New product development processes in small and medium-sized enterprises: Some Australian evidence (*).

This article examines the new product development process (NPDP) in Australian small and medium-sized enterprises (SMEs). Findings from a sample of 276 innovative Australian SMEs suggest that marketing-related activities were undertaken less frequently and were less well executed than technical

activities in developing new products. However, marketing-related activities were important in distinguishing between successful and unsuccessful new products. In addition, resource and skill availability and new product planning were positively associated with the quality with which NPD activities are executed. Further the existence of a new product strategy seemed to have a significant positive impact on the quality of NPD activities.

The successful market introduction of new products or services is crucial to the survival and success of business enterprises. Small and medium-sized enterprises (SMEs) are no exception (Wynarczyk 1997). With increased competition and rapid changes in technology, customer requirements, and business practices, product life cycles have shortened (Wind and Mahajan 1997). This has imposed pressures on all organizations, including SMEs, to innovate more effectively and efficiently.

Research over the past two decades has provided valuable insights into the factors that influence new product performance, suggesting a number of variables that distinguish new product success from failure (see Craig and Hart 1992; Hart and Baker 1994; Montoya-Weiss and Calantone 1994). In a recent extensive study, Cooper and Kleinschmidt (1995) found that a firm's new product performance depends mainly on its processes, resources, and strategies, which are the "cornerstones of successful product development" (Cooper 1996, p. 655).

The present study focuses on the new product development process (NPDP) used in SMEs. The NPDP includes those activities and decisions that move a product from idea to launch (Cooper and Kleinschmidt 1986), and its effectiveness has a significant impact on new product performance (Cooper 1996). Specifically, this study investigaes the completeness and quality of the NPDP in SMEs and their impact on new product performance. Completeness and quality are important indicators of the NPDP's effectiveness and have been found to significantly influence ew product success (for example, Cooper 1979, 1988, 1996; Cooper and Kleinschmidt 1986, 1988, 1991, 1993b, 1995; Rochford and Rudelius 1997). In addition, the study examines the links among a firm's NPDP, its new product strategy, and its resources and skills. Although it has been suggested that the NPDP is influenced by an organization's new product strategy (Booz Allen and Hamilton 1982) and the availability of esources and skills (Rochford and Rudelius 1997), there is little empirical research into the impact of strategy or resources and skills on the quality and completeness of the NPDP, particularly in SMEs. The present study attempts to fill this gap.

The present research departs from previous studies in three important respects. Firstly it investigates the NPDP in SMEs. Despite the fact that SMEs account for more than 90 percent of business enterprises in most developed countries, most NPD research has examined the process only in large organizations (Cooper 1979; Cooper and Kleinschmidt 1993a, 1995), despite research suggesting that SMEs have been as innovative as their large counterparts (Rothwell and Zegveld 1982) and have played dynamically complementary roles to large firms (Nooteboom 1994). SMEs are different from large firms in several important areas of innovation management. For example, SMEs can be more flexible and motivated (Nooteboom 1994), but have fewer managerial, financial, and technological resources (Rothwell 1989; Vossen 1998). Moreover, innovations can also be influenced by owner/managers' proactive personality (Becherer and Maurer 1999). Given these differences, it is not clear whether research on large firms can be applied to SMEs. Secondly, the present study examines NPD at a project level, rather than at a macro level. Finally, the study focuses on the chemical and machinery industries (ANZSIC 25 and 28), thereby reducing inter-industry variation (Cooper and Kleinschmidt 1993a).

Specifically, this paper attempts to answer the following four questions: (1) What activities do SMEs undertake within their NPDP and how well are these activities undertaken in terms of completeness and quality? (2) Is the quality of the NPDP influenced by the presence or absence of a new product strategy? (3) What impacts do a firm's resources and skills have on the quality and completeness, or proficiency in Cooper's (1988) term, with which the NPDP is undertaken? and (4) How does a firm's proficiency in executing these activities influence new product success?

