Leadership And Team Management
250 words about job search, resumes and careers.
What did you learn? What things do you think might help you in your job search? Is there anything you were looking for that you couldn’t find?
Resume red flags….not having internship….diversity in job, but not frequent…overstating experience..too many pages to resume, and not key words, too many leaderships, too much on resume….keep resume short and simple
Nonverbal co mmunication, eyes mouth, looking in other directions
Business etiquette..happy hour with co workers, put cell phone away…..how you eat
Professionalism….timeliness, assertive…respect empathic
Public Speaking – know your topic, don’t talk right away, captivate audience, energy, don’t read presentation, open body language..
Candid career.com in the Library career videos
Rockawin, D. (2012). Using Innovative Technology to Overcome Job Interview Anxiety. Australian Journal of Career Development (ACER Press), 21(2), 46–52. https://doi-org.ezproxy2.apus.edu/10.1177/103841621202100206
Chi, C. G., & Gursoy, D. (2009). How to help your graduates secure better jobs? an industry perspective. International Journal of Contemporary Hospitality Management, 21(3), 308-322. doi:http://dx.doi.org.ezproxy2.apus.edu/10.1108/09596110910948314
Born, A., & Witteloostuijn, A. (2013). Drivers of freelance career success. Journal of Organizational Behavior, 34(1), 24–46. https://doi-org.ezproxy2.apus.edu/10.1002/job.1786
How to help your graduates
secure better jobs?
An industry perspective
Christina G. Chi and Dogan Gursoy
School of Hospitality Business Management, Washington State University,
Pullman, Washington, USA
Abstract
Purpose – Many hospitality programs have developed their own career and placement services to
assist students in job searching efforts. The purpose of this paper is to identify factors that are
important for the success of career and placement services offered by hospitality programs, from the
industry’s perspectives.
Design/methodology/approach – Data were collected through an online survey from hospitality
recruiters and human resources managers. Descriptive statistics were applied for the data analysis.
Findings – Internship requirement was found to be the most important factor for the success of
career services, followed by faculty industry experience and quality of student preparation
for job/internship interviews. These were followed by reputation of the program and quality of
educational curriculum and courses taught.
Originality/value – The important implications drawn in this paper could assist hospitality schools
to allocate limited resources to help create excellent career and placement services.
Keywords Recruitment, Career guidance, Human resourcing, Hospitality management, Graduates
Paper type Research paper
Introduction
Hospitality management programs, students and faculty are always seeking ways to
improve job placement rate for their graduates. Owing to a sharp increase in schools
that offer hospitality degrees over the past quarter century, and a more complex and
sophisticated hospitality industry looking for high-quality management talent, the bar
has been raised in terms of qualification of the hospitality graduates (Stoller, 2008). In
today’s environment, achieving good academic performance is hardly enough to find a
good job after graduation. In order to be competitive in the job market, hospitality
students have to adopt aggressive approaches, such as building hospitality-related
internship experiences, taking more course work, developing networking skills, and
participating in extracurricular activities like hospitality student clubs/societies,
fund-raising initiatives, and community involvement. Acquiring and developing more
skills and abilities help students in job searching endeavors. Some graduates are likely
to find jobs through the recruitment efforts of the industry. It is true that a small
percentage of hospitality companies heavily invest in college graduate recruitment,
and training and development (Doherty et al., 2001). However, studies suggest that
relying on industry’s recruitment efforts as the sole placement tool is problematic
( Jenkins, 2001; Jameson and Holden, 2000), due to the fact that while some employers
invest in graduate recruitment and foundation training, others expect graduates to take
the initiative in job searching and start adding value to the companies from day one by
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0959-6119.htm
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Received 24 September 2007
Revised 7 January 2008,
18 March 2008
Accepted 9 June 2008
International Journal of
Contemporary Hospitality
Management
Vol. 21 No. 3, 2009
pp. 308-322
q Emerald Group Publishing Limited
0959-6119
DOI 10.1108/09596110910948314
performing the expected duties and responsibilities of the position they are hired for
with minimal or no additional training (Doherty et al., 1997).
As the preceding discussion suggests, students are likely to struggle in their quest
for placement after graduation and it is problematic to rely only on industry efforts for
placement. As a result, many hospitality programs have developed their own career
and placement services to assist their students in job-searching efforts. The quality
level of these career services determines how well the graduates are placed in the job
market. Thus, it is important to explore factors that are likely to influence the success
of the career services provided by hospitality programs.
Literature review
One of the general assumptions most hospitality schools subscribe to is that if you
have a relevant, up-to-date and industry-driven curriculum, graduates are likely to
possess the knowledge and skill sets required for a successful career in the hospitality
industry. However, as suggested by several studies, one of the biggest challenges
hospitality programs face today is determining clear objectives for the curriculum that
meets the constantly changing needs of the industry (Dopson and Tas, 2004; Gursoy
and Swanger, 2004, 2005; Okeiyi et al., 1994). Therefore, investigation of the curriculum
of hospitality and tourism programs has been a growing area of research in recent
years. Many studies have investigated the components of hospitality curriculum and
the need to integrate operational and managerial skills for career success (Dopson and
Tas, 2004; Baum, 1991; Burgidge, 1994; Christou, 2002; Dopson and Nelson, 2003;
Knutson and Patton, 1992; Li and Kivela, 1988; Tas, 1988; Tas et al., 1996; Kay and
Russette, 2000; Okeiyi et al., 1994). While some researchers suggested that operational
issues such as working knowledge of product/service were important for success
(Kay and Russette, 2000), others suggested that managerial and behavioral issues such
as interpersonal relations and managerial skills were more important for success in the
industry (Okeiyi et al., 1994). Dopson and Tas (2004) indicated that these two
approaches to curriculum should be integrated in order to prepare students for a
successful career in the industry; the curriculum of hospitality schools should not only
provide the impetus for students to learn necessary skills to operate a business but also
enable them to gain a substantial knowledge on how to manage their staff. Other
researchers argue that in developing a curriculum, educators need to consider three
major components of hospitality education: substantive knowledge, skills, and values
(Dopson and Tas, 2004; Gursoy and Swanger, 2004, 2005).
Several studies (Baum, 1991; Burgidge, 1994; Knutson and Patton, 1992; Li and
Kivela, 1988; Tas, 1988; Nelson and Dopson, 2001; Dopson and Nelson, 2003) have
examined the importance of various skills and abilities necessary for graduates’ career
success. These studies contained a very general list of skills that were identified by
surveying hotel managers and/or students. For example, Knutson and Patton (1992)
identified 15 managerial skills that students believed they should possess to be
successful in the industry by surveying students at Michigan State University.
In another study, Li and Kivela (1988) examined the differences between hotel
managers’ and students’ perceptions of skill importance relative to success in the
hospitality field, by surveying both hotel managers and students using the 34 hotel
management skills identified from the literature. These authors reported that applying
marketing management methods and developing job specifications were perceived as
How to help
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309
more important by managers than by students. They also reported three differences
between managers and students in their perception of graduates’ competencies.
