a new graphic format to facilitate the understanding of technological innovation models: the seesaw...

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A new graphic format to facilitatethe understanding of technologicalinnovation models: the seesaw ofcompetitivenessEduardo Vasconcellos a , Marcos A.C. Bruno b , Milton de A.Campanario a & Sérgio L. Noffs ca School of Economics, Administration and Accounting , Universityof São Paulo – FEA/USP , Brazilb Centre for Technology Policy and Management – NPGCT/USP ,São Paulo, Brazilc Marketing Consultant – Siemens , São Paulo, BrazilPublished online: 02 Jun 2009.

To cite this article: Eduardo Vasconcellos , Marcos A.C. Bruno , Milton de A. Campanario & SérgioL. Noffs (2009) A new graphic format to facilitate the understanding of technological innovationmodels: the seesaw of competitiveness, Technology Analysis & Strategic Management, 21:5,565-582, DOI: 10.1080/09537320902969067

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Technology Analysis & Strategic ManagementVol. 21, No. 5, July 2009, 565–582

A new graphic format to facilitate theunderstanding of technological innovationmodels: the seesaw of competitiveness

Eduardo Vasconcellosa, Marcos A.C. Brunob∗, Milton de A. Campanarioa

and Sérgio L. Noffsc

aSchool of Economics, Administration and Accounting, University of São Paulo – FEA/USP, Brazil; bCentre forTechnology Policy and Management – NPGCT/USP, São Paulo, Brazil; cMarketing Consultant – Siemens,São Paulo, Brazil

Many models exist in the literature to explain the success of technological innovation. However,no studies have been made regarding graphic formats representing the technological innova-tion models and their impact, or on the understanding of these models by non-specialists intechnology management. Thus, the main objective of this paper is to propose a new graphicconfiguration to represent the technological innovation management. Based on the literature,the innovation model is presented in the traditional format. Next, the same model is designed inthe graphic format – named ‘the see-saw of competitiveness’ – showing the interfaces amongthe identified factors. The two graphic formats were compared by a group of graduate stu-dents in terms of the ease in understanding the conceptual model of innovation. The statisticalanalysis shows that the seesaw of competitiveness is preferred.

Keywords: technological innovation models; technological innovation learning; graphicalrepresentation of innovation models; success of technological innovation

Introduction

Innovation means seeking opportunities that are radically new in the market, by exploring the newtechnologies and introducing new concepts for the business. A study published by The Economistshows that approximately 50% of the economic growth of the USA in the late 1990s stemmedfrom business sectors that did not exist in the previous decade (Wolpert 2002).

The commercial exploitation of technological innovations has become one of the most importantways to reach such objectives. Within this context, a company’s technological competencies mayrepresent its most important competitive advantage.

Although there is extensive literature on the factors leading to innovation, no studies have beenconducted on the different graphic formats to present the technological innovation models andtheir impacts on the understanding of these models. The importance of innovation as a success

∗Corresponding author. Email: [email protected]

ISSN 0953-7325 print/ISSN 1465-3990 online© 2009 Taylor & FrancisDOI: 10.1080/09537320902969067http://www.informaworld.com

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566 E. Vasconcellos et al.

factor for companies has made this issue part of both undergraduate and graduate programmes.All contributions to increase teaching efficiency and diffusion efficiency of concepts and mod-els are relevant to strengthen and consolidate the area of technological innovation managementworldwide.

The study has two objectives:

(1) To propose a new graphic configuration for an analytical model on technological innovationmanagement, showing a group of variables and their dynamic interaction that condition thesuccess of technological innovation.

(2) To compare the new graphic format – the see-saw of competitiveness – with the traditional one,so as to evaluate the potential contribution of the new format, to facilitate its understandingby non-specialists in technology management.

In summary, the present study does not aim at developing an innovation model that is morecomplete or more efficient for decision making than the ones already existing in the literature, butrather, to explore the graphic representation, as a tool to facilitate the understanding of the modelby non-specialists in the management of technology. The Management of Technology – MOT –subject has been included very often in courses for executives as well as those for undergraduateand graduate students. The present article aims at contributing towards the teaching of innovationmodels to non-specialists in the management of technology. The strength of the new graphicformat is to show the dynamics of the success innovation factors.

At first, the paper discusses the importance of visual communication. Next, based on the liter-ature, a set of factors that influence success in technological innovation is graphically presentedboth in the traditional way and in a new format suggested by the authors. After this, a surveywas carried out with graduate students in order to compare both formats in terms of the ease ofunderstanding on the part of the students.

Visual communication and innovation models

The concepts related to management innovation recognise that technological, organisational,market and institutional factors interact in a complex manner. The building blocks of a theory insuch a volatile field consist of mental constructions or images. The proposed graphic representationin this article has been conceived to discover regular patterns in the innovation managementpractices.

