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Technovation 28 (2008) 450–463

www.elsevier.com/locate/technovation

Learning and novelty of innovation in established manufacturing SMEs

Nabil Amaraa,�, Rejean Landryb, Nizar Becheikhb, Mathieu Ouimetc

aCHSRF/CIHR Chair on Knowledge Transfer and Innovation, Department of Management, Laval University, Quebec City, Que., Canada G1K 7P4bCHSRF/CIHR Chair on Knowledge Transfer and Innovation, Department of Management, Faculty of Business, Laval University,

Quebec City, Que., Canada G1K 7P4cDepartment of Political Science, Faculty of Social Sciences, Laval University, Quebec City, Que., Canada G1K 7P4

Abstract

For many firms, the challenge is less about whether or not to innovate than about increasing the degree of novelty of their innovations

in order to improve their competitive advantage and create opportunities to access new markets. This paper contributes to advance

knowledge on the degree of novelty of innovation in established small and medium manufacturing firms by using as dependent variables

both innovation and the degree of novelty of innovation. This approach is implemented in two stages. First, we study the presence/

absence of product and process innovations. Second, we adopt a firm’s perspective to examine the degree of novelty of innovations of the

sub-sample of these SMEs that have developed product or process innovations. The results indicate that various types of learning impact

on the presence (or absence) of innovation as well as on the degree of novelty of innovation. Overall, the results show that variables

related to learning by doing, learning by training and learning by interacting have the highest impact on the degree of novelty of

innovation of established SMEs. The results of the paper are also used to derive practical implications for owners and managers of

established SMEs and for policy makers.

r 2008 Elsevier Ltd. All rights reserved.

Keywords: Innovation; Degree of novelty of innovation; Learning; Sample-selected regression; Established manufacturing SMEs

1. Introduction

The novelty of innovations has captured the attention ofboth researchers and policy makers who, in different ways,have looked for answers to the crucial question: ‘‘What canbe done to foster the degree of novelty of innovation inmanufacturing firms?’’ (Green et al., 1995; Cooper andKleinschmidt, 1996; Freeman and Soete, 1997; Danneelsand Kleinschmidt, 2001; Darroch and McNaughton, 2002;Sorescu et al., 2003; Kenny, 2003). Increasing the degree ofnovelty of innovation is important because it improvescompetitive advantages and creates opportunities for firmsto access new markets (Lynn et al., 1996; McDermott andHandfield, 2000; McDermott and O’Connor, 2002). Priorstudies on innovation suggest that increasing the degree of

e front matter r 2008 Elsevier Ltd. All rights reserved.

chnovation.2008.02.001

ing author. Tel.: +1418 656 2131x4382;

2624.

esses: nabil.amara@mng.ulaval.ca (N. Amara),

mng.ulaval.ca (R. Landry), nizar.becheikh@mng.ulaval.ca

mathieu.ouimet@pol.ulaval.ca (M. Ouimet).

novelty of innovations involves higher risks (O’Connor andVeryzer, 2001) and requires more resources, both financialand human, as well as research knowledge (Stringer, 2000).There are two major bodies of literature on innovation.

Mostly based on the Oslo Manual approach (OECD 1992,1996), a first body of studies focuses its attention on thepresence or absence of product/process innovations. Theresults of such studies allow differentiating innovativefrom non-innovative firms, as well as the determinants ofproduct and process innovations (see Becheikh et al., 2006for a systematic literature review). As for the second bodyof literature, it focuses its attention on innovative firms byattempting to explain the degree of novelty of innovationsby adopting either a customer’s perspective or a firm’sperspective. Most of the studies on the degree of novelty ofinnovations consider either large firms or science-basedfirms. This paper contributes to advance knowledge on thedegree of novelty of innovation in established small andmedium Canadian manufacturing firms by using asdependent variables both innovation and the degree ofnovelty of innovation. This approach is implemented in

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two stages. First, we study the presence/absence of productand process innovations by using measures derived fromthe Oslo Manual. Second, we adopt a firm’s perspective toexamine the degree of novelty of innovations of the sub-sample of these SMEs that have developed product orprocess innovations.

The rest of the paper is organized as follows. It firstbriefly reviews the major issues regarding the specificationof the dependent variables innovation and degrees ofnovelty of innovation to indicate how the present studydeals with them. The second part of the paper reviews theempirical literature on the determinants of innovation. Thefollowing parts of the paper present the data, methods andresults. The paper concludes by stressing the major findingsof the study and their implications for established SMEsand policy makers, as well as by pointing out limits andissues that should receive attention in future investigationson the degree of novelty of innovation.

2. Specification of the dependent variables innovation and

degree of novelty of innovation

McDermott and O’Connor (2002, p. 425) pointed outthat in spite of more than 30 years of research on the degreeof novelty of innovation, ‘‘researchers are far from aconsensus regarding a formal definition of radical innova-tion’’. There is an agreement on the fact that innovationsare about novelty or changes in products or productionprocesses. These changes have been described and dis-cussed in a number of ways. Several expressions have beenused to describe the idea of novelty or changes: radicalinnovation (Lee, 1995; Germain, 1996; Koberg et al.,2003); breakthrough innovation (McDermott and Hand-field, 2000; O’Connor and Rice, 2001); disruptive innova-tion (Sandberg, 2002); discontinuous innovation (Lynnet al., 1996; Veryzer, 1998; O’Connor, 1998; McDermottand O’Connor, 2002); significant innovation (Felsenstein,1994); major innovation (Blanchfield, 1983; Inzelt, 1996);pioneering innovation (Ali, 1994), etc.

