of the onlne medum on prce senstvty. Fgure 1 provdes an overvew of how search costs and expected benefts determne prce senstvty. [Fgure 1 about here]

8 5 We further post that the benefts and costs of nformaton search, and therefore prce senstvty, depend on three sets of factors: onlne medum-related factors, customer factors, and ntermedary factors. These are broad sets of factors smlar to the set of factors that have been found to affect prce senstvty n general (Nagle and Holden 1995). By lookng at medum-related, customer, and ntermedary factors, we can develop nsghts on web ste desgn, targetng, and dstrbuton and allance strateges, respectvely. Of these factors, some onlne medum-related factors (web ste factors) are specfc to onlne shoppng, whle the rest are common to both onlne and offlne shoppng. Each of these factors nfluences prce senstvty through ts effects on the benefts and costs of search. Table 1 summarzes these effects. Fgure 2 summarzes the hypotheszed man and moderatng effects of the onlne medum on the determnants on prce senstvty. We elaborate on these hypotheszed relatonshps below. [Table 1 and Fgure 2 about here] Frst, we artculate the man effect of the onlne medum on the two aspects of prce senstvty (prce mportance and prce search), relatve to the offlne medum. The onlne medum per se s unlkely to have a man effect on prce mportance, at least n the short run. However, the medum used by a customer can affect nformaton search (Beatty and Smth 1987). Unlke prce search that s contextspecfc, prce mportance s more a functon of the product category and the characterstcs of an ndvdual, and thus, s less lkely to be drectly nfluenced by the medum. In contrast, the onlne medum may ncrease the proclvty to prce search. Research suggests that search costs are lower when the tme requred for search s lower (Irwn and Smth 1957; Urbany 1986). Search tme s typcally lower onlne than offlne. Although the cost of non-prce nformaton search s lkely to be lower onlne, we expect the reducton n the cost of prce search to outwegh the reducton n the costs of non-prce nformaton search for many products. 1 Ths s because the onlne medum, lke the prnt medum, nherently allows qucker nspecton of prces relatve to the offlne medum, than of other search attrbutes. Indeed, Prasad and Rng (1976) found hgher prce senstvty wth prnt ads relatve to TV ads. Thus, we expect customers nclnaton to search for better prces to be hgher onlne. These arguments suggest hypothess H A. H A : The onlne medum ncreases the proclvty to search for prces (man effect). 1 We recognze that the costs of non-prce nformaton search can be hgher onlne relatve to offlne for certan products that requre evaluaton of sensory attrbutes such as touch or smell.

9 Onlne Medum-Related Factors Among onlne medum-related factors, we propose that the perceved content of a web ste, the perceved nteractvty of the ste, the perceved depth of nformaton at the ste, the nteracton of content and depth of nformaton, the relatve ease of prce search, and the range of avalable product and prce optons are lkely to mpact prce senstvty. We elaborate below the effects of each of these factors. Perceved content of web ste. A web ste can be prce-orented or feature/beneft-orented. Marketng messages that are predomnantly prce-orented should ncrease prce senstvty, whereas nonprce advertsng (e.g., focus on features and benefts) should decrease prce senstvty (Kaul and Wttnk 1995). The relatonshp between a web ste s marketng messages and prce senstvty may also depend on the sze of customers consderaton sets and the relatve strength of preference for the brand n queston (Mtra and Lynch 1995). Prce-orented content may expand the consderaton set and, f the brand preference s not strong, t can elevate the mportance of prce for customers. More nformaton on prces can also lower the cost of searchng for prce nformaton relatve to the cost of searchng for nonprce nformaton. Ths phenomenon s partcularly lkely n computer-medated envronments than n real stores (Burke et al. 1992), leadng to greater proclvty to search for prces. Prce-seekng behavor also occurs when nformaton on non-prce attrbutes s low, but these attrbutes are mportant (Tells and Gaeth 1990). Conversely, feature-orented messages may reduce the costs of non-prce nformaton relatve to prce nformaton, thereby lowerng prce senstvty (Shankar and Krshnamurth 1996). A feature- or beneft-orented web ste that does not emphasze prces s therefore lkely to lead to both lower prce mportance and prce search. These arguments suggest hypothess H 1. For ease of exposton, we state each remanng hypothess for both the aspects of prce senstvty, prce mportance and prce search, but test the hypothess on each aspect separately. H 1 : The more prce-orented the web ste, the hgher the onlne prce mportance and prce search. Perceved nteractvty of web ste. Interactvty refers to the ablty of web stes to dynamcally generate outputs based on customer nputs. 2 A hgh degree of statc messages may ncrease the search costs for non-prce attrbutes. Ths stuaton can make customers, who are passve searchers, rely on prce as a heurstc to compare alternatves (Urbany et al. 1996; Urban, Wenberg, and Hauser 1996), thereby ncreasng the mportance they attach to prce and ther tendency to search for better prces. On 2 Hghly nteractve web stes typcally use nterface technologes that provde quck and effcent nformaton at a level defned by the customer. Examples of such stes are and

