The influence of process concurrency on project outcomes in product development: an empirical study...

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 43, NO. 2, MAY 1996

PROCESS ~

CONCURRENCY

153

-1 PROJECT

The Influence of Proces,s Concurrency on Project Outcomes in Product Development: An

Empirical Study of Cross-Functional Teams ‘Oscar Hauptman, Member, ZEEE, and Karim K. Hirji, Member, ZEEE

Abstruct- This study examines the practice of concurrent engineering (CE) in terms; of process concurrency, and the impact of concurrency on success of product development projects. The study is based on 50 cross-national projects from companies in Australia, Canada, Denmark, Finland, United Kingdom, and the United States in the aerospace, automobile, chemical, computer, electronics, shipbuilding, and telecommunications industries. Four dimensions of process and behaviors of engineering/R&D and manuhcturing members of cross-functional product development teams were reliably operationalized: 1) two- way communication, 2) overlapping problem solving, 3) readiness to make decisions on the basis of uncertain and ambiguous information, and 4) readiness to release uncertain and ambiguous information. These dimensions of process concurrency were found to be reliable predictors of development projects’ success, as measured by product cost and quality, project schedule and budget performance, and project team satisfaction. The paper offers implications for theory and practice and models of CE management for future research.

I. INTRODUCTION

A. Problem Statement

ECHNOLOGICAL, process and product innovation re- T ceived significant attention as a critical factor in the competitive environment of the last 20 years [331, [45], [461, [49]. The intensive research on concurrent engineering (CE) and related new method!; of product development and delivery has proven the value of these methods [9], [12], [13], [18], [19], [28], [29], [33], [ S I , [60]. The traditional ways of developing products through approaches in which functional experts sequentially peirformed their specialized tasks were rendered inadequate and obsolete [ l l ] , [27]. Evidence from the academic and popular press of companies increasingly using CE [40], [S2], [60] shows that this new product development method has been successfully diffusing throughout the industry.

Presently, there is a reasonable consensus of what the principles of CE are. For instance, according to the CALS technical report [37], CE is “a fundamentally new way of looking at

Manuscript received October 5, 1994. Review of this manuscript was arranged by Guest Editor G. Susman.

0. Hauptman is with the Research Program in Managing Technological Change in Manufacturing, Sclhool of Business, Carleton University, Ottawa. Ont. K1S 5B6 Canada.

K. K. Hirji is with the Department of Management Sciences, Faculty of Engineering, University of Waterloo, Waterloo, Ont., N2L 3G1 Canada (e- mail: [email protected]).

Publisher Item Identifier S 001 8-9391 (96)04458-3.

Fig. 1. Conceptual model of CE.

how products-as well as their enabling technologies and manufacturing, testing, and support processes-are conceived, specified, and developed” concurrently, by cross functional teams. The basic factors underlying CE include common goals, complete visibility of design parameters, mutual consideration of all downstream decisions, overlapping problem-solving, collaboration to resolve conflicts, teamwork, and continuous improvement [9], [42], [50], [60]. With very few exceptions [48], [56], [57], the focus of most research has been on the enablers of CE, namely, the tools, methods, information technologies, organizational structure, reward system, physical location, and culture [21]. Obviously, the research focus on the enablers of CE leaves out the reasons for investing in these enablers in the first place-effective and efficient team- level attitudes, processes, and behaviors. These behaviors of problem-solving in R&D/engineering and manufacturing are critical for creating a superior product delivery process [2] , [3] , [ 131, [ 271 and developing manufacturable products quickly and efficiently.

The conceptual framework that guides this research (see Fig. 1) builds on previous models of CE (or effective NPD [21], [65]) by continuing where they typically stop. The focus of this study is to investigate how the “black box” of CE-the project team, its attitudes, processes, and behaviors-influence project outcomes. As shown by Susman and Dean [56]’ these team-level information processing activities determine the

’ They write that these processes have “. , . the most significant direct effect on DFM effectiveness.”

0018-9391/96$05.00 0 1996 IEEE

154 IEEE TUNSACTIONS ON ENGINEERING MANAGEMENT, VOL. 43, NO. 2, MAY 1996

salient outcomes of the NPD/CE process. Similar to Rusinko [48] and Susman et al. [57], [4], [5], we separate the team- level behaviors and processes from their enablers, namely the organizational integrating and coordinating mechanisms, including information technologies, the reward system, and the organizational structure.

To summarize the study’s objectives: 1) To model and operationalize team-level behaviors and

processes that determine the degree of concurrency in the CE process.

2) To test how the identified team-level behaviors and processes contribute to the outcomes of product development projects.

11. LITERATURE REVIEW AND RESEARCH HYPOTHESES

A. Degree of Concurrency in CE

CE, a.k.a. simultaneous, parallel, a.k.a. producibility engineering, a.k.a. DFM, is commonly defined in the literature as the integrated and parallel design of products and their related processes, including manufacturing, test, and support. The evident success of this method [ l l l , [131, [291, [331, [40], [48], [60] stems from advantages in improving product design quality, shortening development lead time, reducing costs in project, product, and process, engineering change or- ders, prototypes, scrap, and rework, and improved production efficiency [40], [42]. Two interrelated questions are: What are the essential process attributes that differentiate CE from sequential engineering and make it what it is? and What is CE from a theoretical point of view?

Considering the nature of CE, its principles emphasize collaboration, communication, cooperation, and consensus. According to the CALS/CE technical report [37], the CE principles include common goals, complete visibility of design parameters, mutual consideration of all decisions, collaboration to resolve conflicts, teamwork, and continuous improvement. Second, CE involves cross-functional overlapping problem-solving which shortens lead time not so much by reducing the duration of the upstream and downstream tasks but by simultaneously executing them. As noted by researchers [ 131, [29], overlapping problem-solving must be matched by changes in the nature and frequency of information flow, the timing of upstream and downstream tasks, and the attitude throughout the organization toward dealing with preliminary information.