Such a study is needed for several reasons. Firstly, an investigation of NPD activities and the quality with which they are executed will improve our overall understanding of the process in SMEs. Secondly, identifying the impact that NPD activities and their quality of execution have on performance can help managers better allocate scarce resources within the process. Thirdly, understanding the relationships between a firm's resources and the quality with which NPD activities are executed in SMEs will assist policy-makers in developing programs that can support innovation within such organizations. The research approach taken to answer these questions is outlined in the next section, while the findings of the present study and their implications are discussed in subsequent sections.

Methodology

The Questionnaire

As data were needed from a number of innovating SMEs, a questionnaire was developed based on previous research that had examined the NPDP Following the definition used in a recent survey conducted by the Australian Bureau of Statistics (1998), respondents were initially asked if they had developed a new product since 1995. If they had, they were considered to be innovators and were asked a series of questions about their most recent new product. Such respondents were asked about the process itself; their strategic orientation, resources and skills, the perceived market potential, the perceived performance of the new product, and a number of questions related to the respondent's organization and personal background. Non-innovative firms were only asked to answer the organization and personal background questions as it was felt that it would be too difficult for them to recall projects that had been undertaken more than four years before the survey was received.

Variables

Stages of the NPDP. The survey included questions based on the 13 NPD stages suggested by Cooper and Kleinschmidt (1986) and used by Rochford and Rudelius (1997), and are shown in Table 2. Respondents were asked to indicate which of these activities they had undertaken and, for those undertaken, how well these steps had been carried out, using a five-point scale ranging from "very poorly done" to "excellently done."

Resources and Skills. A firm's resources and skills have been suggested as a source of competitive advantage (Day and Wensley 1988) and a firm's marketing and technical resources and skills influence the proficiency with which it executes the NPDP (Song and Parry 1997). Consequently, respondents were asked about these factors using the items developed by Song and Parry (1997), which are shown in Table 4. An additional measure asking about financial resources was added because such resources are a major issue in most SMEs.

New Product Strategy. The existence of a new product strategy has long been recognized as a core requirement in the NPDP (Booz Allen and Hamilton 1982). More importantly from this study's point of view, the existence of such a strategy is an important indicator of managerial practice in SMEs. Indeed, Atkins and Lowe (1997) suggested that planning is a useful proxy for the nature of the managerial tasks and processes that take place in SMEs. However, litfie research has explored the relationship between the existence of a product strategy and the quality of an SME's NPD activities.

Planning Formality. The formality of a firm's planning has been examined in previous research (for example, Pearce, Freeman, and Robinson 1987; Olson and Bokor 1995). Commonly identified groups in such studies have been formal and informal planners, and planners and non-planners (Pearce, Freeman, and Robinson 1987). Lyles et al. (1993) pointed out that this broad definition of planning formality is appropriate when studying small businesses. Because new product strategy can be both explicit and implicit (Cooper 1985) and because strategic planning in SMEs may not be very formalized (Gibb and Scott 1985; Schwenk and Shrader 1993), the existence of a product strategy was measured in this study using a simple three-category scale ("no strategy" "an informal strategy," or "a formal strategy") based on previous research and interviews with 20 managers in Australian SMEs. A formal new product strategy involves having a written or explicit plan, while an informal strategy has a plan developed through informal discu ssions and is implicit, rather than explicit.

New Product Success. Measuring the success of a firm's new product is difficult. A number of suggestions have been made (Griffin and Page 1993). A task force set up by the Product Development Management Association (PDMA) suggested 16 core aspects of new product success (Griffin and Page 1993). All were included in the present questionnaire, as well as an additional measure that asked respondents about their perceptions of the overall success of the new product being discussed. Respondents were asked to indicate the product's success on these 17 measures using a fivepoint scale, ranging from "well below average" to "well above average."

These aspects of the NPDP, along with a number of organizational and respondent background questions, were included in a questionnaire that was sent to a sample of Australian SMEs. The sample and the procedures used to obtain responses are outlined in the next section.

The Sample

The sample was drawn from the Australian chemical and machinery industries (ANZSIC 25 and 28). The chemical industry includes petroleum, coal, chemical, and associated products, while the machinery industry includes motor vehicles, shipbuilding, electrical and electronic products, and scientific instruments (Australian Bureau of Statistics 1998). These two manufacturing industries were selected because they are the most active in product innovation (Australian Bureau of Statistics 1998) and account for a majority of manufacturing R&D expenditure (61 percent in 1994-95) in Australia (Industry Commission and Department of Industry Science and Technology 1997). There are approximately 55,000 manufacturing firms in Australia (Australian Bureau of Statistics 1998) and the chemical and machinery industries account for 27 percent of these firms (Industry Commission and Department of Industry Science and Technology 1997), suggesting that there are approximately 15,000 firms in these industries.