As for the discussions on novelty and changes inproducts and production processes, one can differentiatebetween the customers’ perspective and the firms’ perspec-tive (Booz-Allen Hamilton, 1982). Researchers looking atinnovations from the customers’ perspective usually focuson products’ ability to provide benefits or features notfound in previous products. In this literature, novelty andchanges in products refer to superior product advantagesfor customers, greater functionality, improvement inperformance features, and added-value to the marketplace(O’Connor, 1998; Leifer et al., 2000; Danneels andKleinschmidt, 2001). The literature using the firms’perspective on novelty or newness of innovation focuseson the amount of resources, development time and changesto technology necessary for firms to undertake radicalinnovations (Hall and Andriani, 2003; Landry et al., 2002;McDermott and O’Connor, 2002). Students of this

perspective focus on firms. This perspective is adopted inthis paper.Conceptualizations and operationalizations of the degree

of novelty of innovation are abundant. A review of theliterature reveals various approaches: categorizations ofinnovation through unidimensional and multidimensionaltypologies, development of unidimensional scales and,finally, development and validation of multidimensionalscales (see Table 1). A large number of typologies havebeen devised to study the degree of novelty of innovation inproducts or processes (see Garcia and Calantone, 2002, fora review). Such typological approaches raise their ownspecific measurement problems as pointed out by Danneelsand Kleinschmidt (2001). When asked to qualify the degreeof novelty of product innovations, they argued that firmsmay tend to overestimate the degree of novelty or theuniqueness of their innovations or may even haveinaccurate perceptions of the products available onmarkets. One may assume that this tendency is even higherfor SMEs, which have less information about thecompetitive environment in which they operate.From the firms’ perspective, what makes a product or a

production process new involves two ingredients: technol-ogies and markets (Green et al., 1995; Danneels andKleinschmidt, 2001). Products or production processes areneither radical nor incremental; they carry instead a loweror higher degree of novelty of innovation. The novelty ofinnovation is a matter of degree. In this literature, thedegree of novelty of innovation in products or productionprocesses has been associated with four types of knowledgedeficits (Green et al., 1995; Danneels and Kleinschmidt,2001; McDermott and O’Connor, 2002):

�

Technological uncertainty refers to the degree to whichthe development of products or production processesinvolves the creation of new knowledge representing asubstantial challenge. The greater the knowledge-crea-tion demands, the greater the degree of novelty ofinnovation. � Technical inexperience refers to the degree to which the

development of products or production processesinvolves the use of skills and equipment where the firmsuffers from a lack of knowledge. The greater the needto acquire knowledge related to the learning of newskills and absorption of new equipment, the greater thedegree of novelty of innovation.

� Business inexperience refers to the degree to which the

development of products or production processesinvolves the creation of new knowledge for the devel-opment of new business practices (i.e., the developmentof organizational innovations). The greater the need toacquire knowledge related to the creation of newbusiness practices, the greater the degree of novelty ofinnovation.

� Technology costs refer to the degree to which the

development of products or production processesinvolves investments in the acquisition of knowledge

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Table 1

Typical conceptualizations and operationalizations of the degree of novelty of innovations

Approaches to qualify novelty of

innovation

Typical conceptualizations/operationalizations Selective references

Level 1: Degree of novelty of

innovation is unqualified

� Companies indicate if they introduced onto the

market any new or significantly improved products or

processes (Oslo Manual)

Michie and Sheehan (2003), Kam et al. (2003), Tether

(2002), Uzun (2001), Diez (2000)

Level 2: Degree of novelty of

innovation is qualified with

nominal typologies

Unidimensional typologies Schmidt and Calantone (1998), Atuahene-Gima (1995),

Felsenstein (1994)

� Incremental (minor) versus radical (major or

significant) innovations

� Two or more categories of the following: innovations

new to the firm/industry/market/country/world

� Firms for which new products/processes have an

effect on more than half of their sales are considered

as highly innovative firms

� Low/medium/high/super high uncertainty

� Incremental/intermediate/radical innovations

� Radical/incremental vs. compatible/incompatible

innovations

� Really innovative/replacement/imitative products

� Companies using stabilized/relatively new/new and

sophisticated/first application ever in the field

technology

Romijn and Albaladejo (2002), Kaufmann and Todtling

(2001), Amara and Landry (2005), Sadowski and

Sadowski-Rasters (2006)

Molero and Buesa (1996)

Kenny (2003), Freeman and Soete (1997), Shenhar et al.

(1995)

Abetti (2000), Kash and Rycoft (2000), Dismukes (2005)

Sheremata (2004)

Stanton et al. (1994)

Autio (1994)

Multidimensional typologies Hall and Andriani (2003, 2002)

� Nature of new knowledge (additive vs. substitutive)/

amount of new knowledge to be acquired and applied

(low vs. high)

� Product capability (same vs. enhanced)/technological

capability (same vs. advanced)

� Nature of market needs/source of technology

(nominal scales)

� Change (minor vs. major)/scope (firm, industry,

country, world)

� New to the world performance (yes/no)/improved

performance (yes/no)/significant cost reduction (yes/

no)

Sorescu et al. (2003), Veryzer (1998)

O’Connor (1998)

Inzelt (1996)

O’Connor et al. (2002), O’Connor and Rice (2001)

Level 3: Degree of novelty of

innovation is qualified with ordinal/

continuous scales

Unidimensional scales

� The more important the changes made to existing

products or processes, the more radical the

innovation

� Frequency of introduction of strategic innovation (5-

point scale)

� Radicalness is defined and then measured on five-

point scale (from ‘‘low’’ to ‘‘high’’)

Landry et al. (2002), Lee (1995)

Koberg et al. (2003)

Germain (1996)

Multidimensional scale

� Technological uncertainty/technical inexperience/

business inexperience/technology cost

� Incremental innovation/innovation that changes

consumer’s behaviour/innovation destroys business

competencies (5-point scale)

� Differences in core technology/superiority in user

benefits (9-point scale)

McDermott and O’Connor (2002), Green et al. (1995)Darroch and McNaughton (2002)

Chandy and Tellis (2000, 1998)

N. Amara et al. / Technovation 28 (2008) 450–463452

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embodied in equipment important to the firm. Thegreater the costs of acquisition of knowledge inequipment for the firm, the greater the degree of noveltyof innovation.

This review of conceptualizations and operationalizationsof the degree of novelty of innovation suggests that thedegree of novelty of innovation should be represented as acontinuum related to various types of knowledge deficits.

