In business markets, the link between satisfaction and

Transcription

1 Jenny van Doorn & Peter C. Verhoef Crtcal Incdents and the Impact of Satsfacton on Customer Share In busness markets, the long-term nature of relatonshps may prompt partes to conduct busness as usual, but negatve crtcal ncdents (CIs) can cause a destablzaton of these long-term relatonshps. The authors develop a comprehensve dynamc model of customer loyalty to account for the mpact of negatve CIs on both the nature and the magntude of the relatonshps between satsfacton and customer share. The results ndcate that CIs trgger a stronger updatng of the customer relatonshp, whch moves customers from a busness-as-usual mnd-set to a reconsderaton of the relatonshp. Furthermore, nonlneartes n the relatonshps are much more pronounced n the presence of CIs. Dependng on the relatonshp qualty, CIs have dfferent consequences for customer relatonshps, and f relatonshp qualty s hgh, a negatve CI can even have a postve mpact on customer share. Keywords: servce falure, busness-to-busness relatonshps, customer loyalty, servces, customer relatonshp management, crtcal ncdents In busness markets, the lnk between satsfacton and loyalty often appears weak or even absent (Narayandas 2005) because mature, ongong customer suppler relatonshps tend to be characterzed by nerta that causes partes to conduct busness as usual and, n essence, mantan the status quo. However, n some crcumstances, long-term relatonshps can destablze, especally when negatve crtcal ncdents (CIs) occur. Negatve CIs can be defned as out-of-the-ordnary events durng an nteracton that customers perceve or recall as unusually negatve (Roos 2002). Furthermore, a negatve CI can trgger a declne n customer satsfacton and alter customer purchasng behavor (e.g., Btner, Booms, and Tetreault 1990). In ths research, we propose that negatve CIs not only drectly affect customer satsfacton and loyalty but also play a moderatng role n the customer relatonshp, such that they ntensfy relatonshps between current satsfacton and loyalty and thus move the relatonshp away from busness as usual to a more actve state. 1 Marketng lterature has already subjected customer satsfacton and customer loyalty to extensve research. 1In the remander of ths artcle, we study the effect of negatve CIs. Thus, when we use the term CI, we mply a negatve CI. Jenny van Doorn s Assstant Professor of Marketng (e-mal: j.van. doorn@rug.nl), and Peter C. Verhoef s Professor of Marketng (e-mal: p.c.verhoef@rug.nl), Faculty of Busness and Economcs, Unversty of Gronngen. The authors acknowledge the data support of a European logstcs company. They thank Ruth Bolton, Peter Leeflang, and Vkas Mttal for ther helpful comments on a prevous verson of ths artcle. They also thank Klaus Backhaus for hs support durng ths research project. They acknowledge the helpful comments of partcpants at the 2006 Marketng Dynamcs Conference at the Unversty of Calforna, Los Angeles; partcpants at the 2006 Marketng Scence Conference; partcpants at the Marketng Scence Insttute Conference on Marketng Metrcs and Fnancal Performance; and semnar partcpants at the Unversty of Gronngen. They also thank the three anonymous JM revewers for ther helpful comments. 2008, Amercan Marketng Assocaton ISSN: (prnt), (electronc) 123 Whereas early studes devoted attenton to the process of satsfacton formaton (e.g., Olver 1980), recent research has focused on the relatonshp between customer satsfacton and customer loyalty (e.g., Bolton and Lemon 1999; Cool et al. 2007; Mttal, Kumar, and Tsros 1999). In addton to cross-sectonal studes, research has begun to employ a longtudnal desgn to consder carryover effects n the relatonshps between satsfacton and loyalty (e.g., Mttal and Kamakura 2001) (see Table 1). Several crosssectonal and longtudnal studes have also conveyed an ntegrated vew of the process of satsfacton formaton and ts relatonshp to customer loyalty (e.g., Bowman and Narayandas 2004; Mttal, Kumar, and Tsros 1999). The mpact of CIs n the context of satsfacton formaton and customer loyalty has receved some attenton (for an overvew, see Gremler 2004), but to date, many studes reman qualtatve n nature and examne, for example, the categores of CIs that affect customers (ds)satsfacton most (Btner, Booms, and Tetreault 1990; Kelley, Hoffman, and Davs 1993). Another group of studes lnks CIs and behavoral consequences (Gardal, Flnt, and Woodruff 1996; Keaveney 1995; Roos, Edvardsson, and Gustafsson 2004), and a related lterature stream n servces marketng examnes how recoverng from a servce falure affects satsfacton and loyalty ntentons (Maxham and Netemeyer 2002; Smth and Bolton 1998) or perceved justce (Smth, Bolton, and Wagner 1999). As we show n Table 1, most research on CIs centers on ther mpacts on ether satsfacton formaton or customer loyalty. Therefore, as our frst objectve, we attempt to develop a comprehensve vew of the role of CIs n the formaton of servce satsfacton and customer loyalty. We further strve to nvestgate how CIs affect the lnks between attrbute and overall satsfacton and between overall satsfacton and customer loyalty. By takng an ntegratve vew, we provde a complete pcture of how negatve CIs affect customer relatonshps. In addton, an ongong debate centers on the role of CIs n customer relatonshps. Although ample theoretcal ev- Journal of Marketng Vol. 72 (July 2008),

2 TABLE 1 Overvew of Studes n Satsfacton and Loyalty Lterature Lterature Stream Study Desgn Satsfacton Formaton Satsfacton Loyalty Satsfacton loyalty Lterature Cross-sectonal studes Churchll and Surprenant (1982) Olver (1980) Tse and Wlton (1988) Anderson and Sullvan (1993) Bowman and Narayandas (2004) Mttal, Ross, and Baldasare (1998) Agustn and Sngh (2005) Longtudnal studes Bolton and Drew (1991) Johnson, Anderson, and Fornell (1995) Cool et al. (2007) Mttal and Kamakura (2001) Verhoef (2003) Bolton and Lemon (1999) Bouldng et al. (1993) Mttal, Kumar, and Tsros (1999) Impact of CIs Cross-sectonal studes Btner (1990) Btner, Booms, and Tetreault (1990) Kelley, Hoffman, and Davs (1993) Smth and Bolton (1998) Smth, Bolton, and Wagner (1999) Keaveney (1995) Roos, Edvardsson, and Gustafsson (2004) Longtudnal studes Bolton (1998) Gardal, Flnt, and Woodruff (1996) Gustafsson, Johnson, and Roos (2005) Maxham and Netemeyer (2002) Ths research dence suggests a moderatng effect of CIs on customer relatonshps, emprcal evdence has been tenuous. Gustafsson, Johnson, and Roos (2005) fnd nether a sgnfcant drect effect of CIs on retenton nor a moderatng effect on the relatonshp between customer satsfacton and retenton. In an nvestgaton of the mpact of servce falures on relatonshp duraton, Bolton (1998) fnds a postve moderatng effect of unreported servce falures on relatonshp duraton, whch mples that unreported servce falures prolong relatonshps. She argues that ths