A database developed by Dunn and Bradstreet was used to obtain the sample, as it was found to be useful in previous small business research (Atkins and Lowe 1997; Wagar 1998). The targeted population was chemical or machinery firms employing fewer than 200 people. A search of the Dunn and Bradstreet database found 10,320 such firms, suggesting that the database contained about 70 percent of all the firms from the two industries. Dunn and Bradstreet was commissioned to provide a random sample of addresses of 3,440 such firms by selecting every third such firm in the database.

The potential for non-response bias was examined following Armstrong and Overton's (1977) recommendation that early and late respondents be assessed on several important background characteristics (education, age, years of experience, number of employees and sales). No significant differences were found on these characteristics, suggesting non-response bias was not a major problem in the present study.

Data Collection

The questionnaire was pre-tested among managers from ten innovative SMEs, resulting in some minor changes. The revised questionnaire was mailed to the selected firms, resulting in 440 useful returned questionnaires, with 276 from innovative firms and 164 from non-innovative firms. In addition, 375 questionnaires were returned incomplete, either because the person to whom the survey was mailed had left the business or because of an incorrect address. As already mentioned, a firm was considered to be innovative if it had developed a new product in the 1995-98 period. This definition enabled a comparison to be made with Australian Bureau of Statistics data (1998), which had been collected between 1994 and 1997. The effective response rate from the innovative firms was 27 percent, based on the percentage of innovative firms in the chemical and machinery industries (34.8 percent and 33.0 percent respectively, according to the Australian Bureau of Statistics (2998) data). This response rate is typical of surveys o f SMEs (for example, Sullivan and Kang 1999). The data analysis reported here was based on the responses from the 276 respondents in innovative firms.

Analytical Procedures

Several analytical procedures were undertaken. First, a series of t-tests were used to examine whether there was a difference in the quality of executing the NPDP between successful and unsuccessful SMEs. ANOVA was used to investigate the relationships between the quality of the NPD activities and the existence of a new product strategy.

To explore the relationships between the availability of a firm's resources and skills and the proficiency with which these activities are executed, firms were grouped according to their levels of resources and the skills they had in NPD. Following the suggestion of Soutar and Williams (1985), a factor analysis was undertaken on the 14 questions that asked about the availability of resources and skills. The factors obtained were then used as input into a clustering procedure to reach a decision as to the appropriate number of groups. The relationship between these groups and the quality of the NPDP was then assessed using ANOVA.

Results

Sample Characteristics

A profile of respondents and their firms is shown in Table 1. As the table shows, most respondents were CEOs, although about 20 percent were functional managers, including some who were responsible for marketing, R&D, production, and engineering.

A majority of the respondents (62 percent) were from the machinery industry, which is in line with the relative number of firms in each industry (Industry Commission and Department of Industry Science and Technology 1997). Nearly half (49 percent) of the new product projects assessed were seen as incremental innovations, 32 percent were rated radical innovations, and 19 percent were new-to-the-world innovations. Forty-nine percent of the new products were developed for an industrial market and 34 percent were developed for a consumer market.

New Product Development Activities Undertaken by SMEs

The frequency with which NPD activities were undertaken by respondent firms can be seen in Figure 1, which suggests innovators undertake most of the activities described by Cooper (1993). The most frequently undertaken activities were product development, preliminary production analysis, in-house product testing, and preliminary technical analysis, all of which are production- or technology-related. The least frequently undertaken activities came from the later stages of the NPDP and tended to be marketing-related activities. Similar patterns were found by Rochford and Rudelius (1997) in their study of the American medical product industry.