Similar to new product development or delivery (NPD) [5] and R&D tasks [3], the empirical insights about CE seem to correspond to theoretical ideas from information processing* literature. Not surprisingly, researchers [131, [211, [471, [541 have used the information processing perspective to study a variety of issues in NPD. Through this perspective, the development of new products is viewed as an interlinked sequence of information processing tasks where knowledge of market needs and technological opportunities is translated into information assets for production. This means that each

*The view of an organization as an information processing system has received considerable attention in the literature on organization theory [ 141, ~ 7 1 .

stage of product development, from product planning to actual production, which represents the physical embodiment of the product concept, involves the processing of information. One of the relevant findings in this area is the positive relationship between task variety and the amount of information processed [17], [62] and between task analyzability and the amount of information processed [ 191.

It is important to note that we conceptualized CE as a task design or structural feature of work [32], [63]. This does not contradict the idea that product development tasks are by their nature more interdependent than other tasks, e.g., of salespersons in a department store, or teachers in classrooms [59]. The CE approach to NPD makes this interdependence higher than the sequential process of traditional NPD3 [13], [211, [481, [561.

1) The Initial Dimensions: Two- Way Communication and Overlapping Problem-Solving: From the literature reviewed above it is evident that integrated problem-solving requires fre- quent, two-way flow of preliminary information. This height- ens the need for effective coordination by the “heavyweight” project team leader as the driver of this process [27], [50], [60]. The role of communication, especially the two-way interactive feedback mode, was found to be an essential determinant of success in R&D and product development teams [2], [3], [5], [6], [25]. Such communication strongly contributes to the problem-solving activities of engineers involved in simultaneous product development, in which manufacturing is involved in designing the process at the same time R&D/engineering is engaged in designing the product.

Going beyond its categoric description, Clark and Fu- jimoto [13, Fig. 8.31 identified the continuum of overlap between product and process design problem-solving. They found that Japanese automotive development projects exhibited a significantly higher degree of overlap between upstream and downstream functions than either European or American projects. These projects also exhibited a strong negative correlation between degree of simultaneity and development lead time. Similarly, De Meyer [19] found that companies that showed above average performance improvement in development lead time exhibited a significantly higher degree of parallel problem-solving in development [29].

2 ) New Dimensions: Release and Use of Uncertain and Ambiguous Information in the CE Process: How complete is the depiction of the team-level CE process in terms of two- way communication and overlap in problem-solving? Previous research offers several qualitative ideas. The first mention of new principles of CE or DFM can be found in [12] and [13]. For instance, engineering attitude is identified as a specific barrier to CE behavior [ 131:

The upstream group will be even less willing to release information early if the environment is hostile, with design changes triggering accusations of sloth or incompetence. If the attitude of product engineers is “I will not give you anything now because I know I will have to change it later and I know that I will take the blame

Thompson [59] proposed an ordinal model of task interdependence, from pooled, through sequential, and to the most difficult, reciprocal.

HAUPTMAN AND HIRJI: THE INFLUENCE OF PROCESS CONCURRENCY ON PROJECT OUTCOMES IN PRODUCT DEVELOPMENT IS5

1 PROJECT 0 UTCO M ES

CONCURRENCY

schedule 1.. -

* communication - ~ ~ _ _ product cost and overlapping quality problem--solving using incomplete and * - uncertaininiformation

I releasing incomplete and 1 uncertain in,tormation

--

Fig. 2. Model and main variables of process concurrency and project out-- comes in CE. Fig. 3.

:r-- 1 1 -

, PROJECT 1 ATTRIBUTES

IVJODEL OF PROJECT ATTRIBUTES

BETWEEN ~ I

ENABLING i I

MECHANISMS AND { PROCESS, CONCURRENCY -2 - - -

Model of CE for future research.

for it, ” management may have to effect a fundamental change of attitude tlhroughout the engineering organization, both upstream and downstream, a very difficult task.

This insight has been extended and articulated by Susmari and Dean [56]:

. . . group members rnust be willing to share provisional information with one another and to react on the basis ofsuch information (Clark and Fujimoto, 1989), as well as to treat their dectsions as tentative until late in the project . . .. Sharing tentative information, acting upon it, and considering decisions as tentative will render new product teams much more flexible in responding to problems.

This direction of inquiry received support from Dubinskas [20, pp. 132-1331 in his ethnographic study of the joint development by Apple and Hirata (Japan) of an automated production line for Apple PC’s. Dubinskas found analogieis with CE and concluded that

. . . projects within manufacturing-especially large process improvement of automation projects-can be looked at with the same intellectual lens that have examined design-to-manufacturing in te r fa~e .~

Dubinskas used the metaphor of the fermentation vat to challenge the development funnel model [27], [64] as “Tay- loristic” and restricting knowledge development. In the va.t model the engineering and manufacturing actors have to “tolerate ambiguity to test alternatives,” and “share incompletje

extracted from cases on NPI) and CE.5 Several cases‘ are especially informative:

1) In the “McAlasdaire Imaging” case, manufacturing specifically would not commit to a cost estimate for a preliminary design of the product. Manufacturing released the cost estimate with “not approved” scribbled over

2) In the “Plus Development” case, there is a continual tug- of-war between the Japanese manufacturing counterparts of Plus, who are trying to get very specific, quantitative information about product design parameters very early in the process, and the designers from Plus, who are delaying specificity.

This literature8 helps to operationalize the degree of concurrency in the CE process, and for this matter in any process of product and process development, through the following information-processing dimensions:

1) The extent of two-way informa.tion flow between R&D/engineering and manufacturing representatives on the CE team.

2 ) The extent of overlap in problem solving between R&D/engineering and manufacturing representatives on the CE team.