3. Independent variables

As pointed out in Table 1, there is a growing body ofempirical evidence regarding the factors that explain thedegree of novelty of innovation. In the absence of adominant explanatory model, the independent variablesidentified in this literature can be considered as checklistsof variables assumed to explain the degree of novelty ofinnovation, rather than formal heuristic devices. However,the checklists of variables identified in the prior studiesappear to frequently lay stress on how firms proceed tocompensate their internal and external knowledge deficits(Garvin, 1993; Dogson, 1991, 1993; Cohen and Levinthal,1989, 1990). In this paper, we suggest that firms do not onlyattempt to compensate their knowledge deficits by creatingnew information, but that they also attempt to improvetheir ability to identify, assimilate, transform and exploitknowledge from their external environment. Improving theability of firms to identify, assimilate, transform andexploit knowledge has been referred to as improving firms’learning capability (Cohen and Levinthal, 1989). Learningcapabilities are those assets that enable firms to transformand exploit their resources in order to develop product orprocess innovations. The learning capabilities that firmscan mobilize in their innovation projects vary significantlyfrom firm to firm. Moreover, although different forms oflearning capabilities are required in order to successfullydevelop product and process innovations, innovations thatinvolve higher degrees of novelty likely require more orhigher learning capabilities than those innovations thatinvolve lower degrees of novelty in their products orproduction processes. The literature on learning is abun-dant and diversified in terms of forms of learning. Five ofthese forms are especially useful to explain the degree ofnovelty of innovations: learning by searching, learning bytraining, learning by using, learning by doing, learning byinteracting (Cohen, 1995; Lundvall, 1997a, b; Freeman,1995a, b).

Learning by searching: This form of learning is associatedwith internal R&D activities (Lee, 1995; Inzelt, 1996;Romijn and Albaladejo, 2002). R&D investments arenecessary to create the new knowledge required to developinnovations, particularly those of a greater degree ofnovelty (Cohen and Levinthal, 1989; Romijn and Albala-dejo, 2002; Caloghirou et al., 2004; Rogers, 2004).Furthermore, one can assume that the involvement offirms in formally organized R&D activities has two

complementary impacts: first, the creation of new knowl-edge; second, the accumulation of the knowledge created ina pool which increases over time. This second impact is lesslikely to occur when R&D activities are not formallyorganized.

Learning by training: Firms require an adequate pool ofskilled manpower to develop innovations, whether incre-mental or radical (Romijn and Albaladejo, 2002; Darrochand McNaughton, 2002; Subramaniam and Youndt, 2005;Freel, 2005). This pool of knowledge can be enhancedthrough investments in staff training (Romijn and Albala-dejo, 2002).

Learning by using: Learning is also boosted by the use ofadvanced technologies (Rosenberg, 1982; Gatignon andXuereb, 1997; Chandy and Tellis, 1998). Such technologiesembody codified knowledge, which creates new opportu-nities for experimentation and problem solving on the shopfloor that would be otherwise impossible. Use andappropriation of advanced technologies accelerate boththe process of innovation and the resolution of moreradical problems, thus opening the way for a greater degreeof novelty of innovation (Wuyts et al., 2004).

Learning by doing: This form of learning suggests thatfirms become more efficient as they get more practice atdoing what they do. In this paper, we associate this form oflearning to formal and informal efforts, combined withpromotion and marketing, directed at problem solving andexperimentation about the promotion of new products orthe marketing of new products on export markets. Thisform of learning is very important, not just to improve theinnovative capability of firms, but also their growth(Germain, 1996; Gatignon and Xuereb, 1997; Tether,2002; Koberg et al., 2003).

Learning by interacting: The development of innovationsrests on a process occurring primarily within firms.However, this process is reinforced when it is complemen-ted by an interactive process, whereby firms forge relation-ships with other firms and the different actors of theirenvironment (Kline and Rosenberg, 1986; Chipika andWilson, 2006). Forging formal and informal linkageswith clients, suppliers, industry associations, researchorganizations and government agencies provides additionalinputs into the learning process which create newopportunities to have access to knowledge facilitatingthe development of innovations. Interactions boost accessto information about markets, technologies, technicalassistance, research and technical knowledge. Interactionsare determinants of innovations (Lundvall, 1988; Felsen-stein, 1994; Lee, 1995; Edquist, 1997; O’Connor, 1998;Darroch and McNaughton, 2002; Tether, 2002; Romijnand Albaladejo, 2002). Firms’ degree of novelty ofinnovation depends on a larger variety of interactionsources, and they are more likely to forge linkages ofexchange with research organizations than other firms(Landry et al., 2002; Amara and Landry, 2005; Rogers,2004; Subramaniam and Youndt, 2005; Hausman, 2005;Becheikh et al., 2006).

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Table 2

Descriptive statistics

Variables Type of variables Minimum Maximum Mean Standard

deviation

Cronbach’s p

Continuous variables

Percentage of sales invested in R&D Continuous, % 0 100 6.48 14.64

Percentage of employees involved in R&D Continuous, % 0 22 1.87 2.99

Percentage of sales shipped by firms

outside the country

Continuous, % 0 100 14.71 26.66

Percentage of sales to the three most

important clients

Continuous, % 1 100 47.27 29.04

Percentage of purchases from the three

most important suppliers

Continuous, % 2 100 58.05 26.44

Number of advanced technologies used Continuous:

number

0 17 3.72 3.80

Business network Index: 2 items 2 10 5.76 1.83 .70

Research network Index: 5 items 1 21 7.63 3.92 .88

Information network Index: 5 items 1 25 10.74 4.54 .83

Age of the firm Continuous:

number

1 123 20.51 15.57

Number of employees Continuous:

number

1 240 27.51 38.79

Dichotomous variables

Percentage of innovative firms 78.1%

Training of employees linked to innovation 71.7% (YES)

Promotion linked to the introduction on

the market of new or improved products

53.7% (YES)

Technology intensiveness Low=50.8% Medium=49.2%

N. Amara et al. / Technovation 28 (2008) 450–463454

3.1. Control variables

Age and size were introduced into the model as controlvariables for firm characteristics. There is still a debateregarding the impact of these two variables on the degreeof novelty of innovations (Chandy and Tellis, 1998, 2000;Stringer, 2000; Tether, 2002; Landry et al., 2002; Koberget al., 2003; Becheikh et al., 2006). The former accounts forany difference in creative capacity due to accumulatedexperience and knowledge through the years. The latteraccounts for any difference arising from differences in scale(Traore, 2004). For some, as firms grow, they wouldbecome more bureaucratic, providing less incentives tocapture the benefits of a greater novelty of innovations,and are more prone to a lower rather than a higher degreeof novelty of innovation (Schumpeter, 1942; Cohen, 1995;Chandy and Tellis, 2000). For others, as firms grow, theyare more likely to have the financial and technicalcapabilities to bear the costs required for a greater degreeof novelty of innovation (Chandy and Tellis, 2000; Sorescuet al., 2003). Both variables are expected to have anambiguous effect on the degree of novelty of innovation.Empirical research, such as proposed in this paper, is aproductive way to shed new light on these conflicting views.

Another control variable included in our investigation isthe technology intensiveness of industries. This is becausein studies like ours, where a large number of industries arefound, it is useful to aggregate and disaggregate industriesat various levels.