counterntutve fndng may occur because customers who do not report falures may be more tolerant. In contrast, reported servce falures have a negatve but nsgnfcant moderatng effect on relatonshp duraton. The results of these studes notably dverge, whch s surprsng because both use the consumer telecommuncatons market as ther context. Thus, the potental moderatng effect of CIs demands further nvestgaton. In turn, our second objectve s to examne the moderatng effects of CIs n the context of customer relatonshps. In partcular, we suspect that CIs may revve the customer relatonshp and enhance the lnk between current satsfacton evaluatons and customer loyalty, thus reducng the nerta of a busness-as-usual relatonshp state. For our research context, we choose busness-to-busness (B2B) servces, whch are more lkely to be strongly affected by CIs than the consumer telecommuncatons market, n whch CIs, though hghly rrtatng, usually do not have major economc consequences. Moreover, we post that negatve CIs mght not only enhance the magntude of the lnks between current satsfacton and loyalty but also affect ther shape and nduce nonlneartes n the relatonshps. Thus far, studes nvestgatng nonlneartes n the context of customer satsfacton and loyalty have arrved at mxed results (Gupta and Zethaml 2006) and have assumed that nonlnear relatonshps occur for all customers (e.g., Agustn and Sngh 2005; Mttal and Kamakura 2001). In contrast, we examne whether the occurrence and type of nonlneartes depend on the presence of CIs. We assume that CIs nduce nonlneartes n a relatonshp that are not detectable (or may even dffer) for customers who have not experenced a CI. Therefore, as a thrd goal of our research, we nvestgate the extent to whch negatve CIs trgger nonlneartes and asymmetres n the relatonshps among attrbute, overall servce satsfacton, and customer loyalty. Fnally, unlke most prevous research on CIs, we use a dynamc approach that takes nto account past evaluatons of satsfacton and past loyalty. Thus, we explctly acknowledge that CIs occur wthn an exstng relatonshp and that the hstory of a relatonshp may nfluence the effect of CIs. Ths approach enables us to explore (usng smulatons) how CIs can affect relatonshps dfferently dependng on the ntal state of those relatonshps before the CI occurred. We organze the remander of ths artcle as follows: In the next secton, we descrbe our conceptual model and deduce our hypotheses. Subsequently, we descrbe the 124 / Journal of Marketng, July 2008

3 desgn of our emprcal study and dscuss several basc descrptve statstcs. We specfy our econometrc model and dscuss the estmaton results n the next two sectons. We conclude wth a dscusson of our results, the research lmtatons, and avenues for further research. Conceptual Model In Fgure 1, we depct our conceptual model, n whch customer share, whch serves as a measure of customer loyalty, s nfluenced by ts lag, current servce satsfacton, and prce satsfacton. Furthermore, we post that current servce satsfacton s affected by attrbute evaluatons and past servce satsfacton and that current prce satsfacton s nfluenced by past prce satsfacton (Mttal, Kumar, and Tsros 1999). Because the lnks among attrbute evaluatons, satsfacton, and customer share are well establshed (e.g., Bowman and Narayandas 2004; Mttal, Kumar, and Tsros 1999), we do not put forth specfc hypotheses. Lkewse, prevous lterature has documented carryover effects wthn these constructs (e.g., Johnson, Herrmann, and Huber 2006). Followng the lterature, we assume postve lnks between attrbute satsfacton and past and current overall servce satsfacton (Mttal, Kumar, and Tsros 1999), as well as between past and current prce satsfacton. We also expect current servce satsfacton, prce satsfacton, and because of nerta effects (Rust, Lemon, and Zethaml 2004; Verhoef 2003) past customer share to have postve mpacts on current customer share. We allow the magntude and the nature of the relatonshps to dffer across customers who have experenced a CI and those who have not. Accordng to pror lterature, both the occurrence of a servce falure and the qualty of subsequent servce recovery have strong mpacts on customer relatonshps (e.g., Smth and Bolton 1998). Therefore, we nclude servce recovery as a determnant of servce satsfacton and customer share n our model. In lne wth pror lterature, we assume that satsfacton wth the servce recovery has a postve effect on both servce satsfacton and customer share for customers who have experenced a CI (Maxham and Netemeyer 2002; Smth, Bolton, and Wagner 1999). Because ths relatonshp s well establshed, we do not nclude explct hypotheses regardng servce recovery effects. Furthermore, n the course of ongong servce relatonshps, customers may experence not just one but multple CIs. Pror research has suggested that multple CIs can lead to less favorable evaluatons of the customer relatonshp and dfferent outcomes than when only one CI occurs (Maxham and Netemeyer 2002). Thus, we account for the FIGURE 1 Conceptual Model Crtcal Incdents and Customer Share / 125

4 occurrence of multple CIs n our conceptual model and expect that multple CIs lower both the level of satsfacton and customer share. Hypotheses Satsfacton Formaton Impact of lagged on current servce satsfacton. In the context of an ongong buyer suppler relatonshp, servce satsfacton represents a cumulatve construct that reflects the customer s overall evaluaton of the servce provder, as opposed to transactonal satsfacton that reflects an evaluaton of a sngle servce encounter (Bolton 1998). Therefore, overall servce satsfacton ncorporates a customer s past and recent experences wth the servce provder and can be descrbed accordng to the general anchorng-andadjustment model, as specfed by Hogarth and Enhorn (1992). Current opnon, whch serves as an anchor, adjusts on the bass of successve experences. In the context of customer satsfacton, ths model mples that past satsfacton evaluatons get updated through current experences (Bolton 1998), whch can be captured n recent measures of attrbute satsfacton. Therefore, we arrve at the followng: L 1 servsat t ϕ servsatt 1 ω l attsat l t l = 1 () = + + ε t, where servsat t s servce satsfacton at t and attsat lt s satsfacton wth the attrbute l at t. When CIs occur, they can have two effects accordng to ths model. On the one hand, they could lead to lower satsfacton ratngs on the attrbute level. On the other hand, they could affect the adjustment weghts ϕ and ω l and determne how strongly customers weght current nformaton compared wth prevous nformaton. We base our consderatons regardng the adjustment weghts on Fske and Neuberg s (1990) contnuum model of mpresson formaton, n whch they dstngush between conceptually drven top-down processes that are largely guded by atttudes and stereotypes and data-drven bottomup processes, n whch mpresson formaton