Customer Price Sensitivity and the Online Medium

Transcription

1 Customer Prce Senstvty and the Onlne Medum Venkatesh Shankar* Arvnd Rangaswamy** Mchael Pusater*** February 1999 *Assstant Professor of Marketng, Robert H. Smth School of Busness, Unversty of Maryland, College Park, MD Tel: (301) Fax: (301) Emal: **Professor of Marketng, The Smeal College of Busness Admnstraton, The Pennsylvana State Unversty, Unversty Park, PA Tel: (814) Fax: (814) Emal: ***Vce Presdent, Interactve Sales & Marketng, Marrott Lodgng, Marrott Internatonal, 1 Marrott Drve, Washngton, D. C , E-mal: mke.pusater@marrott.com 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.

2 2 Customer Prce Senstvty and the Onlne Medum Abstract The nature of the onlne medum and the characterstcs of web stes can sgnfcantly alter the degree of customer prce senstvty n onlne markets. Many managers fear that the Internet wll ncrease 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 comprsng web ste factors and several customer and ntermedary factors, to explan the man and moderatng effects of the onlne medum on customer prce senstvty. We use ths framework to develop hypotheses, whch we test usng data from both onlne and offlne customers n the hosptalty ndustry. We examne two mportant aspects of prce senstvty: the relatve weght a customer attaches to prce compared to other attrbutes (prce mportance), and customer s perceved value of undertakng a search for better prces (prce search). Our results ndcate that the onlne medum does not have a man effect on prce mportance, but t ncreases the perceved value of undertakng a prce search. However, some aspects of the onlne medum and some web ste tactcs can actually dampen prce senstvty. Specfcally, web ste tactcs such as the degree of nteractvty and the depth of nformaton provded at a web ste reduce both aspects of prce senstvty. Interestngly, the perceved content of the ste (n terms of prce or 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, leadng to lower prce senstvty relatve to the offlne medum. These moderatng effects are dfferent for the two aspects of prce senstvty. Compared to the offlne medum, the onlne medum ntensfes the effect of product optons n lowerng prce mportance and dampens the effect of prce bundlng n ncreasng prce mportance. The onlne medum also dampens the ncrease n prce search that results when customers use ntermedares to compare prces. We outlne the mplcatons of our results for developng Internet marketng strateges. In partcular, our results suggest that web stes should make t easy for customers to search for both prce and non-prce attrbutes. Key words Prce senstvty Internet marketng Onlne medum Hotel ndustry

3 1. Introducton In many product categores, onlne shoppng on the Internet s becomng ncreasngly more convenent than offlne shoppng. The Internet helps buyers and sellers to transcend geographc and temporal barrers n makng one-tme transactons or for establshng long-term exchange relatonshps through Electronc Commerce. It enables sellers to offer detaled nformaton about ther products and servces (often embedded wthn rch edtoral content), engage buyers n rcher dalogs than s afforded by drect mal, dynamcally nfluence choces at the pont of decson makng, and even delver dgtal products (e.g., CDs, consultng reports) nstantaneously (well, almost). It helps buyers shop among a large number of sellers, explore more choce alternatves, harness vast amounts of potentally relevant nformaton at the pont of decson-makng, and customze ther shoppng envronments. Several authors have noted the potental transformatons of compettve marketng actvtes that could be brought about by transactons between buyers and sellers n the onlne envronment (Glazer 1991; Hoffman and Novak 1996; Rangaswamy and Wnd 1994). In recent years, the World Wde Web (WWW) s emergng as the most mportant marketng vehcle on the Internet for facltatng transactons between buyers and sellers. The onlne shoppng ndustry, comprsng travel servces, books, and computer products, to name a few, 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 $9.8 bllon n 1998, could grow to a $327 bllon ndustry by the year The onlne medum 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). As another example, accordng to a recent study by JD Power and Assocates, less than 10

