Technological newness: An empirical study in the process industries

ELSEVIER

J. Eng. Technol.Manage. 13 (1996) 263-282

Journalof ENGINEERINGAND TECHNOLOGY MANAGEMENT JET-M

Technological newness" an empirical study in the process industries Brent D. Barnett a,*, Kim B. Clark b a Marriott School of Management Brigham Young University, Provo, UT 84602, USA b Graduate School of Business Administration Harvard University, Boston, MA 02163, USA

Abstract

Technological change in product development is a crucial issue in the management of technology. The entire sweep of technological changes over the past hundred of years is, in essence, the sum of thousands of individual product development projects in thousands of firms. Yet the degree of technological change in individual development projects has not been extensively studied. This paper presents a four-dimensional characterization of technological newness for product development projects in the process industries in which product development is closely tied to process innovation. The characterization uses four dimensions of change that are required in the development of new products: chemistry, production equipment, fabrication technology and process control. Based on the framework of technological newness, the paper presents data collected on the degree of process change in a set of 20 product development projects conducted by a large manufacturer of advanced polymers. The data provide a clear demonstration of the value of the measurement framework, showing a strong relationship between the characterization of change and the project performance. © 1997 Elsevier Science B.V. Keywords: Technologicalchange; Technologicalnewness; Developmenttime; Process industries

1. Introduction

Technological change is a prime determinant of economic growth and the evolution of industry structure (Schumpeter, 1934; Solow, 1957; Freeman, 1982) and plays a central role in the dynamics of competition and strategy (Nelson and Winter, 1982; Porter, 1985; Dosi et al., 1988). Adding to the importance of understanding technologi-

* Corresponding author. 0923-4748/97/$17.00 Copyright © 1997 Elsevier Science B.V. All rights reserved. Pll S0923-4748(96)01009-0

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cal change is the current emphasis on the management of technology and effective development of new products (Stalk, 1988; Blackburn, 1991), reflecting the current competitive pressures in the world economy. Effective product development has become recognized as a primary contributor to competitive strength. This new focus on product development performance and :its effect on competition has brought a new emphasis on the management of rapid technological change. Technological change is, thus, a crucial aspect of product development. The entire sweep of technological changes over the past hundred years is, in essence, the sum of thousands of individual product development projects in thousands of firms. Yet the degree of technological change in individual development projects has not been extensively studied or understood. Underlying empirical knowledge of the technological factors that affect the performance of product development is still limited. Few empirical studies have examined the role of technological change in product development. This paper examines the problem of characterization of technological change specifically in product development projects. It builds on recent studies (Clark et al., 1987; Schoonhoven et al., 1990; Iansiti, 1995) in which the characterization of technological change and the relationship between technological change and measures of product development performance such as development time have been important themes. To explore technological change in product development, the paper draws on an in-depth study of the development process in a manufacturer of high-value specialty polymers. This study of product development in a single firm, typical of firms in this industry, allows examination of the characterization of technological changes in an important set of industries - the process industries. It is hoped that understanding technological change in this environment will also provide generalization of insights into technological change in a broader context.

2. Characterizing technological change in product development How can the construct of technological change be operationalized in individual product development projects? In the study of technological change, many empirical characterizations of change have been used. Ettlie et al. (1984) and Dewar and Dutton (1986) examined the different effects of incremental and radical change on industry evolution. Abernathy and Clark (1985) found that periods in the history of the automotive industry have been dominated by one or another type of technological change. Tushman and Anderson (1986) found that competence-enhancing and competence-destroying change had dramatically different effects on the structure of the cement, airline and computer industries. Such characterizations of technological change have been predominantly used to examine the role of technological change in industry evolution. But the context of product development seems to need a different approach that might help clarify the relationship between technological change and performance measures of development projects. Our intent was to dewflop a characterization of technological change that could in some way be related to performance parameters such as time, cost and quality (Wheelwright and Clark, 1992).