s based on attrbutes. They conceptualze these two processng mechansms not as mutually exclusve but rather as endponts of a contnuum (Fske and Neuberg 1990; Kunda and Thagard 1996). Transferred to our context, n both the presence and the absence of CIs, current servce satsfacton should be guded by attrbute satsfacton and prevous evaluatons, but the occurrence of CI mght affect the weghts of both peces of nformaton. We propose that f no CIs occur, the updatng process s less pronounced, more atttude-based, and more relant on past servce satsfacton as the strongest nfluence on current satsfacton. If CIs occur, more data-drven processng results because, n most cases, CIs wll be connected to one or several attrbutes relevant for the evaluaton and thus wll ncrease attrbute accessblty and facltate attrbute retreval. In these crcumstances, more attrbute-based processng s lkely (Mantel and Kardes 1999; Sanbonmatsu and Fazo 1990). Evaluatons should be guded more by the new data and less by past experence; n turn, a stronger update of overall servce satsfacton should occur. Thus, n reference to Equaton 1, we post that ϕ wll be smaller for customers who experence a CI than for those who do not. H 1a : Lagged servce satsfacton has a weaker mpact on current servce satsfacton for customers who experence a negatve CI than for customers who do not experence a CI. Nonlnear mpact of lagged on current servce satsfacton. Pror customer satsfacton lterature has acknowledged potental nonlnear relatonshps (e.g., Anderson and Mttal 2000). To the best of our knowledge, such nonlnear relatonshps n the lnk between past and current evaluatons of servce satsfacton have not yet been nvestgated. However, the many studes that fnd nonlnear effects n the context of customer satsfacton formaton have ndcated that ths relatonshp may dsplay nonlneartes as well. In partcular, we post that the presence and nature of these nonlnear effects may depend on the occurrence of a CI. A negatve CI provdes strong negatve cues that should alter satsfacton evaluatons consderably and lead to lower attrbute and overall servce satsfacton levels. However, prevous research n the doman of mpresson formaton has suggested that people are reluctant to alter ther atttudes completely. Defensve processng resultng from the desre to confrm a preferred poston can lead people to dscount negatve nformaton and weght t less heavly than postve nformaton (Ahluwala 2002; Kunda 1990). Therefore, prevous mpressons or atttudes can be resstant to change, even n the presence of a negatve cue. In the context of customer satsfacton, defensve processng mples that customers wth a hgh level of servce satsfacton who experence a negatve CI may dscount the negatve nformaton and attach more weght to ther prevously held postve overall atttudes. When a CI occurs, hgher satsfacton ratngs should have a stronger tendency to carry over to the next perod than lower ratngs; n other words, we expect a postve asymmetry. H 1b : For customers who experence a negatve CI, hgher past servce satsfacton ratngs have a stronger mpact on current servce satsfacton than lower ratngs. In the absence of a CI, the updatng process should be much less pronounced, and the theoretcal consderatons of the nature of the lagged relatonshps lkely wll not apply. Therefore, we must seek other ratonales for the nature of the relatonshps n the absence of a CI. On the one hand, the relatonshp between past and current servce satsfacton mght be straghtforward and lnear. On the other hand, negatve or postve past evaluatons may have a stronger mpact on current satsfacton. Prospect theory, whch posts that losses loom larger than gans, would suggest a negatve asymmetry, that s, a greater mpact of less favorable satsfacton evaluatons (Kahnemann and Tversky 1979; Mttal, Ross, and Baldasare 1998). The so-called negatvty effect also offers theoretcal grounds for negatve asymmetry because t argues that negatve nformaton stands out and s weghted more heavly than postve nformaton (Fske 126 / Journal of Marketng, July 2008

5 1980; Kanouse and Hanson 1972; Mttal, Ross, and Baldasare 1998; Peeters and Czapnsk 1990). However, postve past satsfacton scores concevably mght exert a greater nfluence on customer satsfacton as well. Theoretcally, ths clam s grounded n the general postvty bas (Anderson 1998; Whtney 1971). Such an effect s especally lkely n hghly ndvdualzed servce settngs, n whch customers antcpate and accept greater varablty n servce levels (Folkes and Patrck 2003). Thus, from a theoretcal pont of vew, the nature of the lagged relatonshps n the absence of a CI s dffcult to predct. Therefore, we do not put forth specfc hypotheses but rather test for nonlneartes n the relatonshps n our emprcal model. Impact of servce attrbutes on servce satsfacton. In the next step, we nvestgate the magntude and nature of the mpact of attrbute satsfacton on servce satsfacton. As we noted prevously, we expect a CI to nduce stronger updates of the otherwse cumulatve construct of overall servce satsfacton than a stuaton wthout a CI. In turn, current evaluatons of the attrbutes should become more salent for overall servce satsfacton ratngs. Thus, we assume that ω l (see Equaton 1) s greater for the group of customers who experence CIs than for those who do not. H 2a : Current attrbute satsfacton has a stronger mpact on current servce satsfacton for customers who experence a negatve CI than for customers who do not experence a CI. Nonlnear mpact of servce attrbutes on servce satsfacton. Next, we nvestgate the nonlneartes n the lnk between attrbute satsfacton and overall servce satsfacton. In Table 2, we provde an overvew of studes pertanng to these nonlnear relatonshps and dstngush between (1) studes that nvestgate nonlneartes n the attrbute satsfacton overall customer satsfacton lnk and (2) studes that nvestgate nonlneartes n the overall satsfacton customer loyalty lnk. Regardng the relatonshp between attrbute satsfacton and overall customer satsfacton, the majorty of the emprcal evdence ponts to a negatve asymmetry, whch mples that negatve attrbute scores exert a stronger nfluence on overall satsfacton than postve ones. Because we assume a strong mpact of current attrbute satsfacton n the presence of a CI, we transfer the fndngs of crosssectonal studes n prevous lterature to relatonshps n the presence of a CI. For customers who experence a CI, we assume a negatve asymmetry n the lnk between attrbute satsfacton and overall servce satsfacton. Theoretcally, we base ths ratonale on prospect theory (Kahnemann and Tversky 1979) and the negatvty effect (Peeters and Czapnsk 1990). H 2b : For customers who experence a negatve CI, lower ratngs of current attrbute satsfacton have a stronger mpact on current servce satsfacton than hgher ratngs. The nature of ths effect for customers who do not experence CI s not clear. On