New Project Selection In The Context Of Sports
The
screening factors that influence the accept or reject decision
Yannick Abrahams University of Groningen Faculty of Economics and Business
Msc Business Administration: Business Development Student number: s1333267
Groningen, 30 September 2008
Abstract
The main purpose of this study was to see which known and new screening criteria are appropriate for selecting new projects within the context of sports and which of these screening factors explain the most of the critical accept / reject decision. A two phase research was adopted for this research. Eight known and thirteen context specific screening factors were identified. A comparison and integration of these yielded in seven main screening factors: project-company fit and project resources (company construct), two triangular relationships (team construct), product superiority (product construct) and market volume and market competition (market construct). Then, the impact of each of the seven screening factors on the reject / accept decision was investigated. Three of the seven screening factors were actual used in practise: the presence of a triangular relationship, having a superior product and having an interesting, big market.
First supervisor: dr. J. Kratzer
1.
INTRODUCTION
Sports is not just about talent. And winning is not just the result of the physical performance of a sport athlete anymore. Every tenth or hundredth of a second an athlete can get in every possible way can help to defeat an opponent and win a match. Technological improvements in for example sport products, training monitoring, information systems, health and talent development, and/or sport facilities and stimulating can make a contribution to get this one hundredth of a second. In several countries, organizations work behind the scenes to develop such technical improvements to win a golden medallion at the Olympic Games or to become World Champion. But also to stimulate recreational sports – to sport more and with less injuries – within society.
In 1978, the Lotus Formula 1-team developed a car with a new sort of bottom, what resulted in a better grip on the track. Mario Andretti got World Champion in this new car. In the field of ice-skating, in 1996, the klapskate was introduced by Dutch researchers. As a result, many World Records were broken at the Olympic Games in Nagano in 1998.
On the other hand, in 2006, Nike presented a new swimming suit, the Swift Skin 2. This suit should have provided less resistance during the race, but the opposite was true. Because of a mistake during the production of the suits, the suit provided even more resistance than with current suits. And also the Swift Skin 3, in 2008 the successor of the Swift Skin 2, became a failure. Here, a lighter, tighter, smoother and faster suit didn’t mean an improvement for swimmers. The Podiatry Today, an American scientific journal in the field of foot-care, gave in 2004 comment on the quality of modern football boots. Podiatry Today compared these new sort of boots with wearing ballet shoes on a football field. The inadequacy of this new sort of boots explained the explosive increase of injuries in the National Football League in the United States. As much as 346 players suffered from injuries (an average of 11 injured players per team). Eighteen per cent of these injuries concerned foot injuries. A fateful sign, according to this journal.
knowledge, and/or to create economical growth (Ministry of Health, Wellness and Sports, 2006).
When we look at industrial product development, Hopkins research (1981) said that for every 100 industrial new product launches, about 40 fail in de marketplace. Crawford (1979) estimated the failure rate to be about 35%. And Cooper showed in his study (1984) that for every 100 products, only 60 become a commercially success. In these studies, success and failure is defined from a financial standpoint: the degree to which the new product’s profits exceeded the firm’s acceptable profitability level for this type of investment. Determining the dependent variable (the main objective) in the context of sports is therefore different than in other studies, because there’re more objectives than just financial.
As the figures above show, in industrial product development, a lot of products fail in the marketplace. There’re often far more new product ideas or projects conceived than resources to commercialize them. Selecting the right project (the project that will become a success) therefore, is a very important task, but is also a very difficult one. In the new product process, the idea screening stage is the first of the project selection stages. It’s the decision point at which more new projects are killed than at any other subsequent stage. Because there’s relatively little reliable information available at this stage on, for example, the proposed product’s market, it costs, and the nature of the investment required, selecting the right project is very hard and complex (Brentani, 1984). A weak screening procedure will fail to weed out the obvious ‘losers’ or ‘misfits’, which results in the misallocation of scarce resources and the start of a creeping commitment to the wrong projects. In contrast, sometimes a screening decision results in viable and worthwhile projects being rejected. This is perhaps even more costly to the firm in terms of lost opportunities (Cooper, 1985).
The screening stage is the first of the project selection stages. The project selection and evaluation process must be designed in such a way as to encourage the generation of ideas, an organisational climate in which people will put forward ideas even if they do not foresee the possibility of being able to develop them further (Pearson, 1974).
the participation of sports, knowledge institutes and market parties, the funding etc. When the project proposal is judged to be favourable, a final proposal will be submitted. After the approval of the project, the project manager will contact the applicant to work out the total project design. The screening decision in this context is the accept or reject decision after judging a sports project proposal.
During the development of an accepted project, several evaluation moments are build in. The progress of a project will – in general – be discussed at the so-called go/no go moments. These evaluation moments are different at every organization, but every organization has different evaluation moments. At such a go/no go moment, the project has to report the progress results to the project leader. Sometimes a project is stopped. For example: assumed was that a certain ice skate application decreased friction. However, it turned out to be the opposite after testing the application in practise. The field-test proved that the investigated theory didn’t work and the project was stopped.
Most of the project leaders also have to report to the Dutch government (for example the Ministry of Health, Wellness and Sports and the Ministry of Economic Affairs), because they’re subsidized. Usually two times a year account has to be given.
Innovation in sports is becoming a hot issue, also in The Netherlands. From 2006, and for a period of five years, the Dutch government reserved 15 million euro’s specific for innovative applications and to become successful in sports (top sports as well as recreational sports). Much research has been done within the context of industrial new product development on critical success factors. But what screening factors are really used by project managers when selecting new, innovative projects within the context of sports? Are the same, classical screening factors that are used within industrial development also being used in this context? And what new, context specific factors are supposed to be important? Little research has been done within this context of sports.
It’s obvious that the ability to select the right project for investment is a key activity to success. In an ideal new product process, management would be able to identify the probable new product winners in advance, and be able to allocate the firm’s development resources to these projects (Cooper, 1992). A need exists, therefore, to probe how managers screen new projects in order to gain a better insight into this vital decision area.
dimensions are used here. The study is useful for organizations that execute highly, innovative sports projects, and especially for the responsible project managers within these organizations: to see which classical screening factors are useful to consider at the screening phase, which screening factors are considered to be typical for this context and which screening factors explain the most of the critical accept / reject decision. The research will be executed among seven Dutch organizations, who all execute highly, innovative sport projects. Eighteen cases (projects) are selected: nine accepted and nine rejected projects.
