Factors for Success in R&D Projects and New Product Innovation: A Contextual Framework R. Balachandra, Member, IEEE, and John H. Friar

Abstract— There have been many attempts over the last 30 years to discover the critical factors that can indicate the success or failure of R&D projects and new product introductions. Because of the large number of studies that currently exist, the authors undertook an extensive review of the germane literature to find whether a general agreement exists about the factors leading to success or failure in new product development and R&D projects. The review shows first that even with a conservative approach to listing significant factors, the list is very long. Second, comparing the factors across studies demonstrates that different authors have found that the magnitude of significance and the direction of influence vary. Third, given the differences in context, the meaning of similar factors may also vary. The contradictory findings lead us to propose a contingency framework for the new product and R&D project models. This framework consists of a contingency cube with three contextual dimensions. Based on this framework, we propose a set of propositions. We conclude this paper with a discussion of the implications of a contingent approach for both researchers and managers in the area of management of new products and R&D projects. Index Terms— Contextual variables, contingency model, new product development, R&D projects, success factors for new products.



RINGING NEW products successfully to market is the lifeblood for most organizations, but it is also a complex and difficult task. Of the nearly 16 000 new products introduced in 1991, almost 90% did not reach their business objectives [73]. It is hard to predict why some new products succeed while most fail. Because the task is so difficult, much has been written from a variety of research streams and disciplines attempting to identify factors leading to successful new products. A precursor to a new product in a majority of the cases is the R&D project needed to produce it. As in the case of a new product, it is hard to predict why some commercial R&D projects succeed while most fail. Though the two—new product development (NPD) and commercial R&D projects—appear to be different at the outset, there is a great deal of similarity between them. They are projects undertaken by the firm to increase the product line and the firm’s competitiveness in the market. In both cases, there is Manuscript received March 6, 1995; revised March 1996. Review of this manuscript was arranged by Editor-in-Chief D. F. Kocaoglu. R. Balachandra is with Northeastern University, Graduate School of Business Administration, Boston, MA 02115 USA. J. H. Friar is with Clark University, Graduate School of Management, Worcester, MA 01610 USA. Publisher Item Identifier S 0018-9391(97)05703-6.

uncertainty about the market, the technology, the costs of production, and the process of development itself. Many research studies have attempted to discover the critical factors that can indicate the success or failure of R&D projects and new product introductions. Some have looked at success factors in new product introduction, some have looked at factors causing failure, and still others have looked at both sets of factors. The studies show that there are a large number of factors influencing the success of a new product or an R&D project (aimed at developing a new product). Some of them are controllable from within the organization but others are external and uncontrollable. All of these studies have several things in common: they identify a number of factors, based on the literature or on common sense, that have been surmised to have some influence on the success of projects; they select a sample of projects and evaluate the factors for these projects; and they perform statistical analysis to identify the significant factors. While some studies collect data from actual projects, others ask executives to rate the contribution and importance of factors for the success of projects of which the executives have some personal understanding. Even though successful outcomes of a new product or commercial R&D project are hard to predict, the research to date has attempted to derive a comprehensive model of what leads to success or failure. A comprehensive model usually lists a number of factors contributing to the success or failure of a new product or an R&D project. Even from a cursory review of the literature in these areas (NPD and R&D project success), one comes away with the conclusion that there is a plethora of factors deemed critical for success or failure. To study this issue in greater depth, we undertook a review of the literature of R&D project management and NPD. The areas of R&D project management (especially in the commercial sector, where projects are taken up with a commercial motive) and new product management deal essentially with the common problems of linking technology and development efforts to manufacturing and marketing, albeit with differing emphasis. We also looked at the literatures of related fields to which the factors pointed, e.g., for market variables, we looked at marketing literature. We wanted to investigate further if what the factors found in these studies agreed with the findings of the specific field. We therefore reviewed over 60 articles in the literature of the related fields to find whether there existed general agreement about the factors leading to success or failure in NPD and R&D projects.

