Innovation project technology, information processing and performance: A test of the Daft and Lengel conceptualization

Journal of Engineering and Technology Management, 9 (1992) 303-338 Elsevier

303

Innovation project technology, information processing and performance: A test of the Daft and Lengel conceptualization Lawrence

Gales, Pamela Porter

and Dina Mansour-Cole

University of Cincinnati, Management Department, Cincinnati, OH 45221-0165, USA

Abstract This study applies the Daft and Lengel (1984,1986) information processing model to a sample of 45 innovation projects. In characterizing innovation projects as an unfolding process, we suggest that project-related technological variety and analyzability become more problematic as projects progress, requiring more information processing. Findings partially support our contentions that managers will match information processing to the project context. Results show significant increases in the amount and richness of information used as projects move from idea generation to commercialization. Additionally, at commercialization there is a significant interaction between project analyzability and emphasis on rich information with respect to project performance. Managers of successful low-analyzability projects emphasize rich information more than managers of successful high-analyzability projects. Furthermore, among high-analyzability projects, managers of successful projects emphasize rich information less than managers of unsuccessful projects. In concluding, we discuss the practical implications of these findings for innovation project managers, along with directions for further research in this field. Keywords. Information

processing;

Information

richness;

Innovation

management

1. Introduction Successful innovation has been identified as critical to continued competitiveness, success, and survival of organizations (Ancona and Caldwell, 1987; Clark, 1989; Von Hippel, 1988). Management research has focused particular attention on better understanding the innovation process (e.g., Ancona and Caldwell, 1987; Drucker, 1985; Ettlie, 1988; Kanter, 1988; Tushman and Nadler, 1980; Von Hippel, 1988). One subject of great interest has been a better understanding of the role of information in innovation. In studies of innovation project management, information availability has Correspondence to: Prof. Lawrence Cincinnati, OH 45221-0165, USA.

0923-4748/92/$05.00

Gales, University

of Cincinnati,

0 1992 Elsevier Science Publishers

Management

B.V. All rights reserved.

Department,

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been identified as a critical factor in project success or failure (e.g. Kanter, 1988; Link and Zmud, 1987, Tushman, 1978, 1979). In fact, Fischer (1979, 1980) has suggested that innovation is essentially an information exchange process. However, the role of information processing (and related actions such as boundary spanning and information gathering) appear to be more complex than first envisioned. Recent works have proposed contingency frameworks for predicting and understanding diverse information-related activities (e.g., Ancona and Caldwell, 1987; Tushman and Nadler, 1980; Von Hippel, 1990). Focusing on applied innovations in the development of toxic waste treatment technology, this study extends understanding of the role of information in innovation project management by applying Daft and Lengel’s (1984,1986) organizational technology and information processing framework to innovation projects. The Daft and Lengel framework, based on Perrow’s (1967) conceptualization of work unit technology, proposes two distinct task characteristics (i.e., task variety and task analyzability) that create specific information processing needs, and two corresponding dimensions of information (i.e., amount and richness ). This emerging framework extends the information processing perspective (e.g. Galbraith, 1977; Tushman and Nadler, 1980; Daft and Macintosh, 1981) and refines variable conceptualization. It has also provided a theoretical basis for research in areas such as top-management decision making (Daft et al., 1987) and information system design and implementation (Lind and Zmud, 1991). We apply portions of this model to innovation project management and examine relationships among innovation project work unit technology, information processing, and project performance. This study responds to criticisms that past innovation research has lacked theoretical grounding or consistent definition and operationalization of constructs (Downs and Mohr, 1976; Utterback, 1974; Van de Ven, 1986). By using Daft and Lengel’s (1984,1986) conceptualization to predict the information processing needs of innovation projects, we respond to calls for the use of consistent and stable measures in innovation/R&D organizational research (Downs and Mohr, 1976 ), thus, avoiding the “varied definitions” (Utterback, 1974, p. 625) that have plagued this area of research. Task variety, task analyzability, information amount and information richness provide measures that are comprehensive, generalizable across innovation projects and generalizable across diverse organizational settings. We propose that innovation project managers should tailor information processing to characteristics of innovation project technology (i.e., the tools, knowledge, skills and actions necessary to complete a task). Additionally, we propose that as projects progress through phases, managers’ information requirements will also change, and may in fact increase. Finally, we hypothesize that project performance is dependent upon how well managers adapt information processing to task characteristics. Thus, managers who continually monitor and match information processing activities to project technology

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characteristics over all phases of the innovation tential for commercial success.

process will enhance

the po-

2. Theoretical framework and research hypotheses The organizational information processing perspective asserts the importance of matching organizational information processing capabilities to the specific work (in this case, innovation project) context (Galbraith, 1973,1977; Tushman, 1978,1979; Tushman and Katz, 1980; Tushman and Nadler, 1978, 1980; Ungson et al., 1981). Central to the information processing perspective is the idea that organizations seek to reduce potential uncertainty arising from the context (Galbraith, 1973,1977; Tushman, 1978,1979; Tushman and Nadler 1978, 1980). Research in a variety of organizational settings has generally been supportive of the contention that, to be effective, work units must match task uncertainty (i.e., predictability, standardization) and equivocality (i.e., ambiguity, shared meaning) with appropriate information processing capabilities (Daft and Macintosh, 1981; Tushman, 1978,1979). Although uncertainty and equivocality may arise from environmental conditions, task interdependence or the type of work unit technology (Galbraith, 1973, 1977; Tushman, 1978,1979; Tushman and Nadler 1978,1980), the current study focuses solely on the influence of innovation project work unit technology. Typically, researchers have operationalized information processing capabilities either through structural dimensions of organizations such as decentralization, boundary spanning, gatekeeping, or vertical information systems (e.g. Allen, 1977; Galbraith, 1973, 1977; Tushman and Katz, 1980; Tushman and Nadler, 1980), or through information characteristics such as amount, source, timeliness, or reliability of information (Daft and Lengel, 198P, 1986; Daft and Macintosh, 1981; Holland et al., 1976; Tushman, 1978,1979; Zmud, 1978,1983). Our focus is on the latter area-characteristics of information, such as richness (i.e., potential information carrying capacity) and amount (i.e., volume or quantity of data), and their relationship to project uncertainty and equivocality. Innovation researchers have identified a variety of project characteristics similar to existing definitions of task uncertainty and equivocality that are related to project information processing requirements (e.g., Allen, 1977; Allen and Hauptman, 1990; Crane, 1972; Pelz and Andrews, 1966; Rogers, 1982; Tushman, 1978,1979; Tushman and Nadler, 1980; Whitley and Frost, 1975). In the following sections we discuss the concepts of variety, uncertainty, analyzability, and equivocality, demonstrate their similarity to characterizations used in the innovation management literature, describe the resulting information processing requirements, and state specific hypotheses concerning the relationships among these factors. Figure 1, based on Tushman and Nadler (1980), graphically presents those relationships and guides our further discussions of these concepts.

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Innovation Project

Activity

Project Manager 1ntelnal grate

Characteristics

uncertainty :

- Number of Information m Bases m Predictability m - Phase

I

:

->I predictability, standardization)

: m

1 Processing 1

I >I 3

-

- Information Amount (i.e.. volume or quantity of data)

: I : I :

:__--_-_.-_--_----,-

i I I

I w Proiect

Analvzabilitp

a 1 - Ease/Difficulty 1 Obtaining information I - 1ncrementa1ism/ 1 Radicalism 1 Phase

1 : :

:

>I

-->

i

Equivocality (i.e.. ambiguity. shared meaning)

I Information Richness (i.e., potential information--

I 1 : I

: 1

:

PROJECT PEBPOBlldllCE

Fig. 1. Project manager information processing model.

2.1. Project variety, uncertainty & information amount Variety, one dimension of work unit technology, is defined as the number of unrelated tasks or unexpected, novel events encountered while carrying out one’s work (Withey et al., 1983). Many unrelated operations or unanticipated events produce high task variety, while repetitive tasks with few different operations or few unanticipated events produce low task variety. Uncertainty, defined as “the absence of information” (Daft and Lengel, 1986, p. 556), results from project variety because of the number of different tasks or information domains that must be mastered, and because unanticipated events produce gaps in knowledge. High-variety tasks require processing greater volumes or quantities of information than do low-variety tasks (Daft and Macintosh, 1981; Daft and Lengel, 1984,1986). The solution to variety-related uncertainty is to ask more questions and to gather and interpret more information (Downey and Slocum, 1975; Galbraith, 1977; Tushman, 1978). With increasing levels of uncertainty, organizations engage in increased information gathering activity (e.g., Daft and Lengel, 1986; Tushman, 1978,1979). The implication is that firms will structure work units or act in ways to increase flows of information as they face increased variety or unpredictability of tasks (Allen et al., 1979; Ancona and Caldwell, 1987; Tushman and Nadler, 1980). This may involve more organic structures that

