Barriers to Innovation among Spanish ... - Wiley Online Library

Journal of Small Business Management 2009 47(4), pp. 465–488

Barriers to Innovation among Spanish Manufacturing SMEs jsbm_279 465..488

by Antonia Madrid-Guijarro, Domingo Garcia, and Howard Van Auken

Innovation is widely recognized as a key factor in the competitiveness of nations and firms. Small firms that do not embrace innovation within their core business strategy run the risk of becoming uncompetitive because of obsolete products and processes. Innovative firms are a perquisite for a dynamic and competitive economy. This paper reports on the results of a study that examined barriers to firm innovation among a sample of 294 managers of small and medium-sized enterprises (SMEs) in Spain. The study examined the relation between (1) product, process, and management innovation and (2) 15 obstacles to innovation, which can limit a firm’s ability to remain competitive and profitable. Findings of the study show that barriers have a differential impact on the various types of innovation; product, process, and management innovation are affected differently by the different barriers. The most significant barriers are associated with costs, whereas the least significant are associated with manager/employee resistance. Additionally, the results demonstrate that the costs associated with innovation have proportionately greater impact on small than on larger firms. The findings can be used in the development of public policy aimed at supporting and encouraging the innovation among SMEs in Spain. Government policies that encourage and support innovation among all firms, especially small firms, can help countries remain competitive in a global market. Public policy that encourages innovation can enable firms to remain competitive and survive, both of which have

Antonia Madrid-Guijarro is an assistant professor at the Business Faculty. She participates as researcher in the SMEs Economic Observatory in the Murcia Region. She has a Ph.D. (2004) in economics from the Politechnical University of Cartagena. Her main research interests are innovation, firm performance, policy implications, and business failure. Domingo García-Pérez-de-Lema is Head of Accounting and Finance Department in the Politechnical University of Cartagena, and Coordinator of SMEs Economic Observatory in the Murcia region (Spain). He holds a Ph.D. (1988) in economics from the Murcia University. His main research focus is SMEs, with especial interest in innovation, financing, regional economics, and managerial control systems. Howard Van Auken is a University professor of management at Iowa State University. He has a Ph.D. in Finance (1980). His research interests are in the area of small firm innovation and finance. Address correspondence to: Howard Van Auken, 3363 Gerdin, Iowa State University, Ames, IA 50011. E-mail: [email protected]

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direct implications for employment and a country’s economic viability. The results may also be insightful for managers who are attempting to encourage innovation. Understanding barriers can assist managers in fostering an innovative culture by supporting new ideas or by avoiding an attitude that creates resistance to new ideas.

Introduction Innovation is widely recognized as a key factor in the competitiveness of nations and firms (Galia and Legros 2004; Tourigny and Le 2004; Storey 2000). Freel (2000) stated that innovation is essential for economic development and critical for firms to remain competitive. Its importance is intensified by increased global competition, decreased product lifecycles, increased technological capabilities of firms, and rapidly changing consumer demands. Small firm success in increasingly competitive markets is often dependent on the degree to which they embrace innovation. Small firms that successfully pursue innovation as a core business strategy increase productivity, growth potential, and likelihood of survival (Cefis and Marsili 2006; Heunks 1998; Geroski, Machin, and Van Reenen 1993). Firms’ adoption of innovation strategies also contribute to economic growth, new employment, and increased wealth (Bertuglia, Lombardo, and Nijkamp 1997; Nijkamp and Poot 1997). Small firms that do not embrace innovation as a core business strategy may become uncompetitive because of obsolete products and processes (Cotec 2006). McAdam and McConvery (2004) believed that firms that embrace innovation outperform those that do not. However, innovation requires overcoming certain obstacles inherent in change. Innovation can expose the firm to additional risk from both internal (e.g., financial and human resource) and external (e.g., external environment)

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factors. Innovation leading to higher returns has a positive impact on the firm, whereas innovation leading to poor investment decisions can have a detrimental impact on firm profitability (Genus and Coles 2006). The negative impact of the potential risk can be a significant obstacle to innovation within firms (Borgelt and Falk 2007). The lag of productivity in Spain, compared with other EU countries, may be attributable to the large growth in the Spanish labor market without a concurrent investment in human resource development and higher investment in technology (Bank of Spain 2006). The lag in innovation among Spanish firms relative to those of other industrialized countries could result in reduced competitive capacity of Spanish firms (Cotec 2006). This paper reports the results of a study that examined barriers to innovation among a sample of 294 manufacturing small and medium-sized enterprises (SMEs) in the Murcia region of Spain. Very few studies have examined barriers to innovation among Spanish firms. Specifically, the study examined the relation between (1) product, process, and management innovation and (2) 15 obstacles to innovation. Teece (1996) emphasized the need to understand and clarify how SMEs can overcome barriers to innovation. Better understanding of barriers to innovation can assist firms to foster development of an environment that supports innovation (Hadjimanolis 1999). Small firms have the advantages of flexibility and adaptability, but also have the disadvantage of resource constraints

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when attempting to become more innovative (Freel 2000). The Murcia region is interesting because of its specific economic characteristics. Although Murcia economy has been growing above the national average in terms of GDP during the period 2000– 2005 (INE [National Statistical Institute] 2006), this growth is mainly based on the building and services sectors. In this context, the GDP growth is not accompanied by productivity growth and wage increases. Manufacturing sector weaknesses are characterized by low investment in innovation, limited internationalization of manufacturing firms, and higher competition from Asian markets (CES 2005). Furthermore, the recent enlargement of the European Union will lead to a decrease in financial support for the Murcia region of Spain by the European Union. To remain globally competitive, manufacturing firms in Murcia must embrace innovation activities that improve productivity. To remain competitive in global markets, manufacturing firms in Murcia must embrace innovation activities that improve productivity, because innovation contributes to sustained long-run economic growth through industry-wide spillover (Grossman and Helpman 1990; Romer 1986). Regional economic development may depend on the manufacturing sector replacing the building sector to lead regional economic growth. This study provides insight into barriers to achieving this goal and into ways in which manager perceptions impact adoption of innovation. The results will help develop a deeper understanding of the barriers to and motivators of innovation. The remaining sections of the paper are organized as follows: the second section presents previous research on innovation, the third section describes the methodology used in the analysis, which is discussed in the fourth section. The fifth section concludes the paper.

