Innovativeness in industrial organizations: A two-stage model of adoption

165

Innovativeness in industrial organizations: A two-stage model of adoption * Stephane

Gauvin

1. Introduction

Universite’Laval, Ste-Foy, Q&bee GIK 7P4, Canada

and Rajiv K. Sinha Arizona State University,Ternpe, AZ 85284-4107, USA Received August 1991 Final version received June 1992

Micro-models of adoption of innovations have provided contradictory results regarding the impact of organizational characteristics upon “innovativeness.” We suggest that these differences can be reconciled by explicitly incorporating the notion that firms differ in terms of their Opportunitiesfor Adoption. We propose a modeling approach to capture the probability of adoption as a function of opportunities for adoption, innovativeness and past adoptions. The model is tested on a sample of 3,000 firms in the context of the adoption of personal computers. The results are compared with those obtained from a conventional logit model. Splitsample validation attests to the robustness of the results from the two-stage model.

Correspondence to: Stiphane Gauvin, Assistant Professor, Universite Laval, Pavillon Palasis-Prince, Ste-Foy, QuCbec, Canada, GlK 7P4. Telephone (418) 656-2158. * The authors wish to acknowledge the Institute for the Study of Business Markets (Penn State University) for its financial support, and COMTEC Inc (Gartner Group) for providing access to their data. Intern. J. of Research in Marketing 10 (1993) 165-183 North-Holland

0167-8116/93/$06.00

In the field of marketing, considerable efforts have been directed at gaining a better understanding of the diffusion process at an aggregate level, along the path suggested by BWS(1969). Thus far, the major use of diffusion models has been for the purpose of forecasting the sales rate of new products over time. These models provide fairly accurate sales estimates but they are of little use in identifying the potential adopters of the innovation. While the importance of identifying factors that impact on intrinsic organizational innovativeness has been highlighted in the marketing (Mahajan et al., 1990a; Robertson and Gatignon, 1986), economics (Mansfield, 1968; Rose and Joskow, 1990) and organizational behavior literatures (Kimberly and Evanisko, 1981), the micro-modeling of adoption behavior has seen very little consensus about firm and industry specific factors that accelerate or retard adoption. Downs and Mohr (1976) noted that: Perhaps the most alarming characteristic of the body of empirical study of innovation is the extreme variance among its findings. [ . . . ] Factors found to be important for innovation in one study are found to be considerably less important, not important at all, or even inversely important in another study. This phenomena occurs with relentless regularity. One should certainly expect some variation of results in social science research, but the record in the field of innovation is beyond interpretation. (p* 700)

0 1993 - Eisevier Science Publishers B.V. All rights reserved

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S. Gauh,

RX. Sinha / Innovations in industrial organizations

Consider the impact of firm size on the probability of adoption. It is generally believed that firm size is one of the most effec-

tive predictors of innovativeness Kimberly and Evanisko, 1981), with larger firms being more likely to be first to adopt new technologies. A positive relationship between firm size and the probability of adoption has been reported in the case of the adoption of technological innovations such as automated telling machines (Hannan and McDowell, 1984); microcomputers (Lind et al., 1989); technological and managerial innovations in libraries (Damanpour, 1987); flexible automation systems (Cainarca et al., 1989); and softwares (Zmud. 1982). Yet, the Schumpeterian thesis ’ is frequently opposed to the belief that smaller organizations are more innovative, and the empirical evidence on the impact of firms size on their innovativeness is not without controversy. For instance, Nabseth and Ray (1974) report several cases of a negative impact of firm size on innovativeness in their study of the adoption of process innovations. The diffusion of the basic oxygen furnace (EgF), introduced in the mid-50s, is particularly well documented. Adams and Dirlan (1964) and Maddala and knight (1967) argued that larger steel plants were slower to adopt the BOF technology because they were insulated from domestic competition. Meyer and Herregat (1974) found no significant size effect in a sample of international steel plants. Davies (1979) found a positive relationship in a sample of British plants. In the US, Oster (1982) found a negative impact of firm size when controlling for the plant size. Levin et al. (1987) report similar results in the case of optical scanners.

’ The Schumpeterian thesis argues that excess resources are required to fuel the process of “creative destruction.” Large firms are more likely to have excess resources, and thus expected to be more innovative than small firms.

We argue that these conflicting results can be reconciled if adoption models explicitly consider the opportunities for adoption 2 of new technologies. Noteboom (1989) sums it up in a succinct fashion when he writes that “[. . . ] the opposition between the ‘small firm’ and ‘large firm’ theses is still unresolved. The issue is too complex to allow for a single sweeping statement concerning the relation between innovation and firm size, regardless of types and conditions of innovation” (p. 109, emphasis added). Since different firms are faced with different types and conditions of innovations, the purpose of our research is to express the probability of adoption as a function of both opportunities for adoption and intrinsic organizational innovativeness. In Section 2 we present a brief discussion of the concepts of innovativeness and opportunities for adoption. Section 3 proposes a two-stage framework that can be applied in cases where the extent of adoption within each organization is known. Section 4 develops a procedure in which we use a first-stage Poisson regression to capture opportunities for adoption, and a subsequent logit regression to capture intrinsic organizational innovativeness. In Section 5 we apply the estimator to the case of personal computers and compare the results of a single-stage logit specification with those of a two-stage model. Section 6 discusses the implications of our findings.

