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Post-MBA industry shifts An investigation of career, educational and

demographic factors

Alvin Hwang Pace University, New York, New York, USA

Regina Bento Merrick School of Business, University of Baltimore,

Baltimore, Maryland, USA

J.B. (Ben) Arbaugh University of Wisconsin Oshkosh, Oshkosh, Wisconsin, USA

Abstract

Purpose – The purpose of this study is to examine factors that predict industry-level career change among MBA graduates.

Design/methodology/approach – The study analyzed longitudinal data from the Management Education Research Institute (MERI)’s Global MBA Graduate Survey Dataset and MBA Alumni Perspectives Survey Datasets, using principal component analyses and a three-stage structural equations model.

Findings – Perceptions about career growth and opportunity for advancement were the strongest predictors of industry shifts. The type of program was also found to have an influence, with part-time MBA programs positively predicting industry shift, and full-time programs having an indirect effect through significant associations with each of the intermediate predictors of industry shifts. Women were found to be more likely to change industries. Satisfaction with the MBA degree was not a predictor of industry change behavior: they were found to be related only to the extent that graduates valued the importance of certain career factors, such as the objective career factor of career growth.

Originality/value – This is a first large scale study of industry-level career change among MBA graduates.

Keywords MBA students, Careers, Part-time MBA programs, Graduates, Career development

Paper type Research paper

Introduction The MBA degree often is perceived as a tool to facilitate career change (DeMeglio, 2006; McCormack, 2007), but its role in career change has not been the subject of substantial and rigorous academic research. After groundbreaking research in the 1970s and 1980s on the role of the MBA in increasing socioeconomic mobility (Dreher et al., 1985; Pfeffer, 1977; Weinstein and Srinivasan, 1974), there have been calls for more research on learning skills that may benefit career development and the

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1362-0436.htm

The authors have relied on data supplied by the Graduate Management Admission Council (GMAC) to conduct the independent research that forms the basis for the findings and conclusions stated by the authors in this paper. These findings and conclusions are the opinions of the authors only, and do not necessarily reflect the opinion of GMAC.

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Received 2 May 2011 Revised 21 July 2011 Accepted 25 July 2011

Career Development International Vol. 16 No. 6, 2011 pp. 592-615 q Emerald Group Publishing Limited 1362-0436 DOI 10.1108/13620431111178344

importance of cross-cultural generalization of career patterns (Sullivan, 1999). The first decade of the twenty-first century has brought a resurgence in research on the role of the MBA in career development (Datar et al., 2010; Gioia and Herman, 2005; Khurana, 2007; Montgomery and Ramus, 2011), but still little is known about the types of career change MBA degrees help produce, or the extent to which they influence such outcomes.

This paper examines the role of the MBA experience in influencing occupational change, particularly decisions to change industries. We study how the likelihood of the MBA degree to facilitate industry change is affected by variables such as student orientations toward career success, program characteristics, and student demographics.

In the next section, we present a literature review and develop hypotheses based on existing research about objective and subjective career success, the role of the MBA in career development, program characteristics that may encourage industry shifts, and the types of students that would be more likely to shift industries after their MBA experience.

The third and fourth sections describe our methodology (research sample, measures and analytical techniques) and report the results of our analyses. In the final sections, we discuss our findings and examine the limitations of our study and its potential implications for business schools and future research.

Literature review and hypotheses Objective and subjective career success During the last decade, careers researchers have broadened the definition of a successful career to include factors other than compensation or organizational rank. These factors are commonly classified as:

. objective career success factors, i.e. external indicators of career achievement such as promotions and compensation; or

. subjective career success factors, including attitudes or perceptions of how people feel about their career accomplishments, such as perceived skill development, organizational commitment, or career satisfaction (Feldman and Ng, 2007; Judge et al., 1995; Wise and Millward, 2005).

Researchers have explored the role of an MBA education on career development intentions (Dreher et al., 1985; Simpson et al., 2004), with the MBA seen as having a positive impact on careers over time (Baruch, 2009; Baruch and Peiperl, 2000; Pfeffer and Fong, 2004; Zhao et al., 2006).

We expect both objective and subjective career factors to be affected by completion of an MBA degree and to be related to career changes such as industry shifts. Research suggests that learning effects may play a role in industry shifts for relatively younger workers, and that they may level off over time (Gibbons et al., 2005). Because MBA students tend to be comparatively younger, they may be particularly subject to such learning effects (Marks and Edgington, 2006; Simpson et al., 2005). As they progress in their careers, they are likely to acquire additional self-awareness and knowledge of their initial industry choice, which in turn, may lead them to conclude that subjective success factors such as job fulfillment due to skill utilization, satisfaction, reasonable benefits (such as opportunity to learn and grow, flexible hours, childcare, and others)

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and work-life balance may be better achieved in other industry settings (Hall and Chandler, 2005). Because their relative youth makes them less likely to be firmly embedded in their pre-MBA employment situation (Mitchell et al., 2001), and learning effects have been shown to influence industry change for such careers (Gibbons et al., 2005), recent MBA graduates should be more likely to pursue employment in a different industry to seek subjective career success. As argued by Hinds (2005), it is the deep realization of one’s existing work context and acquisition of new knowledge about alternative new context – both often take place in the MBA learning environment that could initiate career moves into new careers. The importance of subjective factors was demonstrated in Vigoda-Gadot and Grimland’s (2008) study where over a period of time, individuals were shown to prefer jobs that have certain embedded values which were consistent with those of their own, with effects above and beyond gender and age considerations. Likewise, Wise and Millward (2005) found values to be a determinant of types of jobs being sought by job-seekers in the 30-40 age range.

Therefore, we anticipate that interest in both objective and subjective career success will motivate the pursuit of careers in new industries:

H1. MBA student interest in career success factors, both subjective (such as skill utilization and meaningful benefits) and objective (such as internal achievement and benefits, external career growth or starting own business), will be positively associated with post-educational industry change.

The MBA role in career development Initial work about the influence of MBA education on the development of managerial skills (Boyatzis and Renio, 1989) and socioeconomic development (Pfeffer, 1977; Weinstein and Srinivasan, 1974) has prompted increased research in the role of the MBA in career development. Although research during the 1990s found no relationship between graduate business education and career achievement for senior managers (Hurley et al., 1997; Judge et al., 1995), one must remember that most of these studies used American samples and focused on subjects who had progressed to senior professional positions prior to the emergence of “boundaryless” (Arthur, 1994; Arthur and Rousseau, 1996) or “protean” (Hall, 1996) careers, and before the proliferation of MBA programs and the exponential growth in the numbers of MBA graduates all over the world.

