An analysis of factors impacting performance of Spanish manufacturing firms.
Madrid-Guijarro, Antonia ; Van Auken, Howard ; Garcia-Perez-de-Lema, Domingo 等
Introduction
Firm performance is one of the most basic issues of concern for
many firm stakeholders, including, for example, owners, employees,
suppliers, and investors. The ultimate responsibility for the evaluation
and performance of a firm resides with the firm's managers.
Research on firm performance has commonly argued that a wide variety of
factors can impact firm performance (Chandler and Hanks, 1998). A
manager's beliefs about performance and factors that impact
performance ultimately determine the development and implementation of
management policies (Brigham and Houston, 2004).
Performance is also related to economic development and social
policy issues. An economy composed of strong performing firms will
generate employment and a wide variety of taxes that are supportive of
government policy initiatives. Weak performing firms will be unable to
provide strong employment growth or tax revenues. A better understanding
of factors influencing performance will enable policy makers to develop
programs that promote economic growth and firms to develop better
operational and strategic plans. Perceptions of what factors influence
performance would directly impact a wide variety of operational and
strategic decisions by the firm. The nature of these decisions,
including for example, expenditures on staff, research and development,
product improvement, and employment, may ultimately impact the economic
conditions.
This paper reports the results of a study that examined the
relationship between the performance and competitive influence factors
(product innovation, staff and planning issues, quality product,
customer orientation and financial attractiveness) of 543 Spanish
manufacturing firms. Performance measures used included (1) manager
perceptions of productivity and (2) financial performance. Few studies
of this type have been conducted on firms in Spain despite the
importance of productivity as a key indicator of firm efficiency and
ability to remain competitive. Studies on performance and productivity
can provide important insight and information to multiple constituents.
Understanding factors that impact performance can provide firms with
more insight into issues affecting investment decisions. Better
investment decisions can enable the firm to become more competitive and
lead to greater productivity and employment. Becoming more competitive
is especially important as countries continue economic integration and
expand world trade.
The remaining paper is organized as follows. Section 2 provides
background information on the Spanish economy and role of manufacturing
in the Spanish economy. Section 3 develops background information on
financial performance. Section 4 explains the methodology used in the
analysis that is reported in Section 5. The results are discussed in
Section 6. Section 7 concludes the paper.
Overview of Spanish Economy
The Spanish economy has recently been experiencing one of the
strongest rates of GDP growth in the European Union. The GDP growth
rates during 1999 and 2000 were 4% and 4.1%, respectively. During 2001
and 2002 the Spanish economy experienced slower growth as a result of
the general economic slowdown throughout much of the world (including
Europe and the United States) and weaker domestic demand. During 2001
and 2002, Spanish GDP growth was 2.7% and 1.9%, respectively--one of the
lowest economic growth rates since 1993. Nevertheless, this economic
growth was still the highest among the large European economies.
However, in spite of this growth, the index of Spanish firms'
productivity is the lowest in the OCDE context (Medel and Martinez,
2004).
The manufacturing sector in the Spanish economy has been important
and a vibrant component to economic growth. Approximately 2.5 million
workers were employed by manufacturing firms in 2002, an increase of
more than 3% since 1999. Sales among manufacturing firms increased
approximately 8% between 1999 and 2002. Research and development
expenditures increased more than 8.5% between 1999 and 2002. In 2002,
the percentages of domestic and European Union manufacturing sales were
approximately 47% and 39%, respectively. The remaining 14% of sales were
made to other parts of the world (Instituto Nacional de Estadistica,
2004). Slowdown in the manufacturing sector due to weak domestic and
international demand that began in the second half of 2000 continued
throughout 2001. The industrial production index increased by 4.5% in
2000, declined 1.3% in 2001 and rose by only 0.2% in 2002. Weakness in
the manufacturing sector of the Spanish economy led the Ministerio de
Ciencia y Tecnologia to initiate programs that were designed to (1)
improve the financial and tax incentives for investment in innovation,
(2) promote public guarantees for the acquisition of capital (especially
for SMEs), and (3) provide incentives for sectors that required specific
assistance (e.g., aerospace industry, the automobile industry,
shipbuilding, textiles and the defense industries).
Firm Performance
Previous studies have examined factors affecting SME performance
from different perspectives. These perspectives include different
methods of measuring performances as well as different variables
influencing performance. Previous studies used both quantitative and
qualitative measures of performance. Factors that have been examined as
influencing performance included strategy, human capital, reputation,
strategic relationships, and resource availability.
Measuring of Performance
Strong performance should be one of the most important goals of the
firm. Highly performing firms are positioned to generate a wide range of
company and society benefits that include attracting resources,
generating jobs, and creating wealth. Firms that are under-performing
are often uncompetitive and experience financial distress (Brigham and
Houston, 2004). Venkatraman and Ramanujam (1987) considered that
financial performance measured the fulfillment of the economic goals of
the firm. Chandler and Hanks (1994) and VanderWerf (1994) suggested that
measures of financial performance were among the most important goals of
the firm. An accurate measure of performance can provide reliable
insight into what affects performance and how firms can develop better
strategies, deploy resources, meet consumer needs, and compete.
