Directory UMM :Data Elmu:jurnal:J-a:Journal Of Business Research:Vol47.Issue3.2000:

Firm Characteristics Influencing Export Propensity:
An Empirical Investigation by Industry Type
Rajshekhar (Raj) G. Javalgi
CLEVELAND STATE UNIVERSITY

D. Steven White
UNIVERSITY OF MASSACHUSETTS DARTMOUTH

Oscar Lee
CLEVELAND STATE UNIVERSITY

This study examines 20,204 manufacturers in a midwestern state to
determine whether firm characteristics significantly influence the propensity to export. By examining census data, the researchers seek to address
the speculation that the lack of representativeness of previous studies has
skewed the interpretation of the influence of firm characteristics on export
propensity. The variables examined are number of employees, total sales,
years in business, international trade activity (exporter/nonexporter),
primary industrial classification, and firm ownership. The results of the
study indicate that the value of using firm characteristics to predict export
behavior varies by industry. J BUSN RES 2000. 47.217–228.  1999
Elsevier Science Inc.

I

n recent years, overseas business has become a matter of
necessity for U.S. firms of all sizes. Such factors as market
saturation and the trade deficit have fueled the need to
consider exporting as a desirable alternative for growth. Although the United States is still the world’s largest economy
and market (USDC, 1994), its trade deficit has grown dramatically for the last two decades. In 1975, the U.S. share of world
trade was 15.4%. This declined to 12.3% in 1988 and 12.2%
in 1991 (Jain, 1993). In 1992, the trade deficit exceeded $40
billion, and in 1993, it reached $75.7 billion. This change
from market leader to market follower has “occurred more
quickly and completely than most Americans thought possible” (Serey, Lindsay, and Myers, 1989, p. 6).
U.S. exports account for relatively little of the country’s
Gross National Product (GNP) (12%). However, the relative
importance of foreign trade as a percentage of the GNP has
almost doubled in the past 20 years. Table 1 presents the
total volume of exports of goods and services, total volume

Address correspondence to D. Steven White, University of Massachusetts

Dartmouth, 285 Old Westport Rd., North Dartmouth, MA 02747-2300.
Journal of Business Research 47, 217–228 (2000)
 1999 Elsevier Science Inc. All rights reserved.
655 Avenue of the Americas, New York, NY 10010

of imports of goods and services, and the trade balance of
goods and services for the period 1960 through 1994.
Historically, from 1891 through 1970, the United States
enjoyed an unbroken string of trade surpluses (USDC, 1994).
Interestingly, the first year of surplus for the service sector
(1971) coincides with the first year of an over-all trade deficit
and a deficit in goods exports. Both sectors have retained their
respective balance of trade orientations since 1971, with two
exceptions: a $900 million goods surplus in 1973 and a $8.9
billion goods surplus in 1975.
A closer examination of the data contained in Table 1
provides interesting insight. Since 1983, the average annual
rate of growth in service exports is 10.75%. During the same
period, the average annual growth rate in the exports of goods
registers 8.87%. When comparing the percentage increase per

year, the growth in services surpasses the growth in goods
exported every period with two exceptions: 1987 to 1988 and
1993 to 1994. Conversely, the average annual increase in the
growth of goods imports equals 8.81% for the period 1983
through 1994, and the average increase in the import of services during the same period is 8.70%. On average, service
exports grew at an average annual rate in excess of three
percentage points over the increase in imports; whereas, goods
exports grew on average .06% faster than the increase in goods
imports. The United States’ declining position in balance of
trade indicates the need for its manufacturing firms to compete
more effectively in the global market (Dertouzos, Thurow,
and Solow, 1989; Hill, Hitt, and Hoskisson, 1988; Kedia,
1993; Young, 1985). To address the trade deficit, the U.S.
government is renewing efforts to expand manufacturing exports (Kotabe and Czinkota, 1992).
The U.S. potential for export growth is colossal: over 85%
of all U.S. manufacturers do not export. Therefore, a great
opportunity exists to expand the international trade efforts of
the nations’s domestic manufacturing firms. In addition, the
ISSN 0148-2963/00/$–see front matter
PII S0148-2963(98)00065-4

218

J Busn Res
2000:47:217–228

R. G. Javalgi et al.

Table 1. U.S. International Trade in Goods and Services Balance of Payments (BOP) Basis (Billions of U.S. Dollars)
Exports

Imports

Trade Balance

Year

Total

Goods

Services

Total

Goods

Services

Total

Goods

Services

1960
1961
1962
1963
1964

1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994

25.9
26.4
27.7
29.6
33.3
35.3
38.9
41.3
45.5

49.2
56.6
59.7
67.2
91.2
120.9
132.6
142.7
152.3
178.4
224.1
271.8
294.4
275.2
266.0
290.9
288.8
309.5
348.0
430.2

489.0
537.6
581.2
616.9
641.7
698.0

19.7
20.1
20.8
22.3
25.5
26.5
29.3
30.7
33.6
36.4
42.5
43.3
49.4

71.4
98.3
107.1
114.7
120.8
142.1
184.4
224.3
237.0
211.2
201.8
219.9
215.9
223.3
250.2
320.2
362.1
389.3
416.9
440.4

456.9
503.0

6.3
6.3
6.9
7.3
7.8
8.8
9.6
10.7
11.9
12.8
14.2
16.4
17.8
19.8
22.6
25.5
28.0
31.5
36.4
39.7
47.6
57.4
64.1
64.2
71.0
72.9
86.1
97.8
110.0
126.8
148.3
164.3
176.6
184.8
195.0

22.4
22.2
24.4
25.4
27.3
30.6
36.0
38.7
45.3
49.1
54.4
61.0
72.7
89.3
125.2
120.2
148.8
179.5
208.2
248.7
291.22
310.6
299.4
323.8
400.1
410.9
448.3
500.0
545.0
579.3
616.0
609.1
657.3
717.4
804.0

14.8
14.5
16.3
17.0
18.7
21.5
25.5
26.9
33.0
35.8
39.9
45.6
55.8
70.5
103.8
98.2
124.2
151.9
176.0
212.0
249.8
265.1
247.6
268.9
332.4
338.1
368.4
409.8
447.2
477.4
498.3
490.7
536.5
589.4
669.0

7.7
7.7
8.1
8.4
8.6
9.1
10.5
11.9
12.3
13.3
14.5
15.4
16.9
18.8
21.4
22.0
24.6
27.6
32.2
36.7
41.5
45.5
51.7
54.9
67.7
72.8
79.8
90.2
97.9
101.9
117.7
118.4
120.9
128.0
135.0