Managers rated higher in importance with regards to using food service techniques
and applying second language skills, while students ranked higher in applying group
dynamics methods.
While previous studies examined the skills, a graduate needs to be successful in the
hospitality industry (Baum, 1991; Burgidge, 1994; Christou, 2002; Dopson and Nelson,
2003; Knutson and Patton, 1992; Li and Kivela, 1988; Tas, 1988; Tas et al., 1996, 2000),
a small number of studies examined the approaches hospitality programs used to place
their students before or after graduation. In order to assist students and graduates in
their quest for job in today’s rapidly changing and highly competitive job market,
a large number of hospitality programs have developed their own career and
placement services. The purposes of these career and placement services are to provide
support to students in all phases of their job search and professional development by
helping them gain hospitality-related internship experiences, providing extracurricular
activities, and helping them develop networking skills. Since the success of these
career services heavily depends on the type of relationships they have with the
hospitality companies and how they are perceived by those companies, it is crucial to
understand how hospitality company recruiters and human resources personnel
perceived the importance of various aspects of career services provided by hospitality
schools.
Feiertag (1998) reported that since the late 1990s corporations have been
implementing extensive and competitive on-campus recruiting programs to counter a
growing skill shortage and to attract qualified candidates. Some companies have even
developed extensive corporate recruitment programs that have a year-round presence
on campuses. In addition, companies are holding more elaborate career fairs to secure
graduating talents, as well as outsourcing on-campus recruiting to professional
consultants to ensure that their organizations can effectively compete to attract top
graduates. Nationwide, most medium to large college placement offices host an
average of 348 companies annually, and 56 percent of graduating seniors register with
college/university placement offices (Feiertag, 1998).
The hospitality industry understands that college degree in hospitality
administration is a valuable degree, but in the past many hospitality graduates
found themselves wound up in jobs that do not require a hospitality degree and do not
pay much as well. Hospitality recruiters are now looking for college graduates that
possess good industry skills upon graduation (Carey and Franklin, 1991). Professional
as well as academic qualifications have become progressively more important in the
hospitality industry and at the same time operational experience is viewed as less
important than specific or generic business management skills for hospitality
graduates (Farkas, 1993; Feiertag, 1998). Several studies suggested that in the near
future generic business credentials are likely to become more important (Gursoy and
Swanger, 2004, 2005; Farkas, 1993; Purcell, 1993). They are likely to become a
prerequisite to mid and senior-level hospitality management jobs. With the constant
restructuring of hospitality firms, new jobs and added responsibilities have emerged
from the continuous organizational change. In order to determine needed competencies
in hospitality graduates by industry, one approach is to address the needs on a
continuous basis from the perspective of hospitality recruiters and human resources
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directors, and to identify which aspects of career and placement services provided by
hospitality schools are important for corporate recruiters and human resources
directors.
Earlier studies suggested that service sector employers used to rely on unstructured
application forms and interviews often conducted by untrained interviewers
(Poppleton, 1989), and the general consensus was that selection in the hotel industry
tended to be informal, simplistic, and reactive (Kelliher and Johnson, 1987; Croney,
1988; Price, 1994). However, recent trends indicate that this is rapidly changing.
Recruitment is becoming more complex and sophisticated. These rapid changes in
recruitment style make identifying and understanding of the factors that will create
excellence in career services critical for hospitality programs and their students and
graduates.
Methodology
Survey instrument
The purpose of this study is to identify factors that were important for the success of
career and placement services offered by hospitality programs. An online survey
instrument was developed to gather data on human resources managers’ and college
recruiters’ perceptions. A questionnaire was created to systematically measure factors
that were perceived as important for the success of career and placement services,
based on the literature review and input from human resources managers and
hospitality educators. The instrument was then reviewed by a group of hospitality
educators and college recruiters. They were asked to provide feedback regarding the
layout, wording, and ease of understanding of the measurement items. The feedback
was then taken into account in the revision of the questionnaire. Based on their
feedback, the instrument was revised and finalized (please see Appendices 1 and 2 for
the final version of the cover letter and the online survey instrument). Both college
recruiters and hospitality educators were consulted throughout the survey instrument
development process to make sure that the final instrument had both content and face
validity. The content validity of survey instruments is assessed by overview of the
items by trained individuals and/or by the individuals from the target population.
The individuals make their judgments about the relevance of the items and the clarity
of their formulation. On the other hand, the face validity of survey instruments is
assessed by cursory review of the items (questions) by untrained individuals.
The individuals make their judgments on whether the items are relevant.
The final version of the survey instrument consisted of two parts. Part I was
designed to collect information on the 21 factors that captured various aspects of
hospitality career and placement services; respondents were asked to rate importance
of each of the 21 factors according to a five-point Likert scale (5 – essential, 4 – very
important, 3 – important, 2 – somewhat important, 1 – not important). Part II of the
questionnaire included demographic questions.
Data collection
A self-administered online survey questionnaire was used to collect data, using the
Dillman (2000) Tailored Design Method. The web site link for the online questionnaire
was e-mailed to all 400 hospitality recruiters and human resource personnel contained
in a data base that’s maintained by a large hospitality school located in the
How to help
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311
Northwestern USA. The subjects were informed of the purpose of the study and that
their participation was voluntary. In order to enhance the response rate, e-mail was
personalized to address to each respondent retrieved from the database. A week later,
a reminder e-mail was sent to those who had not responded.
Data analysis
Descriptive statistics (means and standard deviations) were used to report the rank
order importance of the 21 subject matter variables. Frequencies were employed to
summarize the demographic profile of the respondents.
Results
Of the 400 surveys sent out, 102 usable ones were returned resulting in a response rate
of 25.5 percent. Considering the survey population (busy business professionals) and
the data collection method (on-line survey), this was considered to be an acceptable
response rate by the researchers.
Respondents’ demographic profile
Demographic characteristics such as gender, education level, ethnicity, present
position and name of company were included in this study in an attempt to provide a
descriptive profile of the survey respondents.
Gender. Of the 100 individuals who provided gender information, 49 (49 percent)
were male and 51 (51 percent) were female.
Present position/name of company. A total of 67 different companies were
represented in the study. Respondents were asked to list their present position. The
vast majority (98 percent) of the respondents were working for the human resources
department of hospitality companies. A couple of respondents worked for professional
recruitment/employment agencies.
Education level. Of the 100 individuals who answered this question, 62 of them
indicated they had a bachelor’s degree (62 percent); 11 respondents had a graduate
degree (11 percent); seven had some graduate level work (7 percent); 18 had some
college work or a technical degree (18 percent); and two respondents indicated they
were high-school graduates (2 percent).
Ethnicity. The majority of the respondents, 87 (87 percent), were Caucasian/White;
five (5 percent) were Asian-American/Pacific Islander; four (4 percent) were
American-Indian/Alaska Native; three (3 percent) were Hispanic/Latino; and one
(1 percent) was Black/African-American.