2. The concept OFinnovativeness There is a considerable amount of confusion surrounding the meaning of the term innovativeness in the context of adoption.

’ The term opportunities for adoption has been coined by industrial economists to designate the opportunity cost borne by a potential adopter using a less efficient technology. This concept is explained in more details in Section 2.3.

S. Gauvin, R.IC Sinha / Innovations in industrial organizations

We can readily identify three definitions in the literature: (1) a label assigned to those who are the first to adopt a new technology (Rogers, 1983); (2) a force that increases the probability of being first to adopt a new technology (Bass, 1969); (3) a force that accelerates adoption of a new technology, over and above the relative opportunities for adoption (Mansfield, 1968). 2.1 Innovatars as first adopters Among the first 3 to present an extensive and coherent discussion of this question, Rogers (1983) has proposed a categorization scheme which, to this day, is the most widely accepted. Noting that in most instances the diffusion of innovations closely follows a normal distribution, Rogers defined innovators as “the first 2.5% of the individuals to adopt an innovation” (p. 246). Innovators are contrasted with the early adopters, early majority, late majority, and finally laggards. The greatest asset of this classification scheme, its simplicity, is also its most serious drawback. First, as noted by Mahajan et al. (1990b), it is arbitrary. The category boundaries received no justification, be they theoretical or empirical. Second, it is of limited conceptual usefulness. Espousing Rogers’ definition is equivalent to saying that individuals or organizations are innovative because they are among the first to adopt. But knowing that the first 2.5% of the sales of a new product will be made to innovators does little in the way of helping management design a product launch strategy. If we were able to explain VU&an organization is an innovator, producers of innovation could incorporate this knowledge in designing more effective strategies. It is more useful to consider innovativeness as a force leading to being first to adopt, because

3 Rogers introdud

this definition in 1962,

167

innovators are, for example, more flexible (Gee, 1978), less risk-averse (Ettlie and Vellenga, 1979) or process information more effectively (Jensen, 1988). 2.2 Innovators are more likely to be first adopters

Bass (1969) defined innovators as those who “decide to adopt an innovation independently of the decisions of other[s]” (p. 216). In Bass’ theory of diffusion, innovators are no longer defined ex post, in term of the probability of their adoption. Instead, they are contrasted with imitators, those who adopt an innovation as result of word-ofmouth. According to Bass “initial purchases of the product are made by both ‘innovators’ and ‘imitators’ [. . . The] importance of innovators will be greater at first but will diminish monotonically with time” (p. 217). Critics have noted that these definitions led to a confusion in terms (Lekvall and Wahlbin, 1973). An innovator according to Bass could belong to any of the five categories suggested by Rogers and vice-versa. It is now generally agreed that Bass’ model should be understood in terms of categories of purchase influence rather than categories of innovativeness, since Bass’ model assumes a population homogeneous in every dimension, including its innovativeness (Mahajan et al., 1990a). 2.3 Innovativeness accelerates adoption Chatterjee and Eliashberg (1990) expanded the Bass framework to heterogeneous populations. Their model assumed that potential adopters evaluate an innovation along performance and price dimensions. POtential adopters also differ in their risk aversion and update their perceptions of a new product as its diffusion in the market provides information on its “true” performance level. The probability of adoption varies over

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time, as the amount of available information increases, and across potential adopters, reflecting variations in its relative value t0 POtential adopters. They suggested a composite measure of innovativeness (y ) itself a function of risk aversion, expectations of performance, price hurdle, reliability of incoming information about the innovation, the “true” value of the innovation and its price (p. 1066). The key contribution of this line of research was to express the probability of adoption as a function of the valuation of the innovation by potential adopters and the process by which they act on their perception of this value, in a format germane to aggregate diffusion models. There should be, however, a sharp conceptual difference between organizational factors affecting the decision making process (e.g., risk aversion), and the characteristics of the innovation itself (e.g., productivity gains). A decision to reject an innovation cannot be regarded as a signal that a firm is not innovative-it may simply be that the firm has no need for this particu-

I

lar innovation. We therefore suggestthat the meaning of innovativeness be restricted to the process by which an organization reaches a decision for adoption or rejection, rather than to the outcome of the decision process. This argument has been made by Rose and Joskow (1990) in their study of adoptions in the electric utility industry. They illustrate their view as follows: Compare two utilities: a large firm with 1000 Megawatts (Mw) of capacity and a small firm with 100 Mw of capacity, each growing at 10% a year. Assume that both will use the technology at the first available opportunity; that is, they are equally “innovative.” If new units come in 100 Mw increments, the large utility will build a new unit next year, while the small utility may not build a new unit for 10 years. We attribute this gap to differences in opportunities, not to differences in the propensity to adopt new technologies (p. 7).

Organization

The probabilityof adoptionis a functionof three categoriesof variables:the opportunitiesfor adoption,the innovativeness of potential adopters, and the extent of past adoptions. The conceptual linkages are formalized in a seriesof propositions. Fig. 1. Determinants of adopth.