10 7 the other hand, nteractve web ste messages, by ther desgn, could lower the costs of search for both prce and non-prce nformaton. More mportantly, nteractvty can prompt shoppers to examne nonprce attrbutes more than prce attrbutes. Interactvty can also ncrease customer nvolvement, whch s postvely related to non-prce nformaton search (Jacoby, Chestnut, and Fsher 1978). When nvolvement s hgh, the search costs of non-prce nformaton s reduced compared to the search costs of prce nformaton. Interactvty also offers greater control to the shopper that ncreases the pleasure of shoppng--a beneft of nformaton search---and ths n turn lowers the mportance of prce (Marmorsten, Grewal, and Fshe 1992). Combned, we expect these forces to reduce onlne prce mportance and prce search. Ths leads to the next hypothess. H 2 : The hgher the nteractvty of the web ste, the lower the onlne prce mportance and prce search. Perceved depth of nformaton. Some web stes have multple layers of web pages wth detaled or deep nformaton. 3 Mult-layered nformaton decreases the costs of searchng for nformaton on both prce and non-prce attrbutes. If the nformaton s mult-layered and rch, customers wll tend to use heurstcs to make decsons (Glazer 1991). Mult-layered nformaton tends to ncrease the use of nonprce heurstcs, resultng n a reduced mportance on prces (Meyer 1982). Such nformaton formats reduce cogntve processng costs and ncrease non-prce nformaton use (Russo et al. 1986). Because of the rcher nformaton, the opportunty costs of non-prce nformaton search relatve to prce nformaton search s also lowered (Carlson and Geseke 1983: Goldman and Johannsson 1978). Together, these effects result n lower onlne prce mportance and prce search, leadng to the followng hypothess. H 3 : The greater the depth of nformaton at the web ste, the lower the onlne prce mportance and prce search. Interacton of perceved content and perceved depth of nformaton. If the content of nformaton s feature-orented, greater depth of nformaton on product features and benefts wll lkely ncrease the benefts of nformaton search and lower the search costs for non-prce nformaton, consstent wth Urbany et al Ths effect wll, n turn, decrease the proclvty to search further for better prces. On the other hand, f the message s prce-orented, more nformaton may prompt greater attenton to prces, thereby reducng the search costs for prce nformaton. Reduced search costs for 3 A good example s that enables vstors to fnd nformaton on apartments based on ther decson-crtera from over 500,000 lstngs n 32 states. Other examples are and

