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This article was downloaded by: [University of Chicago Library] On: 16 November 2014, At: 15:56 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Economics of Innovation and New Technology Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gein20 Market Structure And The Adoption Of Innovations: The Case Of The Spanish Banking Sector Manuel Espitia Escuer a , Yolanda Polo Redondo a & Vicente Salas Fumás a a University of Zaragoza Published online: 28 Jul 2006. To cite this article: Manuel Espitia Escuer , Yolanda Polo Redondo & Vicente Salas Fumás (1991) Market Structure And The Adoption Of Innovations: The Case Of The Spanish Banking Sector , Economics of Innovation and New Technology, 1:4, 295-307, DOI: 10.1080/10438599100000009 To link to this article: http://dx.doi.org/10.1080/10438599100000009 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

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Page 1: Market Structure And The Adoption Of Innovations: The Case Of The Spanish Banking Sector               ‡

This article was downloaded by: [University of Chicago Library]On: 16 November 2014, At: 15:56Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Economics of Innovation and New TechnologyPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/gein20

Market Structure And The Adoption Of Innovations:The Case Of The Spanish Banking SectorManuel Espitia Escuer a , Yolanda Polo Redondo a & Vicente Salas Fumás aa University of ZaragozaPublished online: 28 Jul 2006.

To cite this article: Manuel Espitia Escuer , Yolanda Polo Redondo & Vicente Salas Fumás (1991) Market Structure And TheAdoption Of Innovations: The Case Of The Spanish Banking Sector , Economics of Innovation and New Technology, 1:4,295-307, DOI: 10.1080/10438599100000009

To link to this article: http://dx.doi.org/10.1080/10438599100000009

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Market Structure And The Adoption Of Innovations: The Case Of The Spanish Banking Sector               ‡

Econ. Innov. New Techn., 1991, Vol. 1, pp. 295-307 0 1991 Harwood Academic Publishers GmbH Reprints available directly from the publisher Printed in the United Kingdom Photocopying permitted by licence only

MARKET STRUCTURE AND THE ADOPTION OF INNOVATIONS: THE CASE OF THE SPANISH

BANKING SECTOR$

MANUEL ESPITIA ESCUER, YOLANDA POLO REDONDO and VICENTE SALAS FUMAS

University of Zaragoza

(Received March 2, 1990: in final form April 25. 1991)

This paper is an empirical study of the determinants of adoption time for the teleprocess terminal by Spanish commercial and savings banks. The explanatory variables include the characteristics of the adopting firms, size, and in the case of the savings banks, the structure of the market and concentration. The results indicate that the speed of adoption is maximized at intermediate levels of size and market concentration, confirming one theoretical prediction of models of diffusion: namely, that adoption time is minimized at intermediate levels of market concentration.

KEY WORDS: Market Structure, Adoption and Diffusion of innovations. Spanish Banking Sector.

I . INTRODUCTION

This paper is an empirical study of the determinants of adoption time for the teleprocess terminal by Spanish commerical and savings banks. The explanatory variables include the characteristics of the adopting firms, size, and, in the case of the savings banks, the structure of the market and concentration. Our results indicate that the speed of adoption is maximized at intermediate levels of size and market concen- tration, confirming the theoretical prediction of models about diffusion of innovations, see Kamien and Schwartz (1972), namely that the adoption time will be minimized at intermediate levels of market concentration.

The theoretical model used to guide the empirical analysis is Davies' (1979) probit model of adoption of process innovations by business firms, complemented with Kamien and Schwartz's (1972, 1982) competitive model of adoption. Previous empiri- cal studies to which this paper is related are those of Mansfield (1961), of Davies (1979) and especially that of Hannan and McDowell (1984) on the determinants of adoption time of automated teller machines by banks in the U.S.A.' There are, however, some differences which are worth pointing out. First, a comparison is established between Davies' probit model of adoption and the "failure rate" model of Kamien and Schwartz, which is also used by Hannan and McDowell; this allows us to test the performance of the two models in fitting the data. Second, as with

$This paper forms part of a larger study on the static and dynamic efficiency of the Spanish banking industry, completed on behalf of FIES, whose financial support the authors' gratefully acknowledge. The comments of two anonymous referees and of the Journal Editor on a previous version of the paper, are also acknowledged.

'Other papers which include theoretical and empirical discussions of the diffusion of innovations across firms are Mansfield (1961, 1963). Nabseth and Ray (1974) and Oster (1982).

