policy. Technological regimes and innovation in services: the case of the Italian banking industry *. Luigi Buzzacchi *'a, Massimo G. Colombo b, Sergio Mariotti b.
research ELSEVIER
Research Policy 24 (1995) 151-168
policy
Technological regimes and innovation in services: the case of the Italian banking industry * Luigi Buzzacchi *'a, Massimo G. Colombo b, Sergio Mariotti b a Politecnico di Torino, Dipartimento dei Sistemi di Produzione ed Economia dell'Azienda, Torino, Italy b Politecnico di Milano, Dipartimento di Economia e Produzione, Milano, Italy
Final version received September 1993
Abstract In this paper we propose a conceptual model to analyse innovations originating from the diffusion of Information Technologies in the banking sector. We argue that technical change in this industry exhibits a revolutionary character. A distinction is made between the 'mass automation' regime, focusing mainly on the mechanization of back-office procedures in the 1960s and 1970s, and the 'smart automation' regime, originating from the introduction of distributed data processing and network technologies and centered around the supply of electronic banking services. A theoretical model is developed which emphasizes the crucial role played by demand-pull variables in stimulating innovative behaviour under the smart automation regime. In contrast, limited importance is attributed to cumulative and learning-by-doing effects relating to back-office automation, at least for banks endowed with sufficient absorptive capacity. The theoretical hypotheses are tested through an econometric analysis of the determinants of the innovative behaviour in electronic payment systems of a sample of Italian commercial banks.
I. Introduction * Corresponding author. t This paper took advantage of the research work carried out by the authors within the framework of the research project 'Information Technology and Innovation in Banking' sponsored by the Adriano Olivetti Foundation. The financial support of a MURST 40% grant (research project 'Performances and Diffusion of Flexible Automation') is also gratefully acknowledged. The authors would like to thank Rocco Mosconi, Valeria Severini, and the participants at seminars held at Groupe HEC, MERIT and Politecnico di Milano for comments and suggestions relating to previous drafts of the paper. The usual disclaimer applies. The authors are jointly responsible for the content of the paper. Luigi Buzzacchi wrote and sections 5 and 6, Massimo G. Colombo wrote sections 1 and 2, and Sergio Mariotti wrote sections 3, 4 and 7.
T h e service i n d u s t r y has b e e n r a t h e r neglected by social scientists i n t e r e s t e d in the economics of technical change, t Most m i c r o e c o n o m i c studies which have addressed issues relating to i n n o v a tion a n d diffusion of technology, have focused o n the m a n u f a c t u r i n g sector, in spite of the fact that services a c c o u n t for the largest share of employm e n t in developed countries a n d a p p e a r to be
1A notable exception is given by Richard Barras' work (see [3-5]).
0048-7333/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0048-7333(93)00756-J
152
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
greatly affected by technical change. On the one hand, service firms are large users of capital-embodied process innovations. On the other, they have been responsible for complementary innovations in the spheres of organization and management and for product innovations with substantial demand-inducing effects. 2 The present paper aims to contribute to filling the gap in the literature. Relying on a conceptual framework inspired by evolutionary theories of technical change, it examines innovations stemming from the diffusion of Information Technology (IT) in the banking industry. In the authors' opinion, the analysis of this industry provides important insights into the forces which shape the innovative behaviour of service firms. The grounds for our choice can be synthesized as follows. First of all, for the last 30 years IT has been playing a pivotal role in stimulating innovation in services. The reason resides partially in the high information intensity of most service activities. More importantly, the diffusion of IT has led to a radical transformation of the p r o d u c e r - u s e r relationship typical of services. As is well known, most services used to be characterized by a simultaneous association between the act of production and that of consumption, whereas in the case of goods these are normally separate activities. In other words, as far as services could not be stored, they had to be consumed as they were produced. 3 By making available equipment for information storage, processing and transmission, IT has made it possible to remove such constraints to a large extent. This has opened new technological and
2 For an analysis of product innovations which took advantage of the diffusion of Information Technology in services and of their likely impact on demand and employment, see Freeman and Soete [14]. 3 "Services are consumed as they are produced in the sense that the change in the condition of the consumer unit must occur simultaneously with the production of that change by the producer; they are one and the same change" (Hill [21, p. 337]). The need for physical proximityof producers and users can be regarded as one of the main reasons for the disappointing productivity increase in the service sector. In addition, it negatively affects demand for services, as it places a binding constraint on the time which users can devote to consumption.
market opportunities, exerting a considerable influence on the organization of service industries and the balance of competitive advantages of service firms. Within this framework, financial services, and especially banking, have perhaps experienced the most dramatic changes. According to Porter and Millar [31], banking offers one of the best examples of information-intensive industry, referring to both products and firms' value chain. Furthermore, the banking sector is a major user of IT equipment, absorbing a large share of the revenues of IT industries (Harianto and Pennings [19]). Accordingly, it provides an ideal testbed for theoretical hypotheses concerning the forces that may favour or inhibit the diffusion of IT-based innovations in services. The objective of the present study is twofold. First, a conceptual model is developed to interpret the paradigmatic characteristics of the innovation process originated by the diffusion of IT in the banking industry. Contrary to previous contributions, and notably to Barras' 'inverse product life cycle model' (see [3,4]), we argue that technical change in banking exhibits non-evolutionary (i.e., revolutionary) characteristics. The diffusion of distributed data processing and telematics involved a shift in technological regime, leading to a substantial devaluation of the stock of knowledge and skills accumulated by firms in the previous regime. A clear distinction should therefore be made between the 'mass automation' technological regime, coinciding with the automation of back-office procedures in the 1960s and 1970s, and the 'smart automation' regime, focused mainly on the provision of Electronic Banking (EB) services. Second, a series of hypotheses relating to the forces which foster firms' innovative conduct 4 in the new regime are derived from the 4 It is important to realize that in services the issues of generation and diffusion of innovation are inextricably linked. The main reason is that it is extremely difficult to separate new products (i.e. new services) from the processes on which they rely. The production of new services requires in most cases the introduction of new production processes or significant modifications of the existing ones. In turn, these often involve the adoption of innovative capital equipment by service firms. This holds also true for the manufacturing sector to a large extent, but it is even more frequent in services.
L. Buzzacchi et al. /Research Policy 24 (1995) 151-168
theoretical model and tested through econometric estimates on data referring to a sample of Italian banks. The organization of the paper is as follows. We outline the conceptual model in section 2; the aim is to point out the peculiarities of and differences between the mass automation and smart automation technological regimes. The implications suggested by the model for the determinants of banks' innovativeness in the current regime are drawn out in section 3. Section 4 is devoted to presenting the data set. In section 5 we specify the econometric model. Particular attention is paid to modeling the consequences of the alleged discontinuity in technical change; we also try to disentangle the effects of technologypush, demand-pull, and structure-performance variables upon banks' innovative behaviour. In section 6 we illustrate the findings of the empirical analysis, which lend clear support to the 'technological revolution' hypothesis. Some remarks concerning policy implications in section 7 conclude the paper.
