Customer Efficiency, Channel Usage and Firm Performance in Retail Banking

Transcription

1 Customer Effiieny, Channel Usage and Firm Performane in Retail Banking Mei Xue Operations and Strategi Management Department The Wallae E. Carroll Shool of Management Boston College 350 Fulton Hall, 140 Commonwealth Avenue Chestnut Hill, MA Phone: (617) Fax: (617) Lorin M. Hitt Operations and Information Management Department The Wharton Shool University of Pennsylvania 571 John Huntsman Hall Philadelphia, PA Tel: Fax: Patrik T. Harker Operations and Information Management Department The Wharton Shool University of Pennsylvania 1000 SHDH, 3620 Loust Walk Philadelphia, PA Phone: (215) Fax: (215) Forthoming in Manufaturing & Servie Operations Management ( 1

2 Customer Effiieny, Channel Usage and Firm Performane in Retail Banking Innovations in tehnology and servie design have inreasingly enabled firms to inorporate selfservie tehnology to augment or substitute for traditional employee-provided servie hannels. Although it is lear that self-servie an redue ost, less is known about how ustomers utilize self-servie hannels in a multi-hannel servie delivery system and the resulting impat on firm performane. An important aspet of servie operations is that ustomers are o-produers of the servie. Thus, the performane of the delivery system and ustomers use of servie hannels an be affeted by ustomers own effiieny or produtivity in servie o-prodution (ustomer effiieny). In this paper we utilize prior theoretial frameworks in servie operations and eonomis to hypothesize relationships among ustomer harateristis (espeially o-prodution effiieny), hannel utilization, and firm performane. We then test these hypotheses using panel data from a large retail bank. Overall, we find that higher ustomer effiieny in selfservie hannels is assoiated with greater profitability and has a omplex relationship with ustomer retention and produt utilization. Key words: servie operations management, servie delivery system, self-servie tehnology (SST), servie o-prodution, ustomer effiieny. 1. Introdution Customer partiipation has always been an indispensable part of any servie delivery proess (Chase, 1978, Lovelok and Young, 1979, Karmarkar and Pitbladdo, 1995). In many proesses, the labor of the ustomers and the employees of the firm are, to some extent, substitutable. Although self-servie has long been an alternative to full servie by employees in many industries (e.g., gas stations), the development of new information tehnologies and innovations in produt design have led to an unpreedented inrease in the sale and sope of self-servie utilization. Around the world, millions of onsumers use the Internet for shopping, managing bank aounts, trading stoks, booking flights, buying movie tikets, traking pakages, and selling everything from a silver spoon to a used ar. The profound impat of this proliferation of self-servie is evident in almost all major servie industries. 2

3 In this wave of the so-alled self-servie revolution, a multi-hannel servie delivery system that ombines a portfolio of employee and self-servie hannels in both physial and virtual environments has beome the dominant model, replaing systems that rely primarily on employee servie delivered in physial hannels. Figure 1 shows suh a multi-hannel servie delivery system in retail banking. Similar systems an also be found in the transportation and retail setors. The popularity of self-servie is a result of deades of servie delivery innovation in pursuit of lower osts and higher quality, fueled by the advanement of tehnology, espeially information tehnology. In some industries, espeially in the Western world, the provision of suh a multi-hannel servie delivery system has beome a ompetitive neessity (The Banker, 2003). Moreover, servie delivery systems an have a signifiant effet on firms operations strategy suh as apaity management (Roth and Menor 2003, Ellram et al. 2004), as well as on the operational performane of other business proesses that diretly interat with the ustomer, suh as sales and marketing. The appeal of adding ustomer self-servie to the overall servie delivery mix is straightforward. By offloading tasks onto ustomers and enabling them to pursue their own servie needs, firms an often provide ustomized servies at mass prodution ost levels. In addition, many of the tehnologies underlying self-servie, suh as Internet-based ordering or ustomer support, also enjoy signifiant eonomies of sale while providing greater aess, flexibility and onveniene. 3

4 Retail Bank Servie Delivery Channel System Physial Channel Virtual Channel Self-servie Channel Employee Servie hannel Self-servie Channel Employee Servie Channel Automati Teller Mahine (ATM) Branh/Teller Internet Call Center Branh/Platform Sales Representative Voie Response Unit (VRU) Automati Clearane House (ACH) Figure 1: The Multi-Channel Servie Delivery System in Retail Banking In a world in whih a signifiant number of ustomers obtain their servie from multiple servie hannels, the design and management of suh a system requires understanding of how ustomers deide whih hannels to utilize and how these hoies affet both the optimal design of servie delivery systems as well as related business proesses. For instane, the widespread adoption of Internet banking has required hanges of traditional banks in the servie apaity and skill requirements of all enter representatives (e.g., to provide omputer tehnial support), the design of different types of banking produts to ompete against the online-only banks, the approahes used to ross-sell or up-sell inremental banking produts, the distribution of physial servie hannels suh as ATMs and branhes, and the mitigation of potential new risks suh as online identity theft. These interdependenies have aused firms to reognize that without an integrated perspetive on hannel utilization, firms may miss opportunities for better resoure alloation, improved operational performane, and stronger ustomer relationships. Management of the performane of self-servie hannels presents an unusual problem in that the performane of the servie system is affeted by the ations of the ustomers with unertain apabilities, inentives and goals. Thus, ustomers own ations and ognitive abilities an have 4

5 a greater impat on servie quality in self-servie hannels than in employee servie hannels. Moreover, beause ustomers are generally free to hoose the hannels in whih they obtain their servie, perhaps shaped by inentives from the servie provider, unertainty in overall ustomer use of a hannel an be further ompounded by unertainty in ustomer behavior (Davis 1993, Ellram et al. 2004). Some institutions have experimented with denying a subset of ustomers aess to ertain hannels, suh as aess to bank branhes or other high ost servies. Often, however, these innovations were either misunderstood or rejeted by onsumers. In some industries, suh as airlines and retail brokerage, expliit prie inentives for hannel use have been implemented (e.g., a booking fee for use of a human tiket agent), although unertainty about ustomer reation to expliit priing for different hannels has limited the diffusion of these types of praties. Despite these diffiulties, many finanial institutions are ontinuing to experiment with a variety of methods to enourage ustomers to utilize the right hannel for their servie needs. The fous of this study is to provide an empirial analysis of the determinants of self-servie use, and how ustomers' hannel hoies are assoiated with firm performane. Utilizing insights from prior models of servie operations (Huete and Roth 1988, Roth 2001, Boyer et al. 2002, Heskett et al., 1997), we hypothesize that ustomers hoose among hannels to perform these transations based on the relative osts and benefits they reeive from eah hoie. Beause selfservie involves an aspet of o-prodution, these osts are diretly influened by ustomer apabilities in self-servie hannels (or ustomer effiieny as in Xue and Harker (2002)) in addition to other ustomer and hannel harateristis. While this theoretial struture is straightforward, it is unlear whih fators, both absolutely and relatively, are important in determining servie demand. Therefore, our first of two major researh questions is: What drives a ustomer s use of different hannels in a multi-hannel servie delivery system? Seond, although ustomers make their hannel hoie deisions based on their own osts and benefits of using different hannels, their deisions may not neessarily maximize the profits of the servie provider. Given that most organizations have made only limited efforts to influene ustomer hannel hoie and that ustomers are heterogeneous, we have the opportunity to observe the effets on the firm of a wide array of individual hannel hoies. The aggregate 5

6 results of these individual ustomer behaviors affet ritial dimensions of firm performane suh as ustomer profitability, ustomer retention and produt adoption. This leads us to our seond major researh question, How does ustomers hannel use influene firm performane? A key empirial hallenge of this work is to measure ustomer effiieny. Xue and Harker (2002) developed a ustomer effiieny measurement framework that utilizes diret measures of ustomer inputs (e.g. time, effort) and output. However, this approah annot be diretly applied when ustomer inputs are hard to measure (espeially if some osts are intangible) or when firms seek to measure effiieny using arhival data that did not inlude diret measures of ustomer osts (e.g., data in transation proessing systems). In this paper, we present an alternative approah in whih we infer ustomer effiieny by revealed hannel hoie. After aounting for non-ustomer fators suh as hannel availability (e.g., loation) and fators unrelated to servie o-prodution effiieny (e.g., transation requirements), ustomers who onentrate their ativity in self-servie hannels when a full-servie alternative is available for the same transation are inferred to be more effiient. We an partially validate this measure by omparing it to fators suh as eduation and experiene, whih should be assoiated with effiieny in our empirial setting, and we also inorporate this measure into models that predit performane. We test our hypotheses using a panel dataset of monthly transation data for approximately 25,000 ustomers of a large retail bank from July 2002 to June Our data inlude detailed observations on transation ativity, ustomer demographi information, and ustomer profitability, loyalty and produt adoption measures. Overall, our results suggest that there is onsiderable variation in revealed ustomer effiieny and that ustomers with higher measured effiieny tend to be younger, have more eduation, and be more experiened with omputers and banking servies in general, whih is onsistent with our prior expetations. We also find that other fators, unrelated to ustomer effiieny, also affet hannel hoie, whih suggests that our measure of ustomer effiieny is related to but distint from a ustomer s overall use of self-servie itself. Seond, ustomers who appear to be more effiient have greater profitability. and utilize more bank servies and have a lower hane to leave the bank, although the effets of 6