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296 M.E. ESCUER, Y.P. REDONDO AND V.S. FUMAS

Hannan and McDowell, we study the diffusion of a new process, developed in an outside industry, across a population of homogeneous firms operating in different geographical markets; therefore the empirical results are free from the criticism that, when we study innovation activity in heterogeneous industries, as is the case in Mansfield and Davies, then the market structure variables, such as concentration, may in fact be determined by the level of innovation in the industry. Third, we find a "v inverted" relationship between time of adoption and market concentration, which was not detected in previous empirical studies: Mansfield finds that concen- tration slows down the speed of adoption, while Davies, Hannan and McDowell reach the opposite conclusion. The "inverted v" relationship has theoretical support in the pioneering work of Kamien and Schwartz, and may also be the point of compromise between the conflicting influences of market concentration in the speed of adoption suggested by the work of Reinganum (1981a, b) and Quirmbach (1986). Reinganum (1981a) argues that in more concentrated markets, firms have larger shares and consequently a higher sales volume, which in turn increases the economic value of the innovation. Quirmbach (1986), on the other hand, shows that collusion among incumbent firms in the market may slow down the adoption of a new process, and collusion will be easier in more concentrated markets. It also has empirical support among the studies that relate R&D intensity and market concentration, see Cohen and Levin (1989).

2. THE BASIC MODEL

The starting point of Davies (1979)' model of diffusion of innovations, is the con- dition for firm i adopting the innovation in time t:

where ER, is the expected return from using such innovation and Ri: is the cut-off point on the return demanded on new investments. Equation (1) indicates that the ratio between expected return and the cut-off point depends on a list of time and firm related variables and that this dependency will allow the firm to adopt at the point in time when both returns are equal.

The function f(s,,) weights the effect of firm size, sit, on the value of the ratio of returns which conditions the adoption decision. It is often argued that size will have a positive influence on the speed of adoption, because larger firms have more ability to technically evaluate the innovation earlier in time, and because their low degree of risk aversion will also imply a lower minimum desired return. But size may also be associated with monopoly power and a low propensity to innovate. The form off (s,,) will be selected empirically from the general function

F,, + ;?;, f(si1) = e

where /3 and y are parameters. For /3 > 0, y = 0, the time of adoption is lower for small firms; for f l c 0, y = 0, larger firms adopt earlier than smaller ones; if f l c 0 and y > 0 the speed of adoption is maximized in firms of intermediate size.

Another function in (1) is the trend factor B(t), which captures the way in which

'See Davies (1979, Chapter 5) .

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ADOPTION OF INNOVATIONS IN BANKING 297

information on the innovation flows over time. Two functions are postulated,

B(t) = at$ (A)

B(t) = ae* (B)

where a and $ are parameters (a > 0, 0 < $ c 1). The specification (A) is con- sidered the appropriate one for simple innovations, in which the information search by the adopting firms is likely to give high returns in the early time periods and lower returns later in time. For complex innovations a more even distribution of returns from information search over time is predicted and therefore Eq. (B) would be appropriate.

Finally, E, is a vector of the explanatory variables, other than those of size and time, which may also influence the evaluation of the expected returns and costs of the innovation.

Equation (I), together with the functional form of %(I), determine the two variables which are often used to describe the diffusion of an innovation, namely the fraction of previous adopters over time, Q,, and the adoption time of a particular adopter, ti Davies (1979) shows that if the set of random variables in E, is jointly distributed as a log-normal density function, Q, is given by,

Q, = N (log 10- 70, I ) for group (A) ( 3 ~ )

Q, = N (910, I ) for group (B)

That is, the proportion Q, is described by a standarized log-normal distribution of time in the case of simple innovations, and by a normal distribution of time for complex innovations.

The adoption time, ti is obtained directly from ( . I ) , after substituting 8(t) and taking logarithms,

log ti = - (1% a)/$ - (1 I$) log f (sit) - (I/$) 1% &it (4A) I. = - (1% a)/$ - (1 I$) 1% f (siO - (I/@) 1% &it (4B)

The parameters $ and jl may vary from one innovation to another and $ is likely to be influenced by the characteristics of the market where the diffusion takes place. Since $ measures the rate at which the innovation is technically improved over time and/or the rate at which firms learns about such an innovation, the market charac- teristics influencing $ will be those that may affect the generation of the information and learning process over time. Davies indicates that two such market variables are growth of demand and level of competition, but how these variables may affect $ is ambiguous. In faster growing markets the potential return for information search is high, as the size of the market is increasing but, on the other hand, in low growth markets, firms may have high incentives to discover new processes which will allow them to cut costs and improve profitability, given that revenues may be decreasing. More market competition will imply pressures by the adopters over non adopters to use the innovation, in order to remain competitive. But it is also likely that com- petition wi!l lower the return from search and, therefore, learning may take place at a lower rate.