2. Technological regimes and innovation in the banking industry As mentioned earlier, the inverse product life cycle model, proposed by Barras [3,4] and applied to the banking industry in Barras [5], constitutes to our knowledge the main attempt so far to develop a microeconomic theory of innovation which explicitly takes into account the peculiarities of services. The model considers the transmission of major capital-embodied innovations to a user sector (which coincides with a service or consumer good sector). From the initial adoption of the new technology up to the subsequent innovations that build on it, technical change is assumed to follow a 'natural technological trajectory' (Nelson and Winter [27]). Its direction and speed depend on the interaction of a series of factors, relating to technological opportunities, demand conditions, market structure, and the institutional environment in the adopting sector. Barras' central argument is that the product life cycle is reversed with respect to Abernathy and Utterback's [1] well-known model. Barras distin-
153
guishes three phases of the cycle: (a) an initial phase heavily oriented towards efficiency gains; (b) an intermediate phase where services are improved incrementally, and (c) a final phase where radical product innovations (i.e. introduction of new services) occur. Barras' model contributes greatly toward an understanding of the innovation mechanisms typical of the services sector. None the less, the model lends itself to a fundamental criticism, which is manifest when it is applied to the banking industry. The process of technological innovation induced by the diffusion of IT in the banking industry, is described as the evolution of a given technological trajectory. By incremental steps and relying on cumulative effects, it leads to full exploration of its innovative potential in the final phase of the cycle, through product innovations destined to radically change the substance and form of banking and to have a strong expansionist effect on demand, strong enough to make the banking sector one of the industries in the vanguard of the 'services revolution', drawing an analogy with the industrial revolution of the last century. This perspective implies that technical change displays strictly evolutionary traits. On the contrary, it is the opinion of the authors that such an assumption does not apply to many service industries, including the banking industry. Just as the development of the microprocessor in the early 1970s prompted a technological revolution in capital goods manufacturing, so the introduction of distributed data processing and datatransmission networks triggered a leap in the technological trajectory in services. In the banking sector, in particular, their rapid diffusion has substantially altered the path followed thus far by technical change, regarding the satisfaction of general economic needs, the characteristics of the relevant knowledge base, the degree of appropriability of innovations, labour- and capital-saving biases, primary agents and locus of the innovative process and, lastly, the selection mechanisms inherent in the economic and institutional environment. As a result, it seems more appropriate to discern two different technological regimes (Nelson and Winter [28]; Winter [33]): the regime of mass automation that characterized the interna-
154
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
tional banking sector until the early 1970s and the regime of smart automation.
2.1. The technological regime of mass automation The technological regime of mass automation drew its origin from the introduction by banking firms in the early 1960s of mainframe computers. Thanks to their computing power and the connected capability of processing efficiently large amounts of standardized transactions, mainframes offered an effective technological solution to the problems of the banking sector at that time. The traditional organization of the production process for banking services, based on the intensive use of clerical manpower, was ill-suited, in fact, to satisfy the mass consumption of such services, as it was subject to diminishing returns to scale, beyond a certain threshold. This limited the ability of banks to expand in size. The advent of mainframes fostered a radical innovation in production processes, which brought on significant changes in the organizational structure of banks: the establishment of EDP departments that centralized information and assumed an essential role in improving efficiency of operations; the creation of a specialized staff function for information services and, later, the development of new professional job categories (system analysts, software programmers) and the disappearance or radical alteration of others. The trajectory followed by technical change was strongly focused in the direction of increased efficiency in producing the portfolio of traditional services (mechanization of operations, with capital-deepening and labour-saving biases). Not until later, when the innovative potential of the new technology had been explored through cumulative effects, learning-by-using and incremental innovations, were they joined first by significant improvements in service quality and later by authentic product innovations, such as the cash dispenser (CD). 5
5 Initially, cash dispensers allowed users to withdraw cash only from the accounts opened at the bank which had installed them. In addition, transactions were processed in batch mode. It is only with the diffusion of telematics that CDs were converted to on-line real-time network operations.
Under the mass automation regime, the locus of the innovative process was largely external to the banking sector, as far as capital-embodied innovations are concerned, and coincided with the fledgling computer industry. None the less, this did not mean that the banking sector displayed the typical characteristics of the supplierdominated sectors, with reference to Pavitt's [29] taxonomy. Early on, in fact, technical change began to manifest a significant disembodied component, whose locus was necessarily inside the user sector. It was related, on the one hand, to technological innovations in complementary areas, especially applications software, and, on the other, to the development of organizational and managerial innovations necessary for an effective utilization of advanced capital equipment. Exploiting the full potential of latent productivity associated with the new technology required a thorough knowledge of banking activity, with key components that were tacit, appropriable, and difficult to transfer beyond the boundaries of the individual user firms. The major financial institutions thus found themselves playing a crucial role as agents of innovation. In fact, at least in the initial phase of the technological trajectory, the need to proceed by trials and errors to discover effective organizational solutions, the cumulative effects, the difficulty of imitation, and the indivisibilities in investment costs, all other things being equal, favoured large size.
2.2. The technological regime of smart automation The diffusion of network technologies and distributed data processing brought a shift in technological regime to the banking industry, in the sense that the technological trajectory took a path different from the past. Partly following the inverse product life cycle model, the initial phase of the new trajectory in the 1970s was characterized by radical process innovations. They led to a structural redesign of banks' information systems, based primarily on the gradual decentralization of computing power to local branches, a widespread shift from off-line batch transactions to on-line real-time transactions and, lastly, interaction with the information
L. Buzzacchi et aL / Research Policy 24 (1995) 151-168
systems of other banks through interbank networks. With the advent of the new regime, however, the opportunities offered by technology in the area of product innovation were incomparably greater than those of the previous regime. Reconfiguration of front-office activity and, more generally, of the interfaces between producers and users laid the foundation for a series of major product innovations (EB services), which were intended to satisfy, and in turn promoted the growth of, a new and more sophisticated pattern of demand on the part of private individuals, business enterprises and other institutions. The biases of the technological trajectory became demand-inducing and capital-widening, in deep contrast with the ones of the previous regime. In addition, the very nature of the process innovations changed, with the manifestation of off-setting capital-saving effects, related to space saving, waste reduction and a more efficient use of working capital. One important implication of the change in technological regime is that the discontinuity largely canceled out the cumulative effects associated with the path previously followed by technology. In general, in the transition between two regimes, the smaller the scientific and technological knowledge base in common with the new regime, the greater the devaluation of knowledge, know-how, skills and capabilities developed by firms moving along the technological trajectory of the former regime. In this specific case, the development of an IT 'culture' during the mass automation regime, with management discovering the key role of data processing and information systems in banking and banking personnel at all levels gaining experience in the use of IT facilities, formed a knowledge base that banks needed in order to proceed along the technological trajectory of smart automation. This might actually be considered as a (weak) evolutionary link between the two regimes. None the less, it is important to emphasize that, in accordance with the revolutionary nature of technical change, the more specific expertise and skills developed in the previous regime, relating to the technological solutions, organizational structures and operating procedures that could ensure maximum effi-
155
ciency, have lost much of their value and may even give rise to lock-in effects, creating a disadvantage with respect to laggard firms which were less innovative in the mechanization of back-office activities. 6 Some other elements of distinction between the two technological regimes deserve additional emphasis. First of all, technological innovations under the smart automation regime enjoy lower appropriability than in the past, due to the fact that the new banking services are subject to significant network economies. A paradigmatic example is offered by the automatic teller machine (ATM). The interest of users in this service is limited if it is only available to them through the ATM facilities installed by their own banks. The utility from the service is closely linked to a widespread geographical diffusion of ATMs, which no single bank is generally capable of providing. In other words, the utility increases with the number of banks that decide to offer this service in a coordinated manner. Therefore, ATM diffusion turns out to depend critically on the level of network economies captured by the individual banks, which in turn depends on the emergence of standards shared by the highest number of banks. The substantial gains generated by cooperation may induce innovative banks, even the large ones, to relinquish any attempts to affirm proprietary standards in favour of a widely shared market standard. In any case, the need for a common standard, be it proprietary or market-wide, stimulates the technological leaders to transfer knowledge and know-how to the laggard banks. Imitation is easier, and the public-good character of the technology is enhanced. Second, although the absence of winning technological and organizational solutions in the phase of new services introduction inhibits the manifestation of most network economies and
6 It is interesting to notice that such development contrasts deeply with the path followed by computer-based automation in the manufacturing sector, which emphasizes the cumulative and evolutionary nature of technical change (see for instance Cainarca et al. [6]).