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permit freer flows of information between departments or more extensive boundary spanning and scanning activities that bring more information into the organization (Daft and Lengel, 1986; Galbraith, 1977; Tushman and Nadler, 1980). In sum, project managers seeking to manage variety and reduce uncertainty will engage in more communications aimed at answering questions and gaining information. Several innovation studies have used technology and information processing variables similar to task variety and uncertainty. For example, studies have included dimensions such as standardization of training and procedures (Holland et al., 1976), risk or predictability of processes and outcomes (Dewar and Dutton, 1986; McFarlan, 1981)) and project constraints such as user application specifications (Fischer 1979,198O; Tushman and Katz, 1980). These project characteristics are logically consistent with task variety (Withey et al., 1983) and use factors such as departures from existing practice, adequacy of training and experience, degree of multidisciplinary interaction, and needs for new or diverse information. Although there is great variability in operationalization of both project technology and information processing, the above studies generally support the information processing contention that increased project variety and the resulting uncertainty require increased information processing activity. This is accomplished through increased communication (Crane, 1972; Fischer, 1979,198O; Pelz and Andrews, 1966, Tushman, 1978) and increased boundary spanning (Allen et al., 1979). Variations in variety, uncertainty, and the resulting information requirements also exist at different phases or sequences of project development (e.g., Gerstenfeld and Berger, 1980; Steele, 1989; Utterback, 1974). It should be noted that although some researchers argue against sequential models of innovation, suggesting overlapping recursive models (Clark and Fujimoto, 1989) or fireworks models (Schroeder et al., 1986), others (e.g., Allen, 1977; Gersick, 1988; Kanter, 1988; Steele, 1989) provide a compelling logic for sequential models of innovation. While this latter group does not suggest rigid discrete staging of project sequences, they indicate that the primary functions and focus of a project will shift in a sequential fashion. Using Gersick’s (1988) framework, we ascribe to the belief that projects may go through phases indicated by specific landmarks or events that cause project members to take stock and shift focus. For example, the initial phase of a project will most likely focus on project conceptualization and domain definition, what we have labelled as idea generation. While activities that characterize this phase may continue or be revisited as the innovation progresses, the primary focus will shift to project design-the acquisition of necessary resources, design of facilities and preliminary design of the innovation. Again, activities that characterize this period of the innovation will not necessarily cease, but the primary focus will eventually shift to full-scale development and production of the innovation. Lastly, although considerable thought should be given to the project’s commercial po-

tential throughout the project, commercialization activities become the final primary focus. These activities include packaging, marketing and shipping the innovation to prospective customers. This type of flexible sequential model does not rule out the possibility that projects may encounter barriers or setbacks, requiring a retreat to an early focus or set of actions. Phases are defined only in terms of focus and dominant activities. The process by which project groups go through these phases is not fixed, but varies from group to group. Furthermore, internal and external conditions and constraints such as reporting requirements may set specific sequential landmarks for projects. Several researchers (e.g., Clark and Fujimoto, 1989; Takeuchi and Nonaka, 1986) have stated or implied that the greatest amount of variety and uncertainty is encountered in the earliest phase of projects. These views are based on two beliefs. First, it is suggested that the largest number of project opportunities, as well as potential gaps in knowledge andunderstanding, exist during the project’s inception. Therefore, more questions are present at the idea generation phase. This belief is supported by a view of innovation as beginning with broadly defined goals and objectives that are subsequently narrowed and focused through a funnelling of decisions (Dean et al., 1990; Ancona and Caldwell, 1987). The second component of this sequentially declining uncertainty model is the belief that the largest and functionally most varied group of potential project participants should be assembled during the earliest project phases. The rationale for this early involvement is to surface divergent points of view and to reduce the likelihood of costly redesign later in the project (e.g., Clark and Fujimoto, 1989; Hayes and Wheelwright, 1984; Leonard-Barton, 1988; Starr, 1990). Together, these factors should contribute to declining uariety and uncertainty as projects progressed, if uncertainty is acknowledged and successfully resolved as it is encountered. While the above logic for decreasing variety and uncertainty appears sound, a compelling counterargument can be made for why project managers’ may perceive that variety and uncertainty increase as projects progress (Ancona and Caldwell, 1987; Kanter, 1988). We propose a model of innovation and the accompanying variety and uncertainty as sequentially unfolding (Gersick, 1988). It may be that, in absolute terms, project managers face the greatest variety and uncertainty at the earliest phases, but that variety and uncertainty are not realized or perceived as such until later when investments are substantial, project parameters are set, and the costs of failures are greatest (March and Shapira, 1987; McFarlan, 1981) . March and Simon (1958) propose that because of bounded rationality, individuals will take action to reduce uncertainty only if they perceive uncertainty to be problematic. Examining factors that signal a need for increased interaction, Gersick (1988) demonstrates that the approach of project transition points makes project members aware of new problems associated with the shifting project focus and mounting time pres-

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sures. She states that, “ (n)ew perspectives appear to enter a group at transitions because team members find old perspectives are no longer viable and initiate a fresh search for ideas” (Gersick, 1988, p. 33 ). Furthermore, in their discussion of uncertainty and stress, Jackson et al. (1987) identify factors that act as triggers, making uncertainty more important and requiring the individual to respond to that uncertainty. These factors include time pressure, threat, and opportunity. Thus, it appears that it is the presence of these triggers or transitions that motivates individuals to respond to uncertainty with increased information processing. Otherwise, uncertainty alone does not necessarily present a problem and will not motivate behaviors aimed at uncertainty reduction. In innovation project management, three factors contribute to this interpretation of sequentially increasing uncertainty. First, as projects progress, the focus of project participants will shift, exposing new and different questions that did not surface during earlier phases (Gersick, 1988). Second, the potential for and impact of meaningful input from diverse functional areas will increase as projects move forward, becoming more focused and elaborated (March and Shapira, 1987; Jackson et al., 1987; Steele, 1989). Third, the consequences of persistent variety and uncertainty become greater as the project progresses. Several researchers have noted that project focus and resulting information requirements will change as projects move through phases of development (e.g., Ancona and Caldwell, 1987; Leonard-Barton, 1988; Newman and Noble, 1990; Steele, 1989; Von Hippel, 1990). For example, problems associated with “scaleup and market development require information quite different from that associated with the work that led to the invention” (Steele, 1989, p. 159). Initially, project managers must answer questions dealing with technical capability-what Steele (1989, p. 139) refers to as the “can we” problem that asks if the project team possesses the skills, knowledge, capital and equipment to create the innovation. As projects progress, managers will potentially face new and different constraints. More “can we” questions will typically arise at critical junctures, transitions, or other milestones (Gersick, 1988). These new questions produce increases in variety and necessitate increased information gathering. Furthermore, as projects progress they will require increasing amounts of information to solve problems associated with diverse applications, application constraints, varied customer preferences, and integration of differing functional domains of project members. Involvement of many perspectives or functional areas creates the need to gather information from multiple perspectives or data bases, which is, by definition, variety. It may also be that meaningful contributions from domains outside of the project’s mainstream are only possible as projects develop and unfold. In early phases of an innovation project the project domain may be so obscure, undefined, or narrowly focused that broad cross-functional interactions are not

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possible’. During the early development of new products or process innovations, personnel from areas outside that most directly involved in the innovation activity may lack a clear understanding of the project and its potential impact. For example, it may be that a marketing department cannot fully appreciate the scope and potential of an engineering innovation such as a new toxic waste treatment until the innovation is far along in the development process. One developer of this type of technology indicated that it was not until the firm began bench testing a new treatment process that they fully understood the number of possible commercial applications. At that point, after considerable time and money had already been spent on the project, the marketing department had some basis for meaningful interaction with the development team. It may also be that participants from diverse functional areas may be willing to suspend judgment or may be coopted by project managers in the early phases. As the project progresses, the coopted participants may begin to see some consequences of their failure to voice opinions earlier. As the project takes form, these individuals may seek to increase their participation. The result would be greater project variety. We have also suggested that it is not just a question of the amount of variety and uncertainty present, but also a question of the consequences of unresolved variety and uncertainty. During idea generation, projects may be ill-defined and subject to so much change that managers fail to recognize the problematic nature of resulting variety and uncertainty. Because resource commitments may be minimal during early project phases, the potential impact of early variety and uncertainty may be perceived as low. Thus, some early variety and uncertainty may not be perceived as important and may be ignored by project managers without serious consequence to the project. As the project progresses, the consequences of unresolved uncertainty grow. In sum, while logic may seem to dictate that projects could not progress if uncertainty continued to increase, the increasing variety and resulting uncertainty are only problematic if they threaten the continued progress and are not ‘The developing literature on concurrent engineering (e.g., Nevins and Whitney, 1989), simultaneous engineering (e.g., Babcock, 1991), and other team approaches to innovation may at first seem to run counter to this argument. Concurrent engineering refers to the concurrent performance of the product development and production functions, while simultaneous engineering is similarly defined as a team concept where “designers, product planners, design engineers, manufacturing engineers, and supplier representatives work together from the beginning to assure that as parts are designed they can be made economically and with good quality” (Babcock, 1991, p. 194). The prescription for cross functional interaction in the early stage of the innovation process does not insure that full understanding of all functions will take place. In fact, Babcock continues to state “ (W)hile this approach saves time and prevents problems, it requires that each team member have some understanding of the other member’s specialties and problems so that they can communicate effectively.” (1991, p. 194). Empirical evidence that this understanding is fully realized in the earliest phase has not yet been presented.

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matched with increased information processing. Thus, both project work unit technology and the phase of project development will contribute to project variety, perceived uncertainty and the consequent need to gather increasing amounts of information. Therefore, we propose that for applied innovation tasks, a significant relationship exists between project variety and amount of information used. As Steele (1989) points out, uncertainty increases as projects progress toward commercialization, requiring greater amounts of information. The uncertainty created by project development will be further compounded by the variety specific to a project’s technology. Projects characterized by high task variety should be associated with greater amounts of information at each point of project development compared with low variety projects. Hypothesis 1-A.

Project managers will use progressively more information as projects move from idea generation to commercialization.

Hypothesis 1-B.

Compared with managers of low-variety project technology, managers of high-variety project technology will process more information at each phase of development.