7 ustomer effiieny on produt adoption and retention seem to have diminishing returns. Overall, this suggests that our onept of ustomer effiieny is relevant for understanding ustomer hannel hoie as well as ustomer profitability and, therefore, has the potential to be produtively used in pratie for understanding ustomer hannel hoies and inorporating these hoies into the design of servie delivery systems. 2. Literature Review 2.1 Self-Servie and Servie Operations The role of the ustomer as a o-produer in servie operations has a long history in servie operations management. Fuhs (1968) notes that ustomers are always either passively or atively involved in the servie prodution proess. Subsequent researh on servie operations management reognized the interation between the ustomer and firm (Chase 1978) and the importane of integrating the prodution role of ustomers into the design of servie delivery systems (Globerson and Maggard 1991). Prior researh has also noted that the design of these self-servie delivery systems an have signifiant ompetitive impliations (Karmarkar and Pitbladdo 1995, Heskett et al. 1997). With ustomers as o-produers, it is inreasingly important to understand and manage ustomers servie hoies and their performane in servie prodution ativities. Lovelok and Young (1979) utilize ase analysis to demonstrate that developing servie systems that aount for ustomer needs and preferenes is essential to realizing produtivity gains from self-servie operations. Subsequent work identified the fators whih drive ustomers deisions to use selfservie hannels, namely time, ontrol, effort, dependene, effiieny, and human ontat (Bateson 1985). Other researh has suggested parallels between managing ustomers and managing employees, using insights from human resoure management (Bowen 1986, Kelley et al. 1990). Combining insights from the prior literature on servie o-prodution with approahes from effiieny analysis, Xue and Harker (2002) propose the onept of ustomer effiieny to apture the notion that ustomers may vary in their ability to effetively partiipate in selfservie, whih in turn affets both ustomer outomes (e.g., satisfation, pereived ost) as well as firm profitability. 7

8 Self-servie systems are more than just a standalone feature of the organization. The literature has inreasingly foused on the observation that self-servie systems need to be integrated with other aspets of organizational design. As Roth and Menor (2003: 151) observe: Central to servie delivery system design is expliit onsideration of the nature of ustomer ontats (e.g., fae to-fae, eletronially mediated, and bak-offie support interations). Whereas these issues have long been part of ustomer ontat theory (Chase 1978, 1981, Kellogg and Chase, 1995, Soteriou and Chase 1998), new types of self-servie hannels introdued new onsiderations into these approahes. For instane, reent work onsidered how eletronially mediated (or virtual ) servie ativities interat with traditional fae-to-fae servie approahes (Froehle and Roth 2004) and how these new servie options affet strategies for market positioning and ustomer relationship management (Voss 2000). In this literature stream, our work is most losely related to the Servie Strategy Design Matrix (Huete and Roth 1988), the Produt-Proess-Proximity ( 3 p ) Matrix for Servies (Boyer, Hallowell, and Roth 2002), the E-servie Customer Retention Model (Roth 2001), the Servie Profit Chain model (Heskett et al. 1997), and the Customer Effiieny Management (CEM) framework (Xue and Harker 2002). Our model extends the Servie Strategy Design Matrix and the 3 p model framework by modeling ustomer hoie of delivery hannel in a multi-hannel system, while the p 3 model fouses on firm hoie of delivery hannel by addressing the tradeoffs between industrialization level (tehnial mediation level at the ustomer touh point) and ustomization. Our model also extends the E-servie ustomer retention model (Roth 2001) by extending the setting from a single hannel (Internet) to a multi-hannel servie delivery system with both physial and virtual employee servie and self-servie hannels. Xue and Harker (2002) propose the ustomer effiieny onept to study the role and impat of a ustomer as a o-produer in servie o-prodution. An effiient ustomer is one who onsumes less resoure to produe more or the same amount of output in her partiipation in the servie o-prodution proess. In essene, the onept of ustomer effiieny parallels the lassial onept of employee produtivity. However, the dual roles of a ustomer (both as oproduer and as onsumer) imply an important distintion between a firm view of effiieny (the firm's benefits versus firm osts for failitating and supporting the ustomer s o-prodution 8

9 ativity) versus a ustomer view of effiieny (the ustomer's value versus diret osts and opportunity osts faed by the ustomer for onduting the o-prodution ativity). Xue and Harker (2002) also note that oordinating servie design with other omplementary firm deisions (e.g., marketing and produt or servie design) to build an effiient ustomer base an have signifiant impat on operational performane, a proess they refer to as ustomer effiieny management (CEM). 2.2 Online Servie in Finanial Servies Prior work has also expliitly examined the role of the Internet as a distribution hannel in the finanial servies industry. Apte and Vepsalainen (1993) examined the tradeoff between using high teh versus high touh hannels and argued that the former is effetive for ost redution and the latter is more effetive for relationship building. Roth and Jakson III (1995) found a negative orrelation between total fator produtivity and servie quality, whih undersores the importane of ost-benefit tradeoffs in servie design. Menor et al. (2001) showed that operations agility, whih is defined as the ability to exel simultaneously on operations apabilities of quality, delivery, flexibility and ost, is as vital for retail banks performane as it is for manufaturing firms. Hitt and Frei (2002) examined ustomer behavior using in retail banks and found that ustomers who utilize online banking used more produts and were more profitable, but that these differenes existed prior to the adoption of online banking. However, this study did not onsider hannel usage. Chen and Hitt (2002) showed that ustomer retention in the online brokerage industry an be influened by the design of selfservie systems and other produt design hoies. Choi et al. (2005) presented a modeling framework for servie delivery in retail banking, examining how two alternative servie delivery proesses (onventional and eletroni) affet ustomer osts, proess eonomis, market segmentation, and ompetition. 2.3 Customer Effiieny and Firm Performane Prior literature suggests a positive link between ustomer effiieny and firm performane suh as profitability and loyalty for two major reasons. First, the lower ost of operating self-servie hannels than employee servie hannels offers the opportunity of signifiant ost savings 9

10 (Chase 1978, 1981, Lovelok and Young 1979, Heskett et al., 1997, Bitner et al. 1997). Seond, a more effiient ustomer gains greater value from self-servie (Xue and Harker 2002), whih, in turn, enourages greater produt adoption and a longer relationship length (Roth 2001, Xue and Harker 2002). However, while effiient ustomers may realize greater value from their servie interations and more extensively utilize low-ost hannels, they may also engage in other behaviors that an negatively impat their loyalty and profitability. Sine effiient ustomers are expeted to utilize self-servie hannels more than employee servie hannels, the redued personal ontat may undermine the bond between the ustomer and the servie provider and thus redue ustomer loyalty (Selnes and Hansen 2001). Also, while self-servie hannels may present a new sales opportunity, it may ome at the expense of redued ontat in employee servie hannels where sales efforts an be more effetive, yielding a sales penalty of self-servie (Heute and Roth 1988). Effiient ustomers, with their deeper knowledge of the firm's produts, may also be better at optimizing their benefits at the expense of the firm by hoosing loss-leader produts. Examples of this behavior in retail banking might inlude minimizing the amount of idle deposits by keeping money only in interest-bearing aounts, or transferring high-ost loans (e.g., redit ards) into redit vehiles with lower interest rates and margins (e.g., home equity lines of redit). Thus, while it is plausible that high effiieny is also assoiated with higher firm performane, the relationship between ustomer effiieny and ustomer profitability is an empirial question. 3. Model 3.1. Measuring Customer Effiieny Using Channel Choie When diret measurement of ustomer inputs and output are possible, ustomer effiieny an be alulated diretly (Xue and Harker 2002). However, when these data annot be utilized (either beause all inputs and outputs are not measurable or not available in arhival data), ustomer effiieny an be inferred from atual ustomer behavior. Presumably effiient ustomers will ondut more transations in self-servie hannels ontrolling for other fators that affet selfservie hoie beause of their relatively lower diret labor and opportunity osts. We utilize this insight to develop a simple model that relates hannel hoie to ustomer effiieny with 10

11 speial attention to measuring variation in effiieny aross ustomers. The model is motivated by our retail banking setting but appears to be onsistent with the struture of other multihannel servie systems where onsumers have disretion over whih hannels to use for their transations. Consider a ustomer that an use a multi-hannel servie delivery system onsisting of C different hannels, indexed by, C = {1, 2,, C} to ondut J possible servie or transation types, indexed j, j J={1,, J}. A ustomer s ost of labor is w (e.g., time opportunity ost) per unit of input ustomer labor (L j ), and the value of the servie is v per unit of output (O j ). Servie value is independent of the hannel through whih the servie is aquired but differs by servie or transation type (e.g., deposit, withdrawal, inquiry, aount transfer). Thus, a ustomer s utility of using hannel to ondut u = v O wl, j J, C. (1) j j j j Note J O j of type j transations is: J is the subset of transation types that an be onduted in hannel. The parameters w and v are assumed exogenous but may vary by ustomer. Furthermore, assume the total utility of banking servies obtained by the ustomer is the sum of the utility for eah C C J j. = 1 = 1 j= 1 individual transation in eah hannel. Thus, total utility U is given by: U = u = u Assuming that there are no prodution omplementarities between ativities of different 2 Oj transation types in different hannels ( L L j ' j ' = 0 ', j' j ), eah ustomer s utility maximization program redues to a set of independent labor hoies aross hannels for different transation types. Consequently, we now fous on haraterizing the input hoies for eah hannel, allowing eah hannel to have a different value, prodution funtion, or set of input quantities. Beause the quantity of transations of a given type are prinipally determined by daily life events, we assume that total numbers of transations are exogenous, although the ustomer is free to alloate her transations aross hannels (thus, O, j J, C are hoies subjet to the onstraint Oj = Oj, j J where the O j are exogenous). C j 11