The completeness of the NPDP can affect the performance of the new products developed (Cooper and Kleinschmidt 1986). Therefore, a series of t-tests were used to compare the activities undertaken by managers of successful and unsuccessful projects. In this case, success was measured through the overall new product success item. Because most of the activities were undertaken by most of the firms, the only significant difference (at the 0.05 level) was in initial screening, which was more likely to have been undertaken by firms with successful projects (96 percent compared to 87 percent).

The Proficiency of Executing NPD Activities and Its Impact on New Product Performance

Although the proficiency with which NPDP activities are conducted has been shown to influence new product performance (Cooper and Kleinschmidt 1988), little is known about this issue in SMEs. Table 2 shows the self-reported proficiency with which the various NPD activities were executed by the firms in this sample.

The mean proficiency scores for all NPD activities ranged from 3.27 to 4.07. Respondents felt they executed several activities well (especially idea generation, product development, in-house testing and preliminary technical analysis). Again, these activities are production- or technology-related. The activities that SMEs did not feel they performed well were marketing-related (including market study, marketing tests and preliminary market analysis).

The quality in implementing the NPD activities can also be affected by the type of innovation (Olson, Walker, and Ruekert 1995) and industry (Cooper and Kleinschmidt 1993a). For this study, a series of ANOVAs were used to test the differences based on the type of innovations (incremental, radical, and new-to-world). No significant differences were found in the 13 activities, suggesting that overall NPDP quality is independent of the type of innovation. T-tests were also used to examine differences by industry. Firms in the chemical industry performed significantly better (at the 0.05 level) in three activities--preliminary financial analysis (3.50 vs. 3.22), market study (3.44 vs. 3.16), and precommercialisation financial analysis (3.47 vs. 3.19).

The Impact of New Product Strategy on the Quality of Execution

The NPD activities undertaken and their quality can be affected by a number of factors, including such managerial practices as having a new product strategy and the availability of resources and skills within a firm. While few organizations in the sample had a formal new product strategy (18 percent), most (61 percent) had an informal one; about a fifth (21 percent) had no strategy. The presence of such a strategy was related to firm size: the average number of full-time employees in firms with a formal strategy was 50; 29 in firms with an informal strategy; firms with no strategy averaged only 22 employees.

A series of ANOVAs was used to examine whether the presence and formality of a new product strategy impacted the quality with which NPDP activities were undertaken, as shown in Table 3. Significant differences (at the 0.05 level) were found in the quality with which every activity was undertaken. For firms without a new product strategy, the quality of the NPD activities was significantly lower than for those with such a strategy. Although firms with a formal strategy had a higher score in most of the activities than those with an informal strategy (11 out of 13), the differences were not statistically significant.

The Impact of Resource and Skill Availability on the Proficiency of Activity

The impact of resource and skill availability was assessed by grouping respondents with similar patterns of these attributes. As a preliminary step, the 14 resource and skill items were factor-analyzed, since muticollinearity between variables can influence clustering (Hair et al. 1995). The principal components analysis revealed three factors that together explained 68 percent of the variance in the data. The factor loadings, after a varimax rotation to achieve simple structure, are shown in Table 4.

As can be seen from Table 4, items that loaded onto Factor 1 were related to marketing resources and skills, while those that loaded onto Factors 2 and 3 were related to technical and financial resources and technical skills, respectively The scores from the three factors were used to group respondents using Ward's hierarchical agglomeration procedure. A large jump was apparent in the clustering criterion when the number of clusters was increased from two to three. Using SPSS's (1997) suggested criterion, a three-cluster solution appeared most appropriate, and this was confirmed by computing the point biserial correlation coefficient, as suggested by Milligan and Mahajan (1980). The profiles of the three groups are shown in Table 5.

Overall, firms in Cluster 3 had more resources and skills than those in Cluster 2 which, in turn, had more resources and skills than firms in Cluster 1 for most items. It was also clear that, while Cluster 3 respondents felt they had adequate resources and skills in most areas, Cluster 2 respondents felt they had adequate market-related resources and skills but only limited technology-related resources. A series of ANOVAs and t-tests found that group membership was not related to sales, size, or industry.