’Many of them are the basis for [64], the related text, and cases publication; they were also the pedagogic material for the “Developing and Managing Technology,” second-year MBA course taught at Harvard Business School by Wheelwright and Clark fom 1988-1990 and by the first author from

6The cases are: 1) “McAlasdaire Tmagiug, PLC: AE-1 Project (A),” HBS Case 690-069, November 199 1, 2) “Digital Equipment Corporation: The VT320 Video Text Terminal,” DMI Case Study, October 1990, and 3) “Plus Development Corporation (A)” HBS Case 687-001, October 1987.

1990- 1992.

information across disciplines.” 7The first author participated in the events described in the case as

Additional infomatiin concerning the reaction of teann Participant Observer and consultant during ‘988. ‘During tbe data analysis stage of the study we become aware of related

developments and ideas in the Laboratory for Machine Tools and production members unce’tain and ambiguous was Engineering of the Technical University, Aachen, Germany [22] from S. Dietz, a visiting graduate student. Dietz also reported that during one of the regular discussion forums held by the laboratory staff with representatives of industry in 1994, the industry representatives brought the issue of dealing with uncertain and ambiguous information during CE to their attention, indicated practical difficulties and asked for solutions.

41t is also evident that hand-off processes exist and are salient to succea,~ of process development projects [61].

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 43, NO. 2, MAY 1996 156

3)

4)

The extent of incomplete, uncertain, and/or ambiguous information usage by R&D/engineering and manufacturing representatives on the CE team, released by their counterparts for decision making. The extent of readiness of R&D/engineering and manufacturing representatives on the CE team to release incomplete and/or uncertain and/or ambiguous information to their counterparts.

These behaviors and attitudes that we specifically asso- ciate with the CE approach to NPD are supported by the literature on teams and work design. For instance, Crawford and Haaland [16] and Johnson [31] found that higher levels of task interdependence are associated with more two-way communication and feedback, mutual assistance in problem- solving, and mutual information sharing. At the same time, the above literature is not informative in relation to CE or NPD- type situations of hand off between team members who have specialized skills, and have to deal with incomplete, uncertain, or ambiguous information.

B. CE Process Concurrency and Product Development Project Outcomes

NPD efficiency and effectiveness are conceptually and prac- tically important even if they are often difficult to measure (see the model in Fig. 3) [21, p. 3751. Clearly, they are important as determinants of company’s competitiveness, to justify resource allocation to R&D, engineering, or other activities that drive technological innovation [8 J . In addition, the measurement of performance in NPD is important for assessing the effectiveness of its determinants, for instance, communication patterns [3] , or such methods as design-for- manufacturability and CE.

The two aspects of NPD effectiveness, the internal (project) and external (market) performance, have been often investigated together in the same study [15], [25], 1301, [33] . At the same time, there is a reason for a separate treatment, namely, because the chain from the performance of product and process development projects is too long and multidimensional to reliably trace the cause and effect from internal to external performance; only a few studies in this genre [13] offer such insights. For assessing the effectiveness and efficiency of the CE process, it is appropriate to measure performance on the level of the project team.

The examples of how to conceptualize performance on the project-level from the NPD literature [5] , [48], [61], [65] and from the R&D literature 131, [26] have several commonalities: First, the measures of project outcomes are almost exclusively subjective, based on evaluations by company managers and staff. Second, these measures are anchored around meeting specific project goals. Third, some use multiple measures of project effectiveness, i.e., meeting product quality or function- ality, and efficiency, i.e., meeting a project budget and schedule 1.51, [26]. From the more general studies of teams, Johnson and Johnson [32J found that high interdependence in teams contributed to learning, achievement, and cognitive complexity; Wageman 1631 found a strong effect of interdependence on

Most of the studies cited above found either direct or contingent relationships between organizational and process dimensions with project outcomes. Because this study focuses on the basic relationship between the nature of CE and project outcomes, the following hypotheses are noncontingent.

Hypothesis One: The higher is process concurrency in CE, the better are the outcomes of the CE project in terms of project effectiveness.

Hypothesis Two: The higher is process concurrency in CE, the better are the outcomes of the CE project in terms of project efficiency.

Another important point that should be addressed is whether CE is a single or multidimensional optimization approach. The initial excitement about CE was related to time-to-market and its advantages in time-based competition [55]. In addition, “common sense” may suggest that there are specific negative relationships between efficiency and effectiveness outcomes (higher quality products might cost more and take longer to develop), or between efficiency outcomes themselves (ceteris paribus, project schedule and budget might be negatively correlated). In contrast, the findings in all the studies cited above offer evidence that such negative relationships seldom exist among project performance dimensions. Namely, project budget and schedule are positively related, and both are positively related to product quality and cost [5], 1261, resulting in the following hypotheses:

Hypothesis Three-a): The higher is process concurrency in CE, the better are the outcomes of the CE project simultaneously in terms of multiple project efficiency goals.

Hypothesis Three-b): The higher is process concurrency in CE, the better are the outcomes of the CE project simultaneously in terms of project efficiency and effectiveness goals.

Finally, most of the above conceptualizations either omit the aspect of project team satisfaction or treat it as a determinant of project success [57]. This leaves the measures wanting, especially considering the importance of quality of working life associated with a new method as interactively intensive as CE [5], [34], [35]. Obviously, team satisfaction could be the cause as well as the effect of the cross-functional, high- interdependence process. At the same time, it is possible to operationalize this construct in terms of postproject attitudes and reactions to the project itself [7], [34], [35]. For instance, Johnson and Johnson [32] found that higher interdependence resulted in better interpersonal relations. Wageman [63, p. 1691 findings are congruent: she found that teams with interdependent task designs had

. . . stronger norms promoting cooperation, higher quality group processes, and greater member satisfaction with work than do groups with hybrid or individual tasks.

The resulting hypotheses address both the single and the multivariate models of relationships between process and outcomes.

Hypothesis Four: The higher is process concurrency in CE, the better are the outcomes of the CE project in terms of team satisfaction.