2.2. Project analyzability, equivocality and information richness Not all information needs within organizations can be met simply by increasing the amount of available information. Task analyzability indicates difficulty encountered searching for task-related information and is related to such things as the extent to which tasks can be programmed or sequenced, the presence of standard operating procedures and/or the existence of a well-defined knowledge foundation, and degree of articulability (Perrow, 1967; Winter, 1987; Withey et al., 1983). Decreasing levels of analyzability are associated with greater difficulty experienced searching for required information (Withey et al., 1983), and greater difficulty transferring information (Winter, 1987). Unanalyzable tasks produce equivocality (i.e., ambiguity about cause-effect relationships, confusion over desired outcomes, disagreements, or misunderstandings about the nature of problems) (Daft and Lengel, 1984,1986; Daft and Macintosh, 1981; Daft and Weick, 1984; Weick, 1979; Winter, 1987). Increasing the amount of available information will not reduce equivocality. High equivocality can only be managed by obtaining increasingly rich information (Daft and Lengel, 1984,1986; Daft and Macintosh, 1981). Although the meaning of information amount should be obvious-“volume or quantity of data” (Daft and Macintosh, 1981, p. 210)-the meaning of richness needs clarification. Daft and Lengel define richness “as the ability of information to change understanding within a time interval” (Daft and Lengel, 1986, p. 560), or “as the potential information-carrying capacity of data” (Daft and Lengel, 1984, p. 196). Richness has been operationalized according to five

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aspects of the method used to convey information: (1) nature of the medium, (2 ) immediacy of feedback, (3) channeL (4) source and (5) language (Daft and Lengel, 1984,1986; Bodensteiner, 1970; Holland et al., 1976; Zmud, 1978). The richest information is conveyed in personal face-to-face interactions, using audio and visual cues embedded in natural and body language. Feedback is potentially immediate. An example of rich communication would be a face-toface meeting in which engineers discussed project design issues. Conversely, lean information is conveyed impersonally, formally, in numeric format encoded in numeric language, and uses only limited visual cues. Feedback potential is limited. An example of lean communication would be a computer printout of project test results. Rich information is of critical importance in reducing equivocality because of the multiplicity of information carried and its ability to handle complex, subjective, impressionistic information (Daft and Lengel, 1984,1986). The immediacy and feedback features of rich modes of communication allow for the type of dialogue necessary to manage the conflicts, confusion, and misunderstandings present in high equivocality situations. In innovation projects, analyzability is embodied in characteristics such as project radicalism or incrementalism (e.g., Allen and Hauptman, 1990; Clark, 1989; Dewar and Dutton, 1986; Ettlie et al., 1984), development of the scientific and technical knowledge base (e.g., Martin, 1983; Sahal, 1981; Utterback and Abernathy, 1981) and clarity of application requirements (Fischer, 1979 ). Fischer (1979)) for example, noted that increasingly nonroutine R&D projects require increasingly nonroutine information sources. Similarly, Bodensteiner (1970) found that non-routine technology projects relied more on interpersonal communication, although his definition of project technology differs somewhat from ours. Zmud (1983) used the term complexity to capture similar aspects of software development projects, and found that different patterns of information processing were associated with complex software development projects when compared with information processing for less complex tasks. Although definitions of routiness, complexity and information processing used in these studies differed from the constructs suggested by Daft and Lengel, there appears to be some association between what are here defined as rich sources of information and non-routine technologies and the resulting high equivocality. Several studies have used project type (e.g., basic, applied, development, or extension) as surrogates for work unit technology (e.g., Allen, 1977; Gerstenfeld and Berger, 1980; Pelz and Andrews, 1966; Whitley and Frost, 1975), although the exact relationship between project type and project analyzability is unclear. Basic research, for example, places greater reliance on written information such as journal articles, conference proceedings, and technical reports, while applied research is more reliant on informal, interpersonal, internal communications. However, some of these information use differences may be at-

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tributable to different communication norms and practices in applied (i.e., industrial) R&D versus basic (i.e., academic) research (Allen, 1977). Finally, in two studies of information system development and implementation, information processing differences have been linked to potential for agreement, ambiguity or conflict, factors that contribute to equivocality. First, Lind and Zmud (1991) found that use of rich information was associated with a greater convergence of understanding among various functional groups involved in information systems development and implementation projects. Second, Newman and Noble’s (1990) case study shows that interactions vary systematically over the life of a project. They show that a major function of much early direct communication is to educate project members, while later communications focus on differences in view points and conflict mediation. As was the case with uncertainty, we propose that project managers will experience increasing equivocality as projects move from idea generation through commercialization. Steele (1989, p. 139) describes the “should we” questions, which exemplify equivocality, as asking whether projects should be initiated or continued. As projects progress into production and commercialization phases, managers will potentially encounter ambiguity about moving from ideas or prototypes to full-scale production and commercialization. Involvement of multiple departments or functional areas will result in different frames of understanding and perceptions of project-related problems (Daft and Weick, 1984; Dearborn and Simon, 1958). In addition to the increased variety associated with involvement of multiple perspectives or functional areas, this broader involvement also creates increased equivocality due to potential disagreements, conflict and confusion (Lind and Zmud, 1991; Newman and Noble, 1990). Furthermore, as projects progress and investments increase, managers may perceive an increase in risk (McFarlan, 1981; March and Shapira, 1987). In sum, we expect that analyzability will decrease as projects move forward, resulting in an increase in equivocality. Thus, we propose that a systematic relationship exists between project analyzability and the importance of rich information. Two sources of analyzability, project work unit characteristics and phase of project development should contribute to information processing requirements. Hypothesis 2-A.

Project managers will emphasize the importance of progressively richer information as they move from idea generation to commercialization.

Hypothesis 2-B.

Compared with managers of high-analyzability projects, managers of low-analyzability technology will place greater emphasis on the importance of rich information at each phase of development.

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2.3. Project technology, information and performance

Up to this point, we have suggested relationships between technology and information processing that should exist. No mention has been made of performance. However, basic to the information processing perspective, and implied in much of the R&D/innovation research on information processing, is the belief that the consequence of coordinating technology and information processing is improved performance. Galbraith states, “the greater the task uncertainty. the greater the amount of information that must be processed ... . to achieve a given level of performance” (1977, p. 37, emphasis added). As we have

already noted, managers of innovation projects characterized by high variety and the resulting uncertainty should be motivated to seek larger quantities of information than managers of low-variety/low-uncertainty projects. The consequence of not matching project conditions with appropriate information processing will be unresolved uncertainty which, in turn, will raise the likelihood of impaired performance. The same is true in the case of matching analyzability and equivocality with appropriately rich information. Managers who fail to respond to low analyzability and the resulting equivocality with rich information are more likely to encounter impaired performance. It is logically clear that high variety and low analyzability demand greater information processing capacity to maintain high performance. Less clear is the impact of too much information processing for routine tasks. We can speculate that such a situation would, at best, seem inefficient and may also harm performance. Thus, hypotheses 3-A and 3-B propose conditional relationships between information processing, technology and project performance. Hypothesis 3-A.

High-variety projects using more information are more likely to be successful than high-variety projects using less information. Conversely, low-variety projects using less information are more likely to be successful than low-variety projects using more information.

Hypothesis 3-B.

Low-analyzability projects using richer information are more likely to be successful than low-analyzability projects using less rich (leaner) information. Conversely, highanalyzability projects using less rich (leaner) information are more likely to be successful than high-analyzability projects using richer information.

3. Research design and measures 3.1.

Sample

This project was part of a larger innovation project management study conducted for the United States Environmental Protection Agency (EPA). Proj-

315 TABLE 1 Sample characteristics 1.

Years firm has been in waste treatment technology development: Mean = 8.84 years S.D.=l.l8years Range = l-40 years

2.

Firms’ primary line of business: Waste treatment is primary= 30 (66.67% ) Waste treatment is division of a diversified firm = 15 (33.33% )

3.

Sale of treatment technology developed in study project: Unsuccessful = 19 (42% ) Successful = 26 (58% )

ect managers for 55 toxic waste treatment projects participating in EPA’s Superfund Innovative Technology Evaluation (SITE) Program were surveyed. Questionnaires were distributed and completed at an EPA-sponsored annual conference for waste treatment developers. Projects represented a wide variety of treatments being developed by private firms and research centers under the SITE Program. The firms developing these technologies ranged in size from Fortune 500 diversified industrial firms to small independent firms specializing in new technology development. The innovations being developed can be classified as product innovations because they were sold to others, although some could also be incorporated by developers into existing production processes. Most of the innovations are actual products intended for clean-up of toxic waste after waste spills, and typical customers include commercial polluters and governmental agencies. A sample profile is provided in Table 1. 3.2. Data collection The survey instrument was developed through a three-step process that began with basic information processing constructs and included structured and unstructured interviews with EPA technical and field supervisory personnel EPA personnel were used because they are trained engineers and scientists, experienced with the development of waste treatment technology. While the technology developers may be knowledgeable about their specific project, the EPA personnel were knowledgeable about a wider range of project types. They typically act as field advisers to l-4 projects at a time. We began by describing or defining basic information processing constructs (i.e., variety, analyzability, and information characteristics) to EPA personnel and then asked them to identify specific examples of these constructs. For example, EPA personnel