11 8 prce, n turn, wll lead to hgher prce mportance and prce search. These arguments lead to the followng hypothess. H 4 : The greater the prce-orentaton and the depth of nformaton at the web ste (nteracton of prce-orented content and depth), the greater the onlne prce mportance and prce search. The remanng hypotheses nvolve the moderatng effects of the onlne medum and these effects occur through factors common to both the onlne and the offlne meda. Therefore, there are two components to each of the remanng hypotheses. The frst component concerns the effect of the factor on prce senstvty regardless of the meda and the second component s about the ncremental or moderatng effect of the onlne medum relatve to the offlne medum. Gven the objectves of our paper, n testng our hypotheses, we focus on the ncremental effect of the onlne medum. Perceved relatve ease of prce search. When the cost of nformaton search s low, customers wll use more nformaton n evaluatng alternatves (Stgler 1961). To the extent that prce becomes easer to search n comparson to other search attrbutes, customers may base ther choces predomnantly on prces, makng prce more mportant to them. Thus, the relatve ease of prce search s also lkely to ncrease the tendency to search for better prces for both onlne and offlne customers. However, prce nformaton search costs are lkely to be lower n onlne channels than n conventonal channels (Bakos 1997). Wth the advent of onlne search agents lke the cost of prce search onlne has come down sgnfcantly. 4 Thus, we expect the mpact of relatve ease of prce search on prce mportance and prce search to be hgher onlne than offlne. Ths leads to our next hypothess. 5 H 5 : The easer t s to search for prce (relatve to other attrbutes), the hgher the prce mportance and prce search, and these effects are more pronounced onlne than offlne. Perceved range of optons. Perceved varety n product and prce optons (e.g., menu of prces for varatons of the product type) can mpact prce senstvty. When a greater varety of offerngs are avalable, customers would typcally derve benefts of nformaton search by fndng products that offer hgher value to them..e., ft ther needs better (Benjamn and Wgand 1995). We expect these benefts of nformaton search to be more transparent onlne than offlne. In many product categores, t s also easer to search for both prce and non-prce nformaton onlne than offlne, especally when there s a 4 Note that search agents work best when a common product or servce s sold by dfferent vendors. 5 We assume that nformaton search s done wthn the respectve medum. If, however, the nformaton search s done n both meda, the net effect of the medum on the mpact of the determnants of prce senstvty wll depend on the relatve degree of search onlne and offlne.

12 9 large number of product offerngs. Whereas the extent of non-prce nformaton search offlne s often low (Urbany, Dckson, and Key 1991), the level of non-prce nformaton search onlne s enhanced by search and sort mechansms. In sum, when a web ste offers a wde range of optons, customers may place much less emphass on prce, and may be much less nclned to search for prces onlne relatve to offlne. These arguments lead to H 6. H 6 : The wder the range of product and prce optons, the lower the prce mportance and prce search, and these effects are more pronounced onlne than offlne Customer Factors We expect the degree of prce senstvty to be governed by such customer factors as brand loyalty, value of tme, and frequency of shoppng. These factors nfluence nformaton search, and may dffer across customers (Furse, Punj, and Stewart 1984). The extent to whch these factors nfluence prce senstvty depends on the medum (offlne or onlne),.e., the medum may moderate the relatonshp between these factors and prce senstvty (Beatty and Smth 1987). Brand loyalty. Brand loyalty s lkely to play an mportant role n determnng prce senstvty both onlne and offlne by reducng attenton to prces. Krshnamurth, Mazumdar, and Raj (1992) demonstrate that customers are relatvely nsenstve to changes n prces of ther favorte brands. Lkewse, Jacoby, Syzabllo, and Busato-Schach (1977) show that loyal customers are more lkely to pay more for a brand than non-loyal customers. Also, wth more product nformaton avalable onlne than offlne, ncreased loyalty could reduce the cost of analytcal processng (Shugan 1980) and lead to less extensve prce search behavor. For example, loyal customers of a hotel chan can use such devces as bookmarks to more easly select ther favorte brands onlne, and are therefore, more lkely to locate a hotel belongng to the same chan even n a new locaton. Loyal customers are thus lkely to be focus less on prces onlne and undertake less search for prces. In partcular, a sgnfcant beneft of nformaton search for loyal customers onlne relatve to offlne s that they can bookmark ther favorte brand and more convenently repeat ther choces, leadng to lower prce mportance onlne relatve to offlne. The above arguments lead to the followng hypothess. H 7 : The hgher the brand loyalty, lower the prce mportance and prce search, and these effects are more pronounced onlne than offlne. Value of tme. Tme-stressed shoppers (.e., those wth hgh costs of nformaton search) are less nclned to search for prces and tend to use smple tactcs such as buy what you bought last tme (Hoyer 1984). Such shoppers may also base ther decsons on mpulse and not spend much effort to fnd

