Introduction

Successful new product introductions are essential to the long-term viability of a firm. Through the creation of new revenue streams with entire product life cycles ahead of them, firms generate potential sources of growth and financial capability for the future. Regular new product introductions are especially necessary for firms in industries that have high clockspeeds or rates of change and evolution. The clockspeed concept was introduced by Fine (1998) in an influential perspective on business strategy. Following Fine’s (1998) rationale, this research utilizes the concept of ‘organizational clockspeed’ as the firm’s ability to change and adapt quickly in response to environmental forces such as intense competition and transformative technologies. Firms recognize that quick competitor response to new product introductions poses a competitive threat (Bowman and Gatignon, 1995) and thereby attempt to develop products even more rapidly, leading to spiraling rates of change and innovation in an industry.

The importance of the supply chain is integral to the clockspeed perspective on strategy (Fine, 1998 §2). Supply chains are argued to be so critical to firm performance and survival that supply chain design is taken to be the ‘ultimate core capability’ of a firm (Fine, 2000, p. 213).  Thus, three central concepts in Fine’s theory are clockspeed, well-integrated supply chains and new product development. However, despite a number of empirical studies using the clockspeed framework, no study has examined the relationship of the supply chain to new product development where clockspeed is explicitly incorporated. This study addresses this gap and extends the literature by developing a theoretical framework that incorporates all three central concepts. Product innovation, clockspeed, and supply chain execution are important business phenomena as well as cornerstones of the clockspeed perspective. The current research, by examining these concepts concurrently, enhances understanding of key operations strategy issues affecting product innovation.  Moreover, the research provides guidelines to upper-level managers who must balance the allocation of resources to new product development strategies to that of supply chain management activities.

The paper is organized as follows.  In the following section, we examine the relevant concepts from the literature, focusing particularly on work which has adopted the clockspeed perspective. We provide a theoretical model that is derived from Fine’s perspective on business strategy which interrelates the concepts of product innovation, clockspeed, and supply chain integration. With regard to clockspeed, we focus on the concept of organizational clockspeed which we revisit below.  We also operationalize key constructs, derive hypotheses, and present a conceptual model specifying the relationships between these constructs and new product development. In the third section, we describe the sample, the data collection procedure, and the empirical methodology for count data used herein. The fourth section contains our findings based on data collected from manufacturers in the automotive industry. Finally, we discuss the implications of the research in the last section.

Literature Review and Model Development

In this section, we discuss key concepts of the model and review the associated background.  Based on this discussion, we develop a conceptual model of the relationships between supply chain integration, clockspeed, and other firm characteristics which appears in Figure 1. In brief, clockspeed is positively related to both the number of entirely new products developed and the number of product updates developed. Supply chain integration is also positively related to both of these new product development measures. Additionally, a moderating relationship between these two primary independent variables is specified based on previous research. Finally, we also identify four firm characteristics which, while not cornerstones of the clockspeed perspective, control and account for important differences which exist across firms.

Figure 1:  Determinants of New Product Introductions: A Clockspeed Framework

Our dependent variable–new product introductions–is an important firm activity that demonstrates a firm’s ability to respond to its customer base through innovation and also evidence of a firm’s continuing commitment to its viability and its future.  Investors tend to react favorably to new product introductions, as these introductions have been empirically demonstrated to have an impact on the market value of firms. For example, Chaney et al. (1991) examined the effect of new product announcements of both wholly new products as well as product updates on stock value in 23 industries. Both were found to cause stock appreciation with new products generating a 0.74% average 3-day excess return and product updates generating a more modest 0.41% average 3-day excess return (see also Bayus et al., 2003).

New product introductions may be categorized with reference to an underlying continuum or dimension (see Benner & Tushman, 2003, for a review). For example, managers are able to determine whether a product they have developed is an entirely new one, or is instead an extension or variant of an existing product. The former represent more substantial innovations than the latter. In the current study we examine the development of entirely new products vis-à-vis product updates or variants. This delineation is very similar to that of Chaney et al. (1991).

Clockspeed

In a high clockspeed environment, the ability to shorten product development times can confer an important advantage as the firm can more quickly react to such dynamic factors as changing consumer tastes and market conditions (Millson et al., 1992). Several authors have empirically examined selected aspects of the clockspeed concepts relevant to the current study. Mendelson and Pillai (1999) examined the effect of clockspeed on three time-based product development measures: development project duration, product-redesign intervals, and production ramp-up time. Using OLS regressions, they found significant negative relationships between clockspeed and the three product development measures. Their findings indicated that as clockspeeds increased, times associated with product development activities were increasingly compressed.