4. Data and descriptive statistics

The data used in this study were collected by a privatefirm specializing in computer-assisted telephone interviewsfrom November 2002 to January 2003. The questionnairewas adapted from the second edition of the Oslo Manualand included questions specifically defined by the sponsorof the survey: Canada Economic Development, a govern-ment agency involved in the promotion of regionaleconomic development and innovation. The purpose ofthe survey was to look into innovation and its determinantsin Laval–Lanaudiere–Laurentides, a region located Northof Montreal (Canada). The population included 2670 firmsof 250 employees or less. The list of these firms wasprovided by the sponsor of the study. After excludingenterprises that were not producing any goods or services,no longer in operation, or impossible to reach after morethan 20 calls, the actual population of respondents was1156. Out of this effective population, 248 firms refused toanswer the survey (21.4%), 84 respondents were out oftown during the survey (7.2%) and 18 questionnaires werenot completed (1.5%). In the end, 806 questionnaires werecompleted and usable, for a response rate of 69.7%, muchhigher than that reached by most surveys administered toprivate firms. Of these 806 respondents, 639 were operatingin manufacturing industries and 167 in service industries.The former subset of the 639 manufacturing firms is used inour study, as we are interested in innovation in manufac-turing industries.

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Overall, 78.1% of the manufacturing firms indicatedthey had developed or improved their products ormanufacturing processes during the last three years. Thedescriptive statistics of the variables used in this study arereported in Table 2. Overall, the average firm had invested6.48% of its sales in R&D activities, allocated 1.87% of itsemployees to R&D, used 3.72 different advanced techno-logies in its manufacturing processes, exported 14.71% ofits production outside Canada, had 27.51 employees,ranked at 5.76 out of a possible maximum of 10 on thescale of business network assets, ranked at 10.74 out of apossible maximum of 25 on the scale of informationnetwork assets and ranked at 7.63 out of a possiblemaximum of 25 on the scale of research network assets.For this average firm, the sales to the three most importantclients represented 47.27% of the total sales, and thepurchases from the three most important suppliersrepresented 58.05% of the total purchases.

Moreover, 71.7% of the manufacturing firms hadprovided their employees with training activities linkedto the development of innovations during the last threeyears, and 53.7% of the firms were involved, during thelast three years, in promotion activities linked to theintroduction on the market of new or improved products.Finally, 49.2% of the firms were in mid-tech sectors,and 50.8% in low-tech sectors. There were no firms inHigh-tech sectors.

5. Instruments and measures

The two dependent variables used in this paper are:Presence/absence of innovation: Binary variable coded 1

(1 ¼ presence of innovation), if the respondent answeredyes to the following question based on the methodology ofthe Oslo Manual: ‘‘During the last 3 years, did yourbusiness unit introduce onto the market any new orsignificantly improved products (goods or services)?’’, andcoded 0 otherwise (0 ¼ absence of innovation).

The degree of novelty of innovation (NOVELTY) ismeasured by an index derived from the four forms ofknowledge deficit identified in Section 2. This indexincludes five items capturing different types of riskencountered by the firm: (1) the risk regarding theintroduction of new equipments (risk associated withtechnical inexperience); (2) the risk regarding marketingstrategies (risk associated with business inexperience);(3) the risk regarding the replacement of former supplierswith new suppliers (risk associated with business inexperi-ence); (4) the risk regarding the hiring of employees withnew qualifications (risk associated with technical inexperi-ence) and (5) the risk regarding the investment in newproduction technologies (risk associated with technologycosts). For each type of risk, the respondents were askedto assess the level of risk they encountered when theydecided to develop or improve products or manufacturingprocesses, using a 5-point scale ranging from 1 (stronglydisagree) to 5 (strongly agree). Hence, the dependent

variable, degree of novelty of innovation of SMEs is thesum of the scores of the items corresponding to theresponses of the firms to these five assertions regardingthe level of five different types of risk encountered by thefirms during the innovation process. The index can rangefrom 5 to 25. The indicators measuring the risk associatedwith technological uncertainty were excluded from themodel because they were correlated with the indicators ofthe explanatory variables related to learning by searching.Considering that this index was based on multiple-itemscales, we conducted a principal components factoranalysis (PCFA) on the construct scales to assess theirunidimensionality. The results of the PCFA indicated thatone factor explained 60.23% of the original variance of thephenomenon studied, with an initial eigenvalue of 1.88. Inaddition, Cronbach’s alpha for this index is .58. It must bepointed out that although there is no strict threshold forhigh reliability, Ahire and Devaray (2001) and Nunally(1967, 1978) recommended the threshold of .50 foremerging construct scales and .70 for maturing constructs.The explanatory variables included in the first model

are the percentage of sales invested in R&D (PERR&D)and the percentage of employees dedicated to R&D(PEEMR&D) as indictors of learning by searching.Learning by training (TRAIN) is measured by a binaryvariable coded 1 if, between 1999 and 2002, the firmundertook training activities linked to innovation, and 0otherwise. Learning by doing is measured by relying ontwo indicators. The first one (MARKET) consists of abinary variable coded 1 if, between 1999 and 2002, the firmundertook promotion activities linked to the introductionon the market of new or improved products. The secondindicator (LNEXPORT) is a measure of the percentage ofsales shipped outside the country. Learning by using ismeasured by the number of different advanced technolo-gies used by the firm (the complete list of these advancedtechnologies is available upon request). This index(USING) can range from 0 up to a maximum of 26technologies.As for the control variables, the age of the firm (AGE) is

measured in years. The size of the firm (SIZE) is measuredby the total number of the firm’s employees in 2002. Thetechnology intensiveness of industries is measured bya binary variable (TECHINT) coded 1 if the firm operatesin a medium technology sector, and 0 if it operates ina low technology sector. The aggregation of industriesreported in Table A1 follows the OECD’s taxonomyand is based on the intensity of R&D (Hatzichronoglou,1997).The checking of the correlation matrix (Table A2)

between the independent variables used in the regressionmodels indicates that the highest correlation coefficientbetween the independent variables is that existing betweenthe size of the firm (LNSIZE) and the variable indicatingthe number of different advanced technologies used byfirms (USING). This correlation coefficient is equal to .48.The first column of Table A2 also reports tolerance statistic

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values for all the predictors used in the regression models.Tolerance statistic values, which are the reciprocal ofvariance inflation factors (VIF), indicated whether apredictor has a strong linear relationship with the otherpredictors. It can be seen that all the tolerance statisticvalues are much higher than .2. This ensures that there isno multicollinearity concern (Menard, 1995; Field, 2006).

