Are All Managers Created Equal?

Transcription

1 VI GOLDFR and OTO YNG* Some managers are better than others. Usng the cogntve herarchy framework of Camerer, Ho, and Chong (2004), the authors develop a structural econometrc model that estmates the level of strategc thnkng. In the model, frms wth a hgh level of strategc thnkng are more lkely to correctly conecture the expected actons of ther compettors. The authors apply ths model to decsons by managers at 2233 Internet servce provders of whether to offer ther customers access through 56K modems n The model s valdated by showng that frms wth a hgher estmated probablty of strategc thnkng were more lkely to have survved through prl The estmaton results show consderable heterogenety n the degree to whch frms behave strategcally and suggest that strategc ablty affects marketng outcomes; specfcally, a smulated ncrease n strategc ablty means that fewer frms offer the technology to ther customers. Keywords: behavoral game theory, emprcal ndustral organzaton, technology dffuson, Internet servce provders, cogntve herarchy re ll Managers Created Equal? Some managers are better than others. Ths (perhaps unsurprsng) statement s mplct n the teachng of busness students and the wdespread reportng of good and bad manageral decsons. To better understand how management ablty affects outcomes, t s necessary to allow for heterogenety n ablty n models. Nevertheless, although several studes model heterogeneous consumers on a varety of dmensons, management heterogenety s rarely examned. Ths s not for a lack of models of strategc heterogenety. For example, Camerer, Ho, and Chong (2004) develop a cogntve herarchy model (herenafter, CH model) of heterogeneous strategc thnkng n whch *v Goldfarb s ssocate Professor of Marketng (e-mal: agoldfarb@ rotman.utoronto.ca), and otao Yang s a doctoral student n Marketng (e-mal: botao.yang04@rotman.utoronto.ca), Rotman School of Management, Unversty of Toronto. The authors are lsted alphabetcally. The authors thank Marc Rysman and Shane Greensten for generously provdng the data and mta lter, Pat aar, Tat Chan, ndrew Chng, llan Collard-Wexler, Shane Greensten, Ernan Haruvy, Teck Ho, Noah Lm, Mke Mazzeo, Jeff Prnce, Sanog Msra, Srdhar Moorthy, ran Ratchford, Mengze Sh, Davd Soberman, Jun Xao, and Yupn Yang, as well as semnar partcpants at Washngton Unversty, Unversty of rzona, Unversty of Chcago, Unversty of Illnos at Urbana Champagn, Unversty of Texas at Dallas, Stanford, Unversty of Calforna, Los ngeles, the 2008 Wnter Meetngs of the Econometrc Socety, the Unversty of Toronto, and the 2007 Internatonal Industral Organzaton Conference for helpful comments. Ths research was partally supported by Socal Scences and Humantes Research Councl Grant No ll opnons and errors are the authors alone. Teck Ho served as assocate edtor for ths artcle. players dffer n how deeply they consder compettor choces. 1 They also provde consderable supportng evdence from laboratory experments. In ths artcle, we develop the frst structural nonlaboratory estmate of management heterogenety based on the CH model and apply t to the decsons of 2233 Internet servce provders (ISPs) to provde 56K modem technology to ther customers. Usng evdence from laboratory experments, we buld an emprcal model n whch players dffer n ther ablty to correctly conecture the behavor of ther compettors. We then explore the consequences of a change n ths ablty for ISPs and for modem manufacturers. Heterogenety n strategc ablty s partcularly mportant n retal markets such as the ISP market. Retalers must choose whch products to offer ther customers, and the beneft of offerng a partcular product depends on whether competng retalers also offer that product. Thus, optmal product assortment decsons are dependent on expectatons over compettor actons. Strategc thnkng by retalers wll then also affect manufacturers. taman, Mela, and Van Heerde (2008) show that wde dstrbuton may be the most mportant factor n determnng the success of a new product. Thus, f strategc ablty affects retaler decsons to offer products, t wll affect manufacturer outcomes as well. 1Haruvy, Stahl, and Wlson (2001) and Ho, Lm, and Camerer (2006), among others, dscuss several other behavoral economc models of player heterogenety, such as McKelvey and Palfrey s (1995) quantal response equlbrum. 2009, mercan Marketng ssocaton ISSN: (prnt), (electronc) 612 Journal of Marketng Research Vol. XLVI (October 2009),

2 re ll Managers Created Equal? 613 In ths artcle, we explore a partcular knd of strategc ablty namely, the ablty to correctly conecture compettor actons through step-by-step reasonng. rch expermental lterature has found that the cogntve requrements of fndng a perfect ayesan equlbrum are substantal (for a revew, see Camerer 2003). These studes show that rather than solvng for the equlbrum, players typcally go through a small (and varyng) number of teratons on the expected actons of other players (e.g., Costa- Gomes and Crawford 2006; Stahl and Wlson 1994). Overall, the expermental evdence on the dffculty of playng these games suggests that small frms wth nexperenced managers n a new ndustry are unlkely to fully solve for the perfect ayesan equlbrum. ecause we are studyng such an ndustry, we adapt Camerer, Ho, and Chong s (2004) CH model to the strategc decsons of ISPs. We operatonalze ths by modelng a Type 0 retaler to act as f t s the only player n the market. Type 1 retaler acts as f t beleves that all other retalers act as f they are the only player n the market. Type 2 retaler acts as f all other players are dstrbuted between Type 0 and Type 1. Type k retaler acts as f all other players are dstrbuted between Type 0 and Type (k 1). Ths structure enables us to develop a predcton of behavor for players of dfferent types. useful consequence of ths model s that the soluton s unque because each frm beleves that t knows what ts compettors are dong. Ths overcomes the common problem of multple equlbra n smultaneous entry games (e.g., aar, Hong, and Ryan 2004; Sem 2006). We then ft these predctons to data to observe whch dstrbuton of types best explans observed behavor. Our context for estmatng ths model s the 1997 decson by ISPs of whether to offer customers a hgher-speed servce (56K bps [bts per second] versus 33K bps) and, f so, whch technology to provde. s ugereau, Greensten, and Rysman (2006) dscuss, frms faced a clear, reasonably well-defned technology choce game among not upgradng, upgradng to Rockwell Semconductor Systems K56Flex modem, upgradng to USRobotcs X2 modem, or upgradng to both. We ask the followng questons: (1) How does strategc thnkng affect the dstrbuton of 56K modem technology? (2) re the players estmated to be greater strategc thnkers