12 In general, transation servies are produed by a ombination of inputs from both onsumers and the firm. Let the prodution inputs for this hannel and transation type be ustomerinvested apital (R), ustomer labor (L), firm-invested apital (K), and firm employee labor (H). Assuming that the effets of ustomer inputs and firm inputs are in multipliative form, this yields an overall prodution funtion for transation servies of type j in hannel (or output of the form: O = g ( R, L ) f ( K, H ) (2) j j Let the ustomers portion of the prodution funtion take the Cobb-Douglas form, ommonly O j ) used in prodution eonomis: α g ( R, L ) = R ( A L ) j j β where α, β are the output elastiities of ustomer apital and ustomer labor, respetively, and A is a ustomer-speifi fator that affets the ustomer s produtivity of labor when using hannel Note that this representation differs slightly from the usual Cobb-Douglas form whih plaes the produtivity term as a multiplier of both apital and labor O= A0 R α L β. The two representations are equivalent beause A0 = A β. We utilize this non-standard representation to make it lear that our hypothesized ause of variations in ustomer effiieny is due to variations in ustomer labor input for a given output.. We represent the mean ustomer as having A=1, with higher values of A representing more produtive ustomers, and lower values of A representing ustomers who are less produtive than the average. Note that the firm portion of the prodution funtion, f( K, H ) is likely to be slow hanging and does not vary aross ustomers with equal physial aess (e.g., geographi loation). Therefore, from the perspetive of an individual ustomer, this term is quasi-fixed. Similarly, sine ustomers do not typially hange the loation of their offie or residene due to ATM or branh loations, or invest in a omputer solely to utilize online banking, we treat ustomer apital ( R ) as quasi-fixed as well. In this formulation, a ustomer then hooses an effort level for eah hannel (whih determines the usage of eah hannel) to solve: J J J, (3) maxu = max u = max ( vo wl ) = max( vo wl ) j j j j j j j L L L 1 L j= j= 1 j= 1 j 12

13 where L = ( L 1,..., L2,..., L ) is the vetor of ustomer labor input for J different types of J transation using hannel. This problem yields a set of first order onditions in whih the marginal produt of labor equals the wage rate (w): L * j Oj w = ( Lj = ). L v Substituting the form of the prodution funtion for O and differentiating with respet to labor O j α β β 1 input yields the first order ondition: L = βr A f( K, H) Lj, whih implies an optimal j j j labor hoie of 1 α β * β R A f( K, H) vj 1 β L j [ w ] =. Substituting this bak into the original prodution funtion yields: α β 1 1 β * 1 β 1 β 1 β 1 β β 1 j [ (, )] j O = R A f K H v w. (4) Beause O J * * Oj j= 1 =, we have α β β 1 1 * 1 J β 1 β [ (, )] β β β j j= 1 O = R A f K H w v, (5) or in logarithms: J 1 * 1 α β β 1 β 1 β 1 β 1 β β 1 j j= 1 log O = log f ( K, H ) + log R + log A + log w+ log( v ), (6) Thus, a ustomer will utilize eah hannel to produe a number of transations * O as a funtion of ustomer effiieny A, level of ustomer apital R, and unit ost of ustomer labor input w, the firm s inputs in the hannels, ( K, H ), and the values of eah transation type, 1 v= ( v,..., v,..., v ). Holding servie values and non-effiieny-related ustomer harateristis j J as onstants, ustomers will onentrate their transations in hannels with the greater output elastiity of ustomer labor and apital ( β, α ), and where the overall firm ontribution to output [ f( K, H ) ] is larger. The onept of most interest in this model is the variation in ustomer effiieny aross ustomers. Hypothetially, if we ould run a regression of transation ount on a onstant, plus measures of ustomer effort osts ( w ), transation value ( v j ) and ustomer apital ( R ), we 13

14 ould retrieve the effiieny measures as the residual of that equation. This is the approah used in the empirial prodution eonomis literature for the omputation of multifator produtivity (see e.g., Grilihes 1994). Formally, onsider an empirial model of the form: J 1 * 1 α β 1 β = 1 β + 1 β + β 1 + j + ε j= log O log f ( K, H ) log R log w log( v ) (7) The residual term of Equation (7) ε then provides an estimate of the ustomer effiieny term β ε β for eah ustomer: = 1 log A or 1 β β A [exp( )] = ε based on Equation (6). 1 There are three issues regarding the diret use of the residual ε as a ustomer effiieny measure. First, this residual applies only to a speifi hannel. Greater preision an be potentially gained in estimation by aggregating estimates of this measure from observations aross multiple hannels, espeially if there is some random variation in the residual due to unrelated fators. Seond, other unobservable ustomer-speifi effets might affet ustomer hannel hoie. Thus, the observed residual an inlude a term (s) whih an be a ustomerspeifi fixed or random effet: ' β ε β = 1 log A + s. Third, the prodution funtion in Equation (2) applies to both self-servie and employee servie hannels due to the inherent o-prodution nature of eah servie delivery proess (Chase 1978), though the level and extent of a ustomer s partiipation an vary substantially. Our expetation and muh of the prior literature suggests that the amount of ustomer labor input in full-servie hannels is smaller than that in selfservie hannels, for a given transation type (see e.g., Xue and Harker 2002). This implies a low elastiity of labor input ( β 0 ) in full-servie hannels, and therefore minimal variation in hannel use due to the diret effet of effiieny ( A β 1). However, the full-servie hannel use may be helpful in estimating individual effets (s), so they an be usefully inorporated into a omposite effiieny measure. Therefore, to minimize the effet of random variation and eliminate the ustomer-speifi effets, we onstrut an overall effiieny measure as a weighted differene between the residuals in full servie (C ) versus self-servie (C ) hannels: 14

15 where (8) CE = θ ε θ ε C' C'' θ = θ in order to eliminate s. The optimal weights are theoretially related to the ' " C C variane of eah residual and are proportional to the marginal produt of labor in eah type of β β hannel (through 1 ). To ontrol for variane in the residuals, we standardize them to a mean of zero and a standard deviation of 1. We then assign weights of the residuals of different hannels. There are a variety of ways of weight assigning depending on the fous of the study and the empirial setting. Our preferred approah is to inlude weights proportional to the monthly transation ounts in eah hannel for eah ustomer beause this weighting aptures a sense of individual ustomer s relative effort aross hannels over time. Alternatively, weights an be done with the population average hannel use or equal weighting. We empirially investigate these alternative weightings and find their use does not alter our major onlusions, so we fous on our preferred measure in most of our analysis (see Setion 4.3 for more details). 3.2 Empirial Implementation Our prior derivation suggests that variation in transation use aross hannels depends on a series of ustomer fators inluding ost of labor, ustomer apital, and relative ustomer value of the servie. In addition, there are firm fators that an affet ustomer hannel hoie. Some firm hoies have effets that an vary by ustomer, suh as the loation of branhes and ATMs relative to eah ustomer's home or plae of work, while others affet all ustomers equally (the design of the Internet banking interfae). Finally, there will be variane in hannel use due to ustomer effiieny, some of whih is due to observable fators and some of whih is not diretly or indiretly observable. Our prinipal empirial task is to onstrut suitable proxies for eah so that we an: a) isolate ustomer effiieny from other fators that affet hannel hoie and b) provide support to the laim that our definition of ustomer effiieny is measuring what we expet by demonstrating that it is orrelated with fators we believe to be assoiated with effiieny (e.g., eduation) in a plausible way. Below we desribe the variables used in our empirial study to apture the fators that are influential on ustomer hannel use in retail banking: 15

16 Fators that affet ustomer effiieny. A large body of literature in labor eonomis has emphasized that skill, training and experiene an affet labor produtivity both generally as well as in tehnology-mediated self-servie (Bartel and Lihtenberg 1987, Bartel 1995, Ihniowski et al. 1997; Gurau 2002, Wang et al. 2003). We apture training as the level of eduation in the household, beause it is plausible that general human apital is assoiated with effiient transation behavior, espeially in the Internet hannel. We apture experiene by a ustomer s tenure. Tenure (measured in years) represents a measure of familiarity with the bank and might be plausibly related to produtivity improvements through learning by doing. We an also ompute the time sine adoption of online banking, whih provides an alternative learning-bydoing measure speifi to the online banking hannel. This measure may also be assoiated with omputer skill, beause early adopters are likely to be more skilled with omputers. Finally, we are fortunate to have an additional potential proxy for omputer skill a market researh indiator of whether the ustomer shows an interest in omputers whih might be assoiated diretly with omputer skill. We would therefore expet that use of self-servie hannels and ustomer effiieny are positively related to eduation, tenure, and omputer experiene and interest. We also have a measure of ustomer age. Greater age and experiene might be assoiated with greater effiieny from learning-by doing argument. However, older ustomers may differ from younger ustomers in that they have less experiene or omfort level with tehnology-mediated self-servie hannels (see e.g., Hitt and Frei 2002, Bitner et al. 2000, Curran et al. 2003, Gurau 2002, Wang et al. 2003). Sine our age measure varies only in the ross-setion, ustomer heterogeneity likely dominates the learning effet and we would therefore expet a negative orrelation between the self-servie usage and ustomer age. Finally, several of the time-dependent variables, suh as age and tenure, may be assoiated with lifeyle effets that lead to a non-linear relationship with effiieny. We therefore allow for these effets by inluding quadrati terms in the empirial analysis for these variables. Opportunity Cost. A number of studies have suggested that inome is a good proxy for the opportunity ost of time (Beker 1993). We therefore approximate the unit labor ost (w) by annual household inome of the ustomer. 16