0018–9391/97$10.00  1997 IEEE


Our analysis shows that the conclusions from the studies are nonuniform, and in some cases, they are even contradictory. Even though our review has led us to conclude that there are a number of well-known methodological weaknesses in the studies and models, we argue that several of the critical factors identified by these studies are contextual. The contexts determine the appearance or nonappearance of some critical factors. Varying contexts also cause the somewhat contradictory nature of some of the factors. This review illustrates some of the major contradictions regarding the effects of some factors on the outcome of an NPD or commercial R&D project. Because of the differences in such factors as approaches, methodologies, definitions, and benchmarks, we created a structure by which we could compare what the various studies concluded for the many variables that were considered. To test whether the conclusions were an artifact resulting from looking across different fields, we undertook an in-depth analysis of 19 studies from only the two narrowly defined fields—R&D projects and NPD. The results of this detailed analysis confirm the overall results. We then analyzed the characteristics of the studies and identified the set of significant factors in NPD and R&D projects. The list of factors identified by these studies is very large (72). The analysis also highlights some important differences between new product studies and R&D project studies. The conclusions from the detailed review and analysis lead us to propose a contextual model for the NPD and R&D project models. This preliminary contextual model consists of a contingency cube with three contextual dimensions. We propose a set of propositions based on this model and framework. We conclude this paper with a discussion of the implications of a contingent approach for both researchers and managers in the area of management of new products and R&D projects.

II. REVIEW OF THE LITERATURE ON SUCCESS/FAILURE FACTORS FOR R&D PROJECTS AND NEW PRODUCTS The literature discussing success in product innovation and R&D projects is vast. We examined over 60 papers in these fields. Success in product innovation or in R&D projects is hard to define, for it is a composite of a number of measures. There is also a time gap between a product’s introduction or the completion of a R&D project and it’s being deemed a success. Cooper and Kleinschmidt [21] identify three factors that are themselves composites—financial performance, opportunity window, and market share. These measures are usually available after a considerable interval. A recent study by Griffin et al. [34] illustrates the divergence of views about and the complexity of the definition of success in NPD. Since there is no one common measure of success, and success is a composite of a number of subjective and objective measures, we have used success as defined by the individual authors of the studies. To provide a better understanding of the phenomenon, we categorized the large number of factors that determine success


using a variant of the method used in marketing strategy studies to structure information [1]—market, technology, environment, or organization. The following review of the relevant literature is ordered into these four major categories. A. Market In both new product introduction and R&D projects, market is an important category. There is universal agreement that there should be a strong market for the new product under consideration or for the outcome of the R&D project. The pioneering SAPPHO study [65] and Cooper [13], [14] gave high importance to “strength of market” based on an analysis of the potential size of the market, the expected market share, and the profitability of the new product. Cochran [11], Cooper [15], and Rubenstein et al. [66] also supported good market analysis without specifically getting into the strength aspect. Mansfield and Wagner [53] went a step further and suggested that the market analysis should be done early. Other authors, however, have noted that market analysis tends to direct projects toward existing markets with small, incremental advances rather than to undeveloped markets with major innovations [31], [35], [55], [74]. Too much market analysis can drive out innovation, so a careful balance must be maintained. Though market analysis will prepare the organization to plan product development better, one wonders whether accurate information on market needs can be obtained. Gaynor [32] suggested that a new product should meet customer needs/wants and that there should be a good understanding of the market. Many authors have analyzed the difficulty of getting good information on customer needs for innovative products in potential markets because customer preferences may not be known by the customers themselves [43], [67], [72]. Existing market-assessment tools are inadequate for highly innovative products, and organizational boundaries inhibit needs assessment [27], [43]. Some authors have argued beyond the expectation of a strong market and have instead concluded that the market must already exist instead of being potential. The need for the existence of a strong market was supported by Balachandra [6], Balachandra and Raelin [5], Hopkins [38], Carter [10], and Islei et al. [39]. An existing market lessens the difficulties in customer research and forecasting but again raises the issue that the ability to do good market analysis may drive companies to small, incremental advances. The expected growth rate of the market for the product is an important factor for the decision to pursue the new product. Merrifield [56] cited this factor as important in evaluating the business side of the new product decision. Balachandra [7] suggested the growth phase in the life cycle of the product as a strong positive factor. Utterback [71], Rubenstein et al. [66], and Cooper [15] also indicated that a new product introduced in response to a growing market has a greater chance of success. Yoon and Lilien [75], however, found higher success rates when products were introduced into markets with slow growth. Maidique and Zirger [49] considered coming into a market early as a strong factor in the success of the new product.