more lkely to survve? and (3) What factors are correlated wth strategc thnkng? We fnd that strategc thnkng slowed the dstrbuton and dffuson of the new technology; that ISPs estmated to be more lkely to be strategc usng 1997 data were more lkely to have survved through prl 2007; and that frms behaved more strategcally f they competed n larger ctes, f they competed n markets wth more educated populatons, and f they competed wth more frms. Thus, although the estmate of strategc thnkng s assocated wth ncreased competton, the ISPs wth hgher levels of strategc thnkng were more lkely to survve. More broadly, our results provde external valdty to the current laboratory research on the CH model: In addton to the fndng on survval, our estmate of the parameter that measures the dstrbuton of strategc ablty across the populaton s at the hgh end of the range found by Camerer, Ho, and Chong (2004). The early ISP market provdes an deal settng for examnng heterogenety n strategc thnkng. In addton to the clear strategc decson we descrbed prevously, many frms competed n several local markets. The dal-up nature of the technology enables us to easly defne markets by local telephone callng areas. Perhaps because ths was a new ndustry, large frms, such OL, coexsted wth very small companes run out of people s homes. Seasoned managers and M students competed aganst recent computer scence graduates who had helped run the modem pools at ther unverstes. Unlke a perfect ayesan equlbrum approach, the CH model can account for ths heterogenety n manageral expertse n the context of smultaneous entry games, and though the decson explored n ths artcle s not truly smultaneous, ugereau, Greensten, and Rysman (2006) provde rch detal on why t can be reasonably vewed as a smultaneous game and, therefore, an approprate settng for the CH model. Overall, the CH model helps explan the varaton n manageral decson makng n a useful way. Our combnaton of behavoral game theory wth the structural methods of new emprcal ndustral organzaton provdes a new framework for understandng varaton n the decsons of managers who face smlar choces. 2 Wthout a model of strategc ablty, t s mpossble to examne how that ablty affects market outcomes. Thus, such a model s a necessary step toward our fndng that strategc thnkng slowed the dstrbuton and dffuson of 56K modem technology, n support of Renganum s (1981) theoretcal work on the subect. More strategc managers are less lkely to adopt new technologes because they antcpate lower profts as a result of competton. Ths suggests an mportant dfference between the dffuson of products to consumers and to busnesses: The lkelhood that a gven frm wll adopt a busness product often depends on the behavor of other competng frms. However, our results suggest that the mportance of ths effect s heterogeneous across managers wth dfferent abltes. For example, strategc consderatons may be less mportant when the product s amed at a new ndustry wth nexperenced management than at a mature ndustry wth lfetme professonal managers. Next, we revew the two key studes on whch the current research s bult. ugereau, Greensten, and Rysman (2006) provde the man data and the emprcal settng, and Camerer, Ho, and Chong (2004) provde the theoretcal bass for the model. REVIEW OF TWO KEY UILDING LOCKS 56K Modem Technology and ugereau, Greensten, and Rysman (2006) The 56K modem was ntroduced n These modems enabled data transfer over the Internet at a faster speed than the prevous technology at a tme when Internet traffc was ncreasng rapdly. Two modem technologes competed for 2rown, Camerer, and Lovallo (2007) undertake a smlar exercse, comparng quantal response equlbrum, cursed equlbrum, and CH n the context of move dstrbutors decsons to show moves to crtcs. Che, Sudhr, and Seetharaman (2007) and Lm and Ho (2007) also explore the consequences of behavoral assumptons to frms. Other related studes document bases exhbted by real-world managers (Chan, Hamlton, and Makler 2007; Hortacsu and Puller 2008) and develop semparametrc models of ratonalzablty (radllas-lopez and Tamer 2008).

3 614 JOURNL OF MRKETING RESERCH, OCTOER 2009 the market: the X2 modem from USRobotcs and the K56Flex modem from Rockwell Semconductor Systems. These technologes had the same performance capabltes, but they dffered n ther ease of connecton dependng on local characterstcs. They were also ncompatble: consumer wth a gven modem could only connect to an ISP at 56K speed f that ISP had the same technology. ugereau, Greensten, and Rysman (2006) study ISPs choce of 56K modem technology. Specfcally, ISPs that offered 33K servce decded whether to offer 56K servce on the X2 modem, the K56Flex modem, both, or nether. ugereau, Greensten, and Rysman model the ISPs problem as an entry game nto two markets and assume a perfect ayesan equlbrum. Then, they use a bvarate probt model to estmate the parameters and show that ISPs were less lkely to adopt the technology that more of ther compettors adopted. uldng on the work of ugereau, Greensten, and Rysman (2006), we model an ISP s technology choce problem as an entry game of mperfect nformaton. Then, we use CH theory to capture heterogenety n ISP use of strategc thnkng. The early ISP market s a partcularly good ndustry to apply CH theory because (1) ISP managers are lkely to be heterogeneous n experence, reasonng ablty, and so forth; (2) each ISP s payoff depends on competng ISPs technology choces; (3) the set of players and markets s well defned, unlke many other entry-type games; and (4) the decsons were largely made over a three-month perod, a perod short enough that a smultaneous game mght be a reasonable model. Our man data set s dentcal to that used by ugereau, Greensten, and Rysman (2006). Ther study provdes a rch descrpton of the data; therefore, we only brefly descrbe some key aspects of the data. ugereau, Greensten, and Rysman use two ISP drectores (thedrectory and oardwatch) to collect nformaton on ISP locaton (through the telephone numbers that could be used to dal n), 56K technology, and some features of the ISP. Followng ugereau, Greensten, and Rysman, we defne markets by telephone swtches. We consder an ISP to compete n a gven swtch/market f there s a local telephone call from that swtch to the ISP dal-n number. We also have demographc data based manly on the zp codes assocated wth each swtch. The data consst of 2233 ISPs n 9070 markets for a total of 216,186 ISP market combnatons. In Table 1, Panel provdes descrptve statstcs by market, Panel provdes descrptve statstcs by ISP, and Panel C provdes descrptve statstcs by ISP market combnaton. Most varable names are self-explanatory. The varable ISP has dgtal connecton s mssng for several observatons. We nclude