Demographic patterns of MBA students have shifted over the last 10 to 20 years, moving from senior managers with significant amount of work experience to relatively younger MBA graduates with much less experience. Therefore, it is important to look beyond monetary or position advancements to examine whether the MBA may actually increase graduates’ career skills. Such an orientation also reflects a shift in careers research toward defining success not merely as material gain, but rather as accomplishing one’s most important life goals (Hall, 1996). In a study of MBA alumni from a southwestern US university, Baruch et al. (2005) found that graduate business degrees are associated with increased competency, income and career success, but that the graduate business degree does not necessarily need to be an MBA to reap these benefits. Other research supports these findings. Baruch and Peiperl (2000) compared matched samples of MBA and non-MBA graduates to managers in four different firms in the UK and found no significant differences between the groups on performance variables, or work and career attitudes. However, they did find that the MBA

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graduates scored significantly higher on perceived workplace skills/competencies. These acquired skills and knowledge are useful in the workplace and therefore should affect MBA graduates’ satisfaction with what the MBA education could do for them, first in their work performance, and then in their careers over time. This satisfaction also is seen in higher self-efficacy in MBA graduates, which has been shown to predict future performance.

Research has shown that MBA graduates were much more likely to attribute the development of their skills to their graduate education than to other factors. Sturges et al. (2003) reported that graduates of a Canadian MBA program reported marked increases in their analytical and people skills (“know-how”), increases in the organizational credibility extended to them, and increased confidence in their ability to present themselves and engage in dialogue with their peers and superiors (“know-why”). Such increases in skills and related confidence may explain in part why MBA students attribute their career choices and development primarily to micro-level factors (individual human capital), rather than meso-level (access to a particular job, and characteristics of the job) or macro-level (structural and demographic) factors (Ozbilgin et al., 2005). All this positive evidence on the usefulness of knowledge, skills and competencies gained in the MBA program indicate graduate satisfaction with their MBA education. Such satisfaction is related to a sense of improved confidence and self-efficacy, a combination that could make career movements across industries an easier task.

MBA programs give students opportunities to interact with people from outside their own industries, thereby increasing their exposure to and awareness of other industries. Acquiring these forms of capital tends to increase the MBA graduate’s inner-value capital, or degree of individual self-esteem, self-efficacy, and confidence (Baruch et al., 2005). Increased inner-value capital through mastery of a curriculum designed to address issues in a variety of industries could increase both student awareness of other industries and the confidence needed to make a transition into a new industry setting, especially when attaining the degree from a prestigious school is associated with increased market value capital (Baruch, 2009; Judge et al., 1995; O’Brien et al., 2010).

The literature shows that MBA graduates are largely satisfied with the acquisition of useful skills and important experiences through their MBA education (Baruch and Peiperl, 2000; Hall and Chandler, 2005; Sturges et al., 2003), and that these skills and experiences are portable to new industry settings. With satisfaction from improved skills and capabilities, these graduates also are likely to be more mobile in search of both objective and subjective career success (Dietz and Orr, 2006; Glosser and Golden, 2004). The sense that one is more competent after an MBA experience and the perception by employers that graduates have a wider skill-set should facilitate career moves by MBA graduates (Pfeffer and Fong, 2004; Zhao et al., 2006). In addition, as industries become more turbulent due to worldwide competition (Glosser and Golden, 2004; Landry et al., 2005), such career movements are likely to involve across-industry changes. Thus, satisfaction with an MBA experience and perceived benefits of such an experience are likely to be related to career change (Ozbilgin et al., 2005), including industry change (Daly et al., 2007). Therefore, we propose the following hypothesis:

H2. MBA Student satisfaction with what the MBA could do for them will be associated with objective and subjective career success factors, and industry change.

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Type of MBA programs and industry shifts Organizational sponsorship of MBA education has been shown to be related to career success (Ng et al., 2005), and not surprisingly, the type of MBA program one pursues and subsequent career decisions. There are costs involved in providing at least partial support for MBA tuition (Baruch, 2009), but there are also benefits: organizational sponsorship, along with supervisor encouragement and access to resources, help create organizational roots for the employee (Ayree et al., 1996; Feldman and Ng, 2007). Such roots may increase the likelihood that employees will stay with their sponsoring organizations instead of pursuing new industries on graduation. The need to consider employees’ needs for development is especially important when the psychological contract for work relationships have shifted from commitment to development for employability (Atkinson, 2002). Organizational investments in human capital are also likely to influence a prospective student’s decision to pursue the MBA on a part-time basis while continuing to work full time for the employer (Wayne et al., 1999). These job, organizational, and community roots may lead an employee to prefer a part-time MBA program, because the financial, social, and professional sacrifices associated with a full-time MBA might imply too high a cost, such as having to leave one’s employing organization. Conversely, students in full-time MBA programs tend to fund their own education and often are seeking a new professional direction (Feldman and Ng, 2007; Mitchell et al., 2001), which means that full-time MBA students are likely to have weaker ties to their previous industry than those who enroll in other types of programs. Accordingly, we expect students in full-time programs to be more likely to move across industries, and part-time students to be more likely to stay in their pre-MBA industries.

H3. MBA program type will moderate post-MBA industry shifts; MBA students who were enrolled in full-time MBA programs will be more likely to change industries on graduation.

MBA student demographics and industry shifts Although recent research suggests that the MBA experience may be less satisfying to women and leave them marginalized within the context of the degree program (Arbaugh et al., 2010; Kelan and Jones, 2010), the emerging literature on boundaryless and kaleidoscope careers suggests significant gender effects for shifting careers on completion of the MBA (Arbaugh et al., 2010; Mainiero and Sullivan, 2005). Family responsibilities tend to have more of an adverse impact on the career progression of female MBA graduates, as compared to their male counterparts (Miree and Frieze, 1999); in response, women are more likely to seek psychological contracts that allow increased job flexibility, rather than pay and advancement (Rousseau, 1995). As a result, women have been more willing than men to pursue options that meet their personal subjective needs, and have been found to have more inter-organizational mobility than men (Valcour and Tolbert, 2003; Simpson, 2000).

The literature on kaleidoscope careers also suggests that as they take on family responsibilities, women are more likely to make compromises early in their careers to accommodate those additional demands (Mainiero and Sullivan, 2005), and may be more willing to move to industries where family-friendly psychological contracts can be arranged (Rousseau, 1995).