4 2 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 qualtyrelated nformaton s mportant to customers and brands are dfferentated, nteractve retalng could lead to lower prce senstvty. Lynch and Arely (1998) 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 (1998) 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. 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. A recent Busness Week artcle (Cortese and Stepaneck 1998) ponts to several onlne markets that realze hgher prces, on the average, than equvalent offlne prce determnng mechansms (e.g., AucNet, Easy Sabre). Gven these conflctng fndngs, we need a better understandng of the effects of the onlne medum on customer 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 Shankar and Krshnamurth 1996 for a revew). Compared to the tradtonal mass meda, the onlne medum provdes for rcher nteracton between customers and manufacturers n determnng the prce assocated wth a transacton. There are several theoretcal ssues of nterest. What are the determnants of onlne prce senstvty? How do these factors dfferentally mpact onlne prce senstvty as compared to offlne prce senstvty? We need research to dentfy the factors that nfluence onlne prce senstvty, examne ther dfferental nfluence onlne relatve to offlne, and artculate how these factors nfluence two dfferent aspects of prce senstvty: (1) Prce mportance - the relatve mportance of prce n

5 3 nfluencng purchase decsons, and (2) Prce search - customers nclnaton to undertake a prce search to fnd better prces. An understandng of prce mportance to customers provdes a bass for formulatng strateges for the marketng mx. Lkewse, nsghts nto prce search behavor provde a manageral bass for mprovng the value of the product or servce, partcularly n the onlne envronment. Although both aspects consttute prce senstvty, they are conceptually dfferent, and not necessarly correlated. Whle prce mportance ndcates the nfluence of prce on customer choce, prce search reflects the customer s perceved value of makng an effort to fnd better prces. 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 prce s 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 prce s hgher than ther reference prces. From a practcal perspectve, managers are anxous to learn how the onlne medum and Internet marketng wll affect prce competton n ther markets. Many managers fear that the onlne medum wll ncrease prce senstvty and ntensfy prce competton. Ths s a partcularly mportant ssue to frms who have consderable nvestments n tradtonal dstrbuton channels, but now face the prospect of havng to strengthen ther Internet presence and grow ther onlne transactons (e.g., automoble manufacturers, hotels). These frms typcally market dfferentated products onlne through ther web stes. How do web ste factors such as nteractvty and depth of nformaton nfluence the two aspects of customer prce senstvty? Is customer s prce mportance hgher onlne? or does a customer search more for better prces on the Internet even among dfferentated products? The answers to these questons have actonable mplcatons for managers of web stes. Our prmary objectves for ths research are to: (1) conceptualze how the onlne medum nfluences prce senstvty, (2) conceptually and emprcally dentfy the condtons that ncrease or decrease onlne prce senstvty relatve to offlne prce senstvty, and (3) artculate Internet marketng strateges that could lower prce senstvty, thereby reducng the potental for prce competton. 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 data from the hotel ndustry. We survey both

6 4 onlne and offlne customers to compare the relatve nfluence of several mportant determnants of prce senstvtes onlne and offlne. Fnally, we summarze and nterpret our results, explore manageral mplcatons, and artculate the contrbutons and lmtatons of the study. Our research bulds on prevous research on ths topc. Burke et al. (1992) and Lynch and Arely (1998) 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 that have a strong offlne presence, but are contemplatng makng major nvestments n onlne markets. Degeratu et al. (1998) also compare shoppng behavor of n real onlne and offlne stores. However, ther study s based only on behavoral data (e.g., does not contan atttudnal nformaton about value of prce search), and the onlne store they used (Peapod) caters only to members who subscrbe to the servce. In contrast, our settng s more representatve of the shoppng mall envronment that characterzes much of the onlne transactons between frms and customers. 2. Conceptual Framework and Hypotheses Our conceptual framework s derved from the nformaton search lterature (e.g., Stgler 1961; Urbany, Dckson, and Kalapurakal 1996). The orgnal model of optmal search behavor of Stgler (1961) has been revsed and extended n many ways (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 nformaton search on prce and the cost of nformaton search on non-prce attrbutes. Based on ths nformaton search perspectve, we can state that the hgher the expected benefts of nformaton search, the lower s the prce senstvty. The hgher the cost of searchng for prce nformaton, the lower s the prce senstvty. 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 search. Fgure 1 provdes an overvew of how search costs and expected benefts determne prce senstvty.