13 10 better prces ether onlne or offlne (Inman and Wner 1999). Search s cheaper onlne for both prce and non-prce attrbutes. Thus, we expect shoppers wth hgh value of tme to do a lttle more non-prce shoppng onlne than offlne, and those wth low value of tme to do more search for prce onlne than offlne. MCI s strategy n sellng musc CDs onlne seems to be based on ths reasonng each of ts onlne CDs s prced between $13.99 and $16.99 compared to the lowest offlne retal prce of $11.99 (Washngton Post 1995). In contrast to prce search, prce mportance s unlkely to be dfferent for shoppers wth dfferent values of tme ether onlne or offlne. That s, shoppers who place a hgh value on tme may stll fnd prce as mportant as those wth low value of tme. Further, shoppers wth hgh value of tme for whom prce s mportant can shop at Everyday Low Prce (EDLP) outlets, both onlne (such as and and offlne. Ths reasonng leads to H 8. H 8 : The hgher the customer s value of tme, the lower the prce search, and ths effect s more pronounced onlne than offlne. Frequency of shoppng. Customers who purchase or shop more frequently ether onlne or offlne have a narrower lattude or zone of prce acceptance than those who shop less often (Kalyanaram and Wner 1995). As a result, customers who shop frequently wll have to search more for prces that fall wthn ther narrow zone of acceptance than those who have a wder zone of prce acceptance. Such customers may also fnd prce more mportant than those who shop less frequently. We use frequency of shoppng as a control varable n our analyses Channel Intermedary Factors Customer prce senstvty s also nfluenced by ntermedary servces avalable onlne and offlne. For example, n the travel ndustry, some onlne customers choose ther arlnes and hotels through electronc channel ntermedary servces such as and just lke customers shoppng offlne make ther decsons through travel agents. Two ntermedary factors are lkely to affect the degree of prce senstvty: (1) prce comparson usng an ntermedary, and (2) product/prce bundlng by an ntermedary. Prce comparson usng an ntermedary. One of the functons of ntermedares s to offer nformaton on competng products. If ntermedares also provde comparatve prces of compettors, customer prce senstvty could ncrease. The avalablty of comparatve prces ncreases the salence of prce as a decson crteron and reduces the costs of prce search. Comparatve prces also form an external source of reference prces to customers (Wner 1986). Combned, these effects may result n an

14 11 expanson of the consderaton sets of customers, promptng them to examne prces of more brands (Mtra and Lynch 1995). Further, the avalablty of comparatve prces from an ntermedary could be perceved as a sgnal to evaluate the dfferent brands on prce. However, when the search costs are low, as n the onlne envronment, reference prces have lttle effect on prce search tendency (Urbany, Bearden, and Welbaker 1988). In contrast, n the offlne envronment, where the search cost for nonprce nformaton s hgher, the presence of comparatve reference prces tends to heghten prce search. Prce mportance, however, s not lkely to be dfferent onlne relatve to offlne. These arguments suggest the followng hypothess. H 9 : Hgher the lkelhood of customers comparng prces usng an ntermedary, the greater are prce mportance and prce search, but the effects on prce search are less pronounced onlne relatve to offlne. Product/Prce bundlng by an ntermedary. Intermedares typcally offer product bundles (e.g., a vacaton package from a travel agent onlne or offlne that ncludes ar travel, hotel, and car rental) and these often nvolve savngs on the components of the bundle. If customers choose from a product bundle, they may focus more on the value of the bundle and less on the prces of ndvdual components. Ths s because the value of the bundle as a whole has a stronger mpact on buyers perceptons of transacton value than does the sum of the components of the bundle (Yadav and Monroe 1993). To ths extent, a bundle may lower prce mportance attached by a customer toward a component of the bundle (Gultnan 1987). Customers also typcally use the psychophyscs-of-prce heurstc n that they perceve the expected savngs from prce search n relatve terms rather than n absolute amounts (Grewal and Marmorsten 1994; Wner 1986). Thus, f the expected savngs on a product or servce s relatvely small, customers typcally decde that such savngs are not worth the tme and effort of search. Because the savngs on a component s smaller than the savngs on the bundle as a whole, customers attach less mportance to the prce of a component, and are less lkely to search for better prces for a component. Often, t s easer to use ths heurstc onlne where such savngs are more transparent than offlne (t s easer to compare the savngs on the bundle onlne than offlne because of the fast avalablty of nformaton onlne). Therefore, we expect the onlne medum to ntensfy the tendency of product/prce bundlng to decrease prce senstvty. Ths leads to our fnal hypothess. H 10 : If products are prce bundled, t reduces both prce mportance and prce search for each component of the bundle, and these effects are more pronounced onlne than offlne.