17 Transation Value. We ontrol for the number of transations of eah type that an be onduted in a hannel as a way of normalizing the transation ounts based on the ustomer s overall transation requirements. This measure is also onsistent with treating total transation ounts of eah type as exogenous. These variables may also broadly apture variation in ustomers value of transations (v) beause value affets the total number of transations of a given type but not their alloation aross hannels. In addition to potentially apturing transation value, there is an additional benefit to inorporating measures of transation mix into the analysis. A number of prior studies have shown that the nature of the produt and transation has a strong relationship with the optimal use of tehnology mediation for servie delivery (Huete and Roth 1988), whih would suggest that more standardized transations would be likely to be done in self-servie hannels that have a high tehnology mediation level (Boyer et al. 2002, Froehle and Roth 2004). By inorporating measures of transation ounts, we an empirially examine whether self-servie hannels are more losely assoiated with more routine transations. Channel Aess. Classial faility and loation theories in servie operations management have long established that the ease of aess to a physial outlet of a servie provider is ruial to a ustomer s deision to hoose a servie provider (Boyer et al. 2002). The availability of a hannel diretly affets the usage of that hannel beause it lowers opportunity osts. Channel availability an also affet the demand for other hannels for whih they are substitutes (Boyer et al. 2002). Thus, ustomers loated in areas with higher branh density may perform more branh transations and less transations in other hannels; similarly, ustomers loated in areas with more ATMs may perform more ATM transations and less transations in other hannels. Customers loated in areas where there is a high density of physial hannels may use virtual hannels less. Beause these fators are really ontrol variables in our analyses, we are prinipally interested in whether these variables perform as expeted as a hek on the model rather than as an expliit empirial hypothesis. Channel Design. Channel design and produt struture an influene hannel hoie. However, beause we only observe the hoies of a single bank over a relatively short time period, the 17

18 variation in behavior due to hannel design is likely to be small. Nonetheless, to ontrol for variation over time in hannel design or inentives, we inorporate monthly dummy variables. In addition, the institution we analyze operates aross a number of different states with slightly different produts and business praties. Due to banking regulations whih are prinipally state-speifi, produts within a state tend to be similar. Thus, we also inlude dummy variables for the ustomer s state of residene to ontrol for variation in hannel design, produts or other aspets of the servie prodution proess Testable Hypotheses Our hypotheses fous on the measurement of ustomer effiieny and its relationship with firm performane. First, we an ondut analyses of individual hannels to understand whether the fators assoiated with effiieny (whih we will refer to as effiieny orrelates) are orrelated with hannel use in the expeted way. From our earlier disussion, effiieny orrelates should be positively related to self-servie use. Moreover, beause self-servie hannels an substitute for other hannels, more use of self-servie may lead to less use of employee servie hannels. Thus, we posit that: Hypothesis 1-1: Fators assoiated with ustomer effiieny (age, tenure, eduation, skill) are positively orrelated with self-servie hannel use and negatively orrelated with employeeservie hannel use. These hypotheses will be tested at a finer level of preision (e.g. orrelation between age and use of teller transations), but are stated broadly here for onise presentation. Our data also allows us to examine the relationship between transation omplexity and hannel hoie as suggested by the prior literature (Huete and Roth 1988, Boyer et al. 2002): H1-2: Customers use self-servie hannels more often than employee-servie hannels to meet routine and standard servie needs in a multi-hannel servie delivery system. 18

19 Seond, our interest is in reating a systemati measure of ustomer effiieny based on the aggregate hannel utilization. Just as our effiieny orrelates should show the proper orrelation with eah hannel, they should show the appropriate orrelation with our aggregate effiieny measures. H2: Customer effiieny, as defined and measured in this analysis, is positively orrelated with tenure, eduation, and omputer skill, and negatively orrelated with age. Finally, we would like to test whether indeed ustomer effiieny is assoiated with firm performane. Although there are theoretial reasons to expet either a positive or negative orrelation between performane and ustomer effiieny in banking, we state our hypothesis in terms of the positive predition. H3: Customer effiieny is positively orrelated with ustomer profitability, produt utilization and retention. The hypothesized relationships among ustomer effiieny, hannel use and firm performane are shown in Figure 2. 19

20 Customer Fators Customer Effiieny (ustomer-view) o Age o Eduation o Tenure o Computer Skill Customer Time Opportunity Cost Transation Value Servie Requirements Combined Fators (Firm and Customer) Channel Aess Firm Fators Channel Design Produt Design (e.g., hannel use inentives) Customer Channel Use Transation ounts Alloation aross hannels Firm Performane/Firm-view Customer Effiieny Profitability Produt Utilization Loyalty Figure 2: Hypothesized Relationships between Effiieny, Channel Usage and Firm Performane 20

21 4. Empirial Analysis Our empirial analysis inludes three omponents. First, we will examine the determinants of hannel utilization to validate our model of hannel use on an individual hannel level (to test H1-1 and H1-2). We will also examine the various ontrol variables for onsisteny with prior theoretial preditions about hannel aess and hannel usage. Next, we will ompute several alternative measures of ustomer effiieny and test the hypothesis that ustomer effiieny is related to ustomer harateristis in a systemati way (H2). Finally, we will use our ustomer effiieny measure to investigate the relationship between effiieny and firm performane, speifially profitability, produt utilization and retention (H3). All analyses were performed using standard proedures in STATA Data The retail banking industry has been a pioneer in applying new tehnology to deliver servies to its ustomers. Most retail banks today have built multi-hannel servie delivery systems similar to the one shown in Figure 1. These systems typially inlude retail branhes (in whih transations are onduted in person by tellers and platform sales representatives), telephonebased ustomer servie representatives (CSRs), automated Voie Response Units (VRUs), Automati Teller Mahines (ATMs), and Internet banking. In addition, ustomers an generate transations by writing heks or by using the automated learing house (ACH) for diret debit or redit of their aounts. Among these hannels, teller, VRU, ATM, Internet and ACH are often used to handle standard and routine transation types suh as straightforward inquiry, deposit, withdrawal, and aount transfers; CSR and platform are often used to handle more ustomized or omplex servie needs. Although we will analyze our model in all the hannels for whih we have data, we will fous our effiieny analysis on one full-servie hannel (tellers), and three self-servie hannels (VRUs, ATMs, and online banking) beause these hannels have the best measurement in our data and are not subjet to issues suh as serving non-standard transations (platform) or being fully automated and not requiring any ustomer effort (ACH). 21

22 The bank used in our study is one of the largest retail banks in the U.S., and its operational praties and ustomer population are regarded as representative of banks of similar size. From a raw data set of the bank s several million ustomers, a random sample of about 25,000 households was drawn and used for this study. We restrited our analysis to ustomers who appear in the banks ustomer information file and who had at least one transational deposit aount, sine it does not make sense to analyze transation behavior for ustomers who do not routinely perform transations. These data inlude monthly transation reords for eah ustomer s deposit aounts organized by transation type and hannel for eah month from July 2002 to June 2003; monthly aount balanes for eah deposit, loan and investment aount for these ustomers over the same period. We define deposit aounts to inlude time deposits, interest and non-interest heking, and money market aounts. Loans inlude onsumer loans, auto loans, redit ards and mortgages. Investment aounts inlude trust, asset management, brokerage aounts and mutual funds (with mutual funds being the most ommon). We also use the bank s bi-monthly profit measure whih is based on an internal model that inorporates interest and non-interest revenue, less servie osts (inluding overhead alloations), expeted loan loss and taxes. For eah ustomer we have a single ross-setion of demographi information whih inludes the date the ustomer first joined the bank, the date the ustomer obtained Internet banking aess (if any), and standard demographis (age, inome, eduation level, gender, marital status, presene of hildren, and zip ode of prinipal residene). In addition, these data also inlude market data purhased from a third party whih aptured interest in omputers (a binary measure). Finally, we obtained information on the bank s own ATM and branh networks, whih provided a ount of the number of branhes and bank-owned ATMs in eah zip ode area. The transational and aount data was obtained from the bank s transational systems and therefore is believed to be of high quality. However, for some hannels, notably platform and telephone CSRs, the bank does not trak all transations in its online systems, so we limit our analyses of these hannels. The transation file also omits non-inquiry VRU transations, but these are believed to be a relatively small portion of overall VRU ativity. The ustomer data (other than those derived from transational data) are olleted as part of the bank s normal 22

23 operations and are supplemented by third-party market researh data. These data are also believed to be highly aurate, although some data are missing. The data of ATM and branh loations are drawn from the bank s operational databases and are also believed to be extremely aurate and omplete. As for the profitability data, although there is some subjetivity in the alulation of ustomer profitability, espeially due to ost alloation proedures, these data are used for internal performane measures. Regardless of its potential flaws, ustomer profitability is one of the measures whih the bank atively monitors and attempts to maximize. All the data desribed above is at individual ustomer level, so we ondut our analysis at the ustomer level with eah observation representing a household in a given month. Observations are not exluded for missing data on the demographi variables. Instead, we inlude a dummy variable to indiate if the data is missing and set the value to the variable mean for a ontinuous variable, or have an expliit missing ategory for all ategorial variables. This proedure ensures that the results are not sensitive to the value used to fill missing data. A onsequene of this data seletion proess is that our data inludes a mix of new ustomers over our period, ustomers who stayed throughout the 12-month period, and ustomers who abandoned some or all of their aounts. Beause the bank retains ustomer information data after ustomer departure, our ounts of departure are not skewed by missing data on the harateristis of ustomers who departed. We also validated that our proportions of new, departing and ontinuing ustomers math the ustomer population. We utilize three outome measures in our performane analysis: ustomer profitability (as omputed by the bank), produt utilization, and ustomer retention. Produt utilization is measured by the aggregate balane for eah of the three types of aounts a ustomer holds: deposit, asset and investment aounts. Beause the eonomis of these three aount types are similar within ategory (e.g., interest and non-interest heking aounts are similar) but different aross ategories (e.g., mutual funds are different from loans), we perform separate analyses for eah of these ategories, but do not use broader aggregates. Customer retention is measured as a binary variable that aptures whether a ustomer losed all her aounts. These measures are onsistent with other researh in banking performane at the ustomer level (e.g., 23