S. Gamin, RX

Sinha / Innorations in industrial organizations

A similar definition had been suggested earlier by Mansfield (1963, 1968), who argued that large firms should be seen as being more innovative “only if the difference in the speed of response between large and small firms is greater than would be expected if a large firm acted as if it were simply the sum of an equivalent number of small, independent firms” (p. 172). The key idea here is that this meaning of innovativeness is technology invariant. Given equal opportunities, innovative firms will adopt a new technology earlier. The implication is that attempts to measure organizational characteristics affecting innovativeness based on the timing of adoption must account for the magnitude of opportunitie:, for adoption.

3. Factors influencing the probability of adoption We suggest a framework to relate a small set of theoretical determinants to the probability of adoption. These determinants are: (1) the opportunities for adoption; (2) the intrinsic innovativeness of potential adopters; and (3) the extent of past adoptions (Fig. 1). 3.1 Opportunitiesfor adoption Opportunities for adoption vary across firms. Larger organizations have more equipment that has to be replaced, and their growth requires more frequent expansions. Opportunities also vary across technologies. Technological opportunities arise from productivity gains that can be achieved with a new technology. Purchase opportunities arise either from an expansion of the demand for the type of technology supplied, or from the replacement of the current technological base. Purchase opportunities are conditional on the existence of technological opportunities, but inter-organizational differences in purchase opportunities are mostly driven by

169

firm-specific factors (scale of operations, see David, 1969), while differences in technological incentives are driven by technological factors (the relative economies of scale provided by a technology in comparison to substitutes). From a research perspective, capturing purchase opportunities is relatively straightforward because of a direct link with the firms’ scale of operations. Various measures such as the size of the installed base, the number of employees, or the sales levels of potential adopters provide a good indication of the magnitude of purchase opportunities. Technological opportunities, on the other hand, pose a difficult challenge. The “correct” measure for technological opportunity should be computed as the difference in the firms’ opportunity costs before and after they adopt the innovation (Mansfield et al., 1977). If the purpose of the analysis were to forecast the impact of a price change or a particular technological improvement on the probability of adoption we would be facing the rather formidable task of measuring the absolute reduction in opportunity costs brought about by innovations (see Nabseth and Ray, 1974, Ch. 11 for a discussion). More specifically, we would need price and performance measures for the new technology and all of its substitutes. If, on the othei hand, we are interested in studying the impact of some other determinant of innovativeness (e.g., risk aversion) we can estimate the relatirye technological opportunities across firms by focusing on a single technology* When an innovation can be acquired in various increments, a measure of the relative opportunities can be approximated from the size of the purchase. Consider the case of a new generation of personal computers. Inter-firm differences in procurement sizes should reflect the relative differences in OPportunities. In other cases, direct usage measurcs will bc rcquircd, as would be the case with facsimile machines for example. For

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personal computers, purchase and usage are closely related; in the case of facsimile machines, they are not. The size of the adopting organization has a positive impact on the opportunities for adoption of most innovations. Larger organizations will generally have a greater number of purchase opportunities than smaller organizations, because their size leads to more frequent replacement of defective equipment and because their growth translates into larger procurements in absolute terms. Similarly, larger organizations operate on a larger scale, offering a greater potential for technological opportunities. 4 Even modest technological advances will translate into considerable incentives to adopt. Thus: Proposition 1: Firm size has a positive impact on its opportunities for adoption.

Although we believe that the magnitude of opportunities for adoption should increase the probability of adoption, an interesting question arises when we consider the development of successive technological generations. There is considerable support for the existence of a learning curve (Ferguson, 1972, Ch. 14; Hart, 1983; Meredith and Camm, 1989). The direct implication is that the rate of technological progress declines over time, and consequently there is less and less room to provide technological opportunities to potential adopters. Assuming that the supplying industry is competitive enough to maintain efficient transfer prices throughout successive generations, we should expect that the technological opportunities will be greatest in the early years following its introduction. Thus: 4 New technologies could also reduce the optimal plant size. A

large

firm

might

more efficient, ple

presented

changes (inertia) adoption

resist

organizations in

the

breaking-up

into

smaller,

Opportunities for adoption have a positive impact on the probability of adoption.

Proposition 2.1:

The impact of opportunities for adoption on the probability of adoption declines over time. Proposition 2.2:

3.2 Past adoptions The probability of adoption of a new technology changes over time. The technology itself evolves and the profile of remaining potential adopters may differ from that of past adopters. But even if we were to keep the same technology and the same profile of potential adopters, there is overwhelming evidence that the probability of adoption will be influenced by the extent of past adoptions. 5 Two effects are hypothesized here: the demonstration effect (Mansfield, 1961; Karlson, 1986), and the word-of-mouth effect (Bass, 1969). Mansfield (1961) introduced the notion of “demonstration effect,” to reflect the fact that adoption depends upon how widespread the use of an innovation is in a sector of the economy. As more and more firms have adopted a new technology, the uncertainty surrounding its value diminishes as nonadopters benefit from the experience of past adopters and learn how to use it (Davies, 19791, new usages are discovered, network externalities arise (e.g., maintenance services proliferate), and, as Bass (1969) suggested, word-of-mouth supplements advertising and other factors external to the diffusion prochess. The demonstration effect is routinely included in econometric studies, and shown to be a key determinant of diffusion (e.g., Gort and Konakayama, 1982; Gottinger, 1987). If

and

(such as the steel plant exam-

introduction).

falls under another

discussed in Section 3.2.

