The Determinants of Technology Adoption The Case of the Banking Firm.pdf

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The Determinants of Technology Adoption: The Case of the Banking Firm Author(s): Timothy H. Hannan and John M. McDowell Source: The RAND Journal of Economics, Vol. 15, No. 3 (Autumn, 1984), pp. 328-335Published by: on behalf of Wiley RAND CorporationStable URL: http://www.jstor.org/stable/2555441Accessed: 24-07-2015 08:45 UTC

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Rand Journal of Economics Vol. 15, No. 3, Autumn 1984

The determinants of technology adoption: the case of the banking firm

Timothy H. Hannan*

and

John M. McDowell**

Using data on the adoption of automatic teller machines by firms in the banking industry, this study examines the relationship between the decision to adopt new technology and its determinants. Since bankingfirms differ considerably in terms of the competitive environments in which they operate, focusing on this one innovation in this industry allows a stronger test of the relationship between market structure and the adoption of new technology than has been previously conducted. Using a failure time estimation procedure, wefind that larger banks and banks operating in more concentrated local banking markets register a higher conditional probability of adopting this new technology, all else equal. We also find that other results are consistent with the underlying model and that the bank's regulatory environment shapes its adoption decision in plausible ways.

1. Introduction

* The rate at which innovations are adopted by firms constitutes an important part of the process of technological change. Thus, investigation of the firm-specific and market- specific characteristics which influence decisions to adopt innovations has long been recognized as an important area of study. In this article we seek to shed further light on this question by choosing as our object of study the decision by banks to adopt automatic teller machines (ATMs).' This choice allows us to avoid a number of the problems encountered by earlier studies of the influence of market characteristics on the adoption of technology because markets in which commercial banks operate are limited geograph- ically. Thus it is possible to compare the adoption decisions by firms in the same industry that operate under widely varying market conditions. By definition we avoid the potentially serious problem frequently faced by previous researchers of accounting for the affect of interindustry differences on the adoption of an innovation. In addition, because

* Federal Reserve Board. ** Arizona State University. The views expressed herein are the authors' and do not necessarily reflect those of the Federal Reserve

Board or its staff. We wish to thank David Walker and Keith Quince for making the data employed in this study available. We also owe a special debt to Robert Avery, without whose skill and advice in using the estimation procedure, this article would not have been possible.

' Past studies of the adoption decision and its determinants include Mansfield (1968), Romeo (1975), and Oster (1982).

328


HANNAN AND MCDOWELL / 329

of the large number of banks and local banking markets in existence, we are able to avoid the problem of few observations and few degrees of freedom encountered in previous studies of the relationship between market characteristics and the adoption decision.2

Another advance over the previous literature involves the estimation procedure that we employ. Our study uses data indicating the point in time that each of a large number of banks introduced an ATM system. The principal task is to assess the dependence of adoption decisions on explanatory variables which may take on different values at different points in time during the period that the innovation could have been adopted. To account for possible changes in the value of explanatory variables over this time period (and thus to avoid the usual assumption of unchanging values), we employ a "failure time" estimation procedure used recently by Lancaster (1979) and Flinn and Heckman (1983) to investigate unemployment duration.

The plan of the article is as follows. Section 2 introduces the explanatory variables to be employed in the analysis and discusses their expected role in the adoption decision. Section 3 describes the data and the estimation procedure, and Section 4 presents the econometric results. The final section concludes with a summary and a discussion of implications.

2. The determinants of ATM adoption * In examining those characteristics of banks and banking markets which are likely to influence the decision to adopt ATM systems, we use as a guide the presumption that an innovation will appear more attractive to a potential adopter, the greater is the positive differential between profits obtainable with and without the innovation.3 This suggests the role of a number of firm and market characteristics in the adoption decision.