Innovation The concept of innovation in the business environment is associated with doing something new or different (Garcia and Calantone 2002). The ability to introduce innovation often depends on the characteristics of the small firm. Less bureaucracy, owner expertise, and closeness between owners and customers can facilitate the implementation. Small firms whose owners have limited external contacts, exert too much control, are not aware of environmental changes, and lack the appropriated education/training may limit the firm’s innovative climate. Additionally, strategic decisions framed within the constraints of family rather than firm goals might encourage firms to reject change needed to implement innovation (Hausman 2005). A number of studies show that firm differences in barriers to innovation were related to cost, institutional constraints, human resources, organizational culture, flow of information, and government policy (Mohen and Roller 2005; Baldwin and Lin 2002). Small firms were particularly restricted by innovation barriers because of their more limited resource base (Hewitt-Dundas 2006; Hadjimanolis 1999). The concept of innovation includes technological innovation and innovation in organizational methods (AECA, 1995). Technological innovation refers to innovation in products (changes in products or commercialization of new products) and innovation in production processes (changes in manufacturing processes or acquisition of new equipment) (Freeman 1974). Managerial and systems innovation (management or administration, purchasing, and commercial/sales) is based on changes introduced in the organizational structure of the company and the administrative process, aspects that are more related to management than with the company’s main activities. This classification was also used by Huiban


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Figure 1 Barriers to Innovation Internal Barriers • • • •

Lack of Financial Resources Poor Human Resources Weak Financial Position High Cost and Risk

Product Innovation

Managers’ Perceptions

Innovation Activities

Process Innovation

External Barriers Management Innovation

• Turbulence • Lack of External Partners Opportunities • Lack of Information • Lack of Government Support

and Bouhsina (1998) and used in the Study of the Harmonized Innovation of the European Union (2004). Figure 1 shows the relationship between barriers to innovation and firm innovation. Barriers to innovation are grouped relative to those that are internal to the firm (believed to be too difficult to overcome and negatively influence implementation of innovation activities) and those that are external to the firm (high operating environment risk). For example, barriers associated with lack of financial resources, inadequately qualified human resources, weak financial position, and high risk may be perceived as too great of a challenge to overcome and thus may limit firm innovation activities. These barriers to innovation are further discussed.

Barriers to Innovation Hadjimanolis (1999) and HewittDundas (2006) used resource-based view to show that differences in innovation activity between firms can be due to resource base differences. A number of studies show that firm differences in barriers to innovation were related to

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cost, institutional constraints, human resources, organizational culture, flow of information, and government policy (Mohen and Roller 2005; Baldwin and Lin 2002). Small firms were particularly restricted by innovation barriers because of their more limited resource base.

Financial Resources Cost has been cited as one of the most significant barriers to innovation. The uncertainty associated with innovation can be a source of conflict with funders (Bergemann 2005). This risk, as well as high monitoring costs and difficulty of assessing the viability of innovation, make the challenge of financing innovation even more difficult (Freel 2000). Conflicts can arise between the need to invest in innovation and the risk aversion common among managers/owners (Hausman 2005; Frenkel 2003), with small firms being especially subject to such conflicts because of their limited financial resources. Souitaris (2001) found that managers of the most innovative firms also were more favorably inclined toward risk acceptance.


Risk and financial exposure are directly linked through much of finance theory, with higher risk being associated with higher financial exposure and lower risk with lower financial exposure (Brigham and Ehrhardii 2005). Activities that increase financial exposure also increase risk, and activities that decrease financial exposure decrease risk. The role of financial exposure and cost of innovation may thus be important constraints on innovation. Transaction cost theory and agency theory suggest that debt financing may lead to lower innovative activities (Jensen and Meckling 1976). Transaction cost theory analyzes the fact that the intangibility and specificity associated with investment in technology, by increasing transaction costs, may prevent firms from financing innovation with debt. Agency theory suggests that the high risk of innovative activities and the existence of information asymmetries can lead to problems with debt financing. An increase in debt may lead to an increase in conflicts between lenders and the firm. Several previous studies point to the negative influence of debt on innovation activity (Giudici and Paleari 2000). H1: For Spanish SMEs, deteriorating financial resources decreases the level of innovation carried out by the firms. H2: For Spanish SMEs, higher perceived risk decreases the level of innovation carried out by the firms.

Human Resources McAdam and McConvery (2004) concluded that SMEs exhibit resistance to innovation. Weak management commitment, which can be a signal that the organizational culture does not support innovation, has been cited as one of the more significant barriers to innovation among SMEs. Employees and innovators

often question the value of a strategy that embraces innovation (Storey 2000). Some of this resistance has been found to be consistent with a very direct management style, in some cases further compounded by an owner-manager relationship (Mosey, Clare, and Woodcock 2002). Several studies have emphasized the role of employee resistance to innovation based on issues such as poor communication, existing corporate norms, weak human resources practices, and lack of commitment of top management (Zwick 2002; Osterman 2000; Kane, Crawford, and Grant 1999). A result of organizational cultures being unreceptive to innovation is the risk of failure to seize new approaches to pursuing market opportunities (Roper and Hofmann 1993). Adoption of innovation requires employee commitment and effort (Acemoglu and Pishke, 1999). Constraints arising from weak management support are an innovation choke point because innovation can disrupt established routines and schedules (Shanteau and Rohrbaugh 2000). Baldwin and Lin (2002) recognized that resistance to change, some of which results from inadequate training or poor employee skills, is an important organizational challenge. Hausman (2005) pointed out that small business managers often lack the types of education and training that have been linked with a successful innovation strategy. Freel (2000) also emphasized that firms are constrained in their ability to attract, train, and retrain managers who are qualified to effectively incorporate innovation into business strategy. H3: For Spanish SMEs, weaker human resources decrease the level of innovation carried out by the firms.

