Exploring the Link between Innovationand Firm Performance

Yannis Hatzikian

Received: 29 May 2012 /Accepted: 18 December 2012# Springer Science+Business Media New York 2013

Abstract In the context of this paper, we examine the relationship between innova-tion and firm performance for Greece. We test the hypothesis that there is a U-shapedrelationship between innovation and firm performance, in the short-term period. Weapply the method for examining nonlinearities, that is, the introduction of squaredterms as independent variables. The collected variables are used in a multiple linearmodel formulation to evaluate the relative performance associated with them. For thisreason, a number of variables are used, like the innovation intensity, the squared termof innovation intensity, the R&D personnel, and the productivity, as well as the firmsize as control variable. We rely on the final results of a research project on women ininnovation, technology, and science, based on 372 questionnaires selected on a 2-yeartime period (2004–2006).

Keywords Innovation . R&D . Productivity . Policy

Introduction

In the context of this paper, we examine the relationship between innovation andfirm performance for Greece. We test the hypothesis that there is a U-shapedrelationship between innovation (innovation expenditure as the percentage ofsales per year) and firm performance (productivity in terms of sales per employee), inthe short-term period. Avariety of studies have examined the link between new productinnovation and sales growth. Some research supports the claim that innovation ispositively associated with rapid growth within small firms (Geroski and Machin 1993;Roper 1997; Wynarczyk and Thwaites 1997; Moore 1995). Geroski (1991a) found apositive link between the rates of entry and innovation. Geroski (1989, 1991b)

J Knowl EconDOI 10.1007/s13132-012-0143-2

Y. Hatzikian (*)Department of Energy Technology, TEI of Athens, Agiou Spyridonos str., 122 10 Aegaleo, Attika, Greecee-mail: hatzikia@otenet.gr

document a productivity enhancing effect of market entry on the industry level. Marketentry is often used as a vehicle for introducing new innovations (Geroski 1995).

New innovative firms challenge incumbents that are often more interested inprotecting their existing position than in seeking new business opportunities. Incum-bents are then forced to increase their R&D investment in order to acquire a lead overtheir rivals due to a more competitive environment. Thus, more resources areallocated to R&D via growing incentives to innovate. Sterlacchini (1989) suggeststhat innovation has a significant and positive effect on productivity growth for mostof the period, except during between 1973 and 1979. However, since each ofSterlacchini’s regressions contain only 15 observations (the number of industries inthe cross-section), the study has very few degrees of freedom, and also imposes theconstraint that the innovation to productivity relationship is the same in each industry.Hall and Bagchi-Sen (2002) find a positive relationship between new product intro-duction and redesigned products and total revenue growth, but only within thebiotechnology sector. Some of the inconsistencies in past research can be explainedby comparing innovators and non-innovators in different sales turnover growthcategories. Furthermore, Hall and Bagchi-Sen (2002) suggest that the lack of employ-ees in marketing functions is a barrier to innovation for firms and also conclude thatmarketing knowledge has a positive impact on firm’s performance.

Mansury and Love (2008) found that the presence of service innovation and itsextent has a consistently positive effect on growth, but no effect on productivity andthere is evidence that the growth effect of innovation can be attributed, at least in part,to the external linkages maintained by innovators in the process of innovation.External linkages have an overwhelmingly positive effect on (innovator) firm perfor-mance, regardless of whether innovation is measured as a discrete or continuousvariable, and regardless of the level of innovation considered. Jansen et al. (2006)distinguish between exploratory and exploitative innovation. They suggest thatcentralization negatively affects exploratory innovation, whereas formalization pos-itively influences exploitative innovation. Interestingly, connectedness within unitsappears to be an important antecedent of both exploratory and exploitative innova-tion. Furthermore, their findings reveal that pursuing exploratory innovation is moreeffective in dynamic environments, whereas pursuing exploitative innovation is morebeneficial to a unit’s financial performance in more competitive environments.

Drews (1998) questioned the innovative capacity of pharmaceutical companies andsuggest that consolidation continues to be a major feature (of the pharmaceuticalindustry), largely due to the impact of the “innovation deficit”. Damanpour et al.(1989) found that administrative innovations led to technical innovations (in publiclibraries) and they imply that organizational (re)structuring leading to administrative andstructural renewal or improvement is a facilitator for the other types of innovations.

The literature provides us with several indicators of the innovation developmentactivity. R&D intensity is used in the literature as measure of intangible assets (e.g.,Delios and Beamish 1999; Lu and Beamish 2004; Qian and Li 2003; Chiao and Yang2011). Traditionally and still the most popular input indicator is the figure onexpenditures on R&D (Loof and Heshmati 2002). In these studies, the innovationexpenditures are divided by total sales to define the R&D or innovation intensity of acompany. Kotabe et al. (2002) indicated that previous studies took intangible assets ascontrol variables.