the varable Mssng for these observatons so that we can nclude the dgtal connecton varable whle lmtng the effect of the mssng data on our results. We supplement ths core data set wth nformaton collected by vstng each ISP s URL to determne whch ISPs stll exsted n prl We observe ISPs makng one of four adopton choces: (1) adopt nether technology, (2) adopt Rockwell Semconductor Systems K56Flex modem, (3) adopt USRobotcs X2 modem, or (4) adopt both. ugereau, Greensten, and Rysman (2006) argue that the decson can be vewed as smultaneous because the dffuson of the technology was so rapd. Table 1, Panel, contrasts the adopton rate for the technologes n July and October ecause the bulk of the adoptons occur n ths short wndow, the authors assume that the game can be vewed as smultaneous. We also make ths assumpton. To explore the consequences of ths assumpton, we estmated a model wth the July decsons taken as gven and examne changes only from July to October. The qualtatve results do not change. The descrptve statstcs reveal a further complcaton: Most ISPs operated n multple markets. The average ISP operated n 96 markets, and the medan served 16 (equvalent to one or two local callng areas). No ISP served all swtches. Multmarket ISPs operated the same technology n all ther markets. Ths complcates our analyss because we need to alter the standard CH model to address multmarket ISPs and to constran ISP decsons to be the same across markets. We dscuss how we deal wth ths subsequently. CH and Camerer, Ho, and Chong (2004) Suppose that many players engage n a smultaneous move game n whch all players payoffs depend not only on ther decson but also on other players decsons. Therefore, players need to form expectatons about what the other players wll do. lthough many models allow players to dffer n ther payoff functons, they typcally assume that all players have the same ablty to thnk through the game. Camerer, Ho, and Chong (2004) argue that ths assumpton s flawed. They develop CH theory, whch allows players to dffer n ther ablty to thnk strategcally. They show that CH works well n both the entry-type game we examne here and a p-beauty contest game (Ho, Camerer, and Wegelt 1998; Nagel 1995). In CH theory, players have dfferent herarches of ratonalty. Type 0 players do not consder ther compettors, Type 1 players assume that all compettors are Type 0, Type 2 players assume that the other players are a combnaton of Type 0 players and Type 1 players, and Type k players assume that the other players are dstrbuted between Type 0 and Type (k 1). Camerer, Ho, and Chong (2004) provde evdence that a Posson dstrbuton effectvely descrbes the observed dstrbuton of players. We rely on ths evdence to support our model and dentfcaton. In the CH model, a Type k player assumes that all other players are dstrbuted truncated Posson between Type 0 and Type (k 1). The model assumes that the dstrbuton of types n the populaton has the same Posson parameter as the truncated Posson used by players to assess compettor types. We nterpret ths herarchy of ratonalty as heterogenety n strategc sophstcaton. Ths nterpretaton reles on pror expermental lterature that shows that players who appear to thnk strategcally show decson processes consstent wth ths dea (osch-domenech et al. 2002; Camerer and Johnson 2004; Chong, Camerer, and Ho 2005; Costa-Gomes, Crawford, and roseta 2001). Therefore, Type 0 managers do not consder compettors. Instead, they consder the characterstcs only of ther frm and ther market. Gven ther own characterstcs, Type 1 players best respond to a stuaton n whch all ther compettors are Type 0, and so on. key dfference between CH and Nash s that n CH models, some players wll be surprsed by the behavor of ther compettors because they dd not correctly conecture ther compettors choces.

4 re ll Managers Created Equal? 615 Table 1 SUMMRY STTISTICS : y Market (N = 9070) Varable M SD Mnmum Maxmum Number of ISPs n the market Number of backbone provders % populaton urban % populaton n dfferent county fve years ago Medan household ncome (n U.S. dollars) 42,644 14,719 6, ,001 % populaton college graduate Number of busness establshments per person : y ISP (N = 2233) Varable M SD Mnmum Maxmum Chose Rockwell Semconductor Systems () n October Chose USRobotcs () n October Chose both n October Chose nether n October Chose Rockwell Semconductor Systems () n July Chose USRobotcs () n July Chose both n July Chose nether n July Number of markets served ISP has dgtal connecton (T1 or ISDN) C: y ISP Market (N = 216,186) Varable M SD Mnmum Maxmum Chose Rockwell Semconductor Systems () n October Chose USRobotcs () n October Chose both n October Chose nether n October Chose Rockwell Semconductor Systems () n July Chose USRobotcs () n July Chose both n July Chose nether n July Number of ISPs n the market Number of backbone provders ISP has dgtal connecton (T1 or ISDN) Mssng % populaton urban % populaton n dfferent county fve years ago Medan household ncome (n U.S. dollars) 50,353 18,249 6, ,001 % populaton college graduate Number of busness establshments per person MODEL ND EMPIRICL STRTEGY In ths secton, we buld on Camerer, Ho, and Chong s (2004) work and apply the CH model to the ISP data. Our specfcaton dffers from the defntons used n laboratory experments. In partcular, to apply the model to data outsde the laboratory, we use observable data to allow ISPs to be heterogeneous n ways other than strategc thnkng. ugereau, Greensten, and Rysman (2006) show that ISPand market-specfc characterstcs nfluence the decson of whether to adopt 56K modem technology at all and, f so, whch technology to adopt. Therefore, we add the ISP- and market-level covarates used n that research. Ths means that rather than choosng randomly, a Type 0 player s choce s the one wth the hgher ntrnsc value to that player, ndependent of compettor choces. Hgherlevel players also consder the ntrnsc value of each choce n addton to compettor behavor. In what follows, we formalze ths approach. Suppose that there are J ISPs ( = 1,..., J) that operate n markets ndexed by. These ISPs observe market-specfc characterstcs x and ISP-specfc characterstcs x. In addton, each ISP has four choces: adopt nether technology, adopt Rockwell Semnconductor Systems K56Flex modem (technology ), adopt USRobotcs X2 modem (technology ), or adopt both. We denote ths choce set as s = {0,,, } and normalze E[ π 0 k] = 0. In our model, E[ π s k] depends on the ISP s level of strategc thnkng, n addton to market-level and ISP characterstcs. s we dscussed prevously, we assume that a Type 0 ISP (denoted by ) does not take compettor actons nto account. Therefore, ts expected proft n market s only a functon of ISP- and market-level characterstcs.