This research doesn’t focus on the impact of the screening factors on the actual success and failure of the innovative sports projects. At this moment, it’s difficult to execute such a research, because of the young age of the participating innovative sports organizations and their projects. Many of the nine selected projects take long and are not finished yet. It’s, therefore, unclear whether these accepted projects actual turned out to be a success.
Because of the little research that has been done on screening dimensions within this specific context, this study will make an extension to the current literature about screening dimensions within the sports context. Practical, it will show managers to what extent classical screening factors are used when selecting new projects within the context of sports and what other, specific screening factors are important. The study also has a social relevance, because the organizations that participated in the research and execute highly innovative sports projects are all financed by public (government’s funds).
The article starts with a theoretical framework. Here, a short literature review is presented about the main studies and the main results so far in the field.
Then, the research method - a two phase research study – is explained. Phase one first describes the results of the quantitative data analysis, using factor analysis. The main goal here is to see which known factors (from literature) project managers use when selecting new sports projects. Then, in phase one’s next step, the screening factors that are observed after the analysis of the interview data are described. Finally, both the factors from the quantitative analysis and the qualitative analysis are compared and integrated.
Phase two looks at the impact of each identified screening factor on the reject / accept decision: to see which factors really are used in practise at the selection phase of new sports projects. A logistic regression analysis will be applied here.
2.
THEORETICAL FRAMEWORK
Much empirical research on the determinants of new product performance has been done. These studies have led to the identification of various determinants of new product performance. In the last forty years, probably the most complete delineation and study of product innovation strategy on success has been done by Cooper with his NewProd Projects. Montoya-Weiss (1994) and Hollander (2002) analysed a large number of success and failure studies. To get an insight in these generally known screening dimensions, the main results of these reviews and Project NewProd are shown.
A comprehensive review on the existing literature was conducted in the study of Montoya-Weiss in 1994. A wide variety of study designs and methodological approaches was observed and a list of determinants of new product performance was developed based on an examination of 47 studies in total, some of them published in well-known journals like the Journal of Product Innovation Management (Griffin and Page, Kleinschmidt, Cooper). Finally, four main categories of determinants were identified: market environment, new product strategy, development process execution and the organization.
Hollander (2002) designed an assessment tool that provided management information for business development teams, in order to increase the product performance. Part of Hollander’s research was an analysis of a large number of success and failure studies. This resulted in four important control constructs which can be used by managers for business development projects: company, product, market and team.
backgrounds and points of view, by providing an agenda of proven discriminating questions for the team and by displaying the results for discussion in a visual and graphic format, NewProd facilitates this project evaluation and analysis (Cooper, 1992). In all the NewProd studies, Cooper measured the development factors related to the company, product and market.
In the two reviews and all the NewProd studies, the market, - product- and company dimension were seen as the most important determinants of new product success. Montoya-Weiss and Hollander also mentioned respectively organization and team as important constructs. They both described communication and structure of the new product project team as important factors for these constructs. In this study, we called this forth dimension the “team construct”.
So, based on an extensive study of the literature, the following four important constructs – which can be used by managers for business development projects – were identified: company, team, product and market. In the next section, each of them is described in depth.
Company
Cooper spoke in 1979 of proficiencies of process activities, a stage wise sequence of goal-oriented activities that determine product success and failure. In 1985, he stated that the project-company fit dimensions (synergy criteria) has a big influence on product outcomes and described the overall project-company resource compatibility and the technological resource compatibility as important success factors. And again in 2001, Cooper said that the technical dimension - having synergy between project and firm in terms of production and technical resources - and well-executed technical and production activities, is critical to new product success.
Montoya-Weiss (1994) described marketing synergy as important factor: the fit between the need of the project and the firm’s resources and skills with respect to the sales force, distribution, advertising, promotion, market research and customer service. Also technological synergy was seen as an important factor: the fit between the needs of the project and the firm’s resources and skills with respect to R&D or product development, engineering and production. Finally, the company resources were described: the compatibility of the resource base of the firm with the requirements of the projects.
Lilien, 1985; Di Benedetto, 1994; Calantone et al, 1993). If some skills are not available or inadequate, the chances to new product success decrease (Montoya-Weiss and Calantone, 1994). And also the resources to develop the new product should be easily accessible (Montoya-Weiss and Calantone, 1994). When a company tries to develop a totally new product (e.g. new technology combined with a new market), the chances for success decrease. The relative newness of a project to the company is therefore an important aspect for a project to succeed or fail (Cooper, 1979b; 1985; Maidique and Zirger, 1984; Baker et al, 1986; Baker et al, 1988; Emanuelides, 1993).
Team
As mentioned before, Montoya-Weiss described the team construct as organizational factors. The most important factors here were the internal and external communication and general organizational factors. The communication factor refers to the coordination and cooperation within the firm and between firms. The general organizational factors refers to the structure of the firm, and specifically with respect to the new product project and project team.
The people in a business development team are the key to every product development process. Without them the best product development process and the best product development organization can not develop a new product successfully. The team construct is measured using two factors: communication and the team (Hollander, 2002). Managing reciprocal interdependence in business development projects requires simultaneous mutual adjustments among the parties involved. This is extremely demanding in terms of communication effort and costs (Emanuelides, 1993; 373). The development team members should be able to integrate and have good technical and communication skills (Rubenstein et al, 1976; Maidique and Zirger, 1984; Gupta et al, 1985; Pozner and Kouzes, 1987; Emanuelides, 1993; Belassi and Tukel, 1996).
Product
According to the review of Montoya-Weiss (1994), the product advantage refers to the customer’s perception of product superiority with respect to quality, cost-benefit ratio, or function relative to competitors.