External Environment The firm’s external environment includes a variety of influences, such as


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global competition, government policy, and economic uncertainty. These challenges require that firms effectively communicate to managers the importance of innovation as a core firm strategy that will help maintain market competitiveness (Frishammar and Horte, 2005). Porter (1985) noted that competitive pressures often force firms to adopt new technologies so as to become differentiated from competitors or gain a cost advantage. Katila and Shane (2005), Souitaris (2001), and Khan and Manopichetwattana (1989) found a positive relationship between external economic uncertainty and the rate of innovation; firms in more turbulent external environments have higher potential for innovation, because turbulent environments trigger firms to incorporate innovation into their business strategy in order to remain competitive and, ultimately, survive (Miller 1987). Information about a firm’s external environment, such as market opportunities, changes in technology, and government policy, impact managers’ adoption of innovation as a strategy to better meet customer needs and to help make the firm more competitive. Information about technology, markets, and government policy initiatives can reinforce the importance and potential advantages of becoming more innovative (Galia and Legros 2004). A lack of information, however, can become another obstacle to innovation (Frenkel 2003; Hadjimanolis 1999), and uncertainty about government policy, especially in European countries, can become a significant barrier to innovation. Piatier (1984) found that lack of government assis-

tance was the third most important barrier to innovation in European countries. H4: For Spanish SMEs, greater difficulties from the firm’s external environment decrease the level of innovation carried out by the firms.

Methodology Data, Sample, and Questionnaire Development The data were collected through personal interviews with 294 managers of manufacturing companies in Murcia (Spain) as a part of the Economic Barometer Project financed by the Instituto de Fomento de la Región de Murcia.1 Sample selection was designed to represent the structure of the region following the stratified sampling principles in finite population. The population of firms was segmented by industry and location. The number of firms in each stratum was calculated relative to information contained in the Central Directory of Firm and as elaborated by the National Statistical Institute. The sample selection framework was the “Panel Empresarial” (http://www.panelempresarial.com) in the Instituto de Fomento de la Región de Murcia. The estimation precision of the sample leads, in the worst case (relative frequency of answers in a specific item is p = .5), to a maximum error of 2.8 percent at a confidence level of 95 percent. The distribution of responding firms by industry is shown in Table 1. Companies that chose to not participate in the project were replaced with a similar (randomly chosen) company in the same industry and geographic area.

1

The Instituto de Fomento de la Región de Murcia is the development agency of the Murcia region. It is a public institution that belongs to the Economy, Firm and Innovation Minister. Its main aim is to foster regional wealth, employment, and economic development through policies that support SMEs and associated development activities in order to improve regional economic competitiveness.

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Table 1 Industry Distribution of the Sample Industry Sectors

Meat Processing Industry Canning Industry Machinery Production of Furniture Chemical Industry, Cork, and Plastics Shoe Industry Stone Industry Other Manufacturing Total

A questionnaire was developed and pretested during the winter and early spring of 2005. Interviews with firm managers occurred during April and May 2005. The questionnaire collected information on the characteristics of the firms, innovation activity (if any) of the firm during the previous two years, and barriers to innovation.

Innovation Variables The questionnaire asked managers to indicate whether their firm had introduced innovation during the previous two years (1 = yes and 0 = no) and to rate importance of that innovative activity (1–5 Likert scales, with 1 = not important and 5 = very important). (1) Product innovation: (a) changes in products or (b) commercialization of new products; (2) Process innovation: (a) changes in manufacturing processes or (b) acquisition of new equipment; and (3) Management innovation: (a) management or administration, (b) purchasing, and (c) commercial/sales. Innovation had a value equal to zero if the firm had not introduced innovation.

Number of Companies

Percent of Firms

23 29 63 44 30 17 43 45 294

7.82 9.86 21.43 14.97 10.20 5.78 14.63 15.31 100

If the firm had introduced innovation, then innovation equaled the respondents’ mean ranking of importance. The analysis used an overall measure of innovation that was compiled by summing all measures for product, process, and management innovation. Hughes (2001) found that subjective measures of innovation were superior to objective measures. Patents, for example, can underestimate innovation activity because some firms cannot afford the exposure and time involved in the patenting process (Kalantaridis and Pheby 1999). Several studies have found that subjective measures, such as manager perception, were highly correlated with objective measures of innovation and allowed comparisons among firms (Frishammar and Horte 2005; Zahra and Covin 1993). Subjective measures such as self-reporting are valid for monitoring, as well as for identifying obstacles that inhibit innovation among SMEs (Kalantaridis and Pheby 1999). The questionnaire asked managers to indicate whether their firm had introduced product, process, and management innovation during the previous two years (1 = yes and 0 = no) and the importance of that innovative activity (1–5 Likert scales, with 1 = not important and


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5 = very important). Innovation had a value equal to zero if the firm had not introduced innovation. If the firm had introduced innovation, then innovation equaled the respondents’ mean ranking of importance. The analysis used an overall measure of innovation that was compiled by summing all measures for product, process, and management innovation.

