Job satisfaction and technology in Mexico

Job satisfaction and technology in Mexico

Steve Lovett*, Tom Coyle1, Russell Adams2

The University of Texas at Brownsville, 80 Fort Brown, Brownsville, TX 78520, USA

Abstract

The introduction of new technologies is one of the greatest benefits that MNCs can bring to developing countries, and the

speed and importance of this technology transfer appear to be increasing. Therefore, the management of developing country

workforces in changing technological settings becomes an important issue for MNCs. In this paper, we address this issue, with

an emphasis on job satisfaction. Data were collected through a survey of line workers at two factories in Mexico. Both were

owned by one parent company, operated under the same set of administrative procedures, and were located within 10 miles of

each other. However, one factory used 30-year-old technology, while the other was state-of-the-art. In the low-tech factory we

found that intrinsic job characteristics were more closely associated with overall job satisfaction and that job commitment was a

relatively more important issue than in the high-tech factory.

# 2004 Elsevier Inc. All rights reserved.

1. Technology transfer and workforcemanagement

The introduction of new technologies has repeat-

edly been cited as one of the greatest benefits that

developed country multinational corporations

(MNCs) can bring to developing countries (Dunning,

1992, 1993; Eden & Lenway, 2001; Kogut & Zander,

1993; Porter, 1990; Vernon, 1998), and the speed and

importance of this technology transfer appears to be

increasing. In the past century, technology transfer

typically proceeded through a pattern that followed a

product life cycle; production technologies were intro-

duced in developed countries and later transferred to

developing countries as they became commonplace

(Akamatsu, 1962). Recently, however, several authors

have noted that this pattern is becoming less and less

the norm. MNCs are using new production technol-

ogies in developing countries, or even introducing

them there (Barker & Goto, 1998; Chen, 1997; Rey-

nolds, 2001). For example, the Chinese government

has recently begun emphasizing what Shi (2001)

refers to as a ‘‘market-for-technology’’ policy, in

which foreign firms that transfer advanced technology

to China are granted special access to Chinese markets

in return. Also, Twin Plant News, a trade journal

focusing on foreign manufacturing plants in Mexico,

has reported on the progress of these plants from

simple assembly and even coupon sorting operations

to ‘‘third generation’’ facilities, citing as an example

of the latter the Delphi Mexico Technical Center,

which designs products and processes for use both

within Mexico and elsewhere, and which has recently

produced more than 30 U.S. patents with more than

100 others in process as of August 2003 (TPN, 2003).

Our research question in this paper is: ‘‘How does

the management of job satisfaction in developing

Journal of World Business 39 (2004) 217–232

* Corresponding author. Tel.: þ1 956 983 7382;

fax: þ1 956 982 0159.

E-mail addresses: [email protected] (S. Lovett), [email protected]

(T. Coyle), [email protected] (R. Adams).1 Tel.: þ1 956 983 7838.2 Tel.: þ1 956 983 7654.

1090-9516/$ – see front matter # 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.jwb.2004.04.002

country workforces differ between low- and high-

technology settings?’’ This is becoming a salient issue

for practitioners because the technological environ-

ments under which developing country subsidiaries

of MNCs operate is changing rapidly. Executives

in these organizations need to understand how job

satisfaction can play a role in improving the produc-

tivity of their workers within this new environment.

Consider, for example, a manufacturing MNC that

had historically performed many of its labor intensive

and ‘‘low-tech’’ tasks in a developing country with

relatively lower wage rates, but that has begun to

perform more technologically intensive tasks in that

country. Local factory managers would presumably

have developed skills in managing a low-tech work-

force, but would the management of job satisfaction

within the workforce under the newer and more

advanced technological system be similar to that

under the older system? We can suggest two reasons

why it would not.

First, the nature of the work itself is different.

Slocum and Sims (1980) propose a theoretical model

of technology based on three elements: workflow

predictability, task predictability, and job interdepen-

dence. Workflow predictability refers to knowledge

about when a task must be performed and depends

more on the complexity and dynamism of the external

environment than on the technology used. Task pre-

dictability, however, refers to knowledge about how a

task should be accomplished, and varies greatly

between low-technology and high-technology set-

tings. In general, task predictability is greater in

low-tech settings because workers perform manual

tasks for which specific procedures can usually be

developed. In contrast, in high-tech settings task pre-

dictability is lower because workers often monitor

automatic processes and must analyze and solve

unique production problems. Job interdependence is

also likely to be different depending on the technology

used. In low-tech settings sequential interdependence

is the norm as parts are passed down an assembly line

to be worked on by different individuals in turn. In

high-tech settings, however, reciprocal interdepen-

dence occurs because workers must often communi-

cate to resolve interrelated production problems.

Therefore, the nature of the work to be performed

changes in fundamental ways as the level of technol-

ogy is increased.

Second, the workers themselves are likely to dif-

ferent in low- and high-tech settings. Biel, in his essay

on the impact of technological change on developing

countries, notes that ‘‘new technologies put an

increased premium on those who possess the needed

education and skills’’ (Biel, 1999: 262). In high-tech

manufacturing situations, workers who have higher

skill levels than their peers are likely to be present.

These workers are likely to differ from their peers on

psychological variables, such as need for achievement,

proactiveness, and perhaps sense of responsibility. In

addition, these more highly skilled workers are likely

to have or at least perceive more alternative job

opportunities than do their peers.

In summary, given the increasing shifts of high-tech

production to developing nations, it is requisite that

management practices also change. This issue is going

to become increasingly critical throughout the fore-

seeable future due to the increasing pressures of

globalization and the related need to cut costs and

increase productivity.

Our research question is also important to academic

researchers because it helps to provide us with a

dynamic picture of job satisfaction relationships.