3.5
4.2
3.4
4.2
6.0
4.7
2.9
2.6
0.3
0.1
2.3
(1.3)
(5.4)
1.9
(4.3)
12.4
(6.1)
(27.2)
(29.8)
(24.6)
(19.4)
(16.2)
(24.2)
(57.8)
(109.2)
(122.1)
(138.8)
(152.0)
(114.8)
(90.3)
(78.4)
(27.9)
(40.4)
(75.7)
(107.0)

4.9
5.6
4.5
5.2
6.8
5.0
3.8
3.8
0.6
0.6
2.6
(2.3)
(6.4)
0.9
(5.5)
8.9
(9.5)
(31.1)
(33.9)
(27.6)
(25.5)
(28.0)
(36.5)
(67.1)
(112.5)
(122.2)
(145.1)
(159.6)
(127.0)
(115.2)
(109.0)
(73.8)
(96.1)
(132.6)
(167.0)

(1.4)
(1.4)
(1.2)
(1.0)
(0.8)
(0.3)
(0.9)
(1.2)
(0.4)
(0.5)
(0.3)
1.0
1.0
1.0
1.2
3.5
3.4
3.8
4.2
3.0
6.1
11.9
12.3
9.3
3.3
0.1
6.3
7.6
12.1
24.9
30.7
45.9
55.7
56.9
60.0

Note: 1. Compiled from official statistics of the U.S. Department of Commerce, Bureau of Economic Analysis. Data reflect all revisions through June 1995.
2. Balance of Payments (BOP) basis for goods reflects adjustments for timing, coverage, and valuation to the data compiled by the Census Bureau. The major adjustments
concern: military trade of U.S. defense agencies, additional nonmonetary gold transactions, and inland freight in Canada and Mexico.
3. Goods valuation are F.A.S. for exports and Customs value for imports.
4. Source: National Trade Data Bank.

global market for manufactured goods is projected to grow
as more nations develop their manufacturing sectors (Mittelhauser, 1994). Approximately 51,000 U.S. firms export regularly, and about 87% of those employ fewer than 500 workers
(Jeannet and Hennessey, 1995), more indication that only a
meager percentage of U.S. export potential is used. Additionally, “eight out of ten new jobs created between 1985 and
1990 were in export-related industries. A $10 billion increase
in exports generates about 193,000 American jobs both directly and indirectly” (Jain, 1993). Exports are not only a
significant aspect of international business activity for the
nation, but also a major economic issue to be dealt with
at the state level as well (Kotabe and Czinkota, 1992). For
manufacturers in each state, global orientation has become a
matter of necessity.
In the past, empirical studies of international trade behavior

have focused on a variety of possible antecedents—including
firm characteristics—of export marketing. Much research on
firm-specific characteristics has been generated (c.f., Bonaccorsi, 1992), and at least three observations can be drawn
from these studies.
1. Little has been done to document the impact of firm
characteristics on export propensity across industries.
2. Research designs, such as sampling procedures and
sample sizes employed, have restricted the generalizability of the findings.
3. The need for empirical studies focusing on a larger data
bases (e.g., census data) is evident (c.f., Bonaccorsi,
1992; Calof, 1994).
This study differs from previous efforts to examine the
influence of firm characteristics on export propensity in that

Export Propensity

the data used comprise a census of manufacturing firms. Furthermore, the study examines the difference in influence firm
characteristics have upon export propensity across 16 industries. The underlying motivation for undertaking this research
is to contribute to the export marketing literature by overcoming the limitations stated above.

Literature Review
For the past two decades, research on the subject of exporting
has been increasing. Topics studied vary widely; however,
some common areas of focus include: obstacles or barriers to
exporting (Alexandrides, 1971; Cavusgil and Nevin, 1981;
Rabino, 1980); factors influencing export performance (Aaby
and Slater, 1989; Axinn, 1985; Cooper and Kleinschmidt,
1985; Dominquez and Sequeria, 1993; Koh, 1991); organizational structuring and exporter profiles (Brasch, 1991; Burton
and Schlegelmilch, 1987; Cavusgil, Bilkey, and Tesar, 1979;
Cavusgil and Nevin, 1979; Diamantopoulos and Inglis, 1988);
pre-identification criteria for potential exporters (Cavusgil,
Bilkey, and Tesar, 1979; Czinkota and Johnston, 1983); the
development of a series of stages of export involvement based
on Rogers’ diffusion of innovation theory (Bilkey and Tesar,
1977; Reid, 1981); and marketing mix issues of exporting
firms (Hill and Still, 1984; Peebles, Ryans, and Vernon, 1977;
Seifert and Ford, 1989). These studies no doubt contribute
to our understanding of export marketing both in the areas
of industrial goods and consumer goods.
The influence of firm characteristics on export potential
has also received much attention in the past (c.f., Bonaccorsi,
1992; Calof, 1994; Delacroix, 1984; Dichtl, Liebold, Kogelmayr, and Muller, 1984; Kaynak and Kothari, 1984; O’Rourke,
1985; Reid, 1982). Mostly, researchers have investigated the
differences between exporters and nonexporters with regard
to readily identifiable firm characteristics. The objective of
studies using the exporter/nonexporter dichotomy is to develop a profile of characteristics that differentiate the categories
(Burton and Schlegelmilch, 1987; Cavusgil and Nevin, 1981;
Christensen, de Rocha, and Gertner, 1987; Yaprak, 1985).
The prevalent belief is that by understanding key differences
between exporters and nonexporters, a concentrated effort to
motivate and assist nonexporters into entering the global market may undertaken.
Czinkota and Ursic (1991) report that the variables of firm
size and age have been the most closely scrutinized of the
characteristics investigated. Internationalization requires appropriate resources; therefore, firm size is an important predictor of export propensity (Calof, 1994; Tookey, 1964).
Larger firms have a greater ability to expand resources and
absorb risks than smaller ones and may have greater bargaining power (Erramilli and Rao, 1993); and larger firms
have specialized managerial resources and can make use of
economies of scale (Samiee and Walters, 1991).
Studies have identified a positive relationship between
firm size and exporting (Ball and Tschoegl, 1982; Cavusgil