Inappropriate measures of performance will provide misleading results
that lead to a weakened competitive position.
A number of previous studies relied on financial information to
measure performance. Chandler and Hanks (1994), VanderWerf (1994), and
Venkatraman and Ramanujam (1987) measured performance using financial
ratios such as net profit margin, return on equity, return on assets.
Santos-Requejo and Gonzalez-Benito (2000) relied on various profit
margin ratios to measure performance of Spanish firms. Shepherd et al
(2000) stated financial profitability was one of the most important
criteria used by venture capitalists in their assessment of the
investment potential of firms. A number of studies (Cainelli et al.,
2004; De Toni, 2001; Lau, 1997; Youndt, Snell, Dean and Lepak, 1996)
have used productivity ratios to assess firm performance.
A potential problem with using financial information is that
financial statements from SMEs may be unaudited and, thus, unreliable
(Sapienza et al., 1988). Furthermore, accounting information may be
altered by accounting norms or management decisions (Camison, 2001;
McGahan, 1999; Kaplan and Norton, 1993; Eccles, 1991). The use of only
quantitative indicators may omit valuable intangible assets that impact
firm competitiveness (Salgueiro, 2001; Camison, 1997; Kaplan and Norton,
1993). A number of studies have emphasized that firm success is better
understood and, thus, analyzed relative to competitors (Alonso and
Barcenilla, 1999; Cuervo, 1993; Salas, 1992; AECA, 1988).
The analysis incorporates both qualitative and quantitative
variables to measure firm performance for several reasons. First, a
single measure (e.g., either qualitative or quantitative variable) has
been cited as being an incomplete measure of performance (Alonso and
Barcenilla, 1999; Cuervo, 1993; Salas, 1992; AECA, 1988). The use of
only quantitative indicators, for example, omits valuable intangible
assets affecting firm competitiveness (Salgueiro, 2001; Camison, 1997;
Kaplan and Norton, 1993). The use of quantitative indicators from
accounting information, such as return on assets, have been used in past
studies to measure firm performance and reflect management decisions as
shown earlier (Camison, 2001; Kaplan and Norton, 1993; Eccles, 1991).
However, even accounting information only captures financial
information. Finally since success may be considered as a relative
measure, the study examines a firm's competitive position relative
to competitors'. Subjective measures are more appropriate for SMEs
since objective measures tend to underestimate degree of innovation
(Hughes, 2001). Earlier studies claimed that perceptual measures were
highly correlated with objectives measures and have the advantage of
facilitating comparisons among firms in different industries (Frishammar
and Horte, 2005; Zahra and Covin, 1993).
Influences on Performance
Issues related to business strategy have commonly been examined in
relation to financial performance. Becchetti and Giovanni (2002) and
Brophy and Shulman (1993) stated that the availability of external
finance can provide liquidity that is necessary to facilitate firm
performance potential. Shrader and Simon (1997) cited
undercapitalization as a prime reason for poor performance. Winn (1997)
recognized efficient asset utilization as being crucial for strong
performance, growth, and business success. Poor asset utilization can be
a precursor to poor performance and a deterioration of the firm's
competitive capabilities. Brophy and Shulman (1993) confirmed that
strong financial performance is a precursor to the firm's ability
to attract investment and support innovation. Strategic market
orientation was also directly linked with performance (Kholi and
Jaworski, 1990; Narver and Slater, 1990; Pelham and Wilson, 1996).
Resource availability has been consistently cited as a pivotal
issue and a common research theme on firm performance. Resource-based
theory emphasizes the importance of a firm's resources (physical
capital, human capital, and organizational resources) and capabilities
in the competitive environment (Collis and Montgomery, 1995). This
theory determines that firms are heterogeneous entities in idiosyncrasy,
difficult to imitate in resources and capabilities (Barney, 1991;
Connor, 1991; Rumelt, 1991; Wenerfelt, 1984). Resources include access
to the full range of support that facilitates the firm's
activities. Abundant resources enable firms to develop and pursue a wide
range of strategic initiatives while resource scarcity can weaken the
firm's competitive position (Chandler and Hanks, 1994; Mosakowski,
1993). Brush and Chaganti (1998) pointed out that effectively utilizing
resources in conjunction with different business strategies can improve
performance. They speculate that human and organizational resources play
a greater role in explaining performance than strategy.
Soh (2003) suggested that increasing the number of strategic
relationships can provide the firm with competitive information that can
lead to improved financial performance. Network theory examines the
relationship between SME network resources, activities and support
relative to firm performance. SMEs that are able to access a broad and
diverse network and who receive much support from their network are more
successful than those who don't (Bruder and Preisendorfer, 1998).
Green and Brown (1997) emphasized that information and resources also
contribute to stronger company performance. SMEs with larger networks
are better positioned to acquire the needed information and resources.