Hypothesis Five: The higher is process concurrency in CE. cooperation, helping, and learning. the better are the outcomes of the CE project simultaneously

HAUPTMAN AND HIRJI: THE INFLUENCE OF PROCESS CONCURRENCY ON PROJECT OUTCOh4ES IN PRODUCT DEVELOPMENT 157

TABLE I SAMPLE BY COMPANY, INDIJSTRY, AND DATA COMPLETENESS

Company Industry

A B C D E

E F H I J K L M

Automotive Automotive Automotive Automotive Automotive

Chemicals Computers Electronics Electronics Electronics Shipbuilding Telecom Telecom

Comments

__ 4 6 3 3 5

2 4 4 3 1 5 9 4

--

Three nearly complete

2

nearly complete

I I I I

*Only two team members reijponded **Only one team member responded

in terms of project efficiency, project effectiveness, and team satisfaction.

Fig. 2 summarizes the relationships between the degree of concurrence in the CE process, as operationalized earlier, and the outcomes of the C13 project.

111. RESEARCH METHODS

A. Research Setting and Sample

The data for this study were collected as part of a 1arge:r study of Global CE by the Intelligent Manufacturing Systems (IMS) international collaboration between academics and industry. The companies that agreed to provide access to projects for the study were from Australia, Canada, Denmark, Finland, the United Kingdom, and the United States in the aerospace, automobile, chemical, computers, electronics, shipbuilding, and telecommunication industries. All the companies that participated in the study have had between one and seven years of experience with CE. We used the following criteria to select the projects: 1 ) the duration of the project was no more than two years, 2) time since project completion did not exceed one year, 3) the project team had to include representatives from engineering/R&D and manufacturing, 4) the project had to be a formal activity led by a project leader, and 5 ) if the project team was distributed geographically, the representatives from engineering/R&D and manufacturing had to be uncol l~cated.~

B. Datu Collection Merhods The data for this study were collected in a single stagle

questionnaire survey. The second author pretested the questionnaire through semistructured interviews with managers involved in the NPD process from two manufacturing companies in the Ottawa-Carleton region; both companies design and manufacture new telecommunication products using CE. The

9This requirement was unrelated to this paper and stemmed from the additional research goals related to the role of IIT tools in CE.

questionnaires were distributed to the project leader and the engineering/R&D and manufacturing representatives on the project tearn by company representatives. Because the Project Leader’s questionnaire was shared with other IMS researchers several items had to be excluded; those were related to project outcomes and several items from Appendix A dealing with process concurrency. Survey instructions required respondents to use a single CE project as the basis to complete the respective questionnaires. The questionnaire were designed to be completed in about an hour. Data collection was completed by the end of June 1994.

C. Measures of Variables

I ) Degree of Concurrency in the CE Process: The four dimensions of concurrency in the CE process were measured by a set of straightforward questions based on five-point ordinal scale items (see Appendix A for details of questions). These questions were designed on the basis of previous exposure to the CE process by the first author, discussions with managers of NPD from R&D/engineering and manufacturing, and detailed information about the Clark: and Fujimoto [13] study and interview data contained in Fujimoto [24]. Each dimension was measured by multiple items. To provide rich information about the new constructs, open-ended qualitative questions asked the respondents to identify three situations in which they had to either use from or release to their counterpart incomplete and uncertain information, and then to describe one situation in more detail. These were used for supporting construct validity.

2) Project Outcomes: Based on the literature reviewed in the previous chapter, questions were designed to measure the outcomes of new product developmeint projects that used CE principles. The seven-point items that measured meeting project effectiveness goals asked the respondent to assess the degree of :juccess, phrased in quantitative terms, in terms of product cost (e.g., “exceeded plan for product cost by 50%”) and quality (e.g., “below target for ove:rall product quality by 25%”). Similar to the above, meeting project efficiency goals was measured by seven-point items of project budget and lead time. Team satisfaction was measure by six five- point items, which asked team members to express the extent of agreement (from “strongly agree” to “strongly disagree”) with statements about the outcome of the team process [34]. The items measured at the end of the project, the extent of mutual trust, satisfaction with the team, amount of stress felt, opinions about group’s effectiveness in resolving conflict and about how well the team members worked together, and readiness to participate in a similar project in the future. Aggregate indexes of performance were constructed to test the relationships between degree of concurrency in the CE process and project outcomes.

Iv. DATA ANAL.YSIS AND RESULTS

A. Survey Response Rates

Fourteen international manufacturing companies participated in this study. The primary contact person in each company nominated one or more new product development


17.3

158

3 6 1 10.6 I 8.7

TABLE I1 FACTOR ANALYSIS AND RELIABILITY OF CO\CURRE'JCY OF THE CE PROCESS

4.60 I 2.78 I 1.69

Factors

1.39

A

.E2

B

.76 .76 .86

C D

A. Two-way communication 1) Information available to manufacturing 2) Information available to engineering 3) Communication was two-way 4) Frequency of communication 5 ) Percent of overlap

Completeness of process design when: 6) Engineering ended their overlap

B. Overlapping Problem Solving Completeness of the product design when:

7) Manufacturing first got involved 8) Manufacturing first feedback 9) Manufacturing first cost estimate I O ) Manufacturing first commitment to purchase

I I ) Engineering to revise decisions 12) Manufxturing to revise decisions 13) Engineering to incorporate information 14) ManuCacturing to incorporate information

C. Use of Incomplete and Uncertain Information

D. Release of Incomplete and Uncertain Information Readiness of

15) Engineering to release information 16) Manufacturing to release information

Percent variance explained Total Eigenvalue Cronhach CY

.76

.83

.77

.70

.44

.65

.5 1

28.7

.66

.65

.80

.68

.so

.45

.85 3 6 .43 .56

.41

.86

1 ) The variables were regrouped for ease of factors identification. The factors' titles correspond to the constructs that emerged from the data 2) The wording of the questionnaire items is in Appendix A, with questions' order and numbers corresponding to the order and numbers of the items in- the table