15 12 Shared cost effect. Prce senstvty s also nfluenced by who pays for the product or servce (e.g., busness travel versus lesure travel). If the customer s not the payer, then prce senstvty would be lower (Nagle and Holden 1995). The effect of ths factor s not lkely to be dfferent between onlne and offlne envronments. We use ths as another control varable n our emprcal analyses. 3. Data and Model for Testng Hypotheses About one-fourth of the revenues n the onlne shoppng ndustry come from travel servces and ths segment of the onlne ndustry s growng at the fastest rate (Forrester Research). Therefore, the travel ndustry provdes a good context for testng our hypotheses Data Collecton Procedure To test our hypotheses, we collected two sets of data: (1) separate samples of onlne and offlne customers (Data set 1), and (2) a completely dfferent sample of customers who used both the onlne and the offlne meda for makng dfferent hotel reservatons (Data set 2). Our use of the two dfferent data sets n the same study s smlar to the use of multple experments n consumer behavor research -- Data set 1 s akn to a between-subjects desgn, whereas Data set 2 s akn to a wthn-subjects desgn. In both the data sets, we focus customer response to a sngle product (hotel) and an assocated web ste. Thus, we reduce the varance that would be ntroduced by alterng these contexts. Although we used dentcal questonnares n both data sets, the questons n Data set 1 were specfc to Marrott hotels, whereas the questons n Data set 2 were applcable to any hotel chosen by the respondent. Further, n Data set 2, the same respondent answered many questons twce, once wth respect to her/hs most recent onlne reservaton and once wth respect to her/hs most recent offlne reservaton. 7 Thus, Data set 2 extends the nvestgaton n Data set 1 from the brand level to the product category level and also overcomes any problems due to self-selecton bas n Data set 1. To ensure that any dfference n the recency of responses between onlne and offlne respondents does not bas results, we take nto account n our analyses, the dfferences n the frequency of shoppng between the samples. 8 6 Note, however, that less than 1% of current travel reservatons are done on the Internet. About 80% of the reservatons are stll done by travel agents (Jones 1998). 7 To control for order bas n answerng onlne and offlne questons, we randomly chose one half of the sample to answer onlne questons frst, whle the other half of the sample were asked to answer offlne questons frst. A subsequent reanalyss of the data separately by order of questons dd not yeld any result that was sgnfcantly dfferent. 8 Even so, we analyzed the tme lag between the date of response and that of the most recent purchase (hotel stay). It s not sgnfcantly dfferent across the onlne and offlne samples n Data sets 1 and 2.

16 13 Data set 1: We collected prmary data from both the onlne and offlne customers of the same servce, namely, Marrott Internatonal. The onlne respondents chose a Marrott hotel on the Internet and the offlne respondents selected a Marrott hotel usng conventonal methods (e.g., travel agents or toll-free telephone number). By havng two separate samples, we are able to: (1) compare the atttudes and behavor of demographcally comparable populatons that dffer only n the medum they use to make hotel reservatons, (2) examne how the onlne medum moderates the effects on prce senstvty of the hypotheszed factors common to both populatons, and (3) hghlght the effects of web ste factors on onlne prce senstvty these are unque to the onlne populaton. The onlne and offlne questonnares ncluded tems on customer shoppng behavor, choces, atttudes, measures of the hypotheszed factors, and demographcs. The questons relatng to factors common to both the onlne and offlne surveys were dentcal n both the onlne and offlne questonnares. In addton, the onlne questonnare ncluded tems on web ste factors. Most of the questons related to the most recent purchase of the product,.e., the last servce encounter. Ths way, we were able to elct atttudes wth respect to a specfc experence. To ensure that the nformaton search undertaken by the respondent s done wthn a sngle medum (onlne or offlne), we ncluded a screenng queston regardng the medum from whch the respondent obtaned nformaton. The offlne sample comprsed respondents from a random sample of 2000 generated from the populaton of 12 mllon customers of Marrott Internatonal s customer database, who were maled the questonnare. For the onlne sample, we posted the survey at Marrott s web ste wth a New tag and requested onlne vstors to fll out the survey. Note that random samplng on the Internet s extremely dffcult to acheve because of a lack of a samplng frame there s no lst of all Internet users or even a lst of potental vstors to a ste, nor s there an onlne equvalent to random dgt dalng. However, t s worth notng that onlne surveys typcally elct responses from a reasonably representatve sample of the unverse of onlne shoppers of a gven product (Intellquest 1997). Ths s because those who are lkely to shop onlne for a product also tend to be the ones who respond to onlne surveys on the product. Respondents n both the samples were offered 500 reward ponts for partcpaton. We collected 214 usable responses from the onlne survey and 306 usable response from the offlne survey, whch represented a response rate of about 15%. Of the responses we obtaned from the offlne surveys, we selected the frst 181 that allowed us to closely match the onlne sample n demographcs. Ths sample formed the matched offlne sample. As s well recognzed, the onlne