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Other researchers have found that women need to change organizations to fully gain the benefits of their MBA experiences (Goldberg et al., 2004; Simpson, 2000). Research has shown that women who move into either less mature or more male-dominated industries tend to earn more and receive more promotions than do women in more traditional roles (Goldberg et al., 2004). The literature also suggests that younger women tend to pursue the MBA degree for career change, while younger men and older women pursue it to improve job opportunities with their current employer (Goldberg et al., 2004). However, these newly acquired competencies may not result in comparable monetary gain or advancement into senior positions for women (Brett and Stroh, 1997; Goldberg et al., 2004; Miree and Frieze, 1999; Simpson, 2000; Simpson et al., 2004). Mainiero and Sullivan (2005) suggest that a possible explanation for these gender differences is that women take a more relational orientation to their career development. That is, they are more likely to factor in the needs of family members, friends, and coworkers when making career decisions. This has caused women to focus on career interests early in their career, on family/relational issues in mid-career, and on authenticity in their late career (Cabrera, 2007). Other research has shown that women are often “type-cast” in their organizational settings, and thus younger women are particularly more likely to see the MBA as a vehicle for moving into new career paths (Simpson et al., 2004, 2005). Based on all the above research findings, we propose that gender will moderate the likelihood of migration to new industries after completion of the MBA:

H4. Gender will moderate post-MBA industry shifts; women will be more likely to change industries on graduation.

Method In order to examine the role of MBA degrees in facilitating career changes across industries, we needed data that captured relevant MBA demographic factors, career-related attitudes, and post-MBA industry affiliation. Thanks to the Management Education Research Institute (MERI) of the Graduate Management Admissions Council (GMAC), we were granted access to the Global MBA Graduate Survey Dataset and the MBA Alumni Perspectives Survey Datasets. Data from 2003-2005 were merged to obtain a variety of student demographic information, satisfaction with MBA experience, career attitudes, and reported industries before and after MBA education.

Sample Our sample consisted of the 5,299 respondents from the 2003 to 2005 GMAC datasets who completed the critical question indicating whether or not they had changed industries and other questions of interest to this study. This group consisted of 1,547 graduates from 2003, 2,040 from 2004, and 1,712 from 2005.

A large segment of our sample self-identified as White Caucasians (45.3 per cent). The remaining were Asians (4.5 per cent), African-American (2.3 per cent), Hispanic (2.2 per cent), Native Indian/Alaskan (0.1 per cent), and Multi-Racial (0.9 per cent). The proportion indicated “Other” or did not indicate specific racial or ethnic background (44.8 per cent). An examination of the gender mix showed that 70.2 per cent of the sample was male. The average sample age was 31.5 years, with a standard deviation of 5.04 years. Most of the respondents were enrolled in full-time programs (70.2 per cent),

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and a sizeable minority in part-time programs (21.1 per cent). There were lesser proportions in executive programs (7.6 per cent) and in other types of programs (1.2 per cent), such as distance learning programs or a mix of programs.

We compared the profile of the 5,299 respondents who completed the questions that were of interest to this study against the profile of non-respondents (n ¼ 10; 868) from the total sample of 16,167 in the 2003 to 2005 GMAC datasets. The profile of non-respondents was very similar to that of respondents. A large segment of the sample self-identified as White Caucasians (42.5 per cent). The remaining were Asians (4.6 per cent), African-American (2.1 per cent), Hispanic (2.1 per cent), Native Indian/Alaskan (0.4 per cent), and Multi-Racial (0.7 per cent). A sizeable proportion indicated “Other” or did not indicate specific racial or ethnic background (47.6 per cent). An examination of the gender mix showed that 66.3 per cent of the sample was male. The average sample age was 30.9 years, with a standard deviation of 4.9 years. Most of the respondents were enrolled in full-time programs (72.6 per cent), and a sizeable minority in part-time programs (20.6 per cent). There were lesser proportions in executive programs (5.5 per cent) and in other types of programs (1.3 per cent), such as distance learning programs or a mix of programs.

Based on the very similar profiles of the respondents and non-respondents, we had some assurance that respondents were not likely to be significantly different from non-respondents.

Measures and analysis Industry shift. The MERI databases used up to 70 industry categorizations. We re-categorized these industries to reflect those developed by the US Department of Labor. Industry change in the dataset was coded as a “change” whenever a student’s industry code before completing the MBA was different from his/her industry in the latest alumni survey. Only students who completed their MBA programs and related survey items were tracked for their movements across industries for this study. Thus, the tested model in this study is of a longitudinal nature, with industry change behavior related to prior MBA survey items and completion of MBA program for each respondent.

Predictor variables. Within the 2003 to 2005 datasets, nine items captured student satisfaction with what they believed their MBA education could achieve for them in their careers, nine items captured how students thought their MBA education could affect their career options, and 14 items captured what students considered important when making job decisions. These were subjective measures covering student satisfaction and perceptions. In addition, we used various objective measures such as type of program (Full-time, Part-time, Exec, and Others) and demographic characteristics (e.g. gender, work experience, and dominant ethnicity categories). These items were subjected through exploratory factor analyses, test for Cronbach Alpha reliability, and confirmatory factor analyses before a smaller subset of the items were used to develop constructs in the final model for hypotheses testing of the study.

Analysis. The first analytical step was to determine the extent to which individual survey items in each of the three survey areas (student satisfaction with what they believed their MBA education could achieve for them, how students thought their MBA education could affect their career options, and what students considered important when making job decisions) might reflect broader underlying factors. This

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step required various principal component analyses of the items in each of the three survey areas. The emerging components, along with various demographic items, were then used in a structural equation model (SEM) to test for relationships among these broader factors and their impact on industry change patterns. Details of the emerging items, along with their confirmatory factor item loadings in the final SEM model are shown in Tables I and II. For example, in the intermediate construct section, the second construct of “start own business” has an item that asks respondents to indicate how their degree could increase their career option of “prepare you to pursue the goal of starting your own business. In the Independent construct section, the first construct (A) has an item that asks respondents to indicate the degree of satisfaction that the degree will help student “preparation to get a good job in the business world.”