Nevens et al. [60] reinforced this idea by suggesting the potential losses a company incurs by delaying the introduction of the product. On the other hand, Yoon and Lilien [75], Freeman [28], and Porter [64] found that coming into a market later may be more advantageous in some cases. Then there is competition. Though we have considered this to be a part of market analysis, some researchers include this in the environmental factors group, especially when the product is completely new with no known competition. One may have to consider competition likely to arise from completely unknown sources. Merrifield [56] looked at competitive analysis as a major factor, while Carter [10] called it competitive environment. The rate of new product introduction in the product category was considered an important factor by Balachandra [7] and Islei et al. [39]. To an extent, this reflects the stage of life cycle of the product category. Higher rate of introduction implies the growth stage, which in turn implies a better chance of success for the new product. Other studies have found that a higher rate of introduction implies greater intensity of competition and is therefore a strong negative factor for commercial success [11], [15], [49], [75]. From this brief discussion, we see that though market strength is a composite factor, its estimation is contextual, because some components for estimating market strength may contribute positively in some situations but negatively in others. The main contextual feature is whether the new product is entering an established market or is an innovative product for which there is no established market. B. Technology The next major category is technology. Many studies [11], [13], [47], [49] found an innovative product to have a greater chance of success in the market, while some found otherwise [54], [65]. Others, like Freeman [28] and Mansfield [51], have found that innovative products are much more likely than less innovative products to fail. The degree of innovativeness, therefore, is an important consideration. Kleinschmidt and Cooper [41] recently found that the relationship between innovativeness and commercial success is not linear but rather U-shaped. A single relationship between innovativeness and commercial success, therefore, is still uncertain. Many studies [13], [46], [49], [50], [66] identified “perceived value of the product” as an important factor. This factor was expressed in a number of different ways, chief among them being “high performance to cost.” Friar [30], however, suggested that customers may have difficulty in actually differentiating performance among competing alternatives in technologically sophisticated areas, and it may take years for the market to sort out the differences. Some studies included patentability of the product or at least a strong technology in the product as an important factor for success [10], [55], [66], [70]. Technology push, though, was not considered as strong a factor as demand pull by a number of studies [51], [54], [65], [71], although in a review of this literature [58], the definitions were found to be so broad as to be meaningless. Further, studies have shown that in many


industries where NPD is important, companies do not even bother to patent their advances [44], [49], [52]. It is apparent that the influence of technology factors on the success of the new product is dependent on other contextual factors, mainly the innovativeness of the technology. C. Environment A product cannot succeed if the environment in which it is introduced is not supportive. The environment consists of a number of different aspects, such as political and social factors, public interest in the product, and social acceptability of the product. The environmental factors analyzed in the studies (except for one) were unique to each study. Four studies [5], [10], [39], [56] included assured availability of raw materials as a critical factor. Though in ordinary times this is not so important, it can become critical in times of hostilities and market-induced shortages. Researchers, therefore, have yet to agree on which factors to analyze, much less on which factors are significant. Cooper [12], moreover, found the environmental factors to be unimportant. By default, so did the authors of the other studies in new product management, as they did not find any significant factors in this area (Table III, factors 1–7). The authors of R&D project studies looked for and found significant factors in the environment but just could not agree on which ones. D. Organization Whatever the technological, market, and environmental factors may be, if the organization is not capable of getting the new product into the market, then the product will fail. All studies in this review have focused on some issues of organization. Many authors have suggested that the organization should receive strong support from the marketing function for the new product (for example, Maidique and Zirger [50], SAPPHO [65], Marquis [54], Cochran [11], Cooper [14], and Link [47]). However, what does strong support mean in this context? Many studies assumed that this aspect is self-evident, and in the questionnaires used by these studies, the executives were asked to evaluate a question such as, “Did the new




product receive strong support from marketing?” But studies by Snow and Hrebiniak [68] and Hitt et al. [36] have shown that companies with strengths in the R&D function often have weak or no marketing skills. Frohman [31], moreover, found that help from the marketing function was a hindrance rather than an aid. Planning and scheduling the process of new product introduction or an R&D project was considered very important by a number of studies [11], [15], [31], [49], [65], [66], [70]. Using quantitative techniques for project selection was deemed important by Liberatore and Titus [45], and their study found that firms using such techniques had better results. Others [53], [55], however, found that quantitative project selection was negatively correlated to economic success. The source of a new product idea has received some attention from researchers. It is suggested by some researchers that the organizational source of a new product idea can generate interest and motivate the technical personnel to devote their energies to making the new product idea work. Some authors recommend that new product ideas be generated from the marketing department, as it is close to customers’ needs [13], [53], [65]. This is disputed by others [31], [36], [67], who suggest that ideas from the marketing department generally lead to incremental products, which may not generate much enthusiasm from the technical staff. They further suggest that the really innovative ideas come from the technical staff. We have presented here a brief review of the relevant literature and highlighted the conflicting nature of some of the conclusions. The set of factors considered important is different in different studies, and even when the factors are similar, their definitions and measurement are different. Even the effect on success is sometimes contradictory. The contradictory nature of these results is presented in Table I, which clearly illustrates our contention.