Modern society is becoming increasingly reliant on graphics to communicate complex con-structs. They are far more readily available and widely used than ever before, so as to become apart of the modern visual culture (Fuery and Fuery 2002). Mirzoeff (1998, 3) states that: ‘Indeed,the gap between the wealth of visual experience in contemporary culture and the ability to analyzethat observation marks both the opportunity and the need for visual culture as a field of study’.Vekiri (2002, 290), based on an extensive analysis of the research conducted by other authors,has concluded that: ‘Graphics are more effective than text for communicating information and forfacilitating concept relation learning’. This opinion is supported in another study, based on 155pieces of research involving 7182 respondents (Levie and Lentz 1982). Another research analysed20 instances of research in order to identify the ideal characteristics of teaching models (Mayer1989). In conclusion the article states that good models contain all the essential parts, states, oractions of the system as well as the essential relations among them, so that the learner can be ableto understand how the system works.

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The Seesaw of Competitiveness 567

Most real innovation comes from a large range of attempts. A model cannot provide rigidnormative guidelines on how to produce efficacious constructs in innovation management. Whatthe authors of the present article have attempted to do is to have a model of how a given set of forcesachieve mindshare, based on the theory and a graphic format. The latter helps to communicatethe structure of an innovation management system and visualise the forces at play.

A set of tools helps to explore, map and visualise structures to foster the interrelationship ofScience and Technology – S&T – structures and networks (Bassecoulard and Zitt 1999; Nederhofand Wiik 1997; Small 1999). The process of learning through graphics is also an object of thesestudies (Brown and Cunningham 1990). Mapping studies are also used to support S&T policies anddecision making (McCain 1998; White and McCain 1998). Science, technology and innovationmanagement are also the object of mapping (Kostoff, Eberhart, and Toothman 1999; Losiewicz,Oard, and Kostoff 2000).

These studies are related to visualisation techniques utilised on data mining and database tomog-raphy methods (Bovack and Wylie 2002; Chen, Paul, and O’Keefe 2001). The visual techniquethat is introduced in this article follows a ‘graphic-as-tool’perspective (Rieber 2000; Winn, Li andSchill 1991). It takes for granted that modern society is becoming increasingly reliant on graph-ics to communicate complex constructs, by means of computer-based instruction (Rieber 2000).The purpose of the graphic format is to provide an easier communication and an argumentativeframework for applying logic for reasoning in a range of situations, within a given set of forces(Roth, Pozzer-Ardenghi, and Han 2005). It requires two main types of background knowledge: thespecific graphic system used to depict the subject matter and the subject matter that is depicted inthe graphic form (Losiewicz, Oard, and Kostoff 2000). Clearly the subject matter is more relevant,but the graphic system can translate it to a better language, to improve decision making and othertypes of communication processes, such as learning and developing a visual culture.

Success innovation factors

The success of the technological innovation is directly related with its acceptance by the market anda sustainable and adequate value added to the company. Pleasing the market is not enough. If theinnovation does not generate adequate results for the company, the shareholders will be penalised.Furthermore, the result must be sustainable, i.e. it must be achieved through mechanisms that canbe maintained for the long term. The result for the company involves tangible and intangibleassets. According to the literature, profit results from an intelligent conception of the business,knowing how to generate this conception, is a strategic ability (Slywotzky and Morrison 1997).These authors define the company’s results by means of the profit zone concept, which involvesreinventing the business conception at least every five years, considering the customer. Innovatingin the business conception generates financial benefits – owing to the growth in sales and theincrease in the production scale – resulting in a rise in the company’s value.

Other authors, discussing the value of the tangible capital as a result of the innovation, statethat obtaining competitive advantages depends on the ability to create a stronger link than thatof the competitors, among the costs of innovation and how much the customers are ready to pay(Ghemawat and others 1999). Thus, according to these authors, a company adds value when thenetwork of customers and suppliers in which it operates is better off with it than without it.

Excellent products or services constitute the essence of a successful brand and they resultmostly from investment in R&D (Kotler and Keller 2005). The link with the brand occurs whenthe customers realise that the company fulfils its promises. Regarding the intangible capital, the

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Figure 1. Factors influencing success of technological innovation. Traditional graphic configuration (GraphicFormat A).

authors state that building a strong image is an important asset and demands careful planning andhuge long-term investments.

Market acceptance and a sustainable result are influenced by four variables:

(1) The technological competence (T ) in itself, as a scientific or technical function, and thecompany’s level of control of such competence.

(2) The company’s strategic use of technological competence, by means of its competence intechnology management (G).

(3) The market resistance to adopt the technological innovation (R).(4) The perception of value of the technological innovation by the market (V ).