CE projects and provided the names of project team members. The final sample included 50 projects; in 40 (80%) the project leader and the R&D/engineering and manufacturing representatives responded to the survey (see Table I for sample breakdown); seven had two team members and three projects had a single team member who responded to the survey.

data from multiple respondents, namely, the project leader, and the R&D/Engineering and Manufacturing representatives, were aggregated to the project level. Because of low oc- currence of systematic missing data the mean substitution method recommended by Tabachnick and Fidel1 [58] was used. After project-level scores were obtained, the entire survey data underwent thorough measurement analysis, including principal components factor analysis for construct validity and calculation of Cronbach's a for internal consistency and reliability. The psychometric properties of the instrument alleviated concerns associated with its newness (criteria suggested by Nunnally [41] were used). In addition to the usual statistical test (e.g., for multicollinearity and heteroscedasticity), a set of tests, using analysis of variance, was conducted to identify company, industry, and country biases in the data. Neither violations of statistical validity nor the above sample selection biases were detected.

C. Results

1 ) Degree of Concurrency of the CE Process: The 50 project-level responses to the 16 items measuring the degree of concurrency in the CE process items were factor-analyzed with principal component analysis and a varimax rotation. Four factors with eigenvalues greater than 1.0 explained 65.3% of the total variance. Inspection of the Scree plot supported the four-factor solution. The project level scores on the various items were summed to form the measures of concurrency of the CE process and tested for reliability. In several cases,

B. Data Analysis

The data underwent two levels of aggregation. First, the individual data from the team leader, and the representatives from R&D/engineering and manufacturing were tested for consistency and reliability. Interrater reliability for each common item was tested by a one-way analysis of variance (for function-based biases), and bivariate correlations and Cronbach a" for interrator reliability [4], 1261, [53]. Both bivariate correlations and the reliability test (Cronbach a) treat the respondents as different measures of the same underlying construct. It should be noted that although these tests will not identify which teams had members with divergent views, they would adequately identify those items for which the views of team members diverged in a large proportion of projects studied. After these tests yielded adequate results,"

"The Cronbach's coefficient (1 indicates the internal consistency or reliability of a multi-item measure. In general, a multi-item measure can be judged to be reliable when the value of its Cronbach cy exceeds 0.70. An D greater than or equal to 0.60 can be considered reliable with exploratory research

" Disagreement among respondents was detected for only two items, which 1411.

were retained for subsequent analysis.

HAUPTMAN AND HIRJI: THE IINFLUENCE OF PROCESS CONCURRENCY ON PROJECT OUTCOMES IN PRODUCT DEVELOPMENT 159

TABLE I11 FACTOR ANALYSIS AND RELIABILITY OF PROJECT OUTCOMES

Factors

A. Team Satisfaction 1) Resolved conflict 2) Enjoyed working with the team 3) Worked well together 4) Stress was low 5 ) High level of trust 6) Like to work in the future

B. Project Efficiency Goals 7) Meeting product cost 8) Meeting project budget 9) Meeting lead time

C. Project Effectiveness Goal 10) Meeting overall product quality

Percent variance explained

Eigenvalue Cronbach a

The variables were regrouped for ease of fac The factors titles correspond to the contructs

I

I rs identification.

C -.

.61

.86 12.3 64.8 1.23 NA

-.

-. -.

-.

]at emerged from the data

when factor loadings of items were ambiguous (e.g., multiple loadings higher than the cutoff point of 0.40) the reliability test (Cronbach a) helped to determine the factor affiliation of the items. Table I1 summarizes this analysis and shows, factor loadings greater than 0.40. Several items did not load as expected: e.g., items five and six, which were expected to load on the overlapping problem-solving and not on the two- way communication factor. This analysis strongly supported1 the psychometric quality of the instrument.

The qualitative data provided by the respondents to the open-ended questions offered further support for construci‘ validity of the novel dimensions related to uncertainty andl ambiguity in the CE process. There were both examples of effective and ineffective behaviors. For instance, a case of over-commitment to uncertain information resulted in itera- tions:

. . . cable drawing did not indicate pin allocation. Po- sition of pin was ambiguous . . . but Manufacturing went ahead without asking and built the cable. The cable had to be redone . . .. (design engineer, aeronautics company).

On the other hand, an example of a successful collaboration was indicated by a telecommunication company’s manufacturing engineer, when engineering released early and uncertain module design changes. Examples of problems with uncertain and ambiguous data and solutions abounded:

. . . insufficient data to align machines, resolved by continual interaction between manufacturing and engineering (project leader, automotive company). . . . the initial design requirements for specific CNC grinder were incomplete and several prototypes had to be developed before the mechanical/electrical limitations of the various components were resolved (manufacturing engineer, automotive company). A processor interface had to be “recycled” without a complete timing analysis done first. Upon consultation with

other group members . . . it was decided that very minor modificaiions were required before “recycle.” We decided the risk to be low and went ahead with the design .. . (hardware design lead, computer company). Instead of testing the transformers first, the packs were built and then the transformers were tested in pack (saved project time). The parts failed the tests and R.&D had to modify parts in the lab.

These examples support the conceptual description of the constructs related to dealing with ambiguous and incomplete information, and the qualitative information [ 131, [20] presented in the literature review section.

2) Projec-t Outcomes: The 50 project level responses to the 10 project outcomes items were factor-analyzed with principal component analysis and a vwrimax rotation. Three factors with eigenvalues >1.0 explained 64.8% of the total variance. Inspection of the Scree plot supported the three-factor solution. The projeci level scores on the various items were summed to form the factor scores and tested for reliability. In the only case of ambiguity of factor loadings the reliability test (Cronbach a ) helped to determine the factor affiliation of the item. Table 111 summarizes this analysis and shows factor loadings >.40. The only item that did not load as expected was product cost (item seven): as a project goal it was expected to be associated with project effectiveness rather than efficiency. This resullt is also different from previous research [ 5 ] , [26]: the fact that three orthogonal factors were identified may suggest thiat project goals in terms of efficiency, effectiveness, and team satisfaction could be accomplished independently. Table IV contains convergent and discriminant validity results for the two aggregate measures. The psychometric quality of the instrument proved to be adequate.