Mendelson and Pillai (1999) made several key contributions by empirically establishing a clear relationship between clockspeed and product development outcomes. However, Mendelson and Pillai (1999) did not directly examine an important aspect of Fine’s framework: the characteristics of the firm’s supply chain considered here. Another important distinction is as follows. The electronics and computer industries considered by Mendelson and Pillai (1999) are known to be the highest velocity business environments (Bourgeois and Eisenhardt, 1988). Indeed, they are held up as the prototypical high clockspeed industries. In the current research however we examine the automotive industry. The automotive industry by contrast is one in which the clockspeed is relatively accelerated (e.g., Fujimoto, 2000) where recent years have brought a proliferation of innovations such as in-vehicle DVD players, onboard navigation systems, superbright LED illumination devices and the like.  Still, the industry is not at the extreme as are the electronics and computer industries. By focusing on an industry in which clockspeed is still moderately rapid, but not extremely so, the results found herein should be more readily applicable to the majority of industries which do not operate at the highest clockspeeds.

Guimaraes et al. (2002) examined the impact of IT (information technology) use effectiveness in a supplier network context from a clockspeed perspective. They found that IT use effectiveness and industry clockspeed needed to be examined concurrently in order to obtain better insight into an examination of supplier network performance. IT use effectiveness was shown to be a determinant of supplier network performance, with industry clockspeed playing an important moderating role. Specifically, there was a stronger relationship between IT use effectiveness and supplier network performance among companies in higher clockspeed environments. The relationship between IT use effectiveness and supplier performance was significantly weaker in lower clockspeed companies. In terms of the concepts under consideration in Figure 1, Guimaraes et al. (2002) provided important insights regarding two of the three concepts displayed (clockspeed and supply chains), but did not examine the concept of new product introductions appearing at the terminus of the arrows.

In the measurement of clockspeed, Fine (1998) indicates that overall clockspeed can be decomposed into different submeasures. First there is organizational clockspeed which involves the rate of change in the structures of organizations. With regard to organizational clockspeed m firms which are able to more rapidly reconfigure themselves to keep pace with industry developments display a higher level of this capacity for rapid proactive change. Based on the clockspeed perspective on strategy, we obtain the following hypotheses regarding organizational clockspeed.

Hypothesis 1a:

Firms exhibiting higher levels of organizational clockspeed will have increased levels of entirely new product development.  

Hypothesis 1b:

Firms exhibiting higher levels of organizational clockspeed will have increased levels of new product update development.  

Fine’s second submeasure is product clockspeed which refers to the pace of innovation. Although we do not attempt to measure product clockspeed directly, we do include a measure of firm innovation (to be described below).  The third submeasure of clockspeed is called process clockspeed and refers to the adoption of new business processes.  Following Mendelson and Pillai (1999), we leave this aspect of clockspeed for future research.

Supply Chain Integration

The potential benefits of a supply chain management-based approach to new product development as opposed to a traditional anticipatory approach were reviewed by Bowersox et al. (1999). The advantages of the former approach revolve around its ability to permit enhanced responsiveness so that the company produces at a level consistent with actual consumer demand. This allows losses to be more rapidly contained in the event of an unsuccessful product launch. It also allows firms to quickly increase production in the event that a product is especially successful. By contrast, in a traditional approach new product rollout decisions are made farther in advance based on forecasts and there are fewer opportunities to rapidly adapt to consumer demand. The potentially excessive costs associated with this approach may lead firms to be more cautious in their new product development efforts, only proceeding when they are particularly certain about both the product and its expected market impact.

Thus, supply chain management has increasingly become the emphasis of organizational efforts to improve both operational efficiency and customer satisfaction objectives (Tan, 2002). Greater supply chain integration in manufacturing industries permits firms to forge and exploit alliances with their most critical suppliers (Porter, 1997) in the continual search for competitive advantage. This effective management of key business processes across a network of buyers and sellers has inspired managers to search for new business designs that can capture and sustain growth. New product development efforts have a large impact on profitability (Morash et al., 1997) and deserve special attention since the inter-functional process integration between sourcing, logistics, production, marketing, and new product development activities is a locus of competitive advantage.

The clockspeed framework emphasizes the role of the supply chain in corporate sustainability. A successful supply chain requires close coordination between a manufacturer and its upstream supply links. Thus, the extent of integration between manufacturer and upstream suppliers was examined. Supply chains are more integrated when member firms co-monitor production lines and regularly calibrate against external demand. In the planning stage for new products, cross-functional and cross-firm teams are used to ensure more synchronized efforts. Structuring financial outcomes for member firms so that payoffs are linked together also leads to greater integration. Finally, when unexpected problems crop up, members of a well-integrated supply chain work together to resolve these problems. Based on these features of supply chains and those of the clockspeed framework, we advance the following hypotheses regarding supply chain integration.

Hypothesis 2a:

Firms exhibiting higher levels of supply chain integration will have higher rates of entirely new product development.

Hypothesis 2b:

Firms exhibiting higher levels of supply chain integration will have higher rates of new product update development.  