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Characterization of technological change at the level of individual product development projects is still in its infancy. Yet, a few researchers have attempted to describe the technological change between product generations in specific industries (Clark et at., 1987; Schoonhoven et al., 1990; Iansiti, 1995). Both the characterization of technological change and the relation between technological change and measures of product development performance such as development time have been important themes in these studies. In their study of product development in the auto industry, Clark et al. (1987) included several measures of technological newness. The dominant measure was the number of parts that were newly designed for each car model. Their data show a strong correlation between development time and parts engineered. But a second measure of newness - the introduction of a leading-edge process innovation - had no significant effect. Schoonhoven's (Schoonhoven et al., 1990) study of entrepreneurial finns in the semiconductor industry utilized a technological newness variable as one determinant of the time required for firms to ship their first product. In the study managers were asked to indicate, on a 10-point scale, the amount of new knowledge created and synthesized in the development project. 7l~ne two variables of knowledge creation and knowledge synthesis proved to be significant factors in predicting development time in the industry although their influence was small. A third variable of technological newness, the degree of circuit miniaturization, provided little explanatory power. Finally, to examine development projects in the technology of ceramic packaging, Iansiti (1995) characterized the technological newness using a technological parameter, the chip density, normalized relative to the best in the industry at the time. The results showed that this measure was weakly correlated with development time. These studies suggest that the absolute level of a technological target is not as useful a measure as a relative one. Put differently, the relevant measure of technological change may be the r e l a t i v e change entailed in a project from one product generation to the next. In short, the crucial raeasure of technological change in product development may be technological newness, a way of comparing the new technological envelope to the past envelope. Characterizing technological change by the degree of change between product generations embodies a view of product development as a process of building knowledge about product and process design. Each new product and each new discovery is built on the previous knowledge that is available in the firm and on the previous technological envelope of capability. This characterization of change as technological newness is, thus, consistent with the philosophy that product development is at its core a process of expanding a firm':s envelope of technological capability (Abernathy and Clark, 1985; Wheelwright and Clark, 1992) and also with the current thrust of capability-driven strategy (Pr~Lalad and Hamel, 1990; Stalk, 1988).

3. The environment: the process industries

The process industries comprise a large segment of the economy. From commodity raw materials like steel, paper, and glass to value-added materials such as advanced

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ceramics, the process industries are a unique set of industries built around the production processes that manipulate material properties to produce raw materials for use in a variety of applications. Process industries have characteristics that are very different from assembly/fabrication industries and may require a different type of management emphasis (Landau, 1989; Utterback, 1994). Yet in much of the literature, a sense of the differences in technological change across different technologies and industries seems to be missing. In particular, the existing literature gives little sense of the specific management challenges in developing of process-based materials characteristic of the continuous process industries. In the literature on strategic product development, for example, (e.g., Wheelwright and Clark, 1992) most research implicitly assumes traditional design processes of fabricated and assembled parts for which design is a sequence of product design and subsequent process design. Innovation in the process industries, however, is enabled primarily by process innovation. Process development is, in fact, the difficult and constraining aspect of product development in the process industries (Clark, 1985; Stobaugh, 1988). 3.1. The production process

Given the role of process innovation in enabling improvements in polymer properties, an understanding of the production process is vital. All plastics are polymeric materials, which are made of long chains of carbon and other atoms chemically linked to form polymers. Under the proper conditions of heat and pressure, the individual organic molecules termed monomers bond together chemically in chains from 100 to 1 million carbon atoms in length forming a substance that can be molded into the hard material we know as plastic. The manufacturing process used to make polymers, although difficult to execute, is fairly straightforward. Fig. 1 gives an outline of the basic production sequence. Following isolation from crude oil (Step 1) the liquid or gas feed stocks are fed into a reaction vessel where, under heat and pressure, the monomer molecules react in the polymerization process (Step 2:) to form polymers. The molten polymer is separated from the liquid solvent and exWuded through small holes to form spaghetti-like strands. These strands are then sliced into small pellets of solid polymer or what is termed polymer resin. After the polymerization, the pure polymer is often mixed or compounded (Step 3) with other materials to modify the properties or add color. The polymer pellets and other additives are combined in an extruder where they are melted, extruded as strands, and again ,;liced into pellets. Further processing of the polymer resin is carried out by firms known as plastic processors. These plastic processors buy polymer resins and mold them into parts for use in industrial and consumer applications ranging from computer terminals to automobiles to construction to packaging (Step 4) before being passed on to the end use (Step 5). 3.2. Innovation in the process industries

The literature on the process industries has focused primarily on cost reduction and economies of scale (Lieberman, 1984, 1989; Linn, 1984; Spitz, 1988; Stobaugh, 1988).

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Monomer Production

Polymerization

Compounding

1 Molding (Fabrication)

End

Use

Fig. 1. Production sequence.

In this view of the process industries, incremental improvements in cost are crucial and firms strive to find ways of whittling down the cost of production (Enos, 1962). The classic work on the increased efficiency of Hollander (1965) in DuPont's rayon plants embodies this view. In this paper, however, our interest lies in a second arena of innovation in the process industries - the development of new and modified materials. Other authors (Freeman, 1982; Hounshell and Smith, 1988; Stobaugh, 1988) discuss this innovation process in large-scale petrochemical innovation. This arena of innovation includes both the advanced materials research by which entirely new polymers and other materials are developed to offer quantum leaps in properties like strength or electrical conductivity and also the research by which F~lymers are modified and blended to provide incremental improvements in industrial properties. In contrast to pure chemicals,, the properties of any given polymer material are not static; as firms learn to modify the manufacturing process they invent polymer variations that offer better properties in targeted markets. Thus, much of polymer innovation includes incremental modifications of one kind or another. Such incremental improvements in properties have played a strategic role in the polymer industry. Such improvements have enabled the penetration of new markets by displacement of other materials (Barnett et al., 1992; Fisher and Pry, 1971). Incremental improvements in polymers, for example, allowed plastics to rel:dace glass as the primary packaging material for many food products. Plastic milk bottles were made possible by a stronger version of