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
156
causes the locus of innovation to remain predominantly with the large banks, this locus is gradually shifted to the area of interbank transactions as the evolutionary trajectory proceeds. Thus institutions such as the central bank and the common carrier responsible for telecommunications services, on the one hand, and interbank associations and consortia, on the other, emerge as the primary actors in the innovative process. The former chiefly provide positive externalities, such as those relating to the data transmission and network infrastructures for automating interbank procedures. The latter play a key role in stimulating cooperative behaviour. Of particular importance is the policy these organizations adopt with regard to standards: promoting and supporting mutually shared standards or permitting free competition among the various proprietary standards is, in light of the foregoing, a fundamental alternative that conditions and shapes the diffusion trajectory of new services. Third, the smart automation regime has fostered a significant increase in the degree of user involvement in the service production process, as a direct consequence of the possibility offered by technology to disassociate the production of a service from its use. In banking, as in many other services (Fontaine [13]), this occurs through the transfer of part of the production transformation function to the user, who manipulates, interrogates, and furnishes information input to the
hardware (ATMs, terminals) that may be owned by the banks but may also belong to the user itself (e.g. remote banking terminals). Within this context, not only the introduction of new services but also the incremental innovations developed over time come to depend on the particular characteristics of the users, the resources and knowledge they possess, and their capacity to adapt to the technology of the new services. Lastly, the transition between the two regimes entails a significant transformation in the selective mechanisms associated with the characteristics of demand and the economic and institutional environment. While under the mass automation regime the size and growth rate of the market for traditional banking services had a driving effect on technical progress, in this case it is the access of banks to an advanced demand on the part of firms, professional customers and a private clientele receptive to innovation in consumption patterns that constitutes a strong incentive to supply EB services. Furthermore, in spite of the reduced appropriability of technology, the vast possibilities for product differentiation permit forms of competition through innovation unknown in the previous regime, in addition to such traditional success factors in banking activity as reliability and cost. Such developments are further promoted by the changes that occurred during the 1980s in the institutional context of all the industrialized countries, though with varying
Table 1 Diffusion of EB services in the Italian banking industry
BANCOMAT c ATM POS CB } HB
RB
Percentage share of adopters
a
31/12/1989
31/12/1991
31/12/1993 (estimates) b
76.5 29.8 52.1
87.4 39.1 63.3
90.7 60.9 74.9
30.7
53.0
6.7 11.3
a Rural banks and branches of foreign banks are excluded. b Estimates consider adoption plans at 31/12/1991. c BANCOMAT services can be offered through either CDs or ATMs. Source: CIPA-ABI [7,8].
at
L. Buzzacchi et aL / Research Policy 24 (1995) 151-168
speeds and different procedures, in the sense of deregulation, internationalization and the integration of previously distinct financial activities.
3. Determinants of the diffusion of Electronic Banking services The term 'EB services' refers to a cluster of product innovations that characterizes the smart automation technological regime. Although the origin of these services dates from the initial experiments in front-office automation under the mass automation regime, 7 they have drawn their fundamental genetic characters, which make them responsible for a radical change in banking activity, from the diffusion of distributed data processing and networking. EB services include: the CD, which allows the user to withdraw cash from his (her) accounts in different banks thanks to the connection to the interbank network; the ATM, which provides the user with a broader range of teller services (for instance, electronic fund transfer, bill payment, provision of information on the user's accounts); the point of sale (POS), an automatic device located in retail outlets for debiting purchases; and remote banking (RB) services, functioning via a network from the user's place of residence and divided, according to the type of user, into two categories, home banking (HB) and corporate banking (CB). Data on the diffusion of such services among Italian banks at the end of 1991 are illustrated in Table 1 (see also CIPA-ABI [8]). The model discussed in the foregoing paragraph suggests a series of hypotheses concerning the determinants underlying the diffusion of EB services. In dealing with this issue, it is useful to distinguish two series of factors: those inherent in the infrastructural, institutional and regulatory environment in which the banking system operates, and those inherent in the structure and conduct of banks, the characteristics of their market and the driving forces of technology.
7We are referring especially to the introduction of CDs. See footnote5.
157
Under the smart automation regime, the former factors are highly significant in shaping firms' innovative behaviour, particularly with reference to the supply of telecommunication networks and services by the common carrier, the conduct of the central bank in its roles of supervisory agency and active party in the creation of infrastructures and common procedures and, lastly, the establishment of standards and laws that discipline electronic funds transfer. 8 None the less, this paper intends to focus on the second series of factors, which seem to us equally important. They include: (a) supply-side factors reflecting the autonomous role of technology in fostering innovation; (b) characteristics of final demand for bank services which, in accordance with demand-pull theories, are likely to stimulate firms' innovative activity; and (c) structure-performance variables concerning the firms and the markets in which they operate traditionally referred to in the literature on the 'Schumpeterian hypotheses'. As to technology-push factors, we claim that the transition from mass automation to smart automation has largely erased the technological advantages connected with the stock of knowledge and skills matured by firms in progressing rapidly along the technological trajectory of the former regime. Consequently, the expected relationship between the innovativeness of firms under the two regimes can be described by a step function, with a critical threshold corresponding to a minimum level of 'literacy' in the use of IT. This indeed appears as the essential prerequisite for being innovative under the new regime. However, above that threshold there should be no positive correlation between a bank's innovativeness in adopting and developing EB services and its former ability in the mechanization of backoffice procedures. More precisely, while substantial delays in adopting data processing equipment which in the 1960s and 1970s permitted the automation and radical transformation of backoffice activity should distinguish banks that are
8The influenceexertedby these factorsupon innovationin the Italian banking industry are analysed elsewhere by the authors (see Mariotti [26]).