III. AN ANALYSIS OF SELECTED STUDIES AND METHODOLOGICAL ISSUES The contradictory nature of the conclusions from the studies is itself very interesting. To test whether the contradictory conclusions are a result of looking at studies in diverse fields, we performed a detailed and in-depth analysis of studies from the two fields of commercial R&D projects and NPD. Since there are a large number of studies in these fields, we focused our attention on a limited number by selecting studies based on the following criteria. 1) The study should have some empirical support, regardless of the methodology used. 2) The study should identify a specific set of factors as being important for success or failure. 3) Only one major study from an author or team should be considered. Nineteen studies met these criteria. The first criterion filtered out a large number of articles that were based on the experiences of an individual. The second criterion excluded those studies whose main focus was not success/failure of new products or R&D projects but that had peripheral references to these issues (including some of the articles cited in Table I). Third, some authors have published numerous studies in this area. They are usually for different samples—industries, countries, etc. From such authors, we selected one study that we considered the most representative, as we found a high degree of consistency in the factors that specific authors found in their studies over time. These criteria reduced the number of studies selected for detailed analysis from a large list to 19 studies. Table II shows the studies and their important features. From these 19 studies, we extracted the factors identified as significant. The authors use a variety of techniques to




derive the significant factors. Typically, they start with a long list of factors (some studies started with a list of over 120 factors) based on earlier studies, the researchers’ personal experiences, and discussions with managers and academics. Then the authors collect data for these factors from actual projects from the executives of the firm (in many cases) or for hypothetical projects from executives attending a conference. The data for the large number of factors are then clustered into a smaller number using statistical techniques such as factor analysis. The derived factors are then used in a classification technique—either discriminant analysis or, in some cases, regression analysis—to identify the most significant factors. Because of the steps involved, the possible variations in each of the steps, and the absence of a universal set of initial factors for which to collect data, the number of significant factors identified by the studies range from a low of three to a high of 16. We used only the final factors the authors put forward in their studies and not all the significant ones found. Even though we used this conservative approach, we ended with 72 factors. We then designed a matrix showing the factors and the studies that identified them. While generating this matrix, we examined the definitions of each factor as described by the authors and grouped them according to their descriptions. Some of the factors are common to a number of studies, while others are unique to specific studies. We assigned to each factor in the matrix an identification letter—M(arket), T(echnology), E(nvironment), or O(rganization)—to designate the category to which it be-

longs. We agreed on 92% of the codings. Where disagreement occurred and could not be reconciled, the factor was jointly coded. The results of such codings are shown in Table III. We also examined whether the factor was influencing the success of the R&D project/new product in a positive or negative manner. We marked those factors providing a positive and those providing a negative influence influence with a . The factors whose influence was either positive with a or negative were marked with an [*]. We coded the factors individually. Studying a complex phenomenon like product innovation or an R&D project to glean the factors leading to success is made more difficult by the quality of data available to researchers. As we reviewed the studies, we categorized the methodological issues. We found four major areas of weakness in the studies: quality of data, definition of new product, factor selection and definitions, and measurement of factors. We do not want to restate what is well known; several authors in the studies discussed some of the methodological issues themselves (Cooper [13], Maidique and Zirger [49], and Rubenstein et al. [66]). We want to suggest, however, that because of the methodological issues, great care must be taken in comparing what was concluded in one study with the results of another. A brief discussion of the methodological issues is presented in the Appendix. There are some interesting conclusions from this detailed study. Though the total list of significant factors identified by the studies is very long (72), half of them are unique to specific




studies. About 75% of the final factors were identified in just one or two studies. Table IV shows the average number of different types of factors in the two types of studies. Two differences between R&D studies and new product studies are immediately apparent from Table IV. The first

is the smaller average number of significant factors found in the NPD studies versus the R&D project studies—about half. The other, somewhat surprising, result is that studies by researchers with a marketing orientation appear to have put more emphasis on internal factors that are organization









