Environmental uncertainty, organizational integration, and new product development effectiveness: a test of contingency theory

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Environmental Uncertainty, Organizational Integration, and New Product Development Effectiveness: A Test of Contingency Theory William E. Souder, J. Daniel Sherman, and Rachel Davies-Cooper

R&D/marketing integration clearly improves new-product development (NPD) effectiveness. However, achieving this integration increases the costs of NPD efforts. If technical and market uncertainty moderate the effects of integration on NPD effectiveness, perhaps a firm can achieve NPD success in a more costeffective manner by seeking the appropriate level of integration, based on the perceived level of uncertainty. In a study of 101 NPD projects at high-tech firms in the U.S. and the U.K., William E. Souder, J. Daniel Sherman, and Rachel Davies-Cooper explore the interplay between technical and market uncertainty, integration, and NPD effectiveness. Their study examines two types of integration: R&D/marketing integration and direct R&D/customer integration. The study measures NPD effectiveness in terms of such indicators as NPD cycle time, prototype development proficiency, design change frequency (a negative performance indicator), and product launch proficiency. The responses from both the U.S. and the U.K. firms provide balanced samples of high and low uncertainty projects, as well as successful and unsuccessful projects. The results of this study support previous research regarding the positive effects of both R&D/marketing integration and direct R&D/customer integration on NPD effectiveness. However, only one measure of NPD effectiveness—R&D comercialization effectiveness—was affected by both R&D/marketing integration and direct R&D/customer integration. This result suggests that the two types of integration are distinct from one another and that managers need to emphasize different types of integration, depending on which aspects of NPD effectiveness their firms need to improve. The results also suggest that technical and market uncertainty influence some aspects of NPD effectiveness. For example, the perceived level of technical uncertainty was found to influence prototype development proficiency and to moderate design change frequency. In other words, these results support the idea that a high level of technical uncertainty warrants paying extra attention to increasing prototype development proficiency in the interest of reducing design change frequency. However, the results also reinforce the idea that NPD activities generally involve high levels of technical and market uncertainty, which means that the high cost of integration may be a requirement for NPD success. © 1998 Elsevier Science Inc. J PROD INNOV MANAG 1998;15:520 –533 © 1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

0737-6782/98/$19.00 PII S0737-6782(98)00033-2

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Introduction

I

ntegration between research and development (R&D) and marketing personnel has repeatedly been found important to new product development (NPD) success [5–7,23–26,29,37– 40,43–54,61]. However, much remains to be learned about the effects of environmental uncertainty on this integration-success relationship. Contingency theory predicts that uncertainty moderates this relationship, with R&D/marketing integration being highly important to NPD success when technical and market environments are highly uncertain, and less important when those environments are less uncertain [17,21,31–33,55–59,61]. This implies that NPD managers need only emphasize integration under uncertain environments, thereby economizing on organizational integration costs. However, mixed support for these notions has been reported in the literature, possibly due to the use of limited definitions of integration and uncertainty and single-item NPD effectiveness indicators [4,17,27,31–33,43–54, 61]. Therefore, a primary objective of this study was to test and reconcile these conflicting findings as reported in the literature. This study of 101 NPD projects sampled from 48 R&D-intensive U.S. and U.K. firms examined the relative NPD effectiveness of R&D/marketing integration and direct R&D/customer integration, under environments of perceived high and low technical and market uncertainties. While many studies of R&D/ marketing integration have been carried out, much less attention has been given to direct R&D/customer integration. The inclusion of R&D/customer integration here extended the integration concept to reflect practices common among today’s short cycle time firms [24,26,47,49,54]. In direct R&D/customer integration, R&D employees are encouraged to build close relationships directly with prospective customers. Multiple NPD effectiveness indicators were used in the interest of increasing the reliability and generality of the findings. The results reaffirmed the broad importance of both R&D/marketing integration and direct R&D/customer integration to NPD effectiveness. The contingency theory prediction that high degrees of integration are important to NPD effectiveness in high uncertainty environments was reinforced. However, minimal mod-

Address correspondence to Dr. William E. Souder, Center for the Management of Science and Technology (CMOST), Suite 126 ASB, University of Alabama in Huntsville, Huntsville, AL 35899.