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The introduction of the Community Innovation Surveys (now Innovation Surveys)led to an increasing variation in measures (e.g., Griffith et al. 2006; Mairesse andMohnen 2005; Loof and Heshmati 2002). In measuring firm performance, variousconcepts are found: sales per employee, export per employee, growth rates of sales,total assets, total employment, operation profit ratio, turnover, and return on invest-ment (Loof and Heshmati 2002). Innovation expenditure, as defined in Oslo Manual(OECD 2005) includes both R&D and non-R&D innovation activities and it is awider measure and much richer than the classical R&D expenditures data.

In the knowledge-based economy (European Commission 2004), innovation hasobtain the central role in the business world achievements. In addition to traditionaltechnological innovation, there is innovation through new business models, newways of organizing work, and innovation in design or marketing. Managing andexploiting to the best effect of all these different kinds of innovation represents amajor challenge to businesses (Foray 2000). The emphasis in the literature is clearlyon an increasing relevance of knowledge and innovation as an input to productionand innovative processes (OECD 2001).

The term the “knowledge-based economy” stems from a “fuller recognition of therole of knowledge and technology in economic growth” as well as from the increas-ing contribution of high-tech sectors (such as computers, electronics, and aerospace)to national output and employment (OECD 1996). The creation of knowledge and itsassimilation are part of a complex process. Firms need to absorb, create, andexchange knowledge interdependently. In other words, innovation and diffusionusually emerge as a result of an interactive and collective process within a web ofpersonal and institutional connections which evolve over time (OECD 2009).

Knowledge transfer may occur through disembodied or equipment-embodieddiffusion. The latter is the process by which innovations spread in the economythrough the purchase of technology-intensive machinery, such as computer-assistedequipment, components, and other equipment. Disembodied technology diffusionrefers to the process where technology and knowledge spread through other channelsnot embodied in machinery.

The evidence suggests that during the l980s, all advanced countries appear to havebeen confronted with a reduction and in some cases even a collapse in the demand forunskilled labor. The reduction and/or collapse in the demand for unskilled labor isexplained partly as a result of technical change, partly as a result of their opening upto international trade. However, different countries appear to have responded indifferent ways. In the USA, labor market adjustment has led to a substantial declinein real wages for the least-educated and least-skilled workers: in the European Union,it has led to much higher levels of unemployment in the unskilled labor force. In othercountries, such as Canada, most of the adjustment has occurred through adjustmentsin labor time (OECD 2002a). The overall long-term tendency towards a more strongknowledge-based economy, in terms of both input proportions and the nature of theoutput, is accelerating. At the firm level, this is reflected in the fact that the shift in thedemand for skills is strongest in firms introducing information technology.

As to innovation management, the first explicit theory is the “technology pushtheory or engineering theory of innovation”. According to this theory, basic researchand industrial R&D are the sources of new or improved products and processes. Theproduction and uptake of research follows a linear sequence from the research to the

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definition of a product and specifications of production. An alternative view, in the1960s, gave birth to the “market pull theory of innovation”. This theory still gives acentral role to research as a source of knowledge to develop or improve products andprocesses. This theory sees the first recognition of organizational factors as contrib-utors in innovation theory; the technical feasibility was still considered as a necessarycondition of innovation, but no longer sufficient in itself for successful innovation(Schmookler 1996; Myers and Marquis 1969).

A new generation called the “chain-link theories” of innovation then emerged toexplain the fact that linkages between knowledge and market are not as automatic asassumed in the engineering and market pull theories of innovation (von Hippel 1994).At the end of the 1980s and during the 1990s, a technological networks theory ofinnovation management was developed by a new group of experts under the label of“systems of innovation”. This view stressed the importance of sources of informationthat are external to the firm: clients, suppliers, consultants, government laboratories,government agencies, universities, etc. (Nelson 1993; OECD 1999). Finally, “thesocial network theory” of innovation management states that knowledge plays a morecrucial role in fostering innovation and “open innovation” assumes that “firms canand should use external ideas as well as internal ideas, and internal and external pathsto market” (Chesbrough 2003). Recently, it is developed the “Quadruple InnovationHelix concept” (Carayannis and Campbell 2009), according to which, government,university, and industry should work effectively with civil society to support andpromote innovation and entrepreneurship.

The growing importance of knowledge as a production factor and as a determinantof innovation can be explained by the continuous accumulation of technical knowl-edge over time, and by the use of communications technologies that makes thatknowledge available very rapidly on a worldwide scale (Foray 2000). It is thereforeof interest to look at the productivity problems faced by enterprises when trying tocarry out innovation.

Literature Review: The Nonlinear Relationship between Innovationand Productivity

Measures of the impact of innovation on enterprise performance are among the mostimportant innovation indicators. The impact of innovations on enterprise performancerange from effects on turnover and market share to changes in productivity andefficiency (OECD 2005). The productivity effects of innovative activities has beenone of the most challenging issues in empirical economics for several decades(Griliches 1958; Mansfield 1965).