Resistance

to

set of determinants

such

’ Past adoptions refers to the number of other firms having

of

purchased the new product, and not to prior purchases made by a given firm.

S. Gawin, R.K. Sinha / Innovations in industrial organizations

new products did not live up to the market’s expectations, the demonstration effect could, conceivably, have a negative impact on the probability of adoption (see models by Kalish and Lilien, 1986; Dockner and Jorgensen, 1988). In practice, however, it is always found to be positive. In their review of the empirical work published in marketing, Sultan et al. (1990) report values ranging from 0.2 to 1.7 for q, the coefficient of internal influence. 6 Thus: Proposition 3.1:

Past adoptions have a positive impact on the probability of adoption. Even though the demonstration effect and word-of-mouth effects are similar the two concepts differ with respect to their impact over time. The demonstration effect is rooted in the economics of information (Stigler, 1961; McCardle, 1985), while Bass’ model traces its origins in epidemiology (Bartlett, 1960). In Mansfield’s perspective, the demonstration effect should weaken over time, since the marginal increase in the cumulative number of adopters will contribute less and less to the reduction in uncertainty regarding the new technology. In contrast, Bass’ model assumes that the impact of word-of-mouth ‘is constant over time. ’ While we are not aware of empirical evidence that could be used to support one position or the other, based upon the economics of information it seems reasonable to use a model in which the value of additional information is marginally decreasing. Thus:

Note that all estimations were made in the case of successful innovations. Few instances of negative word-of-mouth are documented. Eckrich and Grimm (1979) report a case study where negative word-of-mouth is believed to have retarded the diffusion of automated teller machines. The probability of adoption is defined as P(t I= p -I(q/m)Y(t ), where 11 is the coefficient of innovation, q is the coefficient of imitation, M is the size of the population in which the innovation diffuses and Y(r) is the cumulative total of past adopters.

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Proposition 3.2: The imp;act of past adop-

tions on the probability of adoption weakens over time. 3.2 Innovativeness The bulk of the literature on adoption has focused on the impact that organizational or individual traits have on the probability of adoption: leadership (Hage and Dewar, 1973); centralization (Zmud, 1982); specialization (Cohn and Turyn, 1980); size (Rose and Joskow, 1990); risk aversion (Lattin and Roberts, 1988); information processing capabilities (Jensen, 1988); decision-maker information processing characteristics (Gatignon and Robertson, 1989). The impact of these factors should remain essentially constant across a wide variety of organizational behaviors. In other words, they should be technology invariant, and this is why knowledge of these impacts is so crucial to the marketers. As we have indicated earlier, empirical support for stable innovative traits is inconclusive and the theoretical underpinnings of several relationships are weak, because they focus on the outcome of the decision process rather than on the acceleration of the process itself. Consider the case of information processing capabilities. The standard argument is that firms with superior information processing capabilities will adopt faster (see Karlsson, 1988). Organizations with superior information processing capabilities should be among the first to understand and capitalize on opportunities offered by new technologies. On the other hand, Balcer and Lippman (1984, Chatterjee and Eliashberg (1990), Jensen (1988), and McCardle (1985) have developed models in which additional information can lead just as quickly to a rejection of a new technology. Moreover, Jensen (1988) has suggested that since “reliable” information is not free, firms with better information processing need not reach a decision before others. The very

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fact that they engaged more resources in information processing on new technologies indicate that they also have more stringent adoption criteria. The net impact on the probability of adoption may be positive or negative. Jensen’s model can be reduced to two basic conceptual determinants of innovativeness: risk-aversion and inertia. Some firms are more risk-averse than others. If there is uncertainty, the threshold for adoption is higher, and this will reduce the probability of adoption (Ettlie and Vellenga, 1979). The degree of organizational inertia will vary across firms. Very large, bureaucratic, centralized organizations have a harder time adjusting to technological changes. If they do not take measures designed to improve their ability to manage change, they will lag be-

hind innovative organizations. All these factors can be subsumed in what can be called “corporate inertia,” that is, the amount of time required by a firm to reach a decision and to act on it (Gee, 1978).

4. Methodological issues In the previous section, we have asserted that opportunities for adoption of new technologies are a critical determinant of the probability of their adoption, and that consequently models of adoption should include an estimate of these opportunities in order to avoid specification biases. The estimation of two-stage models that contain unobservable variables has now become commonplace in the field of applied

Stage I: Estimate the Opportunities for Adoption

I Stage II: Estimate the Probability of Adoption

The first stage of the estimatorperformsa Poissonregressionof the observed purchase size on measures of firms’ scale of operations.We use these estimatesto generatean index for the opportunitiesfor adoptionto all potential adopters. The second kage of the model integrates this value along with measures of organizational innovativeness an measures of past adoptions, Here, the dependent variable is an indicator variable with value = 1 if the firm has adopted the innovation, and zero otherwise. Fig. 2. Structure of the estimator.