BIOGRAPHICAL SKETCHES William E. Souder holds the Alabama Eminent Scholar Endowed Chair in Management of Technology at the University of Alabama in Huntsville (UAH). At UAH, Dr. Souder is also the founder and Director of the Center for the Management of Science and Technology (CMOST), and he holds positions as Professor of Engineering and Professor of Management. He is the originator and director of the 19-country cross-culture INTERPROD study from which this article derives. Dr. Souder received the B.S. with Distinction in Chemistry from Purdue University, M.B.A. with a Concentration in Marketing from St. Louis University, and Ph.D. in Management Science from St. Louis University. He has 12 years of varied industrial management experience in the chemical industry, 7 years of government laboratory experience, and over 20 years of experience in academe, where he has initiated several courses and programs in the management of technology. Dr. Souder has founded three small start-up firms, has many years of consulting experience with numerous Fortune 500 firms, and has a long track record of research in the management of research and development, engineering, technology transfer, and innovation. He is the author of over 200 publications and six books on the management of technology and the recipient of several prestigious awards from both U.S. and foreign governments, including one from the White House for service on the President’s commission on industrial policies to stimulate innovation. J. Daniel Sherman received a B.S. degree from the University of Iowa, a M.A. degree from Yale University, and a Ph.D. in organizational theory/organizational behavior from the University of Alabama. He has held visiting positions at the Stanford Center for Organizations Research at Stanford University and at the RoseHulman Institute of Technology. Dr. Sherman is Professor of Management and Chairman of the Management and Marketing Department at the University of Alabama in Huntsville. He is the author of over 40 research publications and his research has been published in Academy of Management Journal, Psychological Bulletin, Journal of Management, Personnel Psychology, IEEE Transactions on Engineering Management, and other journals. His current interests focus on the management of innovation, engineering management, and cross-functional integration within organizations. Dr. Sherman holds an appointment as Research Scientist in CMOST, where he is active in the INTERPROD project. Rachel Cooper is Professor of Design Management and Director of the Centre for Design and Manufacture at the University of Salford. She is also the Chair of the European Academy of Design and the Editor of The Journal of Design, Published by Gower Press. Dr. Cooper undertakes research in the areas of new product development, design management, supply chain management, and design and construction processes. She has published over 100 papers in this field and four books, her latest, Marketing and Design Management, was published by Thompson Business Press in 1997. Dr. Cooper directed the U.K. portion of INTERPROD.

erating effects were found, and the prediction that integration is less important under low uncertainties was not supported. Based on these results, recommendations for NPD managers are presented and explanations of the findings are given that resolve inconsistencies in prior studies and form the groundwork for

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future research on uncertainty, integration, and NPD effectiveness.

Theory, Background, and Focus R&D/Marketing Integration NPD typically involves collaborations between R&D and marketing personnel in setting new product goals, identifying market opportunities, and resolving product cost-design-performance tradeoffs [23–26,44 – 48]. Other tasks that may naturally be dominated by either party as an artifact of their differentiated roles and specialized organizational functions still require significant R&D/marketing collaboration [45,46,48]. R&D/marketing integration has been defined as a team spirit of joint commitment in the performance of these types of tasks [43]. Though R&D/marketing integration can be a phenomenon of the firm, it also has been found to be a phenomenon of the project, with high and low integration projects frequently existing within a single firm [24 –27,37,39,45,46,50,51]. Its importance to NPD project success is well established, and a number of approaches to achieving integration have been suggested [5–9,17,23–29,37– 40, 43–54,61]. These approaches include the physical colocation of R&D and marketing personnel [1,5,34,47], project and matrix organizations [11,18,20,32,35], communication arrangements [12,13,37,38,44,45,50], and various types of teams and task forces [6,18, 23,28,33,42,44 – 46,52]. Evidence supports the effectiveness of these approaches, although with attendant organizational and human costs [6,11,20,23,35,44,45]. A Knowledge Gap: The Influence of Uncertainty The notion that technical and market uncertainties moderate the relationship between R&D/marketing integration and NPD effectiveness is rooted in the conceptualization of the NPD process as a sequence of information processing activities [18,23,25,37,38,51, 55]. When the technical and market environments are predictable, well understood, and characterized by low rates of change, decisions and actions may be preprogrammed or routinized and high degrees of R&D/ marketing integration may be unnecessary. On the other hand, high degrees of R&D/marketing integration may be required to succeed under conditions of high market and technical uncertainties, where the information, knowledge, and understanding of the technologies and markets needed to manage a success-

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ful project are poor. It may be noted that this interpretation of contingency theory emphasizes cognition, e.g., the basis for management action is the degree of perceived understanding of the condition [53,61]. This interpretation is adopted in this study. Mixed support for this postulated moderating effect of uncertainty on the relationship between integration and performance can be found in the organizational design literature where integration has been studied in non-NPD settings [2,4,10,12,21,27,31–33,57– 60], and in the recent NPD literature [1,6,19,38,42,45,48,49, 53,61]. The significance of NPD to the wealth of modern firms and nations [41] emphasizes the importance of understanding the effects of integration and uncertainty on NPD effectiveness. If technical or market uncertainty do not moderate the effects of integration on NPD effectiveness, then NPD managers should strive to achieve universally high levels of integration. This would add the costs of achieving and maintaining integration to the normal costs of NPD success, while also presumably generating a positive net return to scale through higher NPD success rates. On the other hand, if technical or market uncertainty do moderate these effects, then managers should strive to carefully match high/low degrees of integration with the respective high/low technical and market uncertainty environments in order to maximize the organizational costeffectiveness of NPD processes. Related Knowledge Gaps Important gaps remain in our knowledge about how R&D/marketing integration actually affects NPD processes. Past studies have focused on integration’s influence on new product success rates, overlooking possibly important impacts on cycle time, product design, and other important internal NPD process activities that relate to the management of the ultimate success or failure of NPD projects. Each of the seven NPD effectiveness measures studied here have been shown to be important aspects of NPD project and product success [5,7–9,26,45,48]. What is incompletely understood from past research is the way integration affects these aspects under various technical and market environments. Though marketers may play an important role in connecting customer needs with technical capabilities, today’s short cycle NPD processes emphasize R&D to customer contacts and the establishment of direct R&D/customer integration [23–26,29,37– 40,44 –54]. The relative effectiveness of direct R&D/customer