In this paper it will be assumed that a and fl are the same for all innovations, given the homogeneity of the firms in the sample. The parameter $ will be allow to vary

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298 M.E. ESCUER, Y .P. REDONDO AND V.S. IFUMAS

across geographical markets as a function of the growth rate of demand and the degree of concentration (as an inverse measure of competition). The sign of the influence of the market variables in J/ will be considered to be an empirical question.

Another class of model of the adoption and diffusion of innovations is that based on the motion of "failure rate", or conditional probability of adopting the innovation for an adopter which has not yet adopted at a given moment in time,

where F(t) is the probability of having adopted the innovation before t. Kamien and Schwartz (1972, 1982) develop a competitive model of firms in oligopolistic markets where the decision variable is the adoption time and it is assumed that h(t) = h, constant over time. The authors describe a set of conditions for which the optimal adoption time is minimized at intermediate values of the probability h. Since h is assumed to be a function of the degree of market competition, the Kamien and Schwartz model (1982, p. 119) would predict that adoption time would be shorter in markets with intermediate levels of concentration, if concentration and competition are (inversely) related. Therefore. assuming that observed adoption time is equal to optimal time, under the assumptions of the Kamien and Schwartz model. then Eqs. (4A) and (4B) could also be considered as the reduced form specification of the optimality conditions for a competitive model of adoption.

But the failure rate model also allows us to derive an expression for the time of adoption which does not involve a process of competitive interaction. Form (5) and h(t) = h, i t can be shown that the expected adoption time E(t) is equal to the reciprocal of h.' So, if observed adoption times are equal to expected ones for a given h, the adoption time may be explained by the same set of variables which explain h. One variant of this approach is followed by Hannan and McDowell(l984) in order to explain the adoption time of automated teller machines among US banks.

All this implies that the actual specification of the model used in the empirical analysis may correspond to different theories of adoption and therefore, the empirical analysis may help to explain which variables influence the time of adoption, but not to choose among alternative theories4 The empirical analysis presented in the follow- ing section follows the steps implied by Davies' model, but references are also made to some results which would be obtained from the application of the failure rate model. with h constant over time.

3. APPLICATION TO THE ADOPTION OF TELEPROCESS TERMINALS BY SPANISH BANKS

The Spanish banking sector is dominated by two groups of institutions, commercial banks and savings banks. Savings banks have mostly specialized in retail banking,

'The following equality is derived in Lancaster (1979). 'Davies points out that to use the number of periods since the first firm adopted the innovation as an

inverse measure of the speed of adoption by firm i requires that all firms of the sample have already adopted the innovation. This is the case in our sample and the reason why a probit model is not used as an alternative. This probit model would have been more appropriate if, say, half of the firms had adopted the innovation at the end of the period studied (1983).

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ADOPTION OF INNOVATIONS IN BANKING

while commercial banks have been offering universal banking services; this means that there has been hardly any direct rivalry between savings and commercial banks, at least for the period under study. Secondly, savings banks have until recently been restricted to operating in a given regional market, whilst commercial banks have been allowed to locate their offices anywhere in the country. For this reason, the relevant geographical market for commerical banks has been a single national market, whilst for savings banks it is possible to define seventeen different regional market^.^

These features of the Spanish banking sector are taken into account in the empirical study in the following ways. Commercial and savings banks are analyzed separately, given that they are considered to be different types of institutions with different product-market orientations. Further, given that commercial banks operate in a single market, it is not possible to incorporate market structure variables in the explanation of adoption times and therefore only firm related variables will be used. For savings banks, on the other hand, market structure variables, such as concen- tration and growth, are included among the explanatory variables.

The data on adoption time was obtained by way of questionnaires mailed to commercial and savings banks; these asked about the year of adoption with respect to a list of product, process and administrative innovations. Table I shows the number of banks which answered the questionnaire, as well as their distribution by firm size and compares this information with the size distribution of the total popu- lation of banks. We consider that the sample of respondents is fairly representative of the total population, although for commercial banks it is biased towards larger banks. Other sources of data were company accounts and general information sources on the Spanish banking sector.