Firms that introduced onto the market product orprocess innovations were asked to answer questionsdealing with other and more specific learning capabilities.Therefore, additional explanatory variables were intro-duced in the model dealing with the degree of novelty ofinnovation. Hence, in addition to the variables used at thefirst-stage model, we introduced two additional indicatorsfor learning by doing. The first one (CLIENT) correspondsto the percentage of sales to the three most importantclients, and the second (SUPPLY) is a measure of thepercentage of purchases from the three most importantsuppliers.

Three additional variables are also introduced tomeasure learning by interacting. The questions used toassess this form of learning are: ‘‘Which of the followingplayed a role as sources of information needed forsuggesting or contributing to the development of new orimproved products or manufacturing processes during thelast three years? Please check all that apply in indicating thedegree of importance on a scale ranging from 1 to 5 where1 ¼ not important at all and 5 ¼ extremely important’’. Inorder to eliminate the information sources that are weaklycorrelated with the overall variables of the same measuringindex, we performed a factor analysis which indicated that12 sources of information were loading on three factors,suggesting the presence of the three types of networks:business networks, information networks and researchnetworks. More specifically, business networks index(BUISNET) is measured as the sum of the scorescorresponding to the responses of the firms to this questionfor the items clients and suppliers. The scores of therespondents can range from 2 to 10. Similarly, informationnetworks index (INFONET) is the sum of the scorescorresponding to the responses of the firms to this questionfor the five following items: (1) trade fairs and exhibitions;(2) professional conferences, meetings and publications;(3) Internet or computer-based information networks;(4) information from professional associations or profes-sional networks; (5) information programs of governments.The scores of the respondents can range from 5 to 25.Finally, the research networks index (RESNET) is thesum of the scores corresponding to the responses ofthe firms to this question for the five following items:(1) patent documents; (2) government research labora-tories; (3) technology transfer organizations; (4) universi-ties; and (5) community colleges. This index can range from5 to 25. The three summated network indices had internalreliability coefficients (Chronbach’s alphas) of .70, .88 and.83. These results indicate that the internal consistency ofeach factor is relatively high.

6. Analytical models and regression results

As indicated earlier, we used a two-stage approach toinvestigate innovation developed by firms. First, we studythe presence/absence of product and process innovations.Second, we use an index of degrees of novelty ofinnovation to study the degree of novelty of innovationsfor the sub-sample of these SMEs that have developedproduct or process innovations. This approach corre-sponds to sample-selected outcomes that refer to thesituation where responses to a continuous variable (Y)are conditional on a dichotomous variable (Z). In ourcase, the first variable (Z) is binary and represents thestage where we examine the presence or absence ofproduct/process innovations (0 ¼ absence of innovation,1 ¼ presence of innovation). The second variable (Y) iscontinuous and represents the degree of novelty ofinnovations.The innovation process described above is estimated

with a sample-selected regression. First, a logit model isestimated for the dichotomous variable Z to examine thepredictors of the presence/absence of innovation in SMEs.Then, a linear regression is estimated for the continuousvariable Y using the sub-sample of innovative firms (i.e.,when Z ¼ 1). Among the substantive predictors would be avariable that represents the predicted probability of beingan innovative firm obtained from the logit regression(PREINNOV). Thus, the linear regression provides a testof the predictors of degree of novelty of innovationconditional on being an innovative firm. In other words,the linear regression estimates are adjusted for the fact thatbeing an innovative firm is the outcome of a non-randomselection process.

6.1. The determinants of the presence/absence of innovation

in SMEs

The model developed for the first stage is

LogðPi=1� PiÞ ¼ b0 þ b1 PERR&Dþ b2 PEEMR&D

þ b3 TRAIN þ b4 LNEXPORT

þ b5 MARKET þ b6 USINGþ b7 LNAGE

þ b8 LNSIZEþ b9 TECHINT;

where bi (i ¼ 0,y,9) are parameters to be estimated.Log(Pi/1�Pi) is the logarithm of the ratio of the

probability that a firm innovates relative to the probabilitythat it does not innovate.Results of the regression of the first stage, that is, the

stage about the presence or absence of innovation, aresummarized in Table 3. The equation has good predictivepower, with 82.2% of correct predictions. The modelcorrectly classified 82.2% of the firms into those who didinnovate and those who did not innovate. The valueof the Nagelkerke R2 (pseudo R2) is .418, which is quitereasonable for qualitative dependent variable models.Furthermore, the computed value of the likelihood ratio

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Table 3

Estimated logit model of factors affecting innovation

Independent variables Dependent variables: innovation/

no innovation

Coefficients (b)a Exp (b)

Intercept .600 1.822

Learning by searching

Percentage of sales invested in R&D [PERR&D] .109 1.115

(.015)***

Percentage of employees in R&D [PEEMR&D] .039 1.040

(.004)***

Learning by training

Training employees in relation to innovation (YES ¼ 1) [TRAIN] .650 1.916

(.058)**

Learning by doing

Percentage of sales shipped by firms outside the country [LNEXPORT]b .071 1.074

(.065)**

Promotion linked to the introduction on the market of new or improved products (YES ¼ 1) [MARKET] .560 1.750

(.077)**

Learning by using

Number of advanced technologies used [USING] .126 1.134

(.033)**

Control variables

Age of the firm [LNAGE]b �.318 .728

(.125)*

Number of employees [LNSIZE]b .073 1.076

(.629)

Technology intensiveness (MEDIUM ¼ 1; LOW ¼ 0) [TECHINT] �.254 .775

(.390)

Number of cases 587

Chi-square (df) 125.60 (9)

Nagelkerke R2 (pseudo R2) .418

Percentage of correct predictions 82.2%

Note: numbers in parentheses indicate p-value.a*, ** and *** indicate that variable is significant at 10%, 5% and 1%, respectively.bLN indicates the logarithmic transformation of the variable whose name it precedes.

N. Amara et al. / Technovation 28 (2008) 450–463 457

(i.e., 125.6) is much larger than the critical value of the chi-squared statistic with 91 of freedom at the 1% level. Thissuggests that the null hypothesis, that all the parametercoefficients (except the intercept) are all zeros, is stronglyrejected. Consequently, the model is significant at the 1%level.