7 5 [Fgure 1 about here] The medum used by a customer can affect nformaton search, and hence prce senstvty (Beatty and Smth 1987). We further post that the benefts and costs of nformaton search and therefore prce senstvty depend on three broad sets of factors: web ste and medum factors, customer factors, and ntermedary factors. These factors are consstent wth the set of factors that have been found to affect prce senstvty n general (Nagle and Holden 1995). Of the broad sets of factors, the web ste factors are specfc to onlne shoppng. Although the customer and ntermedary factors are common to both onlne and offlne shoppng, our prmary focus s on solatng the dfferental mpact of these factors on the two aspects of prce senstvty onlne relatve to offlne. 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. In what follows, we elaborate on these hypotheszed relatonshps. [Table 1 and Fgure 2 about here] Frst, we explore the man effect of the onlne medum on the two aspects of prce senstvty, relatve to the offlne medum. The medum per se s unlkely to have a man effect on prce mportance. However, we can expect the onlne medum to affect prce search by decreasng the costs of search. In general, search costs are lower when the tme requred for search s lower (Irwn and Smth 1957; Urbany 1986). The tme requred for search on the Internet s typcally lower than the tme requred offlne. Although the costs of non-prce nformaton search, n general, are also lkely to be lower, we expect the reducton n the costs of prce search to outwegh the reducton n the costs of non-prce nformaton search. 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 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. 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.

8 6 H A : The onlne medum ncreases prce search (man effect) Web Ste and Medum Factors Among web ste and medum factors, the perceved content of 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 product optons are lkely to mpact prce senstvty. The extent to whch the content of a web ste s perceved as prce-orented or feature/beneft-orented plays an mportant part n determnng prce senstvty (Shankar and Krshnamurth 1996). At the same tme, the degree to whch a web ste s perceved as nteractve could also affect prce senstvty (Urban, Wenberg, and Hauser 1996). Smlarly, perceptons regardng the perceved depth of nformaton and ts nteracton wth the perceved content at the ste (Meyer 1982) wll lkely affect prce senstvty. 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 non-prce 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 advertsng 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 lower the costs of prce search relatve to the costs of searchng for non-prce nformaton. Ths phenomenon s partcularly lkely n computer-medated envronments than n the actual store (Burke, et al. 1992). Conversely, feature-orented advertsng may reduce the costs of nonprce nformaton relatve to prce nformaton, thereby lowerng prce senstvty (Shankar and Krshnamurth 1996). In general, customers encode lttle prce nformaton (Dckson and Sawyer 1990). 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. H 1 : The more prce-orented the web ste, the hgher the onlne prce mportance and prce search.

9 7 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 can make customers, who are passve searchers, rely on prce as a heurstc to compare alternatves (Urbany et al. 1996), thereby ncreasng ther prce senstvty. On the other hand, nteractve web ste messages, by ther desgn, could lower the costs of search for both prce and non-prce nformaton. More mportant, nteractvty can prompt shoppers to examne nonprce attrbutes more than prce attrbutes and smultaneously ncrease the perceved benefts of shoppng onlne. Interactvty ncreases customer nvolvement, whch s postvely related to non-prce nformaton search (Jacoby, Chestnut, and Fsher 1978). The search costs of non-prce nformaton s thus reduced compared to the search costs of prce nformaton. Interactvty also offers greater control to the shopper, thereby ncreasng the pleasure of shoppng, a beneft of nformaton search (Marmorsten, Grewal, and Fshe 1992). In combnaton, these forces could 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 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 focus 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 s also lowered (Carlson and Geseke 1983: Goldman and Johannsson 1978). Together, these effects result n lower onlne prce mportance and search, leadng to the followng hypothess. 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 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