Other studies emphasized the importance of human resources on firm
performance. West and Meyer (1998) found that the quality and diversity
of ideas among management have impact performance. Chandler and Hanks
(1998) and Brush and Chaganti (1998) found that human capital was a
critical factor influencing firm performance. Santos-Requejo and
Gonzalez-Benito (2000) confirmed that smaller staff led to higher profit
margins and found that higher qualified staff had a positive impact on
performance. Watson et al (2003) revealed that human capital is an
important factor in assessing the investment potential of the firm due
to the impact of human capital on financial performance.
Finally, new product innovation is important for firm performance.
Hall (1993) showed that firms with high R&D spending have
above-industry-average financial performance. Regev (1998) and Chaney et
al (1991) found that innovating firms had higher labour productivity and
sales growth than non-innovating firms. Heunks (1998) found that
innovation increased firm productivity but not profits in the short-run
due to the cost of innovation.
The following hypotheses are based on the findings of these
previous studies:
H1: Spanish manager perception of performance is directly
associated with both internal and external operations.
H2: Spanish SME financial performance is directly associated with
both internal and external operations.
Sample, Questionnaire, and Methodology
Sample
The sample consisted of 739 Spanish manufacturing firms with 10 or
more employees. Data came from the Asociacion Espanola de Contabilidad y
Administracion de Empresas) Factores Determinantes de la Eficiencia y
Rentabilidad de las Pyme en Espana project. This database contains
qualitative and quantitative information gathered through a mail survey
sent to managers of each firm. The questionnaire was developed and
pretested during May to September, 2000. A total of 543 usable
questionnaires were returned and used in the analysis. Financial
information (matched with sample firms) was obtained from the SABI
database (Informa S.A. and Van Dijk Bureau).
The sample design was based on a stratified sampling in finite
population considering two variables: activity and size. The number of
firms in each stratum was provided by the Directorio Central de Empresas
elaborated by the Instituto Nacional de Estadistica. Following this
procedure the maxim sampling error was 3.68 with a significance level of
95%.
The sample composition is shown in Table 1. Respondent firms were
partitioned according to OECD (1997) categories of technological
intensity. These categories, which are based on the level of technology
specific to the sector (measured by the ratio of R&D expenditure to
added value) and the technology embodied in the purchases of
intermediate and capital goods, were used to classify technological
intensity. The respondent firms were also segmented according to the
European Union's (2003) criteria for firm size: (1) small firms:
less than 50 employees and annual sales less than 7 million euros or
total assets not larger than 5 million euros; (2) mid-sized firms:
50-249 employees and annual sales less than 40 million euros or total
assets less than 27 million euros; and (3) large firms: more than 249
employees and annual sales more than 40 million euros or total assets
greater than 27 million euros.
Questionnaire
The questionnaire contained two major sections. Each section was
designed to collect specific information necessary for the analysis. The
first section collected demographic information about the firm and
firm's manager (e.g. age of firm and manager, number of employees,
and market served: regional, national, European community, or
international). These questions were developed to collect information on
characteristics of the company that were relevant to the study.
The second section asked respondents to rank the importance of 20
factors that affected firm performance during the previous two years
using a 5-point Likert scale (1 = not important and 5 = very important).
These factors were identified through a review of previous research on
strategic factors affecting firm performance. The list of factors
included growth based on access to new markets, continuous improvement
in products/services, development of new products/services, prices lower
than competition, superior product/ service quality, R&D, cost
reduction, manufacturing/commercial process flexibility, availability
and quality of supplies, technological innovation, staff experience,
customer service, customer orientation, marketing, brand, product
diversification, reputation, centralized controls, and planning. Similar
methodology was previously used in studies by Kotha and Vadlamani
(1995), Arthur (1992), and Segev (1989). The second section also asked
respondents to rank (1) staff productivity, (2) asset productivity, and
(3) ability to obtain external capital relative to competitors using a
five-point Likert scale (1 = much worse and 5 = much better). The
rankings on staff and asset productivity were summed to form a new
variable (referred to as productivity measure) that was used in the
analysis. The scale reliability value of the new variable is 0.7
(Nunnally and Bernstein, 1994). This section also asked respondents to
rank their ability to obtain external capital relative to competitors
using a 5-point Likert scale (1 = much easier and 5 = much more
difficult).
Methodology
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 sample
was then evaluated by several segments to provide greater insight into
sample characteristics and to justify further analysis.
Principal component analysis was used to form groups of related
variables among the 20 factors thought to influence firm performance.
Principle 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 correlates high with
a smaller number of variables and low on the other variables, was
subsequently used to enhance the interpretability of the principal
components or factors. This procedure identified six factors.
Several multiple regression analyses used different dependent
variables to measure firm performance (both qualitative and quantitative
measures). The first multiple regression analysis examined the
relationship between the managers' ranking of firm productivity
(dependent variable), the six factors from the principal components
analysis (independent variables--product innovation, internal controls,
customer orientation, market recognition, efficiency, product quality),
three control variables (age of firm, technological intensity of the
firm, and size of firm), and financial attractiveness of the firm (e.g.,
ability to attract external capital) relative to competition.
