1 Simultaneous versus sequential complementarity in

Simultaneous versus sequential complementarity in the adoption of technological and organizational innovations: the case of innovations in the design sphere Giuliana Battisti Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK [email protected] ph: +44 024 7652 2312 Massimo G. Colombo Department of Management, Economics and Industrial Engineering Politecnico di Milano Via Lambruschini, 4b 20156 Milan, Italy [email protected] ph: +39 02 2399 2748 Larissa Rabbiosi Department of International Economics and Management Copenhagen Business School Porcelaenshaven 24A, DK2000 Frederiksberg, Denmark [email protected] ph: +45 3815 2897 Forthcoming in Industrial and Corporate Change Abstract It is generally suggested that technological and organizational innovations, being complementary need to be adopted simultaneously. Nevertheless, sequential rather than simultaneous adoption of these two types of innovation may be optimal. In this paper, we analyse the pattern of mutual causation of technological and organizational innovations and contribute to the understanding of their interdependencies. By the means of a test of necessary and sufficient conditions for the presence of sequential versus simultaneous complementarity, we explore the adoption of two allegedly complementary innovations in the sphere of design, namely: computer aided design/manufacture equipment (CAD) and interorganizational design teams with customers and suppliers (JOD). The evidence is drowned upon a longitudinal sample of Italian manufacturing plants observed over 27 years (19701996). We find that simultaneous adoption of the two innovations under consideration is unlikely while the likelihood of JOD adoption increases having adopted CAD. The results highlight the driving role of technological innovations, and notably of the decline in the price of IT equipment, upon the diffusion of complementary organizational innovations. Key words: diffusion, adoption sequence, complementarity, technological innovations, organizational innovations JEL: C33, L63, O3 1

1. Introduction In 1979, Rosenberg argued that technological “innovations hardly ever function in isolation” (p. 26) and history has taught that the greatest advancements have always been supported by the availability and the development of complementary innovations. 1 Since the appearance of general purpose technologies such as information and communication technologies (ICTs), scholarly attention has been attracted by the interdependencies between technological and organizational innovations. Specifically, these two types of innovations have generally been considered as complementary meaning that there are synergistic gains when they are used in combination (e.g., Milgrom and Roberts 1990). Thus, the full benefit of the adoption of technological innovations is only achieved if they are not carried out in isolation but accompanied by complementary organizational innovations such as business processes and work practices (for a review, see for instance Brynjolfsson and Hitt, 2000). If these organizational innovations are not implemented or are implemented only partially, the adoption of technological innovations can even create substantial productivity losses (e.g., Brynjolfsson et al. 1997). A small body of theoretical literature on innovation adoption has provided various explanations why complementary innovations are better adopted sequentially rather than simultaneously (e.g., Jovanovic and Stolyarov, 1997, 2000; Smith 2005). Building upon that literature we argue that too often scholarly attention is limited to the synergistic gains generated by simultaneous technological and organizational changes while the dynamics and the timing of their adoption sequence is often ignored. This is a drawback from both a conceptual and methodological point of view. Synergistic gains from the joint use of two innovations can occur, if present, also when the two innovations are adopted in sequence. Accordingly, the aim of this study is to corroborate the role of “sequential complementarity”

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and its important implications for both the empirical and the theoretical treatment of complementarities. In this work, we analyze the sequence in which technological and organizational innovations are introduced, thereby shedding new light on the pattern of mutual causation between these two types of innovation. We use the term “simultaneous complementarity” to refer to a situation in which the gains from the simultaneous adoption of the two innovations are greater than the sum of the gains from the adoption of each innovation in isolation. We use the term “sequential complementarity” to indicate a situation in which the gains from the adoption of a technological (organizational) innovation are greater if an organizational (technological) innovation has already been adopted in comparison to situations where it has not been adopted previously. 2 As far as we know, no large scale empirical study has rigorously examined whether technological and organizational innovations that allegedly are complementary are better adopted simultaneously or sequentially. The present paper aims to fill this gap. This is an important achievement for several reasons. First, without accounting for sequential adoption, the existence and the extent of complementarity effects between technological and organizational innovations risk being underestimated. In addition, because of this underestimation one may fail to detect the causal mechanisms that drive (or hinder) the diffusion of the two types of innovation. On the one hand, the dramatic reduction of the price of IT-based equipment in the last three decades may have been a key driver of the radical transformation we observed in the organization of firms, because of sequential complementarity between technological and organizational innovations (Bresnahan et al. 2002). On the other hand, based upon the evidence that organizational changes occur slowly (see Colombo and Delmastro, 2002) but crucially influence the returns from adoption of IT-

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based equipment, one may argue that the transformation of firm organization is an essential pre-requisite for the rapid diffusion of these latter technological innovations. We disentangle simultaneous versus sequential complementarity pursuing an applied analysis of the adoption of two allegedly complementary innovations. As technological innovation, we consider computer-aided design and/or manufacture equipment (hereafter labeled CAD), and inter-organizational design teams involving personnel from customers and/or suppliers (hereafter labeled JOD) as organizational innovation. CAD is IT-based equipment that allows to modify a product design at low cost, to (re)engineer a product and to evaluate production costs of different potential designs. JOD is an organizational practice based upon close collaboration with customers and suppliers in the design of new products. It allows design teams to quickly meet specific customer requirements at an early stage of the design process and to timely incorporate future technological developments from suppliers in design specifications. Advances in communication and information processing capabilities led by IT-based equipment such as CAD, have been recognized to impact firm organization, enabling new forms of cooperative behavior across intra-organizational as well as inter-organizational boundaries (e.g., Adler, 1995; Fulk and DeSanctis, 1995; Kumar and van Dissel, 1996). In accordance with this view, qualitative evidence suggests that the joint use of CAD and JOD makes the interaction with customers and suppliers in the design of new products far more efficient. Toyota’s development of new products with their suppliers is an “archetypical” example of real-world perceived complementarity between CAD and JOD. Toyota’s suppliers design components into new vehicles in collaboration with Toyota’s manufacture engineers using Toyota’s CAD systems, thereby obtaining substantial efficiencies (Liker and Choi, 2004). Complementarities between CAD technologies and changes in the organization of customer-supplier relationships have been observed also in Procter & Gamble in relation

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with its “Connect and Develop” strategy (Dodgson et al. 2006), in Nike Inc. (Hong, 2002), in the disk drive industry (Scott, 2000), and in the Rocketdyne division of the Boeing Corporation (Sethi et al. 2003). This qualitative evidence indicates that the joint use of CAD and JOD allows firms to shorten development time, reach higher levels of product customization, and make better use of the existing manufacturing resources. Therefore, CAD and JOD provide an ideal test bed to study the sequence of adoption of allegedly complementary technological and organizational innovations. Moreover, relying on a rare 27 year panel data set, our analysis allows us to contribute to the specific literature on CAD and JOD providing new evidence on the pattern of their mutual causation. In order to detect whether CAD and JOD are simultaneously or sequentially complementary (or both), we investigate in a panel data framework i) whether the likelihood of their simultaneous adoption is greater than the likelihood of adoption of each individual innovation in isolation, and ii) whether the adoption of either innovation leads to a subsequent increase of the likelihood of adoption of the other innovation. For this purpose, we estimate the transition probabilities relating to a four-state Markov chain corresponding to discrete-time bivariate survival for (unbalanced) panel data and use the one step ahead non causality and strong simultaneous independence test proposed by Mosconi and Seri (2006) (hereafter MS). To our knowledge, this is the first statistical technique that addresses issues of sequential complementarity in innovation adoption by the means of non-causality analysis in a bivariate discrete-time binary process. With the exception of Åstebro et al. (2012) there is a scant use of the MS method in the adoption literature. This is mainly due to the fact that the original MS model does not control for unobserved heterogeneity, and therefore only provides necessary conditions for simultaneous and sequential complementarity, and also to the well known lack of sufficiently long longitudinal data at firm (or plant) level necessary to

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test in a robust way the presence of complementarities (see Athey and Stern 1998) and the direction of the adoption sequence. In this paper we propose an extension of the MS test of sequential complementarity that accounts for unobserved heterogeneity and can be applied in situations in which one can exclude the presence of simultaneous complementarity (i.e. the necessary condition of the MS test for absence of simultaneous complementarity cannot be rejected). We then use it to test the causal direction in the adoption of CAD and JOD based upon the information contained in the longitudinal panel data on the adoption timing of CAD and JOD as well as other plant characteristics of 438 Italian manufacturing plants observed over a period of 27 years i.e. from 1970, the date of market appearance of the innovations under scrutiny to 1996. The paper is structured as follows. In Section 2, we review the theoretical literature that has examined the sequence of adoption of allegedly complementary innovations (Section 2.1) and the empirical literature on innovation diffusion that has documented the complementarity between technological and organizational innovations (Section 2.2). In Section 3 we describe the dataset and illustrate the diffusion of CAD and JOD from 1970 to 1996 in Italy. In Section 4 we present the econometric methodology. Section 5 illustrates the econometric results. In the final section we discuss our findings and conclude the paper. 2. Literature review 2.1.