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identified variety as a function of the number of different applications for the technology and the number of applications outside of the waste treatment domain. A list of project descriptors for analyzability was generated in a similar manner with EPA personnel, who identified project-related factors that contributed to the ease or difficulty in acquiring appropriate and necessary information. They identified two related factors, the degree of project incrementalism and the extent to which projects built on the project team’s existing expertise in the toxic waste treatment field. From these discussions and examples we constructed the questionnaire (see Appendix 1) and then asked EPA personnel to verify the validity of the items. Similarly, a priori discussions with EPA personnel familiar with the sample projects led to identification of four project phases: idea generation, project design, technology development/production, and commercialization. While EPA personnel agreed that these phases were not necessarily discrete, they did agree that each period had a different central focus (Kanter, 1988; Steele, 1989) and coincided with specific EPA progress reporting requirements. Pretest interactions with EPA SITE personnel identified typical information sources for projects and the potential importance of these sources for project developers, paralleling conceptualizations of rich and lean information sources (Daft and Lengel, 1984; 1986). Information amount and richness were measured retrospectively, with data collected for each of four project phases from key informants (project managers or supervisors) who were directly involved in the activities about which we inquired. We followed procedures outlined by Huber (Huber, 1985; Huber and Power, 1985) for ensuring accuracy of retrospective data. Managers of 45 projects (82% response rate) representing 45 different firms returned completed questionnaires describing their project work unit technology, information processing activity and success in selling their technology. A copy of the survey questionnaire appears in Appendix 1. Some analyses used a sample of 41 due to missing data on the information amount variable. Eight nonrespondents do not differ in any known way from the respondents: they are involved in similar technologies with access to the same information sources through their participation in the EPA SITE program. (We have no reason to assume that they are any more or less successful in selling their technology. ) Two remaining non-respondents were nonprofit research organizations and were excluded since they are not involved in commercialization.

3.3. Measures and instruments Work unit technology Classification of project work unit technology and Withey et al.‘s. (1983) conceptualization opers’ descriptions of their projects.

was guided by Perrow’s (1967) and achieved through devel-

317

Variety. Multiple treatment applications require knowledge of varied technical requirements and varied application constraints (Withey et al., 1983 ). Two five-point Likert scales were used to measure the number of distinct types of applications. First, each scale was a surrogate measure of the number of information bases relevant to the treatment technology. Some treatments had only very narrow, specialized waste treatment applications (i.e., 1 = one specific waste treatment application) resulting in low variety, while others could be applied to a wide array of toxic wastes and situations (i.e., 5 = complex/multiple waste treatment applications) resulting in high variety. Managers of the first type of treatment required knowledge and information covering the narrow range of waste materials and applications, while managers of the second type required mastery of knowledge and information on many toxic wastes and conditions. For example, some treatments were suitable for both organic and inorganic waste and required a more extensive knowledge base than treatment suitable for only one type of waste. Multiple applications outside the toxic waste treatment domain contributed a second independent source of variety. Treatments designed solely for toxic waste treatment (i.e., 1 =only for waste treatment) required information only from the toxic waste treatment domain and had low variety, while projects with multiple applications outside the waste treatment domain (i.e., 5 =many other applications) required additional information from unrelated sources resulting in proportionately more task variety. For example, a treatment that can be incorporated into a manufacturing process so as to reduce toxic waste required familiarity with various manufacturing processes as well as waste treatment. These items constituted a multidimensional count of potential applications of the treatment technology, rather than perceptual measures of variety (Withey et al., 1983). Thus, assessment of internal reliability was inappropriate since the two measures were not expected to correlate (r= - 0.11) (Hattie, 1985). The two items were added together to form a single composite measure of the number of relevant information domains for each project. AnaZyzabiZity. To assess project analyzability, we asked project managers to select project descriptions that most accurately fit their project. Two related factors, the degree of project incrementalism and the extent to which projects built on the firm’s existing experience and expertise in the toxic waste treatment field were used to classify projects as either high or low analyzability. High-analyzability projects were those that were characterized by incremental technologies, combinations of existing technologies, or moderated shifts in technology accompanied with extensive experience in the field. Low analyzability projects were characterized by moderate shifts in technology accompanied by little or no experience in the field, or major technology shifts, regardless of experience.

Projectphuses. In addition to the variety and analyzability arising directly from the project technology, we have proposed that different amounts of variety and analyzability will also result from the different phases of a project. Four project phases were identified: idea generation, project design, technology development, and commercialization. During idea generation the central focus or task is identification of the fundamental project concept. In the current study context, idea generation was associated with application to the EPA for inclusion in the SITE program. Only the most promising ideas were selected. Project design involved feasibility assessment and prototype design, development and testing. Fullscale development and production of the treatment technology take place during the development phase. During the final project phase, the continued production, marketing and sale of the treatment technology take place. Information processing Information amount. Information sources included contact with EPA, potential users, scientists, engineers, and sources identified by respondents. Information amount was measured retrospectively by recording the frequency of communication with each information source (Daft and Lengel, 1984,1986) at each of four periods of project development. The reliabilities for the four measures of information amount (amount of information gathered from ( 1) potential users, (2) other technology developers, (3 ) EPA, and (4) academic scientists/engineers) were: idea generation (alpha= 0.67)) project design (alpha=0.75), technology development/production (alpha=0.65) and technology commercialization (alpha = 0.78). Information richness. The potential importance of information sources for project developers parallel theoretical conceptualizations of rich and lean information sources (Daft and Lengel, 1984,1986). The five information sources, in descending order of richness (richness values are indicated in parentheses) were: face-to-face communication (5)) telephone conversations (4)) formal conferences (3 ), technical reports (2) and newsletters (1). Project managers indicated the importance (1 = not important; 5 = very important) of each type of communication at each of the four project phases listed above. Richness scores were calculated by multiplying the importance rating (l-5) given by the project manager with the assigned richness value (l-5). For example, if a manager rated face-to-face communication (with a richness value of five) as very important (5 ) , the corresponding richness score for the communication would be 25. This was done for each mode of communication at each phase of project development. All communications at a given phase were summed to arrive at a communications richness score for that phase of project development. This procedure is similar to those used in other studies of communication richness (e.g., Daft and Lengel, 1986; Lind and Zmud, 1991). The reliabilities for the

319

richness scales at each project phase are: idea generation (alpha = 0.81)) project design (alpha=0.72), technology development (alpha=0.77), and commercialization (alpha = 0.91) . As with information amount, procedures specified by Huber ( 1985;Huber and-Power, 1985) were followed to ensure accuracy of these data. Project performance Although many possible intermediate measures of performance were possible, actual sale of technology was an unambiguous and unobtrusive measure of commercial success. Furthermore, a stated objective of the EPA SITE Program, which all sample projects participated in, was to commercialize technologies. Project performance was categorized based on project managers’ reports of success in selling their treatment technology as either successful (1) or unsuccessful (0). While recognizing the limitations of these dichotomous data, this was the only commercial performance data that the EPA would permit us to collect. 3.4. Analysis A combination of linear and loglinear models were used to test the hypotheses. Hypotheses 1-A through 2-B were evaluated using split-plot repeated measures ANOVA, testing information use across project phases (within subjects effect) and the impact of variety and analyzability on information processing (between-subjects effect) (Milliken and Johnson, 1984). Hierarchical logistic regression models were used to test the effect of information processing on the likelihood of selling treatment technology (Hypotheses 3-A and 3-B) (Aldrich and Nelson, 1984). At each project phase we began with full models specified in the hypotheses. These include the project variety, project analyzability, information amount, information richness, and two interaction terms (variety x amount and analyzability x richness). The procedure we followed was to test the full model for significance and then proceed to eliminate variables to find a best fitting model. 4. Results 4.1. Descriptive statistics Table 2 presents sample means and standard deviations for information amount and richness at each of the four project phases. Means for information amount increase over the first three project phases and decline slightly in the final period. The importance of information richness means increases with each successive project phase. Frequencies for the two measures of project technology are displayed in Ta-

320 TABLE 2 Mean and standard

deviations

for information

amount and importance

of rich information

Project phase

Information amount (N=41; Range=O-15) Mean (Std.D.)

Richness importance (N = 45; Range = O-60) Mean (Std.D.)

Idea generation Project design Development Commercialization

4.98 6.32 8.24 7.56

27.98 31.63 41.44 47.93

(3.70) (3.76) (3.34) (4.28)

(14.87) (11.69) (11.38) (8.13)

TABLE 3 Frequency

distribution

of project variety and analyzability

Variety

Total

Low

High

Analyzability High Low

7 13

14 11

21 24

Total

20

25

45

ble 3. The distribution uniform, except for an low variety) projects. Among 45 projects commercializing their

-

of variety and analyzability among projects was fairly under-representation of routine (high analyzability and surveyed, treatment

26 were successful technology.

and 19 were unsuccessful

4.2. Information processing and project phases The within-subjects phase differences in the amount of information used and importance of rich information that we hypothesized were tested using separate repeated-measures analysis of variance. Variety was included as a block variable in the model for information amount (Table 4)) while analyzability was included as a block variable in the model for importance of rich information (Table 5 ). Univariate tests employed the Huynh-Feldt correction for conservative levels of significance (Milliken and Johnson, 1984 ). The amount of information used at each phase (within subjects effect) varies significantly (F= 10.72, df=3, 117, adjusted p
321 TABLE 4 Repeated measures and mean contrast for project phase and variety effects on information

amount

Part A: Phase and variety effects Source

df

Between subjects Variety Error Within subjects Phase Variety x Phase Error

40 1 39 120 3 3 117

F

MS

2.52 34.43

0.07

84.74 7.62 7.91

10.72* 0.96

*p < 0.0001 adjusted Part B: Contrast

of information

amount over phases of project development

Contrast

Mean difference

F

P<

Idea generation-Project design Project design-Development Development-Commercialization

1.34 1.92 0.68

7.86 15.79 1.82

0.008 0.0003 0.18

used at each project phase. Contrasts of adjacent phases show that information amount increases significantly between idea generation and project design (mean difference = 1.34, F= 7.86, p-c 0.008) and between design and technology development (mean difference = 1.92, F= 15.79, p < 0.0003). Information amount does not change significantly between technology development and commercialization (mean difference = 0.68, F= 1.82, p < 0.18). These findings partially support Hypothesis l-A, but do not support Hypothesis 1-B. In support of Hypothesis 2-A, the analysis shows that the importance of rich information also varies significantly by project phase (F=44.05, df= 3, adjustedp < 0.0001). Mean comparisons indicate significant increases in the importance of rich information at each project phase. Importance of rich information at the design phase is significantly greater than at idea generation (mean difference = 3.65, F= 4.73, p < 0.04). Importance of rich information at the development phase is greater than at the design phase (mean difference=9.81, F= 34.04, p-c 0.0001). Importance of rich information at commercialization is also significantly greater than at the development phase (mean difference = 6.49, F= 17.36, p < 0.0001). The importance of rich information does not, however, vary systematically with respect to analyzability (F= 1.17, df=3, adjustedp c 0.32). Thus Hypothesis 2-B is not supported.