J Busn Res
2000:47:217–228

219

and Naor, 1987; Cavusgil and Nevin, 1981; Christensen,
de Rocha, and Gertner, 1987; Gottko and McMahon, 1988;
Hirsch and Adar, 1974; Malleksadeh and Nahavandi, 1985;
Terpstra and Yu, 1988); and a positive relationship between
firm size and percentage of total export sales (Cavusgil, 1984b;
Madsen, 1987; Reid, 1982). Culpan (1989) concluded that
smaller firms demonstrate less success in exporting than do
medium or large firms. Other studies indicate that the size
correlation only exists to a certain level, beyond which the
relationship fails (Czinkota and Johnston, 1983). Finally, some
studies find that firm size is not significantly related to propensity to export (Diamantopoulos and Inglis, 1988; Hester,
1985). Czinkota and Johnston (1981) question the direction
of causality with regard to firm size: Do exporting activities
increase size or does size lead to exporting?
Calof (1994), in his thorough investigation of the association of firm size to export behavior, brought to light several
critical issues in the status of the research to date. One concern
raised and addressed by his study is the generalizability of
the results of previous studies because of their small sample
size. Building on the earlier work of Bonaccorsi (1992), who
used an Italian national database of 8,810 companies, Calof
investigated 14,072 Canadian firms and concluded that size
may offer limited insight into a firm’s propensity to export. He
ends by questioning the generalizability of his study, however,
because of the bias of the database used, which contained
information on Canada’s largest firms. Others have suggested
that the conflicting empirical evidence regarding firm size may
be attributable to variance in contextual factors, such as the
firm’s industry and market environments (Samiee and Walters,
1990, p. 236).
Given the previous research efforts, our goals are threefold:
(1) to examine the influence of firm characteristics on propensity to export; (2) to address the concerns of Samiee and
Walters (1990) by examining the relationship between the
variables investigated and industry type; and (3) to end the
debate over the contribution of firm characteristics to export
propensity by using data collected as part of a 1994 investigation of 20,204 Ohio manufacturing firms.

Hypotheses and Rationale
Consistent with Calof (1994), this study uses two dimensions
of firm size rather than one: number of employees and total
sales. Those seeking a logical argument as to why one should
investigate multiple measures of firm size will find an excellent
explanation in Calof’s comprehensive examination of the theoretical foundation of the export literature. The additional firm
characteristics included in the analysis are years in business,
international trade activity (export/nonexport), primary industrial classification (by two-digit Standard Industrial Classification (SIC) code), and firm ownership (private vs. public).
The aggregate and industry-level hypotheses developed in the
ensuing section, allow the researchers to investigate the impact
each variable has upon export propensity.

220

J Busn Res
2000:47:217–228

Number of Employees
Numerous studies have measured firm size in terms of the
number of employees (Bilkey and Tesar, 1987; Bonaccorsi,
1992; Burton and Schlegelmilch, 1987; Cavusgil and Naor,
1987; Hirsch, 1971; Holzmuller and Kasper, 1991; Kaynak
and Kothari, 1984; Lee and Yang, 1990; Madsen, 1989; Maleksadeh and Nahavandi, 1985; Mugler and Miesenbock, 1986;
Yang, Leone, and Alden, 1992). Overall, the results indicate
that exporting firms are larger in terms of number of employees than nonexporting firms (Keng and Jiuan, 1989). Kedia
and Chhokar (1986) go so far as to proclaim that most smalland medium-sized firms do not export. Differences in employee size within size groups (small, medium, and large)
investigated are also documented. Kaynak and Kothari (1984)
found that small and medium-sized businesses participating
in international trade have more employees than businesses
within the same size categories who do not participate. The
mixed evidence suggest the following.
H1a: The average number of employees working for exporting firms will significantly differ from the average
number of employees working for nonexporting
firms.
H1b: Within each industry, as the number of employees
increases, the propensity to export will increase.

Total Sales
Studies measuring firm size as the sales level of firm (Calof,
1994; Cavusgil, 1984a; Cavusgil and Nevin, 1981; Christensen, de Rocha, and Gertner, 1987; Czinkota and Johnston,
1983; Hester, 1985; Holden, 1986; Kaynak and Kothari, 1984;
Keng and Jiuan, 1989; Lall and Kumar, 1981; Lee and Yang,
1990; Madsen, 1989; Maleksadeh and Nahavandi, 1985;
Yang, Leone, and Alden, 1992) indicate that firms with higher
sales are more likely to engage in exporting activity. Thus, it
is prudent to hypothesize the following.
H2a: The sales level of exporting firms will significantly
differ form the sales level of nonexporting firms.
H2b: Within each industry, as the level of sales increases,
the propensity to export will increase.

Age of Firm
Czinkota and Ursic (1991) report that much research exists
illustrating the contribution of firm age to export propensity.
Previous studies indicate that younger firms exhibit more
interest in foreign markets than older, established firms (Kaynak and Kothari, 1984; Kirpalani and MacIntosh, 1980; Lee
and Brasch, 1978; Ursic and Czinkota, 1981). Conversely,
evidence also suggests that older firms are more likely to
export than younger firms (Lee and Yang, 1990; Welch and
Wiedersheim-Paul, 1978). Diamantopoulos and Inglis (1988)
report that no relationship between firm age and export pro-

R. G. Javalgi et al.

pensity exists. Finally, Cooper and Kleinschmidt (1985) contend that the age of an exporting firm varies by its strategy.
Firms identified by them as “world marketers” were significantly younger than firms guided by other strategies. Given
the mixed results of previous studies, the following hypotheses
emerge.
H3a: The average age of exporting firms will significantly
differ from the average age of nonexporting firms.
H3b: Within each industry, as the age of the firm increases,
the propensity to export increases.

Firm Ownership
Few studies investigating propensity to export have included
a firm ownership variable, and those that do measure ownership in terms of foreign versus domestic (Keng and Jiuan,
1989). The explanation offered is that foreign-owned firms
are more likely to send goods out of the country, perhaps
back to company headquarters for distribution or inclusion
in other products. Because the goal of delineating exporter/
nonexporter characteristics is to identify, encourage, and assist
potential manufacturers who are not yet exporting to do so,
we believe that measuring firm ownership in terms of private
versus public makes more intuitive sense. Yang, Leone, and
Alden (1992) included private versus public ownership in
their analysis, but found the difference not to be significant.
It is generally believed, however, that the pressure of publicly
held corporations to maximize shareholder wealth will lead
these businesses to explore new markets more readily than
their privately held counterparts. Thus, the mixed evidence
leads us to hypothesize (H4a in null form) the following.
H4a: The ownership structure of exporting firms will not
significantly differ from the ownership structure of
nonexporting firms.
H4b: Within each industry, being publicly held will positively influence a firm’s propensity to export.