Table 6 shows the relationship found between the adequacy of a firm's resources and skills adequacy on the proficiency with which it undertakes NPD. As shown, proficiency level generally followed the adequacy of resources and skills. Firms inCluster 3 were more proficient than firms in Cluster 1 in almost all of the activities, and were more proficient than firms in Cluster 2 in three of the activities. Cluster 2 firms perceived themselves as being better that those in Cluster 1 in eight of the 13 activities listed. This suggests that resources and skills have a significant impact on the quality with which SMEs execute NPD activities. Firms with marketing and technical resources and skills perceive themselves as performing NPD activities better than do firms that are under-resourced in these areas.

Activities Contributing to Product Success and Need for Improvement

A series of t-tests was also used to examine the impact of proficiency on new product success. Successful respondents undertook NPD activities more proficiently (Table 2). However, in only six activities were these differences significant, with most of these activities being marketing-related (preliminary market analysis, customer product testing, and marketing testing).

These results and those from Figure 1 suggest an intriguing theme about NPD in SMEs. Most SME innovators perform well in technology- and production-related activities, but at least some firms have difficulty implementing marketing-related activities. Marketing-related activities were undertaken less frequently and not as well executed as other NPD activities. However, performance in marketing-related activities seemed to distinguish successful developers from unsuccessful ones.

Finally, the research attempted to determine which, if any, of the NPD activities contributed to the financial success of the new products developed and which of the NPD activities needed improvement. Two open-ended questions were included in the questionnaire. The respondents' answers to these questions are summarized in Table 7. From the table it can be seen that product development, commercialization, market study, and preliminary market analysis contributed most frequently to new product success. These activities also ranked high on the need for improvement list, with market study being suggested more frequently than product development.

Conclusions and Implications

The present research investigated the new product development process in Australian SMEs in two industries (chemical and machinery). The great majority of innovating firms undertook most of the activities suggested by Cooper and

Kleinschmidt (1986). However, overall, the Australian SMEs surveyed undertook marketing-related activities less frequently than they did technical activities.

It is clear that the quality with which NPD activities are executed affects new product success. Overall, SMEs undertake technology-related activities, such as product development, in-house product testing, and preliminary technical analysis, very well. However, they do not perform marketing-related activities, such as market study, marketing testing, and preliminary market analysis, as well. Nevertheless, it is these marketing-related activities that seem to distinguish successful and unsuccessful new products.

The quality of executing NPD activities is associated with the firm's resource and skill availability and the existence of a new product strategy. Better-resourced SMEs have a higher level of quality in implementing the NPD activities. The NPD activities also seem to be performed better in firms that have some kind of new product strategy whether formal or informal.

Finally, respondents felt that several NPD activities were more important to their new product success than the others. These key activities were market study, product development, commercialization, and preliminary market analysis. Respondents also recognized that these were activities that their firm most needed improvement.

The results of the present study have important managerial implications. The first relates to NPD activities in SMEs. Although only one activity differentiated between successful and unsuccessful new products, it would be dangerous to underestimate the importance of undertaking all of the NPD activities. A firm can be flexible in executing the NPDP but cutting corners and leaving out important steps may increase chances of product failure or a reduction in the quality with which NPD activities are executed, as Cooper (1993) has pointed out in his rebuttals of common excuses for omitting steps in the NPDP.

It was also clear that marketing-related activities were undertaken less frequently than technology-related activities. Innovating SMEs seem to be better at technology-related activities. However, more effort and resources need to be devoted to marketing-related activities because these were the activities that best differentiated successful and unsuccessful products.

These conclusions are reinforced by the fact that the existence of a new product strategy was strongly associated with the quality with which NPD activities were executed. Through careful strategic planning, SMEs can look at their future in a systematic way, communicate this future through the organization, and develop an appropriate organizational structure (Hegarty and Hoffman 1990). Such planning can facilitate rational decision-making (Gibb and Scott 1985). SMEs must develop appropriate new product strategies, be they formal or informal. More importantly, through such planning, SMEs can learn about problems, develop solutions, and improve their strategic responses to the environmental complexity and uncertainty that is an inherent part of NPD.

For government policy-makers, assistance and encouragement is needed to help innovating SMEs in their strategic planning and marketing, as improvements in these areas are likely to have the greatest impact on eventual new product success.