The empirical application is performed in two stages. First, we use models (3A) and (3B) to explore the diffusion of the innovation over time and to test whether the teleprocess terminal may be considered a simple or complex innovation. Secondly, the focus is placed on the time of adoption by individual firms.

3.1. Diffusion over time Models (3A) and (3B) describe the path of the proportion of adopters over time for simple and for complex innovations respectively. The teleprocess terminal may be considered a priori as a complex innovation, since its installation requires important levels of investment and thorough internal re-organization of the production process, but we are keen to see if this hypothesis is confirmed by the data. With this objective in mind, both models will be fitted and their results compared.

If we define by z, the value from the standardized normal distribution which corresponds to a probability of adoption in t equal to Q, (number of adopters until t over the total number potential adopters), then Eqs. (3A) and (3B) may be written as

z, = a, + b, log t, for group (A) (3'A) Z, = a2 + b2t, for group (B) ( 3 3 )

where ai = -p/a and bi = 110; the parameter b, is defined as the parameter of diffusion and is directly related to the slope of the diffusion curve.

The results of fitting (3'A) and (3'B) to the available data are shown in Table 2. All

'For an overview of the Spanish banking sector, see Vives (1990).

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300 M.E. ESCUER, Y.P. REDONDO AND V.S. FUMAS

Table 1. Number of respondents and bank population by size (n = number of employees).

Commercial banks

Sample Popularion

Large (n 3 5000) Medium (5000 > n 3 600) Small (n < 600) Total

Savings banks

Sample Popularion

Large (n C 1000) Medium (1000 > n 3 500) Small (n < 500)

Total

the models give a fit to the data which does not allow us to reject the null hypothesis that the model actually explains such data. But for model (3'A), the presence of autocorrelation in the residuals is detected, both in commercial and in savings banks. The Durwin-Watson statistic for model (3B) falls in the indeterminacy zone in the case of commercial banks, whilst for savings banks the hypothesis of autocorrelation in the residuals is not accepted. We conclude that the empirical evidence confirms the hypothesis that the teleprocess terminal is a complex innovation, for which trend factor (B) is a more accurate assumption than trend factor (A).

Table 2. Estimation of diffusion over time

Commercial banks Savings banks (Drffusion period, 1971-1983) ( D~ffusion period, 1967-1981)

Model ( 3A ) Model ( 3 8 ) Model ( 3A ) Model (3B)

Parameter of 1 .St9 0.14* 1.47* 0.28* diffusion. b, ( r statistics) (3.7) (5.3) (7.2) (13.0)

R 0.61' 0.77. 0.80° 0.93.

Durbin-Watson 0.77t 1.20: 0.84t 1.89

*Significantly diKcxnl from zero at 99%. t P r e v m ~Tautomrrcla~ion: :Indeterminacy: $Absence ofaulocorrelalion.

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ADOPTION OF INNOVATIONS IN BANKING

Table 3. Adoption time determinants: commercial banks

Model 1 Model 2 (Dependent (Dependent variuble) ti varinble ti

Model 3 (Dependent variable) ti

Constant

Deposits

(~eposits)'

log Deposits

Profitability - 1.41 (- 1.14)

Fixed assets/no of I x lo-'* employees (2.12)

IT 0.78 0.90 0.73

F 23.35*** 42.6*** 18.6*** S.E. of residuals 2.00 1.43 2.21 no of observations 20 20 20

***. **. *: Statistically significant at 99.95 and 90%. mpectivcly.

3.2. Adoption time by individual firms The basic model is now equation (4B) which will be applied to commercial and savings banks.

3.2.1. Commercial banks The main explanatory variable of the time of adoption include in equation (4B) is the "size" of the firm, which will be measured as the volume of bank deposits, as an average for years 1975-78. Other explanatory variables, which would be part of E, , , are "profitability" and "capital intensity" of the production process. The profitability of the bank is measured as the rate of return on total assets (average net profit 1975-1978 divided by net assets). A higher return will generally imply more liquidity and, therefore, more funds to finance the investment required to introduce the innovation. Thus a negative association is expected between adoption time and profitability.

Capital intensity is measured as the ratio between the fixed assets of the bank and the number of employees. The teleprocess terminal may be considered as a labour saving technological innovation and, therefore, the value of adopting the innovation at a given moment in time should be higher for more labour intensive organizations. With this in mind, a positive association should be expected between adoption time and capital intensity (again measured as an average value for the period 1975-1978).