Theoretical literature on sequential adoption of complementary innovations

The importance of the sequence of adoption of complementary innovations has long been recognized in the innovation literature. Benefits from an innovation might have to wait for the achievement of complementary innovations to exert their greatest impact. The diffusion of an innovation can be hold back awaiting for the development of its complementary innovation (see Rosenberg, 1979, p.26). The latter not only affects the initial take off but it can also shape the diffusion across users (Brown, 1981). More recently, theoretical works 6

have indicated several reasons why complementary innovations may be better adopted sequentially than simultaneously. Jovanovic and Stolyarov (2000) show that with non convex adjustment costs, the optimal adjustment of two complementary capital inputs is asynchronous. The same occurs if there is a learning period before one of the inputs can be used effectively by the adopting firm (Jovanovic and Stolyarov, 1997). Smith (2005) highlights two additional factors that may generate sequential adoption of complementary innovations. First, if the prices of the two innovations are expected to decline at different rates, a firm may fist adopt the innovation whose price is decreasing more slowly and wait for a further decrease of the price of the complementary innovation before adopting it. Second, if there is uncertainty about the dynamic paths of either the benefits from or the costs of adoption of the two innovations and the associated investments are partially irreversible, the option value of waiting before adopting the complementary innovation may render sequential adoption of the two innovations more profitable than simultaneous adoption. The above arguments highlight conditions under which sequential adoption of two complementary innovations is the first-best solution for firms, that is the one that maximizes firm’s profits under perfect foresight. However, sequential adoption may also be a result of the constraints that firms face in making decisions relating to innovation adoption. In particular, firms may experience diseconomies of scope arising from simultaneous adoption of complementary innovations due to limits in the ability of managers to deal with simultaneous changes in several spheres of firm’s activity. Alternatively, the existence of financial constraints may hinder simultaneous adoption. 2.2.

Empirical evidence on complementarity between technological and organizational innovations

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Most studies on the existence of complementarity effects in the adoption of technological and organizational innovations tend to rely upon the observation of how often these innovations are jointly adopted. 3

Caroli and Van Reenen (2001) show that plants that introduce

computer-based equipment are more likely to be involved in organizational change. Bresnahan et al. (2002) highlight that work re-organization of large US firms is positively correlated with their stock of IT capital and is a good predictor of firm level demand for IT. Greenan (2003) finds that medium and large sized French manufacturing firms that reported no re-organization were far less likely to use computer-based technologies than re-organized firms. Bartel et al. (2007) find a positive correlation between the adoption of new IT-based production equipment and the adoption of new human resource management practices in valve manufacturing plants, although the complementarity between technological and organizational innovations is not directly tested. Even though these studies suggest that technological and organisational changes indeed are complementary, the sequence of their adoption is not rigorously documented. Other studies examine the effects of past adoption of one type of innovation on the probability of adopting the other type. Hollenstein (2004) provides evidence that in spite of the existence of strong inertial forces, ICT adoption drives organizational change among Swiss firms. Similarly, Lynch (2007) finds that investments in organizational innovations are highly positively correlated with significant past investments in information technology. Smith and Weil (2005) provide evidence that in the apparel industry adoption of Electronic Data Interchange (EDI) is a precursor of the adoption of modular assembly (for early findings see Dunlop and Weil 1996; Pil and MacDuffie, 1996). Conversely, Bocquet et al. (2007) consider the effects of use of several organizational practices on the subsequent adoption of three ICT-based innovations – EDI, Enterprise Resource Planning, and Internet-based exchange systems. Using a cross-section of firms located in Haute Savoie, they find that

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organizational practices generally have no direct positive effects on adoption of the technological innovations under consideration but certain combinations of these practices positively influence their adoption. In a similar vein, studies concerned with technology diffusion have documented the stimulating role played by organizational innovations, while assuming that these latter innovations are antecedent (lagged) to the firm’s decision to adopt a new technology (Stoneman and Kwon, 1994; Colombo and Mosconi, 1995; Battisti and Stoneman, 2003, 2005; Battisti et al,. 2009). Although

extant

work

suggests

that

both

simultaneous

and

sequential

complementarities between technological and organizational innovations is possible, we lack large-scale empirical evidence on these different types of complementarities and whether there is a preferable sequence of adoption of these innovations continues to be an open question. 3. Data The main source of data used in this paper is the FLAUTO database which derives from two national surveys of Italian manufacturing plants conducted in 1989 and 1997 by Politecnico di Milano, followed by telephone interviews with plant managers. 4 The 1989 sample frame was selected by stratification according to industry, plant size and geographical location of the population of Italian plants with more than 10 employees that were in operation in 1986, and included 2,926 plants. Of these plants, 810 returned the survey questionnaire in 1989. 102 of these plants closed down between 1989 and 1997. The sample considered in this work includes the 438 plants out of the remaining 708 plants that responded to the 1997 survey (response rate of 62%). From 1970, the date of first appearance in Italy of the innovations under scrutiny, up to 1996 we have information on adoption by sample plants of IT-based technological and organizational innovations (notably, CAD and

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JOD) and other plant-specific variables. As some plants were established after 1970, the panel data set is unbalanced. We explored the possible effect of sample attrition between the original 1986 sample of 2,926 plants and the 1997 reduced sample of 438 respondents. We found that it was random across industry ( χ 2(6)=5.22, p=0.73) and geographical location ( χ 2(4)=1.34, p=0.72). However, the same cannot be said about the distribution of plant size ( χ 2(5)=21.83, p=0.0002) due to greater attrition in the class 10 to 19 employees and smaller attrition among plants that in 1986 had more than 200 employees. 5 In line with several studies on innovation diffusion (e.g., Karshenas and Stoneman, 1993), in our sample large plants are relatively more represented than small plants. In addition, the FLAUTO sample was drawn in 1986, so it does not include plants that were established after 1986. Whether the adoption behaviour of these newly established plants differs from that of their incumbent peers is questionable. However, in spite of these shortcomings, the sample has a number of strengths. It is large and heterogeneous. The observation period is long (27 years) and covers large part of the diffusion period starting from the first appearance of the two innovations. It also contains very detailed information on the characteristics of the sample plants. That allowed us to insert in the model specification several controls that may affect the likelihood of adoption of CAD and JOD (for a similar approach, see Boucquet et al., 2007). Most importantly, FLAUTO contains information on the year of first adoption of CAD and JOD by the sample plants. The diffusion of these two innovations is illustrated in figure 1. Both innovations appeared in the beginning of the seventies and diffuse following the traditional S-shaped pattern. By 1980 JOD was adopted by about 15% and by 1996, it was adopted by about 56% of the plants in the sample. During the seventies and up to 1987 (which is also the flexus of the diffusion curve) CAD has been diffusing at a slower pace than JOD. By 1987 it was adopted by about 20% of the plants. After that date diffusion has been taking place at a faster 10

rate than that of JOD. By 1996 about 77% of the eligible plants in the sample had adopted CAD. Insert figure 1 about here The existence of complementarities between CAD and JOD is intuitively supported by the fact that by 1996, 49.5% of sample plants had adopted both innovations (see table 1). Nevertheless, only 3% of the sample plants reported to have adopted them simultaneously, 23.1% first introduced JOD and then CAD, while 23.4% introduced first CAD and then JOD. Of the remaining plants, 6.6% had adopted JOD by 1996 but not CAD, 27.2% had adopted CAD but not JOD, while 16.7% still had to adopt either JOD or CAD for the first time. Insert table 1 about here 4. Econometric methodology and model specification 4.1. Econometric methodology In order to detect the presence of complementarity between the two innovations, we use the Mosconi and Seri’s (2006) strong simultaneous independence and one step ahead non causality tests for discrete-time binary time series that allows us to discriminate between simultaneous and sequential complementarity. We also show that under specific conditions that will be illustrated below, such test can be modified so as to control for bias engendered by either fixed or time-variant unobserved heterogeneity. The testing procedure is based on the estimates of the transition probabilities related to a four states Markov chain and consists of two steps (for a detailed illustration of the MS econometric model see the Appendix). The first step uses the MS model to provide necessary (but not sufficient) conditions for simultaneous and sequential complementarity. 6 Given two innovations A and B, the MS model provides a formal test for the following hypotheses: (i) the probability of simultaneous adoption of A and B is the same as the probability of adopting any one of the two innovations independently (rejection leads to simultaneous complementarity); (ii) the probability of 11

adopting A in time t is independent of whether B has already been adopted (rejection leads to sequential complementarity B-A); (iii) the probability of adopting B in time t is independent of whether A has already been adopted (rejection leads to sequential complementarity A-B). The strength of the MS testing procedure is that it allows us to distinguish between simultaneous and sequential complementarity and to establish the causal direction in the adoption sequence. Its major drawback is that while it controls for observable factors that may influence the adoption of the two innovations, it does not allow us to control for unobserved heterogeneity. To overcome this shortcoming, we adapt to the survival data framework the instrumental variable (IV) approach that has been proposed by the extant complementarity literature for a cross-sectional framework (Arora, 1996; Athey and Stern, 1998; Novak and Stern, 2007). We contend that if one observes innovation-specific factors that influence the likelihood of adoption of one innovation but have no direct effect on the likelihood of adoption of the other (allegedly complementary) innovation, these factors can be used as valid instruments for the adoption of the corresponding innovation. Therefore, if an exogenous shift in the innovation-specific factors, i.e. the instruments, is found to significantly affect the adoption probability of the other innovation, one can unambiguously attribute this effect to the existence of complementarity effects between the two innovations. 7 Based on these considerations, we propose a test of sufficient conditions for sequential complementarity, while assuming that the null hypothesis of strong simultaneous independence of the adoption decisions cannot be rejected (see Ǻstebro et al. (2012) for a modification of the MS’s method when the null hypothesis of strong simultaneous independence of the adoption decisions cannot be retained).