We chose SEM for several reasons. Based on our four hypotheses, we needed to develop a three-stage model (independent, intermediate and dependent) to test for relationships among the posited factors within a single equation. This three-stage model could not be accomplished through traditional multiple regressions that allowed for only two-stage testing of relationships. Second, SEM enables us to examine correlations among variables in each stage of the model and across different stages. This is important for us to understand however else independent and intermediate variables may be influencing each other in the underlying data structure. These relationships could reveal fertile ground for future studies. Third, SEM allows us to

Construct Lambda Y coefficients *

Dependent construct item 1 Industry change

Industry change since graduation (Yes/No) 1.00

Intermediate construct items 2 Start own business

Prepare you to pursue the goal of starting your own business 1.00 Allow you to expand the number of organizations with which you can seek employment 20.04

3 Career growth Increase the chances of promotion where you currently work 0.10 Allow you to make a career transition – use the MBA to change from your current occupational area to a specific one 20.12 Allow you to switch industries/diversify the types of organizations with which you can seek employment 20.18 Allow you to remain marketable (competitive) 1.00

4 Achievement Achieving something that you personally value 1.00 Opportunity for advancement 0.24

5 Stability Benefit package 1.00 Job security 0.78 Opportunity to learn new things 0.07

6 Skill utilization Positive organizational climate 1.00 Opportunity to use your skills to the maximum 0.15

Note: * Coefficients are significant at t . 1:96

Table I. Construct item coefficients –

Lisrel model

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determine the fit between all the tested relationships within the single model against the natural variance structure of the data. A high fit assessment provides some confidence that tested relationships do exist in the data and are unlikely to be a function of forced relationships that do not account for other significant influences in the model.

Results Table III presents correlations (phi relationships) among the independent demographic factors and provides a useful profile of typical MBA students over the three years of collected data. Age was negatively correlated with all the MBA Programs, with phi coefficients ranging from 20.27 to 20.58. Similarly, work experience was negatively correlated with all the MBA programs (phi coefficients ranging from 20.06 to 20.32). There were also correlations among experience and ethnic groups, and other demographic variables.

Principal component analyses were carried out on student responses from the three student satisfaction and career attitudinal areas in the 2003 to 2005 datasets. Overall, the exploratory factor analysis showed good factor loadings by items on their respective components and discriminant ability of items against non-intended components. The lowest item loading on intended factors was 0.51, with most of them

Construct Lambda X coefficients *

Independent construct items A Satisfied MBA will achieve career goals

Preparation to get a good job in the business world 0.97 An increase in your career options 1.00 Credentials you desired 0.98 Opportunity to improve yourself personally 0.94 Opportunity for quicker advancement 0.98 Development of your management knowledge/technical skills 0.95 An increase in earning power 0.93 Opportunity to network and to form relationships with long-term value 0.87 Job security 0.74

B MBA Program (Full Time) 1.00 C MBA Program (Part Time) 1.00 D MBA Program (Exec MBA) 1.00 E MBA Program (Other Types) 1.00 F Work Experience 1.00 G Gender (Male ¼ 1; Female ¼ 2) 1.00 H Age 1.00 I Ethnicity – American Native 1.00 J Ethnicity – Asian 1.00 K Ethnicity – African 1.00 L Ethnicity – Caucasian 1.00 M Ethnicity – Hispanic 1.00 N Ethnicity - Multi-ethnic 1.00 O Ethnicity – Others 1.00

Note: * Coefficients are significant at t . 1:96

Table II. Construct item coefficients – Lisrel model

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a b

c d

E F

g h

i J

k l

m n

In d

ep en

d en

t co

n st

ru ct

co rr

el at

io n

s (P

h i

co ef

fi ci

en ts

)

S at

is fi

ed M

B A

M B

A ac

h ie

v e

ca re

er g

oa ls

M B

A P

ro g

ra m

(F u

ll T

im e)

M B

A P

ro g

ra m

(P ar

t T

im e)

M B

A P

ro g

ra m

(E x

ec M

B A

)

M B

A P

ro g

ra m

(O th

er T

y p

es )

W or

k ex

p er

ie n

G en

d er

(M al

e ¼

1; F

em al

e ¼

2) A

g e

A m

er ic

an N

at iv

e A

m er

ic an

A si

an A

m er

ic an

A fr

ic an

A m

er ic

an C

au ca

si an

A m

er ic

an H

is p

an ic

M u

lt i-

et h

n ic

b M

B A

P ro

g ra

m (F

u ll

T im

e) 0.

08 *

c M

B A

P ro

g ra

m (P

ar t

T im

e) 0.

06 *

0. 89

d M

B A

P ro

g ra

m (E

x ec

M B

A )

0. 03

* 0.

76 *

0. 80

e M

B A

P ro

g ra

m (O

th er

T y

p es

) 0.

08 *

0. 28

* 0.

35 *

0. 48

f W

or k

E x

p er

ie n

ce 0.

10 *

2 0.

09 *

2 0.

15 *

2 0.

32 *

2 0.

16 *

g G

en d

er (M

al e ¼

1; F

em al

e ¼

2) 0.

07 *

0. 58

* 0.

58 *

0. 48

* 0.

20 *

0. 06

h A

g e

0. 06

* 2

0. 40

* 2

0. 48

* 2

0. 58

* 2

0. 27

* 0.

86 *

2 0.

15 *

i A

m er

ic an

- N

at iv

e 0.

11 *

2 0.

07 *

2 0.

07 *

0. 09

* 0.

51 *

2 0.

04 *

2 0.

03 *

0. 00

j A

m er

ic an

- A

si an

0. 04

* 2

0. 09

* 2

0. 07

* 0.

00 0.

47 *

2 0.

11 *

2 0.

07 *

2 0.

07 *

0. 34

k A

m er

ic an

A fr

ic an

0. 04

* 2

0. 05

* 2

0. 04

* 0.

00 0.

17 *

2 0.

09 *

2 0.

05 *

2 0.

07 *

0. 65

* 0.

12 *

l A

m er

ic an

– C

au ca

si an

0. 04

* 2

0. 10

* 2

0. 10

* 2

0. 04

* 0.

06 *

2 0.

16 *

2 0.

13 *

2 0.

11 *

0. 18

* 0.

48 *

0. 05

m A

m er

ic an

- H

is p

an ic

0. 08

* 2

0. 16

* 2

0. 15

* 2

0. 07

* 0.

18 *

0. 00

* 2

0. 12

* 2

0. 02

* 0.

29 *

0. 14

* 0.

24 *

0. 08

n M

u lt

i- et

h n

ic 0.

10 *

2 0.

25 *

2 0.

26 *

2 0.

06 *

0. 44

* 0.

06 *

2 0.

12 *

0. 15

* 0.

72 *

0. 35

* 0.