The likelihood of firms to innovate increases as thepercentage of sales invested in R&D increases, as theyincrease the percentage of employees involved in R&Dactivities, as the percentage of their exports increases, andas they increase the number of different advancedtechnologies used in their manufacturing processes. Thenegative relation between the age of the firm and thelikelihood to innovate means that the older the firms,the less likely they are to develop or improve their productsor manufacturing processes. Moreover, as we can see fromthe column labeled Exp (b) of Table 3, an increase by oneunit in the percentage of sales invested in R&D, in thepercentage of employees involved in R&D activities, and inthe percentage of exports, increases the likelihood thatfirms innovate by 1.11, 1.04 and 1.07, respectively. An

increase by one unit in the number of different advancedtechnologies that the firms use in their manufacturingprocesses increases by 1.13 times the likelihood that firmsinnovate. Finally, an increase by one year in the age of thefirms decreases by .73 times the likelihood that firmsinnovate.As for the two dichotomous variables, the results

indicate that being a firm providing training activities toits staff regarding the development or improvement ofproducts and manufacturing processes increases the like-lihood of this firm to innovate. Similarly, being a firminvolved in promotion linked to the introduction on themarket of new or improved products increases theprobability of such a firm to innovate. As suggested byfigures in the column for exp (b), compared to firms that donot provide training activities linked to innovation, firmsthat carry such activities are 1.92 times more likely toinnovate. By the same token, compared to firms that carrypromotion activities linked to the introduction on themarket of new or improved products, firms that carry suchactivities are 1.75 times more likely to innovate.

ARTICLE IN PRESSN. Amara et al. / Technovation 28 (2008) 450–463458

6.2. The determinants of the degree of novelty of innovations

in SMEs

At the second stage of the approach, we have developedthe following ordinary least squares model:

NOVELTY ¼ b0 þ b1 PERR&Dþ b2 PEEMR&D

þ b3 TRAINþ b4 LNEXPORTþ b5 MARKET

þ b6 CLIENTþ b7 SUPPLYþ b8 USING

þ b9 BUISNETþ b10 RESNETþ b11 INFONET

þ b12 LNAGEþ b13 LNSIZEþ b14 TECHINT

þ b15 PREINNOV;

where bi (i ¼ 0,y,15) are parameters to be estimated.Regression results are summarized in Table 4. As

indicated in this table, 10 out of the 15 explanatory variablesintroduced in the model explain the degree of novelty ofinnovation. The total amount of variance in the magnitudeof degree of novelty of innovation explained by this model isshown by the adjusted R2 to be .46. One can see that thedegree of novelty of innovation augments with increases inthe percentage of sales invested in R&D; the percentage ofemployees involved in R&D activities; the number ofdifferent advanced technologies utilized for production

Table 4

Regression equation predicting degree of novelty of innovation of SMEs

Independent variables

Intercept

Predicted propensity to innovate [PREINNOV]

Learning by searching

Percentage of sales invested in R&D [PERR&D]

Percentage of employees dedicated to R&D [PEEMR&D]

Learning by training

Training employees in relation to innovation (YES ¼ 1) [TRAIN]

Learning by doing

Percentage of sales shipped by firms outside the country [LNEXPORT]b

Promotion linked to the introduction on the market of new or improved pr

[MARKET]

Percentage of sales to the three most important clients [CLIENT]

Percentage of purchases from the three most important suppliers [SUPPLY

Learning by using

Number of advanced technologies used [USING]

Learning by interacting

Business network [BUISNET]

Research network [RESNET]

Information network [INFONET]

Control variables

Age of the firm [LNAGE]b

Number of employees [LNSIZE]b

Technology intensiveness (MEDIUM ¼ 1; LOW ¼ 0) [TECHINT]

Number of cases: 228

Adjusted R2: .46

Calculated F ¼ 13.97; theoretical F (15; 213) ¼ 2.17 at 1%

a*; ** and *** indicate that variable is significant at 10%, 5% and 1%, resbLN indicates the logarithmic transformation of the variable whose name i

increases; and the predicted probability of being aninnovative firm. The negative relation between the variableindicating the percentage of purchases from the three mostimportant suppliers and the degree of novelty of innovationmeans that the more firms are dependent on a small numberof suppliers, the lower the degree of novelty of innovation.As for the three explanatory variables concerning

learning by interacting, one can see that only two of themsignificantly explain the magnitude of the degree of noveltyof innovation. As indicated in Table 4, the magnitude ofthe degree of novelty of innovation increases as theinformation network assets and the research networkassets increase. The variable ‘‘business network’’ does notexplain the novelty of innovation.As for the dichotomous variables, being a firm providing

training activities to its staff about the development orimprovement of products and manufacturing processesincreases the degree of novelty of innovation. Similarly,being a firm involved in promotion linked to theintroduction on the market of new or improved productsincreases the degree of novelty of innovation.To assess the scope of the impact of the explanatory

variables on the degree of novelty of innovation, weestimated the partial elasticities of the continuous variables,which significantly explain the magnitude of the degree of

Dependent variables: index

of novelty of innovation

Coefficients (b)a T-ratios

2.435 1.57

4.032 10.21***

.033 2.16**

.013 1.30*

1.021 1.82**

�.021 �.48

oducts (YES ¼ 1) .714 1.66**

�.006 �.79

] �.012 �1.38*

.101 1.66**

.214 1.22

.482 2.51***

.368 1.90**

.111 .37

.296 1.30*

�.306 �.73

pectively.

t precedes.

ARTICLE IN PRESSN. Amara et al. / Technovation 28 (2008) 450–463 459

novelty of innovation of SMEs that innovate. The partialelasticities for the variables, that have been found tosignificantly explain the degree of novelty of innovation offirms that have innovated, were calculated with the equationpresented in Note A of Table 5. These partial elasticitiesreflect the average of the coefficients of elasticity evaluatedfor each of the 228 observations. As can be seen in Table 5,the coefficient of elasticity of the variable research networkassets takes the highest value, .323. This implies that apositive relative change of 10% in the index of the researchnetwork assets increases the degree of novelty of innovationby 3.23%. Likewise, a positive relative change of 10% in theindex of the information network assets increases the degreeof novelty of innovation by 2.15%. Furthermore, a positiverelative change of 10% in the number of different advancedtechnologies used by firms in their production processes, inthe number of employees, in the percentage of sales investedin R&D, and in the percentage of employees involved inR&D, increases the degree of novelty of innovation by.26%, .22%, .15% and .13%, respectively. Conversely, apositive relative change of 10% in the percentage ofpurchases from the three most important suppliers decreasesthe firms’ degree of novelty of innovation by .52%.