Barriers to Innovation Variables Table 2 summarizes the research on barriers to innovation, which have been organized into 15 areas in two large categories: internal and external barriers. Internal barriers are those that originate within the firm, whereas external barriers are those that originate from the firm’s external environment. Measures of barriers to innovation used in this study were developed from studies shown in Table 2. The questionnaire asked managers to rank the importance of 15 potential barriers to innovation, using a 1–5 Likert scale (1 = not important and 5 = very important). This approach was used because previous studies reported that managers’ attitude significantly impacted innovation climate (Storey 2000; Lefebvre, Mason, and Lefebvre 1997; West and Anderson 1996). Variables in the questionnaire, which were identified on the basis of previous studies, included excessive risk, high costs, difficulty of controlling cost, difficulty of access to financial resources, economic turbulence, lack of market information, lack of cooperation possibilities, lack of regional infrastructures, insufficient government support, lack of new technology information, management or employee resistance to change, lack of qualified personnel, lack of formative activity in the company, and problems of retention of qualified employees in the company (Hewitt-Dundas 2006; Bergemann 2005; Gordillo and Herrmann 2005; Mohen and Roller 2005; Scozzi et al. 2005; Galia and

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Legros 2004; Frenkel 2003; Baldwin and Lin 2002; Zwick 2002; Garcia and Briz 2000; Storey 2000; Hadjimanolis 1999; Kalantaridis and Pheby 1999; Oakey 1997; Westhead and Storey 1996; Greis, Bidner, and Bean 1995). Respondents were also asked to rank the change over the past two years in their firm’s (1) liquidity position, (2) cost of debt, and (3) level of debt, using a 1–5 Likert scale (1 = worse and 5 = better). An increase in liquidity suggests that the firm would be in stronger financial condition, whereas an increase in the cost and level of debt would suggest a weaker financial condition. A stronger or weaker financial condition would be expected to enable firms to invest more or less, respectively, in innovation.

Analysis The results were initially summarized using univariate statistics (means and frequencies) to provide a better understanding of the respondents and characteristics of the responding companies. The initial summary statistics included technological intensity, number of employees, educational level of the manager, firm ownership, nature of innovation within the firm, and barriers to innovation. The analysis was completed in several steps. First, correlation analysis was completed among the barriers to innovation variables to provide insight into relationships between the variables. Subsequently, principal component analysis was used to form groups of related variables among the 15 barriers to innovation variables. Principal component analysis determines linear composites of the variables that display certain similar properties. A number of factors are produced, and related variables can be sorted into categories according to the magnitude of loadings under each factor. Varimax rotation, a procedure through which each component is found to cor-


Table 2 Revision of the Literature about Barriers to Innovation Variables

Authors

Internal Barriers High Costs Innovation Cost Difficult to Control Excessive Risk

Lack of Qualified Personnel Difficult Access to Financial Resources

Problems Keeping Qualified Employees Lack of Internal Employee Training Employees Resistance to Change Manager Resistance to Change External Barriers Insufficient Government Support Economic Turbulence

Lack of Market Information

Lacks of Regional Infrastructure Lack of Information about Technologies Lack of External Partners Possibilities

Garcia and Briz (2000); Frenkel (2003); Zwick (2002); Baldwin and Lin (2002); Galia and Legros (2004) Hadjimanolis (1999); Garcia and Briz (2000); Frenkel (2003); Mohen and Roller (2005) Hewitt-Dundas (2006); Galia and Legros (2004); Frenkel (2003); Zwick (2002); Storey (2000); Garcia and Briz (2000); Kalantaridis and Pheby (1999); Hadjimanolis (1999) Mohen and Roller (2005); Galia and Legros (2004); Frenkel (2003); Zwick (2002); Baldwin and Lin (2002); Garcia and Briz (2000); Hadjimanolis (1999) Hewitt-Dundas (2006); Mohen and Roller (2005); Frenkel (2003); Zwick (2002); Storey (2000); Kalantaridis and Pheby (1999); Hadjimanolis (1999); Galia and Legros (2004) Westhead and Storey (1996); Oakey (1997)

Hewitt-Dundas (2006); Galia and Legros (2004); Mohen and Roller (2005); Frenkel (2003); Zwick (2002); Baldwin and Lin (2002); Garcia and Briz (2000); Kalantaridis and Pheby (1999); Hadjimanolis (1999) Hadjimanolis (1999); Freel (2000); Frenkel (2003) Khan and Manopichetwattana (1989); Souitaris (2001); Frenkel (2003); Katila and Shane (2005); Gordillo and Herrmann (2005) Hewitt-Dundas (2006); Galia and Legros (2004); Frenkel (2003); Zwick (2002); Baldwin and Lin (2002); Kalantaridis and Pheby (1999); Hadjimanolis (1999) Scozzi, Garavelli, and Crowston (2005) Galia and Legros (2004); Frenkel (2003); Zwick (2002) Hewitt-Dundas (2006); Mohen and Roller (2005); Hausman (2005); Greis, Bidner, and Bean (1995); Freel (2000); Garcia and Briz (2000); Hadjimanolis (1999); Kalantaridis and Pheby (1999)


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relate strongly with a small number of variables and weakly with the others, was used to enhance the interpretability of the factors. Changes in the firm’s financial position was measured as a binary variable, using information about the changes in the firm’s financial position (liquidity, cost of debt, and level of debt) over the previous two years. This variable was used to identify firms that had experienced a declining financial condition. The econometric analysis takes into account that a certain fraction of the firms in the sample do not innovate. Censoring may exist since only the importance of the firm’s innovation activity can be observed. Four Tobit regression models and four Powell’s (1984) censored least absolute deviations (CLAD) estimators were used to evaluate the relationship between barriers to innovation and firm innovation. Though assuming a functional form for the regression model, Powell’s CLAD estimator does not rely on functional form assumptions for the error process. This estimator is therefore preferable to the standard Tobit estimator in cases of heteroscedastic or non-normally distributed error terms. The independent variables were the same for all regressions, and the dependent variable was different for each of the four models. The dependent variable in the first model (Innovation) was respondents’ mean ratings of all innovation activities, with innovation having a value of zero when the respondent indicated that the firm had not carried out any innovative activity during the previous two years. The dependent variables in the other three models were measures of innovation in the firm’s products, manufacturing processes, and management. Product innovation had a value = 0 if the firm had no product changes or commercialization of new products during the previous two years, and a

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value equal to respondents’ mean rating of importance if the firm had introduced either of the product innovation measures. Process innovation had a value = 0 if the firm had no changes in manufacturing processes or acquisition of new equipment, and a value equal to respondents’ mean rating of importance if the firm had introduced either of the process innovation measures. Management innovation had a value = 0 if the firm had no changes in manufacturing, purchasing, or commercial activities, and a value equal to respondents’ mean rating of importance if the firm had introduced either of the management innovation measures.