In a review of the literature, Guimaraes et al. (2002) point out that high clockspeed environments pose additional challenges for the execution of closely-integrated supply chains.  In such situations, the extensive joint collaboration, coordination and planning required for these endeavors may become particularly demanding (Bensaou, 1997).  As mentioned previously, Guimaraes et al. (2002) found that industry clockspeed played an important moderating role with respect to the impact of IT effectiveness on supplier network performance. Specifically, hypothesized interactions between clockspeed, supply chain integration and IT effectiveness were found such that higher clockspeeds in highly integrated supply chain environments led to decreases in supplier network effectiveness. Given these findings, we propose that organizational clockspeed interacts with or moderates the relationship between supply chain integration and new product development efforts through the following process.  In any organization resources are finite.  Thus, firms with limited resources facing the requirements associated with maintaining high levels of supply chain integration in the face of highly changeable environments will likely have less spare resources available to engage in other activities such as new product development.  Consequently, we would expect that in such circumstances new product development activities would decline.  Thus, we propose the following hypotheses.

Hypothesis 3a:

The co-occurrence of higher organizational clockspeeds and higher supply chain integration leads to lower rates of entirely new product development.

Hypothesis 3b

: The co-occurrence of higher organizational clockspeeds and higher supply chain integration leads to lower rates of new product update development. 

Firm Characteristics: Control Variables

We also consider certain firm characteristics that are likely to be influential in new product development. In the current study, these variables are explanatory variables of secondary concern, and thus may be viewed as control variables.Firm size and scope.  Firm size has been found to be a relevant control variable in empirical operations management research (e.g., Dröge & Germain, 1998; Shah and Ward, 2003; Wei and Morgan, 2004) and indeed Hendricks and Singhal (2001) indicate that “(f)irm size is probably one of the single most influential variables in organizational studies” (p. 271). In terms of the number of employees, passing the 100–employee mark has often been utilized to indicate that a firm is no longer small in size (e.g., Wagar, 1997; U.S. Bureau of the Census, 2001; Plenert, 2002, p. 314; Pullins et al., 2004; Wells, 2004). As such, a firm size variable was employed which was coded 0 for smaller firms (100 or fewer employees) and 1 for larger firms. In the current study, 27% of the firms were of this smaller size.  We may also consider a firm’s financial size.  One measure of this is a firm’s annual sales volume. Clearly, firms with larger sales volumes may have more financial resources to support new product innovation. Thus, we entered log annual sales volume into the model to control for differences in this firm characteristic. In a similar vein, Novak and Eppinger (2001) also found it useful to identify and control for multiple conceptualizations of firm volume in their empirical investigation of product complexity and the supply chain.

Past level of innovation:

Firms which have a larger product line are oriented toward a higher variety of products, and may be expected continue to grow by product innovation. Thus, the breadth of the product line could be viewed as a proxy for the past level of product innovation at the firm. Firms that have already innovated a large number of products might be expected to continue their efforts. Thus, we would expect the relationships between the number of products in the product line and the respective measures of entirely new products and product updates to be positive.  As such, we include a variable that indicates the number of products currently produced by the firm to control for this kind of effect.

Electronic Data Interchange

IT is an integral part of the modern supply chain. The mechanisms by which IT can be used to support supply chains within an organization have been considered by Hoogeweegen et al. (1999). IT has been empirically shown to positively impact firm performance via enhanced supply chain coordination (Ross, 2002). Moreover, increased IT investment within the supply chain can be used to signal increased commitment, thereby leading to greater interorganizational trust (Kent and Mentzer, 2003). This is important as there are many potential sources of conflict and risk in supply chains (Kumar and van Dissel, 1996) which may degrade firm performance.

One form of IT that has been identified as a key technology for supply chain management is electronic data interchange (Ross, 2002). Electronic data interchange (EDI) has been shown to create substantial business value and has long been used in the automotive industry. For example Mukhopadhyay et al. (1995) estimated the annual savings of EDI to Chrysler to be approximately $220 million. The benefit of EDI in the supply chain context is that it facilitates information sharing (Sahin and Robinson, 2002), thereby promoting greater levels of integration and coordination as well as lowering costs (Cachon and Fisher, 2000).

Of particular relevance to the current research was the IT-oriented work by Guimaraes et al. (2002) discussed earlier. In somewhat related research, Jayaram et al. (2000) examined the effects of eight different IT infrastructures on four measures of time-based performance in supply chains. They found a positive relationship between new product development time and the use of IT infrastructures supporting design-manufacturing integration, such as computer-aided design and computer-aided manufacturing. Mendelson and Pillai (1998) also found that firms were more likely to utilize IT such as electronic data interchange in higher clockspeed environments. Here we control for differences in EDI usage by including an EDI measure in the empirical model.

Methods

Sample and Data Collection

To enhance the overall validity of the study, a two-phase data collection effort was used. In the first phase, an initial field study consisted of plant visits and in-depth interviews with several plant managers, manufacturing supervisors, supply-chain managers, and purchasing managers working in the automobile industry in Brazil. This first phase allowed us to refine the face validity of the survey instrument.  Based on these findings, we developed our final survey questionnaire that was administered by mail. The data for this second phase of the research were collected in 2002–2003 through a questionnaire mail survey of Brazilian automobile manufacturers and suppliers as part of a broader research project affiliated with the International Motor Vehicle Program at the Massachusetts Institute of Technology.