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Figure 1. Guiding model.

integration versus R&D/marketing integration is an important NPD management issue that has not been thoroughly studied. Focus of This Study Following the research paradigm outlined in Figure 1, this study attempted to address some of the above knowledge gaps by examining the relationships between integration and NPD effectiveness in a sample of 101 U.S. and U.K. NPD projects. Moderated multiple regression was used to test the moderating effects

of perceived market and technical uncertainty on the relationships. The decision to study U.S. and U.K. firms was motivated by the desire to internationalize this study while also avoiding major country cultural and language differences. The odd numbered sample studied here (101 projects) resulted from the decision to drop projects with incomplete data. Two types of integration were studied: R&D/marketing integration and direct R&D/customer integration. To reflect the multidimensional nature of NPD activities, NPD effectiveness was measured in terms of seven multi-item indicators: NPD cycle time, prototype development

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proficiency, design change frequency, R&D technical effectiveness, R&D commercialization effectiveness, product launch proficiency, and market forecast accuracy.

Methodology Measurements The eight-item integration instrument developed by Souder and Song [53] was adopted here to measure R&D/marketing integration and R&D/customer integration at the project level. The items assess the level of contact, amount of information flow, participation and interactions between R&D and marketing parties, frequency and extent of early user involvements, dependence on user reactions to early prototypes, and information flows between developers and users. Each item is scored on a five-point Likert-type scale, designed for completion by consensus of the marketing and R&D personnel assigned to the NPD project. This instrument is reported to exhibit high reliability in repeated studies [51,53,54]. Following Boyd et al [3], two measures of environmental uncertainty were utilized: technical and market. Perceived technical uncertainty was measured by a scale consisting of four dichotomously scored (high 5 2, low 5 1) items. This scale was originally developed by Souder [48] and further operationalized by Yap and Souder [61], designed for completion by knowledgeable technical personnel assigned to the NPD project. Perceived market uncertainty was measured by a scale consisting of three dichotomously scored items, also originally developed by Souder [48], further operationalized by Yap and Souder [61] and extended by Souder and Song [53]. This scale was designed for completion by senior-level marketing personnel assigned to the project. The items in these scales document the firm’s professed levels of familiarity, knowledge, understanding, and comprehension of the markets, customers, and underlying technologies relative to the NPD project, and the firm’s ability to accurately translate user needs into product performance attributes [48,53,54,61]. High reliability has been reported for both of these measures in repeated field studies and applications in multiple cultures [45,48,49,53,54,61]. Souder [48], Souder and Song [53], and Yap and Souder [61] have all emphasized the perceptual nature of these measures. They point out how a dynamic market may be considered highly uncertain to a new

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entrant, while perceived as much less uncertain to its long-term occupants who have grown comfortable with their surroundings. It may be noted that complexity, dynamicism, variability, and other similar attributes typically used to objectively describe environmental uncertainty are reflected in the measures used here [3,14 –16,31–33,36 –38]. However, note that the focus here is not on the objective measurement of uncertainty. Rather, as Figure 1 shows, the emphasis is on documenting the firm’s perceptions of uncertainty as a basis for their actions, irrespective of how that uncertainty exists. Measurement scales for the seven NPD effectiveness dimensions were adopted from other studies. Cycle time was measured by a single item scored on a five-point Likert-type scale, adopted from Gupta and Souder [26] and designed to be rated by senior technical managers. Prototype development proficiency was measured with three items scored on five-point Likert-type scales, developed for completion by senior technical personnel assigned to the project, and adopted here from prior studies [45,48,49,53]. Design change frequency (a negative performance indicator) was measured with two items scored on five-point Likert-type scales, developed for completion by senior technical managers, and successfully used in other studies [26,61]. R&D technical effectiveness was measured with six items scored on five-point Likert-type scales, and R&D commercialization effectiveness was measured with three items scored on five-point Likerttype scales, designed for completion by senior managers as used in other studies [48,53,54,61]. Product launch proficiency was measured with four items scored on five-point Likert-type scales, designed for completion by senior managers and successfully used in prior studies [9,17,48,53]. The accuracy of the market forecast for the NPD product was measured with two items scored on five-point Likert-type scales, drawn from other studies [7,48,53] and used here with senior-level managers. All the above measures were made at the project level except for cycle time and design change frequency, where the managers at each firm were requested to provide representative scores for projects typical of those studied at their firms. Following the practices in other studies [8,17,22, 26,38,48], these scores were then inputted to the projects studied here. This practice was adopted to avoid nonrepresentative variations and inadequate measurements that might be encountered if these measures had been made at the project level, due to the small numbers of projects studied at each firm.