24 Hitt and Frei, 2002), as well as with the performane metris that the bank utilizes internally. Table 1 presents definitions and summary statistis of our key variables. 4.2 Channel Use Analysis Using our servie o-prodution model in Setion 3, our general empirial model for hannel demand relates the total number of transations in a hannel to orrelates of ustomer effiieny (age, tenure, interest in omputers, online banking tenure), orrelates of ost of ustomer effort (household inome), orrelates of transation value (total transations by type), hannel availability (branh density, ATM density, having online banking), and other ontrol variables for ustomer harateristis (martial status, having hildren, gender) and variation in bank harateristis (month, ustomer state of residene). Time-related variables (age, online banking tenure, ustomer tenure) are both entered as linear and as squared terms to aount for lifeyle effets and other non-linear trends. The variables for inome and eduation are ategorial and are expanded to a set of dummy variables, so no ordering is imposed on these measures. Thus, our baseline empirial model is: log(1 + T ) = β + δ log(1 + T ) + β age + β age + β omp + β ollege + β graduate j j age 2 age omp ollege graduate j J + β tenure + β tenure + β eb tenure + β eb tenure + β atm + β branh 2 2 tenure 2 eb tenure 2 tenure eb tenure atm branh + β web + β has _ eb med inome high inome gender hildren + β married + state _ dummies + time _ dummies + ε (9) married med inome + β high inome + β gender + β hildren We estimate this equation using monthly data of eah ustomer s transations in eah of five hannels (teller, VRU, ATM and online banking, and ACH), although we exlude ACH from our effiieny measure later, beause ACH is fully automated. Beause transation ount variables (transations in a given hannel T and transations of a given typet j ) an be legitimately zero, we add one to the transation ount before taking the logarithm to prevent a zero observation from reating an extreme point. Transation types that annot be onduted in a partiular hannel (e.g., deposits in the VRU hannel) are omitted from the regression. For the two sets of ategorial variables for eduation and inome, the lowest ategory is omitted (high shool eduation, low inome). Beause the dependent variable is the logarithm of transations, the 24

25 oeffiients (exept for the transation ount ontrols, whih are also in logarithms) an be interpreted as perentage hanges (e.g., β ollege is the perentage differene in transations between a ollege eduated ustomer and a high shool eduated ustomer). For variables entered in both linear and quadrati terms (e.g., age), both oeffiients need to be examined to determine the relationship. Where relevant, we will disuss both the trend (inreasing or dereasing) and where the minimum/maximum point is ahieved (for relationships that are βage onvex or onave). For example, for age, this point is ahieved at. In many ases, this 2β will show that the minimum or maximum is at the edge of the data range, so the relationship is effetively monotoni over the sample range. For all hannels exept the online banking hannel, the estimates will be performed using ordinary least squares with Huber-White robust standard errors (lustered by ustomer) to aount for repeated ustomer observations over time (Wooldridge, 2002). These analyses are done with the STATA reg proedure. 2 age For online banking, we have to aount for the fat that some ustomers have not initiated online banking and therefore their online transation ount will be zero. There are two ways of handling this issue. One option is to restrit our analysis only to ustomers with online banking. However, this analysis an be biased by ustomer self-seletion (Tobin 1958, Maddala 1983). Our preferred method is to treat the desired number of online banking transations as a latent variable whih is ensored at zero. This formulation leads to the use of a generalized Tobit model (the interval regression model) that allows both ensored observations of ustomers without Internet aounts and non-ensored observations of ustomers with Internet aounts (Tobin, 1958, Goldberger 1964, Maddala 1983, Long 1997). This model was estimated using the STATA intreg proedure. The results of these analyses are presented in Table 2, with eah olumn representing an analysis of an individual hannel. In our testing of hypothesis H1-2 and some other disussions later, we refer to oeffiient omparisons aross models for different hannels. For these omparisons, we ompute statistial signifiane by noting that oeffiients in two different regressions are statistially independent, so the standard error of the differene an be omputed by the simple formula for the variane of a differene of two independent random variables. Beause the sample size is the same for all 25

26 our hannel use regressions, the appropriate test for the equality of two hypothetial oeffiients β 1 and β2 with regression standard errors SE( β1) and SE( β2) is a t-test given by: t = β β [ SE( β )] + [ SE( β )] Overall, the models are all signifiant (p <.001) and the results (see Table 2) in this analysis are broadly supportive of the hypothesized relationships among self-servie usage and orrelates of ustomer effiieny (H1-1) and the relationship between self-servie usage and transation type (H1-2). Age. Customer age is generally positively assoiated with full-servie transations (tellers) age teller ( β =.021, p <.01) and age has a negative and signifiant orrelation with the use of self- ebanking servie hannels whih is strongest for online banking ( β =.026, p <.01). The quadrati terms are also signifiant but small, generally suggesting that the relationship between transation use and age is monotoni over the sample (the maximum is reahed at.0205 = 64.1 years for tellers and the minimum is reahed at age 60 years for online 2(.00016) banking, whih are both around the 90 th perentile of the sample age distribution). age Experiene/Tenure. The tenure results are mixed. For the most part, greater relationship length with the bank is assoiated with less usage of two self-servie hannels (ATM and online banking) as well as less usage of tellers. There are positive relationships with VRU usage and ACH. Examining the quadrati terms suggests that these trends persist over the entire sample distribution or at least far out in the upper tail (the earliest minimum is reahed in the teller hannel at a tenure of 16.4 years, whih is lose to the 90% perentile of the tenure distribution). We therefore onlude that this analysis does not show any systemati relationship between length of relationship and hoie of hannel. Experiene/Online Banking Tenure. The results are loser to expetation on online banking tenure. An additional year of experiene in online banking is assoiated with a 5% derease in 26

27 the number of teller transations (signifiant at p<.01) and this holds aross the entire sample. The relationship between online banking tenure and the use of other hannels is more omplex. Initially, the number of online banking transations is delining in online tenure, reahes a minimum at approximately 1.9 years and is inreasing thereafter. VRU use initially inreases following the adoption of online banking but dereases as online banking tenure moves beyond the mean. The initial inrease in VRU use may be onsistent with the inreased demand on telephone support for Internet ustomers learning to use the system (this interpretation was suggested by managers we interviewed as part of this researh). The same holds for ATMs, but the dereasing region is reahed muh faster (.65 years) suggesting a negative relationship with online banking tenure. ACH use is inreasing in online banking tenure, although this may be partially expeted for tehnial reasons automati bill pay in the online banking hannel is often fulfilled through the ACH system. Overall, the delining use of teller transations and the inrease use of the online hannel for long-time users suggests hannel substitution, whih is fully onsistent with H1-1. In addition, the onvex relationship between online banking use and online banking tenure suggests that it takes a period of time before online banking is fully utilized, and at that time it beomes a substitute for other self-servie (VRU and ATM) and fullservie hannels (teller). Eduation. The hypothesized relationships also hold generally for self-servie hannel usage and eduation. Point estimates suggest that the highest levels of eduation are onsistently assoiated with greater use of self-servie hannels and less use of full-servie hannels exept for VRUs. Customers with a graduate degree perform 12% fewer teller and nearly 15% more ATM transations than do ustomers with a high shool eduation (these differenes are signifiant at p<.01). The results of online banking are mixed ollege eduated ustomers atually perform fewer online banking transations than high shool eduated ustomers while ustomers with a graduate degree perform higher numbers of transations than either group, but none of the results are signifiant. The one unusual finding is that VRU use is delining in eduation. This may, in part, be due to the fat that the VRU is a gateway to telephone CSRs, whih makes the VRU hannel have some of the appearane of a full-servie hannel as well (that is, alls to the CSR are first routed through the VRU, with an option to speak to a ustomer representative ). For this reason, we omit the VRU hannel from our subsequent effiieny 27

28 analysis sine our measure of VRU transations may onfound self-servie transations with attempted full servie transations through CSRs. Skill/Interest in Computers. Generally, the oeffiients on interest in omputers are in the right diretion but tend to be small and insignifiant for most hannels. Expressed interest in omputers is assoiated with 9.3% greater online banking transations, but even this number is not signifiant at onventional levels (t=1.5). Thus, the results on this variable are inonlusive but not inonsistent with our hypotheses. The lak of power of this variable may simply be due to imperfet measurement, an issue whih an hopefully be addressed in future researh. Channel Availability. There is a lear relationship between ustomer adoption of online banking and lower numbers of transations in all other hannels exept ACH, whih shows a modest inrease. The greatest relationships are the VRU hannel (38% less use for ustomers with online banking after ontrolling for tenure) beause the apabilities between online banking and the VRU are similar. Similarly, ustomers with online banking aess perform nearly 13% fewer teller transations. As observed in the online banking tenure analysis, ACH appears to be a omplement to online banking. The results are not as strong for other hannel availability measures but are largely onsistent with our hypotheses. Greater numbers of available ATMs in the ustomer s home zip ode area is assoiated with fewer teller transations and more ATM transations, suggesting that ATMs are substitutes for tellers, as would be expeted. One additional ATM is assoiated with a 0.8% deline in teller transations and a 2% inrease in ATM transations. ATMs appear to be substitutes for the other hannels, although these oeffiients are not signifiant. The results of branh density are puzzling. More branhes are assoiated with less branh transations and more use of online banking. Although this is inonsistent with our story of branh availability, it ould indiate that branhes tend to have the highest densities in ommunities that also have a propensity to use online banking suh as urban settings. Thus, it appears that it may be ating as a ontrol for unobserved demographi fators. Unfortunately we are not able to test this further beause the branh data and our geographi ontrol are both at the same level of aggregation (zip ode), whih means we annot use a zip ode ontrol to eliminate this soure of variation. 28