Measure of productivity was used as a dependent variable due to the
impact of productivity on performance. Measure of productivity may also
lead to development and implementation of business strategy. The measure
of productivity was developed by summing rankings of (1) labor
productivity relative to competition (1-5 Likert scale with 1 = much
worse and 5 = much better) and (2) asset productivity relative to
competition (1-5 Likert scale with 1 = much worse and 5 = much better)
from the questionnaire. PM = [a.sub.0] + [b.sub.1]Age + [b.sub.2]TI +
[b.sub.3]Medium+[b.sub.4]Large +[b.sub.5]F1 + [b.sub.6]F2 + [b.sub.7]F3
+ [b.sub.8]F4 + [b.sub.9]F5 + [b.sub.10]F6 + [b.sub.11]FA+ [b.sub.12]e
where:
PM = Productivity Measure; Age = Age of Firm in Years (control
variable); TI = Technological Intensity of Firm (control variable:
1=high and medium high, 0 = medium low and low); Size = Size of Firm
(control variables: Medium [1 = medium 0 = other] Large [1 = large 0 =
other]); [F.sub.1] ... [F.sub.6] = Factors 1-6 (from principal
components analysis) FA = Financial Attractiveness
The next multiple regression used a 4-year average return on assets
(1999-2002) as the dependent variable and the same independent
variables. The purpose of this analysis was to understand the
relationship between firm performance using ROA and the independent
variables. The analysis was extended to include total asset turnover and
net profit margin (components of ROA using the DuPont formula).
ROA = a0 + [b.sub.1]Age + [b.sub.2]TI + [b.sub.3]Medium +
[b.sub.4]Large + [b.sub.5]F1 + [b.sub.6]F2 + [b.sub.7]F3 + [b.sub.8]F4 +
[b.sub.9]F5+[b.sub.10]F6 + [b.sub.11]FA+ [b.sub.12]e
TAT = a0 + [b.sub.1]Age + [b.sub.2]TI + [b.sub.3]Medium +
[b.sub.4]Large + [b.sub.5]F1 + [b.sub.6]F2 + [b.sub.7]F3 + [b.sub.8]F4 +
[b.sub.9]F5 + [b.sub.10]F6 + [b.sub.11]FA+ [b.sub.12]e
NPM = a0 + [b.sub.1]Age + [b.sub.2]TI + [b.sub.3]Medium +
[b.sub.4]Large + [b.sub.5]F1 + [b.sub.6]F2 + [b.sub.7]F3 + [b.sub.8]F4 +
[b.sub.9]F5 + [b.sub.10]F6 + [b.sub.11]FA + [b.sub.12]e
where:
ROA = Mean Return on Assets (1999-2002); TAT = Total Asset Turnover
(1999-2002); NPM = Net Profit Margin (1999-2002); Age = Age of Firm in
Years (control variable); TI = Technological Intensity of Firm (control
variable: 1 = high and medium high, 0 = medium low and low); Size = Size
of Firm (Control Variables: Medium [1 = medium 0 = other] Large [1 =
large 0 = other]); [F.sub.1] ... [F.sub.6] = Factors 1-6 (from principal
components analysis); FA = Financial Attractiveness
The control variables were used as a result of discussion and
findings in previous research. For example, technological level was
shown to be related to performance in several studies (Acs and
Audretsch, 1990; Acs and Audretsch, 1991; Oakey, 1991; Poutziouris et
al., 2000; Audretsch, 2002). Additionally, a number of studies (Fu et
al., 2002; Calvo Flores et al, 2000; Sanchez and Bernabe, 2002; and
Majumdar, 1997) found that size and profitability can be related due to
advantages associated with production flexibility (Farinas and Martin,
2001), adaptability and more flexible bureaucratic environment (Camison,
2001), absence of agency problems (Fernandez and Nieto, 2001), and
demand proximity (Vossen, 1998). Jovanovic (1982) and Durand and
Coeurderoy (2001) found that age may be related to performance due to
"liability of newness" (e.g., higher failure rates among newer
firms), "liability of adolescents" (e.g. failure rates
associated with competition during firm's later years), and
"liability of obsolescence" (e.g. failure rates associated
with older firm).
Results
Demographic Characteristics
The demographic characteristics of the sample firms are shown in
Table 2. Almost two-thirds of the responding firms (66.9%) are small,
and 27.3% are medium-sized firms. The manager of more than the 50% of
the responding firms had a university degree. Approximately 45.4% of
sales among the sample firms are to the national market, 37.5% to a
regional market, and 12.1% to the European Union market.