Recently, this research topic has been enforced by new theoretical underpinningsfrom endogenous growth theory showing that economic output is supposed to bepositively correlated with the flow of new products including both radical andincremental innovations (Romer 1990; Aghion and Howitt 1998). The majority ofstudies on the relationship between innovation and firms’ economic performance usesthe production function approach, where different measures of firm performance(mainly productivity) are explained by several independent variables such as physicalcapital, human capital, R&D and other innovation-related investments as well as firm

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size. Within the production function approach, the innovation process itself is treatedas a black box, if it is treated at all. Most studies on R&D expenditure find it to have anet positive effect on both value added and turnover, although the advantages of R&Ddecline when its effect is evaluated over time (Huang and Liu 2005). Arundel et al.(2003) report that almost all studies find a positive and significant relationshipbetween innovation and different measures of firm performance.

A variety of relationships between intangible assets, R&D investment, innovationoutput, and firm performance has been found concerning nonliner relationships, U-shaped curves, inverted U-shaped curves, and horizontal S-shaped curves. Theinverted U-shaped curvilinearity is generally supported by researchers (Gomes andRamaswamy 1999; Hitt et al. 1997; Qian 2002). Some studies have found morecomplex relationships, such as U shape (Ruigrok and Wagner 2003) and S shape (Luand Beamish 2004).

Many researchers have explained curvilinear relationships between R&Dexpenditure with little consistency across studies. Support for a U-shapedrelationship is found using the organizational learning theory (e.g., Ruigrokand Wagner 2003), an inverted U-shaped relationship level, based on anincrease in organizational costs as the diversity grows beyond the optimal level(e.g., Gomes and Ramaswamy 1999), a horizontal sigmoid relationship,connected to multistage approaches (e.g., Lu and Beamish 2004). These studieshave used a diversity of theoretical approaches, from the finance theory ofportfolio diversification, the resource base view, to organizational learningtheory, to predict a generally positive relationship between R&D expenditureand performance (Kim et al. 1993; Ruigrok and Wagner 2003).

Systemic innovation relies on the dynamic capabilities of a firm, which can bedefined as the “firm’s ability to integrate, build, and reconfigure internal and externalcompetences” (Teece et al. 1997; Teece 2007). Institutionalized capabilities crystal-lize into routines, which are the so-called “genes” in innovation (Nelson and Winter1982). Schumpeter was radical (Rosenberg 1994) because he broke conventionaleconomic theories in three ways, which later inspired the development of the threeschools (the capability school, the corporate entrepreneurial school, and the culturalschool) (Tzeng 2009).

The research on innovation is not limited to the Schumpeterian tradition. There areother equally important schools that ground their theories on classical sociologists,philosophers, and the natural sciences. The three Schumpeterian schools mentionedin this work represent only a fraction of the effort to advance the understanding ofinnovation (Tzeng 2009). There are other important schools such as the configurationschool (e.g., Miller and Friesen 1982), the complex adaptive systems school (e.g.,Anderson 1999), the cluster school (e.g., Porter 1990), the knowledge managementschool (e.g., Nonaka 1994), and the population ecology school (Aldrich and Martinez2001, cited by Tzeng (2009)).

Conventional economists favor an equilibrium model and treat change as exoge-nous. In contrast, Schumpeter adopted a disequilibrium perspective and treatedeconomic change as evolutionary and technology as endogenous (Schumpeter1934). According to the above theory and literature, higher investments of innovationwill be able to achieve a better performance. However, this result did not concludewith any consistent support. Based on the theory of the growth of a firm, a company

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has no way to extend itself without limitation because the management capability of acompany limits the growth of the company (Penrose 1959).

Resource-based theory strongly supports the idea that knowledge resources are ofprimary importance to start-ups. It also provides a rationale that knowledge isdependent upon the unique constellation of members in the entrepreneur’s network,the media, or means through which communications take place, and the frequency ofsuch communications (West III 2003). Networking can thus produce a type ofknowledge that is valuable, rare, inimitable, nontradable, and nonsubstitutable (Westand Noel 2009). In contrast, knowledge gained through industry and businessexperience or through previous start-up experience, though valuable, is not necessar-ily as unique and rare. When firms are facing high competition, enterprises have tohave the ability in innovation and networking to, at least sustain capability. Therefore,firm’s resources in order to develop innovation activities should have a positiveimpact on firm performance (Huang and Liu 2005).

Knowledge resources (based on innovation activities development) provide theinitial foundation for competitive advantage. Through a variety of information-processing activities, the firm develops asymmetric knowledge about the opportu-nity’s real potential (West III 2003). Strategic and organizational changes applied inbusiness should take into account the challenge of the new knowledge economy.Within the firms that actually implement strategic and organizational changes, theperspective involved is that strategic and organizational changes can help their firmsto foster competitive advantages by increasing flexibility and efficiency.