Consider first the prevailing wage rate (WAGE) in the market in which the bank operates. Since ATMs are labor-saving, an increase in the wages which must be paid employees should reduce the profitability of ATM operations by less than it effects the profitability of operations involving human tellers. Thus, adoption of ATM systems should appear more attractive to banks operating in higher wage areas, all else equal.4

Market growth (GROWTH) is another characteristic which may influence the profitability of an ATM system relative to its alternatives. To the extent that it allows establishment of ATM operations without having to replace undepreciated capital associated with human teller operations, ATMs should appear more attractive to banks operating in markets experiencing faster growth.

We also include a measure of firm size in the analysis. Most researchers have focused on its role as a proxy for the profitability of an innovation as a result of scale economies.

2 In focusing on the adoption of a given innovation by several different industries, past researchers have been able to use only as many observations as there are adopting industries, and this in practice has turned out to be a rather small number. Thus, Romeo's (1975) investigations of the adoption of numerically controlled machine tools could muster only ten different industry observations and reported estimations with as few as four degrees of freedom. Mansfield (1968) speculates on the relationship between the adoption decision and market structure from the standpoint of even fewer observations.

3 While this simple presumption is sufficient for our purposes, the relationship between the adoption decision and profits that may be obtained with and without the innovation and with and without rival adoption of the innovation is more complex than the presumption may suggest. See Kamien and Schwartz (1982, Chs. 4 and 5) and an earlier version of our article (Hannan and McDowell, 1983) for a fuller discussion.

The precise sense in which "greater attractiveness" translates into an adoption decision is developed fully below. Note further that higher wage levels may make ATM adoption more attractive for other reasons as well. Areas with higher wage rates have a higher proportion of people who value their time relatively highly, thus registering greater demand for ATMs. Also, to the extent it proxies average educational levels, higher wage rates may indicate a population more adaptable to the use of a new technology.


330 / THE RAND JOURNAL OF ECONOMICS

Other routes of causation, however, include the possibility of differences in managerial attitudes between large and small firms and differences in relative risk exposure for firms of different size. Thus, the effect of firm size cannot be predicted. Also included in some estimations is a term representing size squared ((SIZE)2) to allow for more flexibility in the relationship and to investigate the existence of a bank size most conducive to technology adoption.

Of central concern in our study is the much debated relationship between market structure and the tendency of firms to adopt new technology. We examine this question by including in the analysis the three-firm concentration ratio as a measure of market concentration.5 While several economists have undertaken the task of deriving from theory the relationship between market structure and various aspects of firm "innovative- ness," these relationships appear to be at best complex ones, contingent on circumstances about which there may be little information in any empirical application.6 Thus, the impact of market concentration in this study is not suggested a priori.

We also include a variable designed to control for differences in bank product mix. According to Walker (1976, p. 13), cash withdrawals from checking accounts represent the most common transaction performed with ATMs. We therefore include a measure defined as the proportion of the bank's total deposits accounted for by demand deposits (denoted PROMIX), with the expectation that banks specializing more in demand deposits will find ATM adoption more attractive.

Included also is a measure of firm profitability. At issue here is the question of whether a liquidity constraint in funding the adoption of the innovation exists. If the availability of internally generated funds constrains investment in this new technology, then the more profitable firm should evidence a greater tendency to adopt it. Since the validity of such a constraint is open to question, the impact of profits is not suggested a priori.

The data employed also allow us to examine the impact of regulatory restrictions on this aspect of innovative activity, since states differ in terms of the restrictions imposed on banks and their usage of ATMs. One such difference involves state branching restrictions, which are controlled for in our estimation through the use of two dummy variables. UNITBR denotes operation in a state in which no branches (only unit banks) are allowed, while LIMBR denotes operation in a state where branching is allowed only within limited geographic areas. Remaining banks operate in states which allow statewide branching. Some authors have suggested that banks for which branching is restricted tend to substitute other competitive devices for the prohibited branches.7 This in and of itself might suggest that the existence of branching restrictions promotes the adoption of ATMs. Other considerations may also be relevant, however. If, for example, the lack of branching restrictions makes entry into new banking markets less costly, established banks operating in such an environment may face a greater threat of entry. Since we cannot predict the impact of these possible differences in potential competition, the role played by branching restrictions is not suggested a priori.