Limitations

There were several limitations in the present study Firstly, the new products studied may not be representative of all the projects undertaken by the firm. Although respondents were asked to respond for their last new product, it is possible that participants chose only successful projects. Secondly, present results may not be transferable to other industries, although the industries selected were more active and invest more in NPD than others (Australian Bureau of Statistics 1998; Australian Manufacturing Council 1991). Other industries should also be examined. Thirdly, the present research examined the NPDP at a micro or project level. Therefore, care should be taken in generalizing the present findings to a more macro level, such as a program level.

Methodologically, ANOVA and t-tests were used in this study to test the mean differences of multiple items. Such an approach can inflate Type I error, although these analytical procedures have been widely used in studying NPD (for example, Cooper 1988; Cooper and Kleinschmidt 1993b). The approach was used anyway partly because some of the questions asked respondents whether they had undertaken an activity and, if so, how well they felt they had carried out that task. A number of respondents had not undertaken all of the tasks. Consequently their data would have been lost for such activities if a multivariate approach, such as MANOVA, had been used. Another methodological limitation is the use of a single item to measure the existence of a new product strategy Based on the results of the present study future research should consider appropriate ways to measure this construct using a set of specific attributes.

Another potential limitation is that a single key respondent in each firm provided the information. If respondents were biased or lacked knowledge, the results could be affected. However, most respondents were general or senior functional managers who met Mitchell's (1994) guidelines for a single key informant. Another concern is that the data were based on self-report, rather than on observations of behavior. There may be differences between perceptions and behavior. However, previous studies on self-efficacy (for example, Bandura 1992) have found a strong correlation between self-efficacy and action. Finally if Australian SMEs are different from such firms in other countries, the present research may not be generalizable. However, past studies of Australian and North American firms' NPDP have not found major differences (Dwyer and Mellor 1991, 1993).

Future Research

The present findings suggest areas for further research. There is a clear need to examine the impact of strategic planning on the NPDP and new product outcomes in detail. Although the present research suggested that new product strategy influences the quality with which NPD activities are executed, the nature of this influence and the nature of the planning process itself need further research. Moreover, firms in different industries may need different new product strategies, which may be technologically driven, balance-focused or high-budget diverse (Cooper 1985). How these types of new product strategies influence new product activities and performance in SMEs needs to be studied.

Research into the NPDP in services industries is also needed to provide insights into service innovation. Although the majority of SMEs are in service industries, very few studies have examined their new service development processes. Such research would provide invaluable insights into NPDP similarities and differences between the manufacturing and services sectors. In addition, product innovations in low R&D expenditure industries and process innovations in the SME sector are other important areas for future research that would enhance our understanding of technological innovations in SMEs. Finally all organizations undertaking innovations-in products, processes and services--could be considered as innovative, and further research is needed to examine the impact of such an extended definition on known relationships.

Dr. Huang is Senior Lecturer in the Department of Marketing at the University of Southern Queensland in Australia. His research interests include innovation management, marketing and e-business.

Prof. Soutar is Director of the Graduate school of Management at the University of Western Australia. He has a particular interest in SME research and founded the Small to Medium Enterprise Research Centre at Edith Cowan University.

Prof. Brown was until recently the Acting Dean of the Faculty of Business of Edith Cowan University in Australia. His teaching and research interests include quality management, human resource management and small business management.

(*.) The authors thank three anonymous reviewers for their valuable comments on the earlier version of this paper that added considerably to its content and style. Responsibility, of course, remains with the authors.

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Figure 1

The Frequency of the NPD Activities Undertaken by SMEs (n = 264)

                                 Successful project

Idea generation
Initial screening
Preliminary market analysis
Preliminary technical analysis
Preliminary production analysis
Preliminary financial analysis
Market study
Product development
In-house product testing
Consumer product testing
Marketing testing
Precommercialization financial
 analysis
Commercialization

                                 Unsuccessful project  Overall

Idea generation                                         94.9
Initial screening                                       92.0
Preliminary market analysis                             92.4
Preliminary technical analysis                          94.9
Preliminary production analysis                         96.0
Preliminary financial analysis                          94.6
Market study                                            89.5
Product development                                     96.4
In-house product testing                                94.9
Consumer product testing                                86.2
Marketing testing                                       82.6
Precommercialization financial                          83.7
 analysis
Commercialization                                       83.0