D. Degree of Concurrency in the CE Process and Project Outcomes

The descriptive statistics of Ihe main variables used in multivariate analyzes are summarized in Table V. Interestingly, the mean of variable number three-“use of incomplete and uncertain information”-is lower than the other three dimensions of CE process concurrency, suggesting reluctance to use of such information or difficulties encountered in attempting to do that by the project team members.

From the bivariate correlations between the two groups of variables, not reported here, the following were positive and significant at p < 0.05: 1 ) two-way communication with both measures of team process satisfaction and project efficiency, 2) overlapping problem-solving with product effectiveness, and 3) the two dimensions related to dealing with incomplete and uncertain information correlated with project efficiency. The results of multivariate regression analysis are presented in Table VI.

The dimensions of CE process concurrency were used as independent variables to investigate various models of project outcomes. Models A-D use individual dimensions of project effectiveness (Hypothesis One) and efficiency (Hypoth- esis One), namely the extent to which the CE project met product quality goals (Hypothesis One) and project budget, schedule, and product cost goals (Hypothesis Two). Only

160 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 43, NO. 2, MAY 1996

TABLE IV CONVERCEVT AND DISCRIMISAYT V~LIDITY OF PROJECT OUTCOMES

Items 1 2 3 4 5 6 I 8 9

Team Satisfaction 1) Resolve conflict well -

2) Enjoy work with team .49 3) Worked well together .50 4) Stress was low .31 5) High level of trust .45 6) Will work in the future together .34

7) Meeting product goals .20 8) Meeting project budget .20

Project Efficiency Goals

9) Meeting project lead time .03

10) Meet product quality .04 Project Effectiveness Goals

Team Satisfaction: Mean intrascale correlation = 0.42. Project Efficiency Goals: Mean intrascale correlation = 0.46 Mean interscale correlation (absolute values) = 0.14.

-

.72

.28

.41

.5 1

.21

.27

.16

.18

- .3 1 .53 .34 .52 . I 1 .38

-

-

.27 . I2 .01 .14 __

.42 .17 .09 -.05 .4 1

.I4 .02 -.01 -.08 .41 .55 __

.15 - .6 .12 .20 - .7 -.8 .18

model G-meeting the project budget goal-was statistically significant, with variable number three: Use of incomplete and uncertain information, significant at p < 0.05. In addition, in model A (meeting the product quality goal), variable number two (overlapping problem-solving) was significant at p < 0.05.

Model E investigated Hypothesis Three(a)-simultaneous high performance in multiple project efficiency goals, while F tested Hypothesis Three@-simultaneous high performance on project effectiveness and efficiency goals. The structure of project performance factors identified in Table I11 served as a guide for aggregate performance indexes: 1) performance items that loaded on the same factor were aggregated addi- tively and 2) those that loaded on different orthogonal factors were aggregated multiplicatively. For Model E the additive index of meeting project budget and lead time and product cost goals was used as an independent variable; it was significant at p < 0.10, with variable number three positive and significant at p < 0.05. Model F also included the product quality goal, multiplying it by the aggregate index used in Model E; the model was significant at p < 0.10.

Finally, Models G and H investigated Hypotheses Four and Five, namely the index for team satisfaction and the overall project success, respectively. Model G was significant at p < 0.10 and variables number one (two-way communication) and three (use of incomplete and uncertain information) were significant at p < 0.10; surprisingly, variable number three had a negative coefficient. Model H was also significant at p < 0.10.

The relationships between the degree of concurrency in the CE process and project outcomes were also supported by discriminant analysis (not reported here). For instance, the two dimensions dealing with use and release of incomplete and uncertain information were significant in discriminating between successful and unsuccessful projects. Their discriminant power persisted across various indexes used for measuring project success and several cutoff points for classification into the categories of successful and unsuccessful projects. In addition, two-way communication and overlapping problems-solving were significant, respectively, for models F (a multiplicative

index of project effectiveness and efficiency, split by top and bottom 1/3 of the index score) and H (a multiplicative index of project effectiveness, project efficiency, and team satisfaction, split by top and bottom 1/3 of the index score). Mann-Whitney tests supported these results by statistically significant differences for the group means for the variables of the discriminant functions-higher scores on dimensions of CE process concurrency for more successful projects. It should be noted that the accuracy of project classification, based purely on the dimensions of CE process concurrency was between 77% and 83%. Furthermore, the classification accuracy for unsuccessful projects was higher than for successful projects.

The results of both regression and ‘ discriminant analysis offer no support to Hypothesis One-effect of process concurrency on project effectiveness, operationalized as product quality. This analysis offers partial support to Hypothesis Two-effect of process concurrency on project efficiency; only the model which uses meeting project’s budget goal as dependent variable is significant. There is also moderate support to Hypotheses Three(a) and (b), which deal with the multidimensional performance of the project. The hypothesis which deals with the impact of process concurrency on team’s satisfaction (Hypothesis Four) was partially supported: while two-communication contributed to team’s satisfaction, use of incomplete and uncertain information had a negative impact. Finally, the hypothesis dealing with overall aggregate index of project success, which included all three components of performance (Hypothesis Five), was supported by the data. Two-way communication and use of incomplete and uncertain information, contributed the explanatory power in models that were found statistically significant.