Innovation = a0 + b1Size + b2TI + b3 Age + b4 F1 + b5 F2 + b6 F3 + b7 FinPos + b8 e where: Innovation = general innovation, product innovation, process innovation, and management innovation Size = firm size (control variable: 1 if >19 employees and 0 if 50 Percent Controlled by a Family Most of Managerial Positions Are Occupied by a Member of the Family Product Innovation Changes or Improvements in the Current Products Market New Products Process Innovation Changes or Improvements in Manufacturing Processes Acquisition of New Equipments Management Innovation Changes or Improvements in Management Issues Changes or Improvements in Purchases or Provisioning Changes or Improvements in Sales


69.4 30.6 77.9 84.1 78.7 74.4 61.2 80.7 71.0 71.7 59.1 33.4 43.2 43.0

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as only 59.1 percent introduced management innovation. The three innovations with the highest percentages were changes or improvement in current products (74.4 percent), acquisition of new equipment (71.7 percent), and changes or improvements in manufacturing processes (71.0 percent), of which fall within the category of product or process innovation. Less than half of the respondents indicated implementation of any of the management innovation activities.

Mean Rankings of Barriers to Innovation Table 4 shows the mean rankings of the importance of factors influencing adoption of innovation. Ten of the 15 barriers have a mean ranking above 3.0, which suggests the existence of a rela-

tively high number of barriers that limit the adoption of innovation among Spanish firms. The top three barriers (high costs, difficult to control costs, and insufficient government financial support) are directly related to costs associated with innovation, whereas the bottom five barriers (problems finding qualified people, lack of internal employee training, lack of external partners, employee resistance to change, and manager resistance to change) are associated with human elements. Table 3 also shows mean rankings for small and medium-sized firms. The mean responses ranked as being most significant by small firms are almost identical to so those ranked by medium-sized firms. Almost all (11 of the 15) barriers are considered to be more important for

Table 4 Barriers to Innovation: Mean Response (1 = Not Important and 5 = Important) (n = 294) Variables

High Costs Innovation Cost Difficult to Control Insufficient Government Support Economic Turbulence Lack of Qualified Personnel Difficult Access to Financial Resources Lack of Market Information Excessive Risk Lacks of Regional Infrastructure Lack of Information about Technologies Problems Keeping Qualified Employees Lack of Internal Employee Training Lack of External Partners Opportunities Employees Resistance to Change Manager Resistance to Change

Overall Mean

Small Firms

Medium Firms

t-Tests

3.78 3.38 3.39 3.33 3.22 3.09 3.03 3.03 3.02 3.00 2.98 2.92 2.84 2.60 2.41

3.85 3.49 3.48 3.43 3.25 3.25 3.04 3.09 3.10 2.96 2.99 2.91 2.91 2.52 2.33

3.69 3.24 3.27 3.20 3.19 2.88 3.02 2.96 2.93 3.05 2.95 2.92 2.76 2.70 2.53

1.429 2.192** 1.634 2.007** 0.452 2.718*** 0.118 1.037 1.312 -0.730 0.309 -0.083 1.289 -1.479 -1.581

*p < .05. **p < .01. ***p < .001.

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small than for medium sized firms. Only the barriers with the lowest means were ranked higher by medium-sized firms. t-Tests of means between small and medium-sized firms show that three innovation barriers are significantly different between small and medium-sized firms: (1) innovation costs difficult to control, (2) economic turbulence, and (3) difficult access to financial resources. Because these variables are directly linked to financial issues, they suggest that the costs associated with innovation may put small firms at a disadvantage compared with larger firms. This finding is consistent with those from studies that emphasize the challenge of small firms associated with limited access to financial markets (Carter and Van Auken 2006). Hausman (2005) believed that closeness between small firms and their customers facilitates innovation, but the parochial nature of small firms limits their ability to be innovative.

Correlations among Variables Correlations between barriers to innovation variables, presented in Table 5, show significant and positive relationships among many variables. The strong relationship between lack of financial resources and (1) excessive risk (0.310), high cost (0.414), and (3) costs difficult to control (0.506) is consistent with relationships found by Hewitt-Dundas (2006) and Galia and Legros (2004). Manager resistance, previously cited as an obstacle to innovation (Osterman 2000; Kane, Crawford, and Grant 1999; Roper and Hofmann 1993), shows few high correlations with other variables. Manager resistance is highly correlated with high cost, a result that demonstrates managers’ concern for controlling company expenses. The relatively large number of correlations above 4.0 supports the importance of external innovation partnerships as a means of acquiring financial and market expertise (HewittDundas 2006; Inkpen 2001).

Factor Analysis Table 6 shows the results of varimax rotated factor analysis of the rankings of the 15 innovation barriers. Factor loadings above 0.5 were used for factor grouping. The Kaiser–Meyer–Olkin measure of sampling adequacy (K–M– O = 0.805), the degree of common variance among the initial variables, is considered to be excellent. Bartlett’s test of sphericity (c2 = 1651.83, dl = 105 sig. = 0.000) shows that the sample correlation matrix does not come from a population in which the correlation matrix is an identity matrix, so the non-zero correlations in the sample matrix are not due to sampling errors. These tests support the use of factor analysis in the study. The scale reliability values for each factor (coefficient alpha) are also reported in Table 6. All scales have alpha coefficients between 0.77 and 0.81, which suggests high reliability (Van de Ven and Ferry 1980). The factors from the principal components analysis are grouped into three categories: external environment, human resources, and risk. Factor 1, external environment, included six variables related to the firm’s environment: economic turbulence, lack of market information, lack of external collaboration, lack of regional infrastructure, lack of government support, and lack of information about technologies. Issues related to the firm’s external environment have been a common theme in previous studies of barriers to innovation (Hewitt-Dundas 2006; Mohen and Roller 2005; Greis, Bidner, and Bean 1995). Li and Atuahene-Gima (2001) specifically noted the important role of government in promoting innovation and the role of economic turbulence as a stimulus that encourages firms to become more innovative. Factor 1 will be used to test H4. Factor 2, human resources, included manager resistance to change, employee resistance to change, lack of qualified personnel, lack of training, and problems


477

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*p < .05. **p < .01.