322 TABLE 5 Repeated measures and mean contrast of rich information Part A: Phase and analyzability

for project phase and analyzability

effects on importance

effects

Source

df

Between subjects Analyzability Error Within subjects Phase Analyzability x Phase Error

44 1 43 132 3 3 129

MS

F

101.76 298.38

0.34

3798.49 100.54 86.23

44.05* 1.17

*p < 0.0001 adjusted Part B: Contrast

of importance

of richness over phases of project development

Contrast

Mean difference

F

P'

Idea generation-Project design Project design -Development Development - Commercialization

3.65 9.81 6.49

4.73 34.04 17.36

0.04 0.0001 0.0001

4.3. Information processing and project performance

Hypotheses 3-A and 3-B propose conditional relationships between project variety, project analyzability, information amount, information richness and project performance. Hierarchical logistic regression results are reported in Table 6. For the first three project phases no models, full or reduced, showed significant relationships between project characteristics, information processing and commercial performance. However, in the final project phase two significant models were identified. The best fitting model was that which contained analyzability, richness and the analyzability x richness interaction (X2= 8.08, df= 3, p < 0.05). The coefficients for analyzability (/3= - 12.83, X2= 5.18, p < 0.02) and the analyzabilityx richness interaction (p= 0.26, X2= 5.13, p < 0.02) were significant in this model. The main effects coefficient for richness (p= - 0.05, X2~0.73) was not significant. Further reduction of the model did not yield a better fit. Figure 2 graphically presents the pattern over the four project phases of the importance of rich information broken down by project analyzability and proj-

323

TABLE 6 Logistic regression analysis of the effect of technology and information processing on the likelihood of project success Project phase

Model (model x2 )

df Variables

B

Idea generation

Full model: (modelx*= 1.95, n.s.)* Sucess = Info amount, Info Richness, Variety, Analyzability, Amount ~Variety, Richness X Analyzability

6

Info amount Info richness Variety Analyzability Amount x Variety Richness x Analyz.

0.64 -0.02 0.60 - 1.17 -0.08 0.04

Design

Full model: (modelx*=3.08, n.s.)* Sucess = Info amount, Info Richness, Variety, Analyzability, Amount x Variety, Richness X Analyzability

6

Info amount Info richness Variety Analyzability Amount x Variety Richness x Analyz.

- 0.63 -0.03 - 0.45 - 1.29 0.09 0.04

Development

Full model: (model x2=9.05, n.s.)* Sucess = Info amount, Info Richness, Variety, Analyzability Amount x Variety, Richness x Analyzability

6

Info amount Info richness Variety Analyzability Amount x Variety Richness X Analyz.

- 0.90

Commercialization

Full model: (model~~=lO.ll, n.s.) Sucess = Info amount, Info Richness, Variety, Analyzability Amount x Variety Richness x Analyzability

6

Info amount Info richness Variety Analyzability Amount X Variety Richness x Analyz.

0.44 -0.10 0.22 - 10.73 -0.04 0.23

Reduced model: (modelx2=9.66,p<0.05) Sucess = Info amount, Info Richness, Analyzability, Richness x Analyzability

4

Info amount Info richness Analyzability Richness x Analyz.

0.17 -0.08 -9.74 0.21

Reduced model: (model x2 = 8.08, p < 0.04) Sucess = Info Richness, Analyzability Richness x Analyzability

3

Info richness Analyzability Richness x Analyz.

x2

P<

2.77 1.51 3.10 3.56

0.10 0.22 0.08 0.06

-0.05 - 1.03 - 6.45 0.13 0.16

-0.05 0.73 0.39 -12.83 5.18 0.02 0.26 5.13 0.02

*Neither the full model nor any reduced model was significant.

ect success. Two patterns are evident. First, the importance of rich information increases for all project groups as they progress for idea generation through commercialization. Second, the difference between the mean level of information richness in successful and unsuccessful low-analyzability projects (mean difference = 8.0) is more than twice the difference between successful and un-

324

50.0-

47.5-

45.0-

42.5IIlpXt_aIlC~ of Rich Information

40.0-

37.5-

35.0-

32.5-

30.0-

25.0-

I Idea Generation

I Design

Project

I Development

Commercialization

Phase

Fig. 2. Importance of rich information x analyzability x process success plotted over project phases. Key: 1= high analyzability/not successful, 2 = high analyzability/successful, 3 = low analyzability/not successful, 4=low analyzability/successful.

successful high analyzability projects (mean difference = 3.1). Furthermore, among high analyzability projects, information richness was less important (mean = 45.7) for successful projects than for unsuccessful projects (mean = 48.8). These descriptive results offer additional support for the conditional relationship between information richness importance, project analyzability and performance. 5. Discussion Two objectives guided this study. First, we wanted to improve understanding of information processing in R&D/innovation project management by responding to calls for theoretically grounded research using established organizational constructs (Downs and Mohr, 1976; Utterback, 1974). Second, we sought to test, in a somewhat limited fashion, Daft and Lengel’s (1984,1986)

325

conceptualization of information processing and work unit technology. Although the results are mixed, there is support for the information processing contingency framework for understanding and predicting information gathering behaviors and uncertainty/equivocality reduction in work units. In this section, three major findings are discussed, and reasons for counterintuitive findings are suggested. First, the study shows that identifiable patterns of information processing exist throughout innovation project phases and mirror expected levels of variety/uncertainty and analyzability/equivocality at each phase. As projects move from idea generation toward commercialization, they become increasingly uncertain and equivocal (Steele, 1989)) necessitating the processing of more information and emphasis on richer information at each subsequent project phase. This finding counters the commonly held belief that projects should necessarily experience sequentially declining uncertainty and equivocality as they progress. Rather, our findings suggest that uncertainty and equivocality that may exist early in project life may not be perceived as problematic and may not elicit information processing behaviors until later stages. Uncertainty and equivocality will not motivate information processing unless they are seen as problematic. Subsequent transitions in project focus may also serve to heighten the project manager’s awareness of uncertainty and equivocality and can trigger intensive and rich information seeking or processing activities (Gersick, 1988; March and Simon, 1958). Inclusion of work unit technology measures (i.e., variety and analyzability) in the analysis does not improve predictions of information amount or importance of richness at any project phase. Although this result is unexpected, it is not necessarily an indication that technology is an unimportant consideration in information processing. The relationship between project technology (i.e. analyzability and uncertainty) and information processing may be more complex than we initially hypothesized. The relationship is not deterministic: uncertainty and equivocality do not cauSe more information processing to take place. Managers should, however, respond to conditions of low analyzability and high uncertainty by increasing information processing activities. Thus, what may be most interesting about the information processing-uncertainty/ equivocality relationships is what happens when managers fail to engage in necessary information processing. When the relationship between information processing and technology is examined with respect to project performance, a somewhat different picture emerges. While task variety (and the resulting uncertainty) and information amount do not appear to play critical roles in the performance of any project, equivocality and rich information plays a rather complex role. Rich information and project analyzability (and the resulting equivocality) are conditionally related to project performance. Managers of low-analyzability/highequivocality projects who indicate greater importance of rich information are

more likely to be successful selling their treatment technologies than managers of similar projects who indicate that rich information is of lesser importance. This suggests that rich information may play a critical role in reducing equivocality. Interestingly, the opposite relationship is present for managers of highanalyzability/low-equivocality projects. Managers of successful high-analyzability projects indicate that rich information is less important than do managers of unsuccessful high-analyzability projects. This finding suggests that too much information may also be problematic, and raises questions about the efficiency of information processing. Even in this information age, with the ubiquity of high-speed, low-cost information systems, unnecessary information processing may be an inefficient luxury that adds delays and costs to project development. While the results of this study are consistent with the Daft and Lengel information processing perspective, they challenge the information processing expectations of many models of the innovation process which advocate early inclusion of personnel from various disciplines as a way to continually reduce uncertainty/equivocality over the course of project development. As such, the results highlight limitations of these models. First, proponents of the recursive or concurrent phase models offer suggestions based on a rational information processing model that are largely prescriptive. Not enough descriptive research has been undertaken to test competing views, and those studies which have examined interaction patterns (e.g., Gersick, 1989) generally acknowledge that the greatest uncertainty/equivocality may not be realized in the beginning of the project. A comprehensive model of project information processing must also acknowledge that some information processing takes place for reasons other than uncertainty/equivocality reduction (Feldman and March, 1981). Second, Gerwin (1988) notes that much innovation research focuses on incremental innovations. He questions whether findings from such studies can be generalized to more radical innovations. For example, Takeuchi and Nonaka (1986) acknowledge that overlapping and recursive activities advocated for reducing uncertainty in new product development may not be appropriate for breakthrough projects that require radical innovation. Furthermore, Abernathy and Utterback (1988) developed the notion of fluid pattern innovations to deal with situations like those faced by waste treatment developers: vague and unclear performance targets and emerging or ill-defined markets where competition is not on price but rather based on functional performance of the product. The technologies in this study are new product innovations in an emerging industry that resembles fluid pattern innovations. Emphasis is centered on questions of product functionality rather than marketing issues, particularly in the early phases. Therefore, many of the small entrepreneurial firms or departments that fit the fluid innovation pattern do not immediately develop marketing specialization. There is not adequate expertise available to resolve