24 *

0. 22

* 0.

41 *

o E

th n

ic it

y -

O th

er s

0. 10

* 2

0. 25

* 2

0. 26

* 2

0. 08

* 0.

48 *

0. 07

* 2

0. 10

* 0.

16 *

0. 70

* 0.

42 *

0. 22

* 0.

19 *

0. 40

* 0.

89 *

N o te :

* C

oe ffi

ci en

ts ar

e si

g n

ifi ca

n t

at t .

1: 96

Table III. Independent construct

correlations – Lisrel model

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above 0.65. The highest item loading on non-intended factors 0.21, with most of them below 0.11. The results revealed one component in the student satisfaction area, two components in the career option attitude area, and three components in the student considerations in job decisions area.

The student satisfaction component accounted for 77.8 per cent of item variance. This component reflected a general student satisfaction with what they believed their MBA could achieve for them in terms of career development, achievement, earning power and security.

The two career option components and three job decision components were respectively adopted as objective and subjective career success factors.

The two components in the career option attitudinal area accounted for 45.2 per cent of item variance. The first component reflected a desire to start a business (objective career success factor), and the second reflected a desire to be competitive for career growth (objective career success factor).

The three components in the student job decision considerations area accounted for 48.5 per cent of the item variance. The first reflected a concern for achievement (objective career success factor), the second a preference for stability (subjective career success factor), and the third a desire for a good work environment to utilize skills (subjective career success factor).

We also performed Cronbach alpha tests on each component. The results were mixed. On the Satisfaction component, which has nine measurement items based on a five-point Likert scale, the alpha coefficient was high at 0.964. The alpha coefficients for each of the other five components were low to non-meaningful. These low readings could be explained by the dichotomous nature of the scales used on items in these five components, and also the low number of items making up each component – four out of the five components consisted of two or three measurement items and only one had four items. A review of the literature showed that alpha tests underestimate reliability under such conditions, since alpha coefficients require tau equivalence conditions and any slight variation from these conditions have a large impact on test results (Bollen, 1989). Also, the dichotomous nature of measurement items in these five components reduces item ability to capture wider variance patterns of respondents in contrast to items that were designed with Likert type scales (Raykov et al., 2010). The limited number of items in each of these five constructs also caused variations in tau equivalence conditions of a single item to have a disproportionate impact on the remaining other one or two measurement items (Rayvov, 1997, 2001).

Recognizing the limitations of alpha coefficients under such conditions, we took the next analytical step of performing confirmatory factor analysis to assure us that the emerging measurement items in their respective sub-constructs from the good exploratory factor structure do indeed have some validity for further use in our study. Accordingly, we subjected the measurement items and their related factor structures to the measurement part of a structural equation model to test for significance of item to construct relationships and their overall fit in the model. The resultant measurement and structural model vindicated the results from the exploratory factor analysis. All item to factor relationships were significant at the 5 per cent level and overall model fit was good. Fit indices are presented below.

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Item descriptions for these components are presented in Tables I and II, along with their respective lambda Y coefficients from a structural equation validation model.

To test our hypotheses of how industry change behavior may be related to student beliefs about what their MBA could do for them, their career option attitude, job decision considerations, and demographic factors, we developed a three-stage SEM model. Industry change (stage 3 – dependent factor) was regressed against both:

(1) intermediate factors (interest in objective career factors such as: . starting own business; . enabling career growth; . achievement and subjective career factors such as:

– stability and benefit; and

– skill utilization); and

(2) independent factors, such as students’ satisfaction with what the MBA could achieve for them, and demographic factors including gender, age, work experience and ethnicity, as well as type of program (Full-time/Part-time/Exec, and Others).

The intermediate factors were also regressed against the independent factors. Results of the SEM model revealed good fit (GFI ¼ 0:90; AGFI ¼ 0:82; NFI ¼ 0:94;

RMSEA ¼ 0:071). All significant relationships are at the p , 0: ¼ 0:05 level. Table IV presents significant beta relationships from the intermediate factors of starting own business, concern for career growth, concern for achievement, concern for stability and desire for skill utilization on the dependent factor of Industry Change.

Table V presents significant gamma relationships from the independent factors (satisfaction with what the MBA could do for students and demographic factors) on the intermediate and dependent factors.

The dependent factor, industry change after MBA program, was positively predicted by three intermediate factors (Table IV). These positive predictors included two objective career factors – remaining competitive for growth (gamma ¼ 0:10) and concern for achievement (gamma ¼ 0:14), and one subjective career factor – preference for stability (gamma ¼ 0:05), suggesting support for H1. The independent factor, student satisfaction with the MBA as a tool to achieve career goals, was not a significant predictor of industry shift (Table V), and therefore H2 was not supported.

No. Intermediate constructs predicting dependent construct of industry change (beta coefficients)

Dependent construct Industry change

1 Start own business 0.01 2 Career growth 0.10 *

3 Achievement 0.14 *

4 Stability 0.05 *

5 Skill utilization 20.03

Notes: * Coefficients are significant at t . 1:96; Lisrel Structural Model Fit Indices (GFI ¼ 0.90; AGFI ¼ 0.82; NFI ¼ 0.94)

Table IV. Intermediate constructs

predicting industry change

Post-MBA industry shifts

603

D ep

en d

en t

an d

in te

rm ed

ia te

co n

st ru

ct s

1 2

3 4

5 6

N o.

In d

ep en

d en

t co

n st

ru ct

s p

re d

ic ti

n g

in te

rm ed

ia te

an d

d ep

en d

en t

co n

st ru

ct s

(g am

m a

co ef

fi en

ts )

In d

u st

ry ch

an g

e S

ta rt

ow n

b u

si n

es s

C ar

ee r

g ro

w th

A ch

ie v

em en

t S

ta b

il it

y S

k il

l u

ti li

za ti

a S

at is

fi ed

M B

A w

il l

ac h

ie v

e ca

re er

g oa

ls 0.

01 0.

03 *

0. 02

0. 01

0. 09

b M

B A

P ro

g ra

m (F

u ll

T im

e) 2

0. 05

* 0.

02 0.

06 *

0. 17

* 0.

08 *

2 0.

07 *

c M

B A

P ro

g ra

m (P

ar t

T im

e) 0.

16 *

2 0.

06 *

0. 00

0. 05

* 0.

05 *

2 0.

22 *

d M

B A

P ro

g ra

m (E

x ec

M B

A )

2 0.

07 *

2 0.

36 *

2 0.

31 *

2 0.