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econometrics. The standard procedure in these models is to replace the unobservable variables (such as rational expectations or equilibrium unemployment probabilities) with their imputed or predicted values from an auxiliary statistical model. These values are then treated as if they were the observed values for purposes of estimation and drawing inferences from the second-stage model. A two-stage procedure produces consistent estimates of the second-stage parameters under fairly general conditions (Judge et al., 1985). First, we fit a model to estimate the opportunities for adoption. Then, the probability of adoption is estimated with a logit model that includes estimates for these opportunities in addition to the factors deemed relevant to capture innovativeness and past adoptions (Fig. 2). We describe below the details of imputing the values for the opportunities of adoption in the first stage and its subsequent use in the second stage of the model.

Observed

4.1 Choosing a first-stage estimator The objective of the first stage is to be able to derive an estimate of the relative opportunities for adoption as a function of firms’ scale of operations and their sector of activity. The basic problem is that although relatively good measures of scale are readily available-number of employees, sales volume, etc. -we cannot observe directly the opportunities for adoption; all we can see are investments in new technologies. Consider Fig. 3. As the scale of operations increases, so does the value of the innovation. Similarly, when the firm chooses to adopt the new technology, the cost of acquisition of the technology increases as a function of its scale of operations. For firms operating on a relatively small scale-to the left of the dashed line-the cost of acquisition of the new technology exceeds its value. There is no opportunity for adoption and, therefore, acquisition is unlikely.

Value of Innovation

Unobserved

Scale of Operations (e.g., firm size)

As the scale of operations increases (e.g., fii

size), the value of the innovation increases (line O-C), and the cost of acquisition (line O’- B). We will observe the cost of acquisi!ion only if the value exceeds the cost, that is, above and to the right of A. SO does

Fig. 3. The relation

between

vaiuc of innovations

and ohscrvcd

173

purchases.

174

S. Gaucin, R.K Sinha / Innovations in industrial organizations

Whenever the scale of operations falls below some critical level, the value of the innovation falls below its acquisition cost and the “observed” cost falls to zero, there is a censoring problem: zero investments are not “true” zeroes. Using an or_2 estimator on such data would underestimate the impact of the scale of operations (see Amemiya, 1984, 1985, Ch, 10). A review of the literature suggests two possible solutions: tobit (Tobin, 1958) and Poisson models (Jorgenson, 1961). Even though tobit models are somewhat more familiar to marketers (see Doyle, 1977, Dubin and McFadden, 1984; and Malhotra, 1986 for reviews and/or applications in marketing), they will often produce grossly inaccurate estimates or simply fail to converge on a solution when a small fraction of the sampled dependent “true” values are observed. The crucial problem is that tobit assumes a linear relationship between the dependent variable and its predictors and uses the nonzero observations to provide an estimate of the true slope. When the “visible” portion of the sample is small, even a minimal departure from the linearity assumption will have a major impact on the final results. Poisson regression, on the other hand, is well suited for the analysis of this type of data (see Bilker, 1987 for an application in marketing). A Poisson model is of the form P(Y;:= ni) = e-“m”l/ni! (Agresti, 1984, appendix B), where Yi is the unobserved opportunity for adoption for the ith firm, ni is the observed purchase size, and m is the Poisson parameter. The parameter m can be conveniently expressed as a log-linear function of the independent variables. 4.2 Specification of the logit model While the first stage provides us with an estimate of the relative opportunities for adoption, the purpose of the second stage is to determine the probability that acquisition

will occur as a function of: (1) opportunities for adoption, (2) the degree of innovativeness of the firm, and (3) the extent of past adoptions, as discussed in Section 3. The specification of logit models in the context of adoption studies is familiar (e.g., Gatignon and Robertson, 1989). Our approach differs in that it incorporates the estimate of opportunities for adoption from the first stage. Iogit models are of the form 7~(Xi> = ePK/l + epxi (Agresti, 1984, Ch. 6), where rr represents the probability of an event, given vector X. In our case, V(Xi) represents the probability of adoption by a firm whose profile Xi, is defined in terms of opportunities for adoption, innovativeness and past adoptions. 4.3 Sampling issues The approach we suggest imposes two rather strict sampling requirements. The first stage in particular imposes the use of short time-frames. In order to ascribe differences in technological investments to firms’ scale of operations, we must assume that technologies are identical across firms. It would not be reasonable, for example, to fit a Poisson model with 10 years worth of data on firms sizes and investments in computers. Technological change has been too drastic. We can, however, break-up the sample into smaller time intervals for which we can reasonably assume that the technology was constant. The second issue pertains to the effect of past adoptions. Restricting the sample to a short time-frame will obviously limit the potential for variation in the number of past adoptions. For all practical matters, overall penetration will be a constant. In order to generate sufficient variance in the penetration rate, the sample will have to encompass either several industries, regional markets or other sub-populations for which penetration rates differ.