29 Opportunity Cost. The results on inome are onsistent with an argument that ustomers may utilize self-servie to eonomize on opportunity osts. Medium-inome onsumers perform nearly 13% more online banking transations and 3% more ACH transations. The relationship is even more pronouned for high-inome ustomers: high-inome ustomers ondut 28% more online banking transations, 7% fewer VRU transations and 2% fewer teller transations (although the figure for tellers is only marginally signifiant at p<.1). Interestingly, although inome might be proxy for other fators suh as eduation or omputer skill, the fat that these numbers are substantial after ontrolling for both eduation and interest in omputers provides greater onfidene in our interpretation. Transation Volume. The results also support hypothesis H1-2, that simple transations are more often aomplished in self-servie hannels. The oeffiients suggest that a 10% greater number of inquiry transations is assoiated with an approximately 1.8% inrease in online banking transations and a 1.3% inrease in ATM transations, but only a 0.5% inrease in teller transations. In other words, the results are onsistent with the argument that a marginal inquiry transation is more likely to be direted to a self-servie hannel. These differenes between the oeffiients on inquiries for the teller hannel versus the other hannels are all signifiant at p<.001. Other Controls. The ontrol variables for state of residene and month are jointly signifiant and in most ases individually signifiant in all analyses. The same is also true for the family struture variables. Our theory does not provide speifi preditions for these variables, so we annot interpret them further in terms of our model. However, one interesting observation is that households where women are the primary aount holder are signifiantly more likely to perform transations in the online hannel and less likely to use tellers, an observation that might prove useful in marketing efforts for online banking Customer Effiieny Using the approah desribed in Setion 3, we now onstrut measures of ustomer effiieny. We first perform regressions of hannel usage against all ovariates in our model exept those 29

30 assoiated with ustomer effiieny. The standardized residuals from this regression are used to onstrut four effiieny measures as desribed in Setion 3. CE1 is omputed as the differene between the two self-servie hannel residuals (ATM, online banking) weighted by number of transations an individual ustomer performs in eah hannel eah month, and the residual of the teller hannel. CE1 is our preferred measure as it aommodates transation differenes aross onsumers over time and does not inlude the potentially problemati VRU hannel. For the purpose of robustness hek we also onstrut alternative measures. CE2 is the same as CE1 exept that it inludes VRU as a self-servie hannel. We also omputed two additional measures, also omitting the VRU hannel: CE3 uses weights proportional to population means rather than ustomer speifi time-varying weights, and CE4 utilizes equal weights. In all ases, the effiieny measures are standardized to a mean of zero and a standard deviation of 1 to ease interpretation. These measures are then regressed on the ustomer effiieny ovariates using ordinary least squares with Huber-White robust standard errors (Wooldridge 2002) using the STATA reg proedure. (see Table3). Overall, the models are all signifiant (p<.001) and the results on the effiieny measures mirror our earlier analysis (Table 3) and lends support to hypothesis H2. Age is negatively related to effiieny, whih is signifiant for CE2, CE3 and CE4 although not signifiant for CE1. Effiieny is shown to be inreasing in eduation, whih is signifiant for the highest eduation CE1 level graduate for CE1 ( β = 0.13, p <.01 ) and signifiant for both ollege and edu graduate graduate for CE3 and CE4. The effiieny measures are all shown to have a onave relationship with tenure: for CE1 with the maximum roughly in the middle of the sample (9 years) thus, effiieny is initially inreasing in tenure, then dereasing. This is similar to the results for the individual hannel analyses. Effiieny is inreasing in experiene with online banking up to the midpoint of the sample (the maximum is ahieved at 1.6 years) and then delining in tenure although tenure has a net positive ontribution over the entire sample range. Thus, effiieny is broadly related to age, eduation, online banking tenure and overall ustomer tenure though the results on interest in omputers are inonlusive. Overall, examining the olumns of Table 3, it appears that although there are some variations in the results from the different effiieny measures they are broadly onsistent, whih suggests the 30

31 empirial performane of our effiieny measure is not partiularly sensitive to the hoie among plausible weight shemes. 4.4 Customer Effiieny and Performane We onsider three measures of performane: profitability as omputed by the bank, produt utilization, and retention. The baseline model relates the dependent performane variable (designated generially by P) to ustomer effiieny (and its square to aount for nonlinear effets), and to a set of ontrol variables suggested by our hannel use model. We inlude ustomer effiieny orrelates in addition to ustomer effiieny for two reasons: first, it may improve empirial performane of the model if these variables have a diret relationship with performane; seond, our results are more onservative and perhaps more relevant beause the effiieny measure now an be interpreted as effiieny that was not otherwise observable through the effiieny orrelates. Thus, P = β + β CE + β CE + β age + β age + β omp + β ollege CE 2 age 2 CE age omp ollege + β graduate + β tenure + β tenure + β eb tenure + β eb tenure 2 2 graduate tenure 2 eb tenure 2 tenure eb tenure + β atm + β branh + β web + β med inome + β high inome atm branh has _ eb med inome high inome + β gender + β hildren + β married + state _ dummies + time _ dummies + ε (10) gender hildren married We also examined variations of the model whih inlude transation ount ontrols (the ounts of different types of transations of the ustomer) and hannel ount ontrols (the ounts for transations in various hannels of the ustomer). We utilize Huber-White robust standard errors to orret for repeated observations of the same ustomer over time (Wooldridge 2002) exept in fixed-effets and random-effets analyses whih already ontrol for repeated observations. For the profitability analysis, the dependent variable is the bank s internal profit measure. For produt utilization, we utilize the logarithm of aount balane as the dependent variable for eah type of produt (deposit, assets and investment). All of these models are estimated by ordinary least squares (using the STATA reg proedure) as well as by fixed and random effets panel data models (using the STATA xtreg proedure). For the retention analysis, we utilize logisti regression where the dependent variable (depart) is set to one if the ustomer departed the bank and zero otherwise (using the 31

32 STATA logit proedure). Overall the models are all signifiant (p <.001) although some have a relatively small R-squared (see further disussion below) and the results are generally supportive to hypothesis H3. Profitability. The profit results are shown in Table 4. The baseline model (Column 1) suggests that more effiient ustomers are more profitable a ustomer that is one standard deviation above the mean in effiieny ontributes $4.76 of additional monthly profit profit profit ( βce1 βce1 squared p (1) + (1) = 4.76, <.01). This relationship ontinues to hold (and is signifiant) when we ontrol for transation types (Column 2) and atually gets stronger when we utilize a fixed effets analysis that ontrols for all time-invariant ustomer harateristis (Column 4). Results of a random effets panel data model are similar (not shown). In addition, the estimates of the model with hannel ontrols (Column 3) suggest that the alloation of transations aross hannels does explain the relationship between effiieny and profitability. When ontrols for hannel usage are inluded the oeffiients are large and negative (as hannel use inurs ost), and the effiieny oeffiient turns slightly negative. This suggests that more effiient ustomers are assoiated with greater profits prinipally due to alloation of transation ativity aross hannels, not beause of a hange in the overall mix of atual transation types (onsistent with our treatment of these as exogenous), nor due to inremental revenue enhaning behaviors. If ustomer effiieny had no relationship with profits exept through hannel transation volume, this CE1 oeffiient should be lose to zero. We will onsider the possibility of revenue differenes further in the produt usage analysis. The ontrol variables in these regressions also appear to be reasonable. Profitability is inreasing in inome, as would be expeted. Interestingly, although ATM density is largely unrelated to profitability, branh density is assoiated with higher profitability. This is onsistent with our explanation of a prior result that branh density was assoiated with higher online banking adoption. It may be that branh density ats partially as a proxy for unobserved demographi variables in addition to representing the availability of the teller and platform hannels. The overall fit of the regression is somewhat small with an R 2 of around 2%. This is not surprising as ustomer profitability in retail banking is known to vary onsiderably aross ustomers (see e.g., 32

33 Hitt and Frei 2002) for idiosynrati reasons. However, due to our large sample size, most of the variables are signifiant individually and jointly. Overall, these results lend support for H3. Attrition. The attrition results shown in (Table 5) suggest a onvex relationship between attrition rate (the probability of departing the bank) and ustomer effiieny. Column 1 shows the linear CE1 oeffiient to be negative and the quadrati term oeffiient to be positive (both signifiant at p<.01). A ustomer with a very low effiieny has a higher attrition rate that is redued as effiieny inreases. This negative relationship between effiieny and attrition persists until attrition is minimized at.96 standard deviations above the mean of CE1, and is inreasing with effiieny thereafter. The result is similar, although weaker with transation type ontrols (Column 2). In Column 3 we add hannel usage ontrols. Greater use of tellers and the online banking hannel are both assoiated with less attrition, although the teller relationship ( β =.22, p <.01) is more than double the oeffiient on online banking depart lntl ( β =.12, p <.01). This suggests that while more use of self-servie is assoiated with depart ln eb dereased departure, the relationship is muh stronger with the full servie teller hannel. However, unlike in the profitability analysis, the relationship between attrition and effiieny (with hannel use ontrols) beomes muh stronger and suggests a negative relationship between attrition and effiieny over almost the entire sample (the departure rate is minimized at 3.7 standard deviations above the sample mean whih is above 99% perentile of the sample distribution). Thus, while the general relationship between effiieny and departure is negative, the very lowest and highest effiieny ustomers show greater attrition. Sine we believe the results without hannel usage ontrols are the proper way to interpret CE, this provides mixed support for H3. Produt Utilization. The results on produt utilization are similar in diretion to the results on attrition. In Table 6, we estimate separate regressions for eah of the three aount types: liabilities (deposit aounts), asset aounts (loans), and investment produts (prinipally mutual funds). Eah regression was performed using OLS, random effets (not shown but similar to OLS), and fixed effets models. The results onsistently show a onave relationship between produt use and effiieny where balanes are maximized around or above the sample mean. In OLS, deposit balanes are maximized at.84 standard deviations above the sample mean of 33