Importance of factors for success of career and placement services
Table I presents the descriptive statistics (means and standard deviations) for the
factors that were deemed to be important for the success of career and placement
services offered by hospitality programs. The results showed that industry
professionals believed all of the factors examined in this study were important to
the success of career and placement services (mean ranging from 2.96 to 4.43 on a
five-point Likert scale). They agreed that internship requirements (hospitality school
requiring work experience hours prior to graduation) were the most important factor
(mean ¼ 4.43), followed by faculty’s industry experience (mean ¼ 4.31) and
the quality of students’ preparation for job/internship interviews (mean ¼ 4.25).
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These were followed by reputation of the hospitality program (mean ¼ 4.22) and the
quality of students’ educational curriculum and courses taught in hospitality/tourism
management (mean ¼ 4.22). The lowest ranked important factors were: hospitality
school administrators’ and faculty’s involvement in hospitality company retreats and
training programs (mean ¼ 2.96), and visits to hospitality company headquarters by
hospitality school administrators and faculty (mean ¼ 3.14).
Discussion
This study identified factors that were considered to be important for the success of
career and placement services provided by hospitality programs, based on company
recruiters’ and human resources managers’ perceptions. Industry professionals attached
importance to all of the factors examined in this study, but they seemed to place more
emphasis on external factors such as the quality of the students and the quality of the
hospitality school than on internal factors such as the quality of the career and
placement services. The top five most important factors (mean ranging from 4.43-4.22)
Measurement items Mean SD
Career service activities by hospitality dean or director 3.53 1.102
Relationship hospitality dean or director has with company recruiting
representatives 3.92 0.935
Quality of services offered by university career service office 4.02 0.860
Quality of career service office facilities and interview rooms 3.22 0.887
Hospitality school scheduling annual career days or nights 3.86 0.980
Hospitality school requiring work experience hours prior to graduation 4.43 0.806
Hospitality school offering a separate course in hospitality careers and interview
skill development 3.75 0.956
Hospitality school employing full-time career counselor or academic adviser 3.67 0.887
Hospitality school using student career or internship coordinators 3.53 0.966
Hospitality school attracting a core group of hospitality recruiting organizations 4.08 0.659
Hospitality recruiting representatives visiting classes when making on-campus
recruiting trips 3.55 1.083
Hospitality recruiting representatives establishing relationships with hospitality
school faculty members 4.04 0.880
Hospitality school arranging and scheduling evening presentations for career
previews 3.84 0.967
Quality of student preparation for full-time job/internship interviews 4.25 0.796
Quality of students’ educational curriculum and courses taught in
hospitality/tourism management 4.22 0.832
Annual placement rate of hospitality school graduating seniors 3.65 0.913
Visits to hospitality company headquarters by hospitality school administrators
and faculty 3.14 1.040
Hospitality school administrators’ and faculty’s involvement in corporate
hospitality company retreats and training programs 2.96 1.113
Hospitality school distribution of company recruiting materials and schedules 3.84 0.880
Industry experience of hospitality school faculty 4.31 0.648
Reputation of the hospitality school 4.22 0.757
Notes: N ¼ 102; Scale: 5 – essential; 4 – very important; 3 – important; 2 – somewhat important;
1 – not important
Table I.
Industry professional’s
perception of importance
of various aspects of
career services provided
by hospitality schools
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313
were related to how students prepare themselves for the real world (via internship), level
of faculty’s industry experience, the quality of students’ preparation for interviews for
full-time jobs and internships, reputation of the hospitality program, and the quality of
students’ educational curriculum and courses taught in hospitality/tourism
management. As for the quality of services (mean importance ¼ 4.02) and facilities
(mean importance ¼ 3.22) provided by the career services, they were only moderately
ranked by the industry professionals in importance for the success of the career and
placement services. Interestingly, industry professionals did not rank high in priority
hospitality school’s efforts/activities in maintaining a relationship with the recruiting
companies. The two lowest ranked factors in importance for the success of the
hospitality career services were related to hospitality school administrators’ and
faculty’s involvement in visiting the hospitality companies (mean importance ¼ 3.14)
and/or participating in company retreats (mean importance ¼ 2.96). However, a close
contact between the academia and the industry may lead to a more intimate knowledge
of the industry and allow curriculum adjustments as a proactive move to prepare
students for their job quest. These findings are of great importance in helping
hospitality schools to allocate their limited resources. Findings clearly suggest that
hospitality schools need to invest more resources on improving the quality of their
programs and students in order for their career and placement services to be competitive
and successful.
Internship requirement was considered by industry professionals as the most
important factor for the success of career and placement services. Internships provide
opportunities for students to practice what they have learnt in the classroom, gain a
better understanding of the industry and its requirements, evaluate different career
choices and secure invaluable hands-on job skills. Internships greatly contribute to the
development of students’ management competencies (Tas, 1988). Internship
opportunities also provide benefits to the industry. Hiring interns enables
hospitality companies to gain access to a pool of potential workers. During the
internship, companies can screen these potential employees without making long-term
commitments. Internships also enable companies to have direct involvement in
training the industry’s future managers ( Ju et al., 1998; Pauze et al., 1989; Petrillose and
Montgomery, 1998; Walo, 2001). Internships strengthen the relationship between
companies and educational institutions, which may increase collaborative research
opportunities, and raise institution profile and company profile among students.
Establishing long-term relationships between the industry and the educational
institution may benefit both parties by optimizing employment opportunities for future
graduates (Bell and Schmidt, 1996; Walo, 1999, 2001).
This finding affirmed the belief that many hospitality programs hold regarding the
importance of requesting internship/work experience from their students. Many
hospitality programs incorporate internship/work experience in their curriculum and
require their students to fulfill certain hours of hospitality related internship/work
experience prior to graduation. This practice “forces” students to start early in getting
involved in the work world, gradually build up their resume and their knowledge,
skills, and abilities (KSA), and eventually be ready for the real world challenges when
it is time to graduate. Educational institutions and their career services not only need to
be aggressive in helping their students secure quality internships but they also need to
keep an eye on their students’ internships and ensure that the experiential learning
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opportunities for the students are fully realized so that “ideally” qualified candidates
are produced for recruiting companies. If resources allow, an internship coordinator
could be on staff, whose responsibilities should include helping students obtain
internships, as well as keeping track of their progress during their internship by
e-mail/phone correspondences and/or on-site visits, as well as conducting internship
experiences evaluations after students complete their internship.
The second most important factor identified for the success of career services was
the industry experience of hospitality school faculty. It is true that educators with
extensive industry experiences are likely to provide a better learning experience for
students as compared to those without. Faculty with industry experience can help
students relate the textbook to the real world. They can readily draw real life examples
from their arsenal of experience to explain complex theories and principles. Therefore,
hospitality departments, programs and schools should encourage their faculty, with or
without prior industry experience, to participate in faculty externships provided by
hospitality companies to gain first-hand experience in current business practices.