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polyethylene. In a similar fashion, existing plastics were modified to gradually take over the market for many types of containers. The relationship between polymer manufacturers and plastics processors deserves clarification. Plastics processors often conduct their own product development of fabricated plastic parts using off-the-shelf materials independent of polymer manufacturers. In industries, however, that are dependent on leading-edge material properties, polymer manufacturers work together with plastics processors to create a new or improved material for their fabricated parts. The new products thus developed are new polymer resins - new polymers and mixtures of polymers with unique properties. These types of development products - new materials produced in a continuous process and sold to plastic processors - are the focus of this study. 4. Technological newness in process industries Previous studies have demonstrated the utility of relative measures of change, or technological newness. How, then, are relative measures of change to be applied to a specific industry? Characterizations of technological change such as radical versus incremental (Ettlie et al., 1984) or competence enhancing versus competence destroying (Tushman and Anderson, 1986) can, in principle, apply to innovations in any industry. These are, however, uni-dimensional constructs. In this study, we explicitly sought a multi-dimensional construct of technological newness that would apply to a select subset of industries - the process industries. Multi-dimensional measures of technological newness hold potential for providing a richer description of technological change when applied to individual development projects in specific industries (Radnor and Rich, 1980). Many studies of technological progress have relied on a change in a technological performance parameter over time (Sahal, 1981; Foster, 1986) as a quantitative measure of technological change. The use of a single performance parameter is a start. But in the process industries we found that technological progress often proceeds on multiple performance parameters simultaneously. Thus, we face the need to characterize the underlying changes in architecture or process that enable such performance improvement. The reviewed studies also include some measures of change that are not applicable in this industry. For example, of all the measures of technological newness we evaluated, the number of parts affected in innovation (Clark and Fujimoto, 1989) seems to be the simplest. Unfortunately, measure of parts count applies only to assembled products. Products such as semiconductors or polymeric materials are not divisible into components; characterizing innowttions in these products by the number of components affected is simply not possible. To characterize technological newness in the process industries we found it necessary to focus on the central issue in process industries - process development. Close examination of the process industries suggests that the central concern in the development of a new material is the development of a production process that will achieve certain desirable properties needed in a given technological application (Clark, 1985; Stobaugh, 1988). If then, in these industries, every new product is dependent on process

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innovation, might not the technological change entailed in each product best be captured by characterizing the required changes in the production process? We, therefore, postulated that technological newness in the process industries could best be understood by examining changes in the production process. Review of the literature and discussion with research and development (R&D) managers identified four central dimensions of technological change in the production process: (1) modifications in cl)emistry, (2) improved production equipment, (3) new fabrication technology, and (4.) improved levels of process control. The literature suggests that in every product development project, new knowledge and capability must be developed in one or more of these areas to enable desired improvements in the product characteristics (Freeman, 1982; Utterback, 1994). 4.1. Chemistry newness

In the polymer industry, as with other process industries, much innovation effort is expended to obtain new types of molecular structure or micro structure (DuBois, 1972; Freeman, 1982). To create a new molecule or new micro structure, firms must carry out extensive experiments to explore new chemical reactions and mixing behavior. In the polymer industry, for example, experiments are carried out to explore the reactivity of new monomers, to determine temperatures of most effective polymerization and to understand the behavior of polymer mixtures. 4.2. Production equipment newness

In addition to the changes in chemistry, the development of new or modified processed materials often requires design and installation of new equipment such as pipes, reactors and pumps (Stobaugh, 1988). To commercialize an entirely new polymer, an entire manufacturing facility must be designed. For a modified polymer the current equipment must often be modified to enable a new reaction or improve component mixing. Thus, in addition to the associated changes in chemistry, the amount of change in the production equipment or equipment newness is also an important dimension of technological newness. 4.3. Fabrication newness

The development of any new material may include more than changing the chemistry and the production equipment. Penetration of a new application may require that fabrication technology be modified. This fabrication technology is usually owned and operated by plastics processors who are customers of the material producer. Often a market for the material is tapped by investing in the development of this downstream technology (Corey, 1956). An example of such an application is the development of plastic grocery bags. These bags have been used for many )rears, but only in the past few years have they surpassed paper bags as the most economical alternative for carrying groceries. To be competitive with paper bags, plastic bags ]had to be strong enough to hold the weight of heavy canned goods but also very thin so that the actual material content was low, thus making