The new product should possess certain features to be successful in the market, such as product superiority, higher quality, or unique tasks (Utterback et al, 1976; Rubenstein et al, 1976; Cooper, 1979b; 1985; Maidique and Zirger, 1984; Baker and Albaum, 1986; Brown and Eisenhardt, 1995). If the new product has an economic advantage compared to the competitive products, it has a higher probability of achieving success (Baker and Albaum, 1986; 1985; Rubenstein et al, 1976; Utterback et al, 1976). Other authors referred to these as the technical aspects of the product (Baker et al, 1986; Emanuelides, 1993; Calantone et al, 1993; Di Benedetto, 1994). Understanding user needs is an important element in developing successful new products (Rothwell et al, 1974; Utterback et al, 1976; Maidique and Zirger, 1984). The new product should meet customer needs, and it is important that these needs are monitored throughout the course of development, since they very rarely remain completely static (Rothwell et al, 1974). These important product aspects can be derived from customers or users (Brown and Eisenhardt, 1995).
Market
Cooper spoke in 1979 of the firm’s external environment (market variables): the nature of the marketplace at which the new product is targeted, as a factor that plays a small role in deciding new product success. In 1985, he stated that two factors deal with market criteria: the market opportunity and market attractiveness. And also in 2001, obtaining a sound knowledge of the marketplace and customer together with proficiently carrying out the market research and launch activities was seen as an important factor that dominates the success equation. Montoya-Weiss spoke of market environment factors. One of these factors is market potential, a measure of market (and demand) size and growth, as well as an indication of customer need level for the product type. This measure also indicates the importance of the product to the customer. The other factor was market competitiveness. This factor reflects the intensity of competition in the market place in general and/or respect to price, quality, service or the sales force/distribution system.
1985). However, the new product has to compete with other products or substitutes products in the same market (Montoya-Weiss and Calantone, 1994; Constantineau, 1993; Baker et al, 1988; Baker and Albaum, 1986). Environmental hostility is defined as the level of threats a company faces due to the intensity, vigour, and multi-faceted nature of competition, and threats from industry cycles (Khandwalla, 1976-77, in Calantone et al, 1997; 181). Rapid changes in a hostile environment makes it difficult for a company to obtain accurate and timely information (Bourgeois and Eisenhardt, 1988 in Calantone et al, 1997; 181). Product developers are advised to clearly define the market segments for developing their product. This also helps to identify the customers and customer needs, preferences and wishes (Hultink, 1997). In a highly competitive environment, a short NPD process is required in order to be first in the market. Even to be a successful late entrant requires fast NPD capabilities to meet changing customer needs. An important exception involves late market entrants who are well-known, possess exceptional product quality, provide excellent service, and possess persistent industry “staying” power (Milson et al, 1992).
Other important studies that are worthwhile to mention
One of the first studies that dealt with the question of differentiating successful and unsuccessful new industrial products was Project Sappho. In two studies, Rothwell found 5 dimensions (41 variables) that are relevant for predicting success and failure. Successful innovators have a better understanding of user needs, pay more attention to marketing, perform development work more efficiently, make more use of outside advice and have greater management authority (Rothwell, 1972; 1974).
The dependent variable: the accept / reject decision
3.
METHOD
A two phase research study was adopted from Brentani (1984) and adjusted for this study. The main purpose of phase one was to identify the screening factors that managers use to select new projects within the context of sports. The second phase of the research dealt with the screening factors which have the greatest impact on the accept / reject decision. First, both the two phases are shortly described. The methods of quantitative and qualitative data analysis are then explained and more information about the followed procedures is given.
Phase 1: interviews and questionnaire: qualitative and quantitative data
Personal interviews were arranged with project managers closest to the screening decision. Three separated approaches were used and therefore two different interview plans (qualitative data) and one questionnaire (quantitative data) were developed. Before the interviews and questionnaire were subjected, a pre-test was done among two project managers.
1. Attribute elicitation: project managers were directly asked to indicate what screening criteria they used when selecting new, innovative sports project.
2. Modified repertory grid: project managers were asked to compare several screened proposals (accepted as well as rejected proposals), and to indicate how each proposal differed from the others in terms of reasons for the accept / reject decision. With this approach, the focus was moved from screening criteria to actual projects, and a number of additional criteria were discovered.
3. List completion: a comprehensive list of 46 statements, developed from the literature, was shown to the respondents. Cooper (2001) and Hollander (2002) spoke about four general screening dimensions, which contained nine screening factors. These factors were translated into 46 statements (see Appendix A). Project managers were asked to select two sports project proposals: one rejected proposal and one accepted. For each proposal, project managers were asked to provide a rating from zero to ten for every statement, where zero meant not important at all and a ten very important when selecting new projects.
(quantitative and qualitative) were compared and integrated. And as a result, a new, second questionnaire was developed which served as an input for phase two.
Phase 2: impact on the accept / reject decision
This phase dealt with the screening factors (from phase one) which have the greatest impact on the accept / reject decision. These screening factors were translated into several screening variables and into a second questionnaire, which was filled in by the same programme managers. On the basis of this quantitative data, a logistic regression analysis was applied to see which screening factors really are used in practise when selecting new sports projects. The analysis: qualitative and quantitative data
Qualitative data analysis
The qualitative data – derived from the interviews – was analyzed, using Miles and Huberman (1994). The interviews were taken using a schematic interview plan. This interview plan contained two structured schemes with open questions. First, the participating project managers were directly asked to indicate what screening criteria they used when selecting new, innovative sports projects. This was called attribute elicitation. Then, the focus was moved from screening criteria to actual projects. Project managers were asked to compare several screened project proposals. This was called modified repertory grid.
Sampling strategy
The type of sampling in this study was criterion based. All participants had to work at organizations that execute highly, innovative projects within the context of sports. The participants were the project managers closest to the screening decision. In The Netherlands, several organizations execute highly, innovative sport projects, like innovative centres focussed on sports and general innovative advice centres. A selection was made, in close cooperation with an expert. Ten people at seven different organizations participated in the research. For each project (accepted or rejected), one project manager filled in two questionnaires and was interviewed once.
Early steps in qualitative data analysis
The first steps in the analysis was coding: first level and second level coding. First level coding was used to summarize the interview data, using codes. Pattern coding was then used to group the summaries into smaller number of constructs. Pattern coding is more explanatory. The interview data was summarized here and grouped into similar screening factors.
Cross-case displays
Case-oriented and variable-oriented approaches were combined in this study. A series of cases were written up, using a more or less standard set of variables. Each taken interview with a project manager here was considered as one case. Then, each interview was analyzed in depth, using matrices, to indentify the different screening factors. Finally, these case-level displays were stack in a meta-matrix, which was further condensed, permitting systematic comparison.