S, Gauvin, RX Sinha / Innovations in industrial organizations

5. An illustration We have applied the model developed above in the context of the acquisition of personal computers by American industrial establishments. In this section, we describe the survey and compare the results of our two-stage approach with those of a one-stage logit model. 5.1 Data

175

The initial samples sizes were 6996 in 1984, and 6739 in 1987. As it is the practice in research on adoption, where first purchase is treated as synonymous with adoption, we ‘removed establishments that had already acquired personal computers prior to the beginning of the year, leaving us with usable samples of 5271 and 3471, respectively. Descriptive statistics are presented in Table 1. 5.2 Measurements and method

The data come from large scale annual surveys conducted since 1984, and designed to collect detailed information on office automation from a comprehensive national sample. ’ Data pertaining to eight categories of office equipment are collected through telephone and person;il interviews in more than 7,000 establishments from all sectors of activity. Sampling is random stratified nonproportional, in order to increase the efficiency of the sampling process among large establishments. We use data on the acquisition of personal computers from years 1984 and 1987. Schools and colleges were removed because their need for computers has more to do with student training than with office automation. H The survey is conducted by COMTEC Inc., a subsidiary of the Gartner Group. Data are available for academic research through the Institute for the Study of Business Markets, Business Administration Building II, The Pennsylvania State University, University Park, PA 16802. USA.

For all establishments, the surveys provide the total number of personal computers acquired by each establishment, including a break-down of their PCs’ inventory by year of acquisition (similar measures are available for the other categories of office automation equipment as well). Thus, it is possible to know whether an establishment has acquired its first PC in, 1984, 1987 or before. Few variables of theoretical interest are available, other than a very detailed count of office automation equipment. Measures of interest include: the number of white collar and blue collar workers, the 4-digit SIC code, and whether or not the establishment has full authority in the decision to purchase its equipment. In addition to these measures, we have computed two sectorial indicators, at the 2-digit SIC level: the penetration (past adoptions), that is, the proportion of establishments having acquired at least one PC

Table 1 Descriptive statistics Weighted average

Year Users Purchase size Employees % White collar Sector intensity Penetration Authority Otxer~-alions

1984

1987

7.09% 1.49 11.3 68.68% 0.02 7.8Wi 75.1 IV6 s271

9.76’;; 1.52 7.8 68.90% 0.10 19.56V 72.17? 3471

Calendar year covered by the survey Establishments that have adopted PCs during of the year Average purchase size Number of employees per establishment Proportion of white collar workers Ratio of PCs per employee at the beginning of the year (within 2-digit SK%) Average proportion of Users per 2-digit SIC at the beginning of the year Proportion of csMlishments with full authority to purchase Establishments which were not Users at the heginning of the year

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176

prior to the year of the survey; and the sector intensity, that is, the ratio of PCs to number of employees found in establishments having acquired at least one PC. Penetration and sector intensity were computed using COMTEC’s full sample. On the basis of these measures, propositions were mapped into testable hypotheses (Table 2). They are straightforward operationalizations of our propositions. For the first stage of the estimation, we use a Poisson regression in which the dependent variable is the number of PCs purchased, while the independent variables are: (1) the number of white collar workers; (2) the number of blue collar workers; and (3) the average sector intensity. The estimation of the Poisson regression was made on a sample of firms that had no PC prior to the beginning of the year (5271 in 1984 and 3471 in 1987). For the second stage of the estimation, we applied a logit regression to the same sample, in which the dependent variable is dichotomous (one if the firm has purchased at least one PC, and zero otherwise), while the independent variables are: (1) the estimated purchase size computed with the first stage parameters; (2) the number of white collar

workers; (3) the number of blue collar workers; (4) firm authority; and (5) sectorial penetration. 5.3 Results Table 3 presents the results for 1984 and 1987, contrasting a one-stage logit model with the proposed two-stage model (first-stage Poisson; second-stage logit). The first hypothesis, pertaining to the effect of firm size on opportunities for adoption, is supported. The hypothesis was tested by fitting a model in which the joint impact of blue and white collar workers was restricted to zero. 9 The likelihood ratio test, comparing the restricted model with the model reported in Table 3 yields a x2 (2 d.f.) of 1597 ( p < 0.01) for the 1984 sample, and of 514 (p < 0.01) for 1987. Overall, the number of employees has a positive impact on the number of PCs purchased. The number of white collar workers have a positive impact on the magnitude of adoption in both samples, but this effect was not significant for blue collar workers in 1984. As ’ The restricted model is of the form Purchase size = f(Sector intensity ). The log-likelihoods are - 2219.7 and - 1196.9 for 1984 and 1987, respectively.

Table 2 Testable hypotheses derived from basic propositions Proposition

Operationalization

Result

Pl:

Hl:

The number of workers (blue and white collar) has a positive impact on the number of PCs acquired by firms

Supported

P2.1: Opportunities for adoption have a positive impact on the probability of adoption.

H2.1: The expected purchase size of PCs has a positive impact on the probability of adoption

Supported

P2.2: The impact of opportunities for adoption on the probability of adoption declines over time.

H2.2: The impact of demand on the probability of adoption is greater in 1984 than it is in 1987

Rejected

P3.1: Past adoptions have a positive probability of adoption.

H3.1: The impact of penetration adoption is positive

on the probability of

Supported

113.2: The impact of penetration on the probability of ;tdoption i\ greater in 1984 than in 1987

Supported

Firm size has a positive impact on its opportunities for adoption

impact on the

P3.2: The impact of past adoptions on the probability of adoption weakens over time.