Faculty externships connect academic professors to the industry, helping them gain
industry experience or update their knowledge about the industry, which will benefit
their teaching and their students in the short and long run. The educational goal of
such an externship is to increase the faculty’s ability to relate theory to practice and
bring knowledge from the business world (e.g. problem solving methods, practical
applications of theory, and leadership concepts) into the classroom. Faculty
externships may also result in improved relationships between the industry and the
institutions. Most of the faculty externships require the professors to write a report
about their experience with the company, identify problem areas and recommend
possible solutions. Here in a sense faculty externs also act as consultants for the
company, using their fresh eyes and academic expertise to help improve the company.
Another strategy to expose students to people with extensive industry experience
might be to invite industry professionals to classes as guest speakers. Industry
speakers share with students their knowledge and expertise in the industry, bring to
the class the latest industry trends and news, and sometimes may even recruit from the
class. Being well connected in the industry, career placement officers can be a major
resource in securing potential industry experts as guest speakers and identifying
possible externship opportunities for the faculty.
This finding also has important implications for hospitality programs that offer
PhD degrees. It is suggested that extensive previous work experience be one of the
criteria used by PhD programs to decide whether an applicant should be accepted into
a program. The work experience requirement for admittance to a PhD program is
likely to give the PhD students a competitive edge when they start looking for a
teaching job, because hospitality programs would prefer hiring faculty with industry
experience so that their students may receive enhanced learning experience.
The third most important factor for the success of the career services was found to
be the quality of student preparation for interviews for full-time jobs and internships.
The interview process for full-time jobs or internships can be very stressful. Preparing
students for the interviews is likely to reduce this stress and enable them to perform
better in the interview, which may result in better offers. How students perform during
the interview may also influence the perception and reputation of the program. If most
students can cope well with the interview process, the perception of the institution is
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likely to improve in the minds of the recruiters and the interviewing companies. In the
long-term, this may improve the industry-wide reputation of the institution. Some
hospitality schools include in their curriculum a course that is designed to help
students develop and improve their job searching, resume writing and interviewing
skills. This benefits students tremendously once they start their job quest. Most career
and placement services also provide various services to help students prepare for the
job interview, such as resume critique, mock interview, and professional development
workshops on topics like effective presentation and networking skills, professional
dress, and business etiquette; but they need to heavily promote these services among
students and strongly encourage students to take advantage of these services.
Two factors that were found to be equally important for the success of the career
and placement services were the reputation of the program and the quality of students’
educational curriculum and courses taught in hospitality/tourism management.
These two factors are obviously interrelated because quality of curriculum impacts
reputation of the program. Hospitality schools should consult with industry
professionals and develop a curriculum that meets the constantly changing needs of
the industry. Feedbacks from industry representatives regarding what features are
most desirable in an “ideal” candidate can alert a program to modify and adjust its
courses and curriculum. Only with an up-to-date and industry focused curriculum, can
schools produce quality graduates with necessary KSA to be successful in the
industry. These successful graduates in turn enhance the schools’ reputation. If the
industry believes that the quality of education at certain schools is higher than that of
others, they are also more likely to hire future managers from those “better” schools.
This forms a “benevolent” circle for the schools and their graduates. By contrast,
graduates of a program that’s relatively “unknown” may have a harder time in their
employment quest.
Like any other study, this study is not free from limitations. The present study was
limited to soliciting participation from only industry professionals contained in a
database of a hospitality school in the Northwestern USA; therefore, the results cannot
be generalized beyond this population. In addition, the response rate in this study
(25.5 percent) was relatively low, which might lead to non response bias introduced due
to the under-representation of those non-respondents in the population. Other
limitations include the fact that there may be biases in the answers provided from some
of the participants, and the reliability of the questions that form the questionnaire.
Conclusions
The study examined factors that are crucial for the success of career and placement
services programs provided by hospitality schools, using data collected from
hospitality recruiters and human resource managers. Findings of the study indicated
that success of these programs mainly relied on internship requirements, mentoring
and preparing students for the interview process, reputation and quality of the
hospitality program, industry experience of hospitality school faculty, and quality of
students’ educational curriculum and courses taught in hospitality/tourism
management. However, since the placement office/career services have little or no
influence in the industry experience of hospitality school faculty, the quality of
students’ educational curriculum and courses taught in hospitality/tourism
management, they should focus on facilitating and improving students’ internship
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experience, and mentoring and preparing students for the interview process. On the
other hand, since faculty’s industry experience and education curriculum play
important roles in the success of the career services, it is up to the academia and the
industry to work together to develop strategies to enrich faculty’s industry experience
and enhance the quality of educational curriculum. One way to achieve this may
be through developing more faculty externships. Since career placement officers have
the contacts in the industry, they can help identify/provide such experiential learning
opportunities for the faculty. Faculty externships could deepen the mutual
appreciation and bring about closer ties between the academia and the industry.
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Jameson, S.M. and Holden, R. (2000), “Graduateness – who cares? Graduate identity in small
hospitality firms”, Education and Training, Vol. 42 Nos 4/5, pp. 264-71.
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Appendix 1. Cover letter
Excellence in career services:
Understanding hospitality companies perceptions and expectations of career service provided by
hospitality school
This survey is being conducted to obtain hospitality company recruiters/human resources
managers/representatives opinions about how important various aspects of career services
provided by hospitality schools. Your assistance in completing this survey is crucial. The
survey will not take more than 5 minutes of your time.
Why participate?
You are one of the 400 possible respondents representing the entire hospitality industry
recruiters/human resources managers/representative population hiring hospitality program
graduates. Our goal is to use this information to improve career services provided by hospitality
schools. This is an excellent opportunity to voice your opinions.
Your personal opinion is important
In order to answer these questions, you do not have to be an “expert”. We are confident that
everyone included in the sample will be able to take part, not just those with strong views or
particular viewpoints. Please remember that there are no right or wrong responses to the
questions and that your honest and thoughtful answers are appreciated. Your participation is
entirely voluntary. The information you provide will be kept confidential final results of the
research will not reveal the respondent’s identity because the responses from all study
participants will be added together in the final analysis.
Please contact us if you have any questions regarding this study at: dgursoy@wsu.edu or
Tel.: (509) 335-7945.
Thank you for your participation
How to help
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319
Appendix 2. Online survey instrument
Understanding hospitality companies perceptions and expectations of career service provided by
hospitality schools
This survey is being conducted to obtain hospitality company recruiters/human resources
managers/representatives opinions about how important various aspects of career services
provided by hospitality schools. Your assistance in completing this survey is crucial. The survey
will not take more than three minutes of your time.
The following items are about how important
various aspects of career services provided by
hospitality schools. Please read each item
carefully and indicate how important/not
important each item is for the success of career
services provided by a hospitality school by
marking the appropriate response category.