In the resource-based view (Wernerfelt 1984; Barney 1986, 1991; Prahalad andHamel 1990; Peteraf 1993; Conner 1991), knowledge is seen as a strategic asset withthe potential to be a source of sustainable competitive advantage for an organization.Knowledge resources include the understanding of how to start up new organizations,how to manage people and processes, how to attain growth and competitive position,and how to stage technology and new product development (Brush et al. 2001;Wiklund and Shepherd 2003, cited by West and Noel 2009). The knowledge viewtherefore has implications not only for new venture start-up performance, but also forlonger-term growth (West and Noel 2009).

A significant finding within the resource-based view is that a firm’s com-petitive advantage arises from managerial knowledge. Management’s key role isto identify and evaluate resources (Barney 1991) and then decide whichresources to invest in and how to utilize them (Castanias and Helfat 1991).To the extent that managers are more adept in organizing and integratingunderlying resources, firms will be able to compete more effectively (Kogutand Zander 1992). Grant (1995, 1996) discussed the facets of knowledgeintegration and coordination capabilities that are a source of competitive ad-vantage for the firm. He pointed to four mechanisms for integrating specializedknowledge: (1) rules and directives, (2) sequencing, (3) routines, and (4) groupproblem solving and decision-making. Hall (1992) produced a framework forstrategic analysis of intangible resources leading to sustainable competitiveadvantage. Some of the more interesting findings were that employee know-how and reputation are perceived as the resources that make the most importantcontribution to business success and that for most companies operations is the mostimportant area of employee know how.

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Prahalad and Hamel (1990) proposed the notion of core competencies relating tothe internal capabilities of organizations. They emphasized the application of “invis-ible” assets, innovation, leadership and competencies, or knowledge as the basis forcompetitive viability.

In the context of the knowledge economy and at the firm level, it is important toexamine the intangible assets with respect to the “nontechnical” alongside to thetechnical aspects of innovation, such as management techniques, organizationalchange, design, and marketing issues. In the economy of knowledge (OECD 2001),theory and statistical data point out that innovation does not constitute an individualactivity of enterprise. On the contrary, the new technologies influence the total ofoperations and the organization (Kitsos and Hatzikian 2006).

There is an increasing interesting in innovations in service sectors due to theirincreasing significance in the economies. Barras (1986, 1990), who, centering onICT-based innovations in services, proposed a conceptual model about the phases inthe use of ICT in services, rather than a model of innovation in services.

Barras (1986, 1990) introduced the “reverse product cycle” to underline that, as a resultof the information intensity of services, the innovation has special characteristics thattranslate into a product cycle “reverse” as compared to the traditional one. The transcen-dence of the reverse product cycle meant a change in the vision of the literature aboutinnovation in services, from considering them non-innovative to a perspective whichconsiders them potential, even “real” innovators, especially through the use of ICTs.

Some authors (Djellal and Gallouj 2001) stress on nontechnological innovation inservices and they point out that services can be sources of innovation by themselves.Their main assumption is that consumers are interested in satisfying their needs(functions), independently of whether the means of doing so is by a product or aservice. Six modes of innovation in services are identified: radical innovation,improvement innovation, incremental innovation, ad hoc innovation, recombinativeinnovation, and formalization innovation (Djellal and Gallouj 2001). According toNijssen et al. (2006), manufacturing firms increase their amount and range of serviceofferings and the boundaries between goods and services become blurred. Hence, ageneral description for innovation, applicable both to goods and services, becomesrelevant. The results indicate a support for an integrated perspective that there aremany similarities when in terms to success drivers (Nijssen et al. 2006). An entre-preneur’s cognitive passion has a significant positive effect on venture capitalists’funding decisions, while the effect of affective passion is insignificant (Chen et al.2009, cited by Laaksonen et al. 2011). Der Foo et al. (2005) argue that task relateddiversity of team members might enhance team effectiveness, while nontask diversitysuch as age might have negative effects.

Human capital can improve innovation development and the ICT activities of anorganization. Innovation and ICT, as knowledge resources can improve the quality ofhuman capital (Huang and Liu 2005). As the first fundamental expression of therelationship between the market opportunity and a new venture’s behavior, this newunderstanding provides the strategic foundation for moving forward with the newventure idea and relative resources.

The research questions of this paper are: What is the relation between innovationintensity and labor productivity? What is the relation between R&D personnel andlabor productivity? For addressing these research questions, we formulate hypotheses

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to be empirically tested through the use of multiple linear regression. The hypothesespresented below aim to identify the behavior of innovation intensity in relation to thelabor productivity of the Greek firms. According to the above review, we propose thehypotheses:

H1. The relation between innovation intensity and labor productivity is a non-linear one and it takes the form of a U-shaped curve.

H2. The percentage of R&D personnel in total of employees per firm is related tolabor productivity.