Of the 34 individuals interviewed in the initial field study, 19 were either plant managers or manufacturing supervisors from Ford, DaimlerChrysler, General Motors, Volkswagen, and Troller. Four of those interviewed were executives at Anfavea (the Brazilian Automakers Association) and Sindipecas (the Brazilian Auto Suppliers Association). In addition, one faculty member of the University of São Paulo whose research involves the development of the automobile industry in Brazil was also interviewed. A total of five automakers (i.e., Volkswagen, Ford, DaimlerChrysler, General Motors, and Troller) were included in the sample, and multiple individuals were interviewed. The companies represented in the interviews were multinational firms from multiple countries, including one Brazilian firm, two European firms, and two from the United States. In addition to the automaker’s personnel, informal interviews were conducted with 10 plant managers from suppliers. Therefore, the sample reflected a diverse set of manufacturing companies within the supply chain of the automotive industry including automakers and suppliers and, in conjunction with the literature review, was well suited for obtaining a rich set of ideas and insights regarding the supply chain management implications impacting new product development.

For questionnaire development, Likert-type measurement scales for the constructs were generated. In particular, scales related to supply chain integration, clockspeed, and EDI were created and items assessing the remaining firm characteristics were developed. Clockspeed was operationalized as organizational clockspeed as per the discussion above. To address face validity, the development of the items was informed by the field studies and the semistructured interviews with managers and executives working in the automobile industry in Brazil.  Before deciding on the final version of the questionnaire, a pilot version was administered at a Ford plant. In addition, one expert on modular production at the University of São Paulo provided some feedback on a pilot version of the questionnaire, and helped refine key constructs and identify the appropriate use of words in the automotive industry. Then we discussed potential wording problems and possible sources of confusion with a Ford plant manager.  These multiple steps helped to further refine the items and to address any remaining concerns about face validity while also helping to establish that the survey instrument was reliably assessing what it was intended to assess.  After we obtained the final version of the questionnaire, it was translated into Portuguese and then back-translated into English to assure that the translation had not obscured the meaning of the questions. The survey was distributed to those identified in the sample group via hard copies along with a request for participation and a brief description of the research in question.

We began the second phase of the research by identifying our sample frame.  Here, this consisted of the automobile and auto suppliers manufacturers identified through lists provided by Anfavea and by Sindipecas In addition, the two associations’ lists were cross-checked with the Brazilian magazine Automotive News, an annual publication which profiles firms and executives in the automobile industry in Brazil. These use of these three sources allowed us to obtain a comprehensive sample frame for the Brazilian automobile industry. After combining these data sources and deleting duplicated entries, the questionnaire was sent to the sample of 493 business units in the automobile industry of Brazil. The survey was mailed to managers at the plant/divisional level, who were asked to respond based on the products and characteristics of their division. Administration of the survey followed the guidelines prescribed in Dillman (1978).  After the mailing, we received a total of 101 valid questionnaires and a response rate of 22 percent. Firms of a variety of ages, sizes, and geographical scope were represented in the final sample group.  In particular, the sample consisted of 17 OEM assemblers and 84 suppliers of parts and components.

Construct Measurements

The two dependent variables in the model – the number of entirely new products and the number of product updates – were measured by asking our respondents the following questions: 1) How many entirely new products has your business unit introduced during the last 12 months? 2) How many new models/variants has your business unit introduced during the last 12 months?

For the main independent variables, we asked our respondents to indicate the degree to which they agreed with statements using a 5 point Likert scale.  Given the organizational clockspeed construct’s centrality in the current research and its relative newness in the empirical literature, we comment briefly on the formulation of this construct and review its articulation by previous researchers. We begin by noting that Guimaraes et al. (2002) did not appear until after the survey administration was well under way, so their items were unavailable.  Mendelson and Pillai (1999) operationalized organizational clockspeed as a dichotomous variable based on whether or not the organization had experience a major restructuring in the preceding three-year period. While such a variable clearly measures organizational change, organizational restructurings may occur for a variety of reasons, including poor leadership, financial difficulties, and declining product quality. An organizational restructuring therefore may indicate a proactive response to market evolution or a reactive attempt to cure ills arising from poor performance. In the current context it is more useful to assess organizational clockspeed as an organization’s propensity for rapid proactive change without potentially tapping into a construct of organizational dysfunction. Hence, the organizational clockspeed items (a = 0.72) are constructed to reflect this conceptualization through the following questions: 1) In general, our people accept change readily  (a if item deleted = 0.66); 2) New and innovative ideas are welcome in our business unit (a if item deleted = 0.66); 3) We reward people for updating our common methodologies and procedures (a if item deleted = 0.72); 4) We have a very decentralized decision making process (a if item deleted = 0.61).