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the bags less expensive. This combination of strength and thinness was obtained by first modifying the polymer to get high strength and also modifying the fabrication technology to enable fabrication of thin sheets. Penetration of the new application required technological improvements in both areas. 4.4. Process control newness

A fourth concern in introducing new products is the need for increased control of the manufacturing process. At the bench scale the temperature of a reaction can be controlled to within fractions of a degree, thus ensuring uniform properties throughout the material. On a production level, however, control is much more difficult and product properties are typically substantially degraded from the properties obtained using similar chemistry at a bench level. The properties of any material depend on the level of process control in the manufacturing process. Increased process control requires experimentation and various types of analysis (Taguchi and Clausing, 1990) that contribute to the technological newness of the project. This four-dimensional char~Lcterization of technological newness provides a framework for examining technological change in the process industries. It should be noted that these dimensions of change are not necessarily unique to the process industries. The development of new production equipment, for example, is common to almost any development project in traditional fabrication or assembly industries. Similarly one would expect process control to be a factor in other industries. Thus, the issue is one of emphasis. Discrete parts makers make process changes and in some development projects the process change may be the central focus of the project. In the development of new materials, however, process development seems always to be a core issue. Thus, for process industries these dimensions are the central focus of any development project. This characterization of technological change can be summarized as follows: 1. It measures relative change or "newness." 2. It is comprised of multiple dimensions. 3. It addresses the central technological changes in integrated product-process development. 4. It applies specifically to product development projects in the process industries but may also apply to the process development component of other industries.

5. Research methods To operationalize the dimensions of technological newness, we looked for an environment where we could examine projects and determine the relevance of the dimensions of technological newness for characterizing individual product development projects. To study technological newness at the project level, we intentionally chose to limit the study to a single firm, a large manufacturer of advanced polymer materials. By limiting the study to a single firm we were able to ensure internal calibration of the scales of technological newness. Working within a single firm also gave us control over differences in managerial practice seen in comparative studies (Clark and Fujimoto, 1989). We were, thus, able to minimize variance in the organizational form and

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organizational process in order to more closely examine technological change in a specific technological environment. In meetings with R & D and marketing managers, we identified 20 projects comprising the major projects done in the company over the preceding five years. These projects included a range of material types. Each project was the development of a particular material for a particular polymer application. Applications included appliances, automobiles, construction, and electronics. Data on each project were compiled through the use of a questionnaire that was distributed to the project leader. For each project we then carried out interviews with the project leader, one other member of the project team, and a more senior manager responsible for the project. In the interviews we reviewed the results of the questionnaires and clarified any discrepancies. In this way we were able to triangulate (Campbell and Fiske, 1959; Jick, 1979) to more clearly understand the projects and enhance the accuracy of the data. 5.1. Scales o f technological newness

The technological newness entailed in each project was measured using a scale of newness for each dimension of process change. Measurement scales were graduated from one to five where one represented nothing new, that is, no change from current technology, in the dimension and five was entirely new in the dimension. On the scale of chemistry newness, for example, one represented no change, two represented minor changes, three represented moderate changes, four represented extensive changes, and five represented an entirely new chemistry. The use of the scales for each of the four dimensions initiated a pattern of triangulation in data gathering that deserves some comment. We originally asked both project managers and senior managers to rate the changes in each project on a scale of one to five. While most of the r~ttings were in agreement, analysis of the results revealed a few discrepancies. In discussing this with several managers we discovered that while managers agreed on the technological changes that were made, the difficulty was in ensuring uniform calibration of the scale. As might be expected, more senior managers tended to rate projects lower on the scale of newness, a result of their broader experience with many projects. It was less likely that they would consider a project high on any dimension of newness; they reserved such a distinction for a very few projects in their experience. To resolve the differences, we met with managers familiar with all 20 projects in the study and examined the ratings of technological change for each project in the context of the entire set of projects. The fe,w discrepancies in ratings were brought into agreement when the managers rated the technological change in each project relative to the range of change seen in the entire set. Thus, only by examining the entire set of projects simultaneously could the technological changes in each project be accurately calibrated. 5.2. Measuring development pe~Tformance

Our goal was to identify measures of technological change that were useful in understanding the performance of projects. As an important measure of project perfor-

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mance, we evaluated the development time for each project. Development time was measured from the point when the first plans for a formulation of the material with the desired properties were discussed. In discussing the beginning date we found an occasional variance of one or two months between dates given by project managers and senior managers. Apparently many projects had no formal starting date. In each case, we chose the earliest date of those given. Analysis showed that this amounted to a variance of less than 10% in the development time which had no noticeable effect on the results. The completion time used was the date the material was qualified in the production facility. At this point in time the product began to be marketed under the firm's trademark and no further changes in properties were made. This date was easily determined from plant records. The total development time for each project was, thus, considered to be the time from the initial discussion of the concept for the new material to the completion of the start-up phase in the production plant.