Understanding job satisfaction for workers in some

particular environment is challenging and interesting,

but understanding how job satisfaction changes over

time as that environment changes may add a whole

new dimension to our understanding of job satisfac-

tion. The changing technological environments in

developing nations provides us with a useful ‘‘labora-

tory’’ for studying these changes.

2. Job satisfaction facets and responses

The literature on job satisfaction is perhaps the most

extensive of all of management fields. One of the most

comprehensive recent reviews is that of Spector

(1997). In this review, Spector emphasizes two impor-

tant points. First, job satisfaction can be divided into

components or facets. For example, satisfaction with

pay and satisfaction with the supervisor are two dis-

tinct job satisfaction facets. An employee could be

satisfied with one of these facets and dissatisfied with

the other, but both may be components of overall job

satisfaction. Second, job satisfaction may have various

consequences. For example, a dissatisfied employee

218 S. Lovett et al. / Journal of World Business 39 (2004) 217–232

may quit, or may stay on the job but with less

enthusiasm and lowered job commitment. In this

paper, we will refer to these consequences as job

satisfaction ‘‘responses.’’

In order to fully understand differences in work-

force management between high- and low-tech set-

tings, both job satisfaction facets and responses must

be considered. In the 1980s and early 1990s, Hulin and

his colleagues developed an Organizational Adapta-

tion/Withdrawal Model (Hulin, 1991; Hulin, Roz-

nowski, & Hachiya, 1985), which may serve as a

useful framework for studying this issue. A simplified

version of this model can be seen in Fig. 1. Briefly,

various ‘‘inputs’’ can affect job satisfaction. These

include satisfaction with facets or attributes of a job,

such as pay or treatment by the supervisor. In turn, job

satisfaction may result in various response behaviors,

ranging from enthusiasm and commitment to absen-

teeism and quitting. The Hulin model is therefore an

integration of earlier work on job satisfaction or on

specific responses to job satisfaction considered

in isolation. Since our purpose in this paper is to

determine how the ‘‘big picture’’ may differ between

low- and high-tech manufacturing settings, the Orga-

nizational Adaptation/Withdrawal Model is an appro-

priate paradigm.

Rice, Gentile, and McFarlin define job facets as

‘‘individual components that make up one’s experi-

ence at work’’ and facet satisfactions as ‘‘affective

evaluations of individual job facets’’ (1991: 31). Job

satisfaction facets can be grouped into several related

categories. First is the job itself. For example, an

employee may perceive his or her job to be important

or unimportant, to be interesting or boring, or to be

easy or fatiguing. Career opportunities and voice fall

into a different category, based on higher order needs.

Included in this category are opportunities within the

organization that satisfy the individual’s needs for

esteem. Voice in this paper means when an employee

speaks up, either to make a suggestion or to make a

request, how this is treated, and how the employee

perceives his or her inputs to be received. Other facets

include satisfaction with pay, satisfaction with the

supervisor, and satisfaction with the hours that the

employee must work.

However, the strength of the relationships between

job satisfaction facets and responses is likely to be as

least as important as the overall levels of the variables.

lowcommitment,

etc.

absenteeism,quitting, etc.

behavioral intentionsto reduce job

inputs

behavioral intentionsto reduce workrole inclusion

available

frames of reference,

satisfaction with job attributes: wages, working conditions, etc.

job satisfaction

Fig. 1. Simplified Organizational Adaptation/Withdrawal Model.

S. Lovett et al. / Journal of World Business 39 (2004) 217–232 219

Locke’s value-percept theory of job satisfaction

(Locke, 1976) explains job satisfaction through the

following equation:

dissatisfaction ¼ ðwant-haveÞ � importance

This equation indicates that dissatisfaction is the

result of a discrepancy between the amount of a facet

desired and that received, but only for those facets that

are important to the individual. In other words, an

individual may be dissatisfied with a particular job

satisfaction facet, but place little importance on that

facet, so that he or she is quite satisfied in general. The

importance of a facet therefore indicates its relation-

ship to overall satisfaction, which Hulin (1991) pro-

poses is related to various responses.

Hulin also proposes that job dissatisfaction results

from a perceived imbalance between what one receives

from a job and what one puts into the job. Job dis-

satisfaction is stressful, so that individuals seek to

reduce it by ‘‘correcting’’ the imbalance. One possible

means of correcting a perceived imbalance is to put less

physical or psychological energy into a job, or in other

words, to reduce job commitment. We view this kind of

tactic as a response to job dissatisfaction. The other

response that we will consider is turnover intentions.

Turnover intentions refers to ‘‘physical job with-

drawal,’’ which can manifest itself in terms of either

absenteeism or turnover. Hulin (1991) classifies both

as behaviors ‘‘to reduce work role inclusion.’’ In this

study we focused on turnover because it was of greater

importance to the management of the organization we

were working with than was absenteeism. Griffeth,

Hom, and Gaertner (2000), in a meta-analysis of 42

studies, found that overall job satisfaction was a

consistent predictor of turnover. The opposite of job

commitment could be referred to as ‘‘psychological

job withdrawal.’’ This is another possible response to

dissatisfaction, and quite unlike absenteeism or turn-

over intentions. It refers to behaviors that serve to

reduce job inputs, and it is an alternative to reducing

work role inclusion.

Finally, as mentioned in the introduction to this

paper, the relationship between job satisfaction facets

and responses may vary between developing country

low- and high-tech settings for two reasons—the

nature of the work is different, and the employees

themselves are likely to be different. Therefore, we

make the following general proposition:

The relationships between job satisfaction facets

and responses will vary between developing coun-

try low- and high-tech manufacturing settings in

ways that can provide guidance in workforce man-

agement.