5 616 JOURNL OF MRKETING RESERCH, OCTOER 2009 () 1 E[ π 0] = β + x β + x β, Here, x are market-level characterstcs that affect the proftablty of adopton, and x are ISP characterstcs. For a Type k > 0 ISP, ts expected proft n market s ( 2) E[ π k] = β + x β + x β + E[ ψ( n + 1) n n E[ π 0] = β + x β + x β ψ 2 n + ψ 3 n X, θ, k], = ψ 1n ψ 2 n + ψ 3 n X, θ, k]. E[ π k] β x β x β E[ + ( + 1) n Here,,, and are the (expected) number of market compettors that adopt technologes,, and both, respectvely. Therefore, these wll be a functon of the market and compettor characterstcs. Then, ψ represents coeffcents on expected compettor behavor, and β represents coeffcents on other parameters of the proft functon. For Type 1 ISPs, we calculate the values for, n n, and n assumng that all ther compettors are Type 0 ISPs that choose the technology that maxmzes ther profts. For Type 2 ISPs, we calculate these values assumng that all ther compettors are ether Type 0 or Type 1. For Type k ISPs, we calculate these values assumng that all ther compettors are dstrbuted between Type 0 and Type (k 1). In ths way, we assume that all ISP- and market-specfc characterstcs are publc nformaton. Thus, any ISP can observe the characterstcs of all the other ISPs and predct ther behavor accordng to the dstrbuton of types. Gven ISP and market characterstcs and the parameters of the model, the choces of Type 0 ISPs are perfectly predctable up to the dosyncratc error n the proft functon. Consequently, the choces of hgher-level ISPs are also teratvely known gven the dstrbuton of types. Our modelng approach to study ths type-dependent choce problem s smlar to approaches that examne state-dependent choce problems (e.g., Netzer, Lattn, and Srnvasan 2008). Here, we have type dstrbutons and type-dependent choce probabltes, whle those methods have smlar state dstrbutons and state-dependent choce probabltes. Followng Camerer, Ho, and Chong (2004), we assume that ths dstrbuton s a truncated Posson. In partcular, we assume that types are dstrbuted Posson wth parameter τ. The Posson dstrbuton s convenent because a sngle parameter descrbes t. s τ ncreases, the dstrbuton of player types becomes relatvely more strategc. We can assume that a Type k ISP beleves that ts compettors are dstrbuted truncated Posson (at k 1) wth the same parameter τ. lternatvely, to estmate how strategc ablty vares wth market and ISP characterstcs, we modfy ths dstrbuton to allow the Posson parameter to vary wth these characterstcs. In partcular, we set ln(τ )= γ 0 + γ 1 z, where z ncludes three market-level characterstcs (the number of compettors n the market, the percentage of the populaton that lves n an urban area, and the percentage of the populaton that has graduated college) and a frm-level characterstc (number of markets served). 3 3lthough t may seem unntutve to nclude market-level characterstcs, we beleve that t s an emprcal queston whether they matter (and Gven ts type, ISP pcks the choce that maxmzes ts proft: Max s {, 0 π, π, π }. ecause ISPs operate n many markets and offer the same technology n all markets, we assume that they add up the profts across markets and choose the technology that gves the hghest total proft. Then, () 3 = 0, π 0 π π = E[ π k] + v, = E[ π k] + v, π = E[ π k] + E[ π k] + v + v + Γ v v where Γ represents the addtonal payoff of adoptng both technologes beyond the sum of adoptng each technology. ecause ρ and Γ are not separately dentfed n a settng such as ours (Gentzkow 2007), we normalze Γ to be 0. The error terms ν are ISP-level shocks that affect the proftablty of the dfferent technologes observed by the ISPs but not by the econometrcan. We can now predct multmarket ISP s choce probabltes, condtonal on ts type. The general procedure s as follows: We frst calculate every ISP s branch-level profts (or market-level profts). Then, we add them up by ISP to get the ISP-level profts. Next, we consder each ISP s aggregate proft maxmzaton problem to determne ts technology adopton decson. Then, we map every ISP s decson to all ts branches. We repeat ths procedure to get every ISP s expectaton about other ISPs decsons, condtonal on t beng of each possble type. We calculate the ISP s choce probabltes assumng that the ISP s maxmzng profts, condtonal on t beng of each type. Formally, the frst step s to calculate ISP s choce probabltes f t s of Type 0 (wth probablty p 0 k [], where k s the hghest type possble): p0 k() Pr0 s0 {0,,, }. Smlarly, we calculate all the other ISPs choce probabltes f they are Type 0. Then, we map s0 onto s0, Pr0 onto Pr 0,, = 1,...,, = 1, 2,, J (where J = 2233 n our data). Second, f ISP s a Type 1 ISP (wth probablty p 1 k [ ]), based on ts belefs about other ISPs types and branch-level decsons (Pr 0, ), we calculate s expected branch-level profts. ddng up these profts, we obtan s aggregate proft level and ts choce probabltes from ts proft-maxmzng problem: p1 k() Pr1 s1 {0,,, }. We smlarly calculate all the other ISPs choce probabltes f they are Type 1 ISPs, and then we map s1 onto s1, Pr1 onto Pr 1,, = 1,...,, = 1, 2,, J. We repeat ths procedure untl we get all ISPs choce probabltes under all types. Mathematcally, a Type k ISP s expected number of compettors adoptng the technologes n market can be shown by the followng vector: we fnd that they do). We nclude the frm-level covarate number of markets served because we beleve that t makes ntutve sense, even though t s not sgnfcant n many specfcatons. 1 ρ ~ N0 (, Σ) and Σ =, ρ 1