34 effiieny. Similar results hold for asset balanes (onave with maximum at 0.2 standard deviations) and investment balanes (onave with maximum at -.02 standard deviations relative to the mean). The results of the fixed effets analyses are more onsistent all show onave relationships with signifiant linear and quadrati terms and that balanes are maximized in the range of 1.4 to 1.9 standard deviations above the mean (about the 90 th perentile of the sample distribution). Thus, the balanes results also provide partial results for H3, although the relationship between ustomer effiieny and produt utilization is more omplex (being urvilinear rather than linear) than we had antiipated. The ontrol variables in these analyses are mostly insignifiant and generally mixed in sign. Eduation is onsistently positively assoiated with balanes but often not signifiant. One notable exeption is inome, whih as expeted has a large and positive relationship with balanes a high inome ustomer has approximately 87% higher liability balanes, 68% greater asset balanes and 37% perent greater investment balanes than a low inome ustomer. These oeffiients as well as the oeffiients on medium inome are all signifiant at p< Summary and Disussion Operations managers aross servie industries fae the hallenge of designing and managing an inreasingly omplex multi-hannel servie delivery system that onsists of both traditional, physial, employee-provided servie hannels as well as virtual, self-servie hannels. Given the o-prodution nature of servie prodution, a ruial step toward suessful servie design and management is to understand both how ustomers utilize these hannels and the orresponding impat on firm performane in short and long term. This requires identifying a wide array of fators that affets ustomers hannel hoies. Prior literature has developed the theoretial argument that ustomer effiieny, defined as a ustomer s effiieny of partiipation in the servie o-prodution proess, should be assoiated with greater self-servie utilization, and that greater ustomer effiieny is therefore assoiated with greater firm performane. In addition, many of the ustomer harateristis that should be assoiated with greater ustomer effiieny have been previously identified. We make two speifi ontributions to this literature. First, we develop an approah for the measurement of ustomer effiieny utilizing hannel demand, 34

35 enabling examination of ustomer effiieny issues without requiring omplete data on all ustomer inputs and outputs. Instead, we measure effiieny as a residual in a model of selfservie hannel use after ontrolling for hannel availability and other fators unrelated to effiieny. Analysis using this measure suggests that our effiieny measure is orrelated with many demographi harateristis in the expeted way. Seond, we utilize our effiieny measure to examine the relationship between ustomer effiieny and performane, a relationship whih has been hypothesized but rarely tested. Our results suggest that ustomer effiieny is strongly and positively assoiated with ustomer profitability aross the sample. We also find that ustomer retention and produt balanes are inreasing in effiieny for at least half the sample whih is onsistent with expetations, but that the very highest effiieny ustomers have lower retention and lower balanes than other ustomers whih was not antiipated by our model and theoretial disussion. We also find support for theories presented in prior work in servie operations, that self-servie hannels that often have high automation levels are relatively favored by bank ustomers for simple and standard transations in omparison to employee servie hannels. While our effiieny measure has pratial advantages, it also has some theoretial disadvantages shared with other approahes that apture a latent onstrut as a residual (for example, the Solow residual of multifator produtivity see e.g., Solow also has this disadvantage). In partiular, we rely heavily on prior assumptions and model speifiation to establish that our residual measure indeed aptures effiieny and not some other latent onstrut. As noted by Edwards and Bargozzi (Edwards and Bagozzi 2000) the elimination of unwanted ausal fators from empirial measures is well known but diffiult problem for empirial testing of soial siene onstruts. We attempt to minimize this problem by using theoretially motivated ontrol variables and heking the results of our analyses for onsisteny with prior theoretial and empirial arguments. Nonetheless, the ideal test would be to ombine the diret measurement of ustomer inputs and outputs with our indiret measurement approah to see how muh of our measure is due to effiieny as opposed to general ustomer heterogeneity. We hope to pursue this in future work. 35

36 Our empirial analysis of transation behavior unovered two unexpeted relationships. First, greater branh density is assoiated with fewer teller transations and more online banking. This may be a ausality issue beause the fators assoiated with greater online banking usage are also likely to be assoiated with the areas with desirable demographis where branhes might be onstruted for non-transational reasons (e.g., selling investment produts, opening new aounts). However, sine this variable is utilized as a ontrol it does not lead us to question our ore findings about ustomer behavior and effiieny. Seond, we did not antiipate a strong onave relationship between produt utilization and effiieny. Part of this observation is onsistent with the idea that more effiient ustomers are better at managing their money whih leads to lower balanes. It may also be onsistent with the idea that ustomers for whom transational aess is ostly will tend to use fewer produts. Regardless of the explanation, it is lear that the highest effiieny ustomers, by our measures, behave differently than other ustomers and further investigation into this issue is needed. The attrition results are easier to explain as there is a tension between online hannel usage and inreased retention and an even stronger relationship between retention and teller usage. Without knowing the ausal diretion of this relationship, whether loyal ustomers use online banking and tellers more or whether these hannels build lok-in and enourage loyalty, it is diffiult to understand whether interventions to alter hannel utilization are likely to improve retention. It does, however, suggest, that effiieny and performane are related. It may be possible to disentangle these effets with a longer time series, whih represents another opportunity for future researh. There are four additional limitations of the urrent study that suggest avenues for future researh. First, our data is from a single bank. Given the random sampling approah and the sheer size of the bank we onsider, our results are likely to be broadly representative of a large population. However, we annot investigate the possibility that the ustomer hoies we observe are affeted by speifi praties of this bank, suh as aount features (e.g., fee struture), hannel harateristis (e.g., online banking system design or all enter hold times), and brand positioning. We hope to expand the sope of the data olletion in future researh. In addition, although our model was motivated by our disussions with retail banking exeutives, it appears the model should apply to multi-hannel servie delivery systems in other industries as well. A study in a different industry would help to validate our ustomer effiieny measurement 36

37 approah. Seond, our ustomer transation reords only over transations for traditional deposit produts. Beause this represents the vast majority of disretionary retail banking transations, this seems a reasonable starting point. However, as banks are inreasingly reliant on non-traditional produts (investment, brokerage, insurane) and these produts are inreasingly served through the same servie delivery infrastruture, it would be useful to extend our analysis to onsider these produts beyond simply examining produt utilization. Third, the explained variane in some of our performane regressions is relatively small, although all the models are highly signifiant. While we would generally prefer greater explanatory power, this most likely suggests that profitability and other performane dimensions are driven heavily by unobserved differenes aross ustomers. However, we do note that the signifiane levels of our effiieny measure are on the same order as fators known to be important in banking profitability, suh as household inome or transation volume. Thus, explanatory power is a broader onern regarding the empirial setting, rather than a onern speifi to our approah. This also suggests it would be benefiial to identify other onstruts that an be used to further distinguish ustomer profitability differenes among ustomers. Moreover, it also suggests that without substantial improvements in modeling or data on profitability, large sample sizes will be required for this type of researh. Fourth, and finally, beause we measure effiieny as a latent onstrut revealed through hannel hoie, we annot diretly investigate the relationship between effiieny and self-servie usage. Our prior arguments suggest that at least some omponents of effiieny may be immeasurable, but it would be useful to ompare our measure of ustomer effiieny to diret measures of ustomer behavior (e.g., time spent performing transations) for the purpose of examining the auray of the measure and to understand the importane of unobservable omponents to overall ustomer effiieny. This issue exatly mirrors the onern in the firm produtivity literature that fouses on methods for explaining multifator produtivity of firms given that it is also measured as a residual onept. Aknowledgements This study is supported by National Siene Foundation (NSF) grants: SES and SES The authors thank Aleda V. Roth, the senior editor and two anonymous referees for their onstrutive and valuable omments. 37

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41 Table 1: Data Desription and Summary Statistis Variable Definition No. No. of Mean Std. Min Max Subjets Obs. Dev. CE1 Customer effiieny measure ( CE squared is the square term of CE1) CE2 Alternative ustomer effiieny measure (with VRU data) CE3 Alternative ustomer effiieny measure (sample mean proportion weight) CE4 Alternative ustomer effiieny measure (equal weight) profit Customer profitability (as measured by the , , bank) depart 1 if ustomer left the bank and zero otherwise lnliabbal Liability balane (e.g. deposits) lnastbal Asset balane (e.g. loans) lninvbal Investment balane (e.g. mutual fund) lnatm ATM transations lnvru VRU transations lntl Teller transations lneb Internet/online banking transations lninq Aount inquiry transations (all hannels) lndep Deposit transations (all hannels) lnwd Withdrawal transations (all hannels) lnxfr Transfer transations tenure Length of relationship (years), tenure2 is the square eb-tenure Time sine online banking initiated (years), eb-tenure2 is the square age Customer age, age2 is the square ATM Count of ATMs in ustomer s home zip ode area branh Count of bank branhes in ustomers home zip ode area web Has internet banking (1=yes, 0=no) gender Gender (1=female, 0=male) omp Interest in omputers (1=yes) hildren Has hildren at home missing (69.25%); 1-no (11.73%); 2-yes (19.02%) married Primary aount holder is married missing (33.18%); 1-no (33.71%); 2 yes (33.11%) inome Annual household inome (estimated) missing (19.55%); 1-low (<=$40k) (19.18%), 2- medium ($40~75k) (24.87%), 3-high ( >=$75k)(26.39%) eduation Eduation level of primary aount holder missing (87.3%); 1-high shool, vaation or tehnology shool (4.54%), 2-ollege (5.06%), 3-graduate shool (3.1%) Eah observation ( obs. ) is a subjet in a month. All variables an potentially vary over time exept demographi variables (gender, omp, hildren, married, inome, eduation), and physial hannel availability (ATM, branh). 41