10 8 H 3 : The greater the depth of nformaton at the web ste, the lower the onlne prce senstvty (prce mportance and search). Interacton of perceved content and depth of nformaton. If the content of nformaton s featureorented, 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 prce senstvty further. 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 prce, n turn, wll lead to hgher prce senstvty. If content s measured wth respect to the prce-orentaton of the web ste (.e., more content means a more prce-orented ste), these arguments lead to the followng hypothess. H 4 : The nteracton of prce-orented content and depth of nformaton wll ncrease onlne prce senstvty (prce mportance and prce search). 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. Thus, the relatve ease of prce search s lkely to ncrease prce senstvty for both onlne and offlne customers. However, prce search costs are lkely to be lower n onlne channels than n conventonal channels (Bakos 1997). Wth the advent of onlne search agents lke and the cost of prce search onlne has come down sgnfcantly. 4 Thus, we expect the mpact of relatve ease of prce search on prce senstvty to be hgher onlne than offlne. Ths leads to our next hypothess. 5 H 5 : The effects of the ease of searchng for prce (relatve to other attrbutes) on ncreasng prce mportance and prce search, 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 4 Note that search agents work best when a common product or servce s sold by dfferent vendors.

11 9 avalable, customers would typcally beneft by fndng products that offer hgher value to them (Benjamn and Wgand 1995). The benefts are more transparent onlne than offlne. It s also easer to search for non-prce nformaton onlne than offlne, especally when there s a 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 onlne sortng mechansms. Customers may be wllng to place less emphass on prces or be less wllng to search for prces onlne relatve to offlne because they could more easly fnd products onlne that ft ther needs better. These arguments lead to H 6. H 6 : The effects of product and prce optons n lowerng prce mportance and prce search are more pronounced onlne than offlne Customer Factors We can expect the degree of prce senstvty (onlne or offlne) to be governed by such factors as brand loyalty, value of tme, and frequency of shoppng. These factors typcally affect the level of nformaton search and may dffer across customers (Furse, Punj, and Stewart 1984). The extent to whch these factors nfluence prce senstvty may depend 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. Brand loyalty reduces 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. In partcular, as compared to offlne customers, loyal onlne customers may have lower costs of searchng for non-prce attrbutes than nonloyal onlne customers, because t s more convenent to choose ther favorte brands n the onlne envronment. Wth more nformaton avalable onlne than offlne, ncreased loyalty could reduce the 5 We assume that bulk of then 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 10 cost of analytcal processng, provdng another beneft (Shugan 1980). Loyal customers are thus lkely to be less senstve to prces onlne. The above arguments lead to the followng hypothess. H 7 : The effects of brand loyalty n reducng prce mportance and prce search, are more pronounced onlne than offlne. Value of tme. Tme-stressed shoppers may use smple choce tactcs such as buy what you bought last tme (Hoyer 1984). Such shoppers may also base ther decsons on brand recognton. If those shoppng onlne do t for savng tme, ths wll lower prce senstvty. Indeed, MCI s strategy n sellng musc CDs onlne seems to reflect ths as t targets shoppers wth hgh value of tme for ts onlne CDs, each of whch s prced between $13.99 and $16.99 compared to the retal prce of $11.99 (Washngton Post 1995). Frequency of shoppng. Items purchased or searched more frequently ether onlne or offlne have a narrower lattude or zone of prce acceptance than those searched less often (Kalyanaram and Lttle 1994). Customers who search often may be more exposed to prces and have a narrow lattude of prce acceptance (Monroe 1971). These customers may fnd prce more mportant or search for better prces than customers who shop rarely 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 ntermedary and (2) prce bundlng. Prce comparson usng an ntermedary. If customers use onlne or offlne ntermedares that provde comparatve prces of compettors, ther prce senstvty could ncrease. Comparatve prces ncrease the salence of prce as a decson crteron and reduce the costs of prce search. They may also expand the consderaton sets of customers, promptng them to examne prces of more brands (Mtra and Lynch 1995). Furthermore, the avalablty of comparatve prces from an ntermedary could be perceved as a sgnal to evaluate the dfferent brands on prce. Although prce comparson tends to