6 re ll Managers Created Equal? 617 ( 4) { E ( n k), E ( n k), E ( n k)} Here, all Type k ISPs assume that any other ISP (denoted by ) s dstrbuted accordng to a normalzed Posson dstrbuton wth one parameter τ, from Type 0 [ p0 k 1( )] to Type (k 1) [ pk 1 k 1 ( )]. gan, note that each ISP has an dosyncratc Posson dstrbuton parameter τ, whereas n the orgnal CH model, each group of lab partcpants has one dosyncratc Posson dstrbuton parameter τ. In other words, n the orgnal CH research, all partcpants types are drawn from the same Posson dstrbuton wth one parameter τ, whereas here, we draw each multmarket ISP from a generalzaton of the Posson dstrbuton n whch the τ vares wth the ISP s characterstcs accordng to the coeffcents on these characterstcs. Next, we can compute ISP s aggregate choce probabltes (weghted by p m k []) wth respect to the choce set {0,,, }: 1 ( 5) p( 0) p l k( ) l f s = 0 = Pr ( 0), I ( 0) =, l = 0,..., k 0 f otherwse p( ) = p l k( ) Pr l ( ), I 1 f s = ( ) =, l = 0,..., k 0 f otherwse p( ) = p l k( ) l 1 f s = Pr ( ), I ( ) =, l = 0,..., k 0 f otherwse f p( ) = p l k( ) Pr l 1 s = ( ), I ( ) =. l = 0,..., k 0 f otherwse Ths gves the followng lkelhood functon: I ( 0) ( ) ( ) () 6 {[ p ()] 0 I [ p ( )] I [ p ( )] [ p ( I ( )] )}. m = k 1 = [ pm k 1( l) Pr m l, ( )], m = 0 l m = k 1 m = 0 m = k 1 m = 0 l l [ p m k 1 We estmate ths lkelhood functon usng a genetc dfferental evoluton algorthm (Storn and Prce 1997). Ths method s smple and effcent for global optmzaton over contnuous spaces. We combne ths wth a GHK smulator usng 50 draws to smulate the choce probabltes (we choose ths number from Monte Carlo evdence n Keane [1994] and elsewhere). 4 4In the man results, we treat all ISPs technology adopton decsons as smultaneous regardless of whether they frst occur n July or October s ugereau, Greensten, and Rysman (2006) dscuss, the descrptve statstcs suggest that ths s a reasonable thng to do. For example, n Table 1, Panel, more than four tmes as many ISPs had adopted Rockwell Semconductor Systems technology n October than n July. m l, () l Pr ( )], [ pm k 1( l m ) Pr l, ( )]. Our model s dentfed because t predcts that dfferent types wll behave dfferently n otherwse dentcal stuatons. It reles on the assumpton that we can assess the attractveness of adoptng the technologes to each ISP. For example, suppose that we observe a market wth three ISPs and that we know that the optmal number of adopters s two. If we observe three adopt, we can assume that they are all Type 0. If we observe none adopt, they must all be Type 1 (and expect that ther compettors both adopted as Type 0s). The model generates such decson rules, and we compare these predctons wth the data. Each τ generates a dstrbuton of types. For example, f τ = 1, 37% of players wll be Type 1, and fewer than 1% wll be Type 5. In contrast, f τ = 3, 16% of players wll be Type 1, and 11% of players wll be Type 5. Gven that we have a large number of ISPs (2233) servng an even larger number of markets (9070), we can fnd the value for τ that best fts the data to dstrbuton of types predcted by the model. RESULTS Model Estmates In ths secton, we dscuss the parameter estmates. Table 2 presents the man results. Table 2, Column 1, uses four dfferent characterstcs of the ISPs and the markets they serve to defne τ. The results suggest that frms that operated n areas wth more educated populatons, that faced more compettors, and that operated n urban areas had hgher values of τ. (The negatve coeffcent for the number of markets s not robust to alternatve specfcatons, and therefore we do not emphasze t.) For example, ths means that the strategc thnkng dstrbutons for frms that faced more compettors frst-order stochastcally domnate the dstrbutons for other types of frms. Therefore, frms wth these characterstcs are more lkely to be hgher-type players and thus behave more strategcally. These results are consstent wth pror laboratory research. For example, Ho, Camerer, and Wegelt (1998) fnd that strategc thnkng ncreases as the number of compettors ncreases, and Chong, Camerer, and Ho (2005) fnd that laboratory partcpants who attended a hgh-qualty school are more strategc. Column 2 estmates the model n whch we assume τ to be equal across all ISPs. The estmated τ s 2.67 (.e., e.9809 ). Ths value means that the numbers of Type 0, Type 1, Type 2, Type 3, Type 4, and Type 5 and above are 164, 437, 583, 519, 346, and 185, respectvely. Ths s at the hgh end of the range of values for τ found n Camerer, Ho, and Chong (2004). For example, the medan value from all the experments they examne s 1.6, and the maxmum value s 4.9. For a group of portfolo managers, τ s 2.8. We vew ths as provdng external valdty for the CH model: Gven that ths s a busness decson, we expect managers to thnk t through more carefully than undergraduate students n a lab. Stll, the level of strategc thnkng s well wthn the range of the lab, suggestng that the laboratory nsghts ndeed apply n our settng. Rows 6, 7, 9, and 10 n Table 2 show that ISPs typcally dfferentate from ther rvals. The parameter ψ s negatve when estmatng a frm s ncentves to adopt the same modem technology as ts compettors and postve when estmatng ncentves to adopt a dfferent technology. For example, Rows 6 and 7 show that f an ISP adopted the K56Flex modem, all else beng equal, ts compettor was

7 618 JOURNL OF MRKETING RESERCH, OCTOER 2009 Table 2 MIN RESULTS Correlates wth strategc thnkng parameter τ (γ) Compettve ncentves for adoptng Rockwell Semconductor Systems K56Flex (ψ ) Compettve ncentves for adoptng USRobotcs X2 (ψ ) Controls: nonstrategc factors that affect adoptng Rockwell Semconductor Systems K56Flex (β ) Controls: nonstrategc factors that affect adoptng USRobotcs X2 (β ) *Sgnfcant at the 90% confdence level. **Sgnfcant at the 95% confdence level. ***Sgnfcant at the 99% confdence level. 