Used methods for cross-case displays: exploring and describing
The descriptive data from the cases was assembled in one meta-matrix (partially ordered meta-matrix). From here, the data was partitioned (divided in new ways) and clustered so that contrasts between sets of cases on variables of interest became clearer. In practise, this method meant that the same identified screening factors from the different interviews were put together. Then content-analytical summary tables were used. The focus here was primary on the content of the meta-matrix, without reference to which cases it came from. The matrix generated a meta-matrix that got all of the reduced data in it. When the same characteristic appeared in more than one case, this was noted on the matrix. The task was to imagine a matrix display that best captured the dimensions of interest (in this case the different screening factors), and all of the pertinent data was arranged in readily analyzable form.
Used methods for cross-case displays: ordering and explaining
the outcome (to accept or reject the project). This approach was variable oriented, but kept the configuration or variables for each case.
Quantitative data analysis phase one
The numerical data – from the first questionnaire – was analyzed here, using factor analysis and reliability analysis. The questionnaire was derived from Cooper’s NewProd (2001) and Hollander’s Genesis Study (2002) and contained four constructs, nine factors and 46 variables. The questionnaire was filled in by eight programme managers, containing eighteen projects (nine accepted and nine rejected) in total. Every project had one respondent (programme manager).
Factor analysis
The main objective of factor analysis was to understand whether the 46 criteria could be grouped and reduced into a smaller number. In this study, the underlying screening factors that dominated the new project accept / reject decision were identified and quantified. The following assumptions were made: the analysis is linear and the data set is a normal distribution. First, a matrix of correlation coefficients was generated for all possible pairings of the 46 variables. From the correlation matrix, then, ten factors were extracted. Finally, the factors (axes) were rotated to maximise the loadings of the variables on some of the factors and reduce them to others.
Reliability analysis
The reliability of each of the ten discovered factors was analyzed, using Cronbach’s alpha, which should have been at least 0.6 to confirm a reliable scale. The items that didn’t reach the 0.6 value were removed.
Quantitative data analysis phase two
On the basis of the results of the second questionnaire, a logistic regression analysis was applied to see which screening factors really are used in practise when selecting new projects.
Binary logistic regression analysis
(2). The independent variables, who influence this new project decision, were the screening factors, identified after factor, - and reliability analysis. Every regression was a bivariate one, because it described the correlation between two variables: the dependent variable, here the accept / reject decision, and each of the screening factors (the independent variables) that were identified.
External validity
4.
PHASE ONE
The next section shows how the data from the first questionnaire was analysed. Then, the screening factors discovered from this numerical data and the screening factors from the quantitative data were summed up and integrated. This procedure resulted in one list with screening factors and a second questionnaire, which served as an input for phase two.
Factor analysis
Objectives of Factor Analysis
The main objective of factor analysis was to understand whether the 46 criteria could be grouped and reduced into a smaller number. In this study, the underlying screening factors that dominated the new project accept decision were identified and quantified. The following assumptions were made: the analysis is linear and the data set is a normal distribution.
Factor analysis: three stages
First, a matrix of correlation coefficients was generated for all possible pairings of the 46 variables. From the correlation matrix, then, ten factors were extracted. Finally, the factors (axes) were rotated to maximise the loadings of the variables on some of the factors and reduce them to others.
The next table shows the ten factors that have been extracted. Com
pon
ent Initial Eigenvalues
Extraction Sums of Squared
Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 15,283 33,224 33,224 15,283 33,224 33,224 5,838 12,692 12,692 2 6,699 14,562 47,787 6,699 14,562 47,787 5,794 12,597 25,289 3 4,966 10,797 58,583 4,966 10,797 58,583 5,487 11,929 37,218 4 3,420 7,434 66,017 3,420 7,434 66,017 5,242 11,395 48,613 5 3,145 6,838 72,855 3,145 6,838 72,855 5,061 11,002 59,615 6 2,805 6,097 78,953 2,805 6,097 78,953 4,354 9,465 69,081 7 2,193 4,768 83,720 2,193 4,768 83,720 4,054 8,813 77,894 8 1,787 3,884 87,604 1,787 3,884 87,604 3,270 7,109 85,003 9 1,379 2,999 90,603 1,379 2,999 90,603 2,092 4,549 89,552 10 1,005 2,186 92,789 1,005 2,186 92,789 1,489 3,237 92,789 11 ,925 2,011 94,800
Eigenvalues
The first block of three columns, labelled initial eigenvalues, comprised the eigenvalues and the contributions they made to the total variance. They determined which factors remained in the analysis. Following Kaiser’s criterion, factors with an eigenvalue of less than 1 were excluded. Here, the ten factors that met the Kaiser’s criterion accounted for nearly 93 % of the variance.
Scree plot
The eigenvalue plot is known as scree plot. The scree plot provided a graphic image of the eigenvalue for each component extracted. The amount of variance accounted for the eigenvalue by successive components initially plunged sharply as successive factors (components) were extracted.
The point of interest was where the curve began to flatten out. Here, the ‘scree’ began to appear between ten and eleven. Component eleven was the first component with an eigenvalue of less than one (0.93).
Interpreting the factors
Varimax – in rotating the matrix the most common method – was used to rotate the factors and to maximise the loadings of the variables on some of the factors and reduce them to others. The purpose of rotation was to arrive at a factor matrix with a pattern of loadings that was easier to interpret than the original factor matrix. Per factor, then, the most significant loadings were identified, using the component score coefficient matrix. As a result, the 46 criteria were subdivided into ten factors (see table 2). These variables can be found in Appendix A. Factors F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 17 39 33 19 9 8 1 10 14 6 24 40 34 21 13 41 2 11 18 7 25 46 35 29 15 42 3 12 36 20 26 37 30 16 43 4 23 27 32 22 5 28 44 31 38 45
Table 2: results derived from the Component Score Coefficient Matrix
Reliability analysis
The reliability of each of the ten discovered factors was analyzed, using Cronbach’s alpha, which should have been at least 0.6 to confirm a reliable scale. The items that didn’t reach the 0.6 value were removed (these values are shown between the brackets in table 4). The following eight factors, including 34 variables, remained.