3. Hypotheses

3.1. Job satisfaction facets

In their review of the job satisfaction literature,

Judge and Church find that ‘‘the work itself consis-

tently emerges as the most important job facet’’ (2000:

170). Intrinsic job characteristics include the per-

ceived significance of the work, the extent to which

the job is boring, and the extent to which the job is

fatiguing (Stone & Gueutal, 1985). Because work in

low-tech settings work is often repetitive and tedious,

we believe that the ability to deal with these intrinsic

job characteristics will be more crucial in low-tech

settings—the low-tech workers who have difficulty

tolerating boring, fatiguing or seemingly unimportant

work will tend to be quite dissatisfied. However, in

high-tech settings workers tend to experience more

variety and more challenging work, so satisfaction

with intrinsic job characteristics will be less of an

issue.

Hypothesis 1a: Satisfaction with intrinsic job char-

acteristics will be more closely associated with overall

job satisfaction in low-tech manufacturing settings

than in high-tech settings.

Judge and Church (2000) also identify two other

well-accepted job satisfaction facets—pay and the

supervisor. In addition, the work schedule or shift

may be important to many workers. Informally, we

expect that satisfaction with pay, the supervisor and

the shift will be associated with overall job satisfaction

equally in low- and high-tech manufacturing settings

because we consider these facets to be independent of

the technological setting. However, since we do not

expect to find a difference, we offer no formal hypoth-

esis relating to these three facets—such a hypothesis

would be a ‘‘weak hypothesis,’’ because it would be

supported by failing to reject rather than by rejecting

the null hypothesis of no difference.


A final job satisfaction facet identified by Judge and

Church (2000) is opportunities for advancement. In

addition, ‘‘voice’’ at work, which occurs when super-

visors consider the suggestions and requests of sub-

ordinates (Folger, 1977), should also be considered.

The workers in high-tech manufacturing situations are

likely to be more skilled than their peers, and so are

also likely to differ from their peers on some set of

psychological characteristics, such as need for

achievement, proactiveness, and/or sense of respon-

sibility. We will not attempt to precisely define the set

of personal characteristics by which the workers in the

high-tech environment may differ from those in the

low-tech environment. Rather, we hypothesize that

these characteristics will be associated with greater

emphasis or value on opportunities for advancement

and on voice at work. High-tech workers who perceive

that they have few opportunities for advancement and

little voice at work will tend to be more dissatisfied

than their low-tech counterparts. In low-tech settings,

satisfaction with opportunities and voice will be less of

an issue than in high-tech settings.

Hypothesis 1b: Opportunities for advancement and

voice at work will be more closely associated with

overall job satisfaction in high-tech manufacturing

settings than in low-tech settings.

3.2. Responses

We believe that workers in high-tech settings, by

virtue of being more skilled, are likely to have or at

least perceive more alternative job opportunities avail-

able to them than do their peers in low-tech settings.

Therefore, a dissatisfied worker in a high-tech setting

is more likely to think of quitting.

Hypothesis 2a: Overall job satisfaction will manifest

itself in terms of turnover intentions more strongly in

high-tech manufacturing settings than in low-tech

settings.

If a dissatisfied employee perceives few alternative

employment possibilities, quitting may simply not be

an option. Still, the dissatisfied employee must

respond in some way in order to avoid the cognitive

dissonance that would result from making no response

to feelings of dissatisfaction. The only possible

response may be through psychological job withdra-

wal, or reducing job commitment. However, we

believe that this response would be more characteristic

of an employee in a low-tech setting, because they are

likely to perceive fewer employment possibilities.

Hypothesis 2b: Overall job satisfaction will manifest

itself in terms of job commitment more strongly in

low-tech manufacturing settings than in high-tech

settings.

4. The setting

Data for this paper were collected through surveys

of line workers at two factories in Northern Mexico.

These were part of the ‘‘maquiladora’’ or offshore

assembly industry. This industry has its origins in a

1966 law that allowed component parts to be imported

into Mexico, assembled and reexported with taxes

paid only on the value added. Currently, tax payments

are computed based on OECD transfer price metho-

dology, which has the benefits of removing disincen-

tives to increasing wage rates and of ‘‘leveling the

playing field’’ across the developing world labor

market. This policy has resulted in sustained growth,

financed mostly by U.S. capital, of maquiladoras not

only along the 2,000-mile U.S./Mexican border, but

also deep into the interior of Mexico. The labor force

of this industry was once almost exclusively made up

of young women, although recently enough men have

been hired to make up almost half of the total maqui-

ladora labor force (MacLachlan & Aguilar, 1998). As

of January 2002 there were 3,367 maquiladoras

employing 1,070,710 workers in Mexico (INEGI,

2002).

The second author of this paper has spent much of

his working life in the maquiladora industry. When the

program was started in the mid-1960s, the benefit to

U.S. companies was to provide a source of low-

cost labor to help U.S. manufacturers compete with

low-cost imported goods, especially from Asia. The

benefit to the Mexican government was new jobs

for its citizens. Most of these were low-skill assembly

line jobs. Turnover could be as high as 20% per month

in a maquiladora plant. This was often tolerated

because employees could be hired and trained within


a matter of days or even hours because of the low

skill requirements. More recently, however, factories

using much more advanced technology have been

built in Mexico under the maquiladora program,

often alongside factories using extremely old tech-

nology. It is against this backdrop that we were

fortunate to have the opportunity to study the work-

ers in two factories working at extreme ends of the

technological spectrum, but operated by the same

organization.

The two factories were located within the same

urban area in Northern Mexico, within 10 miles of

each other, and both were owned by the same U.S.

parent company. Both plant managers, as well as the

managers for human resources, accounting, purchas-

ing, MIS, and site facilities, reported to a single site

manager, and were often rotated between the two

factories. Personnel policies, including those covering

hiring, firing, vacation, safety, leave of absence and

benefits, were the same for both factories. These

policies were apparently effective, as monthly volun-

tary employee turnover in both plants had been a

consistent two to five percentage points below the

city-wide average for maquiladoras over the previous

year.

