The study and practice of customer relationship management

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1 Raj Srnvasan & Chrstne Moorman Strategc Frm Commtments and Rewards for Customer Relatonshp Management n Onlne Retalng Academc studes offer a generally postve portrat of the effect of customer relatonshp management (CRM) on frm performance, but practtoners queston ts value. The authors argue that a frm s strategc commtments may be an overlooked organzatonal factor that nfluences the rewards for a frm s nvestments n CRM. Usng the context of onlne retalng, the authors consder the effects of two key strategc commtments of onlne retalers on the performance effect of CRM: ther brcks-and-mortar experence and ther onlne entry tmng. They test the proposed model wth a multmethod approach that uses manager ratngs of frm CRM and strategc commtments and thrd-party customers ratngs of satsfacton from 106 onlne retalers. The fndngs ndcate that frms wth moderate brcks-and-mortar experence are better able to leverage CRM for superor customer satsfacton outcomes than frms wth ether low or hgh brcks-and-mortar experence. Lkewse, frms wth moderate onlne experence are better able to leverage CRM nto superor customer satsfacton outcomes than frms wth ether low or hgh onlne experence. These fndngs help resolve dsparate results about the value of CRM, and they establsh the mportance of examnng CRM wthn the strategc context of the frm. The study and practce of customer relatonshp management (CRM) has experenced explosve growth over the past decade. Extant research provdes two sets of nsghts nto the relatonshp between a frm s CRM nvestments and ts performance. The frst set focuses on CRM as expenses. Gupta, Lehmann, and Stuart (2004) fnd that customer acquston and retenton expenses have a sgnfcant, postve effect on frm value. Other studes report a postve relatonshp between a frm s CRM technology nvestments and CRM performance (Jayachandran et al. 2005; Mthas, Krshnan, and Fornell 2005). The second set of studes envsons CRM as a frm capablty and, agan, reports ts postve effects on both CRM and busness performance (Day and Van den Bulte 2002; Renartz, Krafft, and Hoyer 2004). However, these fndngs are n contrast to ncreasng practtoner skeptcsm of CRM expendtures. As Day and Van den Bulte (2002) note, practtoners report that the majorty of CRM ntatves fal to meet expectatons (Dgnan 2002). Indeed, CRM has been decred as one of the bggest blunders of the early twenty-frst century (Infoworld 2001); there s evdence that most CRM ntatves do not delver the antcpated return on nvestment (Gartner Group 2003). Raj Srnvasan s Assstant Professor of Marketng, Red McCombs School of Busness, Unversty of Texas at Austn (e-mal: mccombs.utexas.edu). Chrstne Moorman s T. Austn Fnch Jr. Professor of Busness Admnstraton, Fuqua School of Busness, Duke Unversty (e-mal: The authors thank the Marketng Scence Insttute for ts fnancal assstance; Dave Rebsten for hs assstance n securng the data; BzRate.com for provdng data; the consultng edtors Bll Bouldng and Rck Staeln; and commentators Rajesh Chandy, Marnk Dekmpe, Abhjt Guha, Jennfer Francs, Wayne Hoyer, Jule Irwn, Mtch Lovett, John Lynch, Vjay Mahajan, Arvnd Rangaswamy, Roland Rust, Raj Srvastava, and Rajan Varadarajan for helpful comments on prevous versons of the artcle. 2005, Amercan Marketng Assocaton ISSN: (prnt), (electronc) 193 Ths dvergence n the effectveness of CRM across theory and practce s both troublng and ntrgung. It s troublng because though CRM as a way to manage customers s here to stay, ncreasng skeptcsm among practtoners sgnals that CRM wll face ntense scrutny and accountablty. From a theoretcal perspectve, the dvergence s ntrgung because t mples that the observed varablty n CRM performance may be explaned by moderatng factors. Consderng possble explanatons for the observed varablty, researchers have examned (1) CRM data-related technques (e.g., Ansar and Mela 2003), (2) marketng strateges for customer proftablty (e.g., Renartz and Kumar 2000, 2003; Rust, Lemon, and Zethaml 2004; Rust and Verhoef, 2005; Venkatesan and Kumar 2004), (3) the balance between customer acquston and retenton efforts (e.g., Renartz, Thomas, and Kumar 2005), and (4) effectve CRM mplementaton (Day and Van den Bulte 2002; Renartz, Krafft, and Hoyer 2004). Although mpressve n scope, extant research offers few nsghts on the strategc choces that are assocated wth the effectve deployment of CRM. Ths s remss because strategc conduct nfluences the effect of customer satsfacton on frm value (Anderson, Fornell, and Mazvancheryl 2004). We address ths gap n the lterature. Specfcally, we ask whether the effect of a frm s CRM on CRM performance, as measured by customer satsfacton ratngs, s nfluenced by ts pror strategc commtments. Strategc commtments can nvolve any long-term frm decson, such as the choce to enter specfc markets or nvest n products, brands, channels, or partnershps. Usng the emprcal context of the emergng onlne retalng market, we nvestgate whether CRM performance s weakened or strengthened by two relevant pror strategc commtments of the retaler: (1) brcks-and-mortar or offlne experence and (2) onlne entry tmng. We test our predctons usng manager ratngs of Journal of Marketng Vol. 69 (October 2005),