the bass of pror lterature that assumes no moderatng effect of CI, we mght expect a negatve asymmetry. However, n the absence of a CI, we expect updatng to be less promnent and current attrbute evaluatons to be less mportant for overall servce satsfacton. As a consequence, nonlnear effects mght be less lkely. Therefore, we do not specfy a hypothess about the nature of ths relatonshp but rather examne the presence and nature of nonlneartes n our emprcal analyss. Prce satsfacton. Exstng lterature contans evdence that the evaluaton of the economc costs and benefts of a servce has a dstnct mpact on customer behavor (Bolton and Lemon 1999; Bolton, Lemon, and Verhoef 2008). Therefore, we nclude the construct of prce satsfacton, whch reflects a customer s evaluaton of the economc exchange rato, n our model. Drawng on the same ratonale we appled n the context of servce satsfacton, we expect smlar effects. When CIs occur, customers should be guded to a lesser extent by past evaluatons, whereas n the absence of CI, current prce satsfacton should be determned largely by past prce satsfacton. H 3a : Past prce satsfacton has a weaker mpact on current prce satsfacton for customers who experence a negatve CI than for customers who do not experence a CI. Usng the same ratonale that underles H 1b, we expect a postve asymmetry wth respect to the mpact of past prce satsfacton ratngs on present ratngs. H 3b : For customers who experence a negatve CI, hgher ratngs of past prce satsfacton have a stronger mpact on current prce satsfacton than lower ratngs. Customer Share Formaton Impact of satsfacton on customer share. The second part of our model pertans to the formaton of customer share over tme. As we noted prevously, nerta effects and, therefore, past customer share are mportant determnants of current customer share (Rust, Lemon, and Zethaml 2004; Verhoef 2003). The extent to whch current ratngs of servce satsfacton and prce satsfacton nfluence current customer share lkely s affected by CIs. Agan, we expect that CIs trgger a stronger update of customer share and mply a stronger mpact of both overall servce satsfacton and prce satsfacton (Gustafsson, Johnson, and Roos 2005). H 4a : Current servce satsfacton has a stronger mpact on current customer share for customers who experence a negatve CI than for customers who do not experence a CI. H 5a : Current prce satsfacton has a stronger mpact on current customer share for customers who experence a negatve CI than for customers who do not experence a CI. Nonlnear mpact of satsfacton on customer share. Emprcal evdence regardng nonlneartes n the relatonshp between satsfacton and loyalty s somewhat ambguous (see Table 2); both ncreasng returns (suggestng a postve asymmetry) and decreasng returns (ndcatng negatve asymmetres) have been observed. Theoretcally, we mght expect negatve asymmetres accordng to Crtcal Incdents and Customer Share / 127

6 TABLE 2 Nonlneartes n the Context of Customer Satsfacton and Loyalty Researched Relatonshps Authors Industry 1 2 Results Anderson and Sullvan (1993) ACSI data x x 1. Negatve asymmetry (dsconfrmaton satsfacton) 2. Lnear relatonshp between satsfacton and repurchase ntentons DeSarbo et al. (1994) Bank, dental servces x Negatve asymmetry (SERVQUAL dmenson qualty) Mttal and Baldasare (1996) Health care x Negatve asymmetry (attrbute evaluatons overall satsfacton) Bolton (1998) Telecommuncatons x Negatve asymmetry (perceved losses/gans on relatonshp duraton) Mttal, Ross, and Baldasare (1998) Health care, automotve ndustry x x Negatve asymmetry (attrbute satsfacton overall satsfacton, attrbute satsfacton repurchase ntentons) Anderson and Mttal (2000) Bowman and Narayandas (2001) ACSI data x Negatve asymmetry, greater effect at the extremes (satsfacton repurchase ntentons) Consumer products x x 1. Negatve asymmetry (dsconfrmaton satsfacton wth a customer-ntated contact) 2. Decreasng returns (satsfacton wth a customerntated contact purchasng behavor) Mttal and Kamakura (2001) Bowman and Narayandas (2004) Gómez, McLaughln, and Wttnk (2004) Automotve ndustry x Satsfacton loyalty ntentons: decreasng returns; satsfacton behavoral loyalty: ncreasng returns B2B: processed metal x Increasng returns (satsfacton customer share) Supermarket x x 1. Negatve asymmetry (qualty overall satsfacton); postve asymmetry (customer servce and value overall satsfacton) 2. Negatve asymmetry (satsfacton sales performance) Streukens and De Ruyter (2004) Agustn and Sngh (2005) Retal servce ndustres x x Lnear relatonshps (servce qualty satsfacton, servce qualty behavoral ntentons, satsfacton behavoral ntentons) Retalng and arlne x Decreasng returns only n the retal data (satsfacton loyalty ntentons) Cool et al. (2007) Bank x Segment-specfc nonlneartes Notes: 1 = antecedents of satsfacton/attrbute satsfacton overall satsfacton, and 2 = satsfacton loyalty. ACSI = Amercan Customer Satsfacton Index. prospect theory (Kahnemann and Tversky 1979) and the general negatvty effect (Peeters and Czapnsk 1990). A stronger mpact of postve satsfacton evaluatons on loyalty s lnked to the noton of customer delght, whch posts that only truly delghted customers are loyal to a company (Bowman and Narayandas 2004). However, most emprcal evdence ponts to a negatve asymmetry (see Table 2). Because CIs may drect specal customer attenton to less favorable aspects of the servce, we suggest a negatve asymmetry n the relatonshp between satsfacton and loyalty n the presence of CIs. H 4b : For customers who experence a negatve CI, lower ratngs of current servce satsfacton have a stronger mpact on current customer share than hgher ones. H 5b : For customers who experence a negatve CI, lower ratngs of current prce satsfacton have a stronger mpact on current customer share than hgher ones. For customers who do not experence a CI, the presence and nature of nonlnear effects reman uncertan. On the one hand, the ongong busness relatonshp of these customers mght mply the lack of a relatonshp between satsfacton and loyalty (Narayandas 2005). On the other hand, cus- 128 / Journal of Marketng, July 2008