State regulatory restrictions on the usage of ATMs themselves differ in at least one important respect. While some states allow banks to locate ATMs away from the premises of an established banking office, others do not. This distinction should prove important in states which prohibit or restrict branching activity, since offpremise ATMs may enable banks partially to circumvent these restrictions. To account for this, we use a dummy

5 This is the most commonly used measure of market concentration in studies of banking market structure and bank performance. See Rhoades (1982) for a summary.

6 See, for example, the extensive work of Kamien and Schwartz as summarized in their book (1982, Chs. 4 and 5).

' See, for example, Flannery (1982, p. 1).


HANNAN AND McDOWELL / 331

variable (OFFPRM) to indicate operation in a state which prohibits or restricts branching activity but which allows offpremise ATMs. Thus, the case in which OFFPREM = 1 should signify a situation in which ATMs appear more attractive to potential adopters.

We also account for two additional considerations. To control for differences between urban and rural environments, we include a dummy variable (URBAN) which indicates operation in an SMSA. We also introduce a dummy variable (BHCDUM) which indicates ownership by a bank holding company. In neither case is the impact of these two variables suggested a priori.

3. The test * While we have thus far discussed determinants of the "attractiveness" of ATM adoption to potential adopters, an empirical analysis clearly requires a more concrete specification of the relationship between firm adoption behavior and explanatory variables. As noted above, this should also be done in a way which accounts for changing values in the explanatory variables during the time period in which the innovation could have been adopted. We accomplish this by using the econometric specifications employed in "failure time" estimation procedures.8 Allowing F(T) to denote the probability that a bank will not have introduced an ATM system by period T (sometimes referred to as the survivor function), it can be shown that

F(T) = exp(-J' h(t)dt) (1) where h(t), called the "hazard rate," is defined as the conditional probability that the firm will adopt the innovation at time t, given that it has not done so by that time. The link between observed behavior and firm or market characteristics in the model is obtained by specifying the conditional probabilities (the h(t)'s) as functions of firm or market characteristics. In view of the nonnegativity of h(t), the most natural and commonly used form is the exponential,

h(t) = exp(X43), (2) where X1 is a vector of firm and market characteristics prevailing at time t and : is a vector of coefficients. Note that substitution of (2) into (1) yields F(T) as functions of the firm and market characteristics prevailing in each period up to period T. The estimation procedure employed maximizes a likelihood function composed of these probabilities.9 Estimated coefficients may be interpreted in terms of the relationship between explanatory variables and the conditional probability of adoption, as expressed in (2).

The data set that we employ is extensive. It consists of annual observations of ATM adoptions and bank and market characteristics for the period 1971-1979 and covers

8 See Kalbfleisch and Prentice (1980) and Lancaster (1979) for a full discussion. 9Because of problems with our adoption data for the years 1977 and 1978, we exclude adoption

information for these two years and use as our likelihood function NX N2 N3

L = J [Fi(Ti - 1) - Fi(Ti)] fJ [Fk(T'976) - Fk(T'979)] 17 Fj(T'979), i= I k=I j=I

where N', N2, and N3 denote the number of banks that introduced ATMs by 1976, between 1977 and 1979, or did not adopt by 1979, respectively. The first bracketed expression refers to the first six years of information and notes that the probability of adoption during any of these years is the probability of not having the innovation by the end of the previous year, minus the probability of not having it by the end of the year in question. The second bracketed expression denotes the probability of adoption sometime during 1977 through 1979 (since we do not use 1977 and 1978 adoption information). The last expression denotes the probability of not adopting by the end of the last year, 1979. Because of the omission of two years of adoption information, this likelihood function can be shown to be superior to the alternative of using probability densities.



3,841 banking firms operating in 392 different SMSAs or counties which have been judged to approximate local banking markets.'0 Of these banks, 740 had introduced ATM systems by 1979, the last year for which data are available.

