Fig. 1. A model of contextual variables and performance criteria associated with competitive priorities emphasized.
differences in the relative emphasis on competitive priorities between and within groups. Third, it ex-
plores the relationship between the groups’ orienta- Ž
tion i.e., competitive priorities emphasized by the .
group and the industry membership. Finally, per- ceived managerial performance of the respective
groups on several measures is examined for differ- ences between and within groups.
3. Methodology
3.1. Data and procedures The data used in this study was collected as a part
of the bigger project that involved five employees — a manufacturing manager, three subordinate employ-
ees, and a general manager — from each participat- ing unit. For details regarding the bigger project,
Ž .
please refer to Kathuria and Partovi 1999
and Ž
. Kathuria et al. 1999 . Given the nature of the pro-
ject and the extent of data collection, a low response rate was anticipated. Thus, a large potential pool of
over 1300 manufacturers was identified from the
Ž .
Pennsylvania Directory of Manufacturers 1995 ,
Ž .
New Jersey Directory of Manufacturers 1995 , and Ž
. Delaware Directory of Manufacturers 1995 . The
sample included different industries to facilitate gen- eralizability of the results. Given the low response
rate of mail surveys and limited resources, first a letter accompanied by a postage-paid reply card was
mailed to solicit participation in the study. About 100 letters came back undelivered due to change of
address or incorrect address. Of the remaining, over 300 responded of which 158 agreed to participate.
The rest, though expressing interest in the study, were unable to participate due to other commitments
at that time or reduced manufacturing activity.
The focus of this study was a manufacturing unit where the manufacturing manager would have im-
plemented or pursued the competitive priorities of the unit, based on hisrher perception. These units
included manufacturing units or divisions of some large firms, and for smaller manufacturers, the entire
organization. The data used in the study came from
Ž two questionnaires one for the manufacturing man-
. ager and one for the general manager from each
Ž .
participating unit. Of the 316 s 158 = 2 question- naires distributed at the two levels, 197 responses
from 99 units were received. One general manager’s response was discarded since the matching manufac-
turing manager’s response was not received. A com- parison of the units in this study with non-participat-
ing units showed no statistically significant differ-
Ž ences for size
number of employees and annual .
sales . The average manufacturer in the sample had annual sales of US43 million with about 75 of the
sample being below US100 million. The average number of employees in a sample plant was 275,
which is on the higher side considering the national
Ž .
average Compton, 1997 . A comparison of the sam- ple with the national statistics is provided in Table 1.
The Manufacturing Manager’s survey, shown in the top panel of Appendix A, was filled out by
managers whose titles included Operations Manager, Director of Operations, and Manufacturing Manager.
The average job tenure for the participating manu-
Table 1 Sample statistics
Industry mix Percent
1. Fabricated metal 15
2. Machinery except electrical and computers 09
3. Electrical machinery and other electric goods 11
4. transportation and aerospace 07
5. Consumer non-durable 20
6. Other 38
Total 100
Industry type: Other Break-up Chemicals
4.1 Plasticrextrusionrtape manufacturing
4.1 Construction-related manufacturing
4.1 Packaging products
4.1 Miscellaneous products
4.1 Components and instruments
3.1 Food products
3.1 Printing
2.0 Bio-tech
2.0 Pharmaceutical packaging
2.0 Communication
2.0 Detergents
1.0 Tooling
1.0 Steel mill, plate
1.0 Ž
. Total other
38 a Employees
a
Range Sample
National distribution for manufacturing 1 to 49
10 83
50 to 99 30
08 100 to 249
37 06
250 to 499 12
02 500 to 999
07 - 01
1000 or more 04
- 01 Total
100 100
Ž .
Annual Sales US million Range
Sample Below 50
64 50–99
11 100–199
06 200 and above
19 Total
100
a
Ž .
Source: Compton 1997 .
facturing managers was 5.11 years with a stan- dard deviation of 4.53 years. Second, the General
Manager’s Survey, shown in the bottom panel of Appendix A, was completed by a superior of the
manufacturing manager who responded to the manu- facturing manager’s survey. Thus, the term General
Manager refers to a superior to whom the manufac- turing manager reports directly. The average years of
association for the two managers in the sample were over 5 years, with a standard deviation of 4.45 years.
3.2. Industry mix This research focused on six industries in the
manufacturing sector, as done in some recent studies Ž
. Ž
. by Boyer et al. 1996 , Swamidass 1994 , and Ritz-
Ž .
man et al. 1993 . Specifically, manufacturing units in the following industries were studied: fabricated
metal, machinery except electrical and computers, electrical machinery including computers, transporta-
tion and aerospace, consumer non-durables, and a miscellaneous industry that was called AotherB. Table
1 contains the composition of the sample, based on the manufacturing managers’ response to the indus-
try-related questions. In terms of the type of indus- try, 15 of the units are in fabricated metal, 9 in
machinery except electrical and computers, 11 in electrical machinery and electrical goods, 7 in
transportation and aerospace, 20 in consumer non- durable, and 38 are in the miscellaneous category
with no more than 5 in any single industry.