2 CRM and strategc commtments and customer ratngs of satsfacton n 106 onlne retalers. Predctons CRM and Frm Brcks-and-Mortar Experence Brcks-and-mortar experence refers to the frm s level of offlne experence before ts onlne entry. Brcks-andmortar experence s a form of strategc commtment that reflects the retaler s ncumbency n offlne retalng. Tradtonally, studes of ncumbency have focused on an ncumbent s ablty to nnovate an emergng technology (e.g., Chandy and Tells 2000). In ths study, we examne the effects of ncumbency on the effectveness of a frm s CRM nvestments n an emergng market. Our revew of the lterature suggests that there are both customer and frm explanatons for ths queston. Customer factors. Several customer-based factors mply that a frm s brcks-and-mortar experence may strengthen the effect of ts CRM on performance. Frst, brcks-andmortar retalers have exstng supply chan nfrastructures, whch should mprove fulfllment effcency, a key success factor n onlne envronments. Second, brcks-and-mortar onlne retalers have access to extensve customer nformaton from ther offlne operatons, whch may mprove ther ablty to deploy CRM effectvely to serve onlne customers. Thrd, brcks-and-mortar frms offlne brand and relatonshp equtes can be leveraged n ther onlne operatons (Geyskens, Gelens, and Dekmpe 2002). These market-based assets (Srvastava, Shervan, and Fahey 1998) may help the frm establsh strong onlne customer relatonshps. For example, brcks-and-mortar operatons may serve as a source of advertsng to pre-sell merchandse (Alba et al. 1997, p. 48). Fnally, brcks-and-mortar retalers enable onlne customers the opton to experence products before they purchase them, whch reduces customers uncertanty and helps them dentfy products that closely match ther preferences, thus ncreasng ther satsfacton (Alba et al. 1997). Customers may also prefer returnng products purchased from onlne retalers to the offlne store, savng shppng costs. Thus, the brcks-and-mortar experence of retalers can complement ther onlne operatons and ncrease the returns on ther onlne CRM nvestments. Frm factors. Although there are postve effects nvolvng customer-based factors, several features of brcks-andmortar onlne retalers suggest the opposte. Frst, ncumbents wth a long hstory of offlne retalng may be concerned about cannbalzng ther brcks-and-mortar operatons (Alba et al. 1997; Lynch and Arely 2000). As Ghosh (1998, p. 127) notes, Establshed busnesses that have carefully bult brands and physcal dstrbuton relatonshps rsk damagng all they have created when they pursue commerce n cyberspace. As a result, brcks-andmortar frms may be less aggressve n ther onlne CRM efforts. As Kanter (2001, p. 92) notes, Ask bg companes about ther goals for the Web, for example, and they are lkely to reply, Cautous testng. Ask dot-coms and they declare, Total world domnaton! Second, brcks-and-mortar experence of onlne retalers may be vewed as an organzatonal routne nvolvng tact knowledge (Nelson and Wnter 1982). Transfers of such tact knowledge are often characterzed by stckness and msapplcaton (Szulansk 1996). Thus, brcks-and-mortar retalers may napproprately transfer knowledge from ther offlne operatons to ther onlne operatons, negatvely affectng onlne performance. Indeed, because offlne and onlne busness models are dstnct, brcks-and-mortar retalers may need to unlearn what led to ther offlne success n the desgn of ther onlne CRM systems (Kanter 2001). Predcton. Integratng the evdence, we expect that moderate levels of brcks-and-mortar experence should produce the hghest performance returns on a frm s CRM nvestments. Moderate brcks-and-mortar experence provdes access to key market-based assets wthout concerns about cannbalzaton or the well-entrenched routnes that may create ncumbency nerta. Conversely, low brcks-andmortar experence offers only freedom from the nhbtng aspects of ncumbency, and hgh brcks-and-mortar experence offers only access to customer relatonshps and knowledge. Thus: H 1 : The postve effect of CRM on performance s stronger for frms wth moderate brcks-and-mortar experence than for frms wth low or hgh levels of brcks-and-mortar experence. CRM and Frm Onlne Experence A retaler s decson to enter onlne markets represents an mportant strategc commtment. We use the term frm onlne experence to capture the frm s entry-tmng strategy, and we defne t as the frm s onlne experence relatve to the frst entrant n the ndustry. Several aspects of onlne retalng, especally n ts early years, suggest that the returns on a frm s CRM nvestments are nfluenced by ts entry tmng. In lne wth H 1, we offer both customer and frm explanatons to derve our predcton. Customer factors. Some aspects of onlne customers mply that a frm s onlne experence may nfluence ts performance rewards for CRM nvestments. Customers gan effcences when swtchng to onlne (from