7 tomers may modfy the share they offer to the suppler only when they are very satsfed, n lne wth the prncple of customer delght. In the absence of a CI, therefore, customer share changes mght occur only as a result of very postve evaluatons of both servce satsfacton and prce satsfacton. In consderaton of these dvergent vews, we do not put forth specfc hypotheses about the presence and nature of a nonlnear effect for customers who do not experence a CI but rather examne the presence of nonlnear relatonshps n our emprcal analyss. Research Desgn Data Collecton Our samplng frame conssts of approxmately 850 customers n Germany and the Netherlands of a European professonal logstcs servce provder. The servce portfolo of the logstcs company manly conssts of physcal transportaton servces wthn Europe; only a small porton of the company s customer base uses addtonal logstc servces such as packagng or warehousng. We conducted a telephone survey n yearly teratons durng , wth one-year tme spans between surveys. The collaboratve project ensures that the logstc servce provder s heavly nvolved n the data collecton. We ntervewed key decson makers regardng supplers choce of transport servces and obtaned an average response rate of 60%. Because we need at least two observatons per frm over tme (t 1 and t), we ncluded only those companes that partcpated n at least two subsequent teratons of the survey and n whch the same key decson maker responded to the survey. As a consequence, our fnal sample conssts of 399 (response rate of 47%) dfferent companes 230 companes that partcpated n two subsequent teratons of the survey and 169 that partcpated n three teratons. Thus, our data set comprses companes partcpatng n 1999 and 2000 (36% of our sample of 399 companes); those partcpatng n 2000 and 2001 (22%); and those partcpatng n 1999, 2000, and 2001 (42%). Of the 399 dfferent companes, 109 belong to the ore, coal, and steel ndustry; 82 belong to the constructon ndustry; 83 belong to the chemcal ndustry; 59 belong to the ndustral and agrcultural ndustry; and 66 belong to the manufacturng ndustry. We pooled the data over all respondents and perods to obtan 568 cases that contan a present and a lagged perod. In addton, we verfed whether the 399 companes dffer sgnfcantly from the respondng companes for whch we had only one observaton. These results reveal no sgnfcant dfferences for ndustry sector, satsfacton, or customer share, suggestng that sample bas due to attrton s not a problem. As noted prevously, we requre a mnmum of two observatons over tme, whch s rather common n longtudnal research wthn customer satsfacton lterature (e.g., Bolton and Lemon 1999; Mttal, Kumar, and Tsros 1999; Verhoef, Franses, and Donkers 2002). We acknowledge the queston whether one year between two surveys s too long to measure feedback effects, but pror research ndcates that t s not. Mttal, Kumar, and Tsros (1999) employ a 21- month span between surveys and reveal feedback effects; the same holds true for Verhoef, Franses, and Donkers (2002), who use a perod of exactly one year between surveys. Johnson, Hermann, and Huber (2006) even allow two years between surveys and stll show substantal carryover effects n customer perceptons and loyalty ntentons. Thus, a tme span of one year should not be too long to assess feedback effects. Measures We developed the questonnare on the bass of nput from expert ntervews. Specfcally, we asked customers to ndcate (on a seven-pont bpolar ratng scale; see the Appendx) ther overall satsfacton wth servce; prce; and transport-related attrbutes, nteractons wth sales agents, and handlng and bllng. Because we am to develop a longtudnal database and gven the few customers n our data, as s typcal n ths type of B2B market, suffcent customer partcpaton s an mportant ssue. Furthermore, because the length of the survey represents an mportant determnant of response levels (e.g., Yammarno, Sknner, and Chlders 1991), we strve for a short survey and therefore use snglenstead of multple-tem measures (see also Bergkvst and Rosster 2007; Rosster 2002). Customers ndcated the customer share they have wth the company on a sx-pont scale (1: <10%; 2: 10% 20%; 3: 20% 30%; 4: 30% 40%; 5: 40% 60%; and 6: >60%). The customer share measure recognzes that busness customers usually purchase from multple vendors (e.g., Bowman, Farley, and Schmttlen 2000), whch mples that loyal behavor must be defned as awardng greater customer share to a partcular suppler (Bowman and Narayandas 2004). By measurng loyalty through customer share nstead of absolute purchase volumes, we can avod the problem of purchase volumes dstorted by the success of the frm, whch can occur n multperod nvestgatons (for a more detaled dscusson, see Cool et al. 2007; Verhoef 2003). We chose a categorcal scale rather than the actual measure because assessments of actual customer share tend to become unrelable and more dffcult to answer. In addton, the company tself wanted consstency n ts measurement and had used ths measure n prevous surveys. CIs Usng the CI technque, we asked customers about the specal occurrences among ther nteractons wth the servce provder they recalled as beng unusually negatve (Roos 2002), whch matches our defnton of a negatve CI. On the bass of dscussons wth management, we ntally formed CI categores related to the attrbute categores n the questonnare. Two judges subsequently assgned the CI to these categores. The proporton of parwse nterjudge agreements s 94.2%; thus, the classfcaton has a suffcent level of relablty (Rust and Cool 1994). The cases on whch the judges dd not agree were resolved through dscusson. In total, our data contan 530 CI reports. In 159 of the 568 cases, respondents experenced one CI, and n 153, they encountered more than one CI, wth an average of 2.4 CIs Crtcal Incdents and Customer Share / 129

8 for the cases n whch multple CIs occurred. In addton, 325 CIs are categorzed as transport-related and consttute core servce falures (Keaveney 1995; Maxham and Netemeyer 2002), such as delayed or cancelled transport servces ( A transport arrved n the mddle of the nght nstead of n the mornng ) or damaged cargo ( Our multfunctonal machne was damaged durng transport; the consequence was producton downtmes ). All these CIs refer to the core servce of physcal transportaton, not to addtonal logstc servces. We assgned 40 CIs to nteractons wth sales agents, ncludng ssues such as long response tmes to transport nqures ( Snce July 14th, we are watng for an offer for transports to Austra ) or unavalablty of the sales agent. Thus, these CIs consttute servce encounter falures (Keaveney 1995). The CIs assgned to the handlng-andbllng category consst of problems durng nteractons wth customer servce ( The customer servce could not tell me where my cargo was at that moment ) or bllng errors ( Our bll was 250,000 too hgh ). Thus, these CIs also manly stem from nteractons. Fnally, 24 CIs are related to prcng ( We just receved the second prce ncrease for ths year ), and 42 CIs cannot be assgned to any of the categores. To assess servce recovery, we used a sngle tem that measures how satsfed customers are wth the frm s resoluton of ther problems, agan wth a seven-pont bpolar ratng scale. Descrptve Statstcs In Table 3, we summarze the descrptve statstcs of our sample. The CIs are assocated wth decreases n attrbutes, overall servce satsfacton, and prce satsfacton; the transport-related attrbutes, whch contan the most CIs, yeld the greatest satsfacton decrease. Evaluatons by customers who dd not experence a CI reman stable or show slght ncreases. The customer share among both customer groups decreases