1. High Cost 2. Costs Difficult to Control 3. Insufficient Government Support 4. Economic Turbulence 5. Lack of Qualified Personnel 6. Lack of Financial Resources 7. Lack of Market Information 8. Excessive Risk 9. Lack of Regional Infrastructure 10. Lack of Information about Technologies 11. Problems Keeping Qualified Employees 12. Lack of Employee Training 13. Lack of External Partners 14. Employee Resistance 15. Manager Resistance

1

0.282**

0.328** 0.113

0.506**

0.323**

0.457** 0.276**

0.258**

0.274**

0.253**

0.425**

0.098 0.032

0.317***

0.378** 0.186**

0.414**

0.280**

0.576** 0.212**

0.285**

0.272**

0.174**

0.337**

0.047 0.009

2

1 0.588**

1

0.210** 0.173**

0.378**

0.294**

0.409**

0.475**

0.241** 0.543**

0.255**

0.359**

0.397** 0.322**

1

3

0.273** 0.197**

0.448**

0.282**

0.295**

0.347**

0.281** 0.406**

0/367**

0.377**

1 0.214**

4

0.456** 0.366**

0.271**

0.550**

0.511**

0.339**

0.140** 0.332**

0.331**

0.173**

1

5

0.207** 0.112

0.449**

0.268**

0.324**

0.314**

0.310** 0.345**

0.236**

1

6

0.241** 0.223**

0.452**

0.259**

0.210**

0.448**

0.193** 0.350**

1

7

0.135** 0.099

0.350**

0.198**

0.223**

0.200**

1 0.255**

8

0.264** 0.222**

0.407**

0.319**

0.254**

0.461**

1

9

0.273** 0.253**

0.406**

0.336**

0.337**

1

10

11

0.393** 0.296**

0.352**

0.526**

1

Table 5 Correlations among Barriers to Innovation (n = 294)

0.471** 0.387**

0.316**

1

12

0.306** 0.230**

1

13

1 0.695**

14

Table 6 Component Loadings for Barriers to Innovation (n = 294) Variables

Excessive Risk High Costs Innovation Cost Difficult to Control Difficult Access to Financial Resources Economic Turbulence Lack of Market Information Lack of External Partners Opportunities Lacks of Regional Infrastructure Insufficient Government Support Lack of Information about Technologies Manager Resistance Employee Resistance Lack of Qualified Personnel Lack of Internal Employee Training Problems Keeping Qualified Employees Cronbach’s alpha Kaiser–Meyer–Olkin Percentage of Total Variance Explained Eigenvalue

maintaining qualified employees. This result is consistent with a number of previous studies that found human resources to be a significant barrier to innovation (Hausman 2005; McAdam and McConvery 2004; Baldwin and Lin 2002; Mosey et al. 2002; Zwick 2002; Storey 2000). Factor 2 will be used to test H3. Factor 3, risk, included excessive risk, high costs, innovation costs difficult to control, and lack of financial resources. Risks associated with financial exposure and financing innovation were previously reported as barriers to innovation (Hewitt-Dundas 2006; Hewitt-Dundas 2006; Bergemann 2005; Hausman 2005; Frenkel 2003; Sivades and Dwyer 2000). Factor 3 will be used to test H2. Information about changes in the firm’s financial position (liquidity, cost of

Factor 1: External Environment

Factor 2: Human Resources

Factor 3: Risk

0.077 0.192 0.214 0.337 0.539 0.602 0.526 0.768 0.690 0.732 0.116 0.141 0.300 0.214 0.207 0.805 0.860 36.20 5430

0.108 0.009 0.040 0.141 0.174 0.172 0.239 0.164 0.167 0.219 0.776 0.827 0.674 0.708 0.617 0.811

0.751 0.820 0.789 0.588 0.350 0.173 0.420 0.114 0.193 0.104 0.060 0.020 0.059 0.197 0.315 0.777

49.54 2.001

57.10 1.134

the debt, and amount of debt) is incorporated into the analysis through an additional factor in the factor analysis presented in Table 7. To assess the firm’s financial position as a barrier to innovation (H1), a binary variable (financial position) was constructed. The financial position variable was equal to 0 when the value was above the mean (increase in liquidity and decrease in debt) and equal to 1 when the value of the factor was lower than the mean (decrease in liquidity and increase in debt).

Regression Analysis The results from the Tobit estimates (Table 8) and the CLAD estimates (Table 9) are different relative to both significant and importance of the coefficients. Analyses of the distribution of


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Table 7 Component Loadings for Financial Position (n = 294) Variables

Financial Position

Liquidity and Cash Cost of Debt Level of Debt Cronbach’s alpha Kaiser–Meyer–Olkin Percentage of Total Variance Explained Eigenvalue

0.699 0.839 0.843 0.706 0.637 64 1.90

our dependent variables reveal bimodal behaviors. Although this does not invalid Tobit regression analysis, the distribution of the residuals shows non-normality. In this case, under misspecified error distributions, the maximum likelihood estimator of the Tobit model is inconsistent (see Shapiro–Francia statistic for normality of residuals in Table 8). Accordingly, we followed the Powell (1984) CLAD method, which is consistent under both heteroskedasticity and non-normality. Unlike the standard estimators of the censored regression model, such as Tobit or other maximum likelihood approaches, the CLAD estimator is robust to heteroskedasticity and is consistent and asymptotically normal for a wide class of error distributions. Consequently, we employ the CLAD method on the following regression specification:

Yi = β0 + β1 X i + β2 Zi + ε i where Yi is the importance level of the innovation carried out by the firm and is bounded between (0, 5), and i, j, e is the error disturbance that is uncorrelated with the independent variables, but is allowed to be correlated with each other

480

if and only if the two observations are from the same plan (i.e., we allow for plan-level common effects). The coefficients are identified by assuming that the median of ei is zero. Therefore, it is essentially a median regression with adjustment on data censoring. We concentrate our interpretation of results on the CLAD estimations because of the clear rejection of central assumption on the Tobit model’s residuals questions the reliability of the resulting estimates and points toward using more flexible models such as CLAD. The CLAD results for the general innovation estimates show that the coefficients for external environment (0.38038) and financial position (-0.53901) are significant at 5 percent, whereas human resources (-0.3284) is significant at 1 percent. These results suggest that when managers perceive more challenging external environments, the level of innovation in the firm increases. However, high perception of Human Resources barriers and weak financial position lead to a decrease in the level of innovation. The process innovation CLAD estimates show that the coefficient for one control variable (size) is positive (0.68, significant at 5 percent), whereas the remaining coefficients for control variables are not significant. The positive coefficient for external environment (0.5994, significant at 1 percent) indicates that respondents believed that more challenging external environments result in a greater level of process innovation. The negative coefficient for human resources (-0.388, significant at 1 percent) suggests that more challenging human resource issues are associated with lower levels of process innovation. Similarly, the negative coefficient for financial position (-0.7583, significant at 1 percent) show that more limitations on financial resources and a weak financial position are associated with lower levels of process innovation.



481

1.2 0.00517

0.000

0.526* (2.03) 0.493 (1.37) -0.004 (-0.59) 0.196 (0.86) -0.188 (-1.04) -0.078 (-0.42) -0.470* (-1.84) 2.655** (3.71) -571.36 11.47 (0.119) 0.022

Coefficient (t-Statistic)

Coefficient (t-Statistic) 0.526** (2.98) 0.204 (0.83) -0.004 (-0.86) 0.332** (2.13) -0.242* (-1.97) -0.191 (-1.51) -0.433** (-2.48) 2.491** (5.15) -508.68 23.25 (0.001) 0.053

Product Innovation

General Innovation

0.000

0.807** (3.27) 0.073 (0.21) -0.004 (-0.95) 0.555** (2.55) -0.345** (-2.01) -0.391** (-2.21) -0.527** (-2.16) 3.244** (4.81) -567.5 25.87 (0.000) 0.065


Process Innovation

Dependent Variables

a t-Values in brackets; *p < .05; **p < .01. Reference category: small size. Reference category: low and medium low technology. Reference category: good evolution in reference to the firm’s financial position.

Size Technology Age External Environment Human Resources Risk Change in Financial Position Intercept Log Likelihood Likelihood Ratio c2 (p-Value) R2 between Predicted and Observed Values Mean Variance Inflation Factor Shapiro–Francia W’ Test for Normal Data (Prob > z)

Independent Variable

Table 8 Tobit Regression Results (n = 294)a

0.000

0.515* (1.70) 0.1330.32 0.007 (0.07) 0.477* (1.81) -0.403* (-1.91) -0.296 (-1.35) -0.701** (-2.34) 1.584* (1.90) -493.23 14.30 (0.040) 0.029


Management Innovation

482

JOURNAL OF SMALL BUSINESS MANAGEMENT 0.7005* (0.42903) -0.0223 (0.57214) -0.0198 (0.01670) 0.08579 (0.41012) -0.29235 (0.24516) -0.19725 (0.37659) 0.06447 (0.40604) 4.1358 0.017

Coefficient (Standard Error)

Coefficient (Standard Error) 0.3186 (0.27219) 0.0367 (0.3983) 0.0034 (0.0107) 0.38038* (0.2021) -0.3284** (0.16679) -0.23741 (0.19997) -0.53901* (0.30892) 2.8305 0.045 1.2

Product Innovation

General Innovation

b

CLAD, censored least absolute deviations. The CLAD standard errors are bootstrapped estimates from resampling 100 times. Reference category: small size. Reference category: low and medium low technology. Reference category: good evolution in reference to the firm’s financial position. *Significant at 10 percent level t-statistic. **Significant at 5 percent level t-statistic.

a

Intercept Pseudo-R2 Mean Variance Inflation Factor

Change in Financial Position

Risk

Human Resources

External Environment

Age

Technology

Size

Variable

Table 9 CLADa Estimates (n = 294)b

0.6795** (0.29344) -0.29086 (0.35742) -0.0097 (0.01213) 0.5994** (0.24336) -0.3882** (0.16296) -0.32568 (0.25032) -0.7583** (0.29438) 3.7789 0.06502


Process Innovation

0.35437 (0.40706) 0.102529 (0.58585) -0.00358 (0.01180) 0.704416 (0.45888) -0.534436* (0.29828) -0.614041* (0.35204) -0.80489** (0.40700) 2.741858 0.04317


Management Innovation

The management innovation CLAD estimates show negative coefficients for human resources (-0.534436, significant at 5 percent), risk (-0.614, significant at 5 percent), and financial position (-0.804, significant at 1 percent). These results indicate that more challenging human resources issues, higher risk, higher costs, more limitations on financial resources, and a weak financial position are associated with lower levels of management innovation.