Factors F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 17 (0,51) 39 33 19 9 8 (0,16) 1 10 14 (0,57) 6 (0,22) 24 40 34 21 13 41 2 11 18 (0,71) 7 (0,11) 25 46 (0,43) 35 29 15 42 3 12 36 (0,51) 20 (0,32) 26 37 30 16 43 4 23 (0,43) 27 32 22 5 28 (0,15) 44 31 38 (0,16) 45
Interpreting the factors
After the analysis of the quantitative data - using factor, - and reliability analysis - eight screening factors that influence the accept / reject decision remained. Each factor consisted of several known (derived from literature) screening criteria and was labelled again. Then, the eight factors were subdivided into four constructs: company, team, product and market.
Factor 1: company construct: project company fit
When the product type itself is totally new to the company and has never made or sold before to satisfy this type of customer need or use, the chances on success decrease. And this is also the case when the potential customers for the product, the technology required to develop the product (R&D) and the nature of the production process are totally new to the company.
Factor 2: company construct: project resources
The management skills, the engineering skills and people and the production resources or skills should be more than adequate for the project.
Factor 3: team construct: triangular relationship 1: company, team and product
The project resources - the financial resources, the marketing research skills and people and the sales force and/or distribution resources and skills – should be more than adequate for the for the project. The participating parties should have enough communication with the other project team members to do their work efficiently and in an effective way. All the team members should be focusing on collecting knowledge for the project. The product should be mechanically and/or technically very complex.
Factor 4: team construct: triangular relationship 2: market, team and product
The project should make a contribution to the competitive advantage of the company and should meet the applicable laws (e.g. product liability, regulations and product standards). When a project is a success, the participating parties should be willing to participate in the same project team again. The product should be highly innovative, totally new to the market, a very high technology one and first into the market.
Factor 5: product construct: product superiority
features or attributes to the customer and should permit the customer to do something he or she couldn’t do with what’s currently available.
Factor 6: market construct: volume and environment
The monetary value of the market for this product should be large and the market should grow quickly. The new product should also have a positive effect on the environment.
Factor 7: market construct: competition
The market shouldn’t be a highly competitive one, with many competitors in it, or where’s a strong dominant competitor and/or a market where new product introductions by competitors are frequent.
Factor 8: market construct: market volume
Potential customers should have a great need for this type of product, will definitely use the product and the product should have a high potential (e.g. can create additional products, multiple styles, price ranges, etc.).
Factors after qualitative data analysis
After the analysis of the qualitative data - using Miles and Huberman (1994) - thirteen screening factors that influence the accept / reject decision were discovered. Each factor was subdivided into one of the four constructs: company, team, product and market.
Company construct: project company fit and project resources
Factor 1: The project should fit into existing programme lines
Factor 2: There should be sufficient resources to execute the project Team construct: p roject team, communication and trust
Factor 3: The project should contain a triangular, - or a quadratic relationship
Factor 4: The participating parties should have sufficient communicative skills
Factor 5: The participating parties should trust each other Product construct: product superiority and product aspects
Factor 6: The project should contribute to top sports
Factor 8: The project should create knowledge
Factor 9: The project should generate a certain reputation
Factor 10: The project should be innovative Market construct: market competition and volume
Factor 11: There should be market opportunities for this project
Factor 12: The project should create economical growth by realising new business within existing companies and/or the creation of new business activities
Factor 13: There should be possibilities for the project to be applied in other sectors Comparison and integration
Several tactics were used to make a good sense out of the two lists of screening factors (identified from the two types of data sets) and to verify conclusions out of them. Searching to patterns of variables was done to look for similarities and differences among the identified screening factors from the two kinds of data. Plausibility intuitions were trusted here, but were supplemented by the clustering of the identified screening factors. Clustering was done to try to get a better understanding of the several screening factors by grouping them and then conceptualize the screening factors that had similar patterns and characteristics. Both the quantitative and the qualitative data contained screening factors that arose from the main four constructs, as identified in the literature: company, team, product and market. As a result, the identified screening factors were clustered into these four constructs. Similar screening factors were then – per construct – put together. The screening factors from the two different data types also were compared and contrasted to each other, and different relationships between screening factors were identified (Miles and Huberman, 1994).
Company construct
As a result of the quantitative data analysis, two factors that dealt with the company construct arose: project-company fit and project resources. These factors where also found after the analysis of the qualitative data: the project has to fit into existing programme lines and there should be sufficient resources to execute the sports project. But a sports project also has to be innovative.
Factor 1: Project-company fit
With regard to the project-company fit factor, the chances of success decrease when the product type itself is totally new to the company and has never made or sold before to satisfy the type of customer need or use. Also the newness of the potential customers for the product, the newness of the technology required to develop the product (R&D) and the newness of the production process influence project success in a negative way. Therefore, the project should fit into existing programme lines. Many organizations have predetermined fields of expertise, like – for example – sport products, training monitoring, information systems, health and talent development, and/or sport facilities. A potential project must fit within such predetermined fields. “You have to predefine on which fields of expertise your focus is on,
you can not do everything. We don’t execute sport projects in fields of which we know that there are other parties with more expertise.” In the context of sports, projects should also fit
with the kinds of sports an organization focuses on. Many organizations focus on a limited number of sports. In general, the focus is on sports with a great market, like, in The Netherlands soccer, cycling, tennis, ice-skating and hockey. “We won’t develop, for example,
a new, innovative billiard cue, because the billiard federation in The Netherlands is not big enough: the billiard market is too small.”
also contain an existing technology in a new product, or new market. It doesn’t have to be completely new.”
Factor 2: Project resources
The project resources should be sufficient to execute the project. The financial resources, management skills, technical resources, production skills and marketing resources have to be sufficient to execute a project. Often, a market party, a knowledge institute, a sports party and a project leader together execute an innovative sports project. From the perspective of the project leaders, such a relationship is often called the “golden triangle”. Here, the project leader has the management skills, the knowledge institute has the technological skills, the market party has the production, - and the marketing skills, and, finally, the sports party has – in theory – the needs to have. “If you look black-and-white, the knowledge institute invests in
man hours, the market party in cash and the sports party in the needs to have.” Also time
plays a role. “Capacity, in the sense of time and manpower, plays a role. We cannot execute
all projects, because of the small labour force. We often do have the skills, but not the time to execute all interesting projects.”