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For each multi-item measure used here, the individual item scores were summed to obtain the total measure score, e.g., the total score of a project on the market forecast measure was computed as the sum of its two item scores. The alternative of averaging the item scores (possibly a more popular but certainly not a better method for computing measure scores) was not used here in the interest of avoiding computer roundoff errors that might be introduced as a result of the division arithmetic. Questionnaires and Their Administration The above measurement items and their scales were formatted in questionnaires that were personally administered by the authors to their designated respondents to collect retrospective information on samples of completed NPD projects. Some questionnaires were administered in one-on-one interview formats. Others were distributed to key individuals who supervised their completion by the designated respondents within each firm. These procedures encouraged 100% response and allowed the collection of additional interpretive information. Follow-up visits to the participating firms to verify questionable responses, cascading interview methods [48], and verification of details with neutral observers supplemented the data collections. When available, internal company reports and archives also were consulted to verify critical events. Previous experience-based recommendations and successful procedures with cross-sectional NPD studies and static models such as that shown in Figure 1 were followed here in administering the questionnaires to minimize learning effects and distortions in responses [49,53,54]. U.S. and U.K. Firms and Projects Using published sources, a quota sample of 25 U.S. and 25 U.K. high-technology firms was assembled for this study, for a total of 50 firms. The inclusion of both U.S. and U.K. firms added national diversity to the sample within an English-speaking culture, thus avoiding national uniqueness. The choice of this design was influenced further by considerations of sample size adequacy based on observed variations, the availability of firms for this study, obtaining equal numbers of U.S. and U.K. firms, and comparability between the U.S. and U.K. firms on the following dimensions. Each firm had introduced two or more new products during each of the past 5 years and

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exhibited 5-year sales and profit growth rates above their industry averages, thus evidencing experience in NPD activities. “High-technology” firms developed and marketed products in two-digit SIC codes 28, 35, 36, and 38 (chemicals, computer equipment, electronics, and instruments), consistent with the conventional definitions of high-technology firms. All firms had more than 2,000 employees, current sales in excess of $250 million ($US), and R&D expenditures in excess of 5% of sales. No consistent patterns of differences were detected between the U.S. and U.K. firms selected for study here with respect to their organizations, policies, and general NPD management, project management, or business practices. While necessary care thus was taken in selecting a sample of firms as the source of the projects for this study, it should be emphasized that the level of analysis and primary data collection was the NPD project within those firms. Candidate NPD projects for study were solicited from the Chief Executive Officer of each firm. Candidates were required to be directed at the development and commercialization of products new to that firm (not just a new model or old product modification), and care was taken to solicit products that represented each firm’s operations and involved significant development efforts. Two commercial (in the marketplace) success projects and two commercial failure projects were sought from each firm, with each firm’s success/failure assessment based on the project’s actual performance against its original sales, profit, and market share targets set for it at the start of its development. This multicriteria measure has been used successfully in other large scale empirical studies [37,45,48,49,53,54,61]. The objective in sampling equal numbers of success and failure projects from each firm, thus resulting in equal numbers of success and failure projects in the entire sample, was to avoid biasing the sample toward project success or failure outcomes. For similar reasons, a range of high and low uncertainty projects was sought from each firm, using the uncertainty measurement scales discussed above. In attempts to improve information accuracy, projects with total development and marketing histories older than 7 years and projects where key personnel associated with their development had left the organization were specifically excluded from study here. Some firms were only able to provide two (one success and one failure) projects meeting all the above criteria. Two other firms were unable to complete their participation (for unrelated reasons). Thus, complete data were collected on 30 success and 30 failure U.S.

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Table 1. Means, Standard Deviations, and Reliability Coefficients Measure

Variable Type

Meana

Range

Standard Deviation

Reliabilityb

R&D/marketing integration R&D/customer integration Technical uncertaintyc Market uncertaintyd Cycle time Prototype development proficiency Design change frequency R&D technical effectiveness R&D commercialization effectiveness Product launch proficiency Market forecast accuracy

Independent Independent Moderator Moderator Dependent Dependent Dependent Dependent Dependent Dependent Dependent

14.44 16.29 5.45 4.26 3.41 11.07 6.46 24.65 10.18 13.87 6.29

4–20 4–20 4–8 3–6 1–5 3–15 2–10 6–30 3–15 4–20 2–10

3.34 3.22 1.41 1.17 .95 2.99 2.20 3.55 2.74 3.13 2.65

.83 .81 .82 .73 — .92 .66 .75 .81 .77 .87

a

The data reported here are not standardized for the numbers of items in each measure, e.g., the mean of 14.44 for R&D/marketing integration is the sum of the means of its four items, the mean for 3.41 for cycle time is for its single item, etc. b For perceived technical and market uncertainty, Kuder-Richardson 20 coefficients are reported because these variables are measured dichotomously. All other numbers are Chronbach alphas. c Skewness 5 .57. Because of the importance of this measure to this study, it is relevant to know that these data were not excessively skewed. d Skewness 5 .28. Because of the importance of this measure to this study, it is relevant to know that these data were not excessively skewed.

projects and 21 success and 20 failure U.K. projects, for a total sample of 101 projects. In spite of this variation from the planned sampling design, in both the U.S. and the U.K., half the projects were rated high and half were rated low on the perceived technical and market uncertainty scales used here, with the high/low uncertainty dimensions distributed evenly within both the U.S. and U.K. project success/failure strata (U.S. 2 U.K. differences all not significant [NS]), binomial tests). The projects were evenly distributed across the four SIC codes (28,35,36, and 38), within both the U.S. and U.K. project success/failure strata (NS, Mann-Whitney U tests). Thus, a balanced sample of success/failure and high/low uncertainty projects was achieved, consistent with the original design of the study.