The variables are detailed below, as well as the factors that impact such variables. This set ofvariables and related influencing factors are represented in a graphic format in Figure 1 – namedGraphic FormatA. However, it should be pointed out that the aim is not to present all the influencingfactors related with each variable, but only some of them, based on the literature, in order to explainthe graphic format proposed in the present paper.

Technological competence

Technological competencies are resources (patents, equipment and infrastructure), people’sknowledge and skills related to scientific research, development and engineering (Mintzbergand Quinn 1996), which the company has and controls, in order to produce goods and services

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that add value (Hamel and Prahalad 1990, 1994) to the company itself, as well as to society(Noffs 2005). Organisations with strong technological competence strive to develop knowledgeand skills ahead of their competitors, seeking to lead the market through basic technologiesthat are both profitable and hard to be imitated (Hamel and Prahalad 1989, 1990; Stalk andHout 1990; D’Aveni and Ravenscraft 1994; Itami 1987; Quinn and Hilmer 1994; Andrews1980).

It is important that technological competence leads to products, processes and services, at acost that allows competitive prices, generating sustainable results for the company as a result ofthe innovation.

Competence in technology management

The alignment between a company’s technological competencies and its strategy reflects theweight of this variable, i.e. to what extent the control of a specific technology will help thecompany reach its objectives (Andrews 1980). For this purpose, the company’s top managementshould foster the integration between R&D and the organisation’s strategy (Roussel, Saad, andBohlin 1991; Andrew and Sirkin 2003; Clark 1989), so as to guarantee that the new productswill be valued by customers (Hamel and Prahalad 1994; Clark 1989; Cohan 1997; Porter 1980;Drucker 2002), and ensure that launch schedules are short and that technological prospecting isdone routinely, in order to guarantee the competitiveness of the company’s technological basis(Hamel and Prahalad 1994; Fundación para la Innovación Tecnológica 2001).

Companies must be able to innovate at the global frontier, creating and commercialising astream of new products and processes that shift the technological frontier in order to maintaintheir advantage over their rivals (Porter and Stern 2002). However, the development of newproducts depends on the capability to integrate R&D, manufacturing, marketing and other areasof the company, to produce something that is valued by the customer and is profitable at the sametime. However, having the R&D teams spread around different countries is a fact that makesthis task even harder (Boutellier et al. 1998). In summary, a strong capability in new productdevelopment is an essential competence.

Research shows that the organisations that invested heavily in R&D identify, as a factor oftheir success, the support and commitment of company officers with technological management(Frohman 1982).

The speed of technological evolution, together with increasing competition, makes the compe-tence of developing and managing technological alliances an essential component of technologicalmanagement. Importance of alliances and cooperation can be managed by tools like models forhigh-sharing technological alliances (Bruno andVasconcellos 2003), frameworks to improve tech-nology valuation in buy-cooperate-sell decisions (Chiesa, Gilardoni, and Manzini 2005) and typesof organisational modes for technological collaboration (Chiesa and Manzini 1998).

One trigger for introducing knowledge management to innovation processes is the increas-ing complexity of products and services, in any singular process technologies are combined andimplanted (Schulze and Hoegl 2005). Another author defines the importance of knowledge as theintangible capital to obtain a competitive advantage as a result of the technological innovationprocess (Crawford 1992). According to this author, knowledge means the capacity to apply infor-mation in a competent way to a task or a specific result. Information in itself is useless without theknowledge of how to apply it in a productive way. The reason for this concern is that companiesoperate in an environment in which the economy is based on knowledge and they therefore seekthe source of this intangible, immeasurable and precious asset: human capital.

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Market resistance to adopt technological innovation

There is a natural resistance of the consumer market to accept new technologies. Being familiarwith the usual way of doing things helps to maintain the status quo (Daft 1989).

Potential users have good reasons for being sceptical about a new technology until they havesufficient proof of the performance of its application. Thus, many of them prefer to adopt theinnovation after its success has been proven (Steele 1989; Roberts and Lilien 1993). Resis-tance arises when the innovation is regarded as difficult to understand and to be used. Fearthat the innovation supplier will not remain in the market is another factor of resistance (Ramand Jung 1991). Technical standards and regulations are another barrier for the introduction oftechnological innovations. The latter will not only support the dissemination of a technologythat has already been accepted by the market but also hinder the entry of new technologies(Lewis 1990).

Limitations imposed by government regulations, as in the case of environmental laws, or thoseimposed by society, such as religious, ecological or social principles also represent sources ofresistance (entry barriers) to the adoption of new technologies (Porter 1980). In order to reduceresistance, the market should be the one to set the pace for R&D: that which the customer demandsis what should be researched and developed (Kodama 1992).