13 11 ncrease prce senstvty, ths effect s lkely to be lower onlne than offlne. Comparatve prces provde reference prces for customers. 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 non-prce nformaton s hgher, the presence of comparatve reference prces tends to heghten prce search. The mportance of prce, however, s not lkely to be dfferent onlne relatve to offlne due to prce comparson usng an ntermedary. These arguments suggest the followng hypothess. H 9 : The effects of prce comparson usng an ntermedary n ncreasng prce mportance and prce search, are less pronounced onlne for prce search. Prce bundlng. Product bundles are typcally offered by ntermedares, and often nvolve savngs on ndvdual tems. If customers choose from a product bundle (e.g., a vacaton package from a travel agent onlne or offlne whch ncludes offerngs of arlnes, hotels, and car rentals) offered by an ntermedary, they may focus more on the value of the bundle and less the on prces of ndvdual components. Ths s because the value of the bundle has a strong mpact on buyers perceptons of transacton value (Yadav and Monroe 1993). To ths extent, bundles may lower prce mportance (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). Because the value of a bundle s hgher than that of a component, customers expect hgher absolute savngs on a bundle than on ts components. Accordng to the psychophyscs heurstc, f the 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 the component s smaller than the savngs on the bundle as a whole, customers attach less mportance to prce, and are less lkely to search for better prces for the component. It s much easer to use ths heurstc onlne where such savngs are more transparent than offlne. Therefore, we expect the onlne medum to ntensfy the tendency of prce bundlng to decrease prce senstvty. Ths leads to our fnal hypothess. H 10 : The effects of choce of a prce bundle n reducng both prce mportance and prce search are more pronounced onlne than offlne.

14 12 Shared cost effect. Customer prce senstvty s also nfluenced by who the payer of the product or servce s. If the customer s not the payer, but hs/her organzaton pays for the product or servce, then prce senstvty would be lower (Nagle and Holden 1995). We wll control for ths varable n our emprcal analyss. 3. Data and Model for Testng Hypotheses To test our hypotheses, we collected prmary data n 1998 from both 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 both 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. By focusng on a sngle product and web ste to test our hypotheses, we reduce error varance due to these condtons. Furthermore, by keepng these condtons fxed, we establsh a common frame of reference for our respondents (e.g., n terms of ther pror brand experences). To test the hypotheses, we develop regresson models lnkng prce senstvty to the varous factors we dentfed n the prevous secton. We descrbe the data and measures and the model specfcaton n the followng subsectons Data and Measures 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 to test our hypotheses. 6 To collect data on shoppng behavor n the travel ndustry, we developed separate questonnares for onlne and offlne customers of the Marrott Internatonal hotel chan. The onlne and offlne customers were mutually exculsve n that they used only the respectve meda to make ther purchases. The questonnare ncluded tems on 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).

15 13 shoppng behavor, choces, customer atttudes, measures of the hypotheszed factors, and demographcs. The questons that pertan to factors common to both the onlne and offlne surveys were dentcal n both questonnares. In addton, the onlne questonnare ncluded tems on web ste factors. Most of the questons pertaned to the last purchase of the product,.e., the last servce encounter. Ths way, we were able to elct shoppers atttudes more specfcally. To ensure that the bulk of the nformaton search by the respondent s done wthn a sngle medum (onlne or offlne), we had a screenng queston to determne the medum from whch the respondent obtaned nformaton. The offlne sample s a random sample generated from the populaton of 12 mllon customers of Marrott Internatonal s customer database. 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. Furthermore, we screened onlne respondents by askng them to ndcate whether they had ever stayed at a Marrott hotel. Although random samplng on the Internet s extremely dffcult to acheve, 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. We collected 214 usable responses from the onlne survey and 306 usable response from the offlne survey, whch represented a response rate of 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. As s well recognzed, the onlne 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). 7 To ensure that any dfference n the recency of 7 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.