over tme, n lne wth pror research ndcatng that the customer share of exstng customers decreases over tme (e.g., Verhoef 2003). Lagged customer shares do not dffer between customers who experenced a CI and those who dd not (p >.1), whch suggests that CIs are unrelated to prevous customer share. Econometrc Model We use a system of equatons to test our conceptual model. We frst dscuss the general structure of our model, and then we extend the model to allow for nonlnear effects. As we noted, we explan current servce satsfacton usng lagged servce satsfacton and current attrbute ratngs as explanatory varables. In the second equaton of our model, we specfy dynamc relatonshps for prce satsfacton, and n the thrd, we let customer share depend on ts lag, current servce satsfacton, and prce satsfacton. We pool the data across ndustres and perods and control for ndustry sector and tme effects by ncludng the correspondng dummy varables. We allow the relatonshps to dffer for customers who experenced and dd not experence CIs and estmate dstnct regresson parameters for these two groups. Because we have no reason to assume that CIs affect our control varables, we mantan unform parameters for these. 2 For customers who experenced CIs, we add a dummy varable to denote whether the respondent experenced one CI or multple CIs and a varable to measure satsfacton wth servce recovery. 3 2Accordng to a Chow test, there s no sgnfcant dfference n the parameter vectors for our control varables at p <.05. 3We assume a drect mpact of the occurrence of multple CIs on satsfacton and customer share, though the occurrence of multple CIs also mght functon as a moderator. However, the relatvely small number of observatons wth one CI and multple CIs means that the estmates would become unrelable f we were to model addtonal moderatng effects. The type of CI mght also matter and could serve as a moderator, but agan, the relatvely small number of observatons for several CI types would lead to unrelable estmates. TABLE 3 Descrptve Statstcs All M (SD) Wth CIs M (SD) Wthout CIs M (SD) t 1 t t 1 t t 1 t Transport-related attrbutes 4.33 (1.18) 4.19 (1.22) 4.20 (1.20) 3.81 (1.18) 4.46 (1.15) 4.62 (1.11) Interacton wth the sales agent 5.11 (1.25) 4.97 (1.31) 5.03 (1.30) 4.78 (1.36) 5.19 (1.20) 5.19 (1.22) Handlng and bllng 4.39 (1.33) 4.34 (1.29) 4.27 (1.33) 4.07 (1.26) 4.53 (1.32) 4.64 (1.25) Servce satsfacton 4.25 (1.12) 4.12 (1.16) 4.18 (1.11) 3.81 (1.15) 4.34 (1.13) 4.46 (1.07) Prce satsfacton 3.83 (1.20) 3.78 (1.22) 3.76 (1.19) 3.62 (1.24) 3.92 (1.20) 3.96 (1.19) Customer share 3.90 (1.66) 3.73 (1.72) 3.98 (1.63) 3.83 (1.69) 3.80 (1.70) 3.63 (1.75) Servce recovery N.A. N.A. N.A (1.47) N.A. N.A. Notes: N.A. = not applcable. 130 / Journal of Marketng, July 2008

9 ( 2) servsat = α01 + I ( k) ϕ servsat 1, + N ( k) ϕ servsat where servsat t = servce satsfacton at t for subject, attsat lt = satsfacton wth the attrbute l at t for subject, recov t = servce recovery at t for subject, prce t = prce satsfacton at t for subject, cshare t = customer share at t for subject, multc t = dummy varable (1 f respondent experenced multple CI and 0 f otherwse), d ndustry,m = ndustry-dependent dummy varable, and d tme = tme-dependent dummy varable. In addton, I (k) and N (k) are ndcator varables, where and prce t CI t t noci t 1, L + [ I ( k) ω attsat l = 1 l, CI + N ( k) attsat ] ω l, noci lt + I ( k) φ re cov + I ( k) ψ multc 4 I ( k ) 1, f subject had negatve CI = 0, f otherwse N ( k ), = 0 f subject had negatve CI 1,fotherwse. We nvestgate nonlnear relatonshps n our model by estmatng several nonlnear model specfcatons (quadratc, exponental, logarthmc, and splne models). In our case, the splne model specfcatons offer the best ft. We defne splne adjustment varables Z j at the thresholds j = 1, lt t t + χ 1 m dndustr ym, + τ dtme + ε m = 1 = α + I ( k) θ prce 02 CI t 1, 1 1t + N ( k ) θ prce + I ( k) ϑ multc noci t 1, 4 χ 2 m m = 1 + d + τ ndustry, m csharet = α03 + I ( k ) βci cshare t 1, + N ( k) β cshare noci t 1, ( ) γ C I servsat t + I k + N ( k) γ servsat d + ε 2 tme 2t noci t ( ) δc I prce t + N ( k) δ noci prce t + I k + I ( k) κ recov + I ( k) ρ multc t t 4 χ 3 m dndustry, m + τ3 d tme + ε3, t m = 1 + t, 6, wth correspondng threshold values of X j (Marsh and Cormer 2001): (3) Z j = D j (X X j ), where D j = 0 for X X j and D j = 1 for X > X j. Thus, we arrve at the followng: servsat = α01 + I ( k) ( ϕ servsat 1, prce cshare t CI t t t = + ϕ CI Z servsatt 1, j + N( k) (ϕnoci servsatt + ϕ Z noci + [ I ( k) ( In terms of nterpretaton, the splne model specfcaton mples that f, for example, servsat t 1 les below the threshold j, the mpact of servsat t 1 on servsat t can be denoted by the regresson coeffcent ϕ CI for a customer who experenced a CI, whereas the mpact for servsat t 1 > ) servsatt 1, j ) 1, L attsat ω l, CI attsat t CI Z t l + l j l l = 1 ω, ) attsat t l noci lt l noci jl + N( k) ( ω, attsat + ω, Z + I ( k) φ re cov + I ( k) ψ multc t t 4 + χ 1 m d ndustry, m + τ dtme + ε m = 1 1 1t prce α0 2 + I ( k ) ( CI prcet 1 + CI Z t, j 1, θ θ ) pr ( ) ( noci t 1, noci j cet + N k θ prce + θ Z + I ( k) ϑ multc + χ d, t 2m ndustry m m = 1 + τ d + ε 2 tme 2t = α + I ( k) ( β cshare 03 + β Z CI 4 CI t 1, csharet 1, j ) + N ( k) ( β cshare 1, noci t csharet 1, noci j ) + β Z servsatt CI t CI j + I ( k) (γ servsat + γ Z servsat ( ) ( γnoci t γ t noci j + N k servsat + Z prce δ t CI t CI j + I ( k) ( prce + δ Z ) prce + N ( k) ( δ prce + δ Z t ) noci t noci j + I ( k) κ re cov + I ( k) ρ multc 4 t t + χ 3 m dndustr ym + τ3 + ε3 m = 1, d tme t. ) )] 1, ) ) Crtcal Incdents and Customer Share / 131

10 j s ϕci + ϕ CI. The lne segments are connected at j, whch s also referred to as a splne knot (for a dscusson of splne models n marketng, see Rust and Bornman 1982). For the model estmaton, we use a three-stage least squares (3SLS) estmator to account for the correlaton of the dsturbances ε and endogenety. Estmaton Results Model Ft and Stablty To assess the performance of our nonlnear moderated CI model, we estmate three rvals for comparsons. The frst s a baselne model that does not allow for any effects of CI (log-lkelhood [LL] = ; Akake nformaton crteron [AIC] = ). The second model examnes the effects of CI n the formaton of satsfacton and customer share. The drect CI model corresponds to the models mostly nvestgated n lterature and examnes drect effects of CI on satsfacton and customer share (LL = ; AIC = ). The thrd model nvestgates moderatng effects of CI on customer satsfacton and customer share but constrans tself to lnear effects (LL = ; AIC = ). The nonlnear moderated CI model (LL = ; AIC = ) outperforms the others n terms of the AIC statstc; the lkelhood rato test (Greene 2003) also favors the nonlnear moderated CI model over the alternatves (p <.01). To verfy that the regresson coeffcents dffer between customers who experenced a CI and those who dd not, we performed Chow tests (Greene 2003) for the three equatons; these confrm the dfference n the parameter vectors (for servce satsfacton and customer share formaton, p <.01; for prce satsfacton formaton, p <.05). Thus, CIs have a moderatng effect on the formaton of satsfacton and customer share over tme because ncludng the moderatng effects sgnfcantly mproves the model. We assess the stablty of our model by drawng ten random samples that each contan 75% of our sample and runnng the model wth these samples (for a smlar procedure, see Bolton, Lemon, and Verhoef 2008). In these tests, 87% of the estmated parameters reach the same level of sgnfcance as our reported model. 