Data and Analyses Measure Validation and Data Quality Check A full range of responses was collected on each measure. The statistics in Table 1 indicate that the database demonstrated well-behaved standard deviations and generally acceptable reliabilities for the measures. Though some of the measures were positively skewed, it did not appear that these aspects interfered with the phenomena being tested here. It may be observed that although the design change frequency measure exhibits a relatively weak reliability in Table 1, it was retained here for completeness and for comparability with other studies.

A factor analysis with eight cross-functional integration items revealed two factors with eigenvalues . 1.00, whose loadings differentiated between the R&D/ marketing integration and R&D/customer integration items. Discriminant validity for these two integration constructs was reinforced further by the patterns of item-to-item correlations and only 3.24% common variance. These results reinforce previous findings of independence and high reliabilities for the R&D/marketing and R&D/customer integration measures [51,53,54]. Table 2 presents the intermeasure correlation matrix for the collected data, demonstrating that several important structural requirements of the research model depicted in Figure 1 were met. Neither perceived technical nor perceived market uncertainty (for brevity, “perceived” is hereafter omitted) are significantly correlated with either of the integration measures, thus further evidencing independence between the integration and uncertainty parameters. Consistent with findings that technical and market uncertainty are intertwined in NPD settings [48,49,53,54,61], technical and market uncertainty are shown to be significantly intercorrelated in Table 2. Only three significant correlations are shown in Table 2 between the two uncertainty parameters and the NPD effectiveness measures, and all are logical. Consistent with the logical flow of activities within an NPD process, several of the NPD effectiveness measures are shown to be significantly intercorrelated in Table 2. Specifically, cycle time is negatively correlated with design change

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frequency but positively correlated with R&D commercialization effectiveness. R&D commercialization effectiveness is negatively correlated with design change frequency but positively correlated with R&D technical effectiveness. Prototype development proficiency is interrelated with product launch proficiency and market forecast accuracy. It may be noted that an alternative design here would consist of factor analyzing the measures, and then conducting all further analyses on the resulting factors rather than on the individual measures. However, these seven measures are conceptually important in the NPD literature, and they are important individual features that are considered in managing realworld NPD efforts. Analytical Procedures Moderated multiple regression analyses were used to test whether or not uncertainty moderated the relationships between integration and each of the seven NPD effectiveness measures. In these analyses, first the independent variable (integration) was entered into the model and the explained variance was examined. Second, the potential moderator variable (uncertainty) was then entered into the model. The partial F associated with the resulting change in R2 was then examined to statistically test whether or not uncertainty (the hypothesized moderator variable) acts as an independent predictor variable. Third, the cross-product term, that is, integration 3 uncertainty, was then entered into the model. The partial F associated with that resulting change in R2 was then examined to test

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whether or not a moderating effect exists. This procedure follows standard moderated regression approaches and resulted in a total of 28 such analyses being conducted. The choice of moderated regression methods over other alternatives (e.g., subgroup analysis) was influenced by statistical power-effectiveness considerations (cf, Zedeck [62]).

Results Tables 3 through 6 present the results of the moderated regression analyses. Tables 3 and 4 present the results for R&D/marketing integration when technical and market uncertainty are entered into the regressions, while Tables 5 and 6 present the results for R&D/ customer integration when technical and market uncertainty are entered into the regressions. The results shown under the column “R&D/Marketing Integration” in Table 3 show that R&D/marketing integration was significant for prototype development proficiency, R&D commercialization effectiveness, product launch proficiency, and market forecast accuracy. Entering technical uncertainty into the regression model explained significant additional variance in prototype development proficiency (F 5 5.89, p , .05), as shown under “Technical Uncertainty.” As the results in the last two columns of Table 3 show, no moderating effects were found from the interaction term, technical uncertainty 3 R&D/marketing integration. Additional interpretations might be made on the basis of the signs of the nonsignificant regression coefficients. However, this approach was not followed in the interest of avoiding type I statistical errors.

Table 2. Correlation Matrix Correlation Coefficientsa Measure 1 2 3 4 5 6 7 8 9 10 11

R&D/marketing integration R&D/customer integration Technical uncertainty Market uncertainty Cycle time R&D commercialization effectiveness Prototype development proficiency Product launch proficiency R&D technical effectiveness Design change freqency Market forecast accuracy

1

2

3

4

5

6

7

8

9

10

11

— .18 .02 2.02 .16 .25a .61a .62a .17 2.04 .47a

— .12 .03 .29a .28a .08 .14 .33a .01 .09

— .42a .21a .08 2.17 .01 .20a 2.07 2.11

— .03 .02 2.12 2.17 .12 .00 2.30a

— .66a 2.02 .05 .36a 2.56a 2.09

— .01 2.05 .38a -.42a 2.11

— .58a .10 2.03 .47a

— .10 2.08 .60a

— 2.24a 2.03

— .12

—

Pearson product moment correlations $ 0.20 for N 5 101 are significant at the .05 level (two-tailed test). Note that all numbers in this table are rounded to the nearest two significant decimal places. a