There were also dramatic differences between the

factories. Factory 1 (low-tech) was very much the

traditional maquiladora, using approximately 30-year-

old technology, and producing a product in the declin-

ing stages of its life cycle. The equipment had been

moved to Mexico from a U.S. factory, and, with the

exception of safety or quality modifications, had not

changed in many years. While the equipment was

clean and well maintained, it was clearly old and the

site manager did not expect that it would ever be

replaced or upgraded.

In Factory 2 (high-tech), there are several major

departures from the traditional maquiladora concept.

First, the product in Factory 2 was not an end product

but a ‘‘feeder product’’ that went to other assembly

factories. This meant that an entirely new skill set was

required for many of the jobs of the factory workers. In

this plant the workers did not touch the product, but in

many cases had to operate sophisticated machinery.

There were some state-of-the-art machines that had

never been in the U.S., but rather were shipped directly

from the manufacturer to the factory. Of course, this

was a radical departure from the traditional mode of

technology transfer for a maquiladora factory; instead

of imitating a production process that had already been

perfected elsewhere, a brand new process was being

developed in Mexico. The result was an increase in not

only worker skill level, but also the skill levels of the

engineers, technicians, machinists and managers

responsible for bringing the new equipment on line.

In fact, the company had to invest considerable

resources in the training of the engineers or techni-

cians who worked with these systems. The site man-

ager reported that as soon as these professional

employees were trained, they were recruited by other

companies, so that retention became a serious pro-

blem.

The result was a considerable activity on the floor of

Factory 2 beyond that of normal production opera-

tions. There were frequent visits of representatives

from the U.S. parent company’s central engineering

group. Visits from senior managers of the parent were

also common. The perception that Factory 2 was very

important to the corporation was clearly obvious

during our visit and conversations with the workers,

many of whom seemed to take pride in working in a

factory where new techniques were being developed.

All this was in direct contrast to Factory 1, where few

outsiders ever visited, and the mood on the factory

floor seemed to be one of a well-established routine.

The work done at the two factories was quite

different. To illustrate, the initial operation in Factory

1 is called chip loading. The primary elements in this

job are a semi-finished chip and a wire bent in the

shape of a U. Ten to twenty of these U-shaped formed

wires are attached to a cardboard carrier, called a

strip. This job is done by placing the strip in a fixture

that spreads the opening of the U slightly and then

placing a chip into each U. This is a paced operation

and failure to keep pace will result in stopping the next

operation in the sequence. However, if pace is kept

then no communication whatsoever is required

between the worker performing this operation and

the worker performing the next operation. With refer-

ence to the Slocum and Sims (1980) model it is evident

that: (1) the workflow is extremely predictable, (2) the

task is extremely predictable, and (3) job interdepen-

dence is sequential.

The initial operation in Factory 2 is called pad

making. In this operation, one worker operates a

machine that builds a pad by stacking a series of thin


layers together. Each pad will contain between 250

and 12,500 individual devices. In order to successfully

accomplish this job, the worker must be able to

properly set up the machine, and monitor and adjust

speed, pressure, temperature and alignment, all of

which are critical to successfully producing a pad

of product. During the normal course of pad making

it is necessary for the worker to take several measure-

ments of the product to insure that it conforms to

specification and to take corrective action when it does

not. As each pad is produced, it goes to the next

operation where another worker cuts the pads into

individual devices. The pad making operation influ-

ences the success of the cutting operation and it is

common for the worker making pads to make an

adjustment based on feedback from the worker doing

the cutting. With reference to the Slocum and Sims

(1980) model it is evident that: (1) the workflow is

extremely predictable, (2) the task has an element of

unpredictability, and (3) job interdependence is reci-

procal; although the flow of product is strictly from

pad making to cutting, the pad maker requires infor-

mation from the cutter in order to successfully com-

plete his/her task.

Since there was a difference in the skill sets required

for the jobs in the two factories, there was also a

different set of hiring criterion. Factory 1 required

little formal education; the minimum educational

requirement was primary school. However, Factory

2 required job applicants to have completed either

secondary school (middle school) or preparatory

school (high school). Also, salaries were about 18%

higher in Factory 2. This was partly because of higher

educational requirements, and partly because Factory

2 was located in a more affluent part of the urban area.

Finally, productivity was much higher at Factory 2. In

Factory 1 it required an average of 80–100 workers to

produce one million finished devices per month, while

in Factory 2 it took only three to four workers to

produce one million devices per month.

5. The survey

The questionnaire was derived from both pub-

lished job satisfaction literature and the first author’s

experience doing job satisfaction surveys in

maquiladoras over the past 5 years. Admittedly, most

of these surveys were conducted in traditional

maquiladora or low-tech settings. The questionnaire

was not a translation from English, but was

written originally in Spanish. English translations

of the questions used in this study are shown in

Appendix A.

Items in the questionnaire included the respondent’s

sex, civil status (married or unmarried), number of

children, academic level, age, organizational tenure,

the name of the respondent’s supervisor, and the

number of days that the respondent had been absent

during the year.

Three questions about intrinsic job characteristics

were included. These referred to the perceived sig-

nificance of the work, the extent to which the job was

boring, and the extent to which it was fatiguing. One

question each about satisfaction with pay, with the

supervisor, and with the employee’s shift was also

asked, as well as a question about satisfaction with

opportunities for advancement. Two questions relat-

ing to voice at work were included. One of these

asked about follow-up on suggestions and the other

about follow-up on requests. In previous studies, the

first author had found these to be important factors of

job satisfaction, perhaps because the classic or stereo-

typical Mexican supervisor tends to be quite auto-

cratic (Rodrı́guez Estrada & Ramı́rez Buendia, 1996).