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4.2.

Explanatory variables of the adoption of CAD and JOD

The covariates influencing the adoption probability of CAD and JOD are selected following the indications of the technology diffusion literature (among others, see, Karshenas and Stoneman, 1993; Stoneman and Kwon, 1994; Colombo and Mosconi, 1995). Such literature suggest that the factors affecting the operating profit gains from use of an innovation – and thus the likelihood of its adoption, can be summarized by rank, stock, order and epidemic effects. Rank effects suggest that returns from adoption are firm specific so that different returns reflect differences across firms. Rank effects are usually measured via a number of firm (or plant) specific characteristics and other socio-economic variables. Stock effects relate to the expected decrease in the net profit gains from adoption generated by the increasing number of competitors using the technology. Order effects relate to differences in the net profit gains from adoption derived from the firm’s position in the order of adoption, with the assumption of first-mover advantages making early order more attractive. Epidemic effects capture the uncertainty reduction about the performance of an innovation that arises from greater information available on it, the latter being positively related to the number of adopters. The list of rank, stock, order and epidemic covariates used in the model is reported in table 2, where we also indicate the predictions relating to the corresponding coefficient based on the findings of previous studies. Summary statistics for all variables are reported in table 3. Insert table 2 about here Insert table 3 about here Two final remarks are in order. First, among the rank variables we include the variables Production technologies, HRMP, Just in time. In line with the literature on complementarity effects in the diffusion of multiple technologies (e.g., Colombo and Mosconi, 1995), the variable Production technologies controls for potential complementarity

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between ITs in production and design. Thus, a positive effect on the adoption probability of CAD is expected. Conversely, we expect this variable not to affect the adoption probability of JOD. Similarly, although the theory on the factors affecting the diffusion of an innovative work practice across firms is still quite undeveloped and large scale empirical evidence is rather scarce, we extend this complementarity expectations to the diffusion of JOD and we expect HMRP and Just in time to have a positive effect on the adoption probability of JOD because of complementarity between organizational changes in different spheres of plants’ activity. However, these two variables should not influence the likelihood of adoption of CAD. Second, following the extant diffusion literature, the prices of the two innovations should have a direct effect upon the adoption decision in that a lower price increases the likelihood of adoption of the corresponding innovation. In the presence of complementarities they also exert an indirect positive effect on the likelihood of adoption (either simultaneously or sequentially) of the complementary innovation. As it will be apparent later, this observation will play a key role in our identification strategy of complementarity effects. We do not have the series of Italian quality adjusted CAD prices. As a proxy we use the quality adjusted computer price at the UK factory gate (pt). 8 Computers are goods for which quality changes have been spectacular; in the UK the quality adjusted price of computers declined by 96.8% between 1970 and 1996. If one wants to compare prices over time it is therefore fundamental to correct prices for quality changes and to use a factory gate real price at “constant quality”. We acknowledge that this variable is not ideal. However, given the internationalization of the computer market we here assume that the series available can reasonably approximate the decline in Italian computer prices and therefore that of CAD (see, Battisti, 2000 and Battisti and Stoneman, 2003 and 2005 for an application of quality adjusted computer price decline and price expectations to the inter- and intra-firm diffusion of innovations). Accordingly, we capture the opportunity cost of investing in CAD with the

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variable Price measured as pt×rt where r is the Italian official interest rate (source: Banca d’Italia). Conversely, the introduction of JOD is mostly affected by indirect (non-capital) costs that are plant-specific and difficult to measure. Accordingly, no reliable proxy for the adoption costs of JOD was available. 4.3.

Choice of the instruments of the adoption of CAD and JOD

In order to control for unobserved heterogeneity and provide sufficient conditions for sequential complementarity, we resorted to IV estimates. In order for a variable to be a valid instrument of the adoption of innovation j, it must be (partially) correlated with the probability of adoption of this innovation, but uncorrelated with the probability of adoption of the other innovation. We use as instruments for CAD and JOD adoption (i) the adoption decisions of innovation j by other firms in the same industry (Stockji,t,) and (ii) the expected change in the number of adopters of innovation j in the same industry (Orderji,t). The former variable is operationalized as the predicted cumulative number of adopters of innovation j (CAD or JOD) in time t within the sector the firm belongs to (Stockji,t= Nji,t). The stock effect assumes that one firm’s adoption impacts (negatively) upon the profitability (thus the likelihood) of further adoption by others (and also the profitability of existing adopters). However, if epidemic, non-pecuniary learning effects dominate, the variable will have the opposite sign. We leave to the empirics to determine the sign of this variable. The order effect is here measured as Orderji,t =

1 j (N i,t+1-Nji,t) (table 2). It tests whether one firm’s adoption reduces r

the returns to all other non-adopters as they are moved down the order of adoption. We expect that the higher the value of this variable the more likely the adoption. In summary, while the Stock variable captures profitability based and learning effects, Order variable captures the first mover advantage. Both effects are innovation and industry

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specific. Those industry level instruments are chosen because they are not affected by firm level unobserved heterogeneity and meet the requirements of being an instrument. While they are expected to influence the probability of adoption of innovation j, they should not affect the adoption of the other innovation. Following a similar reasoning, we also use Price, i.e. the price of CAD time the interest rate, as instrument for CAD adoption. Price is an innovation specific variable that is neither industry specific nor firm specific. Price is determined on the international market; as such it is not affected by either industry level or firm level unobserved heterogeneity. 5. Results We have estimated the econometric models using STATA and OX Professional (Doornik, 1999). The results are reported in three steps. As a preliminary step, we use a cross-sectional version of the Markov model estimated using only the last year of our adoption data (i.e. 1995). As explanatory variables we only consider rank effects (see table 2), measured in 1994. The estimates are reported in table 4. Insert table 4 about here The correlation rho between the error terms of the two equations is positive (0.367) and significant at 1%. In accordance with the extant complementarity literature (see for example Arora and Gambardella, 1990) this evidence provides a first (admittedly very partial) indication of the existence of complementarity effects generated by the joint use of the two innovations under scrutiny. Of course, due to the static nature of this model, nothing can be said as to whether complementarity effects are simultaneous or sequential (or both), and what is the sequence of adoption. In order to shed light on these issues we turn to the second step where we estimate a survival discrete-time bivariate panel data model. In table 5 we report three different model specifications. In Model 1 we specify only the constants. In the other models we control for a number of covariates believed to influence 16

the adoption decision. In Model 2 we add the rank variables and the price effects. In Model 3 we introduce the stock and order variables. It should be noted that, as in the more known probit model, also in the MS model the coefficients cannot be interpreted as marginal effects. We can only interpret the sign and the significance of the individual regression coefficients being the probability of adoption a function of the linear combination of the regressors. Given the difficult interpretation of the magnitude of the effects based on the regression coefficients, in Section 5.3 we use the counterfactual analysis approach to evaluate the magnitude of key results. 5.1. The effect of the explanatory variables on the adoption of CAD and JOD Most explanatory variables have the effects predicted by the extant diffusion literature as reported in table 2. Previous studies (see among others Hannah and McDowell 1984, Karshenas and Stoneman 1993, Baldwin and Rafiquizzaman 1998, Battisti 2000, Battisti and Stomenan 2003, 2005) indicate that the declining price of an innovation positively affects the decision to adopt while too rapidly declining prices can have the opposite effect. Consistently with this view, the estimated opportunity cost to invest in CAD (Price) is negative and significant at 1%. However, price expectations (Price change) are not significant, possibly as a consequence of measurement errors generated by the proxy we use. As to rank effects (Karshenas and Stoneman, 1993), younger and larger plants are more likely to adopt both CAD and JOD, while higher levels of education of the workforce (Education) has a positive and statistically significant influence (at 1% level) only in the CAD equation. The coefficient of the variable Multi plant is not statistically significant at any conventional level in the CAD equation suggesting that, on average, plants part of multi-unit organizations show the same likelihood of adopting CAD as plants belonging to single-plant firms. On the other hand, the coefficient of Multi plant in the JOD equation is negative and significant at 10% suggesting that the adoption of JOD is less likely for plants part of a multi17

unit organization, possibly as a result of greater organizational inertia. The coefficient of Production technologies shows a positive impact upon the adoption of CAD significant at 1%. This result is in line with previous findings (see among others Stoneman and Kwon, 1994; Colombo and Mosconi, 1995; Stoneman and Toivanen,Ǻstebro, 1997; 2002) suggesting that plants that previously adopted advanced production technologies are more prone to adopt complementary computer-based design technologies. Similarly, HRMP and Just in time are found to exert a positive, significant impact upon the likelihood of adoption of JOD. This confirms that organizational innovations are better adopted in clusters than in isolation (see among others Huselid, 1995; Ichniowski et al., 1997). Among the socio-economic context variables, in the CAD equation R&D intensity and Concentration index show a positive and negative sign respectively, both significant at 10%. However, neither competitive pressure nor R&D intensity seems to exert any influence upon the adoption of JOD. There is also evidence of significant positive localization externalities on the adoption of CAD and JOD as is apparent from the positive, statistically significant coefficients of Infrastructure index. 9 As far as order and stock effects are concerned (e.g., Karshenas and Stoneman, 1993), the Stock and Order variables exhibit positive coefficients significant at 1% in the CAD equation. Therefore first-adopters of CAD benefit from higher profits than later adopters (order effect), while spillovers from the increasing number of users lead to technology diffusion across plants (epidemic effect). In the JOD equation we do not find evidence neither of stock nor of order effects. The absence of any epidemic effects may be traced to the tacit and context-specific nature of organizational innovations, which makes them difficult to imitate. Insert table 5 about here