158
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
innovative in EB from those that are not, a faster and more extensive adoption of such equipment and the development of specific capabilities regarding their efficient use (in particular in the field of applications software) are unlikely to generate incremental opportunities for innovation under the smart automation regime. Though we expect variables mirroring a firm's technological prowess to have little influence on innovative performances in EB (within the aforementioned constraints), the opposite holds true for demand-pull variables. The role of the characteristics of demand in shaping banks' innovative behaviour seems magnified in EB due to the accrued importance of the relation between the producer and the consumer of the services. In particular, the degree of services' usability by customers and the adaptability of their consumption pattern to the new forms of service delivery are important driving factors in the diffusion of EB. In accordance with such argument, banks operating in local markets with higher economic development and where the population served displays attitudes conducive to electronic payments (which can be considered to be proxied by factors such as wealth or educational levels) should clearly be favoured in pioneering EB. We now turn to the influence exerted by firm structure and market organization. In spite of growing criticism, due also to the weak evidence generally provided by empirical studies (for a survey see Cohen and Levin [9]), it is usual to assume that there is a positive correlation between firm size and innovation rates owing to the existence of economies of scale and scope both in R & D and in the utilization of the results of R & D activity. In addition, since the pioneering work by Griliches and Mansfield [15,23,24], a large number of studies on the diffusion of process innovations has shown that large firms tend to be early adopters, with the positive influence of size being more pronounced for major innovations (see for instance Davies [12]). A more restricted literature has analysed the effects of scale on innovative rates and the advantages offered by large size in the adoption of innovations in banking. For instance, using data on the diffusion of ATM in the US banking
industry, Hannan and McDowell [16,18] show that larger banks evidence higher conditional probability of adopting such technology, even though the conditional probability turns out to be smaller than the conditional probability of at least one adoption among a group of smaller firms that together equal their size. In addition, large size seems to be associated with a greater tendency to adopt as initial adopter rather than as follower. Pennings and Harianto [30] document that the propensity of US commercial banks to introduce video-banking services is significantly correlated with their size. The positive curvilinear relation they find is consistent with the results of Bantel and Jackson [2], who analyse factors affecting technological and administrative innovations by US banks. On the role of size in inducing early adoption of EB services, we note the following. On the one hand, there seem to be considerable scale economies due to the indivisibility of certain investment costs (those related to the installation and operation of private telecommunications networks 9 and the development of applications software, for example) and due to the fact that certain benefits increase with the size of adopters (particularly the brand effect derived from offering advanced services to the public). Likewise, large size affords efficient diversification of risk through the possibility of undertaking a variety of innovative projects. Furthermore, a large bank is more likely to possess the specialized complementary assets (Teece [32]) necessary to the commercial success of innovations, such as a skilled sale force for marketing and distributing EB services. Lastly, for services in the pre-paradigmatic phase of their trajectory for which no prevailing (proprietary or market) standard has yet emerged, competition to establish a dominant configura-
9 It is worth stressing that in Italy the deficiencies in the public supply of networks and data-transmission services favoured the diffusion of private networks, obtained by individual banks through dedicated lines leased from the public provider. This triggered a vicious circle that has further depressed public investments in the development of networks and basic services, limiting the externalities offered to the banking sector, and particularly to small banks.
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
tion, the technological and market uncertainty involved, the connected risks of incurring sunk costs, and the possible temporary lack of externalities, all work to the benefit of large banks. 10 On the other hand, the relevance of extramural sources of technology under the smart automation regime 11 and more generally the tendency to transform technology into a public good should ceteris paribus determine basically symmetrical conditions in adopting new services by companies of different size (at least for services which have already entered the development stage). The externalities offered by the proliferation of interbank consortia strengthen this tendency. In the Schumpeterian literature, market power also plays a key role as a stimulus to innovation (see again Cohen and Levin [9]). Market power makes the results of a firm's innovative activity more appropriable and thus more profitable; at the same time, it ensures a greater capacity to self-finance R&D, which by its nature suffers the market imperfections and transaction costs of being financed on the capital market. In the banking sector, such incentives to innovate should play a rather modest role, because of the intrinsic nature of banking, which largely removes the need for self-financing, and the weak appropriability of technology under the smart automation regime. None the less, Hannan and McDowell [16-18] find a positive highly significant effect of market concentration on the probability of adopting ATM in the US. Actually, the concentration of supply may promote forms of oligopolistic competition based on product differentiation and incremental innovations aimed at improving interfaces with users, at least up to a threshold beyond which collusive behaviour and forms of quiet life are likely to prevail, with a consequent
10 This seems to have been the case of remote banking services in Italy up to the 1990s. ll Pennings and Harianto [30] point out that interfirm networks providing access to extramural sources of technology play an important role in favouring introduction of videobanking services in the US. Their empirical analysis shows that linkages with IT firms and spillovers from competing organisations are the most conducive factors to innovation.
159
reduction in innovation rates (see Heggestad [20]). 12
4. The methodology of the field analysis and the sample of firms
In order to study the factors influencing the diffusion of EB services in Italy, a sample was extracted from the universe of Italian commercial banking firms. These are the only institutions that are allowed by the Italian law to supply EB services. At the end of 1990, the universe included 1022 banks operating through 17666 branches. Considering the object of the present paper, the sampling procedure was designed so as to secure the presence in the sample of a sufficient number of innovative banks. Therefore, a questionnaire was mailed to the 600 largest Italian banks (in terms of total deposits); on the basis of the information available from previous studies (see for instance CIPA-ABI [7]) diffusion of EB services in the remaining banks can in fact be regarded as negligible. Such 422 banks altogether account for a share of industry deposits lower than 5%; most of them are rural and artisan banks with just one branch. The questionnaire was mainly aimed at collecting detailed data relating to the supply of the innovative services mentioned in section 3 (CD, ATM, POS, HB, and CB) and the lags in the adoption of data processing capital equipment. Data on the structure and
12 It is important to recognize that in Italy the induction mechanisms inherent in the market structure may have been impeded to some extent by the delay and gradualism with which deregulation has been implemented. T h e institutional barriers to the entry of new banks, decided in 1966, was moderated in 1971 but not removed until 1985. The recent policy orientations of the Banca d'Italia, aimed at introducing greater competitive stimuli, may not have had a significant impact yet, in the sense of instigating competition among firms traditionally used to a heavily regulated environment. In any case, they are unlikely to have already dismantled the tight, collusive local oligopolies inherited from the past, which restrain Schumpeterian competitive dynamics.
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
160
Table 2 Variables employed to measure diffusion Variable
Inter-firm diffusion a Intra-firm diffusion
Service CD
ATM
POS
CB
HB
_ NCD b NOPCD c AOPCD -
LATM NATM b _
LPOS NPOS b NOPPOS d AOPPOS e -
LCB _ _ _ NCOCB f
LHB _ _ _ NCOHB f
e
a b c a e f
--
-
Adoption lag with respect to the innovator within the sample. Ratio of number of facilities installed at 3 1 / 1 2 / 1 9 9 0 to 1990 customer deposits. Ratio of number of cash withdrawal transactions to customer deposits in 1990. Ratio of number of payment transactions to customer deposits in 1990. Ratio of amount of funds involved by transactions to customer deposits in 1990. Ratio of number of subscribers at 31/12/1990 to 1990 customer deposits.
performance of banks, such as size, localization of branches, and profitability, were gathered from other sources (mainly the firms' annual reports and studies published by ABI, Italian Banking Association). The returned questionnaires were completed through direct and phone interviews with banks' EDP and Organization managers; in this way, it was also possible to check the correctness of the mailed data. In this phase of the field analysis considerable effort was devoted to ensuring the presence in the sample of a sufficient number of medium and large-sized banks. The final sample is composed of 77 banks. Referring to Banca d'Italia's size classification of the Italian banking industry, ~3 the sample includes 4 of the 'largest' banks (out of 8), 2 of the 'large' ones (out of 8), 4 'medium' banks (out of 14), 20 'small' banks (out of 65), and finally 47 out of the 927 'smallest' banks.