Industry Type
Samiee and Walters (1991) investigated the differences between regular and sporadic exporters based upon 2-digit SIC
codes and found no difference in distribution between the
two groups. Bonaccorsi (1992) posits that industry type is an
intervening mediator in the relationship between firm size
and export propensity. However, the authors found no research that empirically investigated exporter/nonexporter differences based on industry type. Because the sampling frame
for the current study is a census of Ohio manufacturers, the
opportunity to identify differences between industries presents
itself. Based upon the lack of research in this area, it is prudent
to hypothesize the following.
H5a: The number of exporting and nonexporting firms
will differ by industry type.

Export Propensity

J Busn Res
2000:47:217–228

H5b: The importance of the variables in determining export propensity will differ by industry type.

Population Under Study
The midwestern state of Ohio has become increasingly export
oriented in recent years. In fact, it is the United States’ third
largest exporter of manufactured goods, exceeded only by
California and Texas. The state’s manufacturing exports were
worth $21.6 billion in 1994, an increase of 182% since 1987.
A total of 784,435 manufacturing jobs are accounted for by
firms in the state who export. Over 50% of the firms that
export employ 36 people or less, and over 74% of the state’s
exporters have fewer than 100 employees. Major trade partners for the state, in descending order of dollar volume, are
Canada, France, Japan, the United Kingdom, Mexico, and
Germany. The state proactively supports the export of manufactured goods.
The data used are part of Harris Publishing’s 1994 Ohio
Industrial Directory. Harris collects information annually, with
support from the state’s Department of Development, regarding each firm’s products, size, location, ownership, etc. The
1994 directory provided information on more than 20,000
manufacturers. In 1994, the total number of manufacturing
firms in the United States was 378,000, according to the
Industrial Technology Institute. Ohio, therefore, accounts for
just over 5% of the manufacturing firms in the country. The
manufacturing data of a state so actively involved in exporting
allows one to develop an accurate portrayal of the impact
each of the variables investigated has upon the propensity to
export. By doing so, a clearer picture of the relationship between firm characteristics and exporting should emerge.

Industries Studied
Sixteen different industrial classifications were examined in
this study: Food Products; Apparel; Lumber; Paper; Printing;
Chemical; Rubber; Stone; Primary Metals; Fabricated Metals;
Machinery; Electrical; Transportation; Measuring Devices;
Miscellaneous Manufacturing; and a catchall category labeled
Other. The basis for categorizing the industries in this manner,
that is, consistent with their 2-digit SIC codes, is that each
corresponding category contains over 400 businesses. The 12
or more SIC codes subsumed by the category Other did not
contain enough observations individually to allow for meaningful statistical investigation.

Component Measures
To operationalize the hypotheses, variable measurement consisted of the following.
1. Number of Employees—the number of employees per
firm, a continuous variable
2. Total Sales—Information on total sales is collected cate-

3.
4.
5.
6.

221

gorically by Harris Publishing. Classification follows a
topology similar to that of Cavusgil and Kirpalani
(1993): small (less than $4.9 million), medium ($5–
49.9 million) and large ($50 million and over).
Export—measured dichotomously: yes or no
Age—The age of the firm is a continuous variable.
Ownership—measured categorically: private or public
Industry Type—This variable is categorical and the classification by the first 2-digits of the manufacturer’s primary SIC code as proposed by Samiee and Walters
(1991).

Methodology
To investigate the aggregate and industry-level differences in
the variables, multiple statistical methodologies are enlisted.
Differences in continuous variables are tested with an initial
analysis of variance. Similarly, differences in categorical variables are tested by means of a Chi-square analysis. In addition
to descriptive measures, the multivariate technique of logit is
used to test the hypothesized relationships. Logistic analysis is
one of the most widely used statistical techniques for analyzing
binary dependent variables, such as export (y 5 1) and don’t
export (y 5 0). A brief discussion ensues in which the cumulative logistic probability function is briefly explained.
Logit is known to be robust. The logit model used in this
study is based on the cumulative logistic probability function:
Pi 5

eoj

bjXij

11eoj

bjXij

Maximum likelihood procedures may be applied to the logit
model written directly as an equation of the form:
Pi 5 F(o bjXij)
j

where F(.) is specified as the cumulative logistic function.
This definition has been frequently used in cases where the
dependent variable is binary.
In the present study, the logit model shown above is used
for two purposes:
1. to determine the over-all contribution of independent
variables (firm characteristics) to export propensity,
which is defined in this study as the likelihood of exporting as a change in predictor variables (firm characteristics) used in the logit model represented in the
mathematical form above; and
2. to statistically test the pattern of logit coefficients by
industry type. To accomplish this, the following test
statistic is used:
k

22{L(*P) 2 [ o L(*j)]}
j51

222

J Busn Res
2000:47:217–228

R. G. Javalgi et al.

Table 2. Aggregate Level Analysis Exporters versus Nonexporters
Variables
Employee: (mean)
Sales:
Low ($4.9 million or less)
Medium ($5 to 49.9 million)
High ($50 million1)
Age of the Firm: (mean)
Ownership:
Private
Public
Industry:
Food products (SIC 2000)
Apparel (SIC2300)
Lumber (SIC 2400)
Paper (SIC 2600)
Printing (SIC 2700)
Chemical (SIC 2800)
Rubber (SIC 3000)
Stone, clay and glass (SIC 3200)
Primary metals (SIC 3300)
Fabricated metals (SIC 3400)
Machinery (SIC 3500)
Electrical (SIC 3600)
Transportation (SIC 3700)
Measure/control dev. (SIC 3800)
Misc. manufacturing (SIC 3900)
Other

n 5 20,204

Export
n 5 6,460(31.97%)

Nonexport
n 5 13,744(68.03%)

121.43

n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n

5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5

1010
400
897
441
2437
872
1115
975
737
2786
4205
755
532
547
854
1641

33.86

Significance
t 5 22.22, p 5 0.000

3564 (23.2%)
2418 (57.9%)
478 (70.9%)
38.46

11791 (76.8%)
1757 (42.1%)
196 (29.1%)
30.86

x2 5 2303.97, p 5 0.000
t 5 18.59, p 5 0.000

5679 (30.0%)
781 (62.7%)

13279 (70.0%)
465 (37.3%)

x2 5 575.65, p 5 0.000

146
88
130
150
193
432
593
260
320
1020
1557
418
258
311
216
368

where L(*P) represents the likelihood function of the
pooled sample of subsamples of all of the industries
studied (k 5 16), and L(*j) represents the likelihood
function for the jth industry.
Because the logit model provides the likelihood function
separately for all industries, it is easy to compute the pooled
L(*P). The test statistic shown above is asymptotically distributed as chi-square with k-degrees of freedom, where k is the
number of parameters in the logit model (see Chapman and
Staelin, 1982).
As a refresher, in order to interpret the individual beta
coefficients for each variable in each industry, one must think
of its impact on the odds ratio (exports propensity). A positive
parameter indicates an increase in the odds and negative indicator shows a decrease. Therefore, beta coefficients represent
the odds of increasing or decreasing a firm’s propensity to
export depending on the magnitude and sign of the predictor.