Mean Rankings of Influence Variables
Table 3 shows manager mean rankings of the importance of the
competitive influence factors on firm performance. Results in the table
show that the majority of the mean rankings were above 3.0. This
provides support for the relative importance of these influence factors
on firm performance. Factors with rankings higher than four are product/
service quality (4.30) and firm's reputation (4.17). Factors that
have mean rankings less than 3.0 include planning, research and
development, centralized control procedures, prices lower than
competitors, and marketing. Generally, these mean rankings suggest that
managers believe that variables associated with customers have a greater
impact on firm performance that variables associated with internal
operations.
The top five mean rankings are related to issues directly impacting
the firm's relationship with customers and, thus, highlight
managers' beliefs about the importance of customer orientation.
Product/service quality and products/services improvements provide
evidence of the importance of high-quality products/services while staff
experience, customer orientation, and customer service provide evidence
for the importance of high-quality customer-firm interaction. The impact
of these five variables ultimately affects the firm's reputation
and, thus, ability to effectively compete in the market.
The five lowest mean rankings (planning, research and development,
centralized control procedures, prices lower than competitors, and
marketing) are associated with issues that are related to the internal
operations of the firm. Planning is central to a wide variety of
internal operational decisions. Internal decisions often begin with
development of operational and strategic planning.
Table 3 also shows mean rankings for the managers' rankings of
relative firm productivity (3.48), return on assets (4.62%), total asset
turnover (1.50), and net profit margin (2.95%). The mean ranking of
relative productivity suggests that the managers believe that their firm
is slightly more productive than competitors. The positive average
return on assets and net profit indicate that firms are generally
profitable.
Factor Analysis
Table 4 shows the results of varimax rotated factor analysis of the
rankings of the 20 factors that influence firm performance. Factor
loadings above 0.4 were considered to be high enough to be included in a
factor grouping. According to the Kaiser-Meyer-Olkin measure of sampling
adequacy (K-M-O = 0.874), the degree of common variance among the
initial variables is "meritorious" boarding on
"marvelous". Another indicator of the strength of the
relationship among variables is Bartlett's test of sphericity. This
test (/2 = 3251.92 dl: 190 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.
Factor 1 included four influence variables: growth, product/service
improvements, research and development, and technical innovation. Factor
1 was labeled as Product Innovation. Factor 2 included three influence
variables: staff experience and qualification, centralized control
procedures and planning. Factor 2 was labeled as Internal Controls.
Factor 3 (labeled Customer Orientation) included two influence
variables: customer service and customer orientation. These variables
reflect the importance of customer orientation as an important component
of the firm's market orientation.
Factor 4 included three influence variables: marketing, brand
identity, product diversification. Factor 4 was labeled Market
Recognition. Factor 5 included five influence variables: prices lower
than competitors, cost reduction, flexible manufacturing processes, and
availability/quality of supplies. These variables in Factor 5 are
consistently related to the improvement of the manufacturing process
efficiency. Factor 5 was labeled Efficiency. Factor 6 included two
influence variables: product/service quality and firm reputation. This
last factor shows the importance of quality as a competitive factor and
was labeled Product Quality.
The scale reliability values for each factor (coefficient alpha)
are also reported in Table 4. All scales have alpha coefficients between
0.52-0.74, which suggests moderate to high reliability (Van de Ven and
Ferry, 1980). The factors from the principal components analysis can be
grouped in two categories: competitive factors related to internal
processes (product innovation, internal control and efficiency factors)
and competitive factors related to market recognition (customer base,
market recognition and quality factors).
Regression Analysis
Tables 5, 6, 7, and 8 show the results of the regression analyses
using the different dependent and independent variables. Absence of
multicollinearity was verified after analyzing the correlations among
the different independent variables and the variance inflation factor
collinearity diagnostic. The variance inflation factor collinearity
diagnostic is shown in Tables 5, 6, and 7. Values close to 1.0 indicate
that the independent variables are not correlated and precision of
estimates is not lost due to multicollinearity. The purpose of the
regression was to analyze the relationship between the influence factors
and firm performance rather than to predict future performance. Table 5
shows the results using the manager's perception of productivity as
the dependent variable. The results show acceptable model fit
(F-statistic=26.035; significance= 0.000; adjusted [R.sup.2]=0.344). The
coefficients for the seven independent variables are significant and
positive. The standardized coefficients show that the most important
influence variable is financial attractiveness (0.359), followed by
internal controls (0.249) (which includes employee characteristics),
quality (0.157), and product innovation (0.131). The coefficient for age
(negative and significant), verifies the liability of obsolescent. This
result provides strong support for hypothesis one.
Table 6, which is used to assess short-term performance, shows the
results using average return on assets (1999-2002) as the dependent
variable. The results show modest model fit (F-statistic= 1.932;
significance=0.033; adjusted [R.sup.2]=0.019). The coefficients for
Internal Control (0.097) and Financial Attractiveness (0.082) are
significant. The positive coefficient for Internal Control indicates
that higher ROA is associated with more qualified staff and more
planning/control within the firm. The positive coefficient for Financial
Attractiveness indicates that higher ROA is associated with easier
access to external capital relative to the firm's competitors. The
regression coefficients on the remaining influence factors are not
significant.