17 14 populaton s younger, on the average, than the general populaton. To match the two samples, therefore, we dropped several respondents from the older age groups from the offlne responses. Instead of usng a smple random sample to dentfy respondents to be dropped, we chose to retan respondents n the order n whch we receved ther completed surveys keepng respondents who sent ther surveys early. We dd ths because early respondents are more lkely to have a response tendency smlar to those vstors to a ste who choose to fll out the onlne questonnare (Intellquest 1997). 9 Data set 2: We also collected prmary data by surveyng a random sample of hotel customers usng a lst provded by the Hosptalty Sales and Marketng Assocaton Internatonal. Customers n ths sample were representatve of customers who choose hotels n both the onlne and offlne medum. Usng data from such a sample, we can better solate the mpact of the onlne medum (relatve to the offlne medum) on customer prce senstvty. We receved 144 responses from a sample of 2,000 customers randomly selected from the lst, who had chosen a hotel both onlne and offlne (a response rate of 7.2%). Recall that for ths data set, the same respondents provded data for ther most recent offlne and onlne reservatons. 3.2 Summary of Data Table 2 summarzes the demographcs of the onlne and offlne samples n both data sets. Comparng the onlne sample wth the unmatched or naturally occurrng offlne sample n Data set 1, we see that the samples are very smlar n terms of gender composton, proporton of (35-54) age group, and the percentage of post-graduate educated members. However, on average, onlne customers are younger, less educated, and less affluent than the unmatched offlne customers. Table 2 also summarzes the characterstcs of the offlne matched samples n Data set 1 that we used for all our data analyses. The onlne and matched offlne samples are comparable on: (1) gender (the percentage of women n the onlne sample s 26 compared to 25 n the offlne sample), (2) age (the proporton of customers n the mddle age [35-54] bracket s almost the same n both samples), (3) educaton (the percentage of those wth post-graduate study s 43 n the onlne sample versus 46 n the offlne sample), and (4) annual famly ncome. The demographc composton for Data set 2 s smlar to that of the matched samples of Data set 1. [Table 2 about here] 9 Even so, we subsequently reanalyzed our data usng a smple random sample to dentfy the respondents to be dropped, but the results of our emprcal analyss were not dfferent.