35 *

2 0.

35 *

2 0.

29 *

e M

B A

P ro

g ra

m (o

th er

ty p

es )

2 0.

11 *

0. 01

0. 06

* 0.

22 *

0. 09

* 0.

14 *

f W

or k

ex p

er ie

n ce

0. 11

* 2

0. 03

2 0.

09 *

0. 17

* 0.

04 *

0. 08

g G

en d

er (M

al e ¼

1; F

em al

e ¼

2) 0.

49 *

0. 01

0. 03

* 0.

04 *

0. 06

* 0.

02 *

h A

g e

2 0.

17 *

2 0.

45 *

2 0.

07 *

2 0.

68 *

2 0.

65 *

2 0.

38 *

i E

th n

ic it

y -

A m

er ic

an N

at iv

e 2

0. 04

* 0.

15 *

0. 07

* 0.

11 *

0. 00

0. 10

j E

th n

ic it

y –

A si

an 0.

02 0.

09 *

0. 08

* 2

0. 16

* 0.

07 *

2 0.

14 *

k E

th n

ic it

y –

A fr

ic an

0. 01

0. 00

2 0.

04 *

2 0.

05 *

0. 06

* 2

0. 01

l E

th n

ic it

y –

C au

ca si

an 2

0. 02

* 2

0. 04

* 2

0. 08

* 0.

15 *

2 0.

05 *

0. 12

m E

th n

ic it

y –

H is

p an

ic 2

0. 02

* 2

0. 07

* 2

0. 01

0. 07

* 2

0. 03

* 0.

05 *

n E

th n

ic it

y -

M u

lt i-

et h

n ic

0. 13

* 2

0. 12

* 2

0. 11

* 2

0. 15

* 2

0. 09

* 2

0. 22

o E

th n

ic it

y –

O th

er s

0. 05

* 2

0. 07

* 2

0. 02

2 0.

14 *

2 0.

02 2

0. 12

N o te s :

* C

oe ffi

ci en

ts ar

e si

g n

ifi ca

n t

at t .

1: 96

; L

is re

l S

tr u

ct u

ra l

M od

el F

it In

d ic

es (G

F I ¼

0. 90

; A

G F

I ¼

0. 82

; N

F I ¼

0. 94

)

Table V. Construct coefficients for three-stage model

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In addition to the three explicitly tested relationships, industry change also was predicted by various demographic factors (Table V). The positive predictors were Part-Time Program (gamma ¼ 0:16), Work experience (gamma ¼ 0:11), Female (gamma ¼ 0:49), and Other ethnicity (gamma ¼ 0:05). Negative predictors were Full-Time Program (gamma ¼ 20:05), Exec. MBA Program (gamma ¼ 20:07), Other types of MBA program (gamma ¼ 20:11), Age (gamma ¼ 20:17), American Native ethnicity (gamma ¼ 20:04), Caucasian ethnicity (gamma ¼ 20:02), and Hispanic ethnicity (gamma ¼ 20:02). These findings support H4 (female gender related to industry shift), but do not support H3 (full-time program related to industry shift).

Discussion Summary of findings Our research identified several characteristics related to industry shifts by MBA graduates. Some findings were not surprising, such as the fact that graduates from Executive MBA programs or interested in starting their own businesses were less likely to shift industries. But other findings seem to conflict with conventional wisdom: being in a part-time program had a significant positive effect on changing industries on graduation, and being in a full-time program had a significant negative effect. In the following paragraphs, we address the more noteworthy findings and potential implications for MBA programs and students.

First, our findings indicate that both measures of career success – objective (opportunities for growth and achievement) and subjective (desire for stability) – are significant predictors of industry change, thereby supporting H1. Our findings from this large dataset are consistent with the literature that argues that both subjective and objective career success factors explain career movements (Arthur et al., 2005; Hall and Chandler, 2005). These findings also extend findings from studies that showed the importance of subjective career factors, such as consistency between individual values and purpose in jobs (Vigoda-Gadot and Grimland, 2008; Wise and Millward, 2005) during career decisions. The additional contribution from our findings is that both objective and subjective factors could also influence post-MBA shift to other industries by graduates.

Second, MBA graduates’ satisfaction with what an MBA education could do for them in terms of gaining knowledge, skills and competencies was significantly related to two career factors, both objective (career growth) and subjective (skills utilization), thus supporting H2. These significant relationships are consistent with findings from earlier research. For example, Baruch and Peiperl (2000) found MBA graduates to report higher workplace skills/competencies after their MBA education; and Sturges et al. (2003) found graduates of a Canadian MBA program to have marked increases in skills - especially analytical and people skills (“know-how”), increases in organizational credibility that were extended to them because of their ability to use their skills, and increased confidence in their presentation and engagement of peers and supervisors in dialogue (“know-why”). Acquisition of these important skills and competencies has the potential to move careers; therefore it should not be surprising that students who gained such skills and competencies from their MBA education are also more satisfied with their MBA degree. Although findings on the relationship between satisfaction with the MBA degree and career factors partially supported H2, there was a lack of direct relationship between satisfaction and industry change behavior. In other words,

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the relationship between graduate satisfaction with the MBA degree and industry change behavior is seen only to the extent that graduates value the importance of certain career factors, such as the objective career factor of career growth. Thus, satisfaction with MBA education is mediated by career factors in influencing industry change behavior.

The most surprising finding of the study was the positive relationship between enrollment in part-time MBA programs and the likelihood to change industries. This clearly contradicted H3, which posited that part-time students were more likely to remain in their pre-MBA industries and full-time students were more likely to move across industries. In contrast, enrollment in full-time MBA programs, as well as in Executive and other MBA programs, showed a negative relationship with industry change. Several factors may help explain the negative direct effects of participation in a full-time program on the likelihood of industry shift. First, we examined career change as a shift of industry rather than merely a change of employer, job duties, or position. Second, although the direct effect was negative, participation in a full-time program was positively associated with the three career development variables associated with industry shift:

(1) career growth;

(2) achievement; and

(3) stability.

It could be that graduates from full-time MBA programs are pursuing career changes, but are doing so by securing new job titles and/or new employers within the same industry. This finding may suggest that students in full-time programs may be pursuing the degree purely for pragmatic reasons, which has become an increasing cause for concern from those who study business schools (Bennis and O’Toole, 2005; Pfeffer and Fong, 2004). Another possible explanation for full-time students staying within the same industry on graduation is that the two years of MBA coursework and other activities are not sufficient for them to become familiar enough with new industries. Although these students may have different employers after leaving school, those employers may be in the same or similar industries.