The lower section of Table 5 shows the percentage ofchange, in the degree of novelty of innovation generated bychanges, by the state of the significant binary explanatoryvariables. These results are calculated by using the equation

Table 5

Impacts of the statistically significant variables on degree of novelty of

innovation

(A) Continuous variables Partial

elasticitya,b

Percentage of sales invested in R&D [PERR&D] .15

Percentage of employees dedicated to R&D

[PEEMR&D]

.13

Percentage of purchases from the three most

important suppliers [SUPPLY]

�.52

Number of advanced technologies used [USING] .26

Research network [RESNET] 3.23

Information network [INFONET] 2.15

Number of employees [LNSIZE] .22

(B) Binary variables Impactsc

Training employees in relation to innovation

(YES ¼ 1) [LEARN]

4.82

Promotion linked to the introduction on the market

of new or improved products (YES ¼ 1) [MARKET]

3.32

aThe partial elasticity of each index is calculated in regard of the mean

of partial elasticities of all the observations composing this index. For the

function Y(X) ¼ a0 ¼ a1X, where Y is the explained variable, X is the

explicative variable, and a0 and a1 are parameters, the partial elasticity of

Y in regard of the variable X is given by the expression: ðqY=qX ÞdðX=Y Þ.bElasticities are reported for a 10% increase in the different continuous

variables.cThe impacts of the binary variables on the novelty of innovation are

estimated using the following formula: Impacts ¼ (Z1�Z0)/Z0, where Z1

is the value of degree of novelty of innovation when the reference variable

is set to 1 and Z0 is the value when it is set to 0.

presented in Note C of Table 5. The coefficients show that thedegree of novelty of innovation would be increased by 4.82%if the firms that do not provide training activities to their stafffor the development or improvement of products andmanufacturing processes decided to provide those activities.Similarly, the degree of novelty of innovation would beincreased by 3.32% if the firms that do not undertakepromotion linked to the introduction on the market of new orimproved products decided to undertake such activities.

7. Conclusion and implications

For most established manufacturing SMEs, the challengeis less about whether or not to innovate than aboutincreasing the degree of novelty of their innovations.Likewise, the scholarly debate in innovation studies is lessand less about whether or not a firm has developed new orimproved products or production processes in the last 3years. Like this study, which shows that 78.1% of firms havedeveloped product or process innovations, most studiesindicate that a vast majority of SMEs innovate. Therefore,the practical and scholarly issue is to figure out what suchimpressive innovative performances mean. On the negativeside, such performances mean that a minority of firms(21.9% in the case of our study) claim that they have neitherimproved their products nor their production processes inthe last 3 years. In itself, such a non-innovative performancesuggests that, in a context where a vast majority of firmsinnovate, a significant minority of established manufactur-ing SMEs are progressively losing their competitive advan-tages and are facing the risk of losing their existing clients.Policy makers and innovation scholars should start to paymore attention to these non-innovative firms.On the positive side, one might assume that all the

innovative firms perform similarly with respect to the degreeof novelty of their innovations. We reject this interpretation tosuggest that the sub-group of these SMEs that have innovateddiffers very significantly with respect to the magnitude of thechanges they have brought in their products or processes.Actually, SMEs are likely located along a continuum where atone extreme, some innovative SMEs make minor changes intheir products or processes while, at the other extreme, othersbring major changes in their products or processes. Therefore,the challenge for policy makers as well as for innovationscholars is to capture performance differences in the degree ofnovelty of innovations, as well as to identify the factors thatexplain the degree of novelty of innovations. This papersuggests that measures of the degree of novelty of innovationsrelying on questions that ask firms to qualify the degree ofnovelty of their innovations as radical innovations, worldpremiere innovations, etc. may induce firms to overestimatethe degree of novelty of their innovations. Such overestima-tions are inasmuch likely that SMEs tend to have inaccurateperceptions of the existing products on the markets. Ourinterpretation of the literature is that the novelty of innovationis a matter of degree and that degrees of novelty might be

ARTICLE IN PRESSN. Amara et al. / Technovation 28 (2008) 450–463460

more validly measured by relying on indicators that can beused to construct indices of degrees of novelty of innovations.

There is as yet no consensus as to the most valid andreliable indices. However, there is consensus about the factthat what makes a product or a production process newinvolves changes in technologies and markets. In theconceptual part of this paper, we have claimed that thegreater the changes in technologies and markets, the greaterthe degree of novelty of innovations. This assumption hasled us to develop an index of the degree of novelty ofinnovation based on the risk associated with the lack ofknowledge that SMEs face when they make changes intechnologies and markets that are related to the develop-ment or improvement of products or production processes.Although this study contributes to advance knowledge onthis issue, we think that future research should develop otherindices in order to capture other dimensions that are relatedto changes in the products and production processes.

In this paper, we have suggested that in order to becomeinnovative or to increase the degree of novelty of theirinnovations, SMEs need to improve their learning capabil-ities, that is, their ability to create internally new knowledge,as well as to improve their ability to identify, assimilate andexploit knowledge from their external environment. Theconceptual framework of this paper has suggested thatincreasing learning capabilities increases the capacity ofSMEs to exploit their resources in order to develop productor process innovations. Moreover, we have suggested thatlearning capabilities that firms can mobilize to innovate differfrom one firm to another, and that increasing the degree ofnovelty of innovations requires more or higher learningcapabilities than is the case for innovations that involve lowerdegrees of novelty. The independent variables used to explainthe presence (or absence) of innovation and the degree ofnovelty of innovation draw upon the literature on learning,which with respect to innovation, has stressed the importanceof five types of learning: learning by searching, learning bytraining, learning by doing, learning by using and learning byinteracting. What was the performance of these explanatoryvariables in the two econometric models?

Learning by searching (percentage of sales in R&D andpercentage of employees in R&D), learning by training(training employees in relation to product/process innova-tion) and learning by using (number of different advancedtechnologies used by firms) explain the presence ofinnovation, as well as the degree of novelty of innovationof established SMEs. These results suggest that owners andmanagers of SMEs and policy makers who aim to increasethe presence of innovation or its degree of novelty shouldconsider increasing their investments in learning bysearching, learning by training, and learning by using.