42 Table 2: Channel Use Analysis (1) (2) (3) (4) (5) Teller Channel VRU Channel ATM Channel ACH Channel Online Banking lntl lnvru lnatm lnah lneb lninq (.003)** (.004)** (.004)** (0.010)** lnwd (.006)** (.006)** (.005)** (0.016)** lndep (.007)** (.008)** (.006)** lnxfr (.012)** (0.023)** age (.001)** (.002)** (.002)** (.001)** (0.005)** age (.00001)** (.00002)** (.00002) (.00001)** (.00005)** omp (.020) (.026) (.025) (.020) (.062) ollege (.027) (.035) (.031)** (.024) (.086) graduate (.030)** (.040)** (.036)** (.029) (.101) tenure (.00173)** (.002)** (.002)** (.002)** (.006) tenure (.00006)** (.00007)** (.00007) (.00005)** (.0002) eb_tenure (.013)** (.020)** (.015) (.012)** (.025)** eb-tenure (.002) (.004)** (.003)** (.002)** (.005)** ATM (.003)** (.004) (.003)** (.003) (.009) branh (.003)** (.005)** (.004)** (.003)** (.011)** web (.022)** (.0274)** (.024)** (.021)** Med-inome (.012) (.014) (.013) (.010)* (.036)** High-inome (.012) (.015)** (.013) (.011)* (.036)** gender (.018)** (.023) (.020)* (.016)** (.055)** hildren (.016)** (.021) (.019) (.015) (.053)** married (.011)** (.014)** (.013)** (.010)** (.036) Dummy State, Month State, Month State, Month State, Month State, Month Variables Observations R-squared Eah olumn represents a separate regression. The olumn header is the dependent variable. Huber-White robust standard errors are shown in parenthesis. * - p<.05, ** - p<.01. Dummy variables for missing data (hildren, married, inome, married, and eduation) are also inluded (not shown). All models are signifiant with p<.001. None of the 42

43 regression oeffiients are standardized. Three deimal plaes are used unless to have more deimal plaes is neessary (e.g. the oeffiients and standard errors of age2 and tenure2 ). Table 3: Customer Effiieny Measure and Correlates (1) (2) (3) (4) Customer-speifi VRU Inluded Sample Mean Equal Weights Weights Weights CE1 CE2 CE3 CE4 age (.002) (.002)* (.00207)** (.002)** age (.00002) (.00002) (.00002)** (.00002)** omp (.027) (.027) (.029) (.029) ollege (.036) (.037) (.039)** (.039)** graduate (.042)* (.043) (.043)** (.043)** tenure (.002)* (.002)** (.003)** (.002)** tenure (.00007)** (.00007)** (.00009)** (.00008)** eb_tenure (.020)** (.021)** (.018)** (.018)** eb-tenure (.004)** (.004)** (.003)** (.003)** web (.029)** (.029)** (.031)** (.0309)** Dummy Variables State, Month State, Month State, Month State, Month Observations R-squared Eah olumn represents a separate regression. The olumn header is the dependent variable. Huber-White robust standard errors are shown in parenthesis. * - p<.05, ** - p<.01. Dummy variables for missing data (eduation) are also inluded (not shown). All models are signifiant with p<.001. None of the regression oeffiients are standardized. Three deimal plaes are used unless to have more deimal plaes is neessary (e.g. the oeffiients and standard errors of age2 and tenure2 ). 43

44 Table 4: Customer Effiieny and Profit (1) Baseline (2) Transation type ontrol (3) Channel Control (4) Fixed Effets profit profit profit profit CE (.674)** (0.679)** (1.108)* (.652)** CE1 squared (.228) (.229) (.226)** (.213) age (.180)* (.178) (.180) age (.002)** (.002)* (0.002) omp (2.102) (2.079) (2.082) ollege (2.911) (2.868) (2.885) graduate (3.477)** (3.426)** (3.437)** tenure (.227)** (.229)** (.229)** (6.956)* tenure (.007)** (.007)** (.007)** (.073)** eb_tenure (1.887)** (1.872)** (1.884)** (3.841)** eb-tenure (.356)** (.351)** (.358)** (.619)** ATM (.460) (.456) (.467) branh (.535)** (.530)** (.537)** web (2.213)** (2.231)** (2.257)** (6.532)** Med-inome (1.160) (1.149) (1.151) High-inome (1.628)** (1.630)** (1.615)** gender (2.122) (2.101) (2.101) hildren (1.985) (1.963) (1.967) married (1.426) (1.413) (1.431) lninq (.424)** (.577)** lnwd (.865)** (.834)** lndep (.869)** (.897)** lnxfr (1.175)** (1.357)** lntl (1.159)** lnatm (.732)** lneb (.910)** lnah (.818)** lnvru (.355)** Observations R-squared Eah olumn represents a separate regression. The olumn header is the dependent variable. Huber-White robust standard errors are in parenthesis exept for the fixed effets regression. * - p<.05, ** - p<.01. A onstant and dummy variables for missing data (inome, gender, married, eduation) are also inluded (not shown), state and month are also inluded. Sample size is redued sine 44

45 profit is only available every two months. All models are signifiant with p<.001. None of the regression oeffiients are standardized. Three deimal plaes are used unless to have more deimal plaes is neessary (e.g. the oeffiients and standard errors of age2 and tenure2 ). 45

46 Table 5: Customer Effiieny and Attrition (1) Baseline (2) Transation type ontrol (3) Channel Controls depart depart depart CE (.024)** (.025) (.036)** CE1 squared (.008)** (.008)** (.009)** age (.010)** (.010)** (.010)** age (.0001)** (0.0001)** (.0001)** omp (.199) (.199) (.199) ollege (.226) (.225) (.226) graduate (.254) (.255) (.255) tenure (.013)** (.014)** (.014)** tenure (.0006)** (.0006)* (.0006)* eb_tenure (.094)** (.094)** (.095)** eb-tenure (.019)* (.0192)* (.019) ATM (.02) (.020) (.021) branh (.023)** (.023)** (.023)* web (.143) (.144) (.146) Med-inome (.083) (.083) (.083) High-inome (.088) (.088) (.088) gender (.118) (.118) (.119) hildren (.144) (.144) (.144) married (.092)** (.092)** (.093)** lninq.029 (.024) lnwd (.033) lndep (.038)** lnxfr (.069) lntl (.047)** lnatm.063 (.033) lneb (.039)** lnah (.041)** lnvru.019 (.021) Dummy Variables Month, State Month, State Month, State Observations Eah olumn represents a separate logisti regression. The olumn header is the dependent variable. Huber-White robust standard errors are in parenthesis. * - p<.05, ** - p<.01. Dummy variables for missing data (inome, gender, married) are also inluded (not 46

47 shown). All models are signifiant with p<.001. None of the regression oeffiients are standardized. Three deimal plaes are used unless to have more deimal plaes is neessary (e.g. the oeffiients and standard errors of age2 and tenure2 ). (1) Liabilities: OLS Table 6: Customer Effiieny and Produt Use (2) Liabilities: Fixed Effets (3) Assets: OLS (4) Assets: Fixed Effets (5) Investments: OLS (6) Investments: Fixed Effets lnliab lnliab lnast lnast lninv lninv CE (.018)** (.005)** (.0231)* (.008)** (.01) (.004)** CE1 squared (.006)** (.002)** (.008)** (.003)** (.004)** (.001)** age (.005)** (.008)** (.005)** age (.00005) (.00007)** (.00005) omp (.065) (.132) (.096) ollege (.084) (.169) (.118) graduate (.094)** (.195) (.137) tenure (.006)** (.032)** (.010)** (.050)** (.006)** (.022)* tenure (.0002)** (.0006) (.0004)** (.001)** (.0002)** (.0004)** eb_tenure (.043) (.032)** (.083)** (.051)** (.052)** (.023) eb-tenure (.008) (.005)** (.016) (.008)** (.010) (.004)** ATM (.010) (.017) (.012) branh (.012)** (.021) (.016)* web (.066)** (.054)** (.129)** (.086)** (.072)* (.039)* Med-inome (.037)** (.068)** (.041)** High-inome (.038)** (.072)** (.046)** female (.063) (.100)** (.064) hildren (.052)** (.104) (.070) married (.036)** (.068)** (.04) Dummy Month, State Month, State Month, State Month, State Month, State Month, State Variables Observations R-squared

48 Eah olumn represents a separate regression. The olumn header is the dependent variable. Huber-White robust standard errors are in parenthesis exept for the fixed effets regressions. * - p<.05, ** - p<.01. A onstant and dummy variables for missing data (inome, gender, married, eduation) are also inluded (not shown), state and month are also inluded. All models are signifiant with p <.001. None of the regression oeffiients are standardized. Three deimal plaes are used unless to have more deimal plaes is neessary (e.g. the oeffiients and standard errors of age2 and tenure2 ). 48