Value perception of technological innovation by the market

The level of value perception of a technological innovation is proportional to its relevance regard-ing the satisfaction of the consumer’s latent needs (Hamel and Prahalad 1994). This factor isusually detached from the origin of the technology, the manufacturer’s name or the product brandand marketing campaigns. It is also considered in the literature that compatibility with the existingtechnology, relative advantage and observability are important factors in the decision to acceptthe innovation. These aspects lower the cost of change (Rogers 1995).

The key for the success in new technologies is to find applications in which the advantagesassociated with the use of such technologies are so relevant as to offset the risks of being thepioneers (Steele 1989; Nooteboom 1989). Thus, a new technology in the field of medicine orpharmacology, associated to the cure or prevention of a deadly disease, will be perceived asvitally important by the medical community and its patients (Noffs 2005).

The value perception of a technological innovation to the incongruities between the expectationsand the results, in light of the users’ evaluation, is the larger the incongruity, the lower the valueperception (Drucker 2002).

High value innovations not only fulfil the customer’s needs but also reach a broad spectrumof the market (Cohan 1997; Organisation for Economic Co-operation and Development 2006;Barney 1991). A potential adopter with a higher level of technological development is more likelyto value innovation (Utterback and Abernathy 1975).

The seesaw of competitiveness

Figure 1 (Graphic Format A) presents two variables (acceptance by the market and sustainableand adequate value added for the company), which influence a third variable: contribution to thesuccess of technological innovation. Four other variables, i.e. market resistance, value perceptionby the market, technological competence and competence in technology management influence

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the first two. These four are, in turn, influenced by 18 factors. This is the usual style of presentation,as shown by several authors (Rogers 1995; Frambach and Schillewaert 1999; Thamhain 2005;Balbinot 2001; Prajogo and Ahmed 2006; Twiss 1974; Therin 1998).

The present article proposes a new form of presentation of the same variables and factorsthat allows the reader to visualise the forces and their impact on the scales – the see-saw ofcompetitiveness (Graphic Format B in Figure 2). This is a novel form of presentation in theliterature on technological innovation at this level of complexity. The difference in this methodof presentation is in the fact that it allows the reader to understand the dynamics of the relationsbetween the factors and the success of the innovation.

The conception of this graphic representation may be related to the ideas of Malcom Gladwell,in his book The Tipping Point, as it allows us to define visually how the interaction between thefactors in the model ‘tips the scales’ in favour of, or against, the success in the adoption of aspecific innovation. This way of addressing the issue, with a dynamic character of systematisingand analysing a phenomenon agrees with one of the key ideas in the work of the above-mentionedauthor, according to which, in the business environment little changes can have large effects, andsometimes these changes occur very quickly (Gladwell 2008).

For technology management specialists, the impact of this new graphic format has a lowerrelevance because they are used to working with the interactions between the different variables.However, the area of technology innovation management is expanding fast. Executives, who areinterested in the issue, as well as graduate students, would benefit highly from representationssuch as this one. The contribution of this paper is enhanced by the increasing number of executivesbeing trained in MOT programmes.

The system, made up of the above-mentioned variables, may be analysed by analogy with ascale (or see-saw). This system is herein called ‘the see-saw of competitiveness’and it is illustratedin Figure 2 (Graphic Format B). It is assumed that the scale is an ‘ideal scale’, made of a rigid

Figure 2. The see-saw of competitiveness for the analysis of technological innovation (Graphic Format B)Source: adapted (Noffs 2005).

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balance beam with an axe in its centre. Thus, without the presence of the variables describedbelow, the beam would remain in the horizontal position.

On the right hand side are the driving forces or impulsion forces, related primarily with theorganisation’s internal capability and represented by the technological competence (T ). On theother side are the forces resisting the technological innovation (R) – those that need to be overcomein order to take advantage of the market opportunities.

The technological competence (T ) may vary from weak to strong, depending mainly on thetechnological and manufacturing resources available at the company.

A technological competence (T ), even one of moderate intensity, if used correctly, mayexert a very strong influence on a company’s results. The offering (or delivery), by theTechnology Management department, of competencies with a high degree of utilisation, whichalso enhance competitiveness, indicates that the company has a high level of competence intechnology management (GA). The low contribution of the available technologies for the risein competitiveness represents a low level of competence in the management (GB). The leveragecapacity of the technological competence is proportional to the distance between the force andthe support axis (dt).

The resistance (R) of the market may vary from weak to strong and it depends on the markettrend to continue using the existing technologies, as well as its predisposition to reject newtechnologies.

Regardless of the ‘marketing actions’ to promote the new technology, markets have their ownneeds and they seek solutions for their different problems. Thus, the result of the reaction (R)against a leveraging action (T ) depends on the level of value perception of the technologicalinnovation, represented in the see-saw by the distance (dr). A high valuation (VA) is reachedwhen the user relates the new technology with the complete fulfilment of an actual need, ofsignificant relevance for this user. Even a sophisticated technology, with no apparent reasons forresistance, may have low value (VB) if the user deems it unnecessary or of low importance.