Methodology

Conceptual Framework

In Fig. 1, the conceptual framework of this work is presented. It shows that theinnovation intensity influences labor productivity at the firm level in the short-termperiod, and, also, the R&D personnel affects on labor productivity and the firm size interms of number of employees.

Data Presentation

The survey data provided are based on the corresponding data of the research projectentitled: “Woman and Innovation: The determinants factors and the obstacles ofinnovative activities of Greek firms: 2004–2006, TEI of Athens” funded by theEuropean Union and the Greek Ministry of Education.

The methodological basis of our survey is provided by the “Oslo manual”, a jointpublication of Eurostat and the OECD (1993, 2005). The questionnaire is based onthe Community Innovation Survey. The initial survey methodology was adapted andadjusted by the research team. Using the database of ICAP AE (ICAP SA is thelargest Business Information and Consulting firm in Greece) containing data of

Fig. 1 Model specification of this work

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63,000 firms, covering all over Greece, the selection of the sample was based onrandom sampling techniques and, finally, 372 questionnaires have been collected,covering the whole country—Greece. Not all of the 372 contacted firms wereinnovative and only a proportion of them concerned firms implemented (product,process, and nontechnological) innovation. To avoid the influence of outliers, wedelete samples where the value of dependent and independent variables are more thanthree sigmas of the mean. Finally, 71 firms are used in the final analysis. These firmswere all with innovation activities. The main statistical unit for the survey is theenterprise. In general, innovation activities and decisions usually take place at theenterprise level, which leads to the enterprise being used as the statistical unit.

The following industries are included in the core target population of the survey:manufacturing (NACE 15–37); electricity, gas, and water supply (NACE 40–41),wholesale trade (NACE 51), transport, storage, and communication (NACE 60–64);financial intermediation (NACE 65–67); computer and related activities (NACE 72);architectural and engineering activities (NACE 74.2); technical testing and analysis(NACE 74.3); research and development (NACE 73); construction (NACE 45);motor trade (NACE 50); retail trade (NACE 52); legal, accounting, market research,consultancy, and management services (NACE 74.1); advertising (NACE 74.4); laborrecruitment and provision of personnel (NACE 74.5); investigation hotels and restau-rants (NACE 55); and renting of machinery and equipment without an operator(NACE 71). In the context of this work, we follow the Community Innovation Surveyand our survey is structured along Oslo Manual guidelines (OECD 2005).

The importance of innovation in the services sector and of the servicessector’s contribution to economic growth is increasingly recognized and hasled to a number of studies on innovation in services (OECD 2005). However,we examine the sectors of the economy in total and we do not focus inmanufacturing and services separately due to different reasons. One reason isthat much innovation performance measurement in the services sector in gen-eral, is originated from the Community Innovation Survey, which we adapted inour research (Adams et al. 2008). Most of the available innovation indicatorsfrom such sources (e.g., R&D, patents, total innovation expenditures, andinnovation sales shares) were designed to cover technological innovation inthe manufacturing sector, and, as such, are only partially adequate measuresof innovation performance in the services sector (Kanerva et al. (2006) cited byAdams et al. (2008)). It is recognized that innovation in services sectors candiffer substantially from innovation in manufacturing sectors (OECD 2005).According to Frascati Manual (OECD 2002b), however, distinguishing R&Dand other innovation activities is particularly difficult for services due to thefact that innovation activities in services tend to be less formally organized, andthat R&D is less well defined for services than for manufacturing. Anotherreason is that it is accepted that the conceptual development in addressingservice sector innovation performance measurement is recent, but limited(Adams et al. 2008). The services sector is diverse and it may concern servicesdealing mainly with goods, those dealing with information, knowledge-basedservices, and services dealing with people (Howells and Tether 2004 classifi-cation, cited by OECD (2005)). However, the distinction between products andprocesses is often blurred, with production and consumption occurring

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simultaneously and, also, innovation activity in services also tends to be acontinuous process, which complicates the identification of innovations inservices (OECD 2005). Furthermore, it is emphasized that, in services, salesare often not an appropriate output indicator and do not work as an indicator ofeconomic impact of innovative activities and that a more appropriate measuremight be based on cost reductions due to service innovations (Evangelista andSirilli 1998; Evangelista et al. (1998) cited by Adams et al. (2008)). A widerange of manufacturing and services industries have increased their use ofknowledge-intensive technologies for production processes and service provi-sion. Finally, we recognize that, due to the small sample limitation of thisstudy, it was not possible to examine other issues among others the industrystructure. We intend to examine such issues as industry structure in future.

The research project is conducted from 2004 to 2006. The observation periodcovered by the survey is from the beginning of 2000 to the end of 2003. The referenceperiod of the survey is the year 2003. The analysis of the data and results areconducted using SPSS 17 and multiple linear regression. We apply the method forexamining nonlinearities, that is, the introduction of squared term as independentvariable (Gomes and Ramaswamy 1999).

Variables and Measures

The variables used in the analysis are presented in Table 1.