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Table 3. Results of Moderated Multiple Regression Analyses for R&D/Marketing Integration with Technical Uncertainty R&D/Marketing Integration

Technical Uncertainty

Interaction Term: Technical Uncertainty 3 R&D/Marketing Integration

Measure

R2

Fa

R2

Partial Fb

R2

Partial Fc

Cycle time Prototye development proficiency Design change frequency R&D technical effectiveness R&D commercialization effectiveness Product launch proficiency Market forecast accuracy

.022 .43 .00 .023 .054 .43 .22

2.03 70.11** .25 2.24 5.39* 73.26** 28.32**

.062 .46 .010 .060 .059 .43 .24

3.78 5.89* .44 3.74 .52 .00 1.80

.064 .48 .013 .064 .069 .43 .26

.18 2.96 .43 .39 1.00 .67 2.67

df 5 1,98. df 5 1,97. df 5 1,96. * p , .05; **p , .001. a b c

The results listed under “R&D/Marketing Integration” in Table 4 show that R&D/marketing integration was significant for the same four NPD performance variables reported in Table 3. It may be noted that the numerical results for these aspects are slightly different in Tables 3 and 4 due to differences in the sample sizes as a result of a few missing data points, and the degrees of freedom vary slightly in all the tables due to these few missing points. In none of these analyses does the amount of missing data exceed 7% of the total sample (seven data points were missing in the total 101 project sample, with the missing data concentrated primarily in the R&D/customer integration measure). It was felt that the data should be analyzed as received without using any replacement algorithms. As shown

under “Market Uncertainty” in Table 4, entering market uncertainty into the regression model explained significant additional variance in product launch proficiency (F 5 4.12, p , .05) and market forecast accuracy (F 5 11.94, p , .001). As shown in the last two columns of Table 4, the cross-product interaction term, market uncertainty 3 R&D/marketing integration, had a moderating effect on one NPD effectiveness measure: prototype development proficiency (F 5 8.72, p , .01). Table 5 presents analogous results to Table 3 for R&D/customer integration, showing a significant effect on three NPD effectiveness measures: cycle time, R&D technical effectiveness, and R&D commercialization effectiveness. The results under “Technical

Table 4. Results of Moderated Multiple Regression Analyses for R&D/Marketing Integration with Market Uncertainty R&D/Marketing Integration

Market Uncertainty

Interaction Term: Market Uncertainty 3 R&D/Marketing Integration

Performance Measure

R2

Fa

R2

Partial Fb

R2

Partial Fc

Cycle time Prototype development proficiency Design change frequency R&D technical effectiveness R&D commercialization effectiveness Product launch proficiency Market forecast accuracy

.024 .37 .00 .028 .065 .38 .22

2.22 56.02*** .13 2.80 6.66* 61.45*** 28.60***

.025 .38 .00 .044 .065 .41 .31

.10 1.55 .00 1.58 .05 4.12* 11.94***

.026 .43 .00 .045 .094 .42 .33

.11 8.72** .10 .00 2.99 2.40 2.60

df 5 1,99. df 5 1,98. df 5 1,97. * p , .05; ** p , .01; *** p , .001. a b c

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Table 5. Results of Moderated Multiple Regression Analyses for R&D/Customer Integration with Technical Uncertainty R&D/Customer Integration

Technical Uncertainty

Interaction Term: Technical Uncertainty 3 R&D/Customer Integration

Performance Measure

R2

Fa

R2

Partial Fb

R2

Partial Fc

Cycle time Prototype development proficiency Design change frequency R&D technical effectiveness R&D commercialization effectiveness Product launch proficiency Market forecast accuracy

.085 .010 .00 .11 .076 .021 .010

8.28** .83 .00 10.73** 7.34* 1.97 .62

.11 .043 .010 .13 .079 .021 .022

2.90 3.07 .47 2.24 .26 .00 1.39

.14 .062 .058 .14 .089 .022 .029

3.06 1.72 4.89* .96 .97 .010 .66

df 5 1,91. df 5 1,90. df 5 1,89. * p , .05; ** p , .01. a b c

Uncertainty” in Table 5 show that entering technical uncertainty into the regression model did not explain significant additional variance in any of the seven NPD effectiveness measures. As shown in Table 5, the cross-product term, technical uncertainty 3 R&D/customer integration, had a moderating effect on one measure: design change frequency. Table 6 presents the analogous results to Table 5 for R&D/customer integration and market uncertainty. Significant results are shown in the first two columns of Table 6 for the same three performance measures as in Table 5, with minor changes in the numbers due to missing data. Entering market uncertainty into the model explained significant additional variance in market forecast accuracy (F 5 9.51, p , .01) and the

cross-product term moderated design change frequency (F 5 4.14, p , .05).

Discussion of the Contingency Findings Of the 28 relationships tested here (seven effectiveness measures, each with two integration variables), only three moderating effects of uncertainty were identified through an examination of interaction terms. This relatively low incidence of moderating effects is consistent with the mixed results in the literature [2,10,18,21,30 –33,45,57– 60]. Because uncertainty measurement is known to be problematic [2,3,14 –16,36], suspicions naturally are aroused about the adequacy of the measures used here.