327

marketing issues early in the innovation product development phases, and even if such expertise is added when commercialization issues become the focus, the resulting levels of uncertainty and equivocality are increased. In discussions of market difficulty like those found in fluid innovations where setting and meeting commercial objectives, user’s requirements and financial market goals exist, Souder (1988) notes that information flow and media choice should change and become increasingly important. Our finding of increasing information processing activity supports Souder’s propositions regarding characteristics which assist the successful transfer of technology or innovation through phases. Providing a general guide in selecting the best way to transfer technologies across the phase boundaries, Souder advocates increased richness and amount of information in cases where environmental uncertainty is high and market dynamics are constantly changing. This is also the case when technology is poorly understood, task objectives are fluid, the major goal is on idea generation, emphasis is on creativity, and the familiarity and accessibility of the technology or the customer is low. This set of conditions is complex and is often present in emerging industries or new market niches (Souder, 1988). 6. Implications and directions for future research 6.1. Managerial implications The information processing model provides a way to understand how individual project managers manage information requirements to improve project performance. We know that communication media vary in their capacity to convey information cues, such that rich media are best used for resolving subjective issues that involve different perspectives. The link with effective project manager performance is clear: high-performing managers are more sensitive to the relationship to the relationship between uncertainty or equivocality and media richness and frequency than are low-performing managers (Daft et al., 1987). The major findings of this study show that managers engaging in the right information processing tasks at the right time can contribute significantly to new product commercialization. Several important information processing strategies for innovation project managers are suggested by the current study results. First, information processing activities should be matched to the phase of the innovation. We have discussed how information processing needs differ according to the project phase. Information processing strategies appropriate at early phases may be inadequate as projects become increasingly complex and involve more personnel from varied functional areas. Mechanisms for processing more and richer information must be developed as projects progress. Project managers must also assess the extent of equivocality embodied in their projects, and support information processing strategies that match that

328

equivocality. Radical innovations and technologies employing unfamiliar methodologies require information gathered through rich means: face-to-face interactions, conferences and telecommunications. Project managers and other personnel should be encouraged to interact directly with potential information sources. Conversely, incremental innovations and technologies using familiar or proven methodologies do not require extensive rich communication. Managers of projects of this second type would be well advised to consider restricting extraneous communication to expedite project development. Unnecessary information processing can be costly, draining energy and time from those involved in the project and adding delays and costs to the innovation project budget. There are even accounts of personnel who were encouraged to freely interact on a simple technology ultimately building more elaborate products than the customer wanted (Souder, 1988). These findings suggest that innovation project managers should play an active gatekeeping role, managing inflows as well as outflows of communication resources. Because managers have to contend with changes in the business environment such as intensified competition, shortened product life cycles, and advanced technology and automation, it is tempting to embrace new approaches to product design that promise to reduce uncertainty. While approaches such as concurrent engineering (Nevins and Whitney, 1989), simultaneous engineering (Babcock, 1991), new product rugby (Takeuchi and Nonaka, 1986) and other multidisciplinary team approaches have significantly improved new product development in many cases, their prescriptions for decreasing information processing needs as projects progress may be inaccurate. The difficulty of each project, either from a technical or market standpoint may also play a role in determining effective information strategies. For “easy” technologies where market difficulties and desired technical requirements are known, it may not matter how information is handled and handed off through phases of the innovation process (Souder, 1988). But for difficult technological and market environments, information processing differences can be traced to project success. Given Souder’s definition of the best phase transfer model as “one that fosters the most timely, smooth, barrier-free transfer of technologies across phase boundaries throughout the life cycle of those technologies” (Souder, 1988, p. 235), a task dominant model of innovation, coupled with increasing attention to information richness is more appropriate under conditions of high environmental uncertainty, where market dynamics are constantly changing, technology is poorly understood, task objectives are fluid, and familiarity with the technology and/or the customer is low. The current study underscores the importance of contingent conditions offered by studies such as Souder’s (1988) that assist innovation managers with selecting the best information processing strategies to lead to new product development success.

329

6.2. Limitations and future research directions The study findings also provide some additional validation for the usefulness of Daft and Lengel’s (1984, 1986) conceptualization of information amount and information richness as components in an information processing model As Tushman and Nadler (1978,198O) noted, the information processing model provides a useful theoretical framework for examining innovation project management. The inclusion of variables similar to Daft and Lengel’s information amount and information richness demonstrates the potential of producing more precise models of information processing in R&D/innovation management. Furthermore, these concepts are based in organizational theory and are generalizable not only across diverse R&D/innovation settings, but also across varied organizational settings. Other parts of the Daft and Lengel conceptualization, such as the influence of interdepartmental coordination and organizational interpretation systems (culture ) should be addressed in future studies. Additional investigation in the under-researched areas of innovation in new markets and new industrial product innovations (as opposed to consumer products or industrial processes) may further benefit from this information processing framework. Two limitations inherent in this study should be noted. First, data on information processing were collected during the commercialization phase for projects. Data on the amount of information and importance of rich information at prior phases were retrospective and, even though we followed prescribed procedures for ensuring accuracy (Huber, 1985; Huber and Power, 1985)) may have been subject to some bias. The pattern of increasing information processing that this study found merits further investigation using a longitudinal design. Second, the study sample is small and purposeful. All projects were developing toxic waste treatment technology and all participated in the EPAsponsored evaluation program (SITE). While this sampling served to control for industry and market effects, it is not clear what effect this context may have on information availability or other facets of project development. Although there was diversity in the types of waste treatments being developed, all projects faced limited markets and heavy government involvement. It should be noted that this type and level of government involvement is not unusual in technology development projects. The federal government has traditionally played a major role in innovations in defense-related industries, energy, health care, and a variety of other areas (Mowery and Rosenberg, 1989). However, to increase the generalizability of findings, future studies of innovation project information processing should investigate a broad range of industry sectors, including those without heavy government involvement. Additionally, investigators should look at other sources of uncertainty such as the environment and work unit interdependence.

330

Two additional issues merit further attention in future research. We have defined project phases based on the central task or focus of the phase (i.e., idea generation, innovation project design, innovation development/production, and commercialization). Future researchers should investigate specific indicators and typical time dimensions of these phases in addition to the types of managerial behaviors typical or required at each phase, typical phase length, and types of behaviors or activities marking transitions between phases. Second, we have measured project success by assessing the commercial success of innovation projects. While we believe that this measure is a good surrogate for other aspects of project performance, future researchers should also individually examine factors such as timeliness, innovativeness, technical efficacy, constituent acceptance, and goal achievement. Finally, the findings in this study raise questions about the cost of processing rich information. Based on the performance of high-analyzability projects that overemphasized the importance of rich information, it appears that the processing of rich information is costly and time consuming. Allowing for the possibility of a symbolic aspect to the choice of media, the potential benefits from rich and frequent communication by innovation managers might be elicited though open-ended interviews. Future research should specifically address the cost of gathering and processing information. Acknowledgement The authors wish to thank the Cincinnati, Ohio office of the United States Environmental Protection Agency, Risk Reduction Laboratory. We would specifically like to thank Steven James, Acting Director of the Superfund Innovative Technology Evaluation (SITE) Program. References Abernathy, W.J. and Clark, K.B., 1988. Innovation: Mapping the winds of creative destruction. In: M.L. Tushman and W.L. Moore (Eds.), Readings in the Management of Innovation. Ballinger, Cambridge, MA, 2nd edn. Abernathy, W.J. and Utterback, J.M., 1988. Patterns of industrial innovation. In: M.L. Tushman and W.L. Moore (Eds.), Readings in the Management of Innovation. Ballinger, Cambridge, MA, 2nd edn. Aldrich J.H. and Nelson, F.D., 1984. Linear Probability, Logic, and Probability Models. Sage Publications, Beverly Hills, CA. Allen, T.J., 1917. Managing the Flow of Technology. MIT Press, Cambridge, MA. Allen, T.J. and Hauptman, O., 1990. The substitution of communication technologies for organizational structure in research and development. In: J. Fulk and C. Steinfeld (Eds.), Organization and Communications Technology. Sage, Newbury Park, CA, pp. 275-294. Allen, T.J., Tushman, M.L. and Lee, D. 1979. Modes of technology transfer as a function of position in the spectrum from research through development to technical services. Acad. Manage. J., 22: 694-708.