Dependent Variable (Labor Productivity) The respondents were asked to define thetotal turnover market sales or sales revenue of goods and services (in currency units)which is the total amount of money that the firm has earned from the sales of all itsproducts during a given time period. Furthermore, the respondents were asked todefine the number of employees during a given time period (It was asked the annualaverage or, alternatively, the number of employees at the end of the year). Laborproductivity, in the context of this work, is the ratio between turnover or annual salesand the number to employees in the reference year (i.e., the last year of the observa-tion period: 2003), log-transformed to a normal distribution.

Independent variables (Innovation intensity and R&D personnel) The respondentswere asked to estimate the total innovation expenditure (in currency units) in thereference year (2003). Innovation or R&D intensity is the ratio between innovation

Table 1 List of variables used to multiple regression

Variables Variables code Description of variables

Labor productivity Ln (PROD) Ln (annual sales per number of employees): 2003

Innovation intensity INNO Innovation expenditure per annual sales: 2003

Squared Innovation intensity INNO2 Squared innovation expenditure per annual sales: 2003

R&D personnel RDP The percentage of R&D personnel in total ofemployees: 2003

Firm size Ln (SIZE) Ln (number of total employees)

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expenditure (or R&D expenditure) and annual sales in the reference year (2003). It isan independent variable. Total innovation expenditure concerns innovation activitiesapplied by the firm in 2003 by implementing new or significantly improved products(goods/services) or processes based on science, technology, or other knowledgeareas, including not yet completed or abandoned innovation activities. In the contextof this work, we employ the definition of innovation expenditure given by the Oslomanual (OECD 2005). Total innovation expenditure concerns innovation activitiesapplied by the firm by implementing new or significantly improved products (goods/services) or processes based on science, technology, or other knowledge areas,including not yet completed or abandoned innovation activities (OECD 2005). Inthe Oslo Manual, the total innovation expenditure may include expenditure on theinnovation activities applied by the firm concerning intramural research & experi-mental development (R&D), acquisition of R&D (extramural R&D), acquisition ofmachinery and equipment, acquisition of other external knowledge, training, marketintroduction of innovations, design, and other preparations for production/deliveries.R&D personnel are the percentage of Research and Development personnel from thetotal number of employees per firm in the reference year (2003).

Control variable: Firm size Performance is often correlated with firm size, and so ameasure of firm size is used. The respondents were asked to indicate the number oftheir employees in the reference period (2003). The number of employees, log-transformed to a normal distribution, served as a proxy for firm size.

Empirical Model

The method for examining nonlinearities is the introduction of squared terms asindependent variables (Gomes and Ramaswamy 1999). Thus, this work adoptsthis method and increases the squared terms of innovation intensity to examinethe nonlinear relationship between innovation and productivity. The variable to beexplained, the dependent variable, is the labor productivity. We employ multipleregression models to examine the relationships of innovation intensity, the R&Dpersonnel, the firm size, and the labor productivity. The error term, ε, representsthe collective unobservable influence of any omitted variables. Based on thehypothesis presented in the “Literature Review: The Nonlinear Relationshipbetween Innovation and Productivity” section, we test the significance of thefollowing regression model:

PROD ¼ b0 þ b1INNO þ b2INNO2 þ b3RDPþ b4SIZEþ " ð1Þ

where:

PROD Labor productivityINNO Innovation intensityINNO2 The squared term of innovation intensityRDP R&D personnelSIZE Firm sizeε The error term.

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Empirical Results

Descriptive Statistics

Table 2 presents the descriptive statistics for each variable. The firms in this samplehave an average level of labor productivity of about ln (11.40). On average, the firmsin our sample spent about 0.78 % of their annual sales on innovation activities. Theaverage number of employees for the 71 firms in our sample is 51 persons, with awide range from 3 to 716, indicating that there is a high variation for firm size.

Table 3 shows a Pearson’s correlation matrix for all of the variables. Significantcorrelation among the independent variable and its squared term suggest the presenceof multicollinearity. To test further for the existence of multicollinearity, we utilize theCondition Index. With regard to the existence of multicollinearity, as Table 4 shows,all of the values of the Condition Index are under 15, indicating that there is noserious multi-collinearity (Belsley et al. 1980).

Hypotheses Testing

We conduct a hierarchical regression analysis to test the impact of innovationintensity on labor productivity (Tables 3, 4, and 5). As we can see from Table 4, inmodel 1 (not including the variable “SIZE”), the explanatory power for PROD(productivity) is R2=38.3 % and it reaches a significant level (F=13,877, p<0.01).In model2, the inclusion of the variable “SIZE” increases the explanatory power forPROD significantly (R2=49.5 %, F=16,148; p<0.01).