V. DISCUSSION

A. Theoretical Implications

The study offers several insights useful for theory of small groups and team performance, as well as for information processing. First, it was especially important to translate the CE process into specific behavioral and attitudinal dimensions

HAUPTMAN AND HIRJI: THE IINFLUENCE OF PROCESS CONCURRENCY ON PROJECT OUTCOMES IN PRODUCT DEVELOPMENT 161

TABLE V DESCRIPTIVE STATISTICS OF MAIN VARIABLES USED

IN REGRESSTON AND DISCRIMINANT ANALYSIS ~~~

Std. Variables Mean r)ev K-SZ

CE Process Concurrency I ) Two way communication 3.82 .61 .93 2) Overlapping problem solving 3.31 .64 .99 3) Use incomplete and uncertain information 2.71 .60 1.30 4) Release incomplete and uncertain information 3.67 .79 1.28

5) Team Satisfaction 3.74 .45 0.12 6) Project Efficiency 2.45 .54 1.25 7) Project Effectiveness 3.50 .65 1.98

Project Outcomes

1) Kolmogorov-Smirnov test of normality statistic for two- tail is p < 0.05 is 1.35. 2) Project Efficiency = Aggregate of meeting project budget and lead time and product cost goals. 3) Project Effectiveness = Meeting product quality goal,

related to information exchange and communication and team performance, confirming and elaborating on previous work [2], [12], [15], [26]. Furthermore, the behavior and attitude of CE team members toward ambiguous and uncertain information, especially the use of such information, were found to play a. role concerning CE project outcomes. These findings related to release and use of uncertain and incomplete information are supported by previous research on decision-making and uncertainty; for instance [IO], [36], [51] suggest that actors will attempt to eliminate uncertainty from their decision-making by various means, including avoidance of uncertain information or deeming it less relevant than it should rationally be. It. should be noted here that the use of incomplete and uncertain information-a task structure requirement in CE-had a negative effect on team’s satisfaction. In addition, the recent work by Eppinger [23], [38], [44] on CE, identified task situations, under which uncertain and incomplete information may have: a dysfunctional effect on project performance. These situa-. tions are characterized by high sensitivity of the downstream, function (i.e., manufacturing) to upstream decisions (i.e., by R&D/engineering), and by slow evolution of upstream (i.e.., design) information from high to low uncertainty. Although1 the findings of the current study do not address the specific: dimensions of the project in terms of evolution and sensitivity,, they identify team members’ attitudes and behavior when dealing with uncertain and incomplete information.

The study’s findings are consistent with studies showing ai

positive relationship between team member interdependence and team performance [5 ] , [16], [32], [39], [63]. In this study, performance concerns attainment of the project budget goal. Furthermore, the finding that two-way communication increases team satisfaction is consistent with previous studies of R&D [2], [5] and product development projects [5], [26]. Consequently, the measures of CE process concurrency could be treated as measures of rich information exchange and communication in highly interdependent teams.

B. Practical Implications

For managers of engineering, especially NPD/CE practition- ers, the study’s findings have several important implications.

First, the measures of process concurrence could be applied as a diagnostic tool for measuring an organization’s success in achieving e:ffective concurrency in the NPD process. These measures could serve as internal and external benchmarks for further NPD process improvement. Similarly, for those companies that have not formally implemented CE, this set of measures goes beyond the binary “CE or no CE?’ distinction by offering generalizable quality measures of the product development process. Top management and project leaders alike will benefit from knowing that CE is a necessary but insufficient condition for project success: while not using CE- type processes could ensure failure, using it does not guarantee success. At the same time, after all the investment in CE enablers, management could evaluate whether the company has benefited from it by developing the competitive capability of CE.

The second implication of these finding sheds light on what CE could offer for project performance: CE has a favorable impact primarily on attainment of project budget goals, but achieves {,his without any adverse impact on quality, cost or schedule. The indexes which reflected combinations of these goals remained significant when tested against CE variables, suggesting that the goals were positively correlated. The fact that significance for the combined goals decreased rather than increased suggests that these correlations are modest.

The findings concerning the relationship between dimensions of the CE process and team satisfaction are moderately reassuring: it seems that the requirement to tolerate uncertain and ambiguous information associated with CE, could endan- ger initial team members’ satisfaction from the CE experience. This may have negative ramifications for subsequent buy-in, adoption, a.nd practice of the CE process. This concern is especially valid for the manufacturing representatives on the CE team, who, as the downstream function, are required to receive and use uncertain and incomplete information from R&D/Engineering. But, as the results indicate, this potential team satisEaction cost could be ameliorated by a high level of two-wTy communication between the members of the upstream and downstream functions on the project team.

C. Generalizability and Methodological Osues

Although the various measures were found adequate in terms of construct and internal validity, for a study dealing with new measures it is important to discuss their generalizability. The threat to generalizability of findings stems mainly frorn the use of convenience sampling, which resulted in a nonrandom set of companies and industries. The former issue cannot be comfortably resolved because it is quite possible that the companies contacted were different from the popula1,ion of companies that use CE. But the potential selection bias toward companies that are more advanced in their CE practices is countered. by the sample’s variance on all dimensions of performance and CE practices. consequently, their self-selection bias may stem from misperception and self-deception. Considering the consequence of studying companies in the aeronautical, automotive, chemical, computer, electronics, shipbuilding, and telecommunications industries, we have not found significant industry-based differences be-

162

4 R2

Fdf4,45

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. VOL. 43, NO. 2, MAY 1996

-.02 .15 .15 .04 . I1 1.10 . I 1 7.6 .11 . I4 .21 .09 .18 .16 .I8 .I7

1.24 1.85 2,97** .90 2.55* 2.17% 2.50* 2.33%

TABLE VI RELATIONSHIP BETWEEN CONCURRENCY OF THE CE PROCESS AND PROJECT OUTCOMES (REGRESSION ANALYSIS)

~

A B C D E F G H Model Independent Variables

1 2 3

-.lo .16 .23 .18 .16 1.62 .19* 11.2 .33* .06 -.19 - .07 -.12 1.51 .05 6.2 I .02 .19 .30* .29 .33** 2.61 -.18* 4.8

tween projects. Consequently, although the results could be generalized to the studied industries only, the set of industries included is broad enough to make the results relevant for most technology-intensive manufacturing companies. Finally, the common method bias associated with the use of a survey questionnaire is potentially present in this study, as in most studies cited [2l, [4l, [51, [21l, [26], [48], [551, 1561. The exceptions are the clinical, qualitative studies such as [ 131, [24], which extensively used interviews and company documents.