Our survey questionnaire asked the following questions to our respondents in order to capture the supply chain integration construct (a = 0.85): 1) We cooperate with suppliers in order to resolve problems whenever an unexpected situation arises (a if item deleted = 0.84); 2) Our major suppliers are frequently monitoring the demand variations for our final products (a if item deleted = 0.82); 3) Our major suppliers are frequently monitoring the speed and flow of our assembly line (a if item deleted = 0.82); 4) Our major suppliers usually keep their own key personnel inside or at close distance to our final assembly line (a if item deleted = 0.81); 5) We use cross-functional teams with our people and with people from major suppliers to carry out key activities in the development stage(a if item deleted = 0.82); 6) We use cross-functional teams with our people and people from major suppliers to carry out key activities in the assembly line (a if item deleted = 0.80); 7) Usually our major suppliers are paid only upon the approval of the final assembled product by us (a if item deleted = 0.85).

Besides the two main independent variables of organizational clockspeed and supply chain integration, we included four firm characteristics as control variables.  One, EDI, was a construct (a = 0.84) measured by Likert scale responses to the following items: 1) Our product development engineers depend on electronic databases listing standard components and their interface specifications (a if item deleted = 0.78); 2) Our people follow standard procedures and rely on electronic systems for transferring knowledge across projects and business units (a if item deleted = 0.83); 3) We frequently use an online system for data exchange between our people and our major suppliers (a if item deleted = 0.77); 4) Our business unit and our suppliers frequently use an electronic data exchange system (a if item deleted = 0.81).  The remaining control variables were: firm size, a dummy variable based on whether the number of employees in the firm was large (greater than 100) or small (100 or less); the log of firm sales volume; and the total number of firm products, measured by asking respondents how many different models/variants their business unit is currently manufacturing.

Construct Validation

The construct validation followed standard procedures involving exploratory factor analysis and the creation of indices summarizing the items. The structure of the constructs that were defined in this study was confirmed by factor analysis. The reliability of each of the scales was estimated by computing Cronbach’s alpha. Each of the scales was refined by removing questions that exhibited low inter-question correlations. The reliability coefficients ranged from 0.72 for organizational clockspeed to 0.85 for supply chain integration. These refined scales have reliability coefficients meeting or exceeding the 0.70 criterion recommended by Nunnally (1978). Hence, the scales possess internal consistency.  Mendelson and Pillai (1999) in their clockspeed research used an aggregate mean of the factor variables as their summary factor measure. We adopted this approach here.

Although there have been questions about the validity of data collected from a single key informant, we used this approach since our data collection incorporated the procedures recommended by various researchers (Kumar, Stern, & Anderson, 1993).  We evaluated non-response bias using Armstrong and Overton’s (1977) procedure.  In order to ascertain non-response bias across the survey instrument itself, we performed t-tests comparing early and late respondents on randomly selected items.  We found no significant differences between early and late respondents on any one of these items, suggesting that non-response bias would not likely exist in the survey instrument.

In order to minimize the effects of common method variance, we adopted four approaches. First, we interspersed the open-ended questions pertaining to some constructs throughout the questionnaire so that respondents would not fall into a pattern linked to Likert or semantic differential scales.  Second, we reverse coded some items in the questionnaire.  Third, after collecting the data, we used Harman’s one-factor test to address the common method variance issue.  If common method variance were a serious problem in the study, we would expect a single factor to emerge from a factor analysis or one general factor to account for most of the covariance in the independent and criterion variables (Podsakoff & Organ, 1986).  We performed a factor analysis on items related to the predictor and criterion measures.   No general factor was apparent in the unrotated factor structure and so we did not detect any common method variance problem.  Fourth, the items used in our study were part of a large-scale questionnaire; therefore, “[I]t is unlikely that respondents would have been able to guess the purpose of the study and forced their answers to be consistent” (Mohr & Spekman, 1994: 147). 

Empirical Model Specification

The number of new product introductions in a particular time frame is a count and as such must be integer-valued and non-negative. By contrast, linear regression methods hinge on an important assumption that the dependent variable is real-valued. The analysis of count data through the use of ordinary linear regression models is typically not recommended as biased, inconsistent and inefficient estimates will likely result (Long, 1997, ch. 8). Monte Carlo studies have shown that attempting to model count data with OLS-based methods typically leads to substantively wrong conclusions being drawn (King, 1986). By contrast, Poisson regression models are consistent, unbiased and efficient for count data. Since we have two dependent variables of interest, we require a bivariate regression model for Poisson counts.  Since overdispersion of the counts is commonly encountered in real-world data where the presence of firm-specific random effects is likely, we utilized a bivariate negative binomial regression model.  Additional description of the methodological motivation and theoretical underpinnings associated with this statistical model appear in the Appendix.

Results:

Descriptive statistics for the data appear in Table 1.  Model estimation was by maximum likelihood using the likelihood function in Equation 2 in the Appendix. Maximum likelihood test statistics were formed from the maximum likelihood estimates of the regression coefficients,, and their standard errors. Table 2 contains these values as they pertain to the two dependent variables of entirely new products and product updates.