As we can see in model 2, the stronger between the two aforementionedmodels in terms of explanatory power (Table 4), the independent variable INNO(innovation intensity) is significant (t=−6,790, p<0.01) and its coefficient is:−271,383. So, for every unit increase in INNO, a 271,383 unit decrease inPROD is predicted. With regard to the independent variable INNO2 (thesquared term of innovation intensity), it is, also, significant (t=5,293, p<0.01)and its coefficient is 8,061.724. So, for every unit increase in INNO2, a8,061.724 unit increase in PROD is predicted. These results indicate a U-shaped relationship between innovation intensity and labor productivity. Thismeans that there is a negative contribution to firm performance in terms of laborproductivity at the beginning of innovation expenditure. Over time, however, asfirms learn from their innovation experience, their performance becomes positive.

Table 2 Descriptive statistics ofthe variables of this work

n=71

Variables Mean Minimum Maximum Std. Deviation

Ln(PROD)

11.4008 9.14 14.58 1.04038

INNO 0.0078 0.00002 0.03 0.00781

INNO2 0.0001 0.0000000005 0.001 0.00020

RDP 0.2940 0.00 1.00 0.33008

SIZE 51 3 716 3

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With regard to the independent variable RDP (the percentage of R&D personnel),it is not significant (t=0.227; p>0.05) and its coefficient is 0.075. Finally, theindependent variable SIZE (number of employees) is significant (t=−3,814, p<0.01) and the coefficient is −0.320. So, for every unit increase in SIZE, a −0.320unit decrease in PROD is predicted. The results support H1 concerning thenonlinearity relation between innovation intensity and they does not support H2concerning the relation between R&D personnel and labor productivity. Finally,the control variable SIZE (number of employees) has a negative significant resultin labor productivity.

The results, as far as they concern the innovation intensity and its relation to laborproductivity, are different from results of other studies (Huang and Liu 2005). Ourfindings concern a U-shaped relationship between innovation intensity and laborproductivity and not an inverted one. The difference concerns the minimum or themaximum point. In the case of the inverted U-shaped curve, we are looking for themaximum level while in the U-shaped curve, we are looking for the minimum level,which is the case of this study.

Table 3 Pearson correlation coefficient of variables

PROD INNO INNO2 RDP Size

PROD 1.000

INNO −0.446* (0.000) 1.000

INNO2 −0.302* (0.005) 0.952* (0.000) 1.000

RDP −0.048 (0.344) 0.316* (0.004) 0.264** (0.013) 1.000

SIZE −0.146 (0.112) −0.284* (0.008) −0.216** (0.035) −0.430* (0.000) 1.000

n=71* p=0.01, significance level two-tailed testb p=0.05, significant level two-tailed test

Table 4 Hierarchical regression analysis of the association between innovation intensity, R&D personnel,and labor productivity

Variable code Model 1 Model 2 Condition Index

Coefficient t value Coefficient t value

Constant 12.263 65.902* 13.777 31.909* 1.000

INNO −241.541 −5.620* −271.383 −6.790* 2.086

INNO2 7,191.576 4.356* 8,061.724 5.293* 2.767

RDP 0.529 1.560 0.075 0.227 9.651

Size −0.320 −3.814* 13.399

F value 13.877* 16.148*

R2 0.383 0.495

Adjusted R2 0.356 0.464

n=71* p=0.01, significance level, two-tailed test

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The relationship between innovation intensity and labor productivity is shown inFig. 2. Differentiating Eq. (1), we gain a critical minimum point of 1.68 %. It isindicating a U-shaped relationship between innovation intensity and labor productivity.

Table 5 One-sample Kolmogorov–Smirnov test

PROD INNO INNO2 RDP SIZE

N 71 71 71 164 71

Normal parameters Mean 11.4008 0.0078 0.0001 0.2940 3.9344

SD 1.04038 0.00781 0.00020 0.33008 1.23233

Most extreme differences Absolute 0.105 0.161 0.274 0.207 0.092

Positive 0.105 0.156 0.273 0.207 0.091

Negative −0.065 −0.161 −0.274 −0.187 −0.092Kolmogorov–Smirnov Z 0.883 1.354 2.305 2.651 0.777

Asymp. sig. (two-tailed) 0.417 0.051 0.000 0.000 0.582

Fig. 2 The relationship between innovation intensity and productivity

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This means that there is a negative relation with labor productivity at the beginning ofinnovation expenditure down to the 1.68 % (vertex) which is the turning point of the U-shaped curve. When the expenditures arrive at the above minimum level, continuousinnovation expenditures will increase the labor productivity to the contrary up to the xintercept which is 3.36 %.