Table 3 shows the results of estimating the three functional forms which vary depending upon the specification of the influence of the size variable.

The F statistic of the three models indicates that the null hypothesis, namely that the adoption time is not explained by the independent variables, cannot be rejected at the 99% level of confidence. The implications of Models 1 and 3 are similar, since

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302 M.E. ESCUER, Y.P. REDONDO AND V.S. FUMAS

both indicate that size is negatively related to adoption time. The choice between the two is therefore irrelevant. More important is the choice between these two models and Model 2 since the latter implies that size does not uniformly influence adoption time in the same direction. Given that the coefficient of the variable Deposits squared is different from zero and that the goodness of fit of Model 2 is substantially higher, then Model 2 is selected as the best explanation of adoption times by Spanish commercial banks. According to this model, adoption time is minimized for an intermediate level of size of approximately 400000 million pesetas in bank deposits

. (5 x 10-'16.8 x 2 x lo-"). Considering that the largest bank in the sample has 512000 million pesetas in deposits, and the smallest 3 410 million, the vast majority of Spanish commercial banks (over 90%) have a figure lower than the critical value of 400 000 million pe~etas .~

Capital intensity is the second variable in order of significance, after firm size, to explain adoption time. The coefficient of this variable has a positive sign, which means that the more capital intensive firms adopt the innovation later than the more labour intensive ones. This result confirms our expectations, since the teleprocess terminal is a labour saving innovation and the return obtained by introducing it should be higher for more labour intensive firms. The results of Table 3 also show that the time of adoption is negatively related to the level of profitability of the adopting firm; the sign of the estimated coefficient for the variable "profitability" is negative, although the level of statistical significance is lower than for the other variables.

3.2.2. Savings banks As we mentioned earlier, savings banks in Spain have been restricted by law to operating in a limited geographical market. At the same time, competition between savings and commercial banks has been mild. For these reasons, savings banks are treated separately from commercial banks and we are able to incorporate market structure variables, such as concentration and growth, to explain adoption time.

The empirical analysis again commences with Eq. (4B) together with an specifi- cation for the functionJ(s) chosen from (2). If we want to incorporate into the model variables of market structure, together with firm specific variables, then one solution is to incorporate these variables in E , , as we did earlier with profitability and capital intensity. This approach implies that the possible influence of market structure variables in $, as recognized by Davies (1979), is totally ignored. On the other hand, if the influence of the market structure variables in JI is recognized, then the model to be estimated should be different. One additional difficulty in following this

'A closer look at the data reveals that there is a very high dispersion among the sizes of commercial banks. Therefore, there is a possibility that the presence of outliers in the sample creates heterostedasticity in the estimated linear models, which would be aliviated when the variable "Deposits'" is included in the regression. The Goldfed-Quandt statistic gives values 1.85, 1.89 and 1.3 for models 1, 2, 3, respectively, whilst the critical values for rejecting the null hypothesis of homotedasticity are 6.39, for models I and 3. and 9.28 for model 3; the null hypothesis cannot be rejected. Second, if the two largest banks are removed from the sample the coefficient of the variable "Deposit$" is no longer significantly different from zero; since the two largest banks are the only ones with deposits larger than 400000 million pesetas. the empirical results obtained with the full sample are perfectly consistent. since they imply, from model 2, that a negative relationship exists between time of adoption and size up to the critical value of 400000 millions. Finally, we believe that the full sample is more representative of the total population of banks.

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.; r'

ADOF'TION OF INNOVATIONS IN BANKING 303

approach is that the relationship between $ and the market structure variables is not known.

Let us now consider the results obtained from the application of the two approaches. The two variables of market structure are concentration and growth. Concentration is measured by the Herfindahl index, calculated for each of the regional markets using market shares, by reference to bank deposits, for each firm in the market (including all firms and not just those who responded to our questionnaire). The growth variable is estimated by' the average annual growth rate of total deposits in the market. The concentration index, H and the growth variable, g, are evaluated at their average values for the years 1975-1978. If these variables are assumed to be included in E , of Eq. (4B), then the model to be estimated is

ti, = c, + c* log f(s,,) + c 3 4 + c*q2 + eggj + Uij (6) where ti, is the time period in which firm i in market j adopts the innovation, c, = -log a/$, c2 = I/$ and ui, is the error term. Profitability and capital intensity are excluded in order to preserve higher degrees of freedom, given the small number of observations available. As we noted above, model (6) would not be distinguished from a pure hazard rate model. Equation (6) includes the variable H2 in order to test whether the non linear influence of concentration (competition) on adoption time suggested by Kamien and Schwartz can be accepted or not by our data.