offlne), whch may ncrease swtchng costs, satsfacton, and loyalty (Johnson, Bellman, and Lohse 2003; Zauberman 2003). Thus, early entrants may be able to extract the greatest performance rewards from ther CRM nvestments. However, ths argument overlooks a key feature of emergng markets; namely, early markets are dfferent from the mass market n terms of customers wllngness to take rsks (e.g., Rogers 1995). In addton, customers n early markets have weaker expectatons gven the nascent status of these markets (Bouldng et al. 1993). Thus, early entrants that establsh customer relatonshps wth early adopters may be dsadvantaged when targetng later adopters (Degeratu, Rangaswamy, and Wu 2000). Ths s problematc because most customers enter the market durng the mddle and later stages of market evoluton. Thus, frms may expect the strongest response to ther CRM nvestments when they enter n the mddle stages of market evoluton, not n the earler or later stages. 194 / Journal of Marketng, October 2005

3 Frm factors. Several aspects related to a frm s onlne experence suggest that there are advantages for later entrants to acheve hgher performance from ther CRM nvestments. Frst, onlne retalng s a new technology that s characterzed by frm (e.g., changes n Web desgn) and customer (e.g., learnng how to use the onlne nterface) expermentaton. Indeed, onlne retalers contnually reengneer ther strateges to meet the evolvng needs of onlne customers (Wnd and Mahajan 2002). Thus, later entrants may have an advantage over early entrants n confgurng cost-effectve CRM systems. Second, emergng markets, such as onlne retalng, are characterzed by technologcal turbulence. For example, the performance-prce ratos of onlne CRM technology ncreased dramatcally over tme, such that later entrants mplemented more cost-effectve CRM nvestments than early entrants. As such, early onlne entrants wth large nvestments n vntage CRM technology ncur consderable upgrade costs to reman compettve (The Gartner Group 2003). In turn, early entrants unwllngness to ncur these costs creates gateways for later entrants (Golder and Tells 1993). Predcton. Integratng these arguments, we expect that frms wth moderate onlne experence receve the greatest rewards for CRM nvestments. We argue that when onlne entry moves from moderate to early, there may be reductons n the effectveness of CRM nvestments due to dfferences n onlne customer cohorts or to evolvng nformaton technologes that are costly to upgrade. Conversely, late entrants may fal to acheve strong performance because of customer loyalty to early entrants. Thus: H 2 : The postve effect of CRM on performance s stronger for frms wth moderate onlne experence than for frms wth low (late entrants) or hgh (early entrants) onlne experence. Method Data We test the predctons usng a multmethod approach n a sample of onlne retalers. The populaton conssted of onlne retalers that were enrolled n BzRate.com s ratng servce n the summer of BzRate.com nserts a popup HTML that nvtes an onlne retaler s customers to partcpate n a survey that rates ther satsfacton wth a retaler after completon of a purchase from the retaler. After order fulfllment by the retaler, BzRate.com sends a second e-mal survey to these customers to obtan customer satsfacton ratngs. BzRate.com sent a Web lnk by e-mal to the senor managers of frms enrolled n ts servce on May 1, 2001, nvtng them to partcpate n our study. In return, frms were promsed nformaton about how ther frm compared wth other frms on key varables. A total of 187 of the 978 onlne frms responded to the survey, for a response rate of 19%. Key nformants, who averaged 46 months tenure, reported hgh levels of confdence (5.80/7.00) n the nformaton they provded. The average frm sze n the sample was 202 employees (standard devaton [s.d.] = 747), and the average age of onlne operatons was 41 months (s.d. = 24); n addton, most retalers had offlne experence (63%). To nvestgate selecton bas, we randomly selected 100 nonrespondent frms and compared them wth the respondent frms on varables obtanable from publc sources: (1) publcly held versus prvately held and (2) brcks-andmortar operatons or not. We found no sgnfcant dfferences between respondent and nonrespondent frms. 1 Of the 187 retalers that responded to our survey, BzRate.com had customer satsfacton data for 106, whch formed the sample for ths study. 