4 Of the sgnfcant parameters n our reported model, all have the same sgn n our subsample models. Thus, our model results appear stable. 5 4To account for our measures of satsfacton wth a seven-pont scale and customer share wth a sx-pont scale, we also estmate models wth a log-log specfcaton. The parameter estmates remaned comparable. To account for the categorcal nature of our customer share measure, we estmate an ordered logstc regresson and obtan vrtually the same results as n our 3SLS model. Because the model contans multple equatons and we must account for endogenety, we show only the results of the 3SLS model. 5Unfortunately, the 3SLS estmator does not account for the panel data structure of a part of our data set. Applyng a panel data estmator s complcated because only part of our data set has a panel structure and the tme dmenson s small. The most straghtforward soluton would be to nclude company-specfc fxed effects for the companes wth two observatons n each of the three equatons smultaneously, but ths s not possble because of the lmtatons of our data set. Instead, we estmate three dfferent Because the contemporaneous measures are collected wthn the same survey, common method varance could be a problem. We followed the procedure that Lndell and Whtney (2001) outlne, usng a marker varable to detect a potental bas. 6 The sgnfcance of the correlaton coeffcents does not change; thus, common method varance does not appear to be a problem. We depct the estmaton results n Table 4. Satsfacton Formaton Regardng the formaton of overall servce satsfacton, we fnd strkng dfferences between customers who experenced and those who dd not experence a negatve CI. Among those who experenced a negatve CI, lagged servce satsfacton ratngs at or below 4 do not nfluence ther current servce satsfacton at all; only the splne adjustment varable for ratngs greater than 4 reaches a satsfactory level of sgnfcance. That s, very low servce satsfacton ratngs do not carry over to the next perod, and only hgh overall servce satsfacton ratngs persst over tme. For customers who dd not experence a CI, the results are dfferent. Accordng to the regresson coeffcents, lagged overall servce satsfacton s the most salent determnant of current servce satsfacton (.446), and when satsfacton ratngs exceed 4, the mpact of lagged servce satsfacton on current satsfacton decreases ( =.263). The dfference between the coeffcents assocated wth customers who experenced CI and those who dd not s sgnfcant (p <.01). In Fgure 2, Panel A, we dsplay the relatonshp between lagged and current servce satsfacton dependng on satsfacton level and whether a customer experenced CI. To develop ths fgure, we vary the lagged servce satsfacton ratngs from 1 to 7 and nsert average values for all other explanatory varables. Although the slopes of the curves are comparable for lagged servce satsfacton ratngs that exceed 4, the mpact of lagged servce satsfacton on current servce satsfacton s much greater for customers who experenced a CI, n support of H 1a. Furthermore, we fnd a postve asymmetry for customers who experenced a 3SLS models, n whch we nclude company-specfc effects for each of the three equatons separately. For all three models, model ft deterorates heavly as a result of the many addtonal parameters, but parameter estmates reman comparable. In addton, we estmate our model usng a reduced data set ncludng only one case per company whch also yelds comparable results. Thus, our results seem not to be affected by the panel data structure of part of our data set. 6As a marker varable, we used the respondents assessment of how ther overall satsfacton wth the best compettor has developed over the past sx months because ths measure was ncluded n all three surveys. The sgnfcance level of the correlaton coeffcents remaned the same after correcton. Because ths marker varable mght theoretcally be somewhat related to the overall satsfacton measure we use n our study, we repeated the procedure for the data collected n the years 2000 and 2001 wth the marker varable How wll the proporton of nternatonal transports n your company develop n the near future? (2000) and a queston about how famlar customers were wth the goal and content of a specfc new company polcy (2001); agan, sgnfcance dd not change. 132 / Journal of Marketng, July 2008

11 TABLE 4 Estmaton Results of the Nonlnear Moderated CI Model Coeffcents CI No CI Dependent: Servce Satsfacton Servce satsfacton t *** Z 4.216** Z 4.183* Transport-related attrbutes t.698***.277*** Z 3.321*** Interacton wth the sales agent t.117***.127*** Handlng and bllng t.132***.084** Servce recovery.019 Multple CIs.156* Ore, coal, and steel ndustry.116 Constructon ndustry.124 Chemcal ndustry.158* Industral and agrcultural sector.079 Year: Constant.407* R Dependent: Prce Satsfacton Prce satsfacton t 1.193***.341*** Z 3.290** Multple CIs.025 Ore, coal, and steel ndustry.352** Constructon ndustry.010 Chemcal ndustry.068 Industral and agrcultural sector.283 Year: Constant 2.469*** R Dependent: Customer Share Customer share t *** Z 2.799*** Z 4.327* Servce satsfacton t.377***.006 Z 3.320* Prce satsfacton t.118*.095 Servce recovery.049 Multple CIs.167 Ore, coal, and steel ndustry.157 Constructon ndustry.313* Chemcal ndustry.057 Industral and agrcultural sector.104 Year: Constant.698** R AIC LL *Sgnfcant at the 10% level. **Sgnfcant at the 5% level. ***Sgnfcant at the 1% level. CI; only past servce satsfacton ratngs greater than 4 sgnfcantly nfluence current overall servce satsfacton. Thus, H 1b s confrmed as well. For customers who dd not experence a CI, less favorable servce satsfacton ratngs have a greater tendency to carry over, an effect opposte to the postve asymmetry we observe for customers who experenced a CI. Current attrbute satsfacton ratngs are more mportant for customers who experenced a CI than for customers who dd not. The former group consders the transportrelated crtera, the attrbute category of the majorty of CIs, most mportant. For customers who experenced a CI, we fnd a negatve asymmetry; the nfluence of the lowest ratngs of the transport-related attrbutes (1, 2, and 3) on overall servce satsfacton s almost twce as hgh (coeffcent.698) as the nfluence of the hgher ratngs (net effect =.377). Thus, the mpact of the attrbute ratngs s stronger than t s for customers who dd not experence a Crtcal Incdents and Customer Share / 133