4. The results

* Table 1 defines the variables used in the estimations reported below. We report maximum likelihood estimations obtained for this sample of 3,841 banks in Table 2. Columns (1) and (2) report coefficient estimates obtained with and without the size squared term, (SIZE)2. Note first the positive and highly significant coefficients of market concentration (CR3) in both estimations. All else equal, banks operating in more concentrated markets are found to have a higher probability of introducing this new technology, given that they have not yet done so. These results are important in that they suggest the possibility of a Schumpeterian-like tradeoff in efforts designed to deconcentrate local banking markets. While such efforts are generally undertaken to produce static efficiency gains associated with lower profit rates and lower prices, our results suggest that they may come at the price of slower introduction of new technology.

The coefficients of SIZE are positive and significant, while the coefficient of (SIZE)2 is negative and insignificant. Thus, larger banks are found to exhibit a higher conditional probability of adoption, all else equal. " This finding is consistent with several different explanations, including the existence of economies of scale in the use of ATMs, less relative risk exposure on the part of larger banks, and different attitudes toward new technology on the part of managers of larger banks.

Consistent with a priori expectations, the coefficients of WAGE are positive and highly significant. Since ATMs are labor-saving, this positive impact of the area wage rate on the conditional probability of adoption suggests that cost savings may be at least part of the reason for the adoption of ATMs.

GROWTH fails to register a statistically significant impact in these estimations, while the coefficients of PROMIX, defined as the proportion of the bank's deposits accounted for by demand deposits, are positive and significant. This positive impact is consistent with the expectation that, because cash withdrawals from demand deposits are the most common type of transaction performed with ATMs, banks with greater proportions of demand deposits find ATMs more suitable to their operations.

The impact of PROFITS on the conditional probability of adoption is negative but statistically insignificant. We therefore fail to find statistical support for the notion that profits, because of a binding constraint placed upon technology adoption by the supply of internally generated funds, have a positive impact on the conditional probability of adoption. This is consistent with the results of other studies which, focusing on other aspects of technological progressiveness, also fail to find an important role for firm profitability. 12

The coefficients of BHCDUM are positive and highly significant, suggesting that all else equal, banks that are members of bank holding company organizations have a higher

'0 This sample was obtained from Stephen Rhoades, who has used these market definitions in a number of recent contributions. See Rhoades (1980). Data sources are the FDIC's Summary of Deposits, bank call reports, bank income and dividend reports, the FDIC population surveys of bank ATM usage, the Bureau of the Census, City and County Data Book, and state statutes pertaining to electronic funds transfer systems as summarized by Penny and Baker (1980).

" Accepting the point estimates of the coefficients of SIZE and (SIZE)2, we find that the size of firm consistent with the highest conditional probability of ATM adoption is several times larger than the largest bank in the sample.

12 See Kamien and Schwartz (1982, p. 98) for a brief review of this literature.



TABLE 1 Variable Definitions

CR3 = Market three-firm concentration ratio, measured as the proportion of total market deposits accounted for by the largest three firms (expressed as a percentage).

SIZE = Bank size, measured in total assets (in millions). WAGE = Wage rate (manufacturing) prevailing in the market.

GROWTH = Yearly growth in market deposits. PROMIX = Product mix, defined as the ratio of each bank's demand deposits to its total deposits. PROFIT = Bank profit rate, measured by net income divided by total assets. UNITBR = Dummy variable indicating operation in a unit-banking state.

LIMBR = Dummy variable indicating operation in a state in which branching is restricted to limited geographic areas.

OFFPRM = Dummy variable indicating operation in a state where branching is either prohibited or restricted and offpremise ATMs are allowed.

URBAN = Dummy variable indicating operation in an SMSA. BHCDUM = Dummy variable indicating ownership by a bank holding company.