Note: Table made from bargraph
Table 1

Profile of the Respondents and their Firms

Characteristic           n   Percent

Respondent's position

 CEO                    212   78.5
 Functional manager      49   18.1
 Others                   9    3.4

Respondent's education

 Secondary education     53   20.1
 Tradesperson            29   11.0
 Vocational education/   35   13.3
 training
 College diploma        147   55.7

Firm sales

 AUS$0-0.5M              31   12.7
 0.5-1M                  32   13.1
 1-5M                    99   40.4
 6-19M                   50   20.4
 20 + M                  33   13.5

Number of employees

 0-4                     42   15.8
 5-19                   109   41.1
 20-99                   92   34.7
 100+                    22    8.3
Table 2

The Perceived Quality of Implementing Activities in the NPDP (a)

Activity                  Overall  Unsuccessful  Successful    Rank

Idea generation            4.07        4.05         4.10         1
Initial screening          3.51        3.46         3.56         6
Preliminary market         3.30        3.15         3.42 (*)    10
 analysis
Preliminary technical      3.76        3.63         3.88 (*)     4
 analysis
Preliminary production     3.53        3.48         3.55         5
 analysis
Preliminary financial      3.33        3.30         3.36         9
 analysis
Market study               3.27        3.19         3.34        13
Product development        3.91        3.82         4.01         2
In-house product testing   3.79        3.75         3.86         3
Customer product testing   3.49        3.33         3.63 (*)     7
Marketing testing          3.30        2.98         3.56 (**)   11
Pre-commercialization      3.29        3.13         3.43 (**)   12
 financial analysis
Commercialization          3.42        3.26         3.60 (**)    8

(a)Mean scores on a five-points scale (5 = excellently done and 1 = very
poorly done).

(*)p < .05 (two tails)

(**)p < .01 (two tails)
Table 3

The Influence of Product Strategy on the Quality of Activites (a)

Activity                           No     Informal   Formal     ANOVA
                                Strategy  Strategy  Strategy  (Scheffe)

Idea generation                   3.55      4.00      3.81        2>1
Initial screening                 2.46      3.32      3.71     3, 2>1
Preliminary market analysis       2.32      3.18      3.40     3, 2>1
Preliminary technical analysis    2.98      3.72      3.77     3, 2>1
Preliminary production            2.98      3.48      3.54     3, 2>1
Preliminary financial analysis    2.77      3.19      3.38       3 >1
Market study                      2.30      3.06      3.10     3, 2>1
Product development               3.41      3.79      4.10      3 > 1
In-house product testing          3.30      3.61      3.85      3 > 1
Customer product testing          2.59      3.17      2.92      2 > 1
Marketing testing                 2.25      2.81      2.85      3 > 1
Pre-commercialization             2.41      2.74      3.04     3, 2>1
Commercialization                 2.16      2.98      3.21     3, 2>1

(a)0-5 scale with 0=not undertaken at all and 5=excellently done.
Table 4

The Results of Factor Analysis Based on the Adequacy of Resources and
Skills (a)

Resources and Skills       Factor 1:       Factor 2:
                           Marketing     Technical and
                           Resources  Financial Resources

R&D skills                  .157             .117
Engineering skills          .020             .230
R&D resources               .208             .650
Engineering resources       .018             .768
Manufacturing resources     .080             .824
Marketing research skills   .798            -.016
Salesforce skills           .800             .067
Distribution skills         .800             .109
Advertising and promotion   .828             .088
 skills
Marketing research          .798             .147
 resources
Salesforce resources        .862             .165
Distribution resources      .840             .209
Advertising/promotion
 resources                  .847             .219
Financial resources         .375             .633

Resources and Skills          Factor 3:
                           Technical skills


R&D skills                      .869
Engineering skills              .801
R&D resources                   .338
Engineering resources           .407
Manufacturing resources         .117
Marketing research skills       .141
Salesforce skills               .133
Distribution skills             .013
Advertising and promotion       .097
 skills
Marketing research              .084
 resources
Salesforce resources            .045
Distribution resources         -.064
Advertising/promotion
 resources                     -.025
Financial resources            -.276