Not
important
Somewhat
important Important
Very
Important Essential
1. Importance attached to Career Service
activities by Hospitality Dean or Director
2. Importance of relationship Hospitality Dean
or Director has with company recruiting.
representatives
3. Importance of quality of services offered by
University Career Service Office.
4. Importance of quality of Career Service
Office Facilities and Interview Rooms.
5. Importance of Hospitality School
scheduling annual Career Days or Nights.
6. Importance of Hospitality School requiring
work experience hours prior to graduation.
7. Importance of Hospitality School offering a
separate course in hospitality careers and
interview skill development.
8. Importance of Hospitality School
employing full-time Career Counselor or
academic adviser.
9. Importance of Hospitality School using
student career or internship coordinators.
10. Importance of Hospitality School attracting
a core group of hospitality recruiting
organizations.
11. Importance of hospitality recruiting
representatives visiting classes when
making on-campus recruiting trips.
(continued)
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22. What is the name of the company you work for?
23. What is your current position?
24. How long have you worked for this company? (years and months)?
12. Importance of hospitality recruiting
representatives establishing relationships
with hospitality school faculty members.
13. Importance of Hospitality School arranging
and scheduling evening presentations for
Career Previews.
14. Importance of quality of student preparation
for full-time job interviews and internships.
15. Importance of quality of student’s
educational curriculum and courses taught
in hospitality/tourism management.
16. Importance of annual placement rate of
hospitality school graduating seniors.
17. Importance of visits to hospitality company
headquarters by hospitality school
administrators and faculty.
18. Importance of Hospitality School
administrators and faculty involvement in
corporate hospitality company retreats and
training programs.
19. Importance of Hospitality School
distribution of company recruiting materials
and schedules.
20. Importance of industry experience of
hospitality school faculty.
21. Importance of the reputation of Hospitality
School.
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321
Corresponding author
Christina G. Chi can be contacted at: cgengqi@wsu.edu
25. Please enter your age in the space provided
Age:
26. Sex: (select only one)
Male Female
27. Race: (select only one)
American Indian/Alaskan Native
African-American/Black
Asian/Pacific Islander
White
Multi-racial (please specify)
Other (please specify)
Hispanic Origin
Hispanic Origin
Not of Hispanic Origin
28. What is your highest level of education?
Some High School
High School Graduate
Some College
College Graduate
Some Graduate School
Graduate Degree
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
secure better jobs?
An industry perspective
Christina G. Chi and Dogan Gursoy
School of Hospitality Business Management, Washington State University,
Pullman, Washington, USA
Abstract
Purpose – Many hospitality programs have developed their own career and placement services to
assist students in job searching efforts. The purpose of this paper is to identify factors that are
important for the success of career and placement services offered by hospitality programs, from the
industry’s perspectives.
Design/methodology/approach – Data were collected through an online survey from hospitality
recruiters and human resources managers. Descriptive statistics were applied for the data analysis.
Findings – Internship requirement was found to be the most important factor for the success of
career services, followed by faculty industry experience and quality of student preparation
for job/internship interviews. These were followed by reputation of the program and quality of
educational curriculum and courses taught.
Originality/value – The important implications drawn in this paper could assist hospitality schools
to allocate limited resources to help create excellent career and placement services.
Keywords Recruitment, Career guidance, Human resourcing, Hospitality management, Graduates
Paper type Research paper
Introduction
Hospitality management programs, students and faculty are always seeking ways to
improve job placement rate for their graduates. Owing to a sharp increase in schools
that offer hospitality degrees over the past quarter century, and a more complex and
sophisticated hospitality industry looking for high-quality management talent, the bar
has been raised in terms of qualification of the hospitality graduates (Stoller, 2008). In
today’s environment, achieving good academic performance is hardly enough to find a
good job after graduation. In order to be competitive in the job market, hospitality
students have to adopt aggressive approaches, such as building hospitality-related
internship experiences, taking more course work, developing networking skills, and
participating in extracurricular activities like hospitality student clubs/societies,
fund-raising initiatives, and community involvement. Acquiring and developing more
skills and abilities help students in job searching endeavors. Some graduates are likely
to find jobs through the recruitment efforts of the industry. It is true that a small
percentage of hospitality companies heavily invest in college graduate recruitment,
and training and development (Doherty et al., 2001). However, studies suggest that
relying on industry’s recruitment efforts as the sole placement tool is problematic
( Jenkins, 2001; Jameson and Holden, 2000), due to the fact that while some employers
invest in graduate recruitment and foundation training, others expect graduates to take
the initiative in job searching and start adding value to the companies from day one by
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0959-6119.htm
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Received 24 September 2007
Revised 7 January 2008,
18 March 2008
Accepted 9 June 2008
International Journal of
Contemporary Hospitality
Management
Vol. 21 No. 3, 2009
pp. 308-322
q Emerald Group Publishing Limited
0959-6119
DOI 10.1108/09596110910948314
performing the expected duties and responsibilities of the position they are hired for
with minimal or no additional training (Doherty et al., 1997).
As the preceding discussion suggests, students are likely to struggle in their quest
for placement after graduation and it is problematic to rely only on industry efforts for
placement. As a result, many hospitality programs have developed their own career
and placement services to assist their students in job-searching efforts. The quality
level of these career services determines how well the graduates are placed in the job
market. Thus, it is important to explore factors that are likely to influence the success
of the career services provided by hospitality programs.
Literature review
One of the general assumptions most hospitality schools subscribe to is that if you
have a relevant, up-to-date and industry-driven curriculum, graduates are likely to
possess the knowledge and skill sets required for a successful career in the hospitality
industry. However, as suggested by several studies, one of the biggest challenges
hospitality programs face today is determining clear objectives for the curriculum that
meets the constantly changing needs of the industry (Dopson and Tas, 2004; Gursoy
and Swanger, 2004, 2005; Okeiyi et al., 1994). Therefore, investigation of the curriculum
of hospitality and tourism programs has been a growing area of research in recent
years. Many studies have investigated the components of hospitality curriculum and
the need to integrate operational and managerial skills for career success (Dopson and
Tas, 2004; Baum, 1991; Burgidge, 1994; Christou, 2002; Dopson and Nelson, 2003;
Knutson and Patton, 1992; Li and Kivela, 1988; Tas, 1988; Tas et al., 1996; Kay and
Russette, 2000; Okeiyi et al., 1994). While some researchers suggested that operational
issues such as working knowledge of product/service were important for success
(Kay and Russette, 2000), others suggested that managerial and behavioral issues such
as interpersonal relations and managerial skills were more important for success in the
industry (Okeiyi et al., 1994). Dopson and Tas (2004) indicated that these two
approaches to curriculum should be integrated in order to prepare students for a
successful career in the industry; the curriculum of hospitality schools should not only
provide the impetus for students to learn necessary skills to operate a business but also
enable them to gain a substantial knowledge on how to manage their staff. Other
researchers argue that in developing a curriculum, educators need to consider three
major components of hospitality education: substantive knowledge, skills, and values
(Dopson and Tas, 2004; Gursoy and Swanger, 2004, 2005).