related, especially the launching and marketing activities, (see for example Calantone and di Benedetto [9]), while researchers with a technical orientation have put more emphasis on the external factors that are market and environment related, as seen by the relative percentage of each group of factors. Technology factors were considered about evenly. This would appear to be counterintuitive in that one would assume that project management by definition is organization related and new product management is market and environment related. The small value for the average number of final factors in the new product management studies is due to many researchers’ having started with a large number of significant variables and then reduced them to a more compact list using factor analysis (Table III). This is more common in the NPD studies (60% versus 0%) and so, as seen in Table IV, the average number of factors in these studies is much smaller than in R&D project studies. In trying to understand the counterintuitive second finding, we analyzed the factors that more than three studies agreed upon. As the results in Table V show, there were 14 such factors. Of them, only three were evenly agreed upon by both literatures, and 9 came from studies predominantly of one kind or the other. The other two factors were cited by three studies of one kind or the other. From this list, it becomes apparent that the R&D project studies more readily agreed on project and technology issues—probability of technical success, need to lower cost, availability of raw materials, timing, and wellplanned R&D process—while the new product studies agreed

on marketing and competition factors—emphasize marketing, see marketing and technology as strengths, and recognize the competitive environment. The latter are aggregates, moreover, which are composites of several additional factors. If we were to classify these factors as internal and external depending on the amount of influence the firm can exert on them, the R&D project studies found more significant factors that are external, while the NPD studies agreed more on factors that are internal. A possible reason for this result is that the two types of studies have traditionally looked at the process at different stages. The R&D project studies have looked at the development stage of a new product, where the technology and a number of other external factors can affect the outcome significantly. The NPD studies, on the other hand, have looked at situations where the idea for the product is at a well-advanced stage in development, its technology has been fairly well developed, but the actual launching and marketing remains. Naturally, the emphasis is then on the internal activities. This discussion confirms our conjecture that there are many contradictions in the effect of different factors on the success of NPD or R&D projects. It further leads to a suspicion that there is no one universal model encompassing the success/failure of either new products or R&D projects. Based on this review and analysis of the selected studies and the larger field, we propose that the success/failure factors are related to the context. This is not a novel idea. Other branches of management—organization theory, strategy, marketing, and international business—have moved toward contingency theories [1], [25], [33], [42], [57], [64]. In Section IV, we propose a set of propositions relating the context of the NPD or R&D project to its success or failure. IV. A CONTEXTUAL STRUCTURE NPD AND R&D PROJECT MODELS


A number of different models have been proposed for NPD (see for example [9], [15], and [46]). Some focus on the process [17], while others focus on the outcomes [9]. Mahajan and Wind [47] have suggested the role of models in supporting and improving the NPD process. Most of these are conceptual. We use parts of these models in developing a conceptual model based on contingencies. Contextual models have started appearing for explaining different aspects of NPD. Two recent studies are worth mentioning: Olson et al. [61] look at the relationship between product innovativeness and organizing for NPD, while Ali [2] develops a contingency model involving numerous hypotheses for the type of NPD undertaken by a firm and a number of contextual variables. Tables I, III, and IV and the discussion in the foregoing section lead one to suspect that there may be only a few universal factors to help identify successful new products or R&D projects. We have shown that several important factors deemed significant for successful product innovation can vary not only in magnitude but also in direction depending on the context. It appears that a factor may be helpful in leading to success in some contexts but may lead to failure or be unimportant in a different context.