Discussion
Table 2 presents the aggregate level analysis of exporters versus
nonexporters. Almost one-third (31.97%) of all manufacturers
in the state participate in export activity. To understand better
the contribution of each variable to a firm’s export propensity,
aggregate level results are compared and contrasted with industry level results in the ensuing section.

(14.5%)
(22.0%)
(14.5%)
(34.0%)
( 7.9%)
(49.5%)
(53.2%)
(26.7%)
(43.4%)
(36.6%)
(37.0%)
(55.4%)
(48.5%)
(56.9%)
(25.3%)
(22.4%)

864
312
767
291
2244
440
522
715
417
1766
2648
337
274
236
638
1273

(85.5%)
(78.0%)
(85.5%)
(66.0%)
(92.1%)
(50.5%)
(46.8%)
(73.3%)
(56.6%)
(63.4%)
(63.0%)
(44.6%)
(51.5%)
(43.1%)
(74.7%)
(77.6%)

x2 5 1922.93, p 5 0.000

Number of Employees
As hypothesized in H1a, the average number of employees
working for firms who export (121.43) differs significantly
(p , 0.000) when contrasted with the average number working for nonexporters (33.86). Across all manufacturing industries, exporters average more than 3.5 times as many employees as nonexporters. Thus, the results support the earlier
findings of Keng and Jiuan (1989). At the aggregate level,
based on the logit model, as the number of employees increases, the propensity to export also increases, as evidenced
in Table 3. However, the importance of the number of employees in predicting export propensity within each industry varies. H1b is true with two exceptions: an increase in the number
of employees does not increase a firm’s export propensity in
the apparel industry (SIC 23) and in the lumber industry
(SIC 24).

Total Sales
As evidenced in Table 2, the level of a firm’s sales is significantly related to the export versus nonexport variable (p ,
0.000). To test H2a sales categories of exporters and nonexporters were examined using a chi-square analysis. Manufacturers with sales of $4.9 million or less are more likely not
to export (76.8 versus 23.2%). This changes when total sales
exceed $5 million, where the number of firms within this
category who export (57.9%) slightly exceeds the number

Export Propensity

J Busn Res
2000:47:217–228

223

Table 3. Propensity to Export as Indicated by Logistic Regression

Variable
Employee
SE

Food
Products
SIC 20

Apparel
SIC 23

Lumber
SIC 24

0.0012
0.0007

20.0007
0.0013

20.007
0.0011

21.3872*
0.1192

21.5681*
0.3914

23.4256
1.7969

23.2594*
1.1246

21.186
0.6409

20.0232
0.1134

20.2917
0.3454

21.7372
1.6672

21.9349
1.0791

Total
0.0006*
0.0001

Paper
SIC 26

Printing
SIC 27

Chemical
SIC 28

Rubber
SIC 30

0.0005
0.0008

0.00005
0.0005

0.0017
0.0009

22.2693*
0.8593

21.9328*
0.3908

20.5521
0.5236

20.3883
0.5621

20.5526
0.7900

20.8479*
0.3782

0.0638
0.4683
20.1537
0.5445

0.0019
0.0014

Sales ($)
,4.9 mm
b
SE
5–49.9 mm
b
SE
.50 mm
b
SE
Age
b
SE
Owner
b
SE
x2
Sig.
Hit ratio

Variable

Stone
Clay and
Glass
SIC 32

Primary
Metals
SIC 33

Fabricate
Metals
SIC 34

Machinery
SIC 35

Electrical
SIC 36

0.0058*
0.0018

0.0002
0.0003

0.0004
0.0004

0.0039*
0.0012

Employee
SE
Sales ($)
,4.9 mm
b
SE
5–49.9 mm
b
SE
.50 mm
b
SE
Age
b
SE
Owner
b
SE
x2
Sig.
Hit ratio

0.0711
0.1219

20.9855*
0.3841

1.5742
1.8133

1.0707
1.1429

20.3363
0.6484

20.5631
0.8661

1.0712*
0.3947

0.0041*
0.0006

20.0012
0.0031

0.0098*
0.0043

0.0063
0.0039

0.0055
0.0037

20.0002
0.0022

0.0063*
0.0027

0.4631*
0.0713
2293.80
0.0000
72.53

0.2852
0.2841
83.42
0.0000
85.27

0.2424
0.8036
41.78
0.0000
85.78

0.3664
0.3143
49.67
0.0000
67.29

1.0398*
0.3363
130.94
0.0000
92.05

20.5247
0.8322

21.3010*
0.3918

0.4394
0.7551

20.2770
0.3774

21.2505
0.8474

0.1810
0.4062

0.0049
0.0028
0.6406
0.3718
153.73
0.0000
78.51

0.0081*
0.0029
0.3694
0.3150
78.59
0.0000
65.87

20.2897
1.5531
36.26
0.0000
77.40

0.3758
0.2374
111.72
0.0000
66.03

0.0183*
0.0039
0.1253
0.2628
103.18
0.0000
62.73

Transport
SIC 37

Measure/
Control
Devices
SIC 38

Misc
Manufac
SIC 39

Other

0.0005
0.0007

0.00009
0.0001

0.0061*
0.0025

0.0355*
0.0057

0.0004
0.0003

20.7677*
0.3729

0.9494
1.2413

10.1492*
3.4434

21.1953*
0.3902

20.5968
0.3852

20.8511
0.5839

20.7884
0.6192

0.6565
0.3684

0.9200
0.5450

0.4659
0.5755

0.2047
0.3549

1.6229
1.1013

9.3099*
3.2666

20.0275
0.3961

20.5761
0.5943

0.2383
0.6339

20.1764
0.3728

21.1816
1.2840

211.9497*
3.4771

20.2650
0.4026

0.104*
0.0044

0.0040
0.0047

0.0012
0.0034

20.0006
0.0023

0.8184*
0.3221
66.06
0.0000
66.28

0.6413
0.4643
65.01
0.0000
65.67

0.4727
0.7111
124.75
0.0000
77.90

20.0883
0.2352
72.14
0.0000
76.90

20.7913*
0.3941
0.0141*
0.0018
0.8592*
0.2297
346.52
0.0000
69.70

0.0173*
0.0017
0.1967
0.2364
790.09
0.0000
72.70

0.0129*
0.0038
0.3354
0.3401
100.62
0.0000
64.57

* Denotes significance beyond the .05 level.