Table 1: Descriptive Statistics and Simple Correlations for Study Variables

We first examine the results for entirely new product development that appear in Table 2. With regard to Hypothesis 1a, higher levels of organizational clockspeed were found to be associated with a greater number of entirely new products developed ( = 1.61). This relationship was positive and statistically significant, thereby providing support for Hypothesis 1a. For Hypothesis 2a, higher levels of supply chain integration were also found to be associated with a greater number of entirely new products developed ( = 2.14). This relationship was also positive and statistically significant and hence Hypothesis 2a was also supported. Finally, the interaction term between these two variables supports Hypothesis 3a. The estimated coefficient is negative ( = -0.580) and statistically significant. The pattern of results can be summarized as follows.  As the level of either supply chain integration or clockspeed increases, the pace of entirely new product development also increases. However, a negative synergy exists between supply chain integration and clockspeed with regard to entirely new product development when levels of both are high. In particular, the negative interaction indicates that firms which are able to successfully maintain high levels of supply chain integration in the face of high clockspeeds introduce fewer entirely new products, supporting Hypothesis 3a. The more innovative firms are those with higher organizational clockspeeds and less integration or more integration and slower clockspeeds. The former type of firm could perhaps be thought of as the traditionally innovative firm having more individualistic tendencies as opposed to being involved in extensive collaborative networks. The latter type of firm is one that is highly integrated in a more stable environment and thus may be more able to afford the investment of resources to plant for the future. The pattern of results obtained here corroborate with those of Guimaraes et al. (2002) who found that high clockspeeds tend to inhibit supply chain integration and performance. A certain amount of stability seems to be beneficial for supply chain operation.

Table 2: Parameter Estimates and Test Statistics

We comment very briefly on the impact of the remaining control variables estimated in the model on entirely new product development. Firm size was found to be a firm characteristic with a statistically significant relationship with entirely new product development. The negative coefficient ( = -0.571) indicates that the smaller firms were more likely to generate entirely new product developments than were large firms under a ceteris paribus assumption. Increased log sales volume was strongly predictive (p < .0001) of an increased pace of entirely new product development.  Similarly, the firm’s past level of innovation (as measured by the number of products a firm had in its product line) was also strongly predictive of its entirely new product developments (p < .0001). Finally, the level of firm EDI was not related to entirely new product developments (z = -0.501, n.s.), contrary to our initial expectations. It would seem that higher levels of EDI do not by themselves accelerate the pace of entirely new product development. Given that EDI has long been in use in the automotive industry (Ross, 2002), the relative advantages associated with it may be plateauing and that EDI may be at present a basic entry requirement for competition. Alternatively, perhaps more innovative firms have also found ways to innovate around varying existing levels of EDI.

Next we examine the results for product update development in Table 2. With regard to Hypothesis 1b, higher levels of organizational clockspeed were again found to be significantly associated with a greater number of product updates developed ( = 2.32). Concerning Hypothesis 2b, higher levels of supply chain integration were again found to be associated with a greater number of new models developed ( = 2.95). Thus, both Hypotheses 1b and  2b were supported in the context of product update development. The interaction term between these two variables was also statistically significant, supporting Hypothesis 3b. The estimate was again negative ( = -0.765) and significant, implying the same type of pattern of results that was observed for entirely new product developments. Firm size, however, was not quite a significant predictor for product update developments. The firm’s past level of innovation again was strongly predictive of product update developments (p < .0001) indicating that firms with larger product lines introduced a greater number of new updates than those with smaller product lines. However, log sales volume was not predictive of product update development after controlling for other factors in the model. Finally, the level of EDI was not associated with product update development at conventional levels of significance. We also mention in passing that the maximum likelihood estimate of the overdispersion parameter, q, was 1.74. From the discussion in the Appendix, we can see the small magnitude of q indicates considerable overdispersion was present in the data, so that the adoption of a bivariate Poisson model would have been inappropriate.

Discussion and Implications:

Our study provides theoretical and managerial insights into new product development strategies in buyer-supplier relationships in the clockspeed context by documenting that supply chain integration and clockspeed are important strategic factors influencing two different levels of product innovation. Overall, our empirical findings indicated that the level of supply chain integration and clockspeed were positively related to entirely new product and product update development. Yet the presence of significant moderating relationships between these independent variables indicates the pattern of relationships involves important contingencies. In particular, higher rates of new product development tend to occur more often when either supply chain integration is at a high level or when clockspeed is at a high level. However, when both are at high levels, the observed level of innovation diminishes as indicated by Hypothesis 3a and 3b. This particular pattern of results appears replicable as it has occurred in both the Guimaraes et al. (2002) study and the present one. As such, a useful area of future research could involve empirically identifying techniques for investigating and potentially ameliorating the specific root causes of the challenges of extensive supply chain integration in high clockspeed environments.  The findings also have important implications for managers. Specifically, firms seeking to increase the pace of new product development should consider sharpening their focus either by moving toward a higher clockspeed approach or instead striving for greater supply chain integration.