Finally, one question about overall job satisfaction

was asked. Thus, there were a total of ten questions

about satisfaction with various aspects or facets of

the employees’ work. The employees were asked

to rate their satisfaction with these aspects on a

five-point Likert type scale. For nearly all measures

in this study, low numbers represent more favorable

responses.

To address the hypothesis concerning turnover

intentions, three items were included. We will refer

to the first of these as short-term turnover intentions, to

the second as long-term turnover intentions, and to the

third as job-to-job turnover intentions (this last was the

only negatively worded question, with high numbers

representing a more favorable response). To address

the hypothesis concerning job commitment, three

items were included. The first was a question derived

from Parker, Wall, and Jackson (1997), which we will

refer to as commitment to quality. The second item

was similar, but referred to problems with teamwork

on the line, so we will refer to it as commitment to


teamwork. We will refer to the third item as commit-

ment to company success.

At both factories we took a census of all employees.

They completed the survey during work hours in a

cafeteria in groups of about 25 each. Neither super-

visors nor personnel from the HR department were

allowed to be present while the employees filled out

the questionnaire. One of the authors introduced the

questionnaire to each group with an identical pream-

ble in which he made every effort to assure the

employees that their answers would be confidential

and asked them to respond to each question with their

most honest answer. After completing the question-

naire each employee put it into a sealed container

resembling a voting box.

6. Results

6.1. Descriptive data

A total of 820 usable questionnaires were received.

Descriptive data pertaining to the two factories are

shown in Table 1.

In terms of demographics, the two groups are

remarkably similar. The average age in each factory

differs by just over a year. Average tenure is slightly

greater at the low-tech factory, but this was expected

because the low-tech factory is older. The breakdown

by sex and civil status shows a few more married

women at the low-tech factory and a few more unmar-

ried men at the high-tech factory, but the differences are

small. The major difference between the groups is

academic level; almost 75% of the employees at the

high-tech factory have a high school education, com-

pared to only about 28% at the low-tech factory.

Also, descriptive statistics and correlations for the

combined sample are shown in Table 2.

Table 1

Description of groups

Low-tech

factory

High-tech

factory

Total participating employees 478 342

Average age (year) 24.8 23.6

Average tenure (year) 3.9 3.0

Percent finished high school 27.6 74.7

Percent unmarried women 41.8 39.6

Percent married women 19.3 14.0

Percent unmarried men 26.9 34.8

Percent married men 12.0 11.6

Table 2

Descriptive statistics for combined sample

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Satisfaction with

1. Opportunities 3.05 1.06

2. Requests 2.77 0.92 0.58

3. Boredom 2.12 1.01 0.38 0.31

4. Suggestions 2.43 0.84 0.50 0.63 0.31

5. Shift 2.10 0.90 0.34 0.38 0.32 0.34

6. Fatigue 2.74 0.99 0.30 0.31 0.27 0.20 0.12

7. Supervisor 2.15 0.92 0.30 0.32 0.28 0.27 0.19 0.23

8. Pay 2.51 0.80 0.40 0.38 0.23 0.32 0.32 0.23 0.21

9. Significance 1.36 0.65 0.25 0.29 0.36 0.28 0.25 0.16 0.20 0.17

Turnover intentions

10. Short term 1.85 0.93 0.28 0.30 0.33 0.24 0.30 0.21 0.18 0.23 0.24

11. Long term 1.84 1.01 0.43 0.39 0.45 0.34 0.36 0.25 0.19 0.27 0.34 0.62

12. Job to job 2.68 1.53 0.14 0.17 0.18 0.10 0.12 0.05 0.15 0.06 0.10 0.21 0.24

Job commitment

13. Quality 1.85 0.95 0.25 0.26 0.39 0.24 0.24 0.22 0.20 0.11 0.30 0.31 0.38 0.13

14. Teamwork 1.89 1.04 0.14 0.15 0.22 0.15 0.12 0.14 0.60 0.13 0.22 0.20 0.25 0.02 0.49

15. Company success 1.57 0.83 0.29 0.31 0.38 0.31 0.23 0.21 0.22 0.17 0.38 0.31 0.45 0.12 0.45 0.31

N varies from 804 to 818. All scales are five-point.

Correlations of greater than 0.10 are significant at 0.01 (two tailed).


6.2. Means of job satisfaction facets and responses

Mean responses to the questionnaire items are

shown in Table 3. In every case, the employees at

the low-tech factory provided more positive responses

than those at the high-tech factory. In particular, the

employees at the high-tech factory tended to complain

more about their shifts, opportunities for advance-

ment, and follow-up on suggestions and requests.

Also, they tended to indicate that they had greater

long-term turnover intentions and lower commitment

to quality.

Nonetheless, we continue to believe that it is the

relationships between the job satisfaction facets and

responses will provide the most interesting informa-

tion, and our hypotheses all focus on these relation-

ships.

6.3. Hypothesis testing

Our hypotheses were tested through structural equa-

tion modeling, a technique that lends itself especially

well to multi-part models such as Hulin’s (1991)

Organizational Adaptation/Withdrawal Model. We

used Lisrel 8.52, and the results are shown in Figs. 2

and 3.