The alternative approach assumes that the parameter I++, which measures the return from information search and learning for the firms in a given market, depends upon market structure variables and is therefore different across markets. That is, IG;. is assumed to depend upon the variables of market structure in the following way,

-'I/$, = a, + a 2 4 + a 3 q 2 + a,g, (7) Therefore, the term - log a/& will be

-log a/$, = b, + b 2 4 + b3H;' + b,g, (8) where b, = (log a)a,, k = 1, . . . , 4. Substituting these functions in (4B), we obtain the equation to be fitted to the data.

In what follows, Eq. (6) is first estimated for alternative specifications of the function f(s); this will allow us to select the best functional relationship between the size of the firm and adoption time. Once this function is selected, Eqs. (7) and (8) are incorporated into the model. Table 4 presents the results obtained in these esti- mations. The first six models are variations of Eq. (7) and models six and seven incorporate $j as a function of concentration and growth.

The empirical evidence indicates that the best fit to the data is obtained when f(si,) = 4, that is, size enters into the equation in its logarithmic form. The hypo- thesis that there is a critical size at which adoption time is minimized cannot be accepted for the population of savings banks (all saving banks in the sample have Deposits lower than 400 000 million pesetas). The estimated sign of f l is negative and therefore larger firms adopt earlier than smaller ones. The statistical significance of the coefficient of the concentration variable increases when concentration squared is introduced into the equation; moreover, the positive sign of the coefficient of this variable indicates that a u shaped relationship between adoption time and concen- tration is plausible. But these conclusions are weak, since in neither of the six models are the coefficients of the concentration variables statistically significant at the selected levels.

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Table 4. Determinants of adoption times: savings banks - - -- -

Dependent variable: adoption time. t,

Model I Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Constant 5.96.. 7.02** 29.70°** 6.80.. 7.92*** 29.80*** 29.00 - 74.2' (2.2) (2.4) (3.8) (2.5) (2.72) (3.8) (0.67) (- 1.3)

Deposits - 1.3 x lo-'** -3.9 x - 1.3 x 10-'** -3.9 x lo-'** (- 1.89) (- 1.6) (- 1.82) (- 1.6)

Deposits' 6.10 x lo-'' 6.1 x lo-'' ( 1.08) (1.1)

Log deposits - 2.3*** - 2.2*** -2.10 7.34. (- 3.4) (-3.3) (-0.47) (1.42)

Concentration - 0.99 - 0.22 +0.15 -147' - 13.92. - 12.3. - 54.10°** 307.0°* (- 0.44) (- 0.09) (0.07) (-1.35) (- 1.3) ( 3 (- 2.76) (2.03)

Concentration2 13.0' 13.0. 1 1.8. - 3 12.3.. (1.3) (1.3) (1.3) (- 2.4)

Growth 9.2. 7.6 5.1 13.2'. 11.5 8.7. 86.80 208.2. (1.36) (1.09) (0.8) (1.8) (1.53) (1.3) (0.62) (1.5)

Log deposits x concentration Log deposits x concentration2 Log deposits x growth

R' 0.14 0.14 0.31 0.16 0.16 0.32 0.43 0.52 F 2.6. 2.3* 5.6". 2.4. 2.2' 4.701** 5.73*** 5.60L* S.E. residual 3.7 3.7 3.3 3.6. 3.6 3.3 3.0 2.8 No of observations 32 32 32 32 32 32 32 32

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ADOPTION OF INNOVATIONS IN BANKING 305

Models 7 and 8 are estimated under the assumption that $,, the return for infor- mation search parameter, is a function of market structure variables, growth and concentration. The adjusted R2 of M.odels 7 and 8 is higher than in previous models, and their standard error of the residuals is lower. Thus, we are able to accept the hypothesis that Davies' model, with $, as a function of market structure variables, is a more accurate description of our data than the models in which such a relationship is not acknowledged. As between Models 7 and 8, the latter has a higher adjusted R2, so we shall assume that it is the appropriate model for our data.' To better interpret the results obtained, the estimated functions of -log a/$j and P/$, are written as,

The estimated coefficients bk = (log a)ak of Eq. (12) are approximately - 10 times the estimated coefficients /?a, of Eq. (13). This proportionality is what we would expect under the assumption that a and /? are the same in all geographical markets. If size has a positive effect on the speed of.adoption, fl positive, the result log alp implies that a has to be between zero and one.