Given the importance of contextual variables, we argue that frameworks or models should take into account these contingencies. Models that do not take these contextual issues into account may lead to erroneous conclusions. Based on the review and analysis discussed above, we propose and identify the underlying parameters causing the contingencies. We suggest that there are three major groups of contextual variables for successful new product innovation and R&D projects: • nature of the innovation; • nature of the market; • nature of the technology. We could consider a fourth group—nature of the industry. There is not enough evidence in the literature to develop any hypotheses for this group, although some recent studies have started looking at different industrial segments (for example, Islei et al. [39] and de Brentani [24]). One could argue, however, that even in different industries, what really affects the outcome of an R&D or NPD project is dictated by the above three contextual groups. Each of these groups has been individually described in some of the studies we reviewed, but no study has explored all of them together. Also, the general argument used by the authors when contextual differences have been found is that the magnitude of the effect of the factor on success or failure of a new product differs, but not the direction. By looking across different studies, however, we see that not only can the magnitude vary but also the direction (Table I). Each of these contextual groups will be described briefly in the following paragraphs. We also propose a number of propositions based on the contextual structure and the discussion that follows. These propositions can then be tested with suitable empirical research. A. Nature of the Innovation The level of “newness” in a product innovation can vary broadly. Although Wheelwright and Clark [74] identified three classes of innovation in new products—incremental, new generation, and radically new—the studies that considered the type of innovation in R&D or NPD projects looked at the poles of incremental and radical innovation. Three studies ([3], [41], and [75]) found differences in factors depending on whether the innovation was pioneering (radical) or incremental. The level of innovation for a new product or an R&D project ranges over a wide spectrum. As a first cut, we will consider two levels of innovation—incremental and radical. An incremental innovation is one where the basic technology and product configuration remains essentially the same and only minor modifications are made to the performance, flexibility, appearance, and other characteristics. A radical innovation is one where the technology is considerably different from the earlier product. The incremental innovation is usually attempted in a wellestablished market [2]. For example, Sony’s Walkman came out with a number of incremental innovations in the 1980’s, but the market was well established and the customer was


well identified. The market analysis in such cases can be very thorough. In the case of radical innovation, the market may not exist at all. The product design in such cases may be based solely on the creative instincts of the designer by understanding user needs through empathy with the user world—what LeonardBarton calls empathic design [43]. The market uncertainty is usually very high. Performing a detailed market analysis may be impossible and fruitless in such cases. The nature of the innovation has different effects on market and organization factors affecting the success of the new product or R&D project. In the case of a radical innovation it is much harder, if not impossible, to forecast and perform early analyses on customer needs, market size, market growth, and competition. Consequently, using formal market analysis is relatively less important. On the other hand, an incremental innovation requires a thorough market analysis of an existing market. In organizational factors, source of ideas and use of quantitative techniques are prime examples of factors that will vary depending on the level of innovation. Likewise, how companies organize for radical innovation has been shown to differ from how they organize for incremental innovation [40], [69], [70]. B. Nature of the Market The nature of the market for a new product can be categorized into two types—existing and new. Whether a company is innovating in an existing market or trying to create a completely new market will cause differences in factors. In the former case, the new product meets an existing need but with some improvements, and therefore the market uncertainty is relatively low. In the latter case, it meets a latent need, and the uncertainty of the market can therefore be very high. The market analyses for the two types are completely different [67]. Likewise, the quality of the information one can discover about the market and customers will differ so that the meaning of some factors will change. As Maidique and Zirger [50] found, market understanding for existing markets came from proactive approaches, but in new markets it came from passive understanding of user needs because of gut feel or experience. C. Nature of the Technology Although classifying technology is hard, one useful classification is high tech versus low tech. The uncertainties in market and technology are different for the two groups. Link [47] found differences in factors depending on whether the setting was high tech. In the high-tech field, the technology is developing very rapidly, and so new product introductions come quickly. The applications and customers may not yet be determined if the technologies are still emerging. This turbulence would have an impact on the marketing and technology factors. The perceived value of any given advance can get lost in the large number of advances. Because standards for the products and their performance are not set, early market entrants can be hurt by later developments.




In low tech, the areas are well defined. The new products have to conform to many of the well-established standards and practices and so have to provide an advantage within this structure. One of the axioms of technology is that what is high tech today will be low tech tomorrow. Using this structure, we propose a contingency framework for NPD and R&D project success. This structure can provide a better understanding of the factors that influence success in these areas. The framework comprises a contingency cube with two levels for each of the three contextual variables. Fig. 1 shows this structure. There are a total of eight blocks in this cube corresponding to the different possible combinations of levels and dimensions. Depending upon the specific combination of the three contextual variables, the emphasis to be placed on different sets of success factors is different. For example, if an NPD project is in a block with the characteristics of an incremental innovation with low technology for an existing market, the market factors are very important, implying careful market analysis. It also suggests that the technology factors are not that important. On the other hand, the organizational factors are very important. This means that in studies focusing on such cases, technology factors may not be found, or even if found may receive a low importance. Based on this categorization of the contextual factors relevant in NPD and R&D projects, we propose the set of hypotheses pertaining to the relative importance of the three types of factors—market, technological, and organizational—as shown in Table VI. We have not included environmental factors, as our review of the literature and analysis has shown that they are not important. These hypotheses suggest that even if there were a universal set of factors for predicting the success of a new product or an R&D project, the relative importance for the factors would be different depending on the contextual nature of the project. One may not even have to consider some factors in some situations, while some factors have to be very carefully evaluated because of their high importance for a given combination of contextual factors. The relative importance of the three types of factors for the eight contexts (one for each combination of contextual variables) can be tested through empirical studies with methodologies similar to those that have been used in the NPD and

Fig. 1. The contingency cube for new product development and R&D projects.