Although the hypotheses all relate to the compar-

ison of individual factor loadings within the model, it

is appropriate to begin with some assessment of the

model itself. Generally accepted criteria for alphas are

0.70 for confirmatory or 0.60 for exploratory research,

and some of our alphas fall below those criteria. Using

the combined data set, for job satisfaction (nine items)

Cronbach’s alpha is 0.80, for turnover intentions (three

items) it is 0.56, and for job commitment (three items)

it is 0.68. However, the UCLA Academic Technology

Services webpage states that ‘‘Cronbach’s alpha mea-

sures how well a set of items measures a single

unidimensional latent construct. When data have a

multidimensional structure, Cronbach’s alpha will

usually be low’’ (UCLA, 2004). Since it was important

to keep the survey as brief as possible, we restricted

ourselves to one question each about each of a number

of various aspects or dimensions of job satisfaction,

turnover intentions and job commitment, so that our

questionnaire was in fact designed to produce a com-

posite or multidimensional structure. For example, our

construct turnover intentions includes one specific

job-to-job turnover question, and Royalty (1998) takes

great pains to distinguish job-to-job turnover from job-

to-nonemployment turnover. Likewise, our questions

about commitment to quality and commitment to

Table 3

Comparison of means

Low-tech factory High-tech factory t-value Significance (two tailed)

Job satisfaction facets

Significance of work 1.30 1.44 3.06 0.00*

Boredom 2.04 2.23 2.66 0.01*

Fatigue 2.67 2.84 2.42 0.02*

Pay 2.45 2.60 2.66 0.02*

Supervisor 2.13 2.19 0.92 0.36

Shift 1.91 2.36 7.26 0.00*

Opportunities 2.85 3.32 6.40 0.00*

Follow-up on suggestions 2.33 2.58 4.23 0.00*

Follow-up on requests 2.61 3.00 6.13 0.00*

Responses

Short-term turnover intentions 1.77 1.96 2.89 0.00*

Long-term turnover intentions 1.66 2.10 6.28 0.00*

Job-to-job turnover intentions 3.32 3.33 0.09 0.93

Commitment to quality 1.73 2.03 4.51 0.00*

Commitment to teamwork 1.82 2.00 2.45 0.01*

Commitment to company success 1.49 1.68 3.24 0.00*

For all variables, low numbers represent more desirable responses, except job-to-job turnover intentions, which is reverse scaled.* p < 0:05.


teamwork are purposely designed to measure two

distinct aspects of a multidimensional construct

‘‘overall job commitment.’’

Furthermore, the overall model fit is good. Overall

fit is often ascertained through the root mean square

error of approximation. Browne and Cudeck state that

‘‘We are . . . of the opinion that a value of about 0.08 or

less for the RMSEA would indicate a reasonable error

of approximation’’ (1993: 144). The models for both

factories meet that criteria with RMSEAs of 0.080 for

the low-tech factory and 0.078 at the high-tech factory.

Other fit statistics are also good; the comparative fit

indexes (CFI) for the two samples are 0.924 and 0.948

respectively, and the incremental fit indexes (IFI) are

0.925 and 0.948. These figures meet the criteria stated

by Bentler and Bonett (1980: 600).

Fig. 2. Low-tech factory.


Lisrel 8.52 provides both parameter estimates and

standard errors for the factor loadings, and two factor

loadings can be compared by dividing the difference

between them by the standard error of the difference,

which is the square root of the pooled variance. The

results of these comparisons are shown in Table 4. The

hypotheses will be considered to be supported when

factor loadings in the low- and high-tech factories are

found to be significantly different, and in the direction

predicted.

Hypothesis 1a predicted that satisfaction with

intrinsic job characteristics would be more closely

Fig. 3. High-tech factory.


associated with overall job satisfaction in the low-tech

factory. This hypothesis was supported. The factor

loadings for fatigue and boredom were significantly

greater in the low-tech plant than the high-tech plant.

The factor loadings for significance of work did not

vary significantly by plant.

We had predicted that satisfaction with pay, the

supervisor and shift would be associated with overall

job satisfaction equally in the two factories, and, as

predicted, virtually no difference was found for super-

visor and for shift. However, satisfaction with pay did

vary between the plants; employees at the low-tech

factory were more dissatisfied.

Hypothesis 1b predicted that opportunities for

advancement and voice at work would be more closely

associated with overall job satisfaction in the high-

tech factory than in the low-tech factory. No support

was found for this hypothesis.

Hypothesis 2a predicted that that overall job

satisfaction would manifest itself in terms of turn-

over intentions more strongly in the high-tech fac-

tory. No support was found for this hypothesis.

Hypothesis 2b, that overall job satisfaction would

manifest itself in terms of job commitment more

strongly in the low-tech factory, was supported. The

factor loading for job commitment was much greater

in the low-tech factory, and the difference was sig-

nificant.

7. Discussion

7.1. Analysis and comparisons with previous

studies

In every case, the employees at the low-tech factory

provided more positive responses than those at the

high-tech factory. Some of this effect may be the

result of differences in age—the workers at the low-

tech factory were slightly older. Many researchers

have found positive correlations between job satisfac-

tion and age (e.g., Brush, Moch, & Pooyan, 1987).

Spector (1997) speculates that one reason for the

correlation between age and job satisfaction may

be that younger workers tend to have higher expecta-

tions. This may be doubly true in our case, because the

workers in the high-tech factory were not only

younger, but were better educated as well. Education

may also raise expectations. Overall, our findings

provide some evidence that younger and more edu-

cated workers, even in developing countries, have

higher expectations and will be disappointed if these

expectations are not met.

However, our most important purpose in this paper

was to determine how the management of job satis-

faction in developing country workforces differs

between low- and high-technology settings, and we

did this by examining the relationships between job

Table 4

Significance of differences in factor loadings

Factor

loading low-tech

Standard

error low-tech

Factor loading

high-tech

Standard error

high-tech

Z of

difference

Job satisfaction facets

Significance of work 0.311 0.0275 0.261 0.0394 1.52

Boredom 0.611 0.0435 0.477 0.0575 2.70

Fatigue 0.449 0.0477 0.294 0.0548 3.06

Pay 0.448 0.0378 0.282 0.0423 4.16

Supervisor 0.379 0.0441 0.392 0.0497 �0.28

Shift 0.420 0.0395 0.429 0.0499 �0.20

Opportunities 0.743 0.0447 0.674 0.0527 1.44

Follow-up on suggestions 0.565 0.0357 0.542 0.0439 0.59

Follow-up on requests 0.645 0.0363 0.684 0.0473 �0.96

Responses

Turnover intentions 0.687 0.0728 0.627 0.0698 0.84

Job commitment 0.714 0.0642 0.399 0.0644 4.90

Z’s of difference of 1.65 or greater are significant at 0.10 (two-tailed test), and are shown in bold.