18 15 Table 3 summarzes the varables we use n our analyses, ther operatonalzatons, and ther mean values for both data sets. 10 As mentoned earler, n ths study we consder two aspects of prce senstvty. Frst, we determne the mportance of prce usng a constant sum scale for the prce mportance measure. Second, we measure atttude toward prce search through a 7-pont scale, followng Lchtensten, Rdgway, and Netemeyer (1993). 11 In Table 3, the varables INTERAC, and INFO, OPTION,, LOYAL, and TIMVAL are measured on 5- or 7-pont scales. CONTENT, PRINT, and BUNDL are dummy varables. CONTENT s 1 f the web ste s perceved to be prce-orented, 0 otherwse; PRINT s 1 f the customer went through a channel ntermedary who offered comparatve prces and 0 otherwse; BUNDL s 1 f the customer chose from a product/prce bundle (such as a vacaton package) by an ntermedary and 0 otherwse. Fnally, EASRCH s computed as the rato of the expressed ease of searchng for prce over the expressed ease of searchng for the hghest rated non-prce attrbute. Ths rato provdes an ndex of the relatve ease of prce search. [Table 3 about here] Interestngly, the dfferences n the varable means across the two meda are consstent between the two data sets. The mportance of prce s not sgnfcantly dfferent whether the respondents used the onlne or offlne medum to make hotel reservatons (17.20 versus 15.76, p > 0.05, Data set 1; versus 16.08, p > 0.05, Data set 2). Proclvty to prce search, however, s hgher onlne than offlne (4.17 versus 3.36, p < 0.001; 4.85 versus 3.66, p < 0.001) 12. Recall that the prce mportance measure captures the relatve mportance of prce wth respect to other attrbutes n brand choce. Therefore, t s 10 The means for the key varables such as prce senstvty, brand loyalty, and prce comparson usng ntermedary n the unmatched offlne sample are qute close to the means of the matched sample. Because our purpose s to compare onlne and offlne samples wth smlar demographcs to solate the effects of the medum on prce senstvty, we focus only on the matched samples for all our analyses. 11 The four tems n the prce search measure are: (1) I dd not go to extra effort to fnd lower prces. (2) I shopped around more than one hotel to take advantage of low prces. (3) I do not consder the money saved by fndng low prces to be worth the effort. (4) I consder the tme taken to fnd low prces to be worth the effort. These tems were related to the most recent purchase. 12 For Data set 1, the percentage of respondents who were busness travelers s lower onlne (43%) than offlne (69%). Therefore, we examned the mean prce senstvtes of the lesure and busness travelers separately. The means for the prce mportance measure for the lesure travelers n the onlne sample (20.39) and n the offlne sample (22.81) are not statstcally dfferent at a sgnfcance level of As expected, the means of prce mportance for busness travelers are lower than for lesure travelers. The onlne mean of and the offlne mean of are not sgnfcantly dfferent (p > 0.01). For the prce search measure, however, the mean of the onlne sample s hgher than that of the offlne sample for both lesure (4.37 vs. 3.50; p < 0.01) and busness travelers (3.91 vs. 3.28; p < 0.01). These dfferences are smlar n Data set 2. We subsequently control of ths effect (shared cost effect) n our analyss.

19 16 reasonable to expect t to be the same across demographcally comparable onlne and offlne shoppers, holdng other factors constant. However, the prce search measure could be dfferent onlne versus offlne, because t s drectly related to customers wllngness to spend tme n searchng for prce nformaton. Our results suggest that the two measures of prce senstvty do ndeed, measure dfferent aspects of prce senstvty: The correlatons between prce mportance and prce search are low for both the offlne (0.19) and the onlne (0.36) samples n Data set 1 and Data set 2 (0.29). Consstent wth the mean values of the measures of prce senstvty, we also fnd that onlne customers are wllng to spend more tme n choosng a hotel (p < 0.01) and make less frequent bookngs (p < 0.05) than offlne customers. Further, the relatve ease of prce search onlne (0.89; 0.92) s not sgnfcantly dfferent from that offlne (0.95; 0.87) and the proporton of customers choosng from a prce bundle s smlar across the onlne and offlne samples (0.06; 0.08 n Data set 1 and 0.14 each n Data set 2, p > 0.05). Interestngly, onlne customers are more brand loyal (p < 0.05),.e., the web ste tends to attract loyal customers n both the data sets, although the levels of loyalty are lower n Data set 2 as compared to Data set 1. Ths s because, unlke Data set 1, whch s based on responses from customers of a sngle hotel chan, n Data set 2 the responses were gathered from customers of many dfferent hotels, many of whch probably do not have as strong a brand name as Marrott. Note that the focus of ths research s not to explan the absolute magntudes of prce senstvtes or brand loyaltes n the dfferent samples. Rather, our nterest s n dentfyng the factors that ncrease or decrease prce senstvty and to artculate the role of the onlne medum n enhancng or dmnshng these effects Specfcaton of Models We use regresson models to determne the effects of the varous factors n explanng the observed prce senstvtes n both samples. Multple factors combne to determne overall prce senstvty, and a lnear model helps us to determne the relatve mpact of each factor on the overall level of prce senstvty. We frst specfy separate models for onlne and offlne prce senstvtes these represent the least restrcted models. We then specfy a pooled model that allows us to assess the dfferental effects of the onlne medum on prce senstvty.