R&D project success/failure studies. The only additional step will be in classifying the sample projects into one of the eight groups depending upon their characteristics. The importance of the factors is usually measured by the value of either the discriminant function or regression coefficient (depending on the nature of the analysis). V. CONCLUSION Successful product innovation is an important yet complex and difficult undertaking. Research in the area has attempted to specify those factors that can increase the number of project successes. After many studies, the notion of finding a single set of universal factors is now considered naive [70]. Studies have moved instead to discover the set of factors that influence product success. These studies have attacked the issue from several different approaches and settings. This has been considered as positive by Maidique and Zirger [49] because the use of complementing methodologies and settings is essential for optimum triangulation on describing the “proverbial elephant.” From our structured review of the literature, however, one may conclude that we are not attempting to describe the


elephant but rather a menagerie: the set of universal factors instead appears to be contextually based. The review has shown first that even with a conservative approach to listing significant factors, the list is very long. Second, comparing the factors across studies demonstrates that different authors have found that the magnitude of significance and the direction of influence can vary so that the factors are contingent. Third, given the differences in context, the meaning of similar factors may vary, such as understanding the market. For example, do we “know” the market from proactive searches for information or from intuitive gut feel? What these studies have been describing, then, are different animals and not one. By comparing studies from two different but related fields whose final aim—successful product innovation—is the same, we have found that the already complex task of understanding the factors leading to success cannot be totally explained by one set of factors for all situations. Instead, depending on the situation, different factors become more or less important, and some may actually begin to hinder rather than aid in the already difficult task of NPD. A move to contingency-based theory in factors for successful products would be consistent with the directions other areas of business research have taken. Several authors in product innovation have taken the next step from describing the factors to creating models that explain the relationships between the factors [9], [12], [22], [75], [76]. These models, however, are based on single sets of factors that each author found in earlier work. Such model building should incorporate a contingency element to improve explanatory power. More research is required to understand the contextual nature of the factors. By saying that product success is contingent on contextual variables, we do not mean to imply what Cooper [13] posed long ago: “Perhaps the problem is so complex, and each case so unique, that attempts to develop generalized solutions are in vain.” We believe instead that there are only a few contextual variables that are relevant. Our analysis suggests that the three contextual variables—nature of innovation, nature of market, and nature of technology—can encompass the range of pertinent contexts to be explored. Though these contextual variables have a continuous spread, we suggest two levels for each to provide a parsimonious model. The framework and the propositions developed here can be tested with methodologies similar to those that have been used in the NPD and R&D project success/failure studies, with some modifications. The findings have a number of implications for practicing managers. Depending on the set of contextual variables, management can save much effort by minimizing the effort on factors that are not very important for that configuration of variables (for example, by deemphasizing market research efforts for a high-technology, radical innovation product where there is not going to be much reliable data). Using this structure, management can direct the appropriate source for generating new product ideas—whether it is for a highly innovative product to be introduced into a not yet existing market or for an incremental innovation in an existing market.