2 We found no sgnfcant dfferences between the 106 retalers wth customer-ratngs data and the 81 retalers wthout customer-ratngs data. 3 Customer Satsfacton Measure We focus on customer satsfacton as the performance metrc assocated wth CRM success. In addton to ts nherent value as a key CRM performance metrc, satsfacton postvely affects other performance metrcs, ncludng retenton, share-of-wallet, and even shareholder value (Anderson, Fornell, and Mazvancheryl 2004). Customer satsfacton has been defned ether as transacton specfc or as cumulatve (Bouldng et al. 1993). Transacton-specfc customer satsfacton s the customer s postchoce evaluatve judgment of a specfc purchase occason (Bouldng, Kalra, and Staeln 1999). In contrast, cumulatve customer satsfacton s the customer s overall evaluaton of the accumulated customer experences wth the frm (Fornell 1992). In ths study, we focus on transactonspecfc satsfacton. Gven our emphass on the performance of a retaler s onlne CRM nvestments, cumulatve satsfacton s not approprate, because t also ncludes customers experences wth a retaler s brcks-and-mortar operatons, when such operatons are avalable. Furthermore, because order fulfllment s a crucal element of CRM n onlne retalng (Rebsten 2002), we use satsfacton ratngs that customers provded after order fulfllment. Specfcally, BzRate.com asks, How satsfed are you overall wth ths purchase experence at (merchant name) ste? on a scale that ranges from 1 ( not at all ) to 10 ( hghly ) (see the Appendx). We averaged three months of a frm s postfulfllment customers satsfacton ratngs followng our manager survey (.e., June, July, and August 2001) to the frm level to obtan a frm-level measure of customer satsfacton performance (mean = 8.68, s.d. =.58; α =.80). CRM Measures We use two measures of frm CRM. Frst, we use an eghttem measure that reflects the frm s CRM system nvest- 1Tests fnd no dfferences n publc versus prvate (χ 2 (1) =.13, not sgnfcant) and presence versus absence of brcks-and-mortar operatons (χ 2 (1) = 2.08, not sgnfcant) for respondent and nonrespondent frms. 2At the tme, BzRate.com offered two plans. In the frst plan (the 106 onlne retalers that responded to our survey), BzRate.com surveyed customers and provded frms wth frmspecfc customer data. In the second plan (81 frms), BzRate.com dd not survey customers and offered these onlne retalers overall aggregate data nstead. 3The t-tests of dfference between the 87 frms (wth no customer data) and the 106 frms (wth customer data) were not sgnfcant on sze (t = 1.612, not sgnfcant), CRM system nvestments (t =.787, not sgnfcant), or CRM capablty (t =.973, not sgnfcant). Customer Relatonshp Management n Onlne Retalng / 195

4 ments, whch we obtaned from a senor manager. Sx tems assess the frm s nvestments n CRM actvtes (1 = low nvestments, 4 = moderate nvestments, and 7 = hgh nvestments ; see the Appendx). Two tems assess the onlne retaler s CRM acquston and retenton expenses relatve to the ndustry (1 = worse than ndustry average, 4= on par, and 7 = better than ndustry average ; see the Appendx). 4 Together, these eght tems form our measure of CRM system nvestments (mean = 5.04, s.d. =.93; α =.77). Second, we complement the measure of the frm s CRM system nvestments wth an assessment of ts CRM capablty. Renartz, Krafft, and Hoyer (2004) and Day and Van den Bulte (2002) developed measures of a frm s CRM capablty. Unfortunately, these measures were not avalable when our survey was launched. Fortunately, these new measures are theoretcally founded n the frm s market orentaton, an organzatonwde system for acqurng, dssemnatng, and respondng to customer nformaton (Kohl and Jaworsk 1990). Ths foundaton renforces the mportance of market orentaton to a frm s CRM capablty. However, a frm s CRM capablty extends beyond ts market orentaton, and our use of market orentaton represents a weak test of the role of a frm s CRM capablty. Gven length constrants mposed by BzRate.com, we used 14 tems from Kohl, Jaworsk, and Kumar s (1993) 20-tem market orentaton scale (see the Appendx; mean = 4.92, s.d. =.78; α =.76). 5 Notably, CRM system nvestments and CRM capablty, as measured by market orentaton, are only moderately correlated (ρ =.32, p <.01). Strategc Commtment Measures Brcks-and-mortar experence. We constructed ths measure from managers reports of dates. The dfference (n days) between days snce frm foundng and days snce frm Web entry, both measured from our survey date (May 1, 2001), s our measure of brcks-and-mortar experence (mean = 2161 days or 5.92 years, s.d. = 4432 days or years). Onlne experence. We also constructed ths measure from manager reports of the dates of ther frm s onlne entry. Two coders assgned frms to one of eght ndustres (shoes and apparel, books and musc, electroncs and computers, health and medcne, flowers and gfts, home and ktchen furnshngs, sportng equpment, and specalty occason [e.g., brdal, brthday, baby]). Interjudge relablty was 88%, and dsagreements were resolved through dscusson. Usng ths ndustry classfcaton, we calculated the number of days snce entry for each frm from the date of 4A revewer rased the concern that the two CRM acquston and retenton expense questons may have been answered on a per customer bass so that the better than anchor (ratng 7) may have been vewed as lower (more effcent); thus, a hgher ratng (7) may actually reflect lower CRM system nvestments per customer. However, because we provde explct nstructons to the respondent to evaluate these tems at the overall frm level, these two tems form a relable scale together wth the remanng sx tems; separate analyss nvolvng the sx-tem scale and the two-tem scale produce smlar results, so ths concern does not seem problematc. 