Several studies (Baum, 1991; Burgidge, 1994; Knutson and Patton, 1992; Li and
Kivela, 1988; Tas, 1988; Nelson and Dopson, 2001; Dopson and Nelson, 2003) have
examined the importance of various skills and abilities necessary for graduates’ career
success. These studies contained a very general list of skills that were identified by
surveying hotel managers and/or students. For example, Knutson and Patton (1992)
identified 15 managerial skills that students believed they should possess to be
successful in the industry by surveying students at Michigan State University.
In another study, Li and Kivela (1988) examined the differences between hotel
managers’ and students’ perceptions of skill importance relative to success in the
hospitality field, by surveying both hotel managers and students using the 34 hotel
management skills identified from the literature. These authors reported that applying
marketing management methods and developing job specifications were perceived as
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more important by managers than by students. They also reported three differences
between managers and students in their perception of graduates’ competencies.
Managers rated higher in importance with regards to using food service techniques
and applying second language skills, while students ranked higher in applying group
dynamics methods.
While previous studies examined the skills, a graduate needs to be successful in the
hospitality industry (Baum, 1991; Burgidge, 1994; Christou, 2002; Dopson and Nelson,
2003; Knutson and Patton, 1992; Li and Kivela, 1988; Tas, 1988; Tas et al., 1996, 2000),
a small number of studies examined the approaches hospitality programs used to place
their students before or after graduation. In order to assist students and graduates in
their quest for job in today’s rapidly changing and highly competitive job market,
a large number of hospitality programs have developed their own career and
placement services. The purposes of these career and placement services are to provide
support to students in all phases of their job search and professional development by
helping them gain hospitality-related internship experiences, providing extracurricular
activities, and helping them develop networking skills. Since the success of these
career services heavily depends on the type of relationships they have with the
hospitality companies and how they are perceived by those companies, it is crucial to
understand how hospitality company recruiters and human resources personnel
perceived the importance of various aspects of career services provided by hospitality
schools.
Feiertag (1998) reported that since the late 1990s corporations have been
implementing extensive and competitive on-campus recruiting programs to counter a
growing skill shortage and to attract qualified candidates. Some companies have even
developed extensive corporate recruitment programs that have a year-round presence
on campuses. In addition, companies are holding more elaborate career fairs to secure
graduating talents, as well as outsourcing on-campus recruiting to professional
consultants to ensure that their organizations can effectively compete to attract top
graduates. Nationwide, most medium to large college placement offices host an
average of 348 companies annually, and 56 percent of graduating seniors register with
college/university placement offices (Feiertag, 1998).
The hospitality industry understands that college degree in hospitality
administration is a valuable degree, but in the past many hospitality graduates
found themselves wound up in jobs that do not require a hospitality degree and do not
pay much as well. Hospitality recruiters are now looking for college graduates that
possess good industry skills upon graduation (Carey and Franklin, 1991). Professional
as well as academic qualifications have become progressively more important in the
hospitality industry and at the same time operational experience is viewed as less
important than specific or generic business management skills for hospitality
graduates (Farkas, 1993; Feiertag, 1998). Several studies suggested that in the near
future generic business credentials are likely to become more important (Gursoy and
Swanger, 2004, 2005; Farkas, 1993; Purcell, 1993). They are likely to become a
prerequisite to mid and senior-level hospitality management jobs. With the constant
restructuring of hospitality firms, new jobs and added responsibilities have emerged
from the continuous organizational change. In order to determine needed competencies
in hospitality graduates by industry, one approach is to address the needs on a
continuous basis from the perspective of hospitality recruiters and human resources
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directors, and to identify which aspects of career and placement services provided by
hospitality schools are important for corporate recruiters and human resources
directors.
Earlier studies suggested that service sector employers used to rely on unstructured
application forms and interviews often conducted by untrained interviewers
(Poppleton, 1989), and the general consensus was that selection in the hotel industry
tended to be informal, simplistic, and reactive (Kelliher and Johnson, 1987; Croney,
1988; Price, 1994). However, recent trends indicate that this is rapidly changing.
Recruitment is becoming more complex and sophisticated. These rapid changes in
recruitment style make identifying and understanding of the factors that will create
excellence in career services critical for hospitality programs and their students and
graduates.
Methodology
Survey instrument
The purpose of this study is to identify factors that were important for the success of
career and placement services offered by hospitality programs. An online survey
instrument was developed to gather data on human resources managers’ and college
recruiters’ perceptions. A questionnaire was created to systematically measure factors
that were perceived as important for the success of career and placement services,
based on the literature review and input from human resources managers and
hospitality educators. The instrument was then reviewed by a group of hospitality
educators and college recruiters. They were asked to provide feedback regarding the
layout, wording, and ease of understanding of the measurement items. The feedback
was then taken into account in the revision of the questionnaire. Based on their
feedback, the instrument was revised and finalized (please see Appendices 1 and 2 for
the final version of the cover letter and the online survey instrument). Both college
recruiters and hospitality educators were consulted throughout the survey instrument
development process to make sure that the final instrument had both content and face
validity. The content validity of survey instruments is assessed by overview of the
items by trained individuals and/or by the individuals from the target population.
The individuals make their judgments about the relevance of the items and the clarity
of their formulation. On the other hand, the face validity of survey instruments is
assessed by cursory review of the items (questions) by untrained individuals.
The individuals make their judgments on whether the items are relevant.
The final version of the survey instrument consisted of two parts. Part I was
designed to collect information on the 21 factors that captured various aspects of
hospitality career and placement services; respondents were asked to rate importance
of each of the 21 factors according to a five-point Likert scale (5 – essential, 4 – very
important, 3 – important, 2 – somewhat important, 1 – not important). Part II of the
questionnaire included demographic questions.
Data collection
A self-administered online survey questionnaire was used to collect data, using the
Dillman (2000) Tailored Design Method. The web site link for the online questionnaire
was e-mailed to all 400 hospitality recruiters and human resource personnel contained
in a data base that’s maintained by a large hospitality school located in the
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Northwestern USA. The subjects were informed of the purpose of the study and that
their participation was voluntary. In order to enhance the response rate, e-mail was
personalized to address to each respondent retrieved from the database. A week later,
a reminder e-mail was sent to those who had not responded.
Data analysis
Descriptive statistics (means and standard deviations) were used to report the rank
order importance of the 21 subject matter variables. Frequencies were employed to
summarize the demographic profile of the respondents.
Results
Of the 400 surveys sent out, 102 usable ones were returned resulting in a response rate
of 25.5 percent. Considering the survey population (busy business professionals) and
the data collection method (on-line survey), this was considered to be an acceptable
response rate by the researchers.
Respondents’ demographic profile
Demographic characteristics such as gender, education level, ethnicity, present
position and name of company were included in this study in an attempt to provide a
descriptive profile of the survey respondents.