The results also show that learning by doing, such asmeasured by the percentage of sales exported outside thecountry, is related to the presence of innovation, but notto the degree of novelty of innovation. Furthermore,the results also show that learning by interacting likewiseinfluences the degree of novelty of innovation. Hence,

improving information networks and research networkscontributes to increase the degree of novelty of innovationof established SMEs, while changes in business networkswith clients and suppliers have no impact on the degreeof novelty of innovation. Such results make sense sinceone can expect that ideas and information provided by clientsand suppliers are more likely about minor changes than moremajor changes in products and processes, whereas ideas andinformation discussed in research networks are more likelyabout major than minor changes in products and processes.Likewise, the results show that increases in the percentage ofpurchases from the three most important suppliers have anegative impact on the propensity to increase the degree ofnovelty of innovations, while increases in the percentage ofsales to the three most important clients have no impact onthe degree of novelty of innovations. This suggests thatincreasing dependence on major suppliers works against thedevelopment of major products or process innovations,whereas increasing dependence on major clients has noimpact on the development of major innovations. Theseresults carry important implications. They suggest thatowners and managers of established SMEs and policy makerswho aim to increase the degree of novelty of their innovationsshould invest resources in the improvement of informationand research networks, while they should aim to limit thedependence of SMEs on a small number of suppliers.The results of this study also contribute to add new

evidence to the debate about the impact of firms’ size onthe degree of novelty of innovations. Indeed, our findingsprovide new ammunitions to those who argue that asestablished SMEs grow, they are more likely to have thefinancial and technical capabilities to bear the costsrequired to increase the degree of novelty of innovations.Finally, the results of the computation of the marginal

impact of the variables that significantly explain the degreeof novelty of innovation also carry important implicationsabout where to invest resources efficiently in order tomaximize increases in the degree of novelty of innovations.The size of the coefficients suggests that in order tomaximize the returns of their investments, owners andmanagers of established SMEs, as well as policy makers,should invest resources in the improvement of research andinformation networks of firms (learning by interacting), aswell as in training (learning by training) and in promotionlinked to the introduction on the market of new orimproved products (learning by doing).One of the major hypotheses derived from the conceptual

framework of this paper was that increasing the degree ofnovelty of innovations would require higher and differentforms of learning capabilities from SMEs. The results of thisstudy did not support this hypothesis. With only a fewexceptions, the same forms of learning explain both theabsence/presence of innovations and the degree of novelty ofinnovations. This unexpected result might mean either thatsome or many of the concepts upon which the theoreticalframework rests should be questioned or that indicatorsused to measure the concepts should be modified or refined.

ARTICLE IN PRESS

ET

LNAGE

LNSIZ

ETECHIN

T

�.24

�.03

.09

.02

.41

.10

.04

.46

.07

.02

.40

.10

�.09

.26

.06

�.13

�.16

.001

�.06

�.12

�.006

.12

.48

.16

.001

.02

.02

.03

.11

.02

�.15

.08

.006

1.24

.01

1.01

1

N. Amara et al. / Technovation 28 (2008) 450–463 461

The main limitation of our study is that the findings didnot allow deriving implications for high-tech firms. Indeed,the sample used in this study only includes low- and mid-techSMEs. However, it is directly linked with the real populationof the region under study in which high-tech firms are almostinexistent. Finally, like most studies on innovation, our studyfocused on the innovation capacity rather than on thefinancial performance of firms, which is the ultimate goalthat all firms try to achieve. In future research, it would beinteresting to examine how the degree of novelty ofinnovation drives the financial performance of firms.

RESNET

INFON

�.01

.18

.15

.23

.04

.17

�.01

.14

.07

.26

�.16

�.06

�.14

�.09

.10

.16

.001

�.001

1.003

1

Appendix A

Distribution of enterprises in manufacturing industriesby technology intensiveness and correlations between

Table A1

Distribution of enterprises in manufacturing industries by technology

intensivenessa

Description of industries

1. Medium technology intensiveness Number of

firms

% of

innovative

firms

Petroleum and coal products manufacturing 1 100.0

Basic chemical manufacturing 27 96.3

Rubber products manufacturing 4 100.0

Plastics products manufacturing 29 79.3

Non-metallic mineral products manufacturing 24 75.0

Primary metal manufacturing 8 75.0

Fabricated metal products manufacturing 111 69.4

Industrial machinery manufacturing 42 95.2

Electrical and electronic products

manufacturing

21 95.2

Motor vehicle manufacturing 21 66.7

Total medium technology 288

2. Low technology intensiveness

Food manufacturing 52 78.8

Beverage and tobacco products

manufacturing

4 100.0

Textile product mills 14 92.9

Clothing manufacturing 22 68.2

Leather and applied products manufacturing 2 100.0

Wood products 90 74.4

Veneer, plywood and engineered wood

products manufacturing

54 75.9

Paper manufacturing 8 75.0

Printing and related support activities 52 78.8

Total low technology 298

Other manufacturing industriesb 53 77.4

Grand total 639 78.1

aThe aggregation of industries is proposed by Innovation Policy Branch

of Industry Canada. It follows the OECD’s taxonomy and is based on the

intensity of R&D (Hatzichronoglou, 1997).bThe firms operating in other manufacturing industries are not classified

in terms of technology intensiveness. Table

A2

Correlationsbetweenexplanatory

variables

Variable

(tolerance

statstics)a

PERR&D

PEEMR&D

TRAIN

LNEXPORT

MARKET

CLIE

NT

SUPPLY

USIN

GBUISNET

PERR&D

(.839)

1.27

.10

.13

.19

.14

.07

.16

�.02

PEEMR&D

(.685)

1.28

.33

.30

�.11

�.15

.36

.01

TRAIN

(.782)

1.19

.27

�.04

�.05

.36

.02

LNEXPORT

(.814)

1.28

�.09

�.04

.33

�.04

MARKET(.847)

1�.12

�.10

.27

.03

CLIE

NT(.745)

1.41

�.04

.06

SUPPLY

(.748)

1�.06

.02

USIN

G(.650)

1.12

BUISNET(.688)

1

RESNET(.535)

INFONET(.410)

LNAGE

(.778)

LNSIZ

E(.526)

TECHIN

T(.941)

aTolerance

statistic

values

are

betweenparentheses.

ARTICLE IN PRESSN. Amara et al. / Technovation 28 (2008) 450–463462

explanatory variables are shown in Tables A1 and A2,respectively.

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