Since the coefficient of x2 is positive (data are from Table 4, model 2) , the curvemust be concave up, in other words it is opens upwards. The axis of symmetry of theU-shaped curve is the vertical line which divides the curve into two “mirror-image”halves. It crosses the x-axis midway between the two x-intercepts (at x=0 and x=0.03361). The vertex of the graph is the point where the axis of symmetry meets thecurve itself. In this case, it is the lowest point (the “bottom of the valley”), since thecurve opens upwards. Since the vertex lies on the axis of symmetry x=−b/(2×a) (whereb=−271 and a=8062), its x-coordinate is at the point 0.016807 or 1.68 % ofinnovation intensity. The critical point of 1.68 % (the turning point of the U-shapedcurve) is the lowest point (the “bottom of the valley”) and indicates that when firmsspend about 1.68 % of their annual sales on innovation activities (innovation inten-sity), the influence of innovation expenditures on labor productivity will reach itslowest status. Furthermore, the level of innovation intensity is lower at any point ofthe curve up to the level of innovation intensity between x=0 and x=0.03361, thanthe level of innovation intensity beyond x=0.03361 (x intercept). It is moving “in thenegative direction”. It is the negative aspect of innovation intensity or, in other words,the “dark side of the moon”. In conclusion, the downside of innovation intensityoutweighs the upside of it up to the x=0.03361 or 3.36 % level of the innovationintensity. As Fig. 2 shows, the Greek firm’s innovation intensity is at 0.78 %. Itseems, that, in order to maximize its performance in terms of labor productivity, theyshould continuously invest in innovation activities experiencing a decrease of laborproductivity on the downside at 1.68 % and experiencing an increase of laborproductivity at 3.36 % level of the innovation intensity. After that point (3.36 %),the innovation expenditure is moving “in the positive direction”.

Normality Test

As we can see in Table 5, the variables Ln (PROD), INNO, and Ln (SIZE) follow thenormal distribution and the variable RDP does not. Nevertheless, this variable isinsignificant, the model fits and its explanatory power is strong.

Conclusion

According to the results of this work, the relation of innovation intensity with laborproductivity is nonlinear. It seems that innovation intensity does not follow aninverted U-shaped curve with relation to labor productivity. Rather, it follows a U-shaped curve where the innovation development, in terms of innovation intensity, inthe “initial stages”, is diminishing, reaching a critical point at the minimum level oflabor productivity and, after that minimum point, it is increasing together with theincrease of the innovation expenditure but at the downside level which exceeds the

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upside level at certain point on the x-axis. In the case of the inverted U-shaped curve,we are looking for the maximum level while in the U-shaped curve we are looking forthe minimum level, which is the case of this study. This means that the firms initiallyexperience negative performance when invest on innovation capital. Over time,however, as firms learn from their innovation development experience, their perfor-mance becomes positive. Furthermore, this study calculates the minimum level ofinnovation expenditure per annual sales as 1.68 % for the Greek firms up to 716employees. Given that the firms in our sample spent, on average, about 0.78 % oftheir annual sales on innovation activities, this shows that the firms should continue toinvest in innovation development despite experiencing negative performance up to1.68 %. Therefore, the firms should frame their strategy based on that minimum levelof innovation intensity and make appropriate resource allocations.

These findings are significant for the firm, the managers, and the policy makersdeveloping innovations because, in that way, they are directed to identify the mini-mum point of the innovation intensity trajectory and to avoid, let us say, theinnovation failure shock, in developing innovation, knowing that reaching thisminimum point the labor productivity is decreasing.

In policy making level, the aforementioned firm strategies should be supportedby a collection of top-down policies as well as bottom-up initiatives, includingstrong R&D policies and funding, but going beyond that to the development ofinnovation networks and knowledge clusters across regions and sectors(Carayannis and Campbell 2010). It is fact that the intensity of the effect ofinnovation (and of ICT investment) on productivity rates vary in a considerableway, depending on the context of the study, the time period considered, and theindicators chosen tomeasure the variables innovation and productivity. Furthermore, theselection of the best productivity measure is raised in several studies. Some authorsargue for the positive relationship between innovation and productivity and others arguefor the negative relationship between innovation and productivity (Faria 2004). Thetheoretical arguments for the positive relation may concern the adoption of newtechnologies, the adjustment costs, the lag between the growth in investmentand its benefits, setting up new equipment, training of employees, problems on theintroduction of the innovation stage related with liquidity constrains, etc. (Bessen 2002;Leung 2004).

The theoritical arguments for the negative relation may concern learning issues suchas new skills necessary to adopt correctly new technologies, time and costs of theadoption process, learning cost, and difficulties to change technology (Jovanovic andNyarko 1996; Ahn 1999). According to some authors, the negative relation betweeninnovation and productivity is due to technology and organizational rigidities includingless effective new technologies in early stages than existing ones, changes on manage-ment techniques in order to coordinate the firm’s resources with the innovation, reluc-tance to switch to new technologies with subsequent significant productivity losses,focus on to existing routines, lack of innovation culture and commitment, etc. (Utterback1994; Benner and Tushman 2002; Tripsas and Gavetti 2000).

The limitation of this study is the small sample. Due to that limitation, it was notpossible to examine other issues such as product and process innovation, industrystructure, competitive pressures, untechnical innovations, cross-country relations,longer period of time, and other internal and external factors in general.

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