13 Banca d'Italia's size classification is based on an index which considers commercial deposits, special credit institutions deposits, total funds under management, net foreign deposits and net worth at the end of 1987. Lower bounds (expressed in terms of total funds under management) of the 'largest', 'large', 'medium', and 'small banks' categories at the end of 1990 turned out to be 30000, 15000, 7500, and 1500 bilion Lira respectively.
5. The econometric model
5.1. A factorial measure of the diffusion of EB services The diffusion of EB services was measured by a series of variables which capture both interand intra-firm diffusion at firm level. The former category comprises indexes which reflect lags in the adoption of ATM, POS, HB, and CB with respect to the 'innovator', this being defined as the sampled bank which first supplied the service under scrutiny. 14 As to intra-firm diffusion, we tried to measure it from two viewpoints. On the supply side, we considered the number of facilities installed by a bank at the end of 1990, normalized with bank size. On the demand side, the 'intensity of adoption' of the service was assumed to increase with the number of transactions, the amount of funds involved a n d / o r the number of customers which had subscribed to the service; of course, normalization is required also in this case.
14 It is worth mentioning that adoption lags of CD could not be measured. The reason is that in most cases the interviewed managers were unable to determine the year when CDs, which had typically been installed by Italian banks under the mass automation regime for batch mode transactions regarding the accounts of banks' own customers, were converted to on-line real-time network operations.
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
161
Table 3 The independent variables of the econometric model LMF
Lags in the adoption of mainframes a n d / o r minicomputers with respect to the innovator within the sample; Lags in the adoption of microcomputers with respect to the innovator within the sample; Lags in the adoption of telecommunication linkages between a bank's EDP centre and branches with respect to the innovator within the sample; Dummy variable; it equals 0 for banks which were laggards under the mass automation regime; otherwise it equals 1; Index of average economic development of the provinces where a bank operates, in 1990; Average per-capita savings in the provinces where a bank operates, in 1990; Total deposits in 1990; Average market share in the provinces where a bank operates, in 1990; Ratio of operating returns to total assets (average value in the 19881990 period); Ratio of net profit to equity capital (average value in the 1988-1990 period); Average Herfindahl concentration index in the provinces where a bank operates, in 1990,
LPC LTL MAINN MKETDEV SAVE DEP MKTSH ROI ROE HERF
Table 4 The correlation matrix of the independent variables
LMF LPC LTL MAINN MKETDEV SAVE DEP MKTSH ROI ROE HERF
LMF
LPC
LTL
MAINN
MKETDEV
SAVE
DEP
MKTSH
ROI
ROE
HERF
1.00
0.15 1.00
0.54 0.23 1.00
0.40 0.71 0.46 1.00
0.22 0.25 0.27 0.37 1.00
0.18 0.24 0.21 0.31 0.91 1.00
0.36 0.21 0.55 0.26 - 0.04 -0.01 1.00
0.41 0.07 0.18 0.13 - 0.09 -0.16 -0.02 1.00
-0.14 -0.11 -0.24 -0.13 0.04 -0.04 -0.55 -0.05 1.00
-0.19 0.16 -0.04 -0.04 - 0.17 -0.10 -0.11 0.15 - 0.01 1.00
0.29 0.02 0.11 0.10 0.06 -0.12 0.00 0.71 - 0.02 0.07 1.00
The precise definition of the 13 variables employed is given in Table 2. 15 The aforementioned variables provide a de15 In computing the values of the various variables, we made a series of simplifying assumptions. First, as regards lag variables, firms which at the end of 1990 did not offer a given service were attributed the maximum value; that is to say, data were censored. Second, as to ATM, we considered only cash withdrawal operations which account for the large majority of operations. Lastly, in the case of remote banking services, the number of equipment installed can be assumed to be roughly proportional to the number of customers; hence, we concentrated on this latter more reliable figure.
tailed description of inter- and intra-firm diffusion of the five EB services considered. Since the purpose of the present analysis is mainly to shed light on the determinants of banks' innovative conduct independently from the strategies pursued with respect to each specific service, a more synthetic kind of index was needed. We thus resorted to factor analysis (see Mardia et al. [25]) to build a synthetic index based on the values taken by such variables. The EB diffusion factor (EB) coincides with the linear combination of the original variables which maximizes the explained variance. The share of the total variance ex-
162
L. Buzzacchi et al. / Research Policy24 (1995) 151-168
plained by the factor analysis is 72%; the amount of missed information can thus be considered as rather limited. We therefore obtained:
where the variables at the right-hand side of expression (1) had previously been standardized (i.e. means equal 0 and standard errors equal 1).
The second category of covariates is intended to capture the incentives connected with the characteristics of the demand for banking services in the local markets where the sampled banks operate. We considered two variables, M K T D E V and SAVE, which mirror the attitude of customers towards EB services. This can be assumed to depend on factors such as customers' wealth and educational level, and general economic conditions. M K T D E V and SAVE are the weighted sums of the values of a general economic development index and of the saving rate in the 92 Italian provinces respectively, computed in 1990; weights are given by the shares of a bank's branches located in the various provinces. Thus:
5.2. The independent variables
MKTDEVi = • DEVj' ( b r i J B R i ) , Y
EB = [0.655 NCD + 0.811 N O P C D +0.792 AOPCD] + [ - 0 . 5 5 7 LATM + 0.475 NATM] + [ - 0.607 LPOS + 0.679 NPOS + 0.863 N O P P O S + 0.844 AOPPOS] + [ - 0.48 1LHB + 0.327 N C O H B ] + [ - 0.392 LCB + 0.226 NCOCB],
(1)
The independent variables of the econometric model are presented in Table 3. Table 4 reports the correlation matrix. In accordance with the theoretical hypotheses set forth in section 3, they can be subdivided into three categories. The first set includes technology-push variables which are proxies of a bank's prowess in back-office automation. More precisely, LMF, LPC, and L T L measure the lags in the adoption of innovations typical of the mass automation regime (i.e. mainframes and minicomputers, stand-alone microcomputers, and telecommunication linkages between the E D P centre and the branches of the bank, respectively). 16 An additional explanatory variable (MAINN) was also created. MAINN is a dummy which equals 1 for banks that were innovative in the mass automation regime and 0 for laggard banks. In order to discriminate between the two groups of banks, a cluster analysis, based on the values taken by LMF, LPC, and LTL, was performed, using the Seeded algorithm. ~7 More on the role of MAINN in the econometric model will be said later. 16Actually, such variables measure the difference between the year 1990 and the adoption years. Non-adopters were attributed a value equal to 0. 17Statistical information on the cluster analysis is available upon request.
S A V E / = E SRj. ( b r i J B R i ) , Y where bri~ stands for the number of bank i's branches in province j and BRi = Ejbrij. DEVj is an index of the economic development of Italian provinces calculated by Confindustria (Italian Association of Business Enterprises). It is given by the arithmetic average of the six following indices: per-capita value added, industrialization rate, employment rate, per-capita deposits, percapita consumption of electric power, and motorization rate. SRj gives per-capita savings in province j. is It is interesting to note that MKTDEV and SAVE turn out to be highly correlated, as is shown in Table 4 (the correlation index equals 0.91). In order to take into account firms' structureperformance characteristics and local market structure in the various provinces a number of additional variables was entered into the model. Firm size was measured by total deposits in 1990 (DEP). Other size indices, such as number of employees, number of branches, total assets, and consumer deposits, were also considered; all are highly correlated with total deposits. 19 M K T S H is Data on Italian provinces are provided by ConfindustriaCentro Studi [11] and Istituto G. Tagliacarne [22]. x9All correlation indices between the above mentioned variables are greater than 0.91.