Table 6. Results of Moderated Multiple Regression Analyses for R&D/Customer Integration with Market Uncertainty R&D/Customer Integration

Market Uncertainty

Interaction Term: Market Uncertainty 3 R&D/Customer Integration

Performance Measure

R2

Fa

R2

Partial Fb

R2

Partial Fc

Cycle time Prototype development proficiency Design change frequency R&D technical effectiveness R&D commercialization effectiveness Product launch proficiency Market forecast accuracy

.086 .010 .000 .11 .080 .019 .010

8.51** .65 .00 11.24** 7.87** 1.75 .67

.087 .021 .000 .12 .080 .049 .10

.06 1.28 .00 1.05 .00 2.88 9.51**

.093 .032 .045 .13 .10 .056 .10

.35 .93 .414* .38 2.23 .64 .15

df 5 1,92. df 5 1,91. df 5 1,90. * p , .05; ** p , .01. a b c

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The use of both perceptual and objective measures has been challenged on theoretical grounds [3,14,15,56], leaving researchers few unequivocal alternatives for measuring environmental uncertainty. The measures used here met acceptable reliability, normality, and range requirements (Tables 1 and 2). An important alternative explanation for the lack of moderating effects in this study may relate to the high-technology nature of the NPD projects (SIC codes 28, 35, 36, and 38), the R&D-intensive firms (annual R&D expenditures over 5% of sales), and the frame of reference of the respondents. Three rating items in the questionnaires inquired about the nature of the technologies underlying each project, benchmarked against the universe of technologies. Respondents were asked to classify the technologies underlying each project as belonging to either undeveloped sciences, developing sciences, and well-developed sciences. Undeveloped sciences were characterized in the questionnaire by poorly defined theories, low predictive states of art about phenomena, and trial-and-error research. Developing sciences were characterized by emerging theories and evolving states of art, while well-developed sciences were characterized by established theories and high predictive states of art. Seventy-two percent of the U.S. projects and 74% of the U.K. projects studied here were classified by the respondents as belonging to either undeveloped or developing sciences. Thus, from the respondent’s frame of reference within their individual firms, the projects were perceived as varying from low to high on the uncertainty measurement scales used here. But when viewed from the perspective of a full range of uncertainty experienced by a universe of projects, firms, and industries embodied in the technology rating items, the projects studied here appeared to lie on the high end of the universe of technical uncertainty measurements. This effectively delimits the full range of moderating effects that could be studied here. These findings are consistent with the high uncertainty that often accompanies NPD work, thereby possibly explaining why integration repeatedly has been found important to NPD success. Because of this, it may be that the contingency theory of organizational design is delusory to reproducible testing in some product development environments, with the results varying according to where the projects lie on the uncertainty continuum. This would explain and reconcile the mixed results reported in the literature, e.g., the contingency theory prediction of moderating ef-

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fects may only be supported where the projects span a true universe of uncertainty. Environmental uncertainty may, in fact, generally moderate the relationship between integration and product development success. But this moderating effect may seldom come into play for many NPD efforts because of the high levels of uncertainty that naturally accompany such projects, thus requiring high levels of R&D/marketing or R&D/ customer integration for their success. This idea is consistent with the contingency theory prediction that high integration is required under high uncertainty conditions, based on the notion that NPD projects involve high levels of reciprocal interdependence [44 – 48,55]. To illustrate, nine investigations that tested the moderating effect of uncertainty were reviewed here [2,10,21,30,31,57–59,60]. Three of these nine exhibited empirical evidence of range restrictions on the uncertainty measure, based on an interpretation of reported means, scale ranges, and standard deviations [21,30,60]. This was further corroborated based on the descriptions of the samples given in these studies. Of these three investigations, two reported mixed results regarding the moderating effect of uncertainty [21,30]. In contrast, in five of the nine studies, no statistical evidence or evidence in the description of the sample was presented to indicate that the sample was restricted on the uncertainty dimension [2,31,57–59]. All five of these studies supported contingency theory predictions. Interestingly, three of these five studies using R&D organizations also included technical service functions in their samples, resulting in an increase in the range of the uncertainty measure [57–59]. Mixed results were only reported in one study where there was no evidence of range restriction [10]. This all suggests that careful definitions and accurate measurements of both uncertainty and product development typologies may be controlling elements in testing contingency theory predictions in product development environments. For example, it may be that many types of product development efforts occupy the lower end of the uncertainty measurement continuum, e.g., product modification, product extension, and product improvement efforts. NPDs, on the other hand, may occupy the higher ends of the uncertainty continuum. A distributed sample of these various types of projects might very well exhibit the full range of contingency theory predictions about integration. However, the results here indicate that a disaggregate examination of the NPD projects within such a population only follows the high uncertainty/high

CONTINGENCY THEORY

integration prediction of contingency theory, a result consistent with the position of NPD projects on the uncertainty continuum universe. This distinction is not simply a matter of NPD projects exhibiting a restricted range of uncertainty, though this may necessarily be manifested in any global uncertainty measurement scale. Thus, it seems clear that both uncertainty and technology measurement difficulties are limiting factors in clarifying the application of contingency theory to NPD. The development of reliable objective scales for measuring both NPD uncertainty and the maturity of a technology should be priority prerequisites to further NPD contingency studies.