6. R ~ Each project was, thus, characterized on the dimensions of technological newness and data on the length of the development project were collected. These data provided the basis for examining the relationship between the dimensions of technological newness as well as the relationship between technological newness and the development time performance.

6.1. Patterns of change A summary of the data patterns is given in Table 1. The data showed that one type of change, chemistry newness, was common to all projects. In other words, when the process chemistry newness was rated on a scale of one to five, no project had a chemistry newness of one. This meant that all projects required some change in process chemistry, consistent with the firm's definition of a development project. The range of changes varied but all entailed some modifications of the polymerization or compounding conditions, increasing the length of the polymer molecules or combining them in different ratios.

Table 1 Basic project data Range

Mean

Deviation

Total development time (months)

6-108

34.1

25.6

Dimensions of technological change Chemistry Production equipment Fabrication technology Process control

2-5 1-5 1-5 1-4

2.8 2.4 1.6 2.2

1.1 1.1 0.9 1.2

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Table 2 Correlations of variables Chemistry,

Production equipment

Fabrication technology

Process control

0.42 0.49 0.76

0.05 0.40

0.09

Chemistry Production equipment Fabrication technology Process control Time (months)

0.43 0.05 0.13 0.71

The data also showed that some dimensions of technological change were more common than others. While changes in chemistry were the common thread in every project, changes in equipment were also common; only two projects had no change in equipment. The data also indicated a low incidence of changes in fabrication technology relative to changes in chemistry and production equipment. Of the 20 projects, 11 required no changes in fabrication technology, suggesting perhaps some natural constraints on changes in fabrication technology. The scale for process control newness differed slightly from the scale used for the other three variables. A value of one was used for reduced control requirements; two for no change; three for minor change; four for moderate change; and five for major change in control requirements. This modified scale was used to search explicitly for projects where Taguchi parameter design methods were used to allow easier control in production (Taguchi and Clausing, 1990). In compilation of the data, however, we found no project for which control requirements fell into the first category of change, that of lessening control. To provide a common reference point, all values were reduced by one so that the final range from one to four would be more consistent with the ranges of the other variables. This additive change has no effect on the data analysis. We also examined the correlation between variables. The analysis (Table 2) showed strong correlations between variables such as chemistry newness and equipment newness, suggesting that most projects with large changes in chemistry also included large changes in equipment. Naturally, despite the overall correlation between the individual dimensions, there were also several projects that displayed the opposite pattern. In other words, some projects with a high degree of newness in chemistry had a low degree of newness in equipment. The results, thus, showed that projects are not purely radical or purely incremental. Each project includes a range of changes. A project may, for example, entail radical changes on one dimension but only incremental changes on the other dimensions. In short, the data indicate that technological changes occur, to a different degree, in each of several contributing dimensions,, thus discounting the natural tendency to view projects along a single dimension of change. This result, therefore, demonstrates the importance of characterizing projects on multiple dimensions of technological change by showing that multiple dimensions of technological newness are necessary for a full understanding of the technological change in any project.

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6.2. Simple regression Having examined the patterns of newness, we next examined whether the dimensions of newness each had similar impact on the development time performance. We first examined simple regressions between each variable and the development time (Table 3). These simple regressions give us an important perspective because they characterize differences in development time between "average" projects at different levels of newness. A regression of development time versus equipment newness, for example, characterizes the average difference in performance between all projects with equipment newness of one and all projects with equipment newness of five. The simple correlations indicated a positive relationship between each of the individual technological variables and the development time. However, the magnitude of their importance differed. For both the new chemistry and new production equipment variables, regression coefficients indicated a large increase in development time with increased newness. The coefficient of 18.7 on the chemistry newness regression, for example, indicates that projects differing one increment in chemistry newness were associated with a difference of roughly one and one half years in development time. That is, a project with chemisw¢ newness of 4 required, on average, an extra 18 months in development, relative to a project with a chemistry newness of 3. This magnitude of the relationship is somewhat less pronounced for the other variables. According to the simple regression, a difference of one increment in equip-

Table 3 Regression models for total development time Constant Chemistry Production equipment MODEL 1 Chemistry only

- 13.2 (18.7)

MODEL 2 Equipment only

- 6.9 (9.0)

MODEL 3 Fabrication only

17.0 (10.7)

MODEL 4 Control only

29.6 (12.8)

Process control

17.5 114.2) 16.4 (3.3) 10.4 (5.6) 2.1 (5.3)

MODEL 5 Chemistry and equipment

- 27.3 (9.2)

11.5 (3.3)

12.2 (2.9)

MODEL 6 Chemistry, equipment, fabrication

- 32.1 (9.7)

12.2 (3.3)

10.3 (3.1)

MODEL 7 Chemistry, equipment, control

- 18.8 (8.4)

10.7 (2.8)

15.8 (2.8)

Standard errors in parentheses.