S. Gauvin, RK Sinha / Innovations in industri.al organizations

177

Table 3 Statistical results a. Purchase size First-stage Poisson 1984

1987

-5.05 ** (0.117) 0.0246 (0.0171) 0.798 ** (0.0237) 5.11 ** (1.18)

Intercept Blue collar workers White collar workers Sector intensity

5015.8 - 1420.8

Chi-square Iog-likelihood

- 4.56 * * (0.134) 0.0922 ** (0.0268) 0.701 ** (0.0364) 3.13 ** (0.519) 3508.6 - 929.87

b. Adoption One-stage logit 1984 Intercept Blue collar workers White collar workers Firm autonomy Sector penetration Sector intensity

- 2.91 ** (0.137) 0.00153 ** (0.000255) 0.00326 ** (0.000337) 0.271 ** (0.0644E - 02) 5.33 ** (0.523) -0.183 (1.80)

Second-stage logit a 1987

Opport . for adopt ion

Log-likelihood Observations

1987

1984

- 3.34 ** (0.174) 0.00348 ** (0.000585) 0.00860 ** (0.00122) 0.353 ** (0.0734) 3.07 ** (0.401) -1.00 (0.800)

-2.98 ** (0.139) 0.000735 ** (0.000255) -0.00771 ** (0.00113) 0.283 ** (0.0651) 4.13 ** (0.473) N.A. 5.61* * (0.609)

- 2220.0 5271

- 1466.4 3471

- 2180.1 5271

-3.27 ** (0.167) 0.00180 ** (0.00066) -0.00865 ** (0.00304) 0.354 ** (0.0731) 2.08 ** (0.306) N.A. 5.14* * (0.924) - 1450.9 3471

a Condition numbers for multicollinearity were 11.95 and 6.9 for 1984 and 1987, respectively, well below the critical value of 30 suggested by Belsley et al. (1980) * * Statistically significant at p < 0.01 or less; standard error are in parentheses.

one might expect for this type of innovation, the impact of the number of white collar workers is markedly stronger than that of blue collar workers, although the difference has decreased in 1987. Hypothesis 2.1 is supported: the expected purchase size (proxy for opportunity for adoption) has a considerable impact on the probability of purchase. It is the most significant factor in 1984 (t-value in excess of 9.0)

and the second most significant in 1987 (tvalue in excess of 8). The impact appears to be slightly less in 1987 than it is in 1984 (5.61 vs 5.14). This difference is not significant, however, contrary to our expectations (H2.2). Hypotheses 3.1 and 3.2, relative to the impact of penetration, are also supported by both models: the impact of penetration is positive in both waves (p < O.Ol), and the impact is more important in 1984 than in

178

S. Gauvin, R.K Sinha / Innovations in industrial organizations

1987 (p < 0.011, supporting the notion of a marginally decreasing value of the information provided by adoptions. 5.4 Discussion The two-stage model provides a modest fit improvement over the one-stage logit model. This should come as no surprise since we use the same variables and very similar functional forms in both models. It is important to note, however, that the purpose of twostage models is not to improve the predictive ability, but rather to provide a more valid representation of the underlying process. For instance, incorporating opportunities for adoption in the two-stage model radically alters the interpretation that firm size has on the probability of adoption. The results of the one-stage model suggests that large firms are more innovative while a two-stage model suggests a more subtle relationship: large firms Lave more opportunities for adoption, but given equal opportunity they are not more likely to adopt. Indeed, if we focus on the number of white collar workers, this analysis suggests that small firms are more innovative, given equal opportunity for adoption. Comparing the one-stage and two-stage estimates also underscores two questionable results of the one-stage logit. The first anomaly pertains to the relative impact of white versus blue collar workers. According to the one-stage logit model, in 1984 the impact of white collar on the demand for PCS is only twice as large as the impact of a blue collar (0.00326 vs 0.00153). In comparison, the Poisson regression suggests that the relative importance of white collar workers is more than 30 times that of blue collar workers (0.798 vs 0.0246), a much more plausible ratio. Second, according to the one-stage logit, sector intensity has no impact on the probability of adoption (l-statistic = - I). In contrast, the first-stage Poisson shows posi-

tive impact (t-statistic = 4.33), again a much more plausible outcome. We have tested the stability of the twostage model with split-half samples for both years. Appendices 1 and 2 testify to the robustness of these findings. The analysis shows that the impact of the number of white collar workers on the probability of adoption is negative, contrary to what a onestage model would indicate. It is not likely to be a statistical artefact. First, this relationship persists across waves and sub-samples. Second, unlike what we observe in the case of white collar workers, the impact of the number of blue collar workers on the probability of adoption is positive, just like one would find in a one-stage model, and this positive impact also holds across waves and sub-samples. In short, while both one- and two-stage models fit the data equally well, the two-stage estimates provide a richer and more plausible description of the process of adoption. 6. Conclusion In order to remove the specification bias created by the omission of an estimate of the opportunities for adoption of new technologies, we suggested using a two-stage estimator and tested it on a large sample of firms. Our results show significant differences in comparison with a traditional one-stage logit model. These differences are consistent with hypotheses derived from a set of fundamental propositions established on the basis of an extensive review of the relevant literature, and in agreement with well accepted theories of organizational behavior. Our results raise questions on the determinants of adoption. First, while word-ofmouth is unquestionably a key factor in determining the probability of adoption of an innovation, little concern has been given to the dynamics of social communication. Previ-