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
is a proxy for a bank's market share; it is given by the weighted average of the shares of total branches attributable to a bank in the 92 Italian provinces at the end of 1990; weights are given by the share of a bank's branches accounted for by each province. Hence: MKTSHi = ~] ( b r i j / B R j ) . ( b r i j / B R i ) ,
J where BR i = Y~ibr,i. Profitability was measured by the ratios of operating returns to total assets (ROD and net profits to equity capital (ROE). Average values over the 1988-1990 period were used. Lastly, we considered concentration in the local markets where banks operate. H E R F is the weighted average of the values taken by the Herfindahl index in the various Italian provinces in 1990; weights are the same ones as those above: H E R F / = E HERF~ • ( b r i J B R i ) , J
where HERFj = Y'.i(briJBRj) 2.
5.3. Specification of the econometric model and predicted signs of the explanatory variables We run OLS regressions of a model of the following general form: EBi = a 0 + a1LDEP i + a z X / + a3MAINN / . X i + a 4 H E R F i + asMAINN / • H E R F / + a6Y/ + avMAINN i - Y/+ a8MKTSH i q- a 9 Z i + Ei,
(2)
where X stands for a demand-pull variable ( M K T D E V or SAVE), Y for the lags in the adoption of the innovations pertaining to the mass automation regime (LMF, LPC, or LTL), and Z for banks' profitability, measured alternatively by ROI or ROE. The prefix L to the size variable means that it is entered into the model in logarithmic form. The predicted signs of the explanatory variables are illustrated in Table 5. Some brief considerations concerning in particular the effects of MAINN are in order.
163
Table 5 T h e p r e d i c t e d sign o f t h e e x p l a n a t o r y v a r i a b l e s Coefficient o f the explanatory variables
P r e d i c t e d sign
aI a2
+ ~-0
a2+a 3 a4 a4 + as
++ ~- 0 9
a6 a6+ a7 a8
~- (} ~- 0 9
a9
')
In accordance with the theoretical arguments proposed earlier, we contend that a clear distinction should be made between banks which have been laggards under the mass automation technological regime, for which MAINN equals 0, and the remaining banks. The former, which exhibited significant delays in the mechanization of back-office procedures, are unlikely to be equipped with the home grown skills and expertise required to gain access to skills provided by external sources of technology (i.e. interbank consortia) and enjoy the benefits of network economies and spillovers from rivals typical of EB. Accordingly, they are not sensitive to competitive pressures and are unable to take advantage of favourable market conditions as to pioneer the introduction of EB. Thus we do not expect coefficients a2, a 4 and a 6 to be significantly different from 0. On the contrary, banks belonging to the latter category (i.e. banks for which MAINN equals 1) are assumed to be endowed with substantial 'absorptive capacity' (Cohen and Levinthal [10]) relating to the use of IT. Coherently, they are likely to be very responsive to stimuli on the demand side: M K T D E V and SAVE should figure prominently in explaining rapid and pervasive adoption of EB on the part of such banks (i.e. a 2 + a 3 > 0). A similar reasoning applies also to competitive pressures typical of oligopolistic markets, provided that concentration is not as high as to induce 'quiet life' effects. In Italy, however, most local markets are heavily concentrated, in spite of the fact that average bank size is small and concentration is
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
164
low at the national level. Consequently, it is difficult to predict the sign of the coefficient of H E R F (a 4 + as).
Since technology tends increasingly to become a public good under the smart automation regime for banks equipped with adequate absorptive capacity, we claim that technological prowess in the automation of back-office activity should not pro-
vide any further advantage for banks for which MAINN equals 1. In other words, there should be no cumulative effects between the mass automation and smart automation regimes. We therefore expect the sum of coefficients a 6 and a 7 not to differ significantly from 0. Lastly, let us turn to the influence of the structure and performance of banking firms. As
Table 6 T h e e c o n o m e t r i c results
Coeff. a0
Const.
aI
LDEP
a2
SAVE" MAINN
a3
SAVE
a2 a3
MKETDEV MAINN MKETDEV
a4
HERF- MAINN
a5
HERF
a6
LMF" MAINN
a7
LMF
a6
LPC. MAINN
a7
LPC
a6
LTL. MAINN
av
LTL
as
ROI
a9
MKTSH
R2 Adj. R 2 H0: a 2 + a 3 = 0 n0: a 4 + a 5 = 0 n0: a 6 + a 7 = 0
Eq. (1)
Eq. (2)
-7.2621 * (3.6740) 0.6922 (0.3952) 0.0023 ** (0.0008) 0.0007 (0.0007)
-8.1102 * (3.4754) 1.0046 ** (0.2973) 0.0014 (0.0010) 0.0011 (0.0008)
Eq. (3) - 1 1 . 2 5 1 0 ** (3.8390) 1.2416 ** (0.3432) 0.0021 * (0.0009) 0.0010 (0.0007)
Eq. (4) - 9 . 3 0 9 9 ** (2.7049) 0.9959 ** (0.2694) 0.0015 ** (0.0003) 0.0012 (0.0007)
Eq. (5) - 1 0 . 5 7 7 4 ** (3.4294) 0.9607 ** (0.2718)
0.0378 ** (0.0085) 0.0443 (0.0285) - 0.0084 (0.0128) - 0.0035 (0.0136) - 0.1410 (0.1222) - 0.0136 (0.1050)
- 0.0099 (0.0137) 0.0019 (0.0135)
- 0.0096 (0.0129) 0.0028 (0.0132)
0.2543 (0.4873) - 0.1396 (0.4013)
0.08882 (0.0542) - 0.0042 (0.0058) 0.5334 0.4708 11.5223 ** 0.2335 2.1450
Standard errors are given b e t w e e n p a r e n t h e s e s .
* Significance level g r e a t e r than 95%. ** Significance level g r e a t e r than 99%.