The result of the combination of the different factors of influence, on the forces of action andreaction, shall indicate the level of market acceptance of the technological innovation (verticalscale to the left of the see-saw), as well as its trend towards success (central scale). The result forthe company is evaluated by the level of sustainable value added to the business, as a consequenceof the exploitation of the technological innovation (vertical scale to the right of the see-saw). Thesystem is dynamic and the indicators reflect the situation resulting from the actions and reactionsexisting at the time of the analysis.

The best scenario occurs when the company has a strong technological competence, alignedwith its strategy, and the market not only values the technological innovation, but also has norestrictions regarding its use. In Figure 2, such a condition is represented by the exploitation ofthe areas to the far right of each of the sides of the see-saw, called ‘areas of excellence’ andrepresented by the letter ‘E’ in this figure.

The worst scenario for the company occurs when it lacks a strong technological competenceand, at the same time, the competence that it has is not aligned with the company’s strategicobjectives. As regards the market, the worst scenario is a low degree of valuation, associated witha high degree of resistance to the use of technological innovation. In the see-saw format, the areasthat are not favourable for the company are the ones to the left of each side, called ‘areas ofinefficiency’, represented by the letter ‘I’ in Figure 2 (Noffs 2005).

Values might be assigned to the variables, based on specific criteria for each case that GraphicFormat B is applied. As an example for a generic case, the scales for the variables may be definedas follows:

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T technological competence – from 0 to 10, where 0 is ‘very low’, 5 is ‘moderate’ and 10 is ‘veryhigh’.

G competence in technology management – the distance (dt) from 0 to 10, where 0 equals GB(low level in technological competence), 5 is ‘moderate’ and 10 represents GA (high level intechnological competence).

R market resistance to adopt the technological innovation – from 0 to 10, where 0 is ‘very low’,5 is ‘moderate’ and 10 represents ‘very high’.

V value perception of technological innovation by the market – the distance (dr) from 0 to 10,where 10 represents VB (low valuation of the technological innovation), 5 is ‘moderate’ and 0equals VA (high valuation of technological innovation).

The results are obtained by adding the influence of the acting forces. Multiplying the value of (T )by the distance (dt) has a positive result whereas multiplying the value of (R) by the distance (dt)renders a negative result. The addition of both these results indicates the value in the central scale– contributing to the success of technological innovation (S). The equation is as follows:

S = T × dt + R × dr

The pointer would be on the positive end (SA), showing a value of +100, when: T = 10,dt = 10 and R or dr = 0. It would be on the opposite end (SB), showing a value of −100,when T or dt = 0, R = 10 and dr = −10. The pointer in a central position indicates a bal-ance between the forces in favour and against the contribution to the success of technologicalinnovation (S).

The other indicators follow the criterion adopted for (S) and would be as follows:

C sustainability with CA (+100), for competitiveness with a sustainable technological basis andCB (−100), for competitiveness with an unsustainable technological basis;

A acceptance withAA (+100), for high acceptance of the technological innovation by the market,and AB (−100), for low acceptance of the technological innovation by the market.

The analyses below refer to hypothetical situations and aim at demonstrating the theoreticalapplications of the see-saw, in order to facilitate the understanding of the technological innovationmodels on the part of the non-specialists in MOT. Such analyses consider only some of thecombinations between the four variables, where just one of these variables is altered, while theother three remain unchanged.

For the purpose of this example, the value of 2 represents ‘weak/low’, 5 equals ‘moderate’ and8 is ‘strong/high’.

First situation

Evaluation of the influence of the technological competence force as a technical function with allthe other three variables remaining unchanged (Figure 3).

Second situation

Evaluation of the influence of the strategic orientation of technological competencies, when theother three variables remain unchanged (Figure 4).

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Figure 3. Expected market reaction, solely as a function of the technological competence.Source: adapted (Noffs 2005).

Figure 4. Expected market reaction, solely as a function of the competence in technology management.Source: adapted (Noffs 2005).

Graphic format ‘A’ vs graphic format ‘B’, regarding the degree of understandingby non-specialists in MOT

This part of the study seeks to respond to four research questions: Research Question 1 (RQ1)aims at determining which format is more easily understood, and Research Question 2 (RQ2) triesto define whether Graphic Format B contributes, in a positive way, towards the understanding ofthe factors leading to innovation success by non-specialists in MOT.

Both questions were transformed into statements:

• Graphic Format B (GraphForm B) is easier to understand than Graphic Format A(GraphForm A).

• GraphForm B, even when it is understood, does not add anything meaningful towards theunderstanding of the factors leading to an innovation’s success.

The respondents were asked to identify their level of agreement with these statements on a scalefrom 1 (strongly disagrees) to 7 (strongly agrees).