The results suggest that the competitive factors that are
associated with ROA are those related to employee characteristics,
planning issues and financial attractiveness. Additionally, the
coefficient for the technological intensity (0.108) and for the dummy
variable that represents the medium size category (-0.101) are
significant. This finding reveals the importance of taking into account
the sector and size variables.
Table 7 shows the results using average asset turnover (1999-2002)
as the dependent variable. The results show modest fit
(F-statistic=3.645; significance=0.000; adjusted [R.sup.2]=0.052). The
coefficient for Product Innovation (-0.122) is significant. The negative
coefficient for Product Innovation indicates that higher asset turnover
is associated with less effort devoted toward innovative activities
within the firm. The impact of the costs associated with innovation and
the investment necessary for the firm to achieve a superior competitive
position has a negative impact on asset turnover. Furthermore, companies
that embark on aggressive growth strategies often find their asset
effectiveness severely compromised in the short term (Winn, 1997).
The coefficient for Internal Controls (0.75) is also significant.
The positive coefficient indicates that better asset utilization is
associated with better staff qualifications, planning, and internal
controls. The results from this model also show that smaller firms have
higher asset turnover than larger firms.
Table 8 shows the results using average net profit margin
(1999-2002) as the dependent variable. The independent variable Internal
Control (0.090) is significant and indicates that higher net profit
margin is associated with factors related to better internal controls.
This result should be interpreted with caution, however, because the
overall model is not significant. The results in Tables 6 and 7 provide
limited support for hypothesis two.
Discussion
The findings of the study provide a number of managerial
implications. Factor analysis revealed that managers' perceptions
of their firm's competitive position can be grouped into product
innovation, internal controls, financial attractiveness, customer
orientation, market recognition, efficiency and product quality
categories. Further analysis showed that the more important these
factors, the greater the manager's perception about competitive
position of the firm relative to competition. Additionally, ROA is
positively influenced by internal controls and financial attractiveness.
Asset turnover, on the other hand, is negatively influenced by the
product innovation.
The positive association between innovation and performance
verifies the findings of several studies (Chaney et al., 1991; Grant,
1991; Damanpour and Evans, 1984; Damanpour et al., 1989). These findings
are also consistent with Heunks (1998), who found that innovation tended
to increase productivity but not profits in the short-run due to the
innovation's cost.
The findings in this study also verified that Spanish manufacturing
firms experience higher performance (as measured by ROA) by investing in
human capital and planning issues. This finding is consistent with
previous studies that found that a firm can be a source of sustainable
competitive advantage through human resources that add value to
production processes and are a unique resource (Santos-Requejo and
Gonzalez-Benito, 2000; Youndt et al., 1996; Chandler and Hanks, 1998;
Brush and Chaganti, 1998; Huselid, Jackson and Schuler, 1997).
The standard coefficients of the different regressions provide
evidence for the high importance of financial attractiveness and
internal controls as the most important contributors to the firms'
competitive position in terms of both ROA and productivity. Financial
attractiveness and internal controls may be closely linked. Effective
internal controls provide evidence that the firm is efficiently
operated. The degree to which a firm is able to implement effective
internal controls is a factor evaluated by potential investors. Firms
that have effective internal controls, especially relative to
competitors, will be more financially attractive to potential investors.
The findings also showed that, in terms of productivity, product
quality relative to competitors is one of the most important factors
affecting business performance. Quality not only enhances the reputation
of the firm, it also can allow the firm to earn higher profits, expand
market share, and generally to grow the business (Buzzell and Gale,
1987; Bigwood, 1997). Calantone and Knight (2000) confirmed that product
quality plays an important role in performance. However, quality
standards may be hard to achieve in some markets (Calantone and Knight,
2000). While industrial firms may prefer superior quality products, the
effect of quality on corporate performance can be equivocal. If
manufacturers are unable to pass-on the added costs when improving
product quality, then profit margins will decline (Szymansky et al.,
1993). This is verified for Spanish manufacturing firms in this paper.
Quality is one of the three most important factors that determine the
performance in terms of productivity, but not in ROA.
The findings provide strong support for hypothesis one. Managers
believe that performance will be enhanced with better internal and
external operational activities. This result suggests that mangers
recognize the value of effective strategies and market orientation.
Belief in the value of these activities would likely lead to commitment,
development, and implantation of strategic policy. This is consistent
with findings by Soh (2003), Watson, Stewart, and BarNir (2003),
Santos-Requejo and Gonzalez-Benito (2000), West and Meyer (1998),
Chandler and Hanks (1998), and Brush and Chaganti (1998).
The findings provide limited support for hypothesis two. Factor
analysis provided groupings of variables that were evaluated related to
financial performance measures. The results indicated that variables
grouped into product innovation and internal controls were associated
with higher performance. This is consistent with findings by Hall et al.
(1993), Chan et al. (1990), and Chaney et al. (1991).