12 CI (.277) (p <.01). For customers who dd not experence a CI, we cannot dentfy nonlneartes for ths attrbute category. We depct the dfferences n the relatonshps caused by CIs n Fgure 2, Panel B. In the handlng-and-bllng category, we fnd a smlar pattern; the coeffcent assocated wth satsfacton evaluatons of handlng and bllng for the customers who experenced a CI (.132) s much hgher than the pared coeffcent FIGURE 2 Relatonshps Between the Varables for Customers Wth and Wthout CI 134 / Journal of Marketng, July 2008

13 among customers who dd not (.084) (p <.01). In contrast, the nteracton wth the sales-agent-attrbute category does not follow ths pattern. The coeffcent for customers who experenced a CI s actually smaller than that for customers who dd not, possbly because relatvely few negatve CIs occurred n ths category. For nether the handlng-andbllng nor the nteracton-wth-sales-agent categores could we detect nonlnear effects. Customers who experenced multple negatve CIs are less satsfed than customers who experenced only one CI, an ntutve result. However, servce recovery fals to exert a sgnfcant mpact on servce satsfacton, whch s surprsng n lght of prevous emprcal evdence that supports such an effect (Maxham and Netemeyer 2002; Smth and Bolton 1998). We can only speculate about the reasons for ths dvergence, but a possble explanaton mght be related to the ncluson of measures of attrbute satsfacton n our model, whch s not present n the majorty of lterature. These measures mght contan an mplct evaluaton of the qualty of the servce recovery and, as such, conceal a dstnct servce recovery effect. Tentatve evdence for ths noton ncludes the sgnfcant (p <.01) and substantal correlatons between servce recovery and the transport-related attrbutes (.61) and handlng and bllng (.41). In summary, we fnd support for H 2a for two of the three attrbute categores. We dentfy a negatve asymmetry n the relatonshp between attrbute evaluatons and overall servce satsfacton only for the transport-related attrbutes, possbly because the other two attrbute categores are less mportant n terms of overall servce satsfacton. Thus, we fnd support for H 2b n one of the three attrbute categores. Prce satsfacton s nfluenced by ts lag and the ndustry sector. Agan, we fnd dfferences n the prce satsfacton formaton of customers who experenced a CI and those who dd not. For customers who experenced a CI, we fnd the expected postve asymmetry, n that more favorable evaluatons are subject to greater carryover effects ( =.483) than are less favorable ones (.193), n support of H 3b. Because of ths postve asymmetry, the queston whether the lagged effects are stronger for customers who experenced a CI or customers who dd not s somewhat more dffcult to answer. For prce satsfacton ratngs equal to or less than 3, the carryover effects are greater for customers who dd not experence a CI (.341 versus.193), n lne wth H 3a. However, because of postve asymmetry, for evaluatons greater than 3, the coeffcent ncreases for customers who experenced a CI (.483). In Fgure 2, Panel C, we depct the mpact of past prce satsfacton on current prce satsfacton, dependng on the evaluaton level and whether customers experenced a CI. For very hgh ratngs of prce satsfacton (between 6 and 7), the lagged effects are smlar for customers who experenced a CI and those who dd not. For lower ratngs, the mpact of past prce satsfacton on current prce satsfacton s greater among customers who dd not experence a CI; n addton, the coeffcents dffer sgnfcantly (p <.01), so we fnd partal support for H 3a. Customer Share Formaton In the customer share porton of our model, the comparably large coeffcents assocated wth lagged customer share suggest a hgh level of nerta. 7 The mpact of lagged customer share on current customer share s greater for customers wth a hgher customer share wth the suppler (CI:.002 for customers whose lagged customer share s equal to or less than 2, and.801 for customers whose lagged customer share s greater than 2; no CI:.608 for lagged customer shares less than or equal to 4, and.935 for lagged customer shares greater than 4). That s, customers wth a hgher customer share are subject to a hgher level of nerta, an ntutve result. For customers who experenced a CI, lower lagged customer share values (1 or 2 on the sx-pont scale) do not sgnfcantly affect ther current customer share. Thus, these customers are more nclned to adjust ther suppler portfolo, perhaps even mplyng a total swtch to an alternatve suppler, and are clearly endangered. Yet, as we show n Fgure 2, Panel D, the relatonshp between lagged and current customer share s comparable for customers who experenced a CI and for those who dd not. However, a remarkable dfference between customers who experenced a CI and those who dd not s that servce satsfacton and prce satsfacton nfluence customer share among the former group, whereas both coeffcents are nsgnfcant for the latter group. Therefore, we fnd support for H 4a and H 5a. Furthermore, for the latter group, the only sgnfcant determnant of current customer share s past customer share, whch suggests that these customers are subject to hgh nerta levels. Wth respect to customers who experenced a CI, we also fnd that only very low current servce satsfacton ratngs (less than or equal to 3) affect ther customer share. For ratngs greater than 3, the effect becomes very small ( =.057). Thus, ratngs greater than 3 hardly affect current customer share, n support of the negatve asymmetry predcted n H 4b. We dsplay the relatonshp between overall servce satsfacton and customer share n Fgure 2, Panel E. Counterntutvely, the sgnfcant mpact of servce satsfacton combned wth negatve asymmetry causes the customer share of customers who experenced a CI to exceed that of customers who dd not at a certan pont. In the next secton, we dscuss ths phenomenon further. We cannot dentfy any nonlnear effects for prce satsfacton, whch means that H 5b s not supported. For customers who experenced a CI, nether the occurrence of multple CIs nor satsfacton wth the servce recovery sgnfcantly affects customer share. However, the occurrence of multple CIs has an ndrect negatve effect on customer share through a lower level of servce satsfacton; n the servce satsfacton model, the respectve coeffcent s negatve and sgnfcant. Agan, we can only speculate about why the servce recovery effect s nsgnfcant, but the most straghtforward explanaton nvolves the evalua- 7The dfference between the coeffcents of customers who experenced a CI and those who dd not s sgnfcant at p <.05. Crtcal Incdents and Customer Share / 135