16 14 response between onlne and offlne respondents does not bas results, we account for dfferences n the frequency of shoppng between the two samples n our analyss. Table 2 summarzes the demographc data of the onlne and offlne samples. Comparng the onlne sample wth the unmatched offlne sample, we see that the samples are very smlar n terms of sex, proporton of (35-54) age group, and the percentage of post-graduate educated members. However, on average, the onlne customers are, much younger, less educated, and less affluent than the offlne customers. [Table 2 about here] Table 2 also summarzes the characterstcs of the two matched samples we retaned for our data analyss. The two samples are well matched on: (1) sex (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. However, the percentage of respondents wth busness as the prmary purpose of hotel stay s much lower onlne (43) than offlne (69). We control for the effect of purpose and payer n our analyss. Table 3 summarzes the varables we use n our analyses, ther operatonalzatons, and ther mean values for the onlne and offlne samples (matched and unmatched). 8 As mentoned earler, we measure 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 Lkert scale, followng Lchtensten, Rdgway, and Netemeyer (1993). 9 In Table 3, the varables, TIMVAL, PREXP, LOYAL, FREQ, OPTION, INTERAC, and INFO are measured on 5- or 7-pont 8 The means for the key varables such as prce senstvty, brand loyalty, and ntermedary comparson 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. 9 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.

17 15 scales. CONTENT, PRINT, BUNDL and BUS 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 prce bundle (such as a vacaton package) and 0 otherwse; and BUS s 0 f the hotel stay was for lesure, 1 otherwse (.e., f the hotel stay for only busness). CONINF s the product or nteracton of CONTENT and INFO. 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] We now dscuss the means of the varables for the onlne and the matched offlne samples. Note also that the means of the unmatched and matched offlne samples are not very dfferent for most of the varables. The correlaton between the two measures of prce senstvty, prce mportance and prce search are low for both the offlne (0.19) and the onlne samples (0.36), suggestng that these two measures are dstnct from each other. The overall means of prce mportance measure n Table 3 are not statstcally dfferent between the onlne and offlne samples (17.20 versus 15.76, p > 0.01). In contrast, the mean for the prce search measure s sgnfcantly hgher n the onlne sample than n the offlne sample (4.17 versus 3.36, p < 0.001). We also note from Table 2 that the percentage of respondents who were busness travelers s lower onlne (43%) than offlne (69%). Therefore, we next examne 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 nonbusness travelers, wth an onlne mean of and an offlne mean of (these 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). Consstent wth ths result, we also fnd that onlne customers are wllng to spend more tme n choosng a hotel (p < 0.01), 10 Ths comparson and the subsequent comparsons are based on pared t-tests.

18 16 make less frequent bookngs (p < 0.05), and more of them go to ntermedares to get prce nformaton (p < 0.01) than offlne customers. Interestngly, onlne customers are also more brand loyal (p < 0.05),.e., the web ste tends to attract loyal customers. Furthermore, the relatve ease of prce search onlne (0.89) s not sgnfcantly dfferent from that offlne (0.95, p > 0.01) and the proporton of customers choosng from a prce bundle s also smlar across onlne (0.06) and offlne (0.08) samples (p > 0.01). Recall that the prce mportance measure captures the relatve mportance of prce wth respect to other attrbutes n brand choce. Therefore, t s reasonable to expect t to be the same across demographcally comparable onlne and offlne shoppers, holdng other factors constant. The prce search measure, however, reflects customers perceved value for undertakng a prce search. Ths measure could be dfferent across onlne and offlne shoppers because t s drectly related to ther wllngness to spend tme n searchng for prce nformaton and to other factors. Although there are dfferences between varable means n the two samples, note that our objectve n ths study s not to explan the absolute magntudes of prce senstvtes n the two samples. Rather, our nterest s n dentfyng the factors that ncrease or decrease onlne prce senstvty and explan the role of the onlne medum on the mpact of these factors 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 compensatory model helps us to determne the relatve mpact of each factor on the overall level of prce senstvty. We frst specfy separate models for the onlne and offlne samples because these models 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. The onlne model s gven by: PRSEN = α α 5 0 PRSRCH + α TIMVAL+ α 9 + α CONTENT + 1 α FREQ INTERAC + α PREXP + α LOYAL α 11 + INTPR α INFO α α α CONINF + 4 OPTION INTBUNDL + α 13 BUS + ε (1) where PRSEN = {PRIMP, PRSEARCH}, PRSEN s prce senstvty, PRIMP s prce mportance, PRSEARCH s prce search, s customer, the varables are as defned n Table 3, ε s an error term