Conclusions
This paper examined the relationship between factors influencing
Spanish firms' competitive position and performance. Firm
productivity (qualitative variable) and return on assets (quantitative
variable) were used as performance measures, and rankings of factors
affecting firm performance and ability to attract capital were used as
influencing variables. The availability of better information about
factors impacting performance is important to understand so that firms
are able to explore the ramifications of alternatives, develop
contingent plans, and make informed decisions. The major findings from
the study include (1) the positive impact of competitive factors on firm
productivity; and (2) the importance of (a) financial attractiveness and
(b) staff and planning issues relative to a firm's ROA.
The study provides insight into policy issues that may be useful
for Spanish manufacturing firms. First, government and company policies
should be developed that facilitate access to capital to Spanish SME
manufacturing firms. Access to capital can provide the liquidity
required to pursue market opportunities, develop innovations, and remain
competitive. Second, firms should recognize that developing policies
that promote effective planning and high-quality staff can lead to
higher performance. High-quality staff and effective planning likely
lead to more efficiency of operations, innovations, and effective plans.
Firm managers should develop long-term plans to support near-term
investment decisions. Since more than 50% of the firm managers have a
college degree, university training programs may consider integrating
these issues into their curriculum.
Third, the results suggest the that positive effect of these
factors may not be realized in the short term due to the time lag of
their impact on performance. Short-term performance may decline in order
to achieve long-term success. Firms should not avoid the negative
effects of new investment or obstruct capital access to achieve
increases in long-term performance.
The results can also be used by consultants and support agencies
that provide assistance to manufacturing firms in areas of planning and
capital acquisition. Remaining competitive and viable are complementary
issues that can be supported through the findings of this study. Since
managers' perceptions of issues affecting performance would be
expected to influence policy development and implementation, this study
may aid consultants and support agencies to better assist policy
development among all firms. The information can be used to assist firms
that are in crisis to understand important changes required to improve
performance and be competitive. Successful firms can use the information
from this study to understand how to retain strong performance and
remain competitive. Information in this study could easily be built into
training programs for both new and existing businesses.
The study has several limitations that provide avenues for
potential future research. The sample was limited to only manufacturing
firms. Future studies could compare results across industries. The data
was also collected at a single point in time. A longitudinal study would
provide evidence on the changes in factors affecting performance over
time. Environmental moderators that may affect the manager's
perceptions and decisions, such as industry change and diversity of
market segments, are not incorporated into the analysis. Finally, an
important potential limitation is that the analysis relied on manager
perceptions rather than quantitative data. Future studies could validate
the results in this paper by collecting reliable quantitative data on
factors associated with firm performance among Spanish companies.
Contact Information
For further information on this article, contact
Howard Van Auken, Iowa State University E-mail:
vanauken@iastate.edu
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Antonia Madrid-Guijarro, University of Cartagena
Howard Van Auken, Iowa State University
Domingo Garcia-Perez-de-Lema, University of Cartagena
Table 1. Number of Firms in Sample by OCED Technological Intensity
in Manufacturing Industries and size (n=543)
Sector
High- Medium-High Medium-Low Low
Technology Technology Technology Technology
7 113 161 262
Size
High-
Technology Large Medium Small
7 32 148 363
Table 2. Respondent Characteristics (n=543)
Firm Characteristic Percentage of Firms
Educational Level of Manager
Primary School 19.7
Secondary School / Professional Formation 24.7
3 years-university 19.9
>3 years-university 35.8
Market Served
Regional 37.5
National Market 45.4
European Union 12.1
International 5.2
Number of Employees
<50 363
50-250 148
>250 32
Table 3. Means of Variables (n=543)
Variable Mean
Product/Service Quality 4.30
Firms Reputation 4.17
Products/Services Improvements 3.88
Staff Experience 3.74
Customer Orientation 3.71
Customer Service 3.68