5We also estmated the model wth Homburg and Pflesser s (2000) measure of market-orented organzatonal culture, and we obtan smlar results. the frst entrant n the frm s ndustry. We computed the frm s onlne experence as the dfference between the number of days snce entry for the ndustry s frst entrant and the number of days snce the frm s onlne entry. To facltate nterpretaton such that the frst entrant nto a category had maxmum onlne experence and smaller numbers ndcate less onlne experence, we subtracted the frm s onlne experence from May 1, 2001, our survey date (mean = 1180 days or 3.23 years, s.d. = 670 days or 1.84 years). Control Varable Fnally, we controlled for the well-known effect of consumer experence on customer satsfacton (Johnson, Bellman, and Lohse 2003). We measured the frm s customer onlne experence level by the average number of onlne purchases the frm s customers made n the product category n the prevous sx months (mean = 3.19, s.d. = 1.12). Results Model Testng Approach To examne the moderatng effect of a frm s strategc commtments on the effectveness of ts CRM, we used a threestep herarchcal lnear regresson model. Step 1 ncluded the man effects of frm CRM, strategc commtments, and the control varable. Step 2 ncluded the two-way nteractons between CRM and ts strategc commtments. Fnally, Step 3 ncluded the nteractons between CRM and quadratc forms of the strategc commtment varables. Thus, our model s as follows: () 1 SAT = Step 1: β0 + β1crm _ Invest + β2crm _ Cap + β3bme + β4oe + β5cust _ Exp + ε1 ; Step 2: β6( BME CRM _ Invest) + β7( BME CRM _ Cap) + β8( OE CRM _ Invest ) + β9( OE CRM _ Cap) + ε2; Step 3: β BME2 + β OE β12( BME CRM _ Invest ) + β ( BME2 CRM _ Cap ) β β ( OE CRM _ Invest ) + 2 +ε3 ( OE CRM _ Cap ), where SAT s customer satsfacton, CRM_Invest t s CRM system nvestments, CRM_Cap s CRM capablty, BME s brcks-and-mortar experence, OE s onlne experence, and Cust_Exp s customer onlne experence for frm. We mean centered all explanatory varables before creatng the nteracton terms to avod multcollnearty. To assess the potental threat from multcollnearty, we examned varance nflaton factors and found them to be below harmful levels (Mason and Perreault 1991). Overall Model Results Step 1 (man effects) was sgnfcant (F (5, 100) = 6.78, p <.01). Step 2, wth the two-way nteractons between CRM 196 / Journal of Marketng, October 2005

5 and the strategc commtments, was also sgnfcant (F (9, 96) = 4.16, p <.01) as was the change n F assocated wth entry of ths step (change n F (4, 96) = 3.43, p <.01). Fnally, Step 3, wth the nteractons between CRM and quadratc forms of the strategc commtments, was also sgnfcant (F (15, 90) = 3.85, p <.01) as was the change n F assocated wth entry of ths step (change n F (10, 90) = 3.76, p <.01). Gven these results, we nterpret the full model results n Table 1. The results ndcate that the frm s CRM system nvestments (b =.30, p <.01) and CRM capablty, n the form of market orentaton (b =.29, p < 0.05), postvely affect customer satsfacton. 6 In addton, the control varable, customer onlne experence, negatvely affects customer satsfacton (b =.28, p <.01). We conjecture that ncreasng consumer experence may ncrease customers should expectatons, producng a negatve effect (Bouldng et al. 1993). We next examne tests of H 1 and H 2 pertanng to the moderatng effects of brcks-and-mortar experence and onlne experence on the rewards for CRM. CRM and Frm Brcks-and-Mortar Experence (H 1 ) In H 1, we predct an nverted U-shaped effect of a frm s brcks-and-mortar experence on CRM effectveness. We frst dscuss the results wth respect to a frm s CRM system nvestments, followed by ts CRM capablty. CRM system nvestments. The frst-order nteracton term (BME CRM_Invest ) s postve and sgnfcant (b =.20, p <.10), and the second-order nteracton term (BME 2 CRM_Invest ) s negatve and sgnfcant (b =.80, p <.01), n support of H 1. To determne the nature of the moderatng effect, we examne the returns on CRM system nvestments at dfferent levels of brcks-and-mortar experence. To do so, we use the unstandardzed parameter estmates from Equaton 1 (not the standardzed estmates we report n Table 1) that are pertnent to brcks-and-mortar experence and CRM system nvestments to calculate and plot the estmated coeffcents of CRM system nvestments for dfferent levels of brcks-and-mortar experence n Fgure 1. 7 As expected, the nverted U shape n Fgure 1 ndcates that moderate brcks-and-mortar experence strengthens the effects of a frm s CRM system nvestments more than low and hgh levels of brcks-and-mortar experence. 8 Approxmately 12 years of brcks-and-mortar experence maxmzes customer satsfacton returns on CRM system nvestments (.22). Addtonal analyss ndcates that CRM returns drop 6Note that the regresson coeffcents for the frst-order terms n mean-centered models are condtonal effects at mean values of the other predctor varables and must be nterpreted wth cauton (Irwn and McClelland 2001). As we subsequently show n Fgure 1 and Fgure 2, CRM system nvestments have a postve effect on performance, except at low onlne experence. 