0.0854 (0.0549) - 0.0018 (0.0057) 0.5198 0.4553 6.7562 ** 0.5762 0.1183
- 0.0378 (0.1680) 0.1578 (0.1509) 0.0757 (0.0562) - 0.0023 (0.0056) 0.5278 0.4644 10.1772 ** 0.4294 0.7721
0.0790 (0.0549)
0.5081 0.4808 16.9995 **
0.0863 (0.0545)
0.4978 0.4699 9.9166 **
L. Buzzacchi et al. /Research Policy 24 (1995) 151-168
to firm size, we predict a positive curvilinear relation, in accordance with the findings of previous studies. 20 As regards market share and profitability, the Schumpeterian hypothesis suggests a positive correlation with innovation rates. In the banking industry things may however turn out otherwise since the availability of funds to finance innovation is not constrained by imperfections and transaction costs in the capital market; in addition, appropriability of technology can be considered to be weak under the smart automation regime. Consequently, no predictions are made regarding the sign of coefficients a s and a 9•
6. The empirical findings The results of the regressions are illustrated in Table 6. Model 1, 2 and 3 are equivalent, except for the technology-push variable (the lags in the adoption of mainframe a n d / o r minicomputers, stand-alone microcomputers, and telecommunication linkages between a bank's EDP center and local branches, respectively). Model 4 refers to the best specification on the basis of the value taken by the adjusted R 2. Model 5 is similar to model 4; the only difference is that MKTDEV substitutes for SAVE. The empirical findings generally support the theoretical hypotheses described earlier. In particular, there seems to be evidence of a technological revolution in banking connected with the introduction of distributed data processing and networking. The innovative behaviours of Italian banks in EB appear to differ markedly on the basis of the value taken by the dummy MAINN. In accordance with the arguments set forth in sections 2 and 3, there appears to be a critical threshold
2o Actually, such an assumption applies particularly to services in the pre-paradigmatic stage of development (i.e. HB and CB). For other more mature services (CD and partly ATM) the advantages allowed by large size might be balanced by the positive externalities mentioned earlier, which benefit all banks independently from their size.
165
corresponding to a minimum level of 'literacy' in the use of IT on the part of Italian banks. Banks which have matured sufficient skills and expertise during the mass automation regime, corresponding with banks for which MA/NN equals 1, can be considered to be equipped with substantial absorptive capacity; this allows them to take advantage of spillovers from rival firms and positive technological externalities (through participation in automation consortia, vertical and horizontal cooperative linkages aimed at technology transfer, and so on) that characterize banking under the smart automation regime. Owing to the public-good nature of technology for banks included in this category, technological leadership in the mechanization of back-office procedures, proxied by the technology-push variables LMF, LPC and LTL, does not seem to provide them with additional advantages: consistent with our predictions, the signs of the coefficients of such variables in models 1 to 3 (i.e. a 6 + a 7) do not significantly differ from 0. In addition, while LPC and LTL would seem to have a weak positive influence on the dependent variable, the opposite holds true for LMF. For banks in this category, it is the attitude of private consumers and customer firms towards electronic payment systems which turns out to play a crucial role in fostering innovation. When MAINN equals 1, the EB factor increases considerably with the demand-pull variables SAVE and MKTDEV. The coefficient of SAVE, which measures the average savings rate in the provinces where a bank operates, is positive and significant at 99% in all specifications; in addition, its value proves to be quite robust, varying between 0.0025 (model 2) and 0.0030 (model 1). The same holds true for MKTDEV (for the sake of simplicity, only the best specification including such variable is reported in Table 6). Instead, when MAINN equals 0, the values of the coefficients of the demand-pull variables decrease substantially and do not significantly differ from zero, in accordance with the reduced capabilities of very laggard banks to absorb extramural skills and take advantage of favourable demand conditions. In contrast, both technological leaders and laggard banks appear to be less sensitive to the
166
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
stimuli from the competitive environment. Contrary to the evidence provided by Hannan and McDowell's studies (Hannah and McDowell [1618]), market structure fails to evidence any effect upon innovation rates in EB. 21 The coefficient of H E R F (i.e. a 4 + a 5) is negative and the null hypothesis cannot be rejected. The reason may well reside in the peculiar competitive conditions in the Italian banking industry, where heavy regulation has traditionally impeded the induction mechanisms associated with market rivalry, in spite of the recent more favourable attitude towards competition on the part of Banca d'Italia (see footnote 12). Lastly, let us turn to the influence exerted upon innovation rates by firm structure and performance. The positive, significant at 99% coefficient of DEP comes as no surprise. We found a curvilinear relation, as suggested by previous studies; all else being equal, the EB index increases with total deposits but at a decreasing rate. Similar results are obtained when other size variables (such as total employees, total assets, consumer deposits) substitute for total deposits. On the contrary, the findings concerning market power and profitability are mixed. On the one hand, a bank's market share does not evidence any effect upon the value of the EB factor, with the coefficient of M K T S H being consistently negative and insignificant. The same remark applies to return on equity. 22 On the other hand, a positive, almost significant correlation emerges with the ratio of operating revenue to total assets. The research thus fails to provide decisive supporting evidence for the notion that profitability places a binding constraint on the adoption and development of EB services by Italian banks.
21It is interesting to emphasize that previous evidence on this issue actually is mixed. For instance, in Pennings and Harianto [30] variables capturing the number of competitors and the amount of shake-outs are found to have no explanatory power of the adoption of video-bankingby US banks. 22This finding conforms to the evidence given by Pennings and Harianto [30]; in addition, Hannan and McDowell [16] find a negative but statistically insignificant correlation between the conditional probabilityof adoption of ATM by US commercial banks and the ratio of net income to total assets.
7. Concluding remarks In this paper we propose a conceptual model to analyse innovations originating from the diffusion of IT in the banking sector. We argue that technical change in this industry exhibits a revolutionary character. The diffusion of distributed data processing and telematics has involved a shift in technological regime; a clear distinction is made between the 'mass automation' regime, focusing essentially on the mechanization of backoffice procedures in the 1960s and 1970s, and the 'smart automation' regime, centered around the supply of EB services. The main implication of the change in technological regime is that advantages relating to cumulative and learning-by-doing effects developed by banks while proceeding rapidly along the mass automation technological trajectory are largely erased. In other words, while a minimum level of literacy in the use of IT does represent an essential prerequisite for being innovative in EB, technological leadership in the previous regime does not ensure technological leadership in the new one. In addition, technology tends to become a public good under the smart automation regime for firms equipped with adequate absorptive capacity, owing to the existence of substantial network economies, incentives to cooperation, spillovers from rival firms, and easy access to extramural skills. Coherently, demand-pull variables mirroring potential customers' conducive attitude towards electronic payment systems and the stimuli from intense competition are expected to play a prominent role in favouring innovation. Empirical findings concerning innovative behaviour of a sample of Italian commercial banks basically confirm the hypotheses of the theoretical model (with the exception of the impact of market structure on innovative rates). Of course, the evidence provided could be extended in a series of directions. For instance, one could try to identify and explain differences in the diffusion rates of the various EB services which characterize the smart automation regime. On the one hand, these are likely to depend on the intrinsic nature of the services, with a distinction being possibly made between the innovation
L. Buzzacchi et al. /Research Policy 24 (1995) 151-168
of traditional products and the offer of entirely new products which make extensive use of datatransmission means, such as international RB services. It would be interesting to investigate whether the knowledge and know-how matured by banks in the mass automation regime have different impacts for the two categories of EB services. 23 On the other hand, the crucial role played by demand-pull variables in the smart automation regime in speeding up adoption of EB services suggests that the specific customer base on which banks focus (e.g. large firms active on the international markets vs. the retail domestic customer base) may influence the pace and direction of their innovative activity. Unfortunately, the analysis of the Italian banking sector provides no evidence on this issue as Italian banks usually are not specialized by market segments. It would also be interesting to determine to what extent such results are industry-specific. Our guess is that much of what has emerged from the analysis of the banking industry applies to other services which are increasingly dependent on IT and where there are considerable network economies, such as the insurance and airline industries. Of course, this remains an empirical question which waits for similar studies in different settings. In any case, the findings of our analysis have important policy implications. In particular, they witness in favour of a 'diffusion-oriented' technology policy. For one thing, the primary aim of such policy should be to promote the development of basic skills in the use of IT on the part of the largest possible number of banks. For this purpose, direct financial incentives based on non-discretionary instruments may of course be of some help. However, indirect measures are likely to be even more effective. On the one hand, as frequently pointed out by Banca d'Italia, they should target the removal of the constraints which have historically prevented the increase of
23 Preliminary findings would seem to show that prior experience with back-office automation has similar effects on the offer of A T M , POS and RB services by Italian banks (see again Mariotti [26]).