Fabrication technology

4.5 (3.4) -7.2 (2.6)

R2

Adj. R 2 d.f.

0.50

0.47

18

0.58

0.56

18

0.16

0.15

18

0.01

0.01

18

0.76

0.73

17

0.78

0.75

16

0.83

0.80

16

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ment newness is associated with a difference in project length of 16 months; the average difference in development time between projects at two levels of fabrication newness was 10 months. Finally, the regression between development time and the control newness shows a relatively small coefficient, indicating that the difference in development time between an average project with minor and one with moderate changes in process control would be only two months, much less than the average differences associated with changes in chentistry or equipment. These simple regressions provide an important view of the differences in the dimensions of change because they give a sense of the patterns one might see if the projects were rated solely on one technological variable. In other words, the simple regressions indicate that if used alone as indicators of the degree of technological change in the project, the dimensions of chemistry newness, equipment newness and fabrication newness would all show a strong connection to the development time, while the dimension of process control newness would appear to be less important in understanding the performance variable of development time. 6.3. Multiple regression

Simple regression, of course, only characterizes differences in development time between "average" projects at different levels of newness. Since they don't provide control for the effects of the other variables, such regressions say little about the incremental effect that changes in a single variable exert on the development time. To account for inter-item correlation among the four variables, multiple regression by minimization of least squares was used to explore the patterns. Several regression models aJre summarized in Table 3. Models 1 and 2 show that either the chemistry or equipment newness alone accounts for much of the variation in the development time. Considered alone (Model 1), the chemistry newness accounts for 50% of the variation in the de,velopment time. Equipment newness alone (Model 2) accounts for 58% of the variation in the development time. Including both variables as predictors in the regression of total development time (Model 5) shows that both have a significant relationship to the development time. Adding the fabrication newness variable to the regression results in a model (Model 6) for which the coefficient of fabrication newness is small - only 4.5 compared to 12.2 for the chemistry newness and 10.3 for the equipment newness. This coefficient is the expected sign but not statistically significant. Nor did including dummy variables for the two projects with major changes in fabrication make the fabrication newness variable significant. Apparently the fabrication newness has no systematic relationship with development time. The effect of changes in process control does, however, appear to be significant. Adding the control newness variable to the original regression model containing only the chemistry newness and the equipment newness results in a model (Model 7) with high explanatory power and high significance on all parameters. This regression model shows that of the four variables of technological newness, only three appear independently to affect the development time of the project. Of these three variables, two have the expected positive sign, that is, higher values being associated with longer development

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time. Contrary to what is expected, however, the sign on the process control coefficient is negative, a result to be explored later in this paper. Diagnostic procedures were also used to uncover data points that might exert undue influence on the results. While two points were found with high leverage, further analysis indicated that they did not distort the regression results. Elimination of these points from the regressions had little effect on the regression coefficients or their significance.

7. Discussion and research limitations 7.1. Discussion o f findings

Several important conclusions follow from the data analysis. First, the study shows that technological change is not a uni-dimensional construct. Past studies suggest that technological change can be characterized by measuring the change in a single performance variable over time (Foster, 1986). In this study, however, we found that multiple dimensions of change are necessary to explain the entire technological change. Such a multi-dimensional characterization allows the amount of new knowledge generated in a project to be understood in simple terms but in terms that provide a richer description thar~ simply categorizing projects as radical or incremental. Second, the study shows that there is no universal correlation between individual dimensions of change. Changes. in chemistry, for example, are often associated with other changes but changes in fabrication technology appear to be independent of the other variables. Including multiple dimensions of technological change in the characterization sheds better light on this characteristic of technological change. Third, these results show that technological newness is a straightforward way of characterizing change in individual development projects. A scale of technological newness allowed the technological change in each dimension to be characterized, thus providing an internally consistent assessment of technological newness that could be used to compare a set of projects covering a range of technological change. Fourth, the study shows that a regression model relating degree of technological newness and a performance measure is important in any characterization of change because it can effectively demonstrate the importance of the dimensions of technological change. By relating the dimensions of technological change to a measure of strategic importance, the regression shows that these measures are not only possible measures but also appear to be useful measures of technological change. Fifth, the study shows that a performance variable may well be influenced most strongly by a subset of the applicable technological dimensions of change. In other words, just because a project entails multiple dimensions of change does not mean that each dimension of change is equally important. In any characterization of technological change, some dimensions of change are likely to be more relevant for a particular performance variable. In this study, for example, three variables of technological newness are the most important for understanding development time. A similar regres-