Above and beyond the strategic contributions described above, the current research makes at least three additional contributions to the literature on clockspeed and new product development. First, we have generated and empirically validated a more detailed framework for understanding the relationships between the concepts of new product development, clockspeed and supply chain integration that appears in Figure 1. Second, the current research appears to be the first to document the impact of clockspeed on firm processes in an industry characterized by a moderate clockspeed. Thus, the concepts associated with the clockspeed perspective on business strategy appear to have generality to a broader set of firms than has been empirically documented previously. Third, we utilize count data measures often found in new product development and clockspeed contexts and apply a bivariate negative binomial methodology that is consistent with the data context.  In particular, the negative binomial model is derivable when firm-specific random effects are present. Here, the low values of q indicate that overdispersion due to random effects specific to individual firms is clearly present (see Appendix). Moreover, the count data methodology represents a continuation of a line of thinking originating with Mendelson and Pillai (1999), who called for more rigorous quantitative measures and models for empirical clockspeed research.

The current research is subject to the typical limitations associated with cross-sectional survey research. For example, longitudinal designs would permit more conclusive determinations regarding the direction of causality. Moreover, the current research is more limited in certain respects than some of the other clockspeed research in that only a single industry, the automotive industry, is considered. This has the potential to circumscribe the generalizability of the findings. However, some of the studies which examine multiple industries nevertheless confine themselves to only high clockspeed industries, circumscribing their generalizability as well. The current research which examines a moderate clockspeed industry thus acts in a complementary fashion to the high clockspeed research. Additionally, it may be somewhat more germane to the many moderate clockspeed industries than the higher clockspeed research. Future clockspeed research should examine other moderate clockspeed industries such as the pharmaceutical and airline industries. Another potential limitation of the current study is that it involves Brazilian firms only. However, the Brazilian automotive industry is highly globalized, so we do not suspect that the patterns observed are only applicable there. Given that Brazil hosts all of the global car manufacturers in the world and that it has served as the testing ground for innovative methods of modular production, it was a well-suited setting for the current research. Nevertheless, there may be some important differences in clockspeed phenomena on an international scale. While the functioning of supply chains has been examined on a country-specific basis (Sum et al., 2001), we know of no research which has explicitly examined clockspeed on a cross-country basis, so this remains an area for further investigation.

References

Armstrong, J. S., & Overton, T. S. 1977. Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14: 396-402.

Bayus, B. L., G. Erickson, R. Jacobson. 2003. The financial rewards of new product introductions in the personal computer industry. Management Science 49(2): 197–210.

Benner, M. J. & Tushman, M. L. 2003. Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review 28(2): 238–256.

Bensaou, M. (1997). Interorganizational cooperation: The role of information technology – An empirical comparison of U.S. and Japanese supplier relations.  Information Systems Research 8(2):  107–124.

Bourgeois, L. J., III, K. M. Eisenhardt. 1988. Strategic decision processes in high velocity environments: Four cases in the microcomputer industry. Management Science 34(7): 816–835.

Bowersox, D. J., T. P. Stank, P. J. Daugherty. 1999. Lean launch: Managing product introduction risk through response-based logistics. Journal of Product Innovation Management 16: 557–568.

Bowman, D., H. Gatignon. 1995. Determinants of competitor response time to a new product introduction. Journal of Marketing Research 32: 42–53.

Cachon, G. P., M. Fisher. 2000. Supply chain inventory management and the value of shared information. Management Science 46(8): 1032–1048.

Cameron, A. C., P. K. Trivedi. 1998. Regression Analysis of Count Data. Cambridge University Press, Cambridge.

Chaney, P. K., T. M. Devinney, R. S. Winer. 1991. The impact of new product introductions on the market value of firms. Journal of Business 64(4): 573–610.

Dillman, D. 1978. Mail and Telephone Surveys: The Total Design Method. Wiley, New York.

Dröge, C., R. Germain. 1998. The design of logistics organizations. Transportation Research Part E 34: 25-37.

Fine, C. H. 1998. Clockspeed: Winning Industry Control in the Age of Temporary Advantage. Perseus, Reading, MA.

Fine, C. H. 2000. Clockspeed-based strategies for supply chain design. Production and Operations Management 9(3): 213–221.

Fujimoto, T. 2000. Shortening lead time through early problem-solving – A new round of capability-building in the auto industry. In: U. Jürgens, (Ed.), New Product Development and Production Networks. Springer, Berlin, 23–53.

Guimaraes, T., D. Cook, N. Natarajan. 2002. Exploring the importance of business clockspeed as a moderator for determinants of supplier network performance. Decision Sciences 33(4): 629–644.

Guo, G. 1996. Negative multinomial regression models for clustered event counts. Sociological Methodology 26(4): 113–132.

Hendricks, K. B., V. R. Singhal. 2001. Firm characteristics, total quality management, and financial performance. Journal of Operations Management 19: 269–285.

Hoogeweegen, M. R., W. J. M. Teunissen, P. H. M. Vervest, R. W. Wagenaar. 1999. Modular network design: Using information and communication technology to allocate production tasks in a virtual organization. Decision Sciences 30(4): 1073–1103.