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5.2. Results on the complementarity between CAD and JOD In the lower part of table 5 we report the results of the tests of the necessary conditions for the existence of simultaneous and sequential complementarity (for details about the MS test, see again the Appendix). As reported in the last row of table 5, for each of the three model specifications the null hypothesis of strong simultaneous independence (H0: γο=0) cannot be rejected at conventional confidence levels. This indicates that the decision of simultaneous adoption of CAD and JOD does not occur more frequently than if the two decisions were taken independently. Therefore, the existence of simultaneous complementarity between the two innovations under consideration can be ruled out. Conversely in all the three models the null hypotheses of non causality (H0: JOD does not cause CAD: βBA=0. H0: CAD does not cause JOD: βAB=0) are rejected at least at 5% significance level in favour of the hypotheses of sequential adoption of CAD and JOD respectively. These findings indicate that the previous adoption of JOD increases the likelihood of the adoption of CAD and similarly the previous adoption of CAD increases the likelihood of adoption of JOD. Inserting additional explanatory variables into the model does not change the substance of the results of the non-causality tests, even though the magnitude of the estimated coefficients decreases quite considerably. In order to check for the robustness of our econometric results, we run several additional estimates. First, since we are looking at a relatively long time period, our results could be biased by the presence of omitted variables that are constant across the observed plants but vary over time. An example of such a variable is the technological progress that has characterized CAD hardware and software in the considered time period and may be only imperfectly reflected by our measure of the quality adjusted price of computers. Thus, as a robustness check, we re-estimated Model 3 (table 5) controlling for time fixed-effects. The results remain largely unchanged (see table A1 in the Appendix). Second, in December 1982, 19

the release of the first IBM PC-based CAD system—AutoCAD—made possible for CAD adopters to work with CAD programs on their own work computers and be no longer dependent on large workstations. Our CAD price variable is unlikely to be able to fully capture the effect of the availability of this PC-based CAD package on the CAD diffusion. Accordingly, to the equation of the adoption of CAD estimated in Model 3 (table 5) we added a step-dummy variable that equals 1 from 1983 onwards and zero otherwise. Although the coefficient of the dummy variable is positive and significant at 1% level, adding this new variable does not affect our results of simultaneous and sequential complementarity (see table A2 in the Appendix). Finally, the simultaneous or sequential adoption of innovations might be driven by complementarity perceived at the firm rather than the plant level. For instance, since in a given period multi-unit firms can adopt JOD in one plant and CAD in another plant, ceteris paribus it is more likely that multi-unit firms compared to single-plant firms can perceive a faster reduction of the uncertainty about the performance of an innovation and the synergistic gains from joint use of the two innovations due to the greater information available within the firm. In order to check whether the ownership status of plants affected our results concerning simultaneous and sequential complementarity, we excluded the plants belonging to multi-unit organizations (i.e. 100 plants) and re-ran the analyses on the reduced sample; the results did not change. In summary, we find that the two necessary conditions for the existence of sequential complementarity effects are persistently verified also when we consider less parsimonious specifications of the model. It might be correctly argued that the empirical results presented so far are driven by unobserved heterogeneity. Rather than complementarity, lurking factors, such as positive business opportunities, favourable financial conditions or smart management, not observable

20

to us or not explicitly modelled might have been responsible for the increase of the gains from adoption of a focal innovation, and therefore in the observed probability of its adoption, after adoption of the other innovation. As argued in Section 4.1, even though the null hypotheses of strong simultaneous independence and one step ahead non causality are necessary conditions for complementarity, they are not sufficient. In tables 6a and 6b we report the results of the IV (Weibull) survival regression models for CAD and JOD. In Models 5 and 8 we use as instrumental variables the predicted adoption probabilities of the allegedly complementary innovation. We also estimate the “reduced form” model where the instruments for the adoption probability of JOD and CAD are directly inserted in the model specification of the other innovation (Models 6 and 9). Tests for the validity of the instruments as well as the Hausman specification tests of endogeneity of the instrumented variables are reported at the bottom of the corresponding tables. We also report in table A3 in the Appendix the estimates of the first stage survival models. In these regressions the marginal explanatory power of each of the individual instruments varies somewhat, but the p-value is never above 1% with the exception of the variable OrderJOD that is only weakly significant (p=0.063). The results relating to the influence of the explanatory variables on the adoption probability of CAD and JOD are reasonably close to those of Model 3 and do not deserve any further comments. Insert table 6a about here We now analyze the results of the tests of the sufficient conditions for sequential complementarity. First, we consider the estimates of the CAD equation reported in table 6a. The IV estimates are not in line with the hypothesis that the adoption of JOD leads to an increase of the likelihood of adoption of CAD. Contrary to our expectations, in Model 5 the coefficient of the predicted probability of JOD adoption is not statistically significant at any

21

conventional level. Note that the Hausman specification test indicates that we cannot reject the null hypothesis that JOD adoption is exogenous (p=0.993). However, also in Model 4 the coefficient of the variable JOD is not statistically significant suggesting that the null hypothesis that JOD adoption does not drive CAD adoption cannot be rejected. In Model 6, based on the results of the first stage regression (see again table A3 in the Appendix), we would have expected the coefficients of two instruments StockJOD and OrderJOD to be positive and statistically significant. The coefficient of OrderJOD is positive and significant at 1% while the coefficient of StockJOD is negative (and weakly significant), contrary to our expectations. We conclude that there is no clear cut indication of sequential complementarity in the adoption of CAD associated with the prior adoption of JOD. The increase in the probability of CAD adoption after adoption of JOD suggested by the MS model is possibly to be traced to lack of proper control for unobserved heterogeneity. Conversely, in the case of the JOD equation the results of the IV estimates (see table 6b) are in line with those of the MS model suggesting the existence of significant complementarity from sequential adoption of JOD after CAD. First, the Hausman specification test rejects the null hypothesis of exogeneity of CAD adoption at 1%; accordingly, we must treat CAD adoption as endogenous and rely on the IV regressions. Secondly, in Model 8 the predicted adoption probability of CAD has a positive coefficient significant at 5%. Thirdly, in Model 9, the three instruments (Price, StockCAD and OrderCAD) are found by a Wald test to be jointly significantly different from zero (χ2(3) = 39.51; p-value = 0.000). Moreover, the coefficient of StockCAD, which is statistically significant at 1%, and the one of Price, which is close to significance (p=0.163), are in line with our expectations based on the first stage regression (see again table A3 in the Appendix). The coefficient of OrderCAD is not significant at conventional confidence levels. Insert table 6b about here

22

In summary, we find that the adoption of CAD has a positive effect on the probability of the subsequent adoption of JOD. Hence, we find that the hypothesis of no sequential complementarity is consistently rejected for JOD having adopted CAD. However, the same cannot be said for CAD having adopted JOD. In fact, we find no robust evidence of a significant increase of the likelihood of CAD adoption triggered by the adoption of JOD. Therefore, we cannot exclude that the adoption of CAD is independent of the prior adoption of JOD. 5.3. Assessing the magnitude of the complementarity effects between CAD and JOD: a counterfactual exercise The aim of this section is twofold. First, we want to assess the magnitude of the sequential complementarity effect relating to the adoption of JOD after CAD. For this purpose, we carried out a counterfactual analysis by considering a benchmark plant with all continuous and count explanatory variables of JOD adoption (xBi,t) set at the sample means in 1986 ( x iB,86 ) and the binary variables set at the median value in 1986 (for mean and median values in 1986, see table 3). We then calculated the probability to adopt JOD depending on whether this benchmark plant had previously adopted CAD or not, based on the estimates of Model 5 in table 3. 10 The probability to adopt JOD having adopted CAD was found to be 1.36 times higher than the probability to adopt JOD in absence of CAD (that is, the odds ratio equals 0.1832/0.1349). Second, we were interested in assessing the extent of the increase in the adoption probability of CAD led by the IT price reduction over the observation period and the indirect effect (through the CAD adoption) generated by this IT price reduction on the adoption probability of JOD, its complementary innovation. For this purpose, for the same benchmark plant we calculated how the probability of adopting CAD would have changed in response to the variation in the cost of CAD which occurred between 1971 and 1995, all the other factors 23

remaining unchanged. Over the period 1971-1995, the IT price had decreased of about 94% with respect to its 1971 value. For the benchmark plant this led to an increase in the unconditional probability of adopting CAD from 3.7% in 1971 to 64% in 1986 (the benchmark year) to 97% in 1995. This confirms that the IT price reduction has been a major driver in the diffusion of CAD. Moreover, we noted above that the probability of JOD adoption was found to be 1.36 times higher in the presence of CAD than in its absence. This suggests that for our benchmark plant, the IT price reduction that occurred between 1971 and 1995 led to an indirect increase of the unconditional probability to adopt JOD (i.e. through the higher probability of the adoption of CAD) equal to 33% (i.e. (0.97-0.037) × (0.18320.1349) / 0.1349). In other words, there exists an indirect positive feedback upon the adoption of JOD, of considerable economic magnitude, led by the IT price reduction via the increasing use of CAD. 6. Discussion and concluding remarks The aim of this paper was to disentangle the existence of simultaneous and sequential complementarity in the adoption of technological and organizational innovations and establish the sequence of their adoption. For this purpose, we have analyzed the diffusion of computer aided design and/or manufacture (CAD) and the establishment of interorganizational design teams with customers and/or suppliers (JOD) among Italian manufacturing plants over the period 1970-1996. We have first estimated a discrete-time bivariate survival panel data model and have relied on the one step ahead non causality and strong simultaneous independence tests (first proposed by Mosconi and Seri, 2006) to test necessary conditions for the existence of simultaneous and sequential complementarity between CAD and JOD adoption. The estimates indicate that the decision to adopt CAD and JOD simultaneously does not occur more frequently than if the two innovations were adopted independently, ruling out simultaneous complementarity. This result has allowed us to use a 24