(a)values in the table are factor loadings.
Table 5

The Adequacy of Resources and Skills in Three Clusters of Firms (a)

Resources and Skills             Overall  Cluster 1  Cluster 2

R&D skills                        3.88      3.06       4.03
Engineering skills                3.94      3.42       3.94
R&D resources                     3.31      3.06       2.82
Engineering resources             3.51      3.38       2.91
Manufacturing resources           3.67      3.78       2.94
Marketing research skills         3.23      2.26       3.25
Salesforce skills                 3.27      2.24       3.21
Distribution skills               3.33      2.48       3.16
Advertising/promotion skills      3.10      2.14       3.05
Marketing research resources      2.97      2.12       2.84
Salesforce resources              3.13      2.10       2.94
Distribution resources            3.26      2.42       3.00
Advertising/promotion resources   3.00      2.02       2.82
Financial resources               3.16      3.18       2.44

Resources and Skills             Cluster 3  ANOVA

R&D skills                         4.10     3,2>1
Engineering skills                 4.21     3,2>1
R&D resources                      3.82     3>1,2
Engineering resources              4.06     3>1>2
Manufacturing resources            4.23     3>1>2
Marketing research skills          3.63     3>2>1
Salesforce skills                  3.77     3>2>1
Distribution skills                3.84     3>2>1
Advertising/promotion skills       3.57     3>2>1
Marketing research resources       3.46     3>2>1
Salesforce resources               3.74     3>2>1
Distribution resources             3.86     3>2>1
Advertising/promotion resources    3.61     3>2>1
Financial resources                3.77     3>1>2

(a)Mean score on a five-point Likert scale with 5=strongly agree and
1=strongly disagree.
Table 6

The Impact of Resources and Skills on the Proficiency of Activity

Resources and Skills            Cluster 1  Cluster 2  Cluster 3  ANOVA

Idea generation                   3.70       4.11       4.19     3,2>1
Initial screening                 2.87       3.59       3.69     3,2>1
Preliminary market analysis       2.65       3.34       3.56     3,2>1
Preliminary technical analysis    3.41       3.83       3.85     3,2>1
Preliminary production            3.28       3.39       3.75     3>1,2
Preliminary financial analysis    2.87       3.23       3.60     3>1,2
Market study                      2.62       3.30       3.49     3,2>1
Product development               3.50       3.94       4.04     3,2>1
In-house product testing          3.51       3.66       3.98     3>1,2
Customer product testing          3.30       3.36       3.67      N.S.
Marketing testing                 2.70       3.35       3.79     3,2>1
Precommercialization              2.92       3.21       3.48       3>1
Commercialization                 2.88       3.43       3.63     3,2>1
Table 7

NPD Activities: Contribution to Financial Success and Areas for
Improvement

Activity (a)                                Contributed to Success
                                                    (n=246)

                                            n        Percent   Rank

Idea generation                            27          8.9       5
Initial screening                           5          1.6      12
Preliminary market analysis                33         10.9       4
Preliminary technical analysis             22          7.2       6
Preliminary production analysis            12          3.9       9
Preliminary financial analysis             20          6.6       7
Market study                               33         10.9       3
Product development                        75         24.7       1
In-house product testing                    7          2.3      11
Consumer product testing                   18          5.9       8
Marketing testing                           8          2.6      10
Pre-commercialization financial analysis    5          1.6      13
Commercialization                          39         12.8       2

Total                                     304

Activity (a)                                   Needs Improvement
                                                    (n=170)

                                            n       Percent   Rank

Idea generation                             0         0        13
Initial screening                           2         1.6      12
Preliminary market analysis                10         8.2       4
Preliminary technical analysis              6         4.9       7
Preliminary production analysis             5         4.1       8
Preliminary financial analysis              5         4.1       9
Market study                               30        24.6       1
Product development                        21        17.2       2
In-house product testing                    8         6.6       5
Consumer product testing                    5         4.1      10
Marketing testing                           6         4.9       6
Pre-commercialization financial analysis    3         2.5      11
Commercialization                          21        17.2       3

Total                                     122

(a)Respondents could indicate more than one answer.