Cost Reduction 3.59
Availability/Quality Supplies 3.56
Staff Qualifications 3.53
Flexible Manufacturing Processes 3.46
Product Diversification 3.45
New Products/Services 3.38
Brand identity 3.28
Growth 3.08
Technological Innovation 3.01
Planning 2.99
Research and Development 2.98
Centralized Control Procedures 2.95
Prices Lower Than Competitors 2.78
Marketing 2.85
Financial Attractiveness 3.63
Ranking of Productivity 3.48
Return on Assets 4.62%
Total Asset Turnover 1.50
Net Profit Margin 2.95%
Table 4. Component Loadings for Competitive Factor (n=543)
Product Internal Customer
Variables Innovation Controls Orientation
Growth 0.595
Products/Services Improvements 0.583
New Products/Services 0.736
Prices Lower Than Competitors
Product/Service Quality
Research and Development 0.701
Cost Reduction
Flexible Manufacturing Processes
Availability/Quality Supplies
Technological Innovation 0.560
Staff Experience 0.655
Staff Qualifications 0.686
Customer Service 0.827
Customer Orientation 0.802
Marketing 0.406
Brand Identity
Product Diversification
Firms Reputation
Centralized Control Procedures 0.659
Planning 0.680
Crombach-alpha 0.739 0.744 0.797
Kaiser-Meyer-Olkin 0.874
Percentage of total variance 60.245
explained
Market Product
Variables Recognition Efficiency Quality
Growth
Products/Services Improvements 0.459
New Products/Services
Prices Lower Than Competitors 0.635
Product/Service Quality 0.702
Research and Development
Cost Reduction 0.639
Flexible Manufacturing Processes 0.535
Availability/Quality Supplies 0.537
Technological Innovation
Staff Experience
Staff Qualifications
Customer Service
Customer Orientation
Marketing 0.449
Brand Identity 0.834
Product Diversification 0.687
Firms Reputation 0.417 0.537
Centralized Control Procedures
Planning
Crombach-alpha 0.624 0.519 0.642
Kaiser-Meyer-Olkin
Percentage of total variance
explained
Only loadings>0.4 are shown
Table 5. Regression Analysis: Relationship Between Managers Perception
of Productivity and Independent Variables (n = 543)
Independent Variables
(F = 26.035 (1) Parameter
[R.sup.2] = 0.344) Estimate t-value p VIF *
Age -0.108 -2.923 0.004 1.103
TI -0.023 -0.624 0.533 1.052
Medium 0.015 0.401 0.689 1.106
Large 0.038 1.025 0.306 1.097
Product Innovation 0.131 3.643 0.000 1.044
Internal Controls 0.249 6.818 0.000 1.071
Customer Base 0.119 3.354 0.001 1.014
Market Recognition 0.076 2.117 0.035 1.044
Efficiency 0.129 3.632 0.000 1.016
Quality 0.157 4.380 0.000 1.028
Financial Attractiveness 0.359 9.736 0.000 1.092
Standardized regression coefficients are shown: 1 = Significant at
1%; 2 = Significant at 5%; * Variance inflation factor
Table 6. Regression Analysis: Relationship Between Mean Return on
Assets (1999-2002) and Independent Variables (n = 543)
Independent Variables
(F = 1.932 (2) Adjusted Parameter
[R.sup.2] = 0.019) Estimate t-value p VIF *
Age 0.020 0.433 0.665 1.103
TI 0.108 2.437 0.015 1.052
Medium -0.101 -2.214 0.027 1.106
Large 0.002 0.050 0.960 1.097
Product Innovation 0.008 0.184 0.854 1.044
Internal control 0.097 2.182 0.030 1.071
Customer Base 0.026 0.594 0.553 1.014
Market Recognition -0.002 -0.044 0.965 1.044
Efficiency -0.045 -1.038 0.300 1.016
Quality 0.007 0.163 0.870 1.028
Financial Attractiveness 0.082 1.826 0.068 1.092
Standardized regression coefficients are shown: 1 = Significant at 1%;
2 = Significant at 5%; 3 = Significant at 10%; * Variance inflation
factor
Table 7. Regression Analysis: Relationship Between Mean Asset Turnover
(1999-2002) and Independent Variables (n = 543)
Independent Variables
(F = 3.645 (1) Adjusted Parameter
[R.sup.2] = 0.052) Estimate t-value p VIF *
Age -0.116 -2.594 0.010 1.103
TI -0.042 -0.965 0.335 1.052
Medium -0.137 -3.060 0.002 1.106
Large -0.078 -1.765 0.078 1.097
Product Innovation -0.122 -2.807 0.005 1.044
Internal control 0.075 -1.710 0.088 1.071
Customer Base 0.011 0.264 0.792 1.014
Market Recognition 0.001 0.034 0.973 1.044
Efficiency -0.007 -0.162 0.872 1.016
Quality -0.007 -0.159 0.874 1.028
Financial Attractiveness 0.003 0.071 0.943 1.092
Standardized regression coefficients are shown: 1 = Significant at 1%;
2 = Significant at 5%; 3 = Significant at 10%; * Variance inflation
factor
Table 8. Regression Analysis: Relationship Between Mean Net Profit
Margin (1999-2002) and Independent Variables (n = 543)
Independent Variables
(F = 1.222 Adjusted Parameter t-value p VIF *
[R.sup.2] = 0.005) Estimate
Age 0.062 1.365 0.173 1.103
TI 0.046 1.028 0.305 1.052
Medium -0.100 -2.196 0.029 1.106
Large -0.013 -0.275 0.784 1.097
Product Innovation -0.021 -0.470 0.639 1.044
Internal control 0.090 1.997 0.046 1.071
Customer Base -0.006 -0.141 0.888 1.014
Market Recognition -0.063 -1.422 0.156 1.044
Efficiency -0.024 -0.539 0.590 1.016
Quality -0.036 -0.809 0.419 1.028
Financial Attractiveness 0.036 0.781 0.435 1.092
Standardized regression coefficients are shown: 1 = Significant at 1%;
2 = Significant at 5%; 3 = Significant at 10%; * Variance inflation
factor