Summary and Conclusions Consistent with previous research, this study found positive effects of both R&D/marketing and direct R&D/customer integration on NPD effectiveness. R&D/marketing integration was found important to prototype development proficiency, R&D commercialization effectiveness, product launch proficiency, and market forecast accuracy. R&D/customer integration was found important to cycle time, R&D technical effectiveness, and R&D commercialization effectiveness. Only one measure (R&D commercialization effectiveness) was affected by both R&D/marketing integration and R&D/customer integration, reinforcing the notion that R&D/marketing integration and R&D/ customer integration are distinct constructs that impact different aspects of NPD. These results are consistent with previous findings on the relationships between R&D/marketing integration and NPD effectiveness, as measured by cycle times, design change frequency, prototype development times, technical effectiveness, and product launch efficiency [27,45,50]. The contribution of this study lies in the extension of this body of findings, demonstrating how R&D/marketing and R&D/customer integration are separate factors that managers can use to influence NPD outcomes in different ways. These results suggest that NPD managers should consider emphasizing high degrees of R&D/marketing integration when there is a need to improve either prototype development proficiency, R&D commercialization effectiveness, product launch proficiency, or market forecast accuracy. NPD managers should consider emphasizing direct R&D/customer integration when cycle time reduction, R&D commercialization effectiveness, or R&D technical effectiveness is sought. The joint use of R&D/marketing and R&D/

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customer integration thus appears to represent a powerful combination for promoting multidimensional NPD effectiveness. The results here also indicate that the use of R&D/ marketing and R&D/customer integration for improving some aspects of NPD effectiveness may be guided by considerations of technical and market uncertainties. Technical uncertainty (perceived) was found to influence prototype development proficiency and moderate design change frequency. Market uncertainty (perceived) was found to influence both product launch proficiency and market forecast accuracy, and to moderate prototype development proficiency and design change frequency. These results reinforce the common sense notion that extra attention should be devoted to increasing prototype development proficiency in the interest of reducing design changes when the technology is uncertain, and the results reinforce the additional common sense notion that attention should be focused on improving product launch proficiency and market forecast accuracy in uncertain markets. Criteria to assist NPD managers in uncertainty measurement are detailed elsewhere [53,61]. The results of this study reinforce the overall importance of R&D/marketing and R&D/customer integration to NPD effectiveness. It was observed that because NPD activities often are characterized by high levels of technical and market uncertainties, the expense and burdens of maintaining high degrees of integration may, in fact, be necessary to achieve high degrees of NPD success. Thus, the contingency theory notion advanced at the beginning of this study about economizing on organizational integration expenses by allocating them according to the presence of high/ low uncertainties may not apply to NPD efforts. Rather, the contingency theory prediction that high degrees of integration are necessary for success with high uncertainty NPD projects was reaffirmed here. These results help clarify some of the mixed empirical findings reported for contingency theory in the literature. This study has further highlighted the need for future research that focuses on the development of valid and reliable uncertainty measurement instruments, especially instruments that distinguish between objective and subjectively perceived uncertainties [3,14 –16]. The need for improved measurement and distinctions between product development typologies and classifications also was highlighted. The use of R&D personnel interaction with customers to achieve direct R&D/ customer integration needs more study, especially the

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use of direct R&D/customer integration to augment R&D/marketing integration. The results reported here, as well as elsewhere [6,26,47,49], lend strong support to the importance of greater use of direct R&D/customer contacts. This study also has highlighted the need for longitudinal models that measure the longterm effects of integration under evolving environmental uncertainties.

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12. De Brentani, U. Success and failure in new industrial services. Journal of Product Innovation Management 6:239–258 (1989). 13. Dougherty, D. Understanding new markets for new products. Strategic Management Journal 11:59–78 (1990). 14. Downey, H. K., Hellriegel, D. and Slocum, J. W. Environmental uncertainty: The construct and its application. Administrative Science Quarterly 20:613–629 (1975). 15. Downey, K. and Slocum, J. W. Uncertainty: Measures, research, and sources of variation. Academy of Management Journal 18:562–578 (1975). 16. Duncan, R. B. Characteristics of organizational environment and perceived environmental uncertainty. Administrative Science Quarterly 17:313–327 (1972).

This work is part of the 19-country INPERPROD study being carried out at the Center for the Management of Science and Technology (CMOST) under the direction of Dr. William E. Souder, and funded by National Science Foundation grant SBR9408272, Marketing Science Institute grant 4-386, and Air Force contract F49620-94-1-0456 to Dr. Souder at CMOST. Additional funding for the U.K. portion of INTERPROD reported here was provided by the University of Salford, U.K. Thanks to CMOST Student Assistants Karen Mason and Trish Malloy and CMOST Graduate Research Assistants Preneet Sihota and Xin Wang for data entry and analysis support. Thanks to Research Assistant Joanne Charleton at the University of Salford for assistance with the U.K. data collection. Thanks to Professor Thomas P. Hustad and two anonymous reviewers for helpful comments that improved the presentation of this work. Last, but not least, special thanks to the many firms and their employees who graciously gave their valuable time and efforts to this study. We sincerely hope the results are useful to them, our ultimate customers.

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