19 17 assumed to be normal wth mean We use BUS as a control varable to account for the lower prce senstvty 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 PRSRCH β TIMVAL β 10 + β PREXP + β 6 FREQ + + β 11 7 INTPR LOYAL + β 12 + β OPTION + 8 INTBUNDL + β 13 BUS + ε (2) To facltate comparson, we use the same subscrpts for the factors common to both onlne and offlne customers. The pooled model s: PRSEN = λ + λcontent λ PRSRCH 5 λtimval λ + λ PREXP λ INTERAC FREQ 2 λ LOYAL + λ INTPR λ INFO 3 + λ OPTION + + λ CONINF + + λ INTBUNDL λ BUS + 13 ε (3) where the parameters λ j j = {0, 5, 6, 7, 8, 11, 12} are a functon of the medum accordng to the followng equaton. λ = λ + j 0 j 1 λ MEDIUM 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 = λ k (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 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 of the coeffcent of any varable would suggest that the varable ncreases prce senstvty. Conversely, a negatve sgn ndcates lower prce 11 We tested the error term n ths equaton and the subsequent equatons for normalty. The results dd not reject the normalty assumpton.

20 18 senstvty. Consstent wth our hypotheses, we expect the man effect sgns of INTERAC, INFO, PREXP, LOYAL, OPTION, BUNDL to be negatve, whle the sgns of CONTENT, CONINF, PRSRCH, 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. 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) dd not suggest any major multcollnearty problem. The varance nflaton factors (VIF) for each ndependent varable ranged from 1.3 to 2.9. In partcular, the correlaton between the two explanatory varables, brand loyalty and prevous experence s low for both onlne (0.23) and offlne (0.23) samples, rulng out a potental confound. 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. 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 Onlne Sample Analyss The four statements used to measure the prce search measure onlne showed hgh relablty. The Cronbach's alpha for these tems s The results of model estmaton appear n Table 5. The models ft the data well. The correlatons between predcted and actual values of the dependent 12 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 multple regressons. Furthermore, there s lttle gans n parameter effcency usng SUR f the regressors are the same n the two equatons (Johnston 1984).

21 19 varables are 0.61 and 0.68 for the two models, respectvely. 13 Recall that ths model allows us to examne the parameters for the onlne model under the least restrcted set of condtons. 14 [Table 5 about here] Sgnfcant factors affectng prce mportance: In terms of web ste factors, perceved nteractvty of the message and perceved depth of nformaton on the web ste dampen prce senstvty (p < 0.01), supportng H 2 and H 3, respectvely. Interestngly, nether the content of web ste (prce versus non-prce nformaton) nor the nteracton of content and depth of nformaton at the web ste s sgnfcant. Among customer-related factors, relatve ease of prce search, pror brand experence, perceved range of optons, value of tme, and frequency of shoppng are the sgnfcant factors nfluencng onlne prce mportance. The easer t s to search onlne on prce relatve to other attrbutes, the hgher the onlne prce mportance. The more favorable the prevous experence, the lower the onlne prce mportance. Perceved range of product and prce optons reduces onlne prce mportance. The hgher the value of tme to customers, the lower ther onlne prce mportance. Onlne prce mportance ncreases wth the frequency of shoppng. Wth regard to electronc ntermedary factors, prce comparson usng ntermedary ncreases onlne prce mportance, whereas prce bundlng reduces onlne prce mportance. Sgnfcant factors affectng prce search: These results are mostly consstent wth those obtaned for the prce mportance measure, but reveal a new sgnfcant factor, and two factors that are no longer sgnfcant. Brand loyalty reduces onlne prce search (p < 0.001), mplyng that the more loyal customers are less prce senstve. However, perceved range of optons and prce bundlng that were sgnfcant n the model wth the prce mportance measure, are not sgnfcant n ths model. Importance of factors n explanng prce senstvty: The relatve mportance of a varable n the proposed model s reflected by ts beta weght (.e., the standardzed coeffcent) n Table 5. For both the prce senstvty measures, the perceved depth of nformaton and prce comparson usng 13 Note that R 2 s not nterpretable for WLS-estmated models (Judge et al. 1985, pp ), so we report the correlaton between the actual and predcted values of the dependent varable.