Discussion Small firm success and survival is often dependent on the degree to which they incorporate innovation into their business strategy, especially because of increasing global competition. Small firms that successfully embrace innovation increase their chances of growth and survival (Cefis and Marsili 2006). Product innovation is important to maintain market share, process innovation is important to maintain competitive prices, and management innovation is important to maintain a flexible and durable organization (Heunks 1998). SMEs need to better understand how to overcome barriers and effectively implement innovation practices (Teece 1996). Policymakers who want to encourage and support innovation in SMEs should address those factors that stimulate and constrain innovation. The results indicate that managers’ perceptions of issues related to costs are greater barriers, and issues related to human resources are lower barriers to innovation for small- as compared with medium-sized firms. The finding that cost is an important barrier is consistent with suggestions that innovation may be constrained by financial resources (Bergemann 2005; Hausman 2005; Frenkel 2003; Sivades and Dwyer 2000). This result is consistent with Garcia and Briz (2000), who found that resistance to change among employees were not among the most significant barriers among a sample of Spanish firms.

Factor analysis resulted in the grouping of the barriers to innovation into external environment, human resources, risk, and financial position. These factors are similar to studies of barriers to innovation in other countries (Hewitt-Dundas 2006; Frishammar and Horte 2005; Bergemann 2005; Mohen and Roller 2005; Baldwin and Lin 2002; Mosey, Clare, and Woodcock 2002; Zwick 2002; Giudici and Paleari 2000; Osterman 2000; Storey 2000). The regression results showed that issues related to the firm’s external environment, human resources, and changes in financial position are significantly associated with innovation within the firm. Firms meet external environment challenges by becoming more innovative, while challenges associated with human resources and weakening of financial position act as obstacles to innovation. Regression analysis specifically examined product, process, and management innovation. The results suggest that Spanish manufacturing SMEs react to challenging external environments by implementing more process and management innovation. Challenging external environments require firms to become more innovative to succeed and survive. Less human resource commitment and worsening financial position result in less process and management innovation. This is not surprising, since people and finances are required for innovation. Worsening financial position of the firm suggests that when companies increase debt and reduce liquidity, then innovation activities decrease. This result is consistent with those of Freel (2000) and Chiao (2002). Additionally, because of higher risk exposure, firms may opt against using debt to finance innovation. Lenders who are risk averse may also be averse to funding risky innovation initiatives. As a result, SMEs might pursue relatively safe and non-innovative projects through use of internal capital (Galende and De la Fuente, 2003).


483

Overall, the results support H1 and H3 for process innovation. Limited financial resources, weak financial position, and human resource resistance and low qualification of labor are barriers to process innovation. H1, H2, and H3 are supported for management innovation. A firm’s declining financial position, risk, human resource resistance, and low skilled level of the labor work force are barriers to management innovation. Limited support is evident for H3 product innovation, since regression model was not statistically significant.

Conclusions This paper examines barriers to product, process, and management innovation among a sample of 294 Spanish manufacturing SMEs located in the Murcia region of Spain. Innovation affects firms’ ability to compete successfully in an increasingly global market. Understanding barriers to innovation can aid in the development of firm strategies and government policies that contribute to economic growth, job creation, and increased wealth. The Murcia region economic situation is interesting due to the need to increase the investment in innovation by manufacturing SMEs. This need is because recent regional GDP growth has primarily been a result of substantial financial support from the Structural Funds from the European Union (INE 2006). A problem with this support is that the building sector is dependent on economic cycles and growth is not accompanied by regional productivity growth in the Murcia region. According to data from the CES (2005), the labor productivity growth in the Murcia region during the period 2003–2005 is 0.7 percent, whereas national growth is 1.2 percent. One of the weaknesses of the manufacturing sector in the Murcia region is its concentration on low- and medium lowtechnology industries that are very labor intensive, as well as low investment

484

in innovation. In fact, the percentage of regional R&D investment relative to GDP in 2005 (0.75 percent) is below the national investment (1.13 percent). Additionally, firm R&D investment at the regional level is only 44.7 percent, whereas at the national level, this percentage is 54 percent (Cotec 2006). A major finding of the study is that barriers have a differential impact on the types of innovation. Product, process, and management innovation are affected differently by the different barriers. Process and management innovation are negatively affected by internal barriers, such as human resources and weak financial position, and positively affected by barriers originating from the environment. Furthermore, the risk factor associated with cost and financing problems is significant for only management innovation. The most significant barriers are associated with costs, whereas the lowest barriers are associated with manager/employee resistance. Additionally, the results demonstrate that the costs associated with innovation have a disproportionate impact on small firms, which are affected more than larger firms. One important finding is that although managers do not rate human resources as an important barrier, human resources have a negative impact on innovation in the firm. The results of the study may be useful for both government and SMEs. The finding can be used in the development of public policy aimed at supporting and encouraging innovation among SMEs in Murcia, Spain. Government policies that encourage and support innovation among all firms, especially small firms, can help countries remain competitive in a global market. Public policy that encourages innovation can improve firms, competitiveness, and survival, both of which have direct implications for employment and a country’s economic viability. The results may also provide insights for managers who are attempting to encourage innovation.


Understanding barriers can assist managers to foster an innovative culture by supporting innovation or by avoiding an attitude of resistance to new ideas. Aligning a culture of innovation with the firm’s business strategies can result in greater efficiency and organizational success. The study has several limitations that provide avenues for potential future research. Because the same source was used to gather data for both the dependent and independent variables, the relations between the variables may be inflated due to common method variance. To evaluate this potential bias, we used the Harman’s single-factor test suggested by Podsakoff and Organ (1986). If there were problems with common method variance when doing the factorial analysis, which included all dependent (innovation in products, process, and management) and independent variables (barriers to the innovation), we would have obtained a unique or several factors that would explain a high amount of variance (Christmann 2000). In our factorial analysis, the six factors explained 72.255 percent of the total variance. Between these factors, the first one explained a 19.546 percent of the variance. These results suggest that the bias of the common method variance was not relevant in our study. Nevertheless, it would be important for future studies to check our results using different sources of information for the data. Second, because the study was completed with industrial SMEs in the Murcia region, the results may not be generalized to all firms in Spain. Regional or country cultural values may affect differences in perceptions of barriers to innovation. The results should be interpreted with caution, since the sample reflects perceptions of managers of regional firms. Finally, the results may be improved and more robust using longitudinal data instead of cross-sectional information.

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