VI. DIRECTIONS FOR FUTURE RESEARCH

In addition to the new insights related to our understanding of CE and NPD processes the study offers, it also points to several gaps. First, the identification, measurement, and confirmation of the importance of CE process dimensions, draw attention to the mechanisms that help and enhance these dimensions. The model in Fig. 3 and the previously identified models that guided this study [48], [56] point to a set of variables that should be revisited, namely the coordinative and integrative mechanisms deployed by companies to facilitate the CE process.

Another promising venue is to address the model of fit between project or task difficulty and the CE process dimensions. Several researchers [I], [28] support this line of reasoning and recent empirical studies [4], [7], [ l3l , [19l, [481, [611 support the existence of a contingency relationship. A related problem of interest is the relationship between the empirical project dimensions, such as scope, scale, and radicalness [61], the conceptual constructs of variety and analyzability [43], and equivocality or ambiguity [ 171.

The study’s findings about the various impacts on the team’s satisfaction from CE are interesting. While difficulties were encountered around the use of ambiguous and uncertain information which had a negative effect, two-way communication had a predictable positive effect on the team’s

satisfaction. The former raises research questions about the role of organizational learning and experience with CE, and the use of new tools, techniques, and methods that help alleviate this negative effect.

Finally, although the various dimensions of CE were reliably modeled and measured, more attention should be given to development of these and other process measures. The development of this knowledge may require a combination of clinical methods with traditional survey instrument validation. Strong grounding of future studies of NPD and CE in organizational behavior theory will benefit both the problem-oriented applied effort and theory development.

APPENDIX A QUESTIONNAIRE ITEMS MEASURING THE

DEGREE OF CONCURRENCY OF CE PROCESS

Using the following scale, indicate your degree of agreement with the following statements.

strongly disagreee neutral strongly

agree 1 2 3 4 5

1) As the product design evolved information was made readily available to manufacturing.

2) As the process design evolved, information was made readily available to engineering/R&D.

3) Communication between engineering/R&D and manufacturing during the project was two-way.

4) How often did engineering/R&D and manufacturing communicate between themselves? once once at least

a month a week once a day 1 2 3 4 5

5 ) Please indicate on the scale below the amount of problem-solving overlap between engineering/R&D and

HAUPTMAN AND HIRJI: THE INFLUENCE OF PROCESS CONCURRENCY ON PROJECT OUTCOMES IN PRODUCT DEVELOPMENT 163

manufacturing that most accurately describes the way in which product and process design tasks are/were carried out in your project. No overlap 25% 50% 75% 100%

at all overlap overlap overlap overlap 1 2 3 4 5

6) How complete wtas the process design when engineering/R&D ended their active involvement in the project‘? Use the scale below to estimate how complete was the

1 2 3 4 5 0% 25% 50% 75% 100%

complete complete

productlprocess design when. . .: 1 2 3 4 5

0% 25% 50% 7.5% 100% complete complete

7) manufacturing became actively involved in product design?

8) manufacturing provided feedback for the first time? 9) manufacturing made formal cost estimates?

manufacturing started making commitments to purchase materials, tools, and equipment? Use the scale below to answer “How willing were.. .” not at all moderately extremely 1 2 3 4 5

engineering/R&I) to revise decisions based on incomplete and uncertain information from manufacturing? manufacturing to revise decisions based on incomplete and uncertain infformation from engineering/R&D? engineering/R&I> to incorporate ambiguous informa-. tion from manufacturing when making product design decisions? manufacturing to incorporate ambiguous information from engineering/R&D when making process design decisions? engineering/R&I> to share incomplete and uncertain information with1 manufacturing? manufacturing to share incomplete and uncertain information with engineering/R&D?

ACKNOWLEDGMENT

authors are very grateful to our colleagues at the Research Program for Managing Technological Change ini Manufacturing, D. Gerwin, L. Moffat, and A . Young for their useful ideas and suggestions, to the managers in the Research Program sponsoring companies and especially A . Fabbricino, and to the managers of sample companies that contributed of their time to provide data for this study. The authors would also like to thank the two anonymous reviewers and the co-editor of the special issue, G. Susman.

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Oscar Hauptman (M’86) received the B Eng. in industrial engineering and the M Eng in industrial management from the Technion, Israel Institute of Technology, Haifa, and the Ph.D. degree froin the MIT Sloan School of Management, Cambridge, MA, in 1976, 1982, and 1986, respectively

His nonacademc work experience includes being the CEO of an R&D incubator in Israel, a consultant for UNIDO in Thailand, and a President of small high-tech management consulting firms. Dr. Haupt- man teaching experience includes Harvard Business

School, Technology and Operations Management Group (1986-1992), at Chengchi National University, Taipei (1985), Northeastem University (1985), Melbourne Business School (1988, 1991-1996), and Carleton University (1992-1996) His current research projects focus on the integration and coordination in high-technology product development teams, leadership of R&D and product development teams, and post-project reviews as a tool for learning

Karim K. Hirji (M’96) received the B Sc degree in systems science from Simon Fraser university, Burnahy, British Columbia, Canada, and the M M.S. degree in management of technology from Carleton University, Ottawa, Ontario, Canada He is presently studying for the M A Sc degree at the University of Waterloo, Ontario, Canada

He is currently involved in the design and implementation of several messaging technologies-including enterprisewide clientherver elec- tronic mail, computer-based faxing, and computer

telephony integration His project management and technical consulting work experience has been with the Bank of Montreal’s Institute for Learning, IBM Canada Ltd , Toronto General Hospital, and the University of Waterloo His current research interests include data warehousing, coordination of clientkerver computing, process-oriented software engineering, the role and impact of information technology in CE, and high performance new product development teams

Mr Hirji is a member of ACM, the Academy of Management, and INFORMS

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