Te
am construct
As a result of the quantitative data analysis, two factors that dealt with the team construct arose. The team is considered here as the involving parties in the project, called the ‘golden triangle’ (besides the project leader). Both triangular relationships can be described as a mix of company, team, product and market factors, where each factor contains three out of four constructs. After qualitative data analysis, three similar aspects arose: a triangular relationship, communication and trust were found as important factors within the context of sports that dealt with the ‘golden triangle’. These three aspects all counted for the two triangular relationships as identified after quantitative data analysis.
Factor 3: Triangular relationship: company, team and product
The first identified triangular relationship contained a company construct (project resources), a team construct (communication) and a product construct (product aspects).
enough communication with each other to do their work efficiently and in an effective way. In this context, the project leader brings the involving parties together. The third aspect - the product - should be mechanically and/or technically complex to increase the chance on success. In general, the knowledge institute is responsible for this part of the project.
Factor 4: Triangular relationship: market, team and product
The second triangular relationship contained a market construct (environment), a team construct (project team) and a product construct (product aspects).
In this triangular relation, it means that the project should contribute to the competitive advantage of the company and meet the applicable laws (e.g. product liability, regulations and product standards). The market party – in general - has experience with these aspects. Second, the degree to which the involved parties want to participate in the current team again also influences success. Finally, the chances of success increase when the product is highly innovative, totally new to the market, a very high technology one and first into the market.
The project should contain a triangular, - or a quadratic relationship
Innovations in sports are often achieved when a knowledge institute, a market party, a sports party and a project leader cooperate. This is called a triangular (knowledge, market and sports) of quadratic (knowledge, market, sports and project leader) relationship. The belief exists that this kind of relationship will lead to durable and long-lasting innovations in sports. Every participating party adds value to the project. “Several parties have to work together
and every party has its own added-value. This triangular relationship is a precondition to become innovative.” “A sports party, a knowledge institute, a market party and an intermediary: that’s the chain of sports.”
A sports party has the needs to have, although it doesn’t always work like that in actual practise. Sports has the need to perform better (top sports) or to sport easier and/or with less injuries (recreational sports). “From top sports, there’s always a market pull, because sport
athletes always have to go faster.” A knowledge institute, like a university, has creating
knowledge as main goal. “Knowledge is our capital.” The project needs such an institute to develop, for example, a new technology and create a prototype product. The market party takes care of the distribution of the final product. This party has also the knowledge and experience to finish a project, so that a prototype product becomes a working and reliable product. Financial success is its main goal. “The market party holds a mirror up to the faces
marketable.” The project leader, finally, is necessary to bring the other parties together and
lead the project in an objective and professional way. This party is in the middle of this ‘golden triangle’ and has the best overview.
There are little organizations with all this kind of knowledge in-house. Also the Dutch Ministry of Health, Wellness and Sports attaches great importance to this kinds of triangular, - or quadratic relationships. “The combination of knowledge institutes, business and sports is of
great importance, also internationally.”
The ‘golden triangle’ is connected with the concept of open innovation. Open innovation is becoming more and more important. The reasons to move from closed to open innovation are, in the first place, the increasing availability and mobility of highly skilled employees. There’s an enormous amount of knowledge available outside the R&D labs of large companies. Second, there’s a significant increase of venture capital. This availability causes more promising ideas to be developed. Then, de possibilities to develop promising ideas outside a company, for example through spin-offs or licensing, increase. Finally, there’re other players in the chain (for example suppliers) who influence the innovation process (Chesbrough, 2003). “The innovation has to be open: every single party must be willing to cooperate,
without keeping secrets for the other involved parties.”
The parties should have sufficient communicative skills and trust each other
Trust and communication play an important role when several parties have to cooperate into one innovative, sports project. No one has to keep his cards close to his chest. All parties have to share a lot of sensitive information. As a result, the initial period can take long, because parties have to get to know each other before they will cooperate into the project. “Maybe
trust is one of the most important criteria. Just on the basis of experience and intuition sometimes a decision is made.” An advantage in this context is the fact that all participating
parties have sports in general as an underlying motive. Sports is interesting for them all and the parties are often willing to do something extra to make the project a success. Projects can also fail when the main objectives of the project are not clearly defined. “You got to have
Product construct
As a result of the quantitative data analysis, one factor that dealt with the product construct arose: product superiority. Similar factors were also found after the analysis of the qualitative data: a sports project should make a contribution to top sports or recreational sports, should create knowledge and/or should generate a certain reputation. These factors all had to do with product superiority.
Factor 5: Product superiority
To increase success, the product should be superior to competing products in terms of meeting customer’s needs, offering a higher quality than competing products, offering a number of unique features or attributes to the customer and permitting the customer to do something he or she couldn’t do with what’s currently available. In this context, it means that a sports related project should contribute to top sports or recreational sports.
The main goal of a top sports project is to make a contribution to the Dutch top sports results. So, make the athlete do something he or she couldn’t do before the innovation. “The goal of
top sports projects is to contribute to the Dutch ambition to be one of the ten best sports countries in the world.” The project should contribute to recreational sports. The main goal of
a recreational sports project is to make a contribution to the increase of the Dutch healthy sports participation: to sport more easily and with fewer injuries (safer). “Recreational sports
projects contribute to the healthiness, integration and participation of people in Dutch society.” In relation with top sports: top sports can serve as a shop-window for recreational
sports. When a top athlete scores, it leads to a higher participation in recreational sports. “Top
sports and recreational sports are connected.” The project should create knowledge. The
creation of knowledge is the main goal of knowledge institutes. “We are judged on the
amount of scientific publications, Ph D students, applied patents etc.” In this context, the
main goal of an innovative sports project is to reinforce the Dutch infrastructure of knowledge for sports in a structural way. In a sports project, a knowledge institute is, for example, responsible for developing the technology that is needed. And to stay interesting for other parties, like market parties, knowledge institutes constantly have to improve their selves on this field. “Knowledge is our capital. The current technology is marked-down. Therefore, we
need to improve ourselves in the field of technology.” The project should generate a certain
participating parties. When a top athlete performs better because of a certain innovation, media will give much attention to it. A good reputation can be interesting for knowledge institutes, like universities, because it causes more students to sign up for that university.