We were surprised, at first, to find that graduates of part-time programs were more likely to shift industries. However, a study of a predominantly part-time MBA program had shown that about half of the respondents had new employers on graduation (Zhao et al., 2006), so perhaps our findings should not have been so unexpected. Further examination and consideration of the economic conditions suggest some reasonable explanations for our finding. First, participation in a part-time program was positively related to 2 of 3 career-related predictors of industry shift, achievement and stability. However, it was negatively related to starting one’s own business and organizational climate/skill development. Could it be that these individuals did not intend to shift industries when they entered the program, but had to do so because of economic downsizing? Did they see the MBA degree as a means to escape an industry they perceived to be a “sinking ship”? For example, during this study the outsourcing of IT functions became increasingly common among US firms. Under such conditions, perhaps these graduates were not able to remain with their previous employer, or maybe they believed that they would not find employment in their original industry, and therefore made shifts to new industries. The comparatively high number of

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respondents shifting out of manufacturing and services industries certainly support such conjectures. It also supports the position of Atkinson (2002) that the new psychological contract at the workplace is no longer one of commitment but one of development for employability. As some of the extreme elements of the outsourcing movement show signs of abating, it would be interesting to track the career progression of these alumni over time to see whether they remain in their new industries or return to their original ones.

Another possible explanation for graduates of part-time MBA programs being more inclined to change industries on graduation might be related to the source of funding. Our data did not allow us to examine the moderating effect of source of funding, but it would be reasonable to expect that if an employer pays for a student to attend a full-time MBA, that student would have a strong commitment (both in affective and legal terms) to remain with the employing firm after graduation (Benson et al., 2004). By the same token, a student who does not intend to stay with the same employer would see the part-time MBA route as a way to preserve independence and keep the options open for switching to another employer, or another industry, on graduating.

Another explanation is that MBA programs are emerging as identity workspaces for part-time MBA students (Petriglieri and Petriglieri, 2010). Due to the stability of the business school, compared to the ongoing turbulence in the manufacturing and service industries, part-time students who perceive the loss of an organizational identity workspace through their employer may find it instead in their school, even if the employer is providing financial support for their MBA (Baruch, 2009). Given the degree of curricular similarity in AACSB-accredited programs (Baruch, 2009; Rubin and Dierdorff, 2009), exposure to tools, concepts, and techniques in the MBA curriculum may be similar regardless of the program format, thereby allowing both part-time and full-time students to adopt these tools as potential social defenses against external turbulence. This argument is also consistent with Hinds (2005) position that providing an individual the opportunity for deep introspection about one’s existing work context and discovery of alternative work context are necessary conditions before actions could be taken to make career moves into a new environment – be these into new organizations or industries.

Geographical embeddedness is yet another explanation for this finding. Geography has been found to be a primary rationale in career choice among full-time MBAs, and is a likely influence factor for part-time students as well (McGinty and Reitsch, 1992; Montgomery and Ramus, 2011). In such an environment, part-time students may see the degree as an opportunity to stay in their geographical location of choice by transitioning from one industry to another, due to difficult economic conditions in their original industry (which also may discourage within-industry shifts) or to the perception of greater opportunities in other industries. The MBA may provide these students with enough social and human capital to give them the confidence and contacts necessary to move to a different industry. Therefore, we encourage other researchers to include geography when considering the construct of career embeddedness.

Based on effect size, our findings suggest that the most likely predictor of whether MBA graduates will shift industries is their gender. This finding supported H4, which predicted that female MBA graduates would be more likely to move across industries on graduation. It is also consistent with Cabrera (2007)’s finding that 14 per cent of her

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women sample looked to other industries as they take into consideration growing family and career needs. It was also interesting to note that gender had more than twice as large an effect as any of the other variables on industry shift behavior. In addition to the social, programmatic, and career motivations of women as noted in our hypothesis development, another possible explanation for the influence of gender on industry shift is the role of objective career success measures such as compensation and achievement. Because economic studies suggest that the presence of a graduate degree essentially negates the “gender gap” in compensation (Jensen and Andrews, 2006), and organizations are increasingly looking outside for recruitment and selection purposes (Cappelli, 2005; Hollister, 2004), women may see the MBA as a vehicle for improving their chances for increased salary and promotions by helping them transition to an industry where there are greater opportunities for pay and promotion. However, this explanation may suggest that we could have a more jaded view of MBAs than their actual preferences indicate in the literature (Montgomery and Ramus, 2011). It was also interesting to note that Cabrera(2007) reported 14 per cent of women in her sample reported a change of focus towards a different industry over time.

Regarding the characteristic with the next largest direct effect on industry shift, the fact that older graduates tend not to change industries is consistent with previous literature, particularly when viewed in concert with findings that age was also negatively associated with the career development variables. Research has shown that older students are more likely to pursue the degree for more intrinsic reasons, such as greater confidence in analytical skills, interpersonal communication, or mental stimulation (Simpson et al., 2005), than the potential financial or positional advantages of the degree. They also are less likely to be concerned with attending an elite school or participating in an elite program (Judge et al., 1995) because they can accomplish their learning goals from a wide selection of programs in the country and are more likely to be attracted to programs that can accommodate their family commitments or their needs for intellectual development. Our findings certainly suggest that older students are pursuing the degree for reasons other than transitioning to new industry settings.

Limitations In spite of the relatively generalizable nature of this sample, there are several limitations of our study that warrant caution in interpreting our findings. First, although ours is a relatively large sample, it is a subset of the graduates who provided responses to the career options and job decisions survey items. Because Global MBA Graduate Survey respondents tend to be from full-time programs, the overall sample is likely to be biased toward full-time, classroom-based US MBA programs. Second, MBA programs offered by US doctoral-level institutions comprise about one-third of the sample, thereby limiting generalizability of the findings to part-time MBA programs at Master’s level institutions or programs offered via technology-mediated formats (Arbaugh, 2005; Baruch et al., 2005). Third, our measure of career change was at the industry level, which did not allow us to consider career changes that involved changes of functional duties or employers within the same industry. Fourth, the relatively short time period of the study (three years) does not allow us to determine whether these industry shifts were permanent. Fifth, the nature of Global MBA survey data does not allow us to measure and examine variables in the manner in which is normally the case in careers research, such as objective and subjective career success factors. The

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dataset also does not include a measure for the extent to which the graduates and/or their employers financed their MBA studies. Therefore, we cannot determine the extent to which employer financial support may have helped explain the level of industry shifts. Finally, there is the issue of other potential types of causality. To what extent are these industry shifts attributable to the participation in MBA programs, and to what extent are they attributable to individual characteristics and/or broader socio-economic factors? Unfortunately, the study’s design does not allow us to determine directly the extent to which these factors influenced the employment decisions of MBA graduates. That said, in spite of any other broader factors which might have contributed to these decisions, our results do indicate that completion of an MBA program influences the degree holders’ potential to move across industries.