20 17 The onlne model s gven by: PRSEN = α + α CONTENT + α INTERAC + α INFO + α CONINF + α INTPR α EASRCH + α OPTION + α LOYAL + α TIMVAL α INTBUNDL 7 + α 11 3 FREQ 8 + α 12 4 BUS + ε (1) where PRSEN = {PRIMP, PRSEARCH}, PRIMP s prce mportance, PRSEARCH s prce search, s customer, the varables are as defned n Table 3, ε s an error term assumed to be normal wth mean CONINF s the product or nteracton of CONTENT and INFO. BUS s a control varable to account for the shared cost effect,.e., when a thrd party (busness organzaton) s the payer for the servce. For the offlne sample, the model s dentcal, except that the web ste marketng factors are absent. Specfcally, the model for offlne customers s gven by: PRSEN = β + β EASRCH + β OPTION + β LOYAL + β TIMVAL + β INTPR β 10 6 INTBUNDL + β 11 7 FREQ + β 12 8 BUS + ε (2) To facltate comparsons between the models, we use the same subscrpts for the factors common to both onlne and offlne customers. The pooled model s: PRSEN = λ + λ CONTENT + λ INTERAC + λ INFO + λ CONINF + λ INTPR + λ INTBUNDL + λ λ EASRCH + λ OPTION + λ LOYAL + λ TIMVAL FREQ + λ BUS + ε (3) where the parameters λ j j = {0, 5-12} are a functon of the medum accordng to the followng equaton. 0 1 λ = λ + λ MEDIUM j j j (4) and the parameters λ k, k = {1, 2, 3, 4} are a functon of the medum accordng to the followng equaton (because these parameters are relevant only for the onlne medum). 1 λ MEDIUM k = λk (5) MEDIUM s a dummy varable = 1 f onlne, 0 f offlne. λ 0 k capture the effects of the offlne medum, and λ 1 k capture the dfferental effects of the onlne medum. To ensure that the results are not dosyncratc to model structure, we compare them to the results from alternatve model specfcatons, namely, sem-log, log-log, and log-recprocal models. These models allow us to examne f any of the 13 We tested the error term n ths equaton and the subsequent equatons for normalty. The results dd not reject the Normalty assumpton.

21 18 relatonshps between the dependent and the ndependent varables s nonlnear. The explanatory varables are the same as n the proposed models. In Equatons (1), (2), and (3), a postve sgn for a coeffcent suggests that the correspondng varable ncreases prce senstvty. Conversely, a negatve sgn ndcates lower prce senstvty. Consstent wth our hypotheses, we expect the man effect sgns of INTERAC, INFO, OPTION, LOYAL, TIMEVAL, BUNDL to be negatve, and the sgns of CONTENT, CONINF, EASRCH, and PRINT to be postve. The predcted sgns of the parameters accordng to our hypotheses are summarzed n Table 4. [Table 4 about here] 4. Results We estmated Equatons (1), (2), and (3) separately for the two measures of prce senstvty n the two data sets. In addton, we also estmated the alternatve model specfcatons for both measures of prce senstvty. In estmatng the models, we tested for multcollnearty and heteroscedastcty. The data matrces for Equatons (1), (2), and (3) n both the data sets dd not suggest any major multcollnearty problem. The varance nflaton factors (VIF) for each ndependent varable ranged from 1.3 to 2.9. The lkelhood rato test of heteroscedastcty (Greene 1993, p. 395), however, rejected equal error varances for all the models, so we use the Weghted Least Squares (WLS) estmaton n all the models n both the data sets. We also tested for potental endogenety of the explanatory varables n each equaton usng the Hausman (1978) test, but the results dd not ndcate any evdence of endogenety. 14 Because the same respondent answered both the onlne and offlne questons n Data set 2, there s a possblty that the onlne and the offlne observatons are correlated. However, the correlaton between the error terms of Equatons (1) and (2) were very low, so t was not a problem Onlne Sample Analyss (Data Set 1) The four statements used to measure the prce search measure onlne showed hgh relablty. The Cronbach's alpha for these tems s Model estmates are summarzed n Table 5. The models ft the data well the correlatons between predcted and actual values of the dependent varables are Note that the models wth the two measures of prce senstvty as the dependent varables have the same set of ndependent varables. Ths leads to a possblty of estmatng the equatons as a Seemngly Unrelated Regresson (SUR) or a canoncal correlaton. However, because the equatons have the same set of ndependent varables, the gans from canoncal correlaton are unlkely to be hgher than those from multple regressons. Furthermore, there s lttle gan n parameter effcency usng SUR f the regressors are the same n the two equatons (Johnston 1984).

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