panel data discrete-time survival type IV estimator based on “exclusion restrictions” (i.e. innovation-specific instruments) to test sufficient conditions for sequential complementarity. Our econometric results confirm that the adoption of CAD positively influences the probability of subsequently adopting JOD. However, results relating to the increase of the probability of CAD adoption triggered by the adoption of JOD are inconclusive. These findings not only corroborate the qualitative evidence of the existence of complementarity between CAD and JOD but also suggest that complementarity is more likely to be sequential rather than simultaneous and it depends on the sequence of the adoption of the technological innovation (and the IT revolution) leading organizational changes in the sphere of design. Our study originally contributes to the innovation literature in that it extends our understanding of the diffusion of multiple interdependent technological and organizational innovations. First, the results highlight that firms can reap the benefits of the adoption of complementary technological and organizational innovations not only implementing simultaneously both types of innovation but also through a sequential adoption of the two innovations. In fact, we do not find any evidence in support of simultaneous complementarity of CAD and JOD. These results highlight the practical importance of theoretical work on sequential adoption of complementary innovations (e.g., Jovanovic and Stolyarov, 1997, 2000; Smith 2005) and provide insights into organizational dynamics. The assumption of simultaneous complementarity implies the simultaneous occurrence of technological and organizational change. However, making these changes at the same time can be infeasible due to, for instance, basic coordination problems and difficulties in synchronizing all changes (Brynjolfsson and Milgrom 2013). The presence of sequential complementarities should facilitate the transition from one organizational system to another, for instance facilitating the coordination of and communication between decision makers.

25

Second, our applied analysis shows that for a better understanding of the technological-organizational change framework, examining the sequence order of the adoption matters. Specifically, based on a larger scale and rigorous econometric design, we tested the arguments proposed by previous studies (Hollenstein, 2004; Lynch, 2007) that the benefits from the introduction of organizational innovations (like JOD) are considerably enhanced by the previous use of complementary technological innovations (like CAD for JOD). In spite of the existence of synergistic gains from combined use of technological and organizational innovations, our findings indicate that it may be optimal for firms to postpone the adoption of these latter innovations. However, the opposite is not necessarily true, as our applied analysis has shown. The reason may be that the magnitude of the extra profit gains from the combined use of technological and organizational innovations is considerable in comparison with the extra profit gains that could be achieved through the use of organizational innovations in isolation (i.e. use of JOD in absence of CAD), but this magnitude is more limited in relation to the extra profit gains that could be obtained from technological innovations alone (i.e. use of CAD in absence of JOD). In our empirical analysis this situation can be exemplified by the fact that the creation of design teams that cross firm’s boundaries and include personnel from customers and/or suppliers has a much more positive impact on firm’s design activities if inter-firm collaboration is supported by advanced IT-based equipment (in our case, CAD) which renders coordination of these teams more manageable and communication and knowledge sharing structured and selective. Conversely, adoption of CAD leads to substantial productivity improvements in the process of design and prototyping even in absence of these inter-firm design teams. Moreover, firms may only learn through the experience of using CAD how to modify their organization to capture the full productivity potential inherent in CAD technologies. Hence, we observe sequential and not simultaneous adoption of the two types of innovation.

26

Third, the decline of the quality adjusted price of computers has been found in this study to be an important determinant of the adoption of CAD. Organizational innovations have not directly benefited from a similarly spectacular decrease of adoption costs. However, because of the existence of sequential complementarity, our results suggest that the declining price of computers has exerted a fundamental indirect positive effect on the diffusion of organizational innovations, mediated by the adoption of complementary IT equipment. Absent such decline, organizational innovations would have been diffusing much more slowly. In summary, the reduction of the price of IT equipment is indirectly a powerful engine of the complementary organizational changes that characterize modern manufacturing. A related issue concerns the introduction of PC-based CAD systems in the early 1980s which fuelled the adoption of CAD, thereby favouring the subsequent complementary adoption of JOD. In other words, the adoption of JOD after CAD might have been pushed by the introduction of PC-based CAD systems. After 1982, the availability of PC-based CAD systems made possible for several firms (in particular for smaller firms) to put in place the IT infrastructure necessary to engage in efficient inter-firm collaborations. Finally, an important feature of our study is that contrary to the MS model we do not only suggest the necessary but also the sufficient conditions to detect the existence of sequential complementarity in a panel data framework. The methodology here used is sufficiently general to be used in situations where the decisions of economic agents (e.g. firms, governmental bodies) are discrete and are presumed to be complementary. Examples of suitable applications include the decisions of firms to make or buy different components (e.g., Novak and Stern, 2007; Van Biesebroeck, 2007), to establish alliances with different types of partners (e.g., Arora and Gambardella, 1990), to invest in internal R&D and/or external knowledge acquisition (e.g., Cassiman and Veugelers, 2006), to ask for financing from different sources – from private and public sources, from banks and venture capital

27

firms (e.g., Colombo et al., 2007b; Hellman et al., 2008), and the decisions of governmental bodies to implement different policy schemes (e.g., Mohnen and Röller, 2005). We are aware that this study, in spite of its strengths, also has limitations. First, we have analyzed the diffusion of two specific innovations among Italian manufacturing plants. It would be interesting to investigate whether our results can be generalized to the diffusion of technological and organizational innovations relating to other spheres of firm’s activity in other settings. Second, the evidence we found that complementarity does not lead to simultaneous adoption of CAD and JOD, and that adoption of CAD makes adoption of JOD more likely but the opposite does not hold true, is compatible with different theoretical arguments. They include the non convexity of the investment costs associated with innovation adoption, learning by using effects, uncertainty about the future benefits from and cost of adoption, and limits in firms’ managerial and financial resources. It would clearly be interesting to assess the explanatory power of these different theoretical arguments, a task which lied beyond the scope of this paper and is left for future research. In order to make one step further in this direction, one may examine whether the complementarity effect we detected are moderated by firm- or environment-specific variables. For instance, if sequential complementarity were driven by lack of financial or managerial resources it should be more pronounced for more resource-constrained firms. A similar reasoning may be used to check whether it is environmental uncertainty that drives sequential complementarity. Lastly, in a similar vein, in our estimates we made the assumption that the extra profit gains from CAD and JOD adoption are constant over time, with technological improvements in CAD technologies being captured by the quality adjusted price of computers. Even more importantly, complementarity effects were also assumed to be time invariant. We are aware that these effects may vary over time, because of learning. It would then be interesting to modify our econometric model to accommodate time-varying complementarity effects.

28

In the light of the above considerations, we believe that this study opens interesting directions for future research on innovation adoption.

Acknowledgements

The authors would like to thank editor Fredrik Tell, two anonymous reviewers, and Thomas Åstebro.

29

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Figures and tables

Percentage of adopting plants

Figure 1 Diffusion of CAD and JOD in Italy

90 80 70 60 50 40 30 20 10 0 70

72

74

76

78

80

82

84

CAD

86

88

90

92

94

96 Year

JOD

Source: Personal elaboration based on FLAUTO data.

34

Table 1 Adoption pattern of CAD and JOD: Evidence from the FLAUTO database % of plantsa

a

No adoption

16.7

Adoption of CAD only

27.2

Adoption of JOD only

6.6

Simultaneous adoption of CAD and JOD

3.0

Adoption of CAD after JOD

23.1

Adoption of JOD after CAD 23.4 Percentage of adopting plants out of the total number of plants (438).

Table 2 Variables definition and expected sign Expected sign

βCAD CAD PRICE effect Price: quality adjusted computer prices (pt) times the interest rate (rt) Price change: expected change in the price of computers (pt+1-pt) RANK effects - Plant characteristics Plant size: size of the plant measured by the natural log of number of employees at time t-1 Plant age: natural log of the age of the plant at time t-1 Multi plant: whether the plant is part of a multi-unit organizations (1) or independent (0) Education: percentage of the workforce with secondary education qualification or with M.Sci. degree (measured in 1989) Production technologies: number of advanced production technologies adopted (robots; stand alone numerical or computerized numerical control machine tools; flexible manufacturing systems or cells) by time t-1 HRMP: number of human resource management practices (job rotation; formal team practices/quality circles; total quality management; individual “pay for knowledge” incentive schemes; group incentive schemes) adopted by time t-1 Just in time: whether the plant has adopted by time t-1 just in time techniques for production or supply (1) or not (0) - Socio-Economic context Concentration index: sectorial Herfindahl concentration index (three-digit NACE-CLIO classification, source ISTAT Census) R&D intensity: sectorial R&D intensity (two-digit NACE-CLIO classification, source ISTAT) Infrastructure index: index of general development of the province in which the plant is located measured as the average in 1989 of the following indexes: per capita value added, share of manufacturing in total value added, employment index, per capita bank deposits, automobilepopulation ratio, energy consumption per head (source: Centro Studi Confindustria 1991). STOCK effects (EPIDEMIC effects) Stockj: within industry predicted cumulative number of adopters at time t; j = CAD, JOD. The predictions are based on a first-order autoregressive model of the form Nji,t=a0+a1 Nji,t-1 + uji, with Nj being the number of adopters of innovation j. ORDER effects Orderj: expected change in the within industry predicted cumulative number of adopters in the interval {t; t+1} times the inverse of the interest rate; j = CAD, JOD.