7Rearrangng Equaton 1, the parameter estmate for CRM_Invest s (b 1 + b 6 BME + b 12 BME 2 ). However, Equaton 1 s mean centered; thus, BME s BME (observed) µ (bme). Thus, the effect of CRM system nvestments at BME (observed) s (b 1 + b 6 [BME (observed) µ (bme) ] + b 12 [BME (observed) µ (bme) ] 2 ). 8For presentaton convenence, we plot brcks-and-mortar experence up to 28 years n Fgure 1. We observe smlar dmnshng returns to CRM system nvestments for hgher levels of brcksand-mortar experence. TABLE 1 Onlne Retalers Strategc Commtments to and Rewards for CRM: Model Results Varables Standardzed Coeffcents Step 1 a CRM system nvestments.30*** CRM capablty.29** Brcks-and-mortar experence.67*** Onlne experence.13 Customer onlne experence.28*** Step 2 b Brcks-and-mortar experence CRM system nvestments.20* Brcks-and-mortar experence CRM capablty.26 Onlne experence CRM system nvestments.28*** Onlne experence CRM capablty.17 Step 3 c Brcks-and-mortar experence 2.14*** Onlne experence 2.24*** Brcks-and-mortar experence 2 CRM system nvestments.80** Brcks-and-mortar experence 2 CRM capablty.10 Onlne experence 2 CRM system nvestments.25** Onlne experence 2 CRM capablty.04 Overall Intercept 17.43*** Overall F assocated wth complete model (degrees of freedom = 15,90) 3.85*** Overall R 2 assocated wth complete model.39*** *p <.10. **p <.05. ***p <.01. athe results from the three-stage model are shown. bthe change-n-f assocated wth the ntroducton of the nteracton of the strategc commtment varables (e.g., brcks-and-mortar experence) and wth the CRM nvestment varables s sgnfcant (change-n-f (4, 96) = 3.43, p <.01). cthe change-n-f assocated wth the ntroducton of the squared strategc commtment varables (e.g., brcks-and-mortar experence 2 ) and ther nteracton wth the CRM nvestment varables s sgnfcant (change-n-f (10, 90) = 3.76, p <.01). below the average return of the frms n our sample (.19) when brcks-and-mortar experence s less than 4 years (.17) and greater than 20 years (.18). 9 In summary, the sgnfcant parameter estmates of the frst-order (b =.20, p <.10) and the second-order (b =.80, p <.01) nteracton terms, combned wth the nverted U-shaped relatonshp of the returns on CRM system nvestments at dfferent levels of brcks-and-mortar experence, support H 1. CRM capablty. We next examne the effect of frm brcks-and-mortar experence on the effects of ts CRM capablty. Notably, both the frst-order (BME CRM_Cap ) (b =.26, not sgnfcant [n.s.]) and secondorder (BME 2 CRM_Cap ) (b =.10, n.s.) nteractons are 9The average return level on CRM system nvestments (unstandardzed b =.19, p <.01) corresponds to the man effect of CRM system nvestments (standardzed b =.30, p <.01) n Table 1. Customer Relatonshp Management n Onlne Retalng / 197

6 not sgnfcant. Combned wth the postve man effect of CRM capablty (b =.29, p <.05), these results suggest that the effect of a frm s CRM capablty on customer satsfacton s mpervous to ts brcks-and-mortar experence. CRM and Frm Onlne Experence (H 2 ) CRM system nvestments. The frst-order nteracton (OE CRM_Invest ) s postve and sgnfcant (b =.28, p <.01), and the second-order nteracton (OE 2 CRM_Invest ) s negatve and sgnfcant (b =.25, p <.05), n support of H 2. We plot the estmated coeffcents of CRM system nvestments at dfferent levels of onlne experence n Fgure 2. The expected nverted U shape n Fgure 2 ndcates that moderate onlne experence strengthens the effects of a frm s CRM system nvestments more than low and hgh onlne experence. Onlne experence of 4.5 years maxmzes customer satsfacton returns on CRM system nvestments. Addtonal analyss ndcates that CRM returns drop below the average (.19) when onlne experence s less than FIGURE 1 The Moderatng Effect of Frm Brcks-and-Mortar Experence on Rewards for CRM Customer Satsfacton Returns on CRM System Investments FIGURE 2 The Moderatng Effect of Frm Onlne Experence on Rewards for CRM Customer Satsfacton Returns on CRM System Investments Brcks-and-Mortar Experence (Years) Onlne Experence (Years) 3.0 years (.16) and greater than 6.5 years (.15). Notably, customer satsfacton returns on CRM system nvestments of very young frms (<2 years) are negatve. We conjecture that ths may be due to the challenges that confront these nascent frms n the turbulent onlne retalng market. In summary, the sgnfcant parameter estmates of the frst-order (b =.28, p <.01) and second-order (b =.25, p <.05) nteracton terms, combned wth the nverted U-shaped relatonshp of the returns on a frm s CRM system nvestments at dfferent levels of onlne experence, support H 2. CRM capablty. Fnally, we examne the effect of a frm s onlne experence on the effects of ts CRM capablty. Nether the frst-order (OE CRM_Cap ) (b =.17, n.s.) nor the second-order (OE 2 CRM_Cap ) (b =.04, n.s.) nteractons are sgnfcant. These results, combned wth the postve man effect of CRM capablty (b =.29, p <.05), suggest that the effect of a frm s CRM capablty on customer satsfacton s mpervous to ts onlne experence. Dscusson CRM System Investments and Frm Strategc Commtments The study s fndngs ndcate that frms pror strategc commtments have mpressve