who do not (42.1%). Manufacturers with sales of $50 million
and over are much more likely to export than not. Therefore,
based upon the resultant chi-square statistic of x2 5 2303.97,
p 5 0.000, the relationship in H2a holds true. In terms of

export propensity, at the aggregate level, the category of low
sales is significant. As evidenced by the beta coefficient generated by the logit model, at the aggregate level, low sales reduces
a firm’s propensity to export. At the industry level, an increase

224

J Busn Res
2000:47:217–228

R. G. Javalgi et al.

in sales level only increases export propensity (H2b) in five
of the 16 industries: Apparel (SIC 23), Lumber (SIC 24),
Paper (SIC 26), Chemical (SIC 28), and Primary Metals (SIC
33). It should be noted, however, that the use of categorical
data when determining the effect of total sales on export
propensity may not adequately reflect the true nature of the
relationship between the two.

Age of Firm
The findings of the study provide support for H3a. At the
aggregate level, exporting firms are significantly older than
nonexporting firms (38.46 vs. 30.86; t 5 18.59; p , .000).
Therefore, with respect to manufacturing, this study supports
the results of earlier researchers such as Lee and Yang (1990)
and Welch and Wiedersheim-Paul (1978). In examining export propensity at the aggregate level, as the age of the firm
increases, its propensity to export also increases. Within each
industry, an increase in firm age increases its propensity to
export (H3b) in 14 of the 16 industries. The two exceptions
are Food Products (SIC 20) and Printing (SIC 27).

Firm Ownership
Exporting activity differs significantly by firm ownership category (H4a). Privately owned firms are less likely to export
than publicly owned firms. More than 60% of the publicly
owned manufacturers in this study reported some level of
export activity. At the aggregate level, public ownership of a
firm increases the propensity that the firm will export. This
relationship is also true, as in H4b, for 15 of the 16 industries
investigated, the exception being the Apparel industry
(SIC 23).

Industry Type
H5a is also accepted. Exporters and nonexporters differ significantly by industry type. Manufacturers producing Measuring,
Analyzing, and Controlling Instruments (SIC 3800) are the
most likely to export, followed by producers of Electrical and
Electronic Machinery, Equipment and Supplies (SIC 3600),
and Rubber and Miscellaneous Plastic Products (SIC 3000).
Industries least likely to export include Printing, Publishing
and Allied Products (SIC 2700), Food and Kindred Products
(SIC 2000), and Lumber and Wood Products Excluding Furniture (SIC 2400).
Furthermore, to investigate H5b and test for differences in
the pattern of logit coefficients across industries, the statistic
k

22{L(*P) 2 [ o L(*j)]}
j51

described earlier was computed. The difference (2049.34) is
statistically significant at the a 5 .0001 level. Thus, one concludes that the pattern of logit coefficients is very different for
each industry. This means that export propensity, as influenced
by a change in a firm’s characteristics, differs by industry type.

Industry-Level Implications
Not only do firm characteristics differ in importance between
industries when delineating exporters from nonexporters, the
ability of researchers to predict correctly export behavior based
upon these characteristics varies by industry. To address the
concerns of Samiee and Walters (1991) with regard to the
effect industry type has on exporting, the researchers compiled
summary statistics per 2-digit SIC code. Table 4 presents the
variable differences for each of the manufacturing industry
categories. The ensuing section is dedicated to describing
within industry differences among exporting firms, all of
which are significant (p 5 0.0000).

Food Products (SIC 20)
Exporting firms employ more than three times as many people
than nonexporting firms in this industry. Firms with sales
levels under $4.9 million rarely export. Only 40% of firms with
sales over $50 million export. The average age of exporting and
nonexporting firms in this industry does not differ significantly. Only 31.8% of the publicly held corporations export,
one of the lowest percentages of all industries. The variable
with the greatest impact in predicting export propensity in
this industry is Sales. Firms with either low sales or high
sales are significantly less likely to export than are firms with
medium sales.

Apparel (SIC 23)
Three times as many people are employed in exporting firms
in SIC 23 as are employed in nonexporting firms. More than
83% of the manufacturers with sales over $50 million export.
Exporting firms are, on average, 12 years older than nonexporting firms. One-third of the publicly owned manufacturers
export. The variable with the greatest impact in predicting
export propensity in SIC 23 is Age: an increase in firm age
leads to an increase in export propensity, ceteris paribus.

Lumber (SIC 24)
More than two times as many people are employed in exporting firms than in nonexporting firms in this industrial
classification. Firms with sales of $50 million or more are the
most likely to export. Exporting firms are, on average, 6 years
older than nonexporting firms. One-third of publicly owned
firms in SIC 24 export. The variable with the greatest explanatory power in this SIC is Low Sales. Firms with sales of less
than $US4.9 million are significantly less likely to export than
are firms in the other two sales categories.

Paper (SIC 26)
Exporting firms in SIC 26 employee over twice as many personnel than nonexporting firms. More than 70% of firms with
sales in excess of $50 million export. Exporting firms are, on
average, 11 years older than nonexporting firms. In terms of
firm ownership, more than 56% of publicly owned firms

Export Propensity

J Busn Res
2000:47:217–228

225

Table 4. Industry-Level Analysis Exporters versus Nonexporters

Variable
Employee
Sales ($)
,4.9 mm
(Low)
5–49.9 mm
(Medium)
.50 mm
(High)
Age of
the Firm
Owner
Private

Public

Variable
Employee
Sales ($)
,4.9 mm
(Low)
5–49.9 mm
(Medium)
.50 mm
(High)
Age of
the Firm
Owner
Private

Public

Food
Products
SIC 20
n 5 1010

Apparel
SIC 23
n 5 400

Lumber
SIC 24
n 5 897

Paper
SIC 26
n 5 441

Printing
SIC 27
n 5 2437

Chemical
SIC 28
n 5 872

Rubber
SIC 30
n 5 1115

Stone,
Clay, and
Glass
SIC 32
n 5 975

162.73
(46.73)

99.3
(30.91)

54.69
(24.54)

141.91
(53.25)

109.76
(21.96)