TABLE 2 Determinants of the Conditional Probability of ATM Adoption

Derivatives of the 9-Year Coefficient Estimates Adoption Probability

Variable (1) (2) (la) (2a)

CONST -7.81 -7.96 (-7.73) (-7.86)

CR3 .18E-1* .19E-1* .21E-2 .22E-2 (6.51) (6.68)

SIZE .33E-4** .12E-3* .39E-5 .14E-4 (2.50) (2.81)

(SIZE)2 -. 17E-8 -.20E-9 (-1.63)

WAGE .31* .31* .37E- 1 .37E- 1 (8.63) (8.72)

GROWTH .47E- 1 .11 .56E-2 .13E- 1 (.06) (.13)

PROMIX 1.02* .99* .12 .12 (2.95) (2.85)

PROFIT -8.72 -8.21 -1.04 -.97 (-1.35) (-1.25)

BHCDUM .98* .96* .12 .11 (11.84) (11.50)

URBAN .23*** .23*** .27E- 1 .27E- 1 (1.75) (1.74)

UNITBER .22*** .24*** .26E- 1 .28E- 1 (1.66) (1.85)

LIMBR .88E- 1 .88E-1 .1 OE-1 IOE-1 (.66) (.66)

OFFPRM .42* .43* .50E- 1 .51E-1 (3-95) (4.01)

N 3841 3841 -2 ln k 430.12 420.04

Note: *, * and *** represent significance at the .01, .05, and .10 level, respectively. Numbers in parentheses are coefficients divided by asymptotic standard errors.



conditional probability of adoption. Less relative risk exposure on the part of large, more diversified bank holding company organizations is a possible explanation.

We introduce the variables UNITBR, LIMBR, and OFFPRM to account for potentially important differences in the regulatory environments in which banks operate. The positive coefficients of UNITBR and LIMBR are consistent with the notion that banks are more likely to adopt ATMs as a means of attracting customers when the alternative of providing convenience through branching is restricted. Only the coefficients of UNITBR reach significance at even marginal levels, however. The coefficients of OFFPRM are positive and highly significant, thereby suggesting that banks are more likely to introduce ATM systems in a regulatory environment in which offpremise ATMs may be used as a vehicle to circumvent restrictive branching laws.

The coefficients of URBAN suggest that urban banks have a higher conditional probability of ATM adoption, although statistical significance is at best marginal. This result is roughly consistent with observations by Walker (1979, p. 12), who notes a higher proportion of banks in SMSAs having ATMs than banks operating outside SMSAs.13

Finally, to illustrate the implications of coefficient magnitudes in terms of the adoption probabilities over time, columns (la) and (2a) in Table 2 report the implied derivatives of the probability of adoption by 1979 associated with a once-and-for-all change (lasting throughout the nine-year study period) of each explanatory variable, as derived from estimations (1) and (2), respectively. Derivatives are evaluated at the average values of explanatory variables.'4 Note that the derivative estimate of the three-firm concentration ratio implies that an increase of ten percentage points in CR3 would result in an increase in the probability of adoption by the end of the study period of approximately .02. This change may be viewed in the context of an actual proportion of adoptions by 1979 of .19. Other derivatives may be interpreted similarly.

5. Conclusion * Using data on ATM adoptions by firms in the banking industry, we have examined the relationship between the firm's adoption decision and various regulatory, market, and firm characteristics in a manner that avoids the problems of interindustry differences and few degrees of freedom faced by previous studies. We also use an estimation procedure that allows us to account specifically for changes in the values of explanatory variables over time. Our results are decidedly Schumpeterian. Larger banks, banks operating in more concentrated local banking markets, and banks that are part of bank holding company organizations evidence a higher conditional probability of adoption of this new technology, all else equal. This raises the classic conflict between policies designed to

13 Assuming that the values of explanatory variables are stationary over time, Lancaster (1979) shows that (1) and (2) imply

E(T) = exp(-X'O), where X' denotes a vector of time-invariant explanatory variables and T denotes the number of periods until adoption. Consistent with this, we estimated this relation using the actual time until adoption, as expressed as

ln(T) =-X':,

with Tobit maximum-likelihood with the 1972 values of the explanatory variables. All coefficients were opposite in sign (with the exception of PROFITS) to those reported in Table 2, as this derivation suggests (since a positive change in the conditional probability of adoption for each period of time implies a negative change in the expected time until adoption) and all variables but PROMIX and PROFITS were significant. See Hannan and McDowell (1983) for a fuller discussion.