Gender. Of the 100 individuals who provided gender information, 49 (49 percent)
were male and 51 (51 percent) were female.
Present position/name of company. A total of 67 different companies were
represented in the study. Respondents were asked to list their present position. The
vast majority (98 percent) of the respondents were working for the human resources
department of hospitality companies. A couple of respondents worked for professional
recruitment/employment agencies.
Education level. Of the 100 individuals who answered this question, 62 of them
indicated they had a bachelor’s degree (62 percent); 11 respondents had a graduate
degree (11 percent); seven had some graduate level work (7 percent); 18 had some
college work or a technical degree (18 percent); and two respondents indicated they
were high-school graduates (2 percent).
Ethnicity. The majority of the respondents, 87 (87 percent), were Caucasian/White;
five (5 percent) were Asian-American/Pacific Islander; four (4 percent) were
American-Indian/Alaska Native; three (3 percent) were Hispanic/Latino; and one
(1 percent) was Black/African-American.
Importance of factors for success of career and placement services
Table I presents the descriptive statistics (means and standard deviations) for the
factors that were deemed to be important for the success of career and placement
services offered by hospitality programs. The results showed that industry
professionals believed all of the factors examined in this study were important to
the success of career and placement services (mean ranging from 2.96 to 4.43 on a
five-point Likert scale). They agreed that internship requirements (hospitality school
requiring work experience hours prior to graduation) were the most important factor
(mean ¼ 4.43), followed by faculty’s industry experience (mean ¼ 4.31) and
the quality of students’ preparation for job/internship interviews (mean ¼ 4.25).
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These were followed by reputation of the hospitality program (mean ¼ 4.22) and the
quality of students’ educational curriculum and courses taught in hospitality/tourism
management (mean ¼ 4.22). The lowest ranked important factors were: hospitality
school administrators’ and faculty’s involvement in hospitality company retreats and
training programs (mean ¼ 2.96), and visits to hospitality company headquarters by
hospitality school administrators and faculty (mean ¼ 3.14).
Discussion
This study identified factors that were considered to be important for the success of
career and placement services provided by hospitality programs, based on company
recruiters’ and human resources managers’ perceptions. Industry professionals attached
importance to all of the factors examined in this study, but they seemed to place more
emphasis on external factors such as the quality of the students and the quality of the
hospitality school than on internal factors such as the quality of the career and
placement services. The top five most important factors (mean ranging from 4.43-4.22)
Measurement items Mean SD
Career service activities by hospitality dean or director 3.53 1.102
Relationship hospitality dean or director has with company recruiting
representatives 3.92 0.935
Quality of services offered by university career service office 4.02 0.860
Quality of career service office facilities and interview rooms 3.22 0.887
Hospitality school scheduling annual career days or nights 3.86 0.980
Hospitality school requiring work experience hours prior to graduation 4.43 0.806
Hospitality school offering a separate course in hospitality careers and interview
skill development 3.75 0.956
Hospitality school employing full-time career counselor or academic adviser 3.67 0.887
Hospitality school using student career or internship coordinators 3.53 0.966
Hospitality school attracting a core group of hospitality recruiting organizations 4.08 0.659
Hospitality recruiting representatives visiting classes when making on-campus
recruiting trips 3.55 1.083
Hospitality recruiting representatives establishing relationships with hospitality
school faculty members 4.04 0.880
Hospitality school arranging and scheduling evening presentations for career
previews 3.84 0.967
Quality of student preparation for full-time job/internship interviews 4.25 0.796
Quality of students’ educational curriculum and courses taught in
hospitality/tourism management 4.22 0.832
Annual placement rate of hospitality school graduating seniors 3.65 0.913
Visits to hospitality company headquarters by hospitality school administrators
and faculty 3.14 1.040
Hospitality school administrators’ and faculty’s involvement in corporate
hospitality company retreats and training programs 2.96 1.113
Hospitality school distribution of company recruiting materials and schedules 3.84 0.880
Industry experience of hospitality school faculty 4.31 0.648
Reputation of the hospitality school 4.22 0.757
Notes: N ¼ 102; Scale: 5 – essential; 4 – very important; 3 – important; 2 – somewhat important;
1 – not important
Table I.
Industry professional’s
perception of importance
of various aspects of
career services provided
by hospitality schools
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were related to how students prepare themselves for the real world (via internship), level
of faculty’s industry experience, the quality of students’ preparation for interviews for
full-time jobs and internships, reputation of the hospitality program, and the quality of
students’ educational curriculum and courses taught in hospitality/tourism
management. As for the quality of services (mean importance ¼ 4.02) and facilities
(mean importance ¼ 3.22) provided by the career services, they were only moderately
ranked by the industry professionals in importance for the success of the career and
placement services. Interestingly, industry professionals did not rank high in priority
hospitality school’s efforts/activities in maintaining a relationship with the recruiting
companies. The two lowest ranked factors in importance for the success of the
hospitality career services were related to hospitality school administrators’ and
faculty’s involvement in visiting the hospitality companies (mean importance ¼ 3.14)
and/or participating in company retreats (mean importance ¼ 2.96). However, a close
contact between the academia and the industry may lead to a more intimate knowledge
of the industry and allow curriculum adjustments as a proactive move to prepare
students for their job quest. These findings are of great importance in helping
hospitality schools to allocate their limited resources. Findings clearly suggest that
hospitality schools need to invest more resources on improving the quality of their
programs and students in order for their career and placement services to be competitive
and successful.
Internship requirement was considered by industry professionals as the most
important factor for the success of career and placement services. Internships provide
opportunities for students to practice what they have learnt in the classroom, gain a
better understanding of the industry and its requirements, evaluate different career
choices and secure invaluable hands-on job skills. Internships greatly contribute to the
development of students’ management competencies (Tas, 1988). Internship
opportunities also provide benefits to the industry. Hiring interns enables
hospitality companies to gain access to a pool of potential workers. During the
internship, companies can screen these potential employees without making long-term
commitments. Internships also enable companies to have direct involvement in
training the industry’s future managers ( Ju et al., 1998; Pauze et al., 1989; Petrillose and
Montgomery, 1998; Walo, 2001). Internships strengthen the relationship between
companies and educational institutions, which may increase collaborative research
opportunities, and raise institution profile and company profile among students.
Establishing long-term relationships between the industry and the educational
institution may benefit both parties by optimizing employment opportunities for future
graduates (Bell and Schmidt, 1996; Walo, 1999, 2001).
This finding affirmed the belief that many hospitality programs hold regarding the
importance of requesting internship/work experience from their students. Many
hospitality programs incorporate internship/work experience in their curriculum and
require their students to fulfill certain hours of hospitality related internship/work
experience prior to graduation. This practice “forces” students to start early in getting
involved in the work world, gradually build up their resume and their knowledge,
skills, and abilities (KSA), and eventually be ready for the real world challenges when
it is time to graduate. Educational institutions and their career services not only need to
be aggressive in helping their students secure quality internships but they also need to
keep an eye on their students’ internships and ensure that the experiential learning
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opportunities for the students are fully realized so that “ideally” qualified candidates
are produced for recruiting companies. If resources allow, an internship coordinator
could be on staff, whose responsibilities should include helping students obtain
internships, as well as keeping track of their progress during their internship by
e-mail/phone correspondences and/or on-site visits, as well as conducting internship
experiences evaluations after students complete their internship.