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sion using other performance data on cost, product quality, customer satisfaction, market share, etc. might prove more or less predictive. Sixth, the study provides specific information about the process industries. In particular, these results confirm the pattern predicted by others (Clark, 1985; Stobaugh, 1988; Landau, 1989; Utterback, 1994) that product development in the process industries would depend on variables of process change. It has been proposed in the past that improvements in the product itself are highly dependent on process improvement. This study shows that the development of new knowledge relevant to the process is closely related to the amount of time required in the project. These results may also tell us something fundamental about other industries outside the process industries in which product generations are closely coupled with major improvements in process. The results suggest that for any industry in which changes in product are accomplished by changes in process, these dimensions of newness are vital and may well explain a large part of the variation of development time. Finally, it provides preliminary evidence that some patterns of technology development may differ across different industries even while some pattems are more specific to groups of industries. By broadening the range of industries that have been studied, the study, thus, increases our understanding of differences across industries. 7.2. Generalizability and study limitations

It is important to address the generalizabilty of these findings. First, consider the dimensions of technological newness. These four dimensions of technological newness are generalizable to virtually all other process industries. Every process industry faces the same dimensions of technological newness. In these industries, material components are combined through some sort of chemical or physical mixing. In every industry, equipment must be modified in order to achieve desired properties. In every process industry, downstream fabrication operations must be modified and adapted to create the desired results. And in every process industry, the development of increased levels of process control enable better performance properties to be attained. The framework of technological newness thus applies not only to finns within the polymer industry but more broadly to other process-based development efforts. This leads, however, to two limitations of the study which deserve mention. First, the projects studied were those projects that could be documented in the firm - not a random sample of the projects undertaken in the firm. They were a selection of the major projects for which data had been kept and project participants were available. Second, since the study was caJrried out in a single firm in a single specific industry, there is limited generalizability of the specific relationships between technological dimensions to other firms and other industries. The fact that, in this study, a large portion of the variation in development time is explained by the technological dimensions is interesting but not highly generalizable. Two factors may be responsible for this. First, the study included a high degree of control over the technology, thai: is, within the firm the production technology was very similar. Second, variation in the organizational procedures was minimized. Greater

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clarity about the technological newness and its effects may have resulted from the reduction of technological and organizational variation.

8. Implications for management The analysis reported in this study leads to several implications for managing a set of heterogeneous development projects. Such analysis has potential to improve the ability of firms to deal with multiple dimensions of technological change in product development. First, characterizing the technological change in individual development projects on several dimensions can assist the initial evaluation and selection of development projects. The resulting characterization of both similarities and differences among projects may be particularly important to determining the potential difficulty and required resources for a project. This kind of information may be an important input to project selection and ultimately a firm's competitive strategy. Second, a regression model based on the characterization of change can assist in predicting project performance parameters such as development time and cost. If, for example, a manager could characterize a project from the outset on any four dimensions of change, it would allow estimation of development times accurate to the extent that the four variables explain the data. It may be that in some environments there is no relationship between the degree of technological change and development time. This information might also be valuable since it would indicate that increases in technological difficulty might well be accomplished with no time penalty. Third, when technological changes are interrelated, linking the characterization of change and the performance variable of development time through a regression provides a means for understanding the relationships between different dimensions of technological change. This goes beyond the predictive value of a regression model. A highly predictive regression model may be especially useful in managing projects but even a regression with lower predictive power can be used to explore complex relationships between variables. The data and regression models in this study provide a particularly interesting example of how the characterization of change and the associated regression model can be used to explore the often complex relationships between different dimensions of technological change and the performance variables. This section elaborates more in-depth on this point. As noted, the three-variable regression model (Model 7) includes an apparent paradox with respect to the process control variable. In other words, intuition suggests that increased process control newness should be related to longer development times. Yet the negative slope on the regression coefficient indicates that increased process control newness is actually associated with decreased development time. Explaining this is not immediately obvious. It seems to imply that contrary to our intuition, process control newness actually reduces development time. To understand this apparent paradox, misinterpretation of the regression coefficients must be avoided. In this context, a negative coefficient for process control newness does