1 2 Coeffcent SE Coeffcent SE 1 Constant (γ 0 ).6716*** *** ln(number of markets served).0221*** ln(number of ISPs n market).0403*** % populaton urban.1569*** % populaton college graduate *** Number of ISP s on Rockwell Semconductor Systems *** *** Number of ISP s on USRobotcs *** *** Number of ISP s on both technologes *** *** Number of ISP s on Rockwell Semconductor Systems.2653*** *** Number of ISP s on USRobotcs *** *** Number of ISP s on both technologes *** *** Constant ** *** ln(number of ISPs n market).3648*** *** ISP has dgtal connecton *** *** Mssng.6874*** *** ln(medan household ncome).2110*** *** Number of busness establshments per person ** ** % populaton college graduate ** *** % populaton urban *** % populaton n dfferent county fve years ago *** Number of backbone provders.0428*** ** Constant *** *** ln(number of ISPs n market) *** ISP has dgtal connecton *** *** Mssng ln(medan household ncome).5909*** *** Number of busness establshments per person * % populaton college graduate *** *** % populaton urban.4507*** *** % populaton n dfferent county fve years ago Number of backbone provders.0276*** *** ρ Log-lkelhood more lkely ether to have adopted the X2 modem or not to have adopted at all. Furthermore, we fnd that the ncentves not to adopt the same technology as a compettor were larger than the ncentves to adopt the competng technology. Ths mples that strategc thnkng may have led to an overall decrease n adopton of 56K modems. We examne ths dea n greater detal subsequently. We explore robustness to several alternatve specfcatons n the Web ppendx (see power.com/mroct09). In general, these results confrm our man fndngs. Comparson wth ugureau, Greensten, and Rysman (2006) Our results are consstent wth ugereau, Greensten, and Rysman (2006), though a comparson provdes mportant addtonal nsghts nto the consequences of allowng heterogenety n strategc ablty. Ther obectve was to determne whether ISPs coordnate to take advantage of potental network externaltes or dfferentate to generate local market power. s n our estmaton, they allow for both coordnaton and dfferentaton to arse n ther analyss. The key dfference between our research and thers s that we allow for heterogenety n manageral ablty. Ther prmary contrbuton and man result s that ISPs tended to dfferentate from ther rvals when choosng whch 56K modem technology to adopt. Ths s consstent wth our results for ψ n Rows 6, 7, 9, and 10 n Table 2. Our prmary emprcal contrbuton nstead arses from the estmates of the strategc ablty parameter τ and the smulatons of the consequences of varyng strategc ablty. Thus, our model provdes addtonal and dstnct nsghts because we can assess how ablty affects outcomes. drect comparson of our results wth those of ugereau, Greensten, and Rysman provdes an addtonal nsght: Our estmated level of dfferentaton s much stronger (n sgnfcance and relatve coeffcent magntude) than the one they estmate. Gven the assumptons of the CH model, ths s expected: Low-type ISPs may not dfferentate effectvely. These would be averaged wth the others had we estmated a perfect ayesan equlbrum model. In the CH model we estmate, the coeffcents are drven only by the frms that behave strategcally. It s mportant to note that, n general, the CH model does not ft the data better than the perfect ayesan equlbrum model used n ugereau, Greensten, and Rysman (2006). We estmated each of the specfcatons they presented, and our estmated log-lkelhoods are smlar to

8 re ll Managers Created Equal? 619 thers wth one notable excepton: Our model fts the data better than thers when we treat the July 1997 decsons as exogenous (.e., when makng decsons n October, ISPs observe ther compettors decsons n July). We beleve that our model fts the data better n ths case because these decsons are more lkely to be truly smultaneous because of the short tme horzon. In partcular, suppose that early adopton decsons (e.g., those n prl) are observable by later adopters (e.g., those n October). Then, the ISPs makng a decson n October wll be able to best respond to the early adopters. Ths means that the resultng adopton patterns wll more closely resemble Nash. In contrast, because t takes tme to set up the technology, t s unlkely that late adopters (those n October) wll be able to best respond to ISPs that adopted n ugust. Therefore, t s more lkely that conectures about compettor behavor over a short tme horzon wll rely on k-step thnkng. In ths way, treatng the July decsons as exogenous and observed, and then modelng only the subsequent decsons as smultaneous, s closer to the smultaneous game that we model. Therefore, ths s suggestve of the usefulness of the CH model over the Nash model when the game s truly smultaneous. Dd Hgh τ Frms Do etter? In ths subsecton, we provde a test of the external valdty of our estmates. We cannot explctly test our model aganst the Nash equlbrum. Instead, we examne whether the ISPs that survved untl prl 2007 had a hgher estmated value of τ. If the frms that are estmated to be more strategc are more lkely to survve, we beleve that ths provdes some surface valdty for our strategc ablty parameter. 5 Our data contan the URLs of 2233 ISPs that were operatng n We manually vsted each of these 2233 URLs agan n prl Of the 2233 URLs, 1107 were stll operatng as ISPs that provded dal-up Internet, DSL, or both. nother 933 were no longer operatng as ISPs. The remanng 193 were operatng as ISPs, but the vstor was forwarded to another Web ste. 6 We use the nformaton n Table 3 to assess the correlaton between the strategc ablty parameter (τ) predcted from our model and survval through ll three columns show the same substantve result: ISPs that survved (through contnued operatons or acquston) have a hgher value of τ. We use Table 2, Column 1, to predct τ. Column 1 defnes survval as ether stll operatng as an ISP or havng been acqured. Column 2 takes the ISPs that were acqured out of the data. Column 3 treats acqured ISPs as havng exted. Overall, Table 3 shows that hgher τ frms dd better n that they were more lkely to have survved for ten years. We do not mean to suggest that the 56K modem decson tself led to survval. Instead, we argue that hgh strategc ablty overall s lkely correlated wth observed strategc behavor n the decson to adopt 56K modems. Frms that survved had hgher estmated levels of strategc thnkng n 5Hale, Hortacsu, and Kosenok (2008) suggest ths type of valdaton strategy n ther work on the dffcultes n estmatng quantal response equlbra usng data from outsde the laboratory. 