βJOD

‒ +

+ ‒ ?

+ ‒ ?

+

+

+ + + ?

?

+

+

+

+

‒ (+)

‒ (+)

+

+

35

Table 3 Summary statistics Variable Price Price change Plant size Plant age Multi plant Education Concentration index R&D intensity Infrastructure index Stock CAD Stock JOD Order CAD Order JOD Production technologies HRMP Just in time

Mean 1638.123 -17.668 4.564 3.029 0.232 16.475 1.094 0.019 112.301 0.235 0.242 0.032 0.022 0.474 0.806 0.079

1970-1995 Std. Dev. Min 1016.642 148.000 32.160 -121.887 1.203 1.743 0.896 0.000 0.422 0.000 17.928 3.000 3.063 0.027 0.032 0.002 21.128 43.700 0.140 0.000 0.276 0.004 0.040 0.000 0.025 0.000 0.697 0.000 1.080 0.000 0.270 0.000

Number of observations 10185 Number of plants 438 a The median value is reported for dummy variables

Max 3692.300 47.170 8.700 4.984 1.000 70.000 24.390 0.139 155.000 0.685 1.007 0.500 0.500 3.000 5.000 1.000

1986 Mean a 1542.000 -8.679 4.629 3.352 0.000 16.836 1.146 0.019 112.833 0.162 0.231 0.095 0.032 0.565 0.767 0.000 347 347

Table 4 Estimates of a cross-sectional bivariate probit model of adoption of CAD and JOD by 1995 CAD JOD Constant -1.486 (0.675) ** -1.246 (0.547) ** Plant size 0.562 (0.097) *** 0.287 (0.067) *** Plant age -0.289 (0.138) ** -0.165 (0.116) Multi plant -0.149 (0.234) -0.181 (0.178) Education 0.019 (0.006) *** 0.008 (0.004) ** Concentration index -0.029 (0.022) 0.018 (0.024) R&D intensity 7.685 (3.515) ** 0.395 (2.075) Infrastructure index 0.003 (0.003) 0.004 (0.003) No. of observations 438 Wald chi2 92.73*** Wald test of rho=0 16.98*** In brackets, robust standard errors. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01.

36

Table 5 Estimates of a dynamic discrete time survival type bivariate probit model of the adoption of CAD and JOD Model 1 CAD JOD -1.856 (0.030)*** -2.050 (0.035)***

Model 2 CAD JOD -2.397 (0.193)*** -2.605 (0.169)*** -0.001 (.00005)*** 0.002 (0.002) 0.234 (0.032)*** 0.086 (0.030)*** -0.049 (0.037) -0.096 (0.033)*** 0.009 (0.086) -0.159 (0.087)* 0.005 (0.002)*** 0.002 (0.002) 0.328 (0.044)*** 0.157 (0.027)*** 0.279 (0.112)** -0.020 (0.010)* 0.003 (0.009) 4.356 (0.923)*** 0.307 (0.973) 0.003 (0.001)** 0.003 (0.001)**

Model 3 CAD JOD -2.966 (0.260)*** -2.666 (0.180)*** -.0004 (.00007)*** 0.001 (0.002) 0.244 (0.033)*** 0.089 (0.031)*** -0.066 (0.038)* -0.115 (0.034)*** 0.008 (0.088) -0.150 (0.087)* 0.005 (0.002)*** 0.002 (0.002) 0.283 (0.046)*** 0.144 (0.028)*** 0.248 (0.113)** -0.018 (0.010)* 0.003 (0.010) 2.026 (1.075)* 0.538 (0.990) 0.003 (0.001)* 0.003 (0.001)** 0.743 (0.218)*** 0.417 (0.261) 3.763 (0.633)*** 1.422 (1.123)

H0: JOD does not cause CAD (βΒΑ=0)

0.530 (0.063)***

0.427 (0.071) ***

0.355 (0.078) ***

H0: CAD does not cause JOD (βΑΒ=0)

0.420 (0.060)***

0.172 (0.069) **

0.159 (0.069) **

H0: simultaneous independence of JOD and CAD (γο=0 )

0.242 (0.151)

0.079 (0.168)

0.064 (0.173)

Constant Price Price change Plant size Plant age Multi plant Education Production technologies HRMP Just in time Concentration index R&D intensity Infrastructure index Stock j; j=CAD;JOD Order tj; j=CAD;JOD

In brackets, robust standard errors. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01. See the Appendix for the precise definition of parameters βΒΑ, βΑΒ and γο.

37

Table 6a Test for unobserved heterogeneity: IV survival estimates of CAD adoption Model 4 Model 5 Model 6 Constant -7.538 (0.775) *** -7.258 (0.790) *** -7.522 (0.777) *** Price -0.001 (0.000) *** -0.001 (0.000) *** -0.001 (0.000) *** Price change -0.002 (0.004) -0.002 (0.004) -0.002 (0.004) Plant size 0.469 (0.077) *** 0.475 (0.077) *** 0.491 (0.077) *** Plant age -0.588 (0.126) *** -0.590 (0.126) *** -0.602 (0.123) *** Multi plant -0.024 (0.187) -0.012 (0.187) -0.041 (0.187) Education 0.011 (0.003) *** 0.011 (0.003) *** 0.012 (0.003) *** Concentration index -0.027 (0.016) * -0.026 (0.016) * -0.024 (0.015) R&D intensity 5.631 (2.211) ** 4.676 (2.304) ** 4.743 (2.365) ** Infrastructure index 0.007 (0.003) *** 0.007 (0.003) *** 0.007 (0.003) *** CAD Stock 0.526 (0.454) 0.770 (0.463) * 0.624 (0.475) CAD Order 4.359 (1.222) *** 4.351 (1.227) *** 4.203 (1.209) *** Production technologies 0.367 (0.091) *** 0.357 (0.092) *** 0.360 (0.093) *** JOD 0.191 (0.127) IV for JOD adoption probability -9.400 (5.930) Stock JOD -1.174 (0.715) * Order JOD 4.261 (1.416) *** No. of failures 315 315 315 Wald chi2 279.15 *** 268.02 *** 266.70 *** pa 2.090 (0.208) 2.088 (0.213) 2.154 (0.215) Test for validity of the instruments b 30.04 *** Hausman specification test χ2(12) : Model 4 against Model 5 3.36 In brackets, robust standard errors. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01 a We test the null hypothesis that p=1, i.e. duration independence. b LR χ2(2) test, we can reject the null hypothesis that the instruments have no explanatory power. The instruments are the variables StockJOD and Order JOD.

Table 6b Test for unobserved heterogeneity: IV survival estimates of JOD adoption Model 7 Model 8 Model 9 Constant -4.815 (0.489) *** -5.508 (0.490) *** -4.396 (0.603) Plant size 0.160 (0.071) ** 0.268 (0.069) *** 0.255 (0.069) Plant age -0.284 (0.106) *** -0.317 (0.108) *** -0.318 (0.106) Multi plant -0.247 (0.189) -0.261 (0.192) -0.265 (0.194) Education 0.005 (0.003) 0.007 (0.003) ** 0.006 (0.003) Concentration index 0.014 (0.016) 0.001 (0.017) 0.016 (0.016) R&D intensity 0.698 (2.268) 2.196 (2.208) -3.267 (2.756) Infrastructure index 0.004 (0.003) 0.005 (0.003) * 0.005 (0.003) JOD Stock 1.248 (0.711) * 1.643 (0.737) ** -0.708 (0.858) JOD Order 1.464 (1.887) 2.381 (1.848) 0.018 (1.972) HRMP 0.140 (0.067) ** 0.151 (0.067) ** 0.130 (0.069) Just in time 0.652 (0.212) *** 0.698 (0.219) *** 0.569 (0.209) CAD 0.827 (0.175) *** IV for CAD adoption probability 3.661 (1.820) ** Price -.0002 (.0001) Stock CAD 1.783 (0.491) Order CAD -0.299 (1.795) No. of failures 226 226 226 Wald chi2 104.80 *** 116.33 *** 153.45 *** pa 1.018 (0.109) 1.046 (0.098) 0.957 (0.088) b Test for validity of the instruments 101.01 *** Hausman specification test χ2(11): Model 7against Model 8 29.60 *** In brackets, robust standard errors. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01 a We test the null hypothesis that p=1, i.e. independence from the duration. b LR χ2(3) test, we can reject the null hypothesis that the instruments have no explanatory power. The instruments are the variables Price, stockCAD and Order CAD.