The term -PI#, is equal to the coefficient of the variable size (log Deposits) in Eq. (4B), when $, is assumed to be constant across markets. In Model 6, the estimated value of - PI$,, under the assumption $, = $ for all j, is -PI$, = - 2.2. On the other hand, the expected value of -,!?/$,can also be estimated from Eq. (1 3) substitut- ing H,. y2 and gj by their average values of 0.40, 0.24 and 0.240, respectively. The resulting value is - 2.4; both estimates of the impact of firm size on the speed of adoption are perfectly consistent. Moreover, since $, is positive by assumption, the coefficient fl has to be positive, which would confirm the positive impact of size on the speed of adoption for Spanish savings banks.

To investigate the relationship between market concentration and time of adoption, we write t+bj as a function of the variables of market structure,

The value of $, is maximized for Hj equal to 0.5, that is to say, the return from information search and learning is maximized at an intermediate degree of market concentration and hence of market competition.

The finding of an intermediate value of the concentration index that maximizes the propensity to adopt the innovation'is consistent with Kamien and Schwartz's indi- cation (1982, p. 119), namely that there is an intermediate degree of market com- petition at which the probability of adoption is maximized and, consequently, the adoption time is minimized. This is also an additional item of evidence to be added to the empirical finding of inverted "u' found in most studies on the relationship between R&D intensity and market concentration, see Cohen and Levin (1989). Finally, the index H is the combination of differences in firm size and the number of

'The hypothesis that thecoefficients of the variables Concentration'and "log Deposits x Concentration'" are zero is rejected at the level of significance of 93%.

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306 M.E. ESCUER, Y.P. REDONDO AND V.S. FUMAS

firms in the industry. Therefore, the observed relationship may be the result of a compromise between the positive effect on the speed of adoption due to a greater dispersion of firm size in the market, and the negative effect of the number of firms in that market, see Davies (1979).

The empirical findings reveal a negative association between market growth and the parameter $, Eq. (13). Therefore growth appears to have a negative impact on the speed of adoption of this particular innovation. This would indicate that the tele- process terminal, as a cost reducing innovation, may have more economic value for firms in markets with low rates of growth in demand.

4. CONCLUSION

The influence of market structure and competition on the time of adoption of innovations by firms in the market, has both conflicting theoretical explanations and conflicting empirical evidence, although this evidence is still very limited. This paper provides a new set of data and new empirical results on the subject. The data comes from Spanish commercial and savings banks and their adoption times for a process innovation, namely the teleprocess terminal. Our results indicate that diffusion of the innovation is faster in markets with intermediate levels of concentration and hence of intermediate levels of competition. That is, the "inverted v", detected in many empirical studies of the relationship between market structure and innovation activity, is also obtained in a case where the innovation activity is not measured in terms of R&D expenditures per unit of sales, but rather in terms of time of adoption of an innovation supplied from outside the adopting industry. Secondly, adopting firms are homogenous, since they all provide banking services, and the differences from one market to another are simply geographical. Therefore, the results shown in the paper are free from the criticism often raised with respect to the "inverted v" finding, namely that the data used to estimate the relationship between R&D intensity and market concentration comes from industries with important structural differences (Chemical and Textiles, for example), which may in turn determine their different levels of concentration.

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Industrial Organization. Vol. 11. R. Schmaknsee; R. Willig, eds. North Holland. David, P.A. (1975). Technical Change. Innovation and Economic Growth. Cambridge University Press. Davies. S. (1979). The Difiion of Process Innovations. Cambridge University Press. Hannan. T.H. and McDowell, J.M. (1984). "The Determinants of Technology Adoption: The Case of the

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Advance". Western Economic Journal, 17(1). Kamien, M.I. and Schwartz, N.L. (1982). Marker Structure and Innovation. Cambridge University Press. Lancaster, T. (1979). "Econometric Methods for Duration of Unemployment", Econometrica. (47). Mansfield. E. (1961). "Technical Change and the Rate of Imitation", Econometrica, 29(4) October. Mansfield, E. (1963). "The Speed of Response of Firms to New Techniques", Quarterly Journal of

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