APPENDIX METHODOLOGICAL ISSUES IN THE STUDIES ON NEW PRODUCT DEVELOPMENT AND R&D PROJECT SUCCESS There are four major areas of weakness in the studies on success or failure factors in NPD and R&D projects: 1) quality of data; 2) definition of new product; 3) factor selection and definition; 4) measurement of factors. A. Quality of Data Four common characteristics of the data contributed to the general weakness of the studies: timing of the studies, case selection, number of company respondents, and industry specificity. 1) Timing of the Studies: In most cases, the evaluations were done as postmortems, which could possibly lead to some coloring of the evaluations based on hindsight. Very rarely were the data collected during a project’s life. 2) Case Selection: There were more cases of success in the samples even though in actuality, there are more failures than successes [6], [73]. Researchers indicate that it is difficult to get data about failures. Some researchers say that they are fortunate to get even as many examples of failed projects as they have. 3) Number of Company Respondents: Most studies generally had only one respondent in each company providing data through completing the questionnaires. Whether this person can provide an objective evaluation of the factors depends on the extent of the person’s involvement. 4) Industry Specificity: Researchers usually focused on some specific industries and highlighted the factors perceived to be important for those industries (for example, Baker et al. [4] for the agricultural products industry and Islei et al. [39] for the pharmaceutical industry). Even studies that attempted to obtain a cross section of industries did not get many different industries in their samples. We found on average five industries surveyed, when such information was given. Whether the results found in one industry, moreover, can be generalized to all industries has been questioned. Damanpour [25], Miller [57], and Friar [29] have found that strategic success is industry specific, so the reliability of pooling industries is questionable; so is generalizing from industry-specific studies. B. Definition of New Product The definition of a new product poses some problems. The studies generally did not make any distinction between different types of new products—a new product is usually anything that is introduced into the market by the firm, regardless of the extent and type of newness. The newness could be in the technology, market, application, or even just a minor modification or extension of the existing product. This issue recently has been addressed by Leonard-Barton [43] and Ali [2]. They both indicate that there are vast differences in managing different types of new product introduction.



C. Factor Selection and Definition


The areas of R&D project management (especially in the commercial sector, where projects are taken up with a final commercial motive) and new product management deal essentially with the common problems of linking technology and development efforts to manufacturing and marketing, albeit with differing emphasis. The emphasis is different as the two correspond to different stages in the product’s development, leading to some difference in the factors and variables used. The problem in comparing aggregate variables is that the underlying factors are often different. Therefore, even though the aggregate terms may be similar, the underlying meanings can be very different. Also, about half of the factors are unique to each study. Different sets of factors are used in the studies depending on the area of the study, the researcher’s orientation, and the sample of projects. Table IV shows the average number of different types of factors in the two types of studies. There is further confusion in terminology and in definitions of factors. For example, what does “emphasize marketing” mean? Does it imply that management provides the resources asked for by marketing? Or does the marketing group get special attention from management? In several cases, the factors are considered self-evident, so that no clear definitions were given even though a term may have many different meanings; for example, “support of top management” may take many different forms.



D. Measurement of Factors


Very few studies provided any benchmarking for the ratings of the factors. It is usually assumed that the factor descriptions and the scales are self-evident. Many of the qualitative factors are rated on a Likert-type scale.


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R. Balachandra (M’96) received the B.Eng. degree from Madras University, Madras, India, the M.Eng. degree in mechanical engineering from the Indian Institute of Science, Bangalore, India, and the Ph.D. degree in business administration from Columbia University, New York. He also attended Harvard Business School, Cambridge, MA, as an International Teacher. He has been a Foundry Engineer, Metallurgist, and an Industrial Engineering Consultant. He has served on the faculty of a number of Indian and U.S. institutions. He was a Visiting Professor at the University of Kiel, Germany, and a Senior Visiting Researcher with Hitachi Ltd. in their Central Research Laboratory in Japan. He is a Faculty Member of Northeastern University, College of Business Administration, Boston, MA. His major research field of interest is R&D project termination. In addition to numerous articles in this area, he is the author of Early Warning Signals for R&D Projects (Lexington, MA: Lexington Books, 1989). He is currently working on international technology transfer and new product development issues. He has published extensively in these areas in such journals as IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, Decision Sciences, Research Technology Management, and International Journal of Technology Management. Dr. Balachandra is a Department Editor for R&D and Engineering Projects for IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. He recently edited a special issue on international R&D. He is a member of the Institute for Operations Research and Management Science and the Decision Sciences Institute.

John H. Friar received the A.B. and M.B.A. degrees from Harvard University, Cambridge, MA, and the Ph.D. degree in the management of innovation from the Massachusetts Institute of Technology (MIT), Cambridge. He is a consultant to firms such as Intel, NYNEX, EG&G, and Digital on commercialization of emerging technologies and on technology strategy. He previously was with Philips Ultrasound as Director of Marketing and Finance and was with Draper Laboratories as a Software Engineer. He has taught at MIT, Northeastern University, and Melbourne University. He is currently a Marketing Professor at Clark University, Worcester, MA. He has authored several papers on innovation management in such journals as Journal of Product Innovation Management, International Journal of Technology Management, and Technology in Society. Dr. Friar has been an officer of the College of Innovation Management within the Institute for Operations Research and Management Science for several years.

Factors For Success In R&D Projects And New Product ... - IEEE Xplore

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