effects on the performance of ther CRM nvestments. Specfcally, the customer satsfacton effects of CRM system nvestments are greater for onlne retalers wth moderate levels of brcks-and-mortar experence than for frms wth low and hgh levels of brcks-and-mortar experence. In lght of ths fndng, managers of brcks-and-mortar retalers wth moderate brcksand-mortar experence (approxmately 12 years) can consder ther offlne experence an asset. Hgher levels of brcks-and-mortar experence (>20 years) produce dmnshng customer satsfacton returns on CRM system nvestments. Perhaps these older ncumbent retalers core rgdtes dampen the returns on ther CRM system nvestments n the emergng onlne market. From an operatonal perspectve, CRM executves can assess the rewards for ther frm s and compettors CRM system nvestments, gven ther brcks-and-mortar experence. Wth respect to onlne experence, customer satsfacton effects of onlne CRM system nvestments are greater for frms wth moderate onlne experence (approxmately 4.5 years) than for frms wth low and hgh onlne experence. Ths suggests that there s a wndow of opportunty n the onlne retalng market for fast followers to generate greater customer satsfacton returns on ther CRM actvtes than early and later entrants. Fnally, managers can use ths approach to predct the optmal return on ther nvestment and on ther compettors CRM nvestments, gven onlne experence levels. CRM Capablty and Frm Strategc Commtments Unlke CRM system nvestments, our fndngs ndcate that a frm s strategc commtments do not moderate the customer satsfacton effects of ts CRM capablty, whch s emboded n ts market ntellgence acquston, dssemnaton, and responsveness processes. Thus, a frm s market orentaton appears to be a robust and effectve organzatonal capablty 198 / Journal of Marketng, October 2005

7 that operates ndependently of the two strategc commtments of brcks-and-mortar and onlne experence. Ths null result, though not compellng n solaton, s powerful when t s consdered n conjuncton wth the sgnfcant moderatng effects of CRM system nvestments. Lmtatons and Opportuntes for Further Research Gven the complex relatonshp between customer satsfacton and other performance metrcs (Anderson, Fornell, and Lehmann 1994), the generalzablty of ths study s fndngs to other performance metrcs s an mportant ssue. Specfcally, studes usng proft-based performance metrcs would provde nsght nto cost-based effects and revenue-based effects of a frm s CRM nvestments. In ths study, we focused on the role of strategc commtments on CRM returns n onlne retalng, an mportant emergng market. Further research that examnes ths ssue n other emergng and mature markets would extend the study s fndngs n mportant ways. In addton, researchers could nvestgate the generalzablty of our fndngs usng other strategc commtments, CRM nvestments, and CRM capabltes. Concluson In summary, our study makes four contrbutons. Frst, we offer a contngent effect of a frm s CRM nvestments on ts performance, sheddng some lght on the dvergent fndngs between the CRM lterature and CRM practce. Second, we offer a strategc vantage pont, hghlghtng the role of two key strategc commtments on the rewards for CRM nvestments. Thrd, we provde mportant nsghts nto CRM actvtes n onlne retalng, for whch the complex ntersecton of frm and customer forces shapes frm performance. Fourth, our study offers gudance to practtoners on the contngent nature of rewards for ther CRM nvestments, whch should be useful n managng ther frms nvestments and montorng ther compettors CRM nvestments. Appendx Frm Customer Satsfacton (mean = 8.68, s.d. =.58, range = 1 10; α =.80) How satsfed are you overall wth ths purchase experence at (merchant name) ste? (1 = not at all to 10 = hghly ) (Note that the reported mean and standard devaton are across frms, and the alpha s across ndvduals wthn frms.) Frm CRM System Investments (mean = 5.04, s.d. =.93, range = 1 7; α =.77) Rate the level of nvestments your frm makes n the followng areas: (1 = low nvestments, 4 = moderate nvestments, and 7 = hgh nvestments ) 1. Developng a large nstalled base of customers 2. Enhancng the performance of our webste 3. Provdng optmal product prcng 4. Improvng the ease of orderng 5. Buldng a strong attachment to our brands 6. Enhancng the qualty of customer support Rate your frm relatve to ndustry average. (1 = worse than ndustry average, 4 = on par, 7 = better than ndustry average ) 1. CRM acquston expenses 2. CRM retenton expenses Frm CRM Capablty (measured by frm market orentaton) (mean = 4.92, s.d. =.78, range = 1 7; α =.76) Rate the extent to whch the followng statements descrbe your frm: (1 = strongly agree, 7 = strongly dsagree ) Informaton generaton 1. In ths busness, we do and/or buy a lot of market research. 2. We are slow to detect changes n our customers product preferences. 3. We are slow to detect fundamental shfts n our ndustry (e.g., competton). Informaton dssemnaton 1. We have frequent nterdepartmental meetngs to dscuss market trends. 2. Marketng personnel spend tme dscussng customers future needs wth other departments. 