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not mean that any project with increased control requirements will take less time than a project with no control requirements. On the contrary, as shown by the simple regression, a positive relationship exists between control requirements and development time. Instead, the negative coefficient indicates that, other factors (i.e., chemistry newness and equipment newness) being equal, projects that include increased control require less time in development. We sought a mechanism that might account for this unanticipated result. Additional regression analysis allowed the robustness of the result to be evaluated. In agreement with the negative coefficient, residuals of the two-variable regression model (Model 5) showed a clear downward trend with increasing process control newness. Similarly, the residuals for single variable regressions in both chemistry newness (Model 1) or equipment newness (Model 2) also showed a distinct downward trend in development time with increasing process control newness. This confirmed that when controlling for either one or both of the other technological variables, the development time for projects with changes in process control was on the average less than for those projects with no changes. This paradoxical negative correlation between process control newness and development time was eventually unclerstood through subsequent interviews with project managers. In these interviews, we inquired about the process control newness. We found that in actuality the development process for projects with new process control differed somewhat from the normal process in how the product testing was integrated into the sequence of activities. For normal projects without new process control, the applicable new chemistry required much application-specific testing that added considerable time to the project. In contrast, for projects that entailed changes in process control, the new chemistry was developed internally using testing that did not substantially affect development time. Thus true to the regression model, for projects with the same level of chemistry newness, those with increased process control actually did require less development time than projects with no increased control. A similar pattern was seen for equipment newness. Equipment improvements needed for increased control were typically modifications that could be tested more completely before installation and did not :require the same amount of debugging needed in more normal equipment newness. So for projects with the same level of equipment newness, those projects with increased process control did in reality require less development time than projects with no increased process control - just as the regression model indicated. Existing data do not allow this analysis to be made with high statistical confidence. Nonetheless, this example illustrates the potential of such data and the associated regression analysis in understanding technological change. Managers and engineers alike make decisions based on personal observations and patterns they recognize, but complex patterns are less easily recognized (Daft and Lengel, 1986). It is easy to imagine that when decisions are made by management intuition, such nuances often go unnoticed. Imagine for a moment the normal pattern of drawing conclusions from simple observations in this environment. Managers observe that longer projects have certain characteristics. They see that changes in chemistry, equipment, fabrication and process control are all associated with longer projects. In the absence of analysis controlling for

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multiple variables, firms are unlikely to recognize underlying relationships between variables. Thus, they are unlikely to recognize that new fabrication technology has no consistent effect and some kinds of process control take less time. Such effects are only observed when technological change is characterized and regression is used to control for variation in other variables.

9. Future research directions

The characterization of change and the associated analysis presented in this paper suggest several directions for future research. First, the characterization and comparative analysis of patterns of technological change in development projects across firms and industries is a clear direction for future research - for which the dimensions of change identified in this study provide a guide. Some patterns of technological change may occur across multiple industries even while some patterns are more specific to groups of industries. Patterns of change in different industries may well vary according to the maturity of the industry and these different patterns of change may well zfffect industry evolution. The study of such underlying patterns may contribute to the understanding of industry structure and competitive dynamics (Dosi et al., 1988). Second, the relationship between degree of technological change and development time in different industries is a logical extension of the characterization of change. Research across industries may identify unique patterns in particular industries as well as practices that can be transfe]Ted between industries. Hidden patterns in technological change may suggest organizational and procedural changes that can improve project performance. Such research may, thus, be highly valuable in understanding and improving the practice of product dew~lopment. Third, research should be directed at the problem of how firms identify and manage the demands of multiple dimensions of change in a particular project. In practice, firms may need to identify not only the desired changes but also the effect of those changes on other technological dimensions. Clark and Fujimoto (1989) discuss the need to synchronize changes in different technological areas, comparing this to simultaneous solving of equations. Firms may, in fact, easily manage self-contained changes, but have more difficulty with domino-type changes where one change in technology initiates changes in other technologies (Brooks, 1975). Fourth, the mechanism by which technological changes are initiated remains an interesting and important question. In the current study, for example, new chemistry was frequently a technological initiator. In other words, the search for new properties in an application initially started with changes in the chemistry. Yet frequently the changes in chemistry led to changes in equipment and fabrication. In other projects, however, changes in fabrication technology were the starting point and these changes initiated changes in chemistry or production equipment. Such interconnected technological changes and the possibility of technological change initiators is an area for future research.

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Fifth, the relationship between technological change and organizational structure remains to be explored. It may, for example, be possible to partition technological changes (von Hippel, 1990) in a way to allow clearer paths of development. How this is best done organizationally is still one of the great puzzles of technological development. With further study, it might be anticipated that engineering design systems and accompanying human organizations will be seen to fit with specific technologies in much the same way that production systems and associated human organizations are adapted to and shaped by the particular technology (Perrow, 1967). In short, the characterization of technological change is vital to the understanding of technological development and technological competition. In fact, in the literature on management of technology the generic approach to technology is only beginning to yield to characterizations which allow patterns in different industries to be studied. The study of these patterns will require continued examination of the interaction between the technology, markets, and orgarfizations in a variety of industries. Understanding and characterizing technological change should be seen as an important part of this effort. This work in the process industafes is a first step in this direction.

Acknowledgements The research reported in the paper was supported by the Division of Research, Harvard Business School and a grant from the Alfred P. Sloan Foundation.

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