“The profit is publicity, not the cash.” A market party with a good reputation will be able to
sell more when bringing a final product on the market. Recreational sports will be stimulated when, for example, a top athlete performs well on the Olympic Games. “In Belgium, more
people started playing tennis after the successes of Kim Clijsters and Justine Henin.” When a
project is a success, the project leader shows the world that it can handle certain innovative sports projects and will become more interesting for other parties. “Top sports projects can
be interesting for us, because at the end of such a project we can say to the world: ‘Look, our organization did this!’”
Sports projects relate to short, - or long term goals. In top sports, for example, a short term goal can be the contribution to the Olympic Winter Games in 2010 in Vancouver. On the long term, the main goal is to help sports in general on a durable manner, so that on a continuous basis successes in top sports will arise.
Market construct
As a result of the quantitative data analysis, two factors that dealt with the market construct arose: market volume and competition. Similar factors were also found after the analysis of the qualitative data: to increase project success, there should be market opportunities for the project and the project should create economical growth by realising new business within existing companies and/or the creation of new business activities. And when a potential sports project in a later stadium can be applied into other (market) segments, it increases the chance to a sports project to be accepted.
Factor 6: Market volume
To increase the success of a project, the monetary value of the market for the product should be large and the market should grow quickly. Potential customers should have a need for this type of product and should definitely use it.
Factor 7: Market competition
of product development activities has to be adequate to increase the likelihood of project to be accepted.
Creation of economical growth
Most of the participated organizations in this study were innovation institutes and innovation advice centres: the project leaders who lead the innovative sports projects. They can be seen as the party that brings together – as mentioned before – the ‘golden triangle’: a market party, a sports party and a knowledge institute. After a project has been finished, usually a market party brings the final product on the market. The aim of this party is to get a profit as big as possible: financial success is their main goal. Therefore, there should be market opportunities; otherwise the market party shall not join the project. Financial success, however, is not the direct aim of the other parties. The sports party wants an innovation so top sports results increase or people can sport with fewer injuries. A knowledge institute wants to generate knowledge and write publications about it. The main goal of the project leader is to lead the project as good as possible, so that the innovation becomes a success and the other parties can achieve their goals.
With regard to the realisation of economical growth, two possibilities arose. First, a situation where the initial investment must be recouped, so that this money can be used for new innovative projects in a later stadium. The recoup of the initial investment and the creation of economical growth by realising new business for the market party is seen as financial success.
“We have to recover the investment we made for this project.”
Another possibility is a situation where creating general, economical growth by realising new business within existing companies and/or the creation of new business activities for market parties is the main goal. Here, the initial investment - government’s funds - is gone, but the project can be seen as a success because it causes general economical growth. “Our aim is
not to earn the revenues of the innovation; just spent the government’s funds to stimulate innovation.”
In both cases a successful project probably makes a contribution to the stimulation of top sports and/or recreational sports (main goal sports party), the generation of knowledge (main goal knowledge institute) and/or the realising of economical growth (main goal market party).
Appliance in other contexts
can create additional products, multiple styles, price ranges, etc.). This criterion can be grouped into the market volume factor. It can be interesting to apply successfully executed innovations in other contexts, within or outside the context of sports. Particular applications meant for example health care can be tested easier en better in the context of sports, because of statutory regulations. It’s in this context of sports also easier to cause a certain reputation. Furthermore, keeping the lead is an advantage of applying a technology in other contexts. It prevents an organization of inventing the same wheel twice. “When a project in a later
stadium can be applied to another context, the choice for such a project increases.”
Overview of the seven screening factors, per construct
Company construct
Factor 1 Project-company fit Factor 2 Project resources
Team construct
Factor 3 Triangular relationship: company, team and product Factor 4 Triangular relationship: market, team and product
Product construct
Factor 5 Product superiority
Market construct
Factor 6 Market volume
Factor 7 Market competition
Comparison with existing, known literature
The next section describes how the seven identified screening factors relate to the screening factors as found in the literature. As mentioned in the theoretical framework section, Cooper and Hollander speak of four main constructs, which contain nine factors and 46 underlying variables. The main four constructs and nine screening factors are: company (project company fit and project resources), team (communication and project team), product (product superiority and product aspects) and market (market competition, market volume and environment).
A first main similarity with the screening factors that were identified in phase one is the breakdown of the seven found screening factors by the four main constructs. Working in a team in this context, however, meant participating and collaborating into one sports project with four different parties with each of them having its own goal. It’s therefore – from the viewpoint of the project leader – also called ‘golden triangle’.
Both the project-company fit and project resources factors were identified after the analysis of the quantitative data. These factors where also found after the analysis of the qualitative data: the project has to fit into existing programme lines and there should be sufficient resources. In the context of sports, the screening factor project team is known as having a triangular relationship. The team contains – from the viewpoint of the project leader – three participating parties: a market party, a knowledge institute and a sports party, who work together into one sports project. Two of this sorts of triangular relations were found. The communication and trust factors found after qualitative data analysis counted for this both factors.
Of the two product factors: product superiority and product aspects, the product superiority factor was found to be an important factor in this context. The five factors found after qualitative data analysis all had to do with superiority.
The market construct was also seen as important construct: two of the three market factors were as important. It concerned the market volume and the market competition factors. The market environment factor was seen as less important.
5.
PHASE TWO
This phase dealt with the seven screening factors which have the greatest impact on the accept / reject decision. On the basis of quantitative data, derived from a second questionnaire with 54 variables, a logistic regression analysis was applied to see which screening factors explain the most of this critical decision. Also the degree of importance is showed here: the involved programme managers rated each of the seven screening factors on a scale from one to ten. A screening factor considered as very important got a higher score than a screening factor that was considered to be less important when selecting a new project.
Introduction
Logistic regression is useful for situations in which the researcher wants to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. Every regression here is a bivariate one, because it describes the correlation between two variables: the dependent variable, here the accept / reject decision, and each of the seven screening factors that was identified. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model. In this case, the probability that a project will be accepted is in the beginning 9/18 = .50 (50%) and rejected is also 50% (9/18 = .50). At this moment, there’s a new project accept rate of 50% over the 18 selected cases. The purpose of logistic regression is to improve upon this new project accept rate by exploiting any association between the dependent and independent variables to predict the dependent variable with the greatest possible accuracy.
Preparing the data set
The dependent variable is the accept / reject decision: 0 = reject, 1 = accept. The independent variables (covariates) are the seven factors identified in phase one. The method indicates how the independent variables enter the model. The default method is ‘enter’: all variables specified are entered in a single step. None of the covariates is combined.
The logistic regression function
variables: Z = A + B (factorX) where A is the regression constant and B(factorX) the regression coefficient. The logistic regression function itself is: p = e^z / 1+e^z, where p is the probability that a new sports project will be accepted and Z is the function described above.
Classification Table Block 0
For each factor, every block 0 contained the same classification table, which showed that the observed and predicted values of new projects were the same. The project acceptance rate without any regression was 50%.
Table 5: classification table
Aspects
For each screening factor, the following aspects will be showed: the logistic regression and the variables in de equation, the model summary, which includes the – 2 log (likelihood), the Nagelkerke R Square and the results of the classification table.
Model summary
The model summary table includes two statistics intended to be equivalent to the coefficient of determination (r2) in ordinary least-squares regression. The Cox and Snell r2 is based on the log likelihood for the model compared with the log likelihood for a baseline model. The Nagelkerke r2 is an adjusted version of the Cox and Snell r2. It adjusts the scale to the statistic to cover the full range from 0 to 1. It indicates to what extent the model contributes to the prediction of the new project accept or reject decision.
Classification Table Block 1
The classification table shows that, when the full model is applied, if and to what extent the rate for predicting the accept or reject decision will increase.
Classification Table a,b
0 9 ,0 0 9 100,0 50,0 Observed Reject Accept 0=Reject, 1=Accept Overall Percentage Step 0 Reject Accept
0=Reject, 1=Accept Percentage Correct Predicted
Constant is included in the model. a.
Factor 1: Company construct: project-company fit
A project should fit into existing programme lines. Many organizations have predetermined fields of expertise, like – for example – sport products, training monitoring, information systems, health and talent development, and/or sport facilities. A potential project must fit within such predetermined fields.
Variables in the Equation
B S.E. Wald df Sig. Exp(B) 95,0% C.I.for EXP(B) Lower Upper Step 1(a) Factor1 ,017 ,043 ,148 1 ,700 1,017 ,934 1,106 Constant -,577 1,572 ,135 1 ,714 ,562 a Variable(s) entered on step 1: Factor1.
Table 6: variables in the equation
Results
The size of Nagelkerke r2 was 0,011 (1,1%), what meant that the model doesn’t contribute to the prediction of the new project accept or reject decision. The rate for predicting the accept or reject decision doesn’t increase: the classification table showed an overall percentage of 50%, as much as it was at the start. And also the significance level - 0,700 - showed that factor doesn’t influence the accept or reject decision.
Statistic values
The logistic regression Z = -0.577 + 0.017(factor 1)
- 2 log (likelihood) 24,804
Nagelkerke R Square 0,011
Significance 0,700
Implications
Factor 2: Company construct: project resources
The project resources should be sufficient to execute a project. The financial resources, management skills, technical resources, production skills and marketing resources have to be sufficient to execute a project.
Variables in the Equation
95,0% C.I.for EXP(B) Lower Upper Lower Upper Lower Upper Lower Upper Step
1(a)
Factor2 ,084 ,132 ,406 1 ,524 1,088 ,840 1,408 Constant
-2,220 3,523 ,397 1 ,529 ,109 a Variable(s) entered on step 1: Factor2.
Table 7: variables in the equation
Results
The size of Nagelkerke r2 was 0,03 (3%), what meant that the model doesn’t contribute to the prediction of the new project accept or reject decision. The rate for predicting the accept or reject decision doesn’t increase: the classification table showed an overall percentage of 50%, as much as it was at the start. And also the significance level - 0,524 - showed that this factor doesn’t influence the accept or reject decision.
Statistic values
The logistic regression Z = -2.220 + 0.084(factor2)
- 2 log (likelihood) 24,537
Nagelkerke R Square 0,03
Significance 0,524
Implications
Factor 3: Triangular relationship: company, team and product
The first identified triangular relationship contained a company construct (project resources), a team construct (communication) and a product construct (product aspects).
Variables in the Equation
95,0% C.I.for EXP(B) Lower Upper Lower Upper Lower Upper Lower Upper Step
1(a)
Factor3 ,044 ,058 ,573 1 ,449 1,045 ,932 1,171 Constant
-2,314 3,096 ,559 1 ,455 ,099 a Variable(s) entered on step 1: Factor3.
Table 8: variables in the equation
Results
The size of Nagelkerke r2 was 0,043 (4,3%), what meant that the model doesn’t contribute much to the prediction of the new project accept or reject decision. The rate for predicting the accept or reject decision doesn’t increase a lot: the classification table showed an overall percentage of 55,6% (compared to 50% at the start). And also the significance level - 0,449 - showed that this factor doesn’t influence the accept or reject decision.
Statistic values
The logistic regression Z = -2.314 + 0.044(factor3)
- 2 log (likelihood) 24,365
Nagelkerke R Square 0,043
Significance 0,449
Implications
Factor 4: Triangular relationship: market, team and product
The second triangular relationship contained a market construct (environment), a team construct (project team) and a product construct (product aspects).
Variables in the Equation
95,0% C.I.for EXP(B) Lower Upper Lower Upper Lower Upper Lower Upper Step
1(a)
Factor4 ,139 ,070 3,983 1 ,046 1,149 1,002 1,317 Constant
-14,528 7,263 4,001 1 ,045 ,000 a Variable(s) entered on step 1: Factor4.
Table 9: variables in the equation
Results
The size of Nagelkerke e2 was 0,562 (56,2%), what meant that the model contributed much to the prediction of the new project accept or reject decision. The rate for predicting the accept or reject decision increased: the classification table showed an overall percentage of 77,8% (an big increase, compared to the 50% at the start). And also the significance level - 0,046 - showed that this screening factor influences the accept or reject decision.
Statistic values
The logistic regression Z = -14.528 + 0.139(factor4)
- 2 log (likelihood) 15,101
Nagelkerke R Square 0,562
Significance 0,046
Implications