3. Data on customer satsfacton are dssemnated at all levels on a regular bass. 4. When one department fnds out somethng mportant about compettors, t s slow to alert other departments. Responsveness 1. It takes forever to decde how to respond to our compettors prce changes. 2. We tend to gnore changes n our customers products or servce needs. 3. If a major compettor launched an ntensve campagn targetng our customers, we would respond mmedately. 4. The actvtes of the dfferent departments are well coordnated. 5. Customer complants fall on deaf ears n our frm. 6. Even f we came up wth a great marketng plan, we probably would not be able to mplement t n a tmely fashon. 7. When our customers want us to modfy a product or servce, the departments nvolved make an effort to do so. Frm Brcks-and-Mortar Experence (mean = 2161 days, s.d. = 4432 days, range = 0 27,393 days) The dfference (n days) between the days snce frm foundng and days snce frm Web entry, both measured from the survey date of May 1, 2001, s the measure of brcks-and-mortar experence. Frm Onlne Experence (mean = 1180 days, s.d. = 670 days, range = days) The dfference (n days) between the days snce the ndustry s frst entrant and the days snce frm s Web entry. To facltate nterpretaton such that larger numbers denote hgher onlne experence, we subtracted the frm s onlne experence from our survey date of May 1, Customer Onlne Experence (mean = 3.19, s.d. = 1.12, range = 1 10) The number of onlne purchases made by the frm s customers n the product category n the prevous sx months. Customer Relatonshp Management n Onlne Retalng / 199

8 REFERENCES Alba, Joseph W., John Lynch, Barton Wetz, Chrs Janszewsk, Rchard Lutz, Alan Sawyer, and Stacy Wood (1997), Interactve Home Shoppng: Consumer, Retaler, and Manufacturer Incentves to Partcpate n Electronc Marketplaces, Journal of Marketng, 61 (July), Anderson, Eugene W., Claes Fornell, and Donald R. Lehmann (1994), Customer Satsfacton, Market Share, and Proftablty, Journal of Marketng, 58 (July), ,, and Sanal Mazvancheryl (2004), Customer Satsfacton and Shareholder Value, Journal of Marketng, 68 (October), Ansar, Asm and Carl F. Mela (2003), E-Customzaton, Journal of Marketng Research, 40 (May), Bouldng, Wllam, Ajay Kalra, and Rchard Staeln (1999), The Qualty Double Whammy, Marketng Scence, 18 (4), ,,, and Valare A. Zethaml (1993), A Dynamc Process Model of Servce Qualty: From Expectatons to Behavoral Intentons, Journal of Marketng Research, 30 (February), Chandy, Rajesh K. and Gerard J. Tells (2000), The Incumbent s Curse? Incumbency, Sze, and Radcal Product Innovaton, Journal of Marketng, 64 (July), Day, George S. and C. Van den Bulte (2002), Superorty n Customer Relatonshp Management: Consequences for Compettve Advantage and Performance, workng paper, Wharton School of Busness, Unversty of Pennsylvana. Degeratu, Alexandru, Arvnd Rangaswamy, and Janan Wu (2000), Consumer Choce Behavor n Onlne and Tradtonal Supermarkets: The Effects of Brand Name, Prce, and Other Search Attrbutes, Internatonal Journal of Research n Marketng, 17 (March), Dgnan, Larry (2002), CRM: Dream or Nghtmare? (accessed Aprl 3, 2002), [avalable at Fornell, Claes (1992), A Natonal Customer Satsfacton Barometer: The Swedsh Experence, Journal of Marketng, 56 (January), Gartner Group (2003), CRM Success Is n Strategy and Implementaton, Not n Software, (accessed December 2003), [avalable at Geyskens, Inge, Katrjn Gelens, and Marnk G. 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McClelland (2001), Msleadng Heurstcs and Moderated Multple Regresson Models, Journal of Marketng Research, 38 (February), Jayachandran, Satsh, Subhash Sharma, Peter Kaufman, and Pushkala Raman (2005), The Role of Relatonal Informaton Processes and Technology Use n Customer Relatonshp Management, Journal of Marketng, 69 (October), Johnson, Erc J., Steven Bellman, and Gerald L. Lohse (2003), Cogntve Lock-In and the Power Law of Practce, Journal of Marketng, 67 (Aprl), Kanter, Rosabeth Moss (2001), The Ten Deadly Mstakes of Wanna-Dots, Harvard Busness Revew, 79 (January), Kohl, Ajay K. and Bernard J. Jaworsk (1990), Market Orentaton: The Construct, Research Propostons, and Manageral Implcatons, Journal of Marketng, 54 (Aprl), 1 18.,, and Ajth Kumar (1993), MARKOR: A Measure of Market Orentaton, Journal of Marketng Research, 30 (November), Lynch, John G., Jr., and Dan Arely (2000), Wne Onlne: Search Costs Affect Competton on Prce, Qualty, and Dstrbuton, Marketng Scence, 19 (1), Mason, Charlotte H. and Wllam D. Perreault (1991), Collnearty, Power, and Interpretaton of Multple Regresson Analyss, Journal of Marketng Research, 28 (August), Mthas, Sunl, M.S. Krshnan, and Claes Fornell (2005), Why Do Customer Relatonshp Management Applcatons Affect Customer Satsfacton? Journal of Marketng, 69 (October), Nelson, Rchard and Sdney G. Wnter (1982), An Evolutonary Theory of Economc Change. Cambrdge, MA: Belknap Press. Rebsten, Davd J. (2002), What Attracts Customers to Onlne Stores, and What Keeps Them Comng Back? 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