3.3. Measures 3.3.1. CompetitiÕe priorities
Consistent with the literature, the term Acompeti- tive prioritiesB is used to describe manufacturers’
choice of manufacturing tasks or key competitive capabilities, which are broadly expressed in terms of
Ž low cost, flexibility, quality, and delivery Ward et
al., 1995; Skinner, 1969; Berry et al., 1991; Hayes .
and Wheelwright, 1984 . Given the multi-dimen- sional nature of these priorities, multiple items were
used to capture a manufacturer’s emphasis on each competitive priority. These items, listed in Appendix
A, were taken from several published sources, in-
Ž .
cluding Morrison and Roth 1993 , Ritzman et al. Ž
. Ž
. Ž
. 1993 , Nemetz
1990 , Wood et al. 1990 , and
Ž .
Roth and Miller 1990 . Manufacturing managers
rated all items on a five-point Likert scale with values ranging from 1 to 5, with 5 being extremely
important. The items in the questionnaire were ar- ranged in a random order to elicit accurate informa-
tion from respondents.
To determine the underlying dimensions of com- petitive priorities, a principal component factor anal-
ysis with oblimin rotation in SPSS 8.0 was used. The oblimin rotation was used since the competitive pri-
orities are not assumed to be orthogonal, but may actually be mutually supportive of each other. To
ensure that a given item represented the construct underlying each factor, a two-stage rule was used
Ž .
cf., Nunnally, 1978 . First, a factor weight of 0.45 was used as the minimum cutoff. Second, if an item
loaded on more than one factor, with difference between weights less than 0.10 across factors, the
item was deleted from the final scale. The Cost and Quality-of-Conformance scales retained all the items
as expected, and the Flexibility scale retained four of the five items. One item that did not load on Flexibil-
ity was ‘customizing product to customer specifica-
Ž tion.’ For the Delivery scale, one item making fast
. deliveries was dropped due to a low factor loading.
Next, the internal consistency of the competitive priority scales was assessed using Cronbach’s a
coefficients. In the present study, since the a ’s for the revised scales were not significantly different
from those of the original scales, the original scales were retained for subsequent analyses. The original
a ’s for the Cost, Quality-of-Conformance, Flexibil-
ity, and Delivery scales, reported in Appendix A, are above the lower limits of acceptability, generally
Ž .
considered to be around 0.60 Nunnally, 1978 . The Quality-of-Design scale, which had an a of 0.46,
was dropped from further analysis. Finally, the scores for each scale were determined
by adding up the individual scores for the corre- sponding measures and then dividing by the number
of measures. For example, a manufacturing unit’s emphasis on cost was calculated by averaging its
Ž score on three measures — M1 controlling produc-
. Ž
. tion costs , M3 improving labor productivity , and
Ž .
M9 running equipment at peak efficiency . As shown in Appendix A, the individual scores on 6 of the 17
Ž .
measures ranged between 1 not at all important and Ž
. 5 extremely important , while 9 of the 17 measures
ranged between 2 and 5, and only 2 of the 17 between 3 and 5.
3.3.2. Managerial performance Regarding studies of manufacturing strategy,
Ž .
Swamidass and Newell 1987 , among others, noted the difficulty of obtaining objective financial mea-
sures of performance, such as profit growth, profit margin, sales increase, market share, return on in-
vestment, etc. Although it is preferable to use objec- tive measures of performance, such measures are
hard to compare across units with different technolo-
gies, product
lines, and
competitive priorities
Ž .
Bozarth and Edwards, 1997 . Hence, perceptual measures of managerial performance were adopted
from the organizational sciences literature, which included quality of work, accuracy of work, produc-
tivity of the group, customer satisfaction, operating efficiency, quantity of work, and timeliness in meet-
ing delivery schedules. The above measures are generic enough to be applicable to different indus-
tries, and different units pursuing dissimilar strate- gies.
The perceived measures have been used and rec- ommended as a substitute when objective measures
Ž are either not available or not relevant Dess and
Robinson, 1984; Venkatraman and Ramanujam, .
1987; Youndt et al., 1996 . The use of perceptual measures, however, could lead to the common meth-
Ž .
ods variance CMV problem, which was tested us- Ž
. ing the Harman 1967 one-factor test. The same test
has been used in similar studies in the Operations Ž
Management literature cf., Bozarth and Edwards, .
1997 . If the measures were to be affected by CMV, then they would tend to load on a single factor. The
factor analysis for the managerial performance mea- sures resulted in two factors, with the highest factor
loadings spread across the two factors.
To further moderate the problem of common method variance due to the mono-respondent bias,
Ž superiors of manufacturing managers not the manu-
. facturing managers themselves were asked to rate
the manufacturing managers’ performance on a scale of 1 to 7, with 1 being ‘Unsatisfactory’ and 7 being
‘Excellent.’ As seen in Appendix A, four of the seven measures ranged between 2 and 7, and three
ranged between 3 and 7, while the average scores on the seven measures ranged between 4.98 and 5.49.
Ž Further, the high-ranking respondents manufactur-
. ing and general managers in this study also helped
to overcome the common method variance problem, since they tend to be more reliable sources of infor-
Ž .
mation Phillips, 1981 .
4. Results and discussion