117.37
(40.37)

124.97
(53.11)

123.61
(24.70)

40
6.6%
(567)
(93.4%)
74
22.9%
(249)
(77.1%)
32
40.0%
(48)
(60.0%)
45.47
(42.92)

60
17.1%
(290)
(82.9%)
23
52.3%
(21)
(47.7%)
5
83.3%
(1)
(16.7%)
42.77
(30.52)

92
11.7%
(697)
(88.3%)
33
32.7%
(68)
(67.3%)
5
71.4%
(2)
(28.6%)
34.03
(27.82)

46
20.7%
(176)
(79.3%)
84
44.0%
(107)
(56.0%)
20
71.4%
(8)
(28.6%)
43.38
(31.76)

122
5.6%
(2078)
(94.4%)
60
27.5%
(158)
(72.5%)
11
57.9%
(8)
(42.1%)
47.53
(35.15)

154
34.2%
(297)
(65.8%)
209
62.2%
(127)
(37.8%)
69
81.2%
(16)
(18.8%)
43.06
(33.36)

275
42.5%
(372)
(57.5%)
277
66.3%
(141)
(33.7%)
41
82.0%
(9)
(18.0%)
31.13
(22.59)

136
18.0%
(620)
(82.0%)
99
52.1%
(91)
(47.9%)
25
86.2%
(4)
(13.8%)
46.70
(38.01)

118
12.8%
(804)
(87.2%)
28
31.8%
(60)
(68.2%)

87
21.9%
(310)
(78.1%)
1
33.3%
(2)
(66.7%)

127
14.3%
(761)
(85.7%)
3
33.3%
(6)
(66.7%)

113
30.1%
(263)
(69.9%)
37
56.9%
(28)
(43.1%)

172
7.2%
(2204)
(92.8%)
21
34.4%
(40)
(65.6%)

339
45.8%
(401)
(54.2%)
93
70.5%
(39)
(29.6%)

520
51.4%
(492)
(48.6%)
73
70.9%
(30)
(29.1%)

226
24.5%
(698)
(75.5%)
34
66.7%
(17)
(33.3%)

Primary
Metals
SIC 33
n 5 737

Fabricated
Metals
SIC 34
n 5 2786

Machinery
SIC 35
n 5 4205

Electrical
SIC 36
n 5 755

Transport
SIC 37
n 5 532

Measure/
Control
Devices
SIC 38
n 5 547

Misc.
Manufac
SIC 39
n 5 854

Other
n 5 1641

232.19
(73.01)

91.19
(34.92)

93.99
(17.76)

140.69
(50.82)

367.81
(130.33)

104.48
(24.98)

59.57
(12.91)

88.85
(49.30)

116
30.1%
(269)
(69.9%)
158
55.2%
(128)
(44.8%)
46
69.7%
(20)
(30.3%)
43.01
(34.27)

602
28.3%
(1526)
(71.7%)
378
63.3%
(219)
(36.7%)
40
65.6%
(21)
(34.4%)
41.04
(29.75)

1022
28.8%
(2526)
(71.2%)
474
80.3%
(116)
(19.7%)
61
91.0%
(6)
(9.0%)
37.39
(24.65)

218
43.8%
(280)
(56.2%)
166
77.9%
(47)
(22.1%)
34
77.3%
(10)
(22.7%)
35.15
(23.76)

80
32.4%
(167)
(67.6%)
130
60.5%
(85)
(39.5%)
48
68.6%
(22)
(31.4%)
34.13
(24.90)

195
48.2%
(210)
(51.8%)
95
79.8%
(24)
(20.2%)
21
91.3%
(2)
(8.7%)
30.92
(25.19)

163
21.0%
(613)
(79.0%)
49
67.1%
(24)
(32.9%)
4
80.0%
(1)
(20.0%)
39.32
(31.22)

243
18.01%
(1103)
(81.9%)
109
41.8%
(152)
(58.2%)
16
47.1%
(18)
(52.9%)
36.19
(33.01)

276
41.2%
(394)
(58.8%)
44
65.7%
(23)
(34.3%)

925
34.8%
(1731)
(65.2%)
95
73.1%
(35)
(26.9%)

1422
35.3%
(2607)
(64.7%)
135
76.7%
(41)
(23.3%)

351
52.5%
(317)
(47.5%)
67
77.0%
(20)
(23.0%)

194
43.3%
(254)
(56.7%)
64
76.2%
(20)
(23.8%)

270
54.1%
(229)
(45.9%)
41
85.4%
(7)
(14.6%)

205
24.5%
(633)
(75.5%)
11
68.8%
(5)
(31.2%)

334
22.1%
(1181)
(77.9%)
34
27.0%
(92)
(73.0%)

Values in parentheses represent nonexporters.

226

J Busn Res
2000:47:217–228

export. The hit ratio for the logistic function in this SIC is
only 67.3%, and no one variable in the equation is significant.
This may indicate that variables other than the four firm
characteristics examined for this study are better suited for
predicting export propensity in SIC 26.
The remaining 12 classifications may be interpreted in a
similar manner. Of the SIC codes not discussed, Printing
(SIC 27) has the highest hit ratio (92%) when determining
export propensity and the lowest percentage of exporters in
any industry (7.9) as shown in Table 2. What becomes apparent is the usefulness of firm characteristics in correctly predicting nonexporters rather than in predicting exports for
certain industry types.

Conclusions

R. G. Javalgi et al.

data also exist. Would the results differ if the sales variable
were collected as a continuous rather than as a categorical
variable? And, although the sampling frame consists of a census of manufacturing firms, one must question whether the
export behavior of firms in a midwestern state are generalizable
to firms in other states or countries. These are both excellent
questions and should serve as a call for future research to
determine whether the results of this study are typical or
atypical. By thoroughly understanding the influence firm characteristics have upon the export propensity of divergent industries, future researchers can better focus their investigations.
The authors thank John K. Ryans Jr., Hans Muhlbacher, and three anonymous
reviewers for their insightful and constructive comments on earlier versions
of this manuscript.

This study contributes to the literature on exporting by testing
the influence of firm characteristics on export propensity using
a manufacturing census. Through the formulation and testing
of hypotheses, the research expands the knowledge regarding
the influence each of the variables exerts on export propensity
at both the aggregate and industry levels. Additionally, it is
one of the few studies to measure export propensity by industry type utilizing a logit modeling approach.
With regard to the goals of this study, all variables investigated are significant when determining export propensity for
the entire sample. The predictive power of the resultant logistic
function exceeds 72% correct classification. However, the impact and predictive significance of these variables varies by
industry classification. The implication is that researchers targeting manufacturers from differing industries, and using less
than census data, should exercise caution when relying on
firm characteristics alone to determine export propensity. One
should include other variables such as those subsumed by the
label management characteristics in order to predict exporting
better. Finally, the question regarding the contribution of
individual firm characteristics to export propensity is resolved:
the contribution varies by industry.