331 Ancona, D.G. and Caldwell, D.F., 1987. Management issues facing new product teams in hightechnology companies. Adu. Ind. Labor Relat., 4: 199-221. Babcock, D.L., 1991. Managing Engineering and Technology: An Introduction to Management for Engineers. Prentice-Hall, Englewood Cliffs, NJ. Bodensteiner, W.D., 1970. Information channel utilization under varying research and development project conditions: An aspect of inter-organizational communication channel usages. Unpublished doctoral dissertation, University of Texas. Clark, K.B., 1989. Project scope and project performance: The effect of parts strategy and supplier involvement on product development. Manage. Sci.. 35: 1247-1263. Clark, K.B. and Fujimoto, T., 1989. Lead time in automobile product development-Explaining the Japanese advantage. J. Eng. Technol. Manage., 6: 25-58. Clark, K.B. and Fujimoto, T, 1991, Product Development Performance. Harvard Business School Press, Boston, MA. Cooper, R.G. and Kleinschmidt, E.J., 1986. An investigation into the new product process: Steps, deficiencies, and impact. J. Innov. Manage., 3: 71-85. Crane, D., 1972. Invisible Colleges: Diffusion of Knowledge in Scientific Communities. University of Chicago Press, Chicago, IL. Daft, R.L. and Lengel, R.H., 1984. Information richness: A new approach to managerial behavior and organization design. In: B.M. Staw and L.L. Cummings (Eds.), Research in Organizutionul Behavior (Vol. 9). JAI Press, Greenwich, CT. Daft, R.L. and Lengel, R.H., 1986. Organizational information requirements, media richness and structural design. Manage. Sci., 32: 554-571. Daft, R.L. and Macintosh, N.B., 1981. A tentative exploration into the amount and equivocality of information processing in organizational work units. Admin. Sci. Q., 26: 207-224. Daft, R.L. and Weick, K.E., 1984. Toward a model of organizations as interpretation systems. Acud. Manage. Rev. 9: 284-295. Daft, R.L., Lengel, R.H. and Trevino, L.K, 1987. Message equivocality, media selection, and manager performance: Implications for information systems. Manage. Inf. Syst. Q., 11: 355-366. Dean, J.W. Jr., Susman, G.I. and Porter, P.S., 1990. Technical, economic and political factors in advanced manufacturing technology implementation. J. Eng. Technol. Manage., 7: 129-144. Dearborn, D. and Simon, H., 1958. Selective perception: A note on the developmental identification of executives. Sociometry, 21: 140-144. Dewar, R.D. and Dutton, J.E., 1986. The adoption of radical and incremental innovations: An empirical analysis. Manage. Sci., 32: 1422-1433. Downey, H.K. and Slocum, J.W., 1975. Uncertainty Measures, research and sources of variation. Acud. Manage. J. 18: 562-578. Downs, G.W. and Mohr, L.B., 1976. Conceptual issues in the study of innovation. Admin. Sci. Q., 21: 700-714. Drucker, P.F., 1985. Innovation and Entrepreneurship-Practice and Principles. Harper and Row, New York. Ettlie, J.E., 1988. Taking Charge of Manufacturing. Jossey-Bass, San Francisco, CA. Ettlie, J.E., Bridges, W.P. and O’Keefe, R.D., 1984. Organizational strategy and structural differences for radical versus incremental innovation. Manage. Sci., 30: 682-695. Feldman, M.S. and March, J.G., 1981. Information in organizations as signal and symbol, Admin. Sci. Q., 26: 171-186. Fischer, J.A., 1979.The acquisition of technical information by R&D managers for problem solving in nonroutine contingency situations. IEEE Trans. Eng. Manage., EM-26: 8-14. Fischer, J.A., 1980. Scientific and technical information and the performance of R&D groups. TIMS Stud. Manage. Sci., 15: 67-89. Galbraith, J., 1973. Designing Complex Organizations. Addison-Wesley, Reading, MA. Galbraith, J., 1977. Organization Design. Addison-Wesley, Reading, MA.

332 Gersick, C.J.G., 1988. Time and transition in work teams: Toward a new model of group development. Acad. Manage. J., 31: 9-41. Gerstenfeld, A. and Berger, P., 1980. An analysis of utilization differences for scientific and technical information. Manage. Sci., 26: 165-179. Gerwin, D., 1988. A theory of innovation processes for computer-aided manufacturing technology. IEEE Trans. Eng. Manage., 35: 90-100. Hattie, J., 1985. Methodology review: Assessing unidimensionality of tests and items. Appl. Psychol. Measure., 9: 139-164. Hayes, R.H., and Wheelwright, SC., 1984. Restoring Our Competitive Edge: Competing Through Manufacturing. Wiley, New York. Holland, W.E., Stead, B.A. and Leibrock, R.C., 1976. Information channel/source selection as a correlate of technical uncertainty in a research and development organization. IEEE Trans. Eng. Manage., EM-23: 163-167. Huber, G.P., 1985. Temporal stability and response-order biases in participant descriptions of organizational decisions. Acad. Manage. J., 28: 943-950. Huber, G.P. and Power, D.J., 1985. Retrospective reports of strategic-level managers: Guidelines for increasing their accuracy. Strut. Manage. J., 6: 171-180. Jackson, SE., Schuler, R.S. and Vredenburgh, D.J., 1987. Managing stress in turbulent times. In: A.W. Riley and S.J. Zaccaro (Eds.), Occupational Stress and Organizational Effectiveness. Praeger, New York. Kanter, R.M., 1988. When a thousand flowers bloom: Structural, collective, and social conditions for innovation in organization. In: B.M. Staw and L.L. Cummings (Eds.), Research in Organizational Behauior (Vol 10). JAI Press, Greenwich, CT. Leonard-Barton, D.A., 1988. Implementation as mutual adaptation of technology and organization. Res. Policy, 17: 251-267. Lind, M.R. and Zmud, R.W., 1991. The influence of a convergence in understanding between technology providers and users on information technology innovativeness. Organ. Sci., 2: 195217. Link, A.N. and Zmud, R.W., 1987. External sources of technical knowledge. Econ. Lett., 23: 295299. March, J. and Simon, H., 1958. Organizations. Wiley, New York. March, J.G. and Shapira, Z., 1987. Managerial perspectives on risk and risk taking. Manage. Sci., 33: 1404-1418. Martin, M.J.C., 1983. On Kuhn, Popper and teaching technological innovation management. Eur. J. Oper. Res., 14: 221-227. Mowery, D.C. and Rosenberg, N., 1989. New developments in U.S. technology policy: Implications for competitiveness and international trade policy. Calif. Manage. Rev., 32: 107-124. McFarlan, F.W., 1981. Portfolio approach to information systems. Huru. Bus. Rev., 59: 142-150. Milliken, G.A. and Johnson, D.E., 1984. Analysis of Messy Data: Volume 1. Designed Experiment. Van Nostrand Reinhold, New York. Nevins, J.L. and Whitney, D.E., 1989. Concurrent Design of Products and Processes. McGrawHill, New York. Newman, M. and Noble, F., 1990. User involvement as an interactive process: A case study. Inf. Syst. Res., 1: 89-113. Pelz, D. and Andrews, F., 1966. Scientists in Organizations. University of Michigan Press, revised edn. Perrow, C.A., 1967. A framework for the comparative analysis of organizations. Am. Social. Rev., 32: 194-208. Rogers, E.M., 1982. Information exchange and technological innovation. In: D. Sahal (Ed. ), The Transfer and Utilization of Technical Knowledge. D.C. Heath, Lexington, MA. Sahal, D.A., 1981. Patterns of Technological Innovation. Addison-Wesley, Reading, MA.

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Schroeder, R., Van de Ven, A., Scudder, G. and Polley, D., 1986. The development of innovation ideas. In: Van de Ven, Angle and Poole (Eds. ), Research on the Management of Innovation: The Minnesota Studies. Harper and Row, New York. Souder, W.E., 1987. Managing New Product Innovations. Lexington Books, Lexington, MA. Starr, M.K., 1990. The role of project management in a fast response organization. J. Eng. Technol. Manage., 7: 89-110. Steele, L.W., 1989. Managing Technology: The Strategic View. McGraw-Hill, New York. Takeuchi, H. and Nonaka, I., 1986. The new new product development game. Harv. Bus. Rev., 64: 137-146.

Tushman, M.L., 1978. Technical communication in R&D laboratories: The impact of project work characteristics. Acad. Manage. J., 21: 624-645. Tushman, M.L., 1979. Impacts of perceived environmental variability on patterns of work related communication. Acad. Manage. J., 22: 482-500. Tushman, M.L. and Katz, R., 1980. External communication and project performance: An investigation into the role of gatekeepers. Manage. Sci., 26: 1071-1085. Tushman, M.L. and Nadler, D.A., 1978. Information processing as an integrating concept in organizational design. Acad. Manage. Rev., 3: 613-624. Tushman, M.L. and Nadler, D.A., 1980. Communication and technical roles in R&D laboratories: An information processing approach. TZMS Special Issue in Management Science: Research Development and Innovation. North-Holland, Amsterdam, pp. 91-111. Ungson, G.R., Braunstein, D.N. and Hall, P.D., 1981. Managerial information processing: A research review. Admin. Sci. Q., 26: 116-134. Utterback, J.M., 1974. Innovation in industry and the diffusion of technology. Science, 183: 620626. Utterback, J.M. and Abernathy, W.J., 1981. A dynamic model of process and product innovation. Omega, 3: 639-656.