The second most important factor identified for the success of career services was
the industry experience of hospitality school faculty. It is true that educators with
extensive industry experiences are likely to provide a better learning experience for
students as compared to those without. Faculty with industry experience can help
students relate the textbook to the real world. They can readily draw real life examples
from their arsenal of experience to explain complex theories and principles. Therefore,
hospitality departments, programs and schools should encourage their faculty, with or
without prior industry experience, to participate in faculty externships provided by
hospitality companies to gain first-hand experience in current business practices.
Faculty externships connect academic professors to the industry, helping them gain
industry experience or update their knowledge about the industry, which will benefit
their teaching and their students in the short and long run. The educational goal of
such an externship is to increase the faculty’s ability to relate theory to practice and
bring knowledge from the business world (e.g. problem solving methods, practical
applications of theory, and leadership concepts) into the classroom. Faculty
externships may also result in improved relationships between the industry and the
institutions. Most of the faculty externships require the professors to write a report
about their experience with the company, identify problem areas and recommend
possible solutions. Here in a sense faculty externs also act as consultants for the
company, using their fresh eyes and academic expertise to help improve the company.
Another strategy to expose students to people with extensive industry experience
might be to invite industry professionals to classes as guest speakers. Industry
speakers share with students their knowledge and expertise in the industry, bring to
the class the latest industry trends and news, and sometimes may even recruit from the
class. Being well connected in the industry, career placement officers can be a major
resource in securing potential industry experts as guest speakers and identifying
possible externship opportunities for the faculty.
This finding also has important implications for hospitality programs that offer
PhD degrees. It is suggested that extensive previous work experience be one of the
criteria used by PhD programs to decide whether an applicant should be accepted into
a program. The work experience requirement for admittance to a PhD program is
likely to give the PhD students a competitive edge when they start looking for a
teaching job, because hospitality programs would prefer hiring faculty with industry
experience so that their students may receive enhanced learning experience.
The third most important factor for the success of the career services was found to
be the quality of student preparation for interviews for full-time jobs and internships.
The interview process for full-time jobs or internships can be very stressful. Preparing
students for the interviews is likely to reduce this stress and enable them to perform
better in the interview, which may result in better offers. How students perform during
the interview may also influence the perception and reputation of the program. If most
students can cope well with the interview process, the perception of the institution is
How to help
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likely to improve in the minds of the recruiters and the interviewing companies. In the
long-term, this may improve the industry-wide reputation of the institution. Some
hospitality schools include in their curriculum a course that is designed to help
students develop and improve their job searching, resume writing and interviewing
skills. This benefits students tremendously once they start their job quest. Most career
and placement services also provide various services to help students prepare for the
job interview, such as resume critique, mock interview, and professional development
workshops on topics like effective presentation and networking skills, professional
dress, and business etiquette; but they need to heavily promote these services among
students and strongly encourage students to take advantage of these services.
Two factors that were found to be equally important for the success of the career
and placement services were the reputation of the program and the quality of students’
educational curriculum and courses taught in hospitality/tourism management.
These two factors are obviously interrelated because quality of curriculum impacts
reputation of the program. Hospitality schools should consult with industry
professionals and develop a curriculum that meets the constantly changing needs of
the industry. Feedbacks from industry representatives regarding what features are
most desirable in an “ideal” candidate can alert a program to modify and adjust its
courses and curriculum. Only with an up-to-date and industry focused curriculum, can
schools produce quality graduates with necessary KSA to be successful in the
industry. These successful graduates in turn enhance the schools’ reputation. If the
industry believes that the quality of education at certain schools is higher than that of
others, they are also more likely to hire future managers from those “better” schools.
This forms a “benevolent” circle for the schools and their graduates. By contrast,
graduates of a program that’s relatively “unknown” may have a harder time in their
employment quest.
Like any other study, this study is not free from limitations. The present study was
limited to soliciting participation from only industry professionals contained in a
database of a hospitality school in the Northwestern USA; therefore, the results cannot
be generalized beyond this population. In addition, the response rate in this study
(25.5 percent) was relatively low, which might lead to non response bias introduced due
to the under-representation of those non-respondents in the population. Other
limitations include the fact that there may be biases in the answers provided from some
of the participants, and the reliability of the questions that form the questionnaire.
Conclusions
The study examined factors that are crucial for the success of career and placement
services programs provided by hospitality schools, using data collected from
hospitality recruiters and human resource managers. Findings of the study indicated
that success of these programs mainly relied on internship requirements, mentoring
and preparing students for the interview process, reputation and quality of the
hospitality program, industry experience of hospitality school faculty, and quality of
students’ educational curriculum and courses taught in hospitality/tourism
management. However, since the placement office/career services have little or no
influence in the industry experience of hospitality school faculty, the quality of
students’ educational curriculum and courses taught in hospitality/tourism
management, they should focus on facilitating and improving students’ internship
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experience, and mentoring and preparing students for the interview process. On the
other hand, since faculty’s industry experience and education curriculum play
important roles in the success of the career services, it is up to the academia and the
industry to work together to develop strategies to enrich faculty’s industry experience
and enhance the quality of educational curriculum. One way to achieve this may
be through developing more faculty externships. Since career placement officers have
the contacts in the industry, they can help identify/provide such experiential learning
opportunities for the faculty. Faculty externships could deepen the mutual
appreciation and bring about closer ties between the academia and the industry.
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Appendix 1. Cover letter
Excellence in career services:
Understanding hospitality companies perceptions and expectations of career service provided by
hospitality school
This survey is being conducted to obtain hospitality company recruiters/human resources
managers/representatives opinions about how important various aspects of career services
provided by hospitality schools. Your assistance in completing this survey is crucial. The
survey will not take more than 5 minutes of your time.
Why participate?
You are one of the 400 possible respondents representing the entire hospitality industry
recruiters/human resources managers/representative population hiring hospitality program
graduates. Our goal is to use this information to improve career services provided by hospitality
schools. This is an excellent opportunity to voice your opinions.
Your personal opinion is important
In order to answer these questions, you do not have to be an “expert”. We are confident that
everyone included in the sample will be able to take part, not just those with strong views or
particular viewpoints. Please remember that there are no right or wrong responses to the
questions and that your honest and thoughtful answers are appreciated. Your participation is
entirely voluntary. The information you provide will be kept confidential final results of the
research will not reveal the respondent’s identity because the responses from all study
participants will be added together in the final analysis.
Please contact us if you have any questions regarding this study at: dgursoy@wsu.edu or
Tel.: (509) 335-7