6For example, typng forwards the vstor to We nterpret ths as the ISP havng been acqured but show robustness to not ncludng these ISPs. Table 3 ISPS WITH HIGHER τ RE MORE LIKELY TO HVE SURVIVED ll ISPs (τ Defned as n Table 2, Column 3) τ.2259** (.0915) Constant.3848 TO PRIL Only ISPs That Mantan an Independent Web Ste cqured ISPs Treated as Havng Exted.2247** (.0941).4819* (.2413) (.2481) Log-lkelhood ** N ** (.0907).5203** (.2394) *Sgnfcant at the 90% confdence level. **Sgnfcant at the 95% confdence level. Notes: Probt regresson of survval on predcted τ. Standard errors are n parentheses. ths context, and therefore we argue that they lkely had hgher levels of strategc thnkng overall. Stll, ths correlaton between survval and strategc ablty needs to be treated as suggestve rather than conclusve evdence n favor of our model. It s possble that varables correlated wth estmated strategc thnkng, τ, are correlated wth survval for reasons ndependent of strategc thnkng. lthough ths s unlkely to be the case for the number of compettors all else beng equal, more compettors should lead to more falures t s possble that ISPs that operated n more urban and educated areas had hgh survval rates. Subsequently, we descrbe several lmtatons of the model n greater detal. Underlyng ths test s the assumpton that strategc thnkers are more lkely to be proftable and thus more lkely to survve. Whle Stahl (1993) shows n an evolutonary settng that some nonstrategc thnkers survve f they are lucky enough to randomly choose a good strategy, we fnd that strategc thnkers (.e., those wth hgher estmated τ) earn a hgher proft on average n our model. In partcular, Table 4 shows that predcted profts and the predcted strategc ablty parameter τ are strongly and postvely correlated wthn the model. The purpose of ths table s smply to show that n our model, n general, ISPs wth hgher τ earn hgher profts: Strategc thnkers are more lkely to Table 4 THE MODEL PREDICTS THT ISPS WITH HIGHER τ WILL HVE HIGHER PROFITS ll ISPs (τ Defned as n Table 2, Column 3) τ.4671* (.0919) Constant.8348* 1 2 Only ISPs That Mantan an Independent Web Ste.4605* (.0939).8283* (.2479) (.2425) R-square N *Sgnfcant at the 99% confdence level. Notes: Ordnary least squares regresson of predcted profts on predcted τ. Standard errors are n parentheses.

9 620 JOURNL OF MRKETING RESERCH, OCTOER 2009 be proftable. Therefore, the result that strategc thnkers survve beyond the estmaton perod provdes external valdty for our asserton that τ measures strategc thnkng. Consequences of Strategc Thnkng on 56K Modem Dffuson Next, we examne how dfferent levels of strategc thnkng may lead to dfferent outcomes. ased on the coeffcents of Table 2, Column 1, Fgures 1 and 2 show smulaton results n whch we allow the dstrbuton of strategc thnkng to vary. Fgure 1 shows that the percentage of ISPs that provde at least one 56K modem technology falls as strategc thnkng rses. If every frm s a Type 0 player, provson of one or the other technology s greater than 99%. However, provson falls under 50% as τ approaches 2, and t falls under 25% as τ approaches 5. eyond τ = 5, the effect of ncreasng τ appears to have lttle systematc mpact on behavor. Thus, Fgure 1 suggests that strategc thnkng slows the overall dffuson of the technology: If the ISPs are more strategc, fewer wll offer the upgraded Fgure 1 % ISPS THT PROVIDE T LEST ONE 56K MODEM Everyone Is Type TECHNOLOGY Estmated level of τ n Table Degree of Strategc Thnkng n the Market (τ) Notes: Smulatons are based on Table 2, Column 2. Fgure 2 % ISPS THT PROVIDE ECH 56K MODEM TECHNOLOGY Provde both Provde only US Robotcs Technology () Everyone Is Type 0 Estmated level of τ n Table Degree of Strategc Thnkng n the Market (τ) Notes: Smulatons are based on Table 2, Column 2. Provde only Rockwell Semconductor Systems Technology () servce to ther customers. Fgure 2 adds two further nsghts: (1) Fewer ISPs wll adopt both technologes as strategc thnkng rses, and (2) the relatve shares of the compettors wll level off as strategc thnkng rses. These results reflect the ncentve to dfferentate. When frms consder the competton, the model suggests that they understand that provdng a dfferent servce from the competton ncreases proftablty. In addton to these results, we conducted a smulaton n whch all players are Type 1. Under ths stuaton, fewer than 1% adopt both technologes, and more than 95% adopt USRobotcs technology, apparently n an attempt to dfferentate from ther expected Type 0 competton. Ths smulaton shows the mportance of heterogenety n strategc ablty n provdng nterestng and reasonable nsghts. It s not smply bounded ratonalty: If everyone s boundedly ratonal n the same way but no structure s mposed n terms of reasonable belefs, the market outcomes become unbalanced. In summary, the smulaton results suggest that allowng for heterogenety n strategc ablty helps us understand varaton n ISP technology choces. Compettve consderatons slowed the dffuson of 56K modem technology; however, dffuson would have been even slower f the ISPs were more strategc (as mght be expected as the ndustry matures). LIMITTIONS s n any emprcal work, ths research has several lmtatons. Frst, we assume, rather than test, the CH model. lthough we provde some evdence of external valdty, our model does not nest Nash equlbrum assumptons. Our goal has been to understand the drvers of changes n the ablty dstrbuton parameter τ, assumng that the model behnd t s correct. We rely on the pror expermental lterature to support our modelng assumptons. Drawng on ths lterature, we measure strategc ablty as the number of thnkng steps a frm goes through to dfferentate from ts rvals. Therefore, two frms wth dfferent characterstcs behave dfferently n the model n a probablstc sense. It s possble that the observed varaton n manageral ablty s smply a varaton n unobserved heterogenety along other dmensons. Our estmate of heterogenety n the ablty to correctly conecture compettor behavor puts a specfc structure on unobserved heterogenety. Gven the cross-sectonal nature of our data and that ISPs choose the same technologes n every market, we cannot allow for unobserved heterogenety n the lkelhood of ISPs upgradng ther servces or n the attractveness of upgradng across markets. These lmtatons can be overcome n future work wth alternatve settngs n whch frms make dfferent decsons n dfferent markets. In ths case, frm and market (random or fxed) effects can be dentfed. In addton, although we fnd that ISPs wth hgh estmated τ were more lkely to survve (despte beng less lkely to adopt 56K modems and facng more compettors) and that the results on what drves the strategc ablty parameter are ntutve, wthout a clear nstrument that s correlated wth τ but not wth survval, ths evdence remans suggestve because t s largely dentfed off the functonal form. Furthermore, ISPs wth more compettors