167
average firm size in the Italian banking industry. This is an extremely important issue if we consider the positive relation between innovation and firm size in banking and the low concentration of the Italian banking industry in comparison with those of other industrialized countries. On the other hand, the further development of automation consortia and other cooperative bodies should be encouraged, so as to allow banks, especially those of small size, to draw on a large external pool of specialized skills and capital equipment. Automation consortia could also provide advisory services for implementing broadly based training programs. More generally, such consortia prove to play a crucial role in favouring technology transfer among banks endowed with sufficient homegrown skills and expertise. Therefore, their resources should be improved and their role extended for instance to the cooperative setting of industry-wide market standard for innovative services. In addition, further advances in EB appear to be tightly contingent on the provision of an efficient public telecommunications infrastructure and the supply of complementary network services by the common carriers, other institutions and private firms. This is a field where Italy has lagged behind and which should be at the core of diffusion-oriented policy, owing to the huge positive externalities offered to the banking industry and other sectors of the economy. Lastly, some brief remarks regarding the priorities of banks' innovative strategies are in order. The findings of the paper have disclosed the crucial importance of demand conditions in stimulating innovation under the smart automation regime. The sensitivity of potential customers to modifying their consumption patterns, making them compatible with the new forms of service delivery typical of EB, has emerged as a driving force of innovation. This comes as no surprise, if we consider the active role which technical change tends to attribute to the consumer in the production of IT-based innovative services. Nonetheless, it suggests that a bank's innovative strategy has to conform to customers' needs and capabilities; stimulating their needs and developing their ca-
168
L. Buzzacchi et al. / Research Policy 24 (1995) 151-168
pabilities through a well-conceived marketing effort should therefore become an integral part of the innovation process.
References [1] W.J. Abernathy and J.M. Utterback, A Dynamic Model of Process and Product Innovation, Omega 3 (6) (1975) 639-656. [2] K.A. Bantel and S.A Jackson, Top Management and Innovations in Banking. Does the Composition of the Top Team Make a Difference, Strategic Management Journal 10 (1989) 107-124. [3] R. Barras, Information Technologies and the Service Revolution, Policy Studies 5 (4) (1985) 14-24. [4] R. Barras, Towards a Theory of Innovation in Services, Research Policy 15 (1986) 161-173. [5] R. Barras, Interactive Innovation in Financial and Business Services: the Vanguard of the Service Revolution, Research Policy 19 (1990) 215-237. [6] G.C. Cainarca, M.G. Colombo and S. Mariotti, An Evolutionary Pattern of Innovation Diffusion. The Case of Flexible Automation, Research Policy 18 (1989) 59-86. [7] CIPA-ABI, Rilevazione dello Stato dell'Automazione del Sistema Creditizio. Esercizio 1989 (CIPA-ABI, Roma, 1990). [8] CIPA-ABI, Rilevazione deUo Stato dell'Automazione del Sistema Creditizio. Esercizio 1991 (CIPA-ABI, Roma, 1992). [9] W.M. Cohen and R.C. Levin, Empirical Studies of Innovation and Market Structure, in: R. Schmalensee and R. Willig (Editors), Handbook of Industrial Organization (North-Holland, Amsterdam, 1989) 1059-1107. [10] W.M. Cohen and D.A. Levinthal, Innovation and Learning: the Two Faces of R&D, Economic Journal 99 (1989) 569-596. [11] Confindustria-Centro Studi, Indicatori Economici Provinciali (Collana Industria e Territorio, SIPI, Roma, 1991). [12] S. Davies, The Diffusion of Process Innovations (Cambridge University Press, Cambridge, 1979). [13] C. Fontaine, L'Expansion des Services. Un Quart de Si~cle en France et dans le Monde D#velopp# (Rexervices, Paris, 1987). [14] C. Freeman and L. Soete, L'Onda lnformatica. Nuove Tecnologie e Occupazione (Edizioni del Sole 24 Ore, Milano, 1987). [15] Z. Griliches, Hybrid Corn: an Exploration in the Economics of Technological Change, Econometrica 25 (1957) 501-522.
[16] T.H. Hannan and J.M. McDowell, Market Concentration and the Diffusion of New Technology in the Banking Industry, The Review of Economics and Statistics 66 (1984) 685-691. [17] T.H. Hannan and J.M. MeDowell, The Determinants of Technology Adoption: the Case of the Banking Firm, Rand Journal of Economics 15 (1984) 328-335. [18] T.H. Hannan and J.M. McDowell, Rival Precedence and the Dynamics of Technology Adoption: an Empirical Analysis, Economica 54 (1987) 155-171. [19] F. Harianto and J.M. Pennings, Technological Innovation through Inter-Firm Linkage, in: L. Gomez-Mejia and M.W. Lawless (Editors), Managing the High-Technology Firm (JAI Press, Greenwich, CT, 1990) 15-42. [20] A.A. Heggestad, Market Structure, Risk and Profitability in Commercial Banking, Journal of Finance 4 (1977) 1207-1216. [21] T.P. Hill, On Goods and Services, The Review of Income and Wealth (December, 1977) 315-338. [22] Istituto G. Tagliacarne, Reddito Disponibile, Consumi e Risparmio delle Famiglie negli Anni 1985/1988 (Istituto G. Tagliacarne, Roma, 1991). [23] E. Mansfield, Technical Change and the Rate of Imitation, Econometrica 29 (1961) 741-766. [24] E. Mansfield, Industrial Research and Technological Innovation: an Econometric Analysis (Norton, New York, 1968). [25] K.V. Mardia, J.T. Kent and J.M. Bibby, Multivariate Analysis (Academic Press, London, 1979). [26] S. Mariotti, Tecnologie dell'Informazione ed Innovazione nei Servizi: il Caso del Settore Bancario (Quaderni della Fondazione Adriano Olivetti, 35, Roma, 1993). [27] R.R. Nelson and S.G. Winter, In Search of a Useful Theory of Innovation, Research Policy 6 (1977) 36-76. [28] R.R Nelson and S.G. Winter, An Evolutionary Theory of Economic Change (Harvard University Press, Cambridge, MA, 1982). [29] K. Pavitt, Sectoral Patterns of Technical Change: towards a Taxonomy and a Theory, Research Policy 13 (1984) 343-373. [30] J.M. Pennings and F. Harianto, The Diffusion of Technological Innovation in the Commercial Banking Industry, Strategic Management Journal 1 (1992) 29-46. [31] M,E. Porter and V.E. Millar, How Information gives you Competitive Advantage, Harvard Business Review (JulyAugust 1985) 149-160. [32] D. Teece, Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, Research Policy 15 (1986) 285-305. [33] S.G. Winter, Schumpeterian Competition in Alternative Technological Regimes, Journal of Economic Behavior and Organization 5 (1984) 287-320.