Implications In spite of these limitations, our findings raise several interesting potential implications for those looking to change careers, searching for MBA programs, or interested in studying the role of the MBA degree in career development. The study generates implications for both theory and practice. Regarding theory on the MBA’s role in career development, a clear finding of our study is that the traditional reasons for pursuing an MBA degree (i.e. objective measures of career success such as career growth and achievement), are primary drivers of industry change by MBA graduates. Something that is not clear from our findings is the extent to which educational experience influences this change. Are there aspects of students’ educational experiences that give them the confidence to switch industries? Do students learn things about industries that increase their attractiveness? Our study underscores the fact that there are several aspects of the role of the MBA in career development that merit additional interest from researchers who study careers and management education.

We also see the need for further consideration for the role of gender as a moderator of the MBA-career change relationship. In contrast to the findings regarding objective career success factors, the fact that women were found to be most likely to change industries after graduation builds on previous research where women who pursued MBAs also preferred career changes for subjective reasons and benefits (Miree and Frieze, 1999; Ng et al., 2005; Simpson, 2000). Whether our findings suggest the relationship between women and career success may be changing will need more time and study. However, our findings do reflect the conclusions of research that shows that younger women are pursuing the degree as a vehicle for career changes (Simpson et al., 2005). We suspect that as undergraduate populations become more female-dominated, women are likely to comprise an increasing proportion of potential incoming candidates for MBA programs, and therefore will be more likely to seek the degree for objective as well as subjective career success factors (Arbaugh et al., 2010).

Regarding implications for practice, we see issues for both MBA programs and prospective industry shifters. Our most surprising finding, the role of part-time MBA programs in predicting career shifts across industries, suggests that these programs may play a more valuable role for industry shifters than is typically assumed. Although conventional wisdom suggests that students pursue full-time programs if they want to shift out of their present industry and pursue part-time programs if they are interested in getting a promotion with their current employer (DeMeglio, 2006; McCormack, 2007), our findings raise the possibility for part-time programs to be

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conduits for people looking to switch industries. This suggests a potential marketing opportunity for part-time MBA programs as vehicles to facilitate career change and tools for economic development. For regions and industries undergoing economic upheaval, this might even provide opportunities for part-time programs to become a part of a company’s outplacement program in preparing employees for new careers and industries.

One possibility that part-time MBA programs may wish to further emphasize is the opportunity they create for students to accumulate additional career capital (Inkson and Arthur, 2001). In their model of career capital accumulation, Inkson and Arthur (2001) note three mutually-reinforcing types of knowledge: knowing-why, representing the individual’s motivation, sense of purpose, and energy; knowing-how, the skills, expertise, and content knowledge; and knowing-whom, the attachments, relationships, mutual obligations and information sources brought to and accumulated over the course of our careers. The role of the part-time program in accelerating accumulation of these knowledge bases could play out as follows. The student, seeking opportunity for career development (knowing-why), pursues the MBA to acquire further developed and/or new content knowledge and skills (knowing-how). They also seek and are exposed to new people in different industry settings (fellow students, faculty, and MBA program staff) who tend to be employed in a particular geographic area with whom they may create working relationships (knowing-whom). Demonstrating competence with course material and assignments (knowing-how) provides both greater confidence and career identity (knowing-why) and increased credibility with students, faculty, and staff, who in turn will be more likely to recommend or hire them for positions in new settings (knowing-whom). Such an orientation also suggests that students should not be seeking the program solely for the accumulation of content knowledge, but for the opportunity to create new relationships in their geographic region of interest.

Even within the emerging research stream on MBA programs and students’ experiences, part-time programs are often an afterthought, if considered at all (Arbaugh et al., 2010; Baruch, 2009; Navarro, 2008; Rubin and Dierdorff, 2009; Griffiths et al., 2005). Given that only about 20 per cent of prospective MBAs complete their studies in two-year, full-time programs (Badenhausen and Kump, 2005), this study suggests that part-time MBA programs and their potential for economic and regional impact should receive much greater research attention (Baruch and Peiperl, 2000; Datar et al., 2010). The fact that this was such an important predictor in our study should encourage efforts to systematically research the program experiences provided by part-time MBAs and their role in subsequent career development.

Another implication for practice pertaining to women and the MBA is the extent to which these programs should change to be more accommodating to their needs. Our findings suggest that women may find their MBA experiences to be supportive of shifts to new industries. On the other hand, there is reason for concern that MBA programs may be less than friendly toward women (Arbaugh et al., 2010; Kelan and Jones, 2010; Simpson, 2006), which might jeopardize their ability to serve as a catalyst in helping women move to more attractive industry settings. Conversely, if MBA programs became more inviting to women, and therefore an even greater catalyst in helping them pursue careers in new industries, those programs could make a stronger case for being considered vehicles of both social and economic benefit. Women hold 57 per cent of undergraduate degrees in the USA and this gender gap is expected to widen

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in the future (US Department of Education, 2008), which means that MBA programs will need to address their gender issues, if only to maintain present enrollments. We certainly would be interested in seeing how this will impact women’s perceptions of the degree as a vehicle for industry shifts.

Conclusion This is one of the first large scale studies of industry-level career change among MBA graduates. Although some of our results were to be expected (such as objective career success factors motivating industry change and participation in Executive MBAs being negatively related to industry shifts), we obtained some surprising findings, such as the significance of part-time programs in facilitating industry change. Collectively, these findings suggest that for all the concerns associated with management education, the MBA has the potential to be a basis on which industry shifters might construct new identities to facilitate professional transitions. Such a role suggests that MBA programs may be underappreciated as a vehicle for generating regional, economic, and social benefits. Our findings also indicate the important role of part-time MBA programs in creating such benefits, and should encourage further research on this under-examined effect. We believe that in the foreseeable future, with the evolving demographics of the MBA student population and the changing labor markets in different industries, the role of the MBA in career development will continue to be quite fluid, and therefore an important area of study for careers and management education researchers.

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