22 20 ntermedary are very mportant factors. In addton, pror brand experence and prce bundlng are very mportant n explanng onlne prce mportance, whle tme-value s very mportant for explanng onlne prce search. The dfferences n results for these two measures of prce senstvty are ntutve. Brand loyalty reduces the perceved value of a prce search (e.g., hgher perceved swtchng costs), but does not affect the customer s relatve mportance of prce. On the other hand, prce bundlng reduces the mportance of prce relatve to other attrbutes, but does not nfluence the perceved utlty of undertakng a prce search. Note, however, that the number of onlne respondents that purchased a travel package n the sample s only 12, so ths result should be treated wth cauton. The ncreased mportance of the value of tme and the decreased mportance of pror brand experence n the prce search measure (as measured by the beta weghts) are also consstent wth the above arguments Offlne Sample Analyss The four tems n the prce search measure for offlne customers ndcated hgh relablty (wth Cronbach s alpha of 0.72). The results of the proposed model for offlne customers are smlar to that of the onlne model and are summarzed n Table 6. [Table 6 about here] By comparng the beta weghts of the offlne model wth those of the onlne model, t appears that prce comparson usng ntermedares has a much stronger mpact on prce search offlne than onlne. Ths s not surprsng, because t s much easer offlne to do prce search by callng ntermedares than by separately callng each brand (hotel) Pooled Sample Analyss The man value of the pooled model s that t allows us to formally test how the onlne medum moderates the effects of varous factors on prce senstvty. Our prmary nterest here s n the ncremental effects of the onlne medum (both man and moderatng effects). Before poolng the onlne and offlne data, we tested for the homogenety of the ntercept and the slope coeffcents of Equaton (2) across the samples usng the Chow (1960) test. The data suggest that we should reject 14 We also tested for the best-fttng model n both the onlne and offlne data usng the Box-Cox approach (Box and Cox 1982) and found the lnear model to be the best-fttng model for both measures. Detals of the best fttng alternatve model are provded n the Appendx.

23 21 the null hypothess of homogenety (p < 0.001). The results of the pooled model are summarzed n Table [Table 7 about here] The man effect of the onlne medum s sgnfcant for the prce search measure, consstent wth H A (p < 0.01). On average, onlne customers are more lkely to undertake prce searches. Two of the four factors specfc to the onlne medum are sgnfcant (p < 0.05 or better): Perceved nteractvty and perceved depth of nformaton at the web ste reduce both prce mportance and prce search, supportng H 2 and H 3. The moderatng effects of the onlne medum are pronounced for some of the factors (p < 0.05 or better). The effect of perceved range of optons avalable at the ste and of prce bundlng n reducng prce mportance are more pronounced onlne than offlne, supportng H 8 and H 10, respectvely. Favorable pror brand experence and prce comparson usng ntermedares have less pronounced effects on prce search onlne than offlne, supportng H 6 and H 9, respectvely. The effects of the factors are largely consstent wth the results from the separate models summarzed n Tables 5 and 6. Hypotheses H 1 (perceved content of web ste), H 4 (nteracton of content and depth of nformaton), H 5 (relatve ease of prce search), and H 7 (brand loyalty), however, are not supported. The results of the alternatve models are smlar. The results of the best fttng alternatve model are provded n the Appendx. The beta weghts n Table 7 are consstent wth those n Tables 5 and 6. The man effect of the onlne medum s the most nfluental factor n determnng prce search. However, the man effect s not sgnfcant n the case of prce mportance, suggestng hgh varablty n ts effects across customers. Pror experence, prce comparson usng ntermedary, and prce bundlng are the next most mportant factors affectng prce mportance. Pror experence, prce comparson usng ntermedary and perceved depth of nformaton are the most nfluental determnants of prce search. 5. Dscusson 15 Note that the estmates from ths model are not exactly dentcal to those from the onlne and offlne models because the onlne and offlne models are not completely nested n the pooled model (the effects of value of tme, frequency, and busness stay varables are not moderated by the medum). Furthermore, the estmates from the pooled model are based on a GLS model that accounts for heteroscedastcty.