*** *** *** *

*

* ***

***

38

Appendix Simultaneous and sequential complementarity in innovation adoption: illustrative numerical examples These numerical examples illustrate cases of simultaneous and sequential complementarity. Consider that at time t three identical plants X, Y and Z do not use innovations A and B. At time t+1 plant X adopts only A that generates gains P1 equals to 4; plant Y adopts only B that generates gains P2 equals to 2; and plant Z adopts at the same time (simultaneously) A and B that generate gains P3 equals to 8. A and B are “simultaneous complementary” since their simultaneous adoption at time t+1 generates gains P3 that are greater than the sum of P1 and P2. At time t+2 plant X adopts B and the additional gain from such adoption is P4 equal to 3. A and B are “sequential complementary” because the adoption of B in period t+2 when A has already been adopted in the previous period t+1 generates gains (i.e., P4=3) that are greater than those generated by the adoption of B when A has not been adopted previously (i.e., P2=2). It could also happen that at time t+2 plant Y adopts A and the additional gain from such adoption is P5 equal to 7. Also in this case A and B are “sequential complementary” because the adoption of A in period t+2 when B has already been adopted in the previous period t+1 generates gains (i.e., P5=7) that are greater than those generated by the adoption of A when B has not been adopted previously (i.e., P1=4). The MS econometric test of necessary conditions for simultaneous and sequential complementarity Assume that there exist two allegedly complementary innovations, CAD (A) and JOD (B) and that in any time t the firm can decide to adopt either A only (A), B only (B), neither (O) or both innovations (S) based upon profitability considerations. Assume also that i) firms have perfect foresight about future price changes and changes over time of other time-varying variables, ii) firm’s investment decision is path dependent in that in time t the firm choice depends upon the state where the firm was in the previous period and iii) the adoption decision is irreversible.

39

Let gA and gB be the annual operating profit gains in time t from the adoption of A alone and B alone respectively. Simultaneous complementarity means that the operating profit gains from joint adoption (gS) are greater than the sum of those derived from individual adoption, gS=gA+gB+vS>gA+gB. Moreover, if there are sequential complementarities, due to path dependency the annual operating profit gains from the adoption of B (A) are greater if A (B) has already been adopted than when it has not, i.e. gBA= gA+ gB + vBA (gAB= gA + gB + vAB). Hence, for simultaneous and/or sequential complementarity to exist vS , vBA, vAB must be significantly different from zero. It is also assumed that the adoption decision is path dependent in that the size of the expected payoff from the adoption of innovation j (k) depends on whether the innovation k (j) was already adopted. The state-dependency of the adoption decisions is modelled by allowing the firm’s decision process to follow a Markov process where the information relevant to the probability to change status in time t is summarized by the state of the process in time t-1. At any time t the state space Yt(ytA;ytB) is given by the following states: YOt=(0,0); YAt =(1,0); YBt=(0,1) and YSt=(1,1). In this context, the analysis of the adoption sequence is essentially the analysis of the discrete choices of firm i in time t given its status in t-1. In accordance with the survival nature of the model, we impose the irreversibility condition; that is, once the firm decides to adopt an innovation it cannot go back to the previous non-adoption status. As it is illustrated in figure A1, in our model there are five possible transitions. In the figure each box represents the state of the process (i.e. the firm’s adoption status) at time t-1, while the arrows represent the transitions that might occur at time t, after imposing the irreversibility condition of the adoption decision. Insert figure A1 about here Following MS, we operationalize the model by using a latent regression approach and by specifying a dynamic discrete choice model where firm i adopts innovation j in time t if

40

the expected gain from adoption is significantly different from zero and positive. Within a latent regression approach this can be represented via a latent continuous random variable y*ji,t crossing a threshold level which with no loss of generality is set equal to null. The resulting dynamic discrete choice can be written as a discrete-time bivariate binary process Yt of which N = 438 realizations are observed: yji,t = 1 if y*ji,t ≥0 and firm i adopts j at time t, i = [1,….438] and t = [tiE,….1996] yji,t = 0 otherwise, where tiE is the maximum between to=1970 and the firm’s i year of entry τio. 11 We then allow the expected net profit gain from individual adoption to be firm and environment specific. We therefore relax the stationarity assumption of the Markov process transition probabilities by modelling the adoption decision as a function of a set of covariates xi,t = [xAi,t; xBi,t]T. The model is such that for a firm i in state YOi,t-1=(0,0), the probability to change status and move to YAt =(1,0); YBt=(0,1) and YSt=(1,1), i.e. to adopt A, or B, or A and B simultaneously (see again figure A1) can be estimated via the standard integrated bivariate probit model: β AT xiA,t i ,t   ; µ, Σ) T B β B xi ,t 

P{yi,t| YOi,t-1, xi,t} = Φ2([ 

(1)

where Φ2 is the integrated bivariate normal distribution with zero mean µ= 0 and covariance 0 

1.........ρ i , t   , while ρi,t is the z-transformed correlation coefficient reflecting the extent  ρ i , t ........1 

matrix Σ = 

to which in time t the likelihood of adoption of one innovation is related to the other innovation (i.e. the extent of simultaneous adoption) and that after simple parametrization of the model can be expressed as:

41

ρi,t =

2 exp( γ o ) – 1. 1 + exp( γ o )

(2)

However, if a firm is in status YO and the adoption decisions of A and B are found not to be related— i.e., ρi,t (or equivalently γο) in (2) is not significantly different from zero for all i and t — the two adoption probabilities would be independent of each other. In such case the bivariate distribution Φ2 would collapse into its two univariate marginal distributions Φ1 that could be estimated independently. Let us now consider the (transition) probability of sequential adoption of a firm i that in time t-1 is in state YBt-1 (see again figure A1). In the transition from state YB=(0,1) to state YS=(1,1), the bivariate distribution P{yi,t|YBi,t-1, xi,t-1} collapses to its marginal P{yAi,t|YBi,t-1, xi,t-1} since P{yBi,t|YBi,t-1, xBi,t-1}=1. The same happens to the transition from state YA to state YS. Therefore, the conditional probability of sequential adoption i.e. to move to state YSt=(1,1) from either state YBt-1=(0,1) or state YAt-1 =(1,0), can be modelled via the two marginal univariate density functions (Φ1) with normally distributed residuals ε, i.e. ε ~ N(0;1): P{ yAi,t |YBi,t-1, xAi,t}= Φ1 (βAT xAi,t + βΒA; 0,1)

(3a)

P{ yBi,t |YAi,t-1, xBi,t}= Φ1(βBT xBi,t + βAB; 0,1)

(3b)

where (3a) can be estimated independently of (3b). In summary, the whole model involves the estimation of five parameters γο, βA, βΒ, and βΒA, βAB and four transition probabilities (1, 2 and 3a and 3b). 12 Mosconi and Seri’s (2006) strong simultaneous independence and one step ahead Granger non causality tests for a first order Markov process reported below for stationary transition probabilities (although the extension to the case with covariates is straightforward), provide necessary conditions for simultaneous and sequential complementarity, respectively. Thus, it can be stated that:

42

i) YAt and YBt are strongly simultaneously independent given YOt-1, if for a firm that has adopted neither innovation (i.e. it is in state YO) in time t-1, the probability of simultaneous adoption of A and B is the same as the probability of adopting any one of the two innovations independently: P{yAi,t|yBi,t, yOi,t-1} = P{yA i,t|yO i,t-1} P{yBi,t|yAi,t, yO i,t-1} = P{yB i,t|yO i,t-1} Hence, the null hypothesis H0: γο=0 is a necessary condition for simultaneous complementarity. If this hypothesis is not rejected, we can exclude the existence of simultaneous complementarity. ii) YBt does not strongly cause YAt one step ahead given YBt-1, if the probability of adopting A in time t is independent of whether B has already been adopted: P{yAi,t|yBi,t-1} = P{yAi,t|yOi,t-1} Hence, the null hypothesis H0: βΒA=0 is a necessary condition for the sequential complementarity B-A. If this hypothesis is not rejected, we can exclude the existence of the sequential complementarity B-A. iii) YAt does not strongly cause YBt one step ahead given YAt-1, if the probability of adopting B in time t is independent of whether A has already been adopted: P{yBi,t|yAi,t-1} = P{yB i,t|yOi,t-1}. Hence, the null hypothesis H0: βAΒ=0 is a necessary condition for the sequential complementarity A-B. If this hypothesis is not rejected, we can exclude the existence of the sequential complementarity A-B. Condition i) essentially states that for simultaneous independence to hold the extra gain from simultaneous adoption should be zero (i.e. vS=0) so that for a firm that in time t-1 has adopted neither innovations, the adoption decision of A (B) is taken independently of B (A). Condition ii) suggests that the probability of adopting A is independent of whether B has

43

already been adopted. Therefore, there are no extra gains generated by complementarity effects from the adoption of A having already adopted B, i.e. vBA=0 in (4). The same can be said for condition iii) with respect to the adoption probability of B, i.e. vAB=0.

The

hypotheses (i), (ii) and (iii), can easily be tested by testing the significance of the Maximum Likelihood parameters estimates γˆ ο, βˆ BA and βˆ AB in (2) and (3a/b).