10 re ll Managers Created Equal? 621 that operate n educated urban markets are more lkely to be strategc. Stll, we can put forth alternatve explanatons for our ntutve results and the correlaton between havng a hgh estmated τ and survvng. Thus, we cannot mantan that the CH model s somehow better than assumng Nash behavor. Indeed, our model does not consstently ft the data better than ugereau, Greensten, and Rysman s (2006) Nash equlbrum model. Instead, we argue that the assumptons of the CH model enable us to learn dfferent thngs from the data than a Nash model. Second, a somewhat restrctve assumpton nherent n the CH theory s that all players thnk they are smarter than all other players. In other words, the CH theory precludes the possblty that players expect ther compettors to be ther equals n level of strategc thnkng. However, f we allow players to thnk that rvals may have equal ablty, ths wll result n a mutually best response through nfntely many teratons, meanng that the unqueness of the soluton would be lost. Thrd, we do not have rch data on manageral characterstcs. lthough we found that several market-level characterstcs were related to our measure of strategc ablty, we cannot report much about the manager-specfc factors that are related to ablty. More nformaton on managers would allow for a deeper understandng of the types of managers that are more strategc. Fourth, we dentfy a specfc knd of ablty: the ablty to correctly conecture compettor behavor. We cannot offer anythng about the ablty of managers n other dmensons that are relevant to success. Fnally, the emprcal settng mght dffer from the model and affect the results n unforeseen ways. For example, multmarket ISPs may weght markets dfferently than our assumptons suggest. They may be forward-lookng frms that consder future market changes that we cannot measure. There may be unobservable shocks to adopton costs or benefts that affect technology choce. For example, a temporary, locally focused prce promoton for one technology may nfluence our results on strategc behavor. Furthermore, although adopton takes place over a short perod, the game we study s not truly a smultaneous game. The ISPs may respond to each other s decsons quckly. Fnally, some ISPs may also be playng a coordnaton game rather than a dfferentaton game. lthough ugereau, Greensten, and Rysman (2006) fnd that ISPs dd not behave ths way on average, f some ISPs were coordnatng, they would appear n the estmates to be less strategc. CONCLUSION s the frst study to our knowledge to combne behavoral game theory wth the structural models of new emprcal ndustral organzaton, ths research provdes a new framework for understandng varaton n the decsons of managers who face smlar choces. Ths framework enables us to show how strategc thnkng affects outcomes. We fnd that strategc thnkng slowed the dffuson of 56K modem technology, n support of Renganum s (1981) theoretcal work on the subect. In partcular, our results suggest that strategc thnkng by some customers substantally reduced modem dstrbuton for both Rockwell Semconductor Systems and USRobotcs. Ths mpact suggests that compettve consderatons n technology adopton are mportant to managers of busness-to-busness products and for polcy makers tryng to encourage technology dffuson. That sad, the degree to whch compettve consderatons matter depends on the strategc sophstcaton of the frms: Our smulatons suggest that adopton rates would have been lower f the average level of strategc sophstcaton were hgher. In general, n ndustres wth nexperenced managers, compettve consderatons may be less mportant. Therefore, ths research bulds on the rch exstng lterature that focuses on the dffuson of new consumerorented products (startng wth ass 1969). Our results suggest two new varables that should be consdered when a new product s amed at busnesses: (1) the strategc consequences of the product for the targeted ndustry and (2) the strategc ablty of the players. The compettve consderatons of busness customers affect dffuson, and ths s partcularly mportant n ndustres wth sophstcated, experenced managers. Consstent wth Soberman s (2007) theoretcal results, ths means that t may be most effectve for busness-to-busness marketers to target ust one frm n each market because the margnal returns to targetng multple competng customers wll be lower. Our results also ndcate that ncentves for busness customers to dfferentate from compettors may hnder the creaton of wnner-take-all markets. We show that estmatng heterogenety n manageral types s feasble and provdes some notable nsghts. Several opportuntes reman for future work that bulds structural econometrc models from the assumptons of behavoral games. We encourage researchers to examne whether strategc thnkng lmts (or encourages) technology adopton n other ndustres and whether ths mpact ncreases as ndustres mature and managers become more experenced. Smlarly, scholars could apply ths modelng technque to other settngs to explore further how strategc thnkng affects market outcomes. 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