References

Limitations and Future Research

Calof, Jonathan L.: The Relationship Between Firm Size and Export
Behavior Revisited. Journal of International Business Studies (Second
Quarter 1994): 367–387.

By limiting the focus of this study to the exporter/nonexporter
dichotomy, other possible motives for exporting, such as
growth or profitability, are largely ignored. In addition, in
industries known to be internationally oriented, such as Measuring, Analyzing, and Controlling Instruments (SIC 38), the
hit ratio for determining export propensity is 65.67%. The
low predictive power indicates that variables other than firm
characteristics influence export behavior. Some researchers
have included management characteristics in their analyses
and generally report a significant influence on export behavior
to exist (cf. Aaby and Slater, 1989; Cavusgil, 1984b; Cavusgil
and Naor, 1987).
Additional limitations based upon the use of secondary

Aaby, Nils-Erik, and Slater, Stanley F.: Management Influences on
Export Performance: A Review of the Empirical Literature 1978–
88. International Marketing Review 6(4) (1989): 7–26.
Alexandrides, C. G.: How the Major Obstacles to Expansion Can Be
Overcome. Atlanta Economic Review (May 1971): 12–15.
Axinn, Catherine: An Examination of Factors that Influence Export
Performance. Unpublished doctoral dissertation, Michigan State
University. 1985.
Ball, Clifford A., and Tschoegl, Adrian E.: The Decision To Establish
a Foreign Bank Branch or Subsidiary. Journal of Financial and
Quantitative Analysis (September 1982): 411–424.
Bilkey, Warren J., and Tesar, George: Export Behavior of Smallersized Wisconsin Manufacturing Firms. Journal of International
Business Studies 8(1) (1977): 93–98.
Bonaccorsi, Andrea: On the Relationship Between Firm Size and
Export Intensity. Journal of International Business Studies 23(4)
(1992): 605–636.
Brasch, J. J.: Deciding on an Organizational Structure for Entry into
Export Marketing. Journal of Small Business Management 19(2)
(1981): 7–15.
Burton, F N., and Schlegelmilch, Bodo B.: Profile Analyses of NonExporters versus Exporters Grouped by Export Involvement.
Management International Review 27(1) (1987): 38–49.

Cavusgil, S. Tamer: Differences Among Exporting Firms Based on
their Degree of Internationalization. Journal of Business Research
12 (1984a): 195–208.
Cavusgil, S. Tamer: Organizational Characteristics Associated with
Export Activity. Journal of Management Studies 21(1) (1984b):
3–22.
Cavusgil, S. Tamer, and Kirpalani, V. H.: Introducing Products into
Export Markets: Success Factors. Journal of Business Research 27
(1993): 1–15.
Cavusgil, S. Tamer, and Naor, Jacob: Firm and Management Characteristics as Discriminators of Export Marketing Activity. Journal
of Business Research 15(3) (1987): 221–235.
Cavusgil, S. Tamer, and Nevin, John R.: Internal Determinants of

Export Propensity

Export Marketing Behavior: An Empirical Investigation. Journal
of Marketing Research 18(1) (1981): 114–119.
Cavusgil, S. Tamer, Bilkey, Warren J., and Tesar, George: A Note
on the Export Behavior of Firms: Exporter Profiles. Journal of
International Business Studies, 10(1) (Spring/Summer 1979):
91–97.
Chapman, R. C., and Staelin, Richard: Exploiting Rank Order Choice
Set Data within the Stochastic Utility Model. Journal of Marketing
Research 19 (August 1982): 288–301.
Christensen, Carl H., de Rocha, Angela, and Gertner, Rosanne G.:
An Empirical Investigation of the Factors Influencing Export Success of Brazilian Firms. Journal of International Business Studies
(Fall 1987): 61–78.
Cooper, Robert G., and Kleinschmidt, Elko J.: The Impact of Export
Strategy on Export Sales Performance. Journal of International
Business Studies 16 (Spring 1985): 37–55.
Culpan, Refik: Export Behavior of Firms: Relevance of Firm Size.
Journal of Business Research 18 (1989): 207–218.
Czinkota, Michael R., and Johnston, Wesley J.: Exporting: Does Sales
Volume Make a Difference? Journal of International Business Studies
14(1) (Spring/Summer 1983): 147–153.
Czinkota, Michael R., and Ursic, Michael: Classification of Exporting
Firms According to Sales and Growth into a Share Matrix. Journal
of Business Research 22 (1991): 243–253.
Delacroix, J.: Export Strategies for Small American Firms. California
Management Review 26 (Spring 1984): 138–153.
Dertouzos, M., Thurow, Lester, and Solow, R.: Made in America, MIT
Press, Boston, MA. 1989.
Diamantopoulos, Adamantios, and Inglis, Karen: Identifying Differences Between High- and Low-Involvement Exporters. International Marketing Review (Summer 1988): 52–59.
Dichtl, E., Liebold, M., Kogelmayr, H. G., and Muller, S.: The Export
Decision of Small and Medium Sized Firms: A Review. Management International Review 24 (1984): 49–60.
Domingues, Luis V., and Segueira, Carlos G: Determinants of LDC
Exporters’ Performance: A Cross-National Study. Journal of International Business Studies 21(1) (1993): 19–40.
Erramilli, M. Krishna, and Rao, C. P.: Service Firms’ International
Entry-Mode Choice: A Modified Transaction–Cost Analysis Approach. Journal of Marketing 57 (July 1993): 19–38.
Gottko, J., and McMahon, R. O.: Export Marketing Activities of Small
to Medium-Sized Manufacturers. Paper presented at the Annual
Meeting of the Western Marketing Association, Newport Beach,
CA. 1988.
Hester, Susan, Export Trading Companies: A Marketing Vehicle for
Small Textile and Apparel Firms. Journal of Small Business Management (October 1985): 20–27.
Hill, C. W., Hitt, M. A., and Hoskisson, R. E.: Declining U.S. Competitiveness: Reflections on a Crisis. Academy of Management Executive
2(1) (1988): 51–60.
Hill, J. S., and Still, R. R.: Effects of Urbanization on Multinational
Product Planning: Markets