44

Table A1 Estimation of Model 3 (Table 5) with time fixed-effects Model 3 Constant Plant size Plant age Multi plant Education Production technologies HRMP Just in time Concentration index R&D intensity Infrastructure index Stock j; j=CAD;JOD Order tj; j=CAD;JOD D1972b D1973 D1974b D1975 D1976b D1977 D1978 D1979 D1980 D1981 D1982 D1983 D1984 D1985 D1986 D1987 D1988 D1989 D1990 D1991 D1992 D1993 D1994 D1995

-4.494 0.272 -0.095 0.029 0.005 0.249

CADa (0.460) (0.036) (0.041) (0.091) (0.002) (0.047)

*** *** ** *** ***

-2.575 0.103 -0.129 -0.144 0.003

JOD (0.202) (0.032) (0.036) (0.090) (0.002)

0.154 0.194 0.003 0.594 0.003 0.158 -0.560 0.209 0.163 0.208 0.145 -0.800 -0.584 -0.608 -0.232 -0.615 -0.346 -0.411 -0.167 -0.297 -0.227 -0.084 0.154 -0.028 -0.122 0.197 -0.339 -0.011 0.372 0.412 0.144

(0.030) (0.117) (0.010) (1.040) (0.001) (0.472) (1.429) (0.199) (0.203) (0.202) (0.206) (0.370) (0.308) (0.310) (0.243) (0.307) (0.258) (0.262) (0.234) (0.252) (0.244) (0.238) (0.225) (0.244) (0.258) (0.247) (0.295) (0.278) (0.271) (0.292) (0.322)

*** *** *** * *** *

-0.021 (0.011) * 2.047 (1.236) * 0.003 (0.001) ** ** 1.055 (0.318) *** 0.532 (0.895) -4.297 (0.000) *** -0.040 (0.529) -5.152 (0.000) *** -0.128 (0.540) -5.133 (0.000) *** ** 0.156 (0.469) * 0.196 (0.463) ** 0.437 (0.430) 0.437 (0.426) ** 0.265 (0.441) 0.688 (0.407) * 0.244 (0.441) 0.983 (0.398) ** 0.978 (0.401) ** 1.173 (0.406) *** 1.486 (0.406) *** 1.519 (0.413) *** 1.602 (0.415) *** 1.174 (0.431) *** 0.756 (0.452) * 1.320 (0.441) *** 1.115 (0.453) ** 0.947 (0.466) ** 1.320 (0.462) *** 0.252 (0.086) *** βΒΑ 0.127 (0.072) * βΑΒ 0.147 (0.189) γο Robust standard errors are reported in brackets. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01. Number of single-plant firms: 438. a Price variables removed for multicollinearity with the year dummies. b The very low estimated standard errors of the dummy variables for the years 1972, 1974, and 1976 reflect the lack of change in the dependent variable (adoption of CAD) in those years (see also Figure 1).

45

Table A2 Estimation of Model 3 (Table 5) controlling for the AutoCAD first release Model 3 Constant Step-Dummy 1983-1995 Price Price change Plant size Plant age Multi plant Education Production technologies HRMP Just in time Concentration index R&D intensity Infrastructure index Stock j; j=CAD;JOD Order tj; j=CAD;JOD

βΒΑ βΑΒ γο

-3.272 0.460 -0.002 -.0003 0.263 -0.091 0.015 0.005 0.247

-0.019 2.877 0.003 0.547 2.010 0.139 0.354 0.092

CAD (0.303) (0.126) (0.001) (.0001) (0.034) (0.040) (0.089) (0.002) (0.046)

(0.011) (1.116) (0.001) (0.233) (0.733) (0.070) (0.078) (0.178)

JOD -2.666 (0.180) ***

*** *** ** *** *** ** *** ***

* *** ** ** *** ** ***

0.089 -0.115 -0.149 0.002

(0.031) *** (0.034) *** (0.087) * (0.002)

0.145 0.247 0.003 0.540 0.003 0.419 1.418

(0.028) *** (0.113) ** (0.010) (0.990) (0.001) ** (0.261) (1.123)

TableA3 Results of first stage survival regressions Adoption of JOD

Adoption of CAD Constant

-5.094

(0.616)

***

Constant

-4.105

(-0.230)

***

Price

-.0004

(.0001)

***

Stock JOD

2.818

(0.598)

***

3.801

(2.043)

*

0.925

(0.086)

Stock

CAD

Order pa

CAD

1.297

(0.360)

***

7.232 1.560

(0.916) (0.153)

***

Order

JOD

pa

Robust standard errors are reported in brackets. Two-tailed tests applied. * p< .10; ** p< .05; *** p< .01 We test the null hypothesis that p=1, i.e. independence from the duration

a

Figure A1 Adoption dynamics: states and transitions

YA YO

YS YB

Note: Adoption status: YO = neither CAD nor JOD; YA = CAD; YB = JOD, YS = CAD and JOD

46

Endnotes 1

The extent of the interdependencies among technological innovations has been documented by various

scholars. Such relationships have been referred to as ‘interrelatedness’ (David, 1975), ‘complementarities’ (Rosenberg 1979), ‘path interdependence’ (Cowan and Hulten, 1996). 2

See the Appendix for illustrative numerical examples of simultaneous and sequential complementarity. These

examples show that there may be cases in which simultaneous complementarity leads to bigger gains than sequential complementarity, but also cases where the opposite is true and the order of the adoption sequence matters. In general which of the two types of complementarity and which sequence generate bigger gains is an important empirical question. 3

A related stream of the empirical literature has focused on the joint effects of technological and organizational

innovations upon the performance of firms (or plants). This literature includes qualitative work (for a survey see Brynjolfsson and Hitt, 2000) and a limited number of econometric studies based on more comprehensive datasets (among others, see Caroli and Van Reenen, 2001; Bresnahan et al., 2002; Colombo et al., 2007a; Aral et al. 2012). 4

Sample plants operated in the following two-digit NACE-CLIO manufacturing industries: fabricated metals

(31), non-electrical machinery (32), computers and office equipment (33), electrical machinery and electronics (34), automotive and other transportation equipment (35-36), and scientific, precision, medical and optical instruments (37). Detailed information on the FLAUTO database, can be found in Cainarca et al., (1990); Colombo and Mosconi, (1995); Colombo and Delmastro, (1999). 5

As to industry, we use the classification mentioned in footnote 4. The geographical location is distinguished in

North-East, North-West, Center, and South & Islands; the plant size, measured by number of employees, is divided in the classes 10-19, 20-49, 50-199, 200-499, ≥500. 6

This testing procedure differs from the traditional Granger causality test in that it predicts the whole

distribution, rather than just the mean (i.e. strong rather than weak independence) while using a one step ahead (rather than global) prediction horizon. 7

The approach we follow here is conceptually similar to the one adopted by Van Biesebroeck (2007) to analyze

complementarity between different activities (i.e. model variety, use of flexible production technologies, and vertical integration) in a panel of plants involved in automobile production. A different approach is to rely on the restrictions that complementarity places on the distribution of the error terms of the equations modelling the adoption decision of each innovation. For instance, Miravete and Pernias (2006) estimate a structural discrete choice model of production and (product and process) innovation decisions which allows positive associations between firms’ activities to be driven by observed and unobserved factors, in addition to complementarity.

47

8

The quality adjusted series of computer prices for the period 1970-1992 was outsourced from Battisti (2000) which

built the price series from 1957 to 1992 using the studies of Triplett (1989) and Stoneman et al. (1992) based on the hedonic price method. The data for the period 1993-1996 has been here extrapolated using the nonlinear function Price = 1044.8×exp-(0.1351×time); R2 = 0.979. More details are available from the authors upon request. 9

We also experimented replacing Concentration index and R&D intensity with industry dummies. Results are

remarkably similar. For the sake of synthesis, the estimates are not reported here. They are available from the authors upon request. 10

Based on the estimations reported in Table 5 (Model 3), we calculated the unconditional probability of JOD

adoption in absence and presence of CAD, given respectively by: P{yBi,t |Yi,t-1= (0,0)}= Φ1(βΒT xi ,86 ; 0, 1) and 1

P{yBi,t |Yi,t-1= (1,0)}= Φ1(βΒT xi ,86 + βAB; 0, 1), where Φ1 is the marginal density function of the bi-variate standard 1

normal distribution, i.e. N(0;1); β B is the vector of coefficients associated to the explanatory variables in the JOD equation and βAB identifies the coefficient associated with the increase of the probability of adopting JOD once CAD is in place (see again the Appendix for details). Note however, that in the general case when ρ.,. ≠ 0, a change in the covariates would affect the conditional probability via the arguments of the joint distribution (Φ2). In our case ρ.,. or equivalently γo , is not significantly different from zero; therefore the joint distribution collapses to its marginal. The odds ratio is calculated in the following way: P{yBi,t |Yi,t-1= (1,0)}/ P{yBi,t |Yi,t-1= (0,0)}. We also repeated this exercise excluding the explanatory variables of JOD adoption that were found not to be statistically significant at conventional confidence levels. Results were very close to those reported in the text. 11

Some of the 438 plants in the FLAUTO dataset entered the market after 1970. Therefore the calendar time [t ,

T] = [1970, 1996] had to be corrected for the firm’s year of entry [tE, T]. Moreover, it is worth noticing that in our dataset the last observation year is 1996, but this does not require any correction for right truncation “since the censoring time may be regarded as a Markov time with respect to the filtration generated by the process” (see Mosconi and Seri, 2006, p.22). 12

However, it is worth noticing that while the second transition probabilities of sequential adoption (in appendix,

see equations 3a and 3b) follow an univariate distribution (Φ1) and can be estimated independently of each other, the first transitions (in appendix, see equations 1 and 2), from state Yt-1O=(0,0) to either YtA =(1,0) or YtB=(0,1), follow a bivariate distribution (Φ2) and could be estimated independently only if γο is not significantly different from zero.

48