Z’s of difference of 1.96 or greater are significant at 0.05 (two-tailed test).


satisfaction facets and responses. As predicted, we did

find that intrinsic job characteristics were more closely

associated with overall job satisfaction in the low-tech

factory. This is consistent with our argument that

being able to deal with repetitive or tedious work is

crucial in low-tech settings, but is less of an issue in

high-tech settings. It is also consistent with Spector’s

observation that ‘‘It has long been believed that rou-

tine, simple jobs such as we find on the traditional

assembly line are inherently boring and dissatisfying’’

(1997: 31). Finally, it is consistent with the major

conclusion of Slocum and Sims’ (1980) analysis of

technology, organization and job design. Despite the

century-old efforts of scientific managers to reduce

uncertainty at the employee level, there appear to be

motivational benefits to the introduction of uncertainty

and especially task uncertainty to jobs. Therefore, the

introduction of task uncertainty to jobs may be an

unintended but very positive consequence of the chan-

ging technological environments of developing

nations.

We had suggested that satisfaction with pay, the

supervisor and the shift would be equally important in

low- and high-tech settings because these facets were

independent of the technological setting. We found

that both satisfaction with the supervisor and with the

shift were of roughly equal importance in the two

factories, but we found that satisfaction with pay was

more important, or more closely associated with over-

all satisfaction in the low-tech factory. We should note,

however, that the workers at the low-tech factory were

paid about 18% less than the workers at the high-tech

factory. Since the two factories were located within 10

miles of each other, many of the low-tech factory

workers were likely to have been aware of this pay

difference. This may have heightened the importance

of pay in their minds, resulting in the stronger associa-

tion with overall satisfaction. This sort of difficulty

with Locke’s (1976) value-percept theory of job satis-

faction, upon which our research is based, has been

noted by others (Judge & Church, 2000; Rice et al.,

1991). In any case, however, pay was not an especially

important issue at either factory—in terms of its

association with overall satisfaction, it ranked fifth

of nine facets for the low-tech factory and seventh of

nine at the high-tech factory. This is consistent with

Spector’s statement that ‘‘The correlation between

level of pay and job satisfaction tends to be surpris-

ingly small . . . [and] . . . this small correlation suggests

that pay itself is not a very strong factor in job

satisfaction’’ (1997: 42).

We had predicted that opportunities for advance-

ment and voice at work would be more closely asso-

ciated with overall job satisfaction in high-tech factory

than in the low-tech factory, but we found that this was

simply not the case. These factors were quite impor-

tant in the high-tech factory, but they were equally or

even more important in the low-tech factory. We had

suggested that the workers in the high-tech factory

differed from their counterparts in the low-tech factory

in psychological characteristics, such as need for

achievement and proactiveness. People may conjec-

ture that the less educated, lower paid workers in the

low-tech factory had less need for advancement and

expression, but we found that this was not the case at

all. Therefore, it may be that the workers in the two

factories are similar in terms of their personality

characteristics, and differ primarily in terms of the

opportunities that they have had available to them. The

fact that we were unable to measure relevant person-

ality characteristics in our survey is a limitation of our

study. In part, this limitation was due to the need to

keep the survey brief so that we could survey the entire

population of workers in both factories. In any case,

opportunities for advancement was either the most

important or second most important job satisfaction

facet at both factories. Schneider, Gunnarson, and

Wheeler’s (1992) essay is primarily a call for greater

emphasis on opportunity in job satisfaction research,

and they propose that people are almost universally

predisposed to seek opportunity in their work envir-

onments. The current study provides support for that

proposition.

In regards to job satisfaction responses, we found,

as predicted, that job satisfaction manifested itself

strongly in terms of job commitment in the low-tech

factory, but much less so at the high-tech factory. Our

conclusion is that while a dissatisfied worker in a

low-tech setting is likely to withdraw psychologi-

cally and simply ‘‘go through the motions’’ at work,

this response is much less of a concern in high-tech

settings. However, we had also predicted that

job satisfaction would manifest itself more strongly

in terms of turnover intentions in the high-

tech factory, and we found that this was not the

case. Turnover intentions were at least an equally


important manifestation of job dissatisfaction in the

low-tech factory.

Our failure to find differences in regards to turn-

over intentions may be due to the fact that we

focused on intentions rather than actual turnover.

Our logic for predicting that job satisfaction would

be more strongly associated with turnover intentions

at the high-tech factory was based on an assumption

that the more highly skilled workers in the high-tech

factory would perceive more alternative job oppor-

tunities available to them than did their peers in low-

tech factory. This may still be true, but it may be that

these perceived alternatives have a stronger effect on

actual turnover than they do on turnover intentions.

Support for this explanation can be found in the

literature. A number of studies have found strong

connections between job satisfaction and turnover

intentions, as we did (e.g., see Tett & Meyer, 1993)

for a meta-analysis. Studies that focus on alternative

job opportunities tend to use actual turnover as a

dependent variable (Carsten & Spector, 1987).

Therefore, we have identified a second significant

limitation in our study. The key to overcoming this

limitation in future studies will be to somehow deal

with the problem of confidentiality—it’s quite clear

to us that many of our respondents prefer not to be

identified, but in order to study the relationship

between job satisfaction and actual turnover we must

be able to identify the respondent of a completed

survey to later determine whether that respondent

has quit.