14 Since the probability of adoption by the end of nine years may be expressed as 1 - exp(- z exp(Xf)), 1=1

its derivative with respect to any given Xi, affecting all nine periods and evaluated at average values, X, may be expressed as [9 exp(-9 exp(Xfo)) exp(Xfo)] * fi, where fi is the coefficient of Xi.



promote static efficiency and those that promote technological progressiveness. Our findings make more critical the question of whether possible static efficiency gains associated with less concentrated markets outweigh the resulting losses in technological progressiveness. The other results we report are consistent with theoretical predictions, thus tending to validate our underlying model. We also find evidence that the regulatory environment in which banking firms operate shapes their decisions regarding the adoption of new technology in plausible ways.

References

FLANNERY, M. "The Social Costs of Unit Banking Restrictions." Working Paper #82-5, Federal Reserve Bank of Philadelphia, 1982.

FLINN, C. AND HECKMAN, J. "Are Unemployment and Out of Labor Force Behaviorally Distinct Labor Force States?" Journal of Labor Economics, Vol. 1 (January 1983), pp. 28-42.

HANNAN, T. AND McDoWELL, J. "The Structural Determinants of Technology Adoption: The Case of the Banking Firm." Working Paper EC 82/83-33, Arizona State University, 1983.

KALBFLEISCH, J. AND PRENTICE, R. The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons, 1980.

KAMIEN, M. AND SCHWARTZ, N. Market Structure and Innovation. Cambridge: Cambridge University Press, 1982.

LANCASTER, T. "Econometric Methods for Duration of Unemployment." Econometrica, Vol. 47 (July 1979), pp. 939-956.

MANSFIELD, E. Industrial Research and Technological Innovation. New York: W. W. Norton & Co., 1968. OSTER, S. "The Diffusion of Innovation among Steel Firms: The Basic Oxygen Furnace." Bell Journal of

Economics, Vol. 13 (Spring 1982), pp. 45-56. PENNY, N. AND BAKER, P. The Law of Electronic Funds Transfer Systems. Boston: Warren, Gorham and

Lamont, 1980. RHOADES, S. "Monopoly and Expense Preference Behavior: An Empirical Investigation of a Behavioralist

Hypothesis." Southern Economic Journal, Vol. 47 (October 1980), pp. 419-32. . "Structure-Performance Studies in Banking: An Updated Summary and Evaluation." Staff Studies #1 19,

Board of Governors of the Federal Reserve System, 1982. ROMEO, A. "Interindustry and Interfirm Differences in the Rate of Diffusion of an Innovation." Review of

Economics and Statistics, Vol. 57 (Autumn 1975), pp. 311-319. WALKER, D. "An Analysis of EFT Activity Levels, Costs, and Structure in the U.S." Working Paper #76-1,

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Paper #79-1, F.D.I.C., 1979.


Article Contentsp. 328p. 329p. 330p. 331p. 332p. 333p. 334p. 335

Issue Table of ContentsThe Rand Journal of Economics, Vol. 15, No. 3, Autumn, 1984Front MatterReputation and Product Quality [pp. 311 - 327]The Determinants of Technology Adoption: The Case of the Banking Firm [pp. 328 - 335]Durable Good Monopolies with Rational Expectations and Replacement Sales [pp. 336 - 345]Resale Price Maintenance and Quality Certification [pp. 346 - 359]Optimal Pricing in Electrical Networks over Space and Time [pp. 360 - 376]Dual Equilibria and Discontinuous Response in Monopolistic Competition with Two Classes of Consumers [pp. 377 - 384]Integration of the Sales Force: An Empirical Examination [pp. 385 - 395]On the Economics of Repeat Buying [pp. 396 - 403]Litigation and Settlement Under Imperfect Information [pp. 404 - 415]The Walrasian ttonnement Mechanism and Information [pp. 416 - 425]Noncooperative Behavior by a Cartel as an Entry-Deterring Signal [pp. 426 - 433]Conditions for Investor and Customer Indifference to Transitions among Regulatory Treatments of Deferred Income Taxes [pp. 434 - 446]Back Matter

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