S. Gauvin, R.K Sinha / Innovations in industrial organizations

ous research conducted at the aggregate level suggests that later introductions tend to accelerate the diffusion process (e.g., Heeler and Hustad, 1980; Takada and Jain, 1991). Our results however, both in single- and two-stage formulations, indicate that the impact of word-of-mouth is not constant over time as Bass’ model (1969) would imply; rather, it appears to decline, as a Bayesian theory of information updating suggests. More research is needed in this critical area. Second, a related question pertains to the dynamics of opportunities for adoption. There is little doubt that the rate of technological progress in the computer industry was higher in 1984 than in 1987. The technological opportunity was probably lower in 1987, and this should have been reflected in the size of the demand coefficient of the second-stage logit. The fact that the impact of opportunities for adoption remained constant for all practical purposes raises the possibility that (1) the computer industry was more competitive in 1987 than it was in 1984 with price reductions maintaining the level of opportunity at a constant level; or (2) selfselection by potential adopters with large opportunities for adoption is such that, over time, the pool of potential adopters maintains a relatively constant level of opportunities for adoption. In order to verify this, additional research should incorporate data on the level of competition on the supplyside, as suggested by Gatignon and Robertson (1989). More generally, we have established the importance and provided a framework for considering opportunities for adoption in research efforts aimed at understanding the forces driving the diffusion of innovations. Factors susceptible of having an impact on these opportunities, such as firm size and the type of activity in which the firm is engaged, should be analyzed carefully. Even though using a one-stage approach yields useful rcsults if the objective is to predict which firm

179

is most likely to adopt an innovation, they lead to mis-interpretation of the reasons underliing the probability of their adoption. The long-standing debate on the validity and limitations of the Schumpeterian thesis and the recurring reviews concerned with the lack of consistency of empirical results illustrate the current shortcomings of this stream of research. Although two-stage models provide a better insight in understanding the process of adoption, their application is quite limited since they require a measure of the relative opportunities for adoption. For several innovations, these data are impossible to obtain, and when they are available, they may not reflect accurately the opportunities for adoption. In particular, since producers of innovations cannot price-discriminate perfectly, our results do not capture the consumer surplus and, therefore, have probably underestimated the impact of opportunities for adoption on the probability of adoption. Additional research on better ways to measure the existence of a consumer surplus may yield considerable insights for the understanding of adoption processes. In a similar vein, our application was limited by the absence of appropriate measures for several underlying constructs. In particular, we can only speculate as to the meaning of the negative impact of the number of workers on innovativeness. lo More research with better measures of risk-aversion and organizational inertia is needed. Similar concerns can be raised regarding the use of first purchases to signal the adoption of a new technology. Large firms will typically purIQThe

negative impact of the number of white collar workers on the probability of adoption is consistent with the idea that large bureaucratic establishments suffer from organizational inertia. The positive impact of the number of blue collar workers is more puzzling. Since this number represents the size of the establishment, controlling for the size of its bureaucratic apparatus, we speculate that it may indicate a lower risk aversion.

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S. Gauvin, R.K Sinha / Innovations in industrial organizations

chase several test units before they commit themselves to adopting a new technology. Finally, while the two-stage model produces consistent estimates, it fails to account for measurement errors of predicted opportunities for adoption. Consequently, the statistical inferences based upon the estimated covariance matrix of the second-stage estimator will be biased even in large samples. Future work in this area may benefit from procedures for correctly estimating the asymptotic standard errors, along the lines

suggested by Murphy and Topel(1985). On balance, we have shown that a twostage approach provides a better conceptual platform to integrate previous theoretical work on the adoption of innovations, and that the substantive results appear to have more face validity than those of one-stage logit models. Our empirical results also point to promising areas of future research aimed at distinguishing the relative impact of key determinants of adoption.

S. Gauvirs, R.K. Sinha / Innovations in industrial orgmizations

* Yh

*

*

*

S. Gauvin, R.K. Sinha / Innovations in industrial organizations

182

Appendix 2 Split-half

estimations: Predictirle ualidiw

1984-Logit/Poisson Full sample Sub-sample 1 Sub-samp!e 2 0.971”/0.952 b 0.985/0.994 Sub-sample 1 O-984/0.973 Sub-sample 2 0.983/0.994 0.987/0.995 0.987/0.997 Fu!! sample Sample size

2635

2636

5271 ---

1987-Logit/Poisson Sub-sample 1 Sub-sample 2 0.985/0.841 Sub-sample 1 Sub-sample 2 0.980/0.848 0.986/0.945 0.991/0.960 Full sampie Sample size

1635

1636

Full samp:i: 0.994/&982 (X989/0.904 3271

The upper panel presents the results using the first half of the sample to calibrate the model, and t!re second half to test its predictive ability, at both stages of the model; the lower pane! presents similar results using the second half to calibrate and the first half to predict. a Percentage of agreement between the predictions of adsption obtained from a calibration on the first half of the sample and the fitted values obtained from an independent estimation on the second half. b Correlation coefficient between the estimates of the opportunities for adoption of the second half of the sample obtained from calibration done with observations from the first half of the sample, and the fitted values of an independent estimation made on the second half.

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