Jayaram, J., S. K. Vickery, C. Droge. 2000. The effects of information system infrastructure and process improvements on supply-chain time performance. International Journal of Physical Distribution & Logistics 30(3/4): 314–330.

Karlis, D., I. Ntzoufras. 2003. Analysis of sports data by using bivariate Poisson models. The Statistician 52(3): 381–393.

Kent, J. L., J. T. Mentzer. 2003. The effect of investment in interorganizational information technology in a retail supply chain. Journal of Business Logistics 24(2): 155–175.

King, G. 1986. Statistical models for political science event counts: Bias in conventional procedures and evidence for the exponential poisson regression model. American Journal of Political Science 32(3): 838–863.

Kumar, K., H. G. van Dissel. 1996. Sustainable collaboration: Managing conflict and cooperation in interorganizational systems. MIS Quarterly 20(3): 279–300.

Kumar, N., Stern, L. W., & Anderson, J. C. 1993. Conducting interorganizational research using key informants. Academy of Management Journal, 36(6): 1633-1651.

Lee, A. 1999. Modelling rugby league data via bivariate negative binomial regression. Australian & New Zealand Journal of Statistics 41(2): 141–152.

Long, J. S. 1997. Regression Models for Categorical and Limited Dependent Variables. Sage, Thousand Oaks, CA.

Mata, F. J., W. L. Fuerst, J. B. Barney. 1995. Information technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly 19(4): 487–505.

McCullagh, P., J. A. Nelder. 1989. Generalized Linear Models (2nd ed.). Chapman & Hall, London.

Mendelson, H., R. R. Pillai. 1998. Clockspeed and informational response: Evidence from the information technology industry. Information Systems Research 9(4): 415–433.

Mendelson, H., R. R. Pillai. 1999. Industry clockspeed: Measurement and operational implications. Manufacturing & Service Operations Management 1(1): 1–20.

Millson, M. R., S. P. Raj, D. Wilemon. 1992. A survey of major approaches for accelerating new product development. Journal of Product Innovation Management 9: 53–69.

Mohr, J., & Spekman, R. 1994. Characteristics of partnership success:  Partnership attributes, communication behavior, and conflict resolution techniques. Strategic Management Journal, 15: 135-152.

Morash, E. A., C. Droge, S. Vickery. 1997. Boundary-spanning interfaces between logistics, production, marketing and new product development. International Journal of Physical Distribution and Logistics Management 27(5/6): 350–369.

Mukhopadhyay, T., S. Kekre, S. Kalathur. 1995. Business value of information technology: A study of electronic data interchange. MIS Quarterly 19(2): 137–156.

Novak, S., S. D. Eppinger. 2001. Sourcing by design: Product complexity and the supply chain. Management Science 47(1): 189–204.

Nunnally, J. C. 1978. Psychometric Theory (2nd ed.). McGraw-Hill, New York.

Plenert, G. J. 2002. International Operations Management. Copenhagen Business School Press, Copenhagen.

Podsakoff, P. M., & Organ, D. W. 1986. Self-reports in organizational research:  Problems and prospects. Journal of Management, 12(4): 531-544.

Porter, A. M. 1997. OEMs in the 21st century: One focus, one supply base. Purchasing 122(9): 50–59.

Powell, T. C., A. Dent-Micallef. 1997. Information technology as competitive advantage: The role of human, business, and technological resources. Strategic Management Journal 18(5): 375–405.

Pullins, E. B.,  D. A. Reid, R. E. Plank. (2004). Gender issues in buyer-seller relationships: Does gender matter in purchasing? Journal of Supply Chain Management 40: 40–48.

Ross, A. 2002. A multi-dimensional empirical exploration of technology investment, coordination and firm performance. International Journal of Physical Distribution & Logistics Management 32(7): 591–609.

Sahin, F., E. P. Robinson. 2002. Flow coordination and information sharing in supply chains: Review, implications, and directions for future research. Decision Sciences 33(4): 505–536.

Shah, R., P. T. Ward. 2003. Lean manufacturing: Context, practice bundles, and performance. Journal of Operations Management 21: 129–149.

Sum, C.-C., C.-B. Teo, K.-K. Ng. 2001. Strategic logistics management in Singapore. International Journal of Operations & Production Management 21(9): 1239–1260.

Tan, K. C. 2002. Supply chain management: Practices, concerns, and performance issues. Journal of Supply Chain Management 38(1): 42–53.

U.S. Bureau of the Census.  (2001).  Highlights from a profile of U.S. exporting companies, 1998–1999.  Report CB-01-136.  U.S. Bureau of the Census, Washington D.C.

Wagar, T. H. (1997). Factors affecting permanent workforce reduction: Evidence from large Canadian organizations.  Revue Canadienne des Sciences de l’Administration 14: 303–314.

Wei, Y., N.A. Morgan. (2004). Supportiveness of organizational climate, market orientation, and new product performance in Chinese firms.  Journal of Product Innovation Management 21: 375–388.

Wells. J. T. (2004). Small business, big losses. Journal of Accountancy 198: 42­­­–47.