Research Question 3 (RQ3) aims at identifying whether there are any significant differencesin the answers to the first two research questions, given by the graduate students that answered

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the questionnaire, in terms of their undergraduate background. In other words, are there anydifferences between graduate students with undergraduate degrees in the area of Human Sciences,as compared with graduate student respondents with undergraduate degrees in the area of Exactor Biological Sciences? This question was proposed as a way of verifying whether Format Bwould be preferred by respondents with a strong background in physics. This might facilitatethe understanding of the innovation model, as it is based on the dynamics of force fields. Thisinformation may be useful for the professor to choose the format, based on the students’ profiles.

The authors asked the professors for permission to interrupt ongoing classes, in order to conductthe survey. Presentations on both formats were made to 67 graduate students of the BusinessAdministration Program of the University of São Paulo (40 of whom were graduate studentswith undergraduate degrees in the area of Human Sciences and 27 were graduate students withundergraduate degrees in the areas of Exact or Biological Sciences). After the presentation, thestudents were asked to fill out the questionnaire comparing the two formats, with regard to theease of understanding them. Sixty-six responses were obtained.

Results of the comparison between Graphic Format A and Graphic Format B

Table 1 shows the tabulation of Research Questions 1 and 2. In order to apply the proportions testusing the data obtained for RQ1 and RQ2, scores of 1 and 2 were considered as a disagreementand scores 6 and 7 as an agreement. The other scores (3, 4 or 5) were taken as neutral. The test(in Table 2) showed the following:

There is a significant difference between the proportions of agreement (50.8%) and disagree-ment (9%), as regards GraphForm B being easier to understand than GraphForm A. The oppositeresult had been expected. GraphForm B, itself, is more complex. The authors believe that, forexperts in the area of technology management, GraphForm A is simpler because they can moreeasily identify the effects of the variables. As an example, for a scholar in this area, it would

Table 1. Tabling of the research questions RQ1 and RQ2.

RQ1 RQ2

GraphForm B is easier to understand than A GraphForm B does not add anything helpful

Observed Observed

Value frequency % Value frequency %

1 4 6 1 14 20.92 2 3 2 26 38.83 6 9 3 11 16.44 5 7.5 4 4 65 15 22.4 5 5 7.56 17 25.4 6 4 67 17 25.4 7 2 3Sub-total 66 98.5 Sub-total 66 98.5Missing 1 1.5 Missing 1 1.5

Total 67 100 Total 67 100Average value = 5.2 Average value = 2.7

Standard deviation = 1.7 Standard deviation = 1.6

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Table 2. Rate test (z-test) for research questions RQ1 and RQ2.

Value of z p value

Research Question 1 z = 6.20 p = 0.000Research Question 2 z = 7.44 p = 0.000

be extremely clear that a higher market resistance to accept an innovation results in a lowerproduction scale, thereby reducing the company’s profit and the innovation’s degree of success.This person would not need to have this shown graphically, and besides, GraphForm B wouldbe an unnecessary complication that does not add anything meaningful. One of this article’sreviewers also agreed with this opinion and was visibly in favour of GraphForm A. This personis a specialist in technology management and, as such, was eagre to have a larger number ofvariables in GraphForm A and verify how this format could help in the process of decisionmaking.

The above-mentioned reviewer is correct because GraphForm B was not built for specialistsin technology management. For technology innovation experts, the relations shown in the newformat are obvious and, therefore, GraphForm A is clearer. However, for those who are initiatingtheir studies in this area, GraphForm A is extremely hermetical and expressed simply as a list offactors that are linked by an arrow to the degree of success of the innovation. These neophytesfind GraphForm B much easier to understand because it shows, graphically and didactically, thedynamics of the relations between the variables.

Another statistically significant difference to be observed is the one between the percentageof agreement and disagreement (9% vs 59.7%, respectively), as regards the fact that proposedGraphForm B does not add anything meaningful to the understanding of the factors leading toan innovation’s success. This result reinforces the previous one. Those who are not experts in thefield will observe an additional value in GraphForm B.

Research Question 3 refers to the existence, or lack of it, of any significant differences inthe responses to questions RQ1 and RQ2, among the graduate students with an undergraduatebackground in the area of Human Sciences, vs the responses given by the graduate students withan undergraduate background in Exact or Biological Sciences. The Mann–Whitney test was usedbecause it is a useful alternative to the t-Student parametric test, when normality assumptionscannot be made (according to the Kolmogorov–Smirnov test, p = 0,000 – which rejects thehypothesis of normal distribution).

The result (Table 3) has shown that we may not conclude there is a difference between these twogroups, as regards the ease in understanding GraphForm B (p = 0.793) as well as any relevantaddition to the comprehension of the factors leading to an innovation’s success (p = 0.973). Oneway to explain this result would be that, the set of forces used in GraphForm B is not as complexas to warrant a background in Exact or Biological Sciences, for its comprehension.