Van de Ven, A., 1986. Central problems in the management of innovation. Manage. Sci., 32: 5. Von Hippel, E., 1988. The Sources of Innovation. Oxford University Press, New York. Von Hippel, E., 1990. Task partitioning: An innovation process variable. Res. Policy, 19: 407-418. Weick, K.E., 1979. The Social Psychology of Organizing. Addison-Wesley, Reading, MA. Whitley, R. and Frost, P.A., 1975. Task type and information transfer in a government research lab. Hum. Relat., 24: 161-178. Withey, M., Daft, R.L. and Cooper, W.H., 1983. Measures of Perrow’s work unit technology: An empirical assessment and a new scale. Acad. Manage. J., 26: 45-63. Winter, S.G., 1987. Knowledge and competence as strategic assets. In: D.J. Teece (Ed.), The Competitive Challenge. Balinger, Cambridge, MA, pp. 159-184. Zmud, R.W., 1978. An empirical investigation of the dimensionality of the concept of information. Decision Sci., 9: 187-195. Zmud, R.W., 1983. The effectiveness of external information channels in facilitating innovation within software development groups. MIS Q., 7: 43-47. Zmud, R.W., 1990. An attribute space for organizational communication channels. Inf. Syst. Res., 1: 440-457.

Appendix l-Study

questionnaire

Questionnaires were accompanied by cover letters explaining the study purpose (i.e., to provide feedback to EPA concerning management of the SITE Program and for academic research on innovation management). Letters informed participants that the study was authorized and approved by the EPA and that data would be reported to EPA only in aggregate form. Research team members would be the only people able to identify respondents. Cover letters also included comprehensive instructions to guide individuals responding to the questionnaire.

334 EPA SITE Program BACKGROUND

Survey

DATA

1.

Name of your firm's

2.

Development of waste treatment technology is: your firm's primary line of business. a Separate division of a diversified firm.

3.

Your

firm

has

SITE project:

been engaged

in waste

treatment

technology

development

for

years.

TECHNOLOGY 4.

DEVELOPMENT

This technology

is best described

as:

Incremental; minor modification of established waste treatment technology. Major modification of established waste treatment technology. Adoption of established technology from an area other than waste treatment. Combination of two or more existing waste treatment technologies. Development of new technology representing a moderate shift from existing waste treatment approaches. Development of new technology representing a major shift from existing waste 5,Compared process of

treatment

with this

approaches.

previous technology

research has:

and

development

projects,

the

developmental

nearly identical to previous projects. involved modest changes in the research and development process. involved significant changes in the research and development process. 1st time project.

been

For the following

questions,

6. This technology 1 One Specific waste stream

7.

Can

suited 2

this

3 A few related waste streams

technology

1 only for treatment

2

Useful waste

8.

researching

and

In

for you to obtain

circle the number

be

very easy

used

for

4

developing

1

to your

this

technical

4

than

waste

treatment?

5 Useful for many other applications

technology how easy or difficult or scientific information?

3

2

other

4

5

10. and

HOW much marketing Virtualy

compared 11. technology? Much

Smaller

financial of this no

risk

1

2

risk to technology 1

3

your firm pose?

2

with other projects

than most

1

2

3

does

4

the

5

4

5

has it been

Very difficult

development,

Extremely

firm, how large

at your 3

5

4

has it been

Very difficult

In researching and developing this technology how easy or difficult 9. for you to obtain relevant information from potential users? Very easy

firm.

5 Complex waste streams

anything

3 Useful for a few other applications

relevant

that best applies

for use on:

Much

commercialization

risky is your

investment

larger than most

in this

335

Indicate (NA) those sources of capital not relevant to the financing of this 12. project. Now rank in order of importance (1 for the most important) the remaining sources of capital. _ _ _

13. A.

Internal capital. Venture capital. Conventional bank loans. Other sources. Please identify

Given

current

Accomplish

levels of funding,

general,

overall

Unlikely to accomplish any objectives El. Accomplish

1

technical

2

Accomplish

is it that this technology

commercial

2

3

4

5

3

4

5

4

5

Likely to accomplish all objectives

Likely to accomplish all technical objectives

objectives?

Unlikely to accomplish 1 any commercial objectives

2

3

Likely to accomplish all commercial objectives

14. How successful have you been installing or selling Has your firm bid on projects with this technology? ~ Was your bid accepted? If you haven't

will:

objectives?

Unlikely to accomplish 1 any technical objectives C.

how likely

objectives?

Yes

--

been successful

this technology? NO Yes

NO

bidding,

why not?

(Check all that apply.)

Technolgy not yet ready for application. Unproven technology. Your firm's lack of experience. Technology to0 costly to users. Lack of adequate capital to finance project. Describe: Other.

SITE

PROGRAM

15. HOW important SITE Program? Critically

Important

16. How much pose? Virtualy

to your firm's

financial

no risk

1

1

2

success

3

risk to your

2

3

4

TOO

general

information information

18. Is information Not enough TOO general

5

4

5

provide

Extremely

potential

information

in the EPA

in the

SITE

program

risky

users with usable

1

2

3

4

5

Too much

1

2

3

4

5

Too specific

from your SITE demonstrations

information

is participation

Unimportant

firm does participation

17. In general, does the SITE Program information on your technology? enough

in site remediation

adequate

information information

for potential

1

2

3

4

5

TOO much

1

2

3

4

5

TOO specific

users?

information information

336

19. For each of the following areas rate the importance to your SITE project of Z=Not very suprxart or information from the SITE Program. (l=Not at all imports&; important; 3=Moderstely important; 4=Very important; Li=Criticslly important.) Direct

financial support or cost sharing: 2 3 Not important 1

4

5

Critically

important

4

5

Critically

important

Information about markets, commercialization or potential technology users: 3 Not important 1 2

4

5

Critically

important

Information/support on policy Not important 1

constraints: 4 5 Critically

important

over this technology: 4 5 Critically

important

Technical

information: Not important

1

3

2

and regulatory 3 2

Information/support on community Not important 1

concerns 3 2

20. For each of the following areas rate how adequate support or information from the SITE Program is to your project's success. (l=Totslly inadequate; Z=Not very adequate; 3=Moderately adequate; I=Adequate; 5=Very Adequate) Financial

support: Totally

inadequate

1

2

3

4

5

Very

adequate

information: Totally inadequate

1

2

3

4

5

Very

adequate

Information about, markets, commercialization or potential technology users: Totally inadequate 1 3 2

4

5

Very Adequate

3

4

5

Very Adequate

concerns over this type technology: Totally inadequate 1 3 2

4

5

Very Adequate

Technical

Policy

and regulatory constraints: Totally inadequate 1

Community

21. Has site-matching

2

been a problem

Not a problem

1

with your SITE technology 3

2

22. Would you have preferred to conduct streams during your SITE project?

23. HOW would

you describe

Highly

24. Would'your

beneficial

1

2

firm still be working

Highly

25. Are there

the quality

likely

significant

Insignificant

costs

1

4

5

Major

more treatability Yes _ No

of your 3

4

4

3

5

2

3

4

5

Major

waste

to progress

without

Highly

costs to your firm for participating 1

on different

with the SITE Program?

Impediment

on this technology

2

studies

interactions 5

project? problem

the SITE Program?

unlikely

in the SITE Program?

costs

337 If there

are significant

26. I" general,

costs, what is the nature

how does your firm benefit

What are the most helpful/beneficial

What

27. HOW important support be? Unimportant

1

2

28. HOW could EPA improve Financial Other

3

4

5

of the SITE Program?

in the SITE program

Very

in the SITE Program?

of the SITE Program?

features

to your participation

costs?

from participating

features

are the least helpful/beneficial

of those

would

increased

financial

Important

the SITE Program?

support:

areas:

TECRNOLOGY

TRANSFER/COMMERCIALIZATION

29. Indicate the number of potential been contacted. What portion

of potential

users of your firm's technology

users does the above number

represent?

that have already

%

30. Indicate the number of potential users that your firm hae contacted at each stage of technology development. Circle the appropriate number for each stage.

Idea generation Desian Deveiopment Commercialization

0 0 0 0

Number of potential users 11-20 21-50 >50 l-5 6-10 11-20 21-50 >50 l-5 6-10 11-20 21-50 >50 l-5 6-10 11-20 21-50 >50 1-5 6-10

31. Indicate the frequency of communication with each of the following groups at each stase oftechnolosvdeveloument. (O=None; l=Very Infrequent; 2=Infreque"t; 3zFreque"t; 4=Very Frequent; NA=Not Applicable.)

PROJECT

GROUPS Potential Other

Users

Developers

EPA Academic Others

Scientists

Idea Generation

Project Design

STAGE Development/ Production

Commercialization

338

32. Indicate the importance to development of your technology of each method communication with each group listed below. (O=IJnimportant; l=Moderately Uni.nt; Z=Moderately Important; 3=Important; I=Vary Important; NA=Not Applicable.)

of

33. Indicate the importance of each m of communication at each project stage listed below. (O=Unimportant; l=Moderately Unimportant; Z=Moderately Important; 3=Important; 4=Very Important; NA=Not Applicable.)

PROJECT

STAGE

I COMMUNICATION Personal

Project

Idea Generation

METHOD

Commercial-

Development/

Design

Production

ization

Visit

Telephone

Calls

conferences Technical

Reports

I

Newsletter

34.

HOW

Not

valuable

valuable

35. What type development?

THANK

YOU

has 1

the 2

input 3

from

potential

4

5

of input was most useful

FOR YOUR EVALUATE

PARTICIPATION AND UODIFY

TEE

Very

users

of

your

firm's

technology

been?

valuable

to you over the

IN THIS SURVEY. SITE PROGRAM.

course

YOUR

of

this

RESPONSES

technology's

WILL

HELP

EPA