In addition, our findings cast doubt on our assump-

tion that different job satisfaction responses are

alternatives—in other words, that a dissatisfied

employee may either increase turnover intentions

or decrease job commitment, or at least that if an

employee enacts one response this decreases the

likelihood that he or she will enact the other. There

is support in the literature for viewing different

responses as alternatives. For example, both the

Rosse and Miller (1984) and the Hulin (1985;

1991) models of withdrawal behavior hypothesize

that dissatisfied individuals select one or a few pos-

sible responses. Our results in the high-tech factory,

in which dissatisfied individuals apparently tended to

think of quitting rather than reducing job commit-

ment, support this reasoning. However, in the low-

tech factory we found strong associations between

job satisfaction and both turnover intentions and job

commitment, implying that dissatisfied individuals

may respond in both ways. We believe that the

question of whether different job satisfaction

responses should be viewed as alternatives is cur-

rently unresolved, and an important issue for future

research.

7.2. Managerial implications

At this point, it’s important to ask what all this

means to a practicing manager. First, we must empha-

size that employee actions in high-tech factories can

have much greater consequences than in low-tech

factories. The workers in the high-tech factory in

our study were responsible for producing product

worth well over a half million dollars per day, several

multiples greater than the value of the product pro-

duced at the low-tech factory. The cost of employee

errors, whether through negligence or inexperience, is

therefore also greater.

The rather weak association between job satisfac-

tion and job commitment found at the high-tech

factory (see Table 4) is therefore good news. In fact,

the managers of the facilities in this study roundly

agreed that it was much less of a problem for a

dissatisfied employee in the high-tech factory to quit,

so long as he or she remains committed to the job

while on the job. Psychological withdrawal as a

response to dissatisfaction may be tolerable in a

low-tech environment, but could be disastrous in

the high-tech environment.

The managers of the facilities in our study were

very proud of the fact that turnover at both factories

was below the city average. However, we have pointed

out that the employees at the high-tech factory work-

ers had higher intentions to quit than those at the low-

tech factory (see Table 3). This is interesting because

the company used the same policies and procedures at

both factories, the only exception being the higher pay

at the high-tech factory. But the consequences of

turnover at the high-tech factory were more severe

because of the cost of potential errors by inexper-

ienced employees and because the learning curve to

full productivity for a high-tech worker is longer. We

tentatively conclude that MNCs cannot expect to

reduce turnover among the more highly skilled work-

ers in high-tech plants simply by raising pay.


In this regard, we must admit with some disappoint-

ment that in this study we have done a better job of

informing managers of high-tech operations in devel-

oping countries as to what they need to be less con-

cerned about (intrinsic job characteristics and

psychological withdrawal) than we have of informing

them as to what they need to be more concerned about.

Perhaps this should have been expected; as stated

previously, our questionnaire was developed in large

part through the first author’s experience with low-

tech operations, and in retrospect it is not surprising

that the questions in it have more explanatory value in

these settings. This is especially important because a

failure to completely understand job satisfaction

among high-tech employees has implications far

beyond any single plant. For example, this study

focused on Mexico, and it seems quite clear that if

the traditionally high turnover rates experienced in the

maquiladora industry are also experienced in Mexi-

co’s new high-tech plants, this will slow the inflow of

high-tech jobs that Mexico so desperately wants. Our

study indicates that simply hiring better educated

employees and paying them more is not the whole

answer. Therefore, future research is needed to better

identify job satisfaction issues that may be especially

important to developing country workers in high-tech

settings. We believe that this research may provide

guidance to the managers of MNC’s foreign opera-

tions and to researchers interested in this important

issue.

Appendix A. Survey questions

English translations of questions to measure job

satisfaction:

1. In your opinion, how important is your work for

Company X? (Significance of work, 1—very

important, 5—completely without importance).

2. In your opinion, how interesting or boring is your

work? (Boredom, 1—very interesting, 5—very

boring).

3. In your opinion, how relaxing or exhausting is

your work? (Fatigue, 1—very relaxing, 5—very

exhausting).

4. In your opinion, how would you rate your salary at

Company X? (Pay, 1—excellent, 5—terrible).

5. In general, how would you rate the treatment that

your supervisor gives you? (Supervisor, 1—

excellent, 5—terrible).

6. In your opinion, how would you rate your work

schedule? (Shift, 1—excellent, 5—terrible).

7. In your opinion, how would you rate the

opportunities for advancement in Company X?

(Opportunities, 1—excellent, 5—terrible).

8. In your opinion, how would you rate the follow-up

that Company X gives to the suggestions for

continuous improvement in the workplace? (Fol-

low-up on suggestions, 1—excellent, 5—terrible).

9. In your opinion, how would you rate the follow-up

that Company X gives to the reasonable requests

of its workers? (Follow-up on requests, 1—

excellent, 5—terrible).

10. Overall, how would you rate working for

Company X? (Question not used, 1—excellent,

5—terrible).

English translations of questions to measure turn-

over intentions:

1. How likely is it that you will continue working for

Company X next year? (Short term, 1—I am sure I

will stay, 5—I am sure I will leave).

2. I like the idea of continue to work for Company X

for many more years. (Long term, 1—completely

in agreement, 5—completely in disagreement).

3. If I had the opportunity to change to a similar job

to that which I have now, and at the same salary,

but with another company, I would go. (Job to job,

1—completely in agreement, 5—completely in

disagreement).

English translations of questions to measure job

commitment:

1. What degree of interest would you have if your

production line wasn’t meeting its quality goals?

(Quality, 1—of great interest to me, 5-of little

interest to me).

2. What degree of interest would you have if the

people on your production line had problems

among themselves and weren’t working as a real

team? (Teamwork, 1—of great interest to me, 5—

of little interest to me).

3. Everyday I look for more and more ways to

contribute to the success of Company X. (Com-


pany success, 1–completely in agreement, 5–

completely in disagreement).

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Job satisfaction and technology in Mexico