Research Question 4 refers to the format which, the graduate students that answered the ques-tionnaire would use, if they were invited to teach a class on innovation success factors. Thisquestion was included because the graphic format that a professor chooses for teaching is usuallythe one that, in this author’s opinion, facilitates understanding. This question confirms the answersto the previous ones. The response includes three options: (1) GraphForm A; (2) GraphForm B;and (3) the use of both formats. For this research question, there were three possible groups ofaudience of the hypothetical lecture: undergraduate students, graduate students (Master’s andDoctoral degree) and executives. Table 4 shows the results obtained with all 67 respondents.

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Table 3. Statistical analysis of research question RQ3: Does the profile of the respondent in terms of hisprevious undergraduate course result in differences for research questions RQ1 and RQ2?

GraphForm B is easier to GraphForm B does not addunderstand than A anything helpful

Mann–Whitney U 500,500 517,500Wilcoxon W 1320,500 868,500Z −0.262 −0.034Asymp. Sig. (2-tailed) 0.793 0.973

Grouping variable: undergraduate course previously taken by the graduate students that answered the questionnaire.

Table 4. Rate test (z-test) for research question RQ4: If invited to teach a class on innovation successfactors, which format would you use?

GraphForm GraphForm Both A andProfile of hypothetical class A (%) B (%) B (%) Value of z p value

Undergraduate students 22 65 13 z = 4.20 p = 0.000Graduate students 12 67 21 z = 7.10 p = 0.000Executives 8 76 16 z = 10.55 p = 0.000Total 100 100 100

Grouping variable: profile of a hypothetical class profile in case the respondents were asked to give a lecture aboutinfluence factors on innovation success

Respondents preferred GraphForm B (65%) vs GraphForm A (22%) to teach a class to under-graduate students. If the audience is composed of graduate students, the preference for GraphFormB was at 67%. Finally, for teaching a class to executives the preference for GraphForm B was at76%. The three results are significant. These results are coherent with the previous ones and showa clear preference for GraphForm B, as regards the ease in understanding.

Conclusions

The literature has plenty of models that aim to explain the relationship between the conditioningfactors and the success of technological innovation. The present article introduces a graphicapproach that allows us to observe the dynamics of the changes in the impact of the variablesvis-à-vis the chances of success of the technological innovation. The main advantage of theproposed format is allowing the reader to visualise, in a graphic and dynamic way, the effect ofthe change of one variable upon the others. Technology innovation management is a complex areaand this form of presentation facilitates the transmission of knowledge between the specialists andthe people who wish to study the subject further. For the specialists, the dynamics of the see-sawformat add little because they are able to mentally visualise the effects. For them, the format maybe useful to transmit their knowledge to other less specialised audiences. However, such groupswith less knowledge of the issue – as executives, undergraduate and graduate students, as wellas researchers in areas further removed from business administration – play an important role inthe strengthening of an interdisciplinary field as the technology innovation management one. Thearticle’s main contribution is not to create a new model of factors leading to innovation that ismore complete than the existing ones, but rather, to present a graphic format that facilitates theteaching of this subject to graduate students and practitioners.

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Research in the field has demonstrated that, in the case of graduate students, the see-saw formatcontributes to the understanding of the factors leading to an innovation’s success. The see-sawis flexible regarding the use of different variables, replacing the ones presented in this article, toanalyse and understand specific cases.

The results of the present paper are consistent with the research conducted by several authors(Fischer and Dirsmith 1995; Pettigrew 1979; Meek 1988; Bartunek and Moch 1987), accordingto which, a graphic format, as a symbolic language of the process of technological innovation,facilitates the communication between the players and may become part of the corporate culturebecause culture arises from shared symbols, language, ideology, beliefs, rituals, myths, storiesand dominant metaphors.

Limitations of the proposed see-saw format

• The see-saw format focuses on ‘understanding’ and, by itself, does not offer a wide range ofpossibilities to perform a conclusive analysis of competitive situations, related to technologicalprojects.

• The proposed format does not contemplate all the variables that might influence the strategicuse of technological competence, as for example, the weight represented by the marketingactions carried out by the organisation itself, the marketing actions on the part of competingcompanies, the possible reactions from competitors, the market’s economic instability, etc.The organisation’s marketing competence is another critical aspect to be considered in futurestudies.

• The see-saw format does not consider the different degrees of association that may exist betweenthe variables.

• The variables are presented in this article in a synthesised manner, which does not fully consider,therefore, the characteristics of the resources or inputs that make up such variables. As anexample, the characteristics of a technological competence may depend not only on the financialresources for its acquisition, but also on science, research, development, engineering and theproduction process.A more careful analysis would require an increase in the number of variablesbased on the break-down of each one of them.

• It should be highlighted that, the proposed format makes it easy to increase the number of influ-ence inputs of the four variables: value perception, market resistance, technological competenceand competence in technological management. The number of main variables, i.e. acceptanceby the market and sustainable value added, can also be increased, with an additional axis start-ing at the centre of the see-saw. A scale ranging from high to low would be placed at the endof this axis. Forces could then be placed, influencing the movement of this axis. However, itshould also be noted that these alterations would also increase the visual complexity of theproposed graphic format. Splitting it in two parts and defining the links between the parts mayrepresent an adequate alternative.

• The qualification of the variables is subjective, as there is no detailed criterion for their eval-uation. Thus, market resistance (R), for example, might be evaluated as ‘strong’ by a moreconservative marketing team, while a more daring team would call it ‘moderate’. This wouldhinder the comparative analysis between two projects occurring simultaneously, or that of asingle project throughout its life cycle.

• The concepts presented in this work do not include a proper definition for staggering thevariables, which lowers the accuracy level of the results.

• The use of the see-saw format is limited to preset, known or hypothetical situations. Since itdoes not utilise statistical resources, it is not possible to calculate trends, extrapolations, etc.

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• For the three preceding reasons, the operation of this format is, presently, not compatible withcomputer programs.

• The proposed format can only be used to show the trends at one particular moment, based onpast and present situations, which requires a continuous vigilance over the likely movement ofthe see-saw. This could be an advantage if the conditions are permanently updated, but it couldalso turn into a pitfall for those who memorise the figure of a situation that might lose its valueas time passes.

Recommendations

Comparing the proposed format with the introduction in the market of a technological innovationand its corresponding life cycle, the see-saw format is presently in its embryonic stage, waitingfor its attributes to be explored, so that they can be evaluated and perfected. Therefore, the recom-mendations focus primarily on the applicability of this format. Overall, the see-saw format shouldbe applied to different situations involving the strategic use of the technological competence, soas to verify its validity and reduce its previously mentioned limitations.

For this purpose, it is recommended that work be done in order to set the criteria and/or meth-ods to qualify each variable, according to the values attributed to these variables, as shown in thehypothetical example of Figures 3 and 4. Thus, for example, it would be possible to state in a lesssubjective manner what is meant by a ‘strong market resistance’to the acceptance of a new technol-ogy. The subjective interpretation of the variables may cause disagreement among the individualsinvolved in the analysis of the technology management, through the proposed format. Based on anadequate criterion for assigning values to the variables according to their qualifications, the opera-tion of the format using computer programs could also be considered. It would also be interestingto include other influencing variables such as, for example, the company’s cultural aspects.

Theoretically, the see-saw of competitiveness format can be applied to any real situation, inwhich the forces acting in favour or against the desired result may be identified, i.e. when thereis a desired value (objective) for a specific action, and this action is influenced by a process andit appears in a result, that may be equal, or different from the desired one.

Future studies may be able to test the seesaw graphic format, in terms of its usefulness to guidethe diagnostic process of innovation management in different environments, improving its metricsand graphic visualisation formats.

Acknowledgements

The authors wish to express their recognition for the valuable contribution of the research assistants, Miriam Oishi andAilton Conde Jussani, graduate students in Business Administration at the University of São Paulo (USP), Brazil. Theauthors thank the collaboration of the faculty of the Graduate Program in Business Administration of the University ofSão Paulo for allowing interruptions of their lectures to conduct the survey.

Notes on contributors

Eduardo Vasconcellos is a Full Professor of Management at the University of São Paulo. He holds a MBA from VanderbiltUniversity and a PhD in Business from the University of São Paulo. He is a member of the Editorial Board of R&DManagement. Research interests: innovation management and organisation design. [email protected]

Marcos A.C. Bruno is Associate Researcher, Centre for Technology Policy and Management at the University of SãoPaulo and Visiting Professor, SDA Bocconi School of Management, Milan, since 2001. He holds an MSc in ChemicalEngineering from the University of São Paulo and a PhD in Business from the same university. Research interests: businessstrategy management and innovation management. Email: [email protected]

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Milton de Abreu Campanario is Director of the Graduate Program in Administration at the Nove de Julho University, SãoPaulo and Professor at the University of São Paulo. He holds a MS from Harvard University and a PhD in Economics atCornell University. Research interests: economics of innovation, technology change. Email: [email protected]

Sergio Noffs holds a Master’s degree in Business Administration, Graduate Program in Administration, PPGA/Uninove,São Paulo, Brazil. He is Marketing Consultant in Drives Technology to Siemens Brazil. He previously worked asEngineering Manager at Inductotherm and as Product Development Manager at Brown Boveri (now ABB). Email:[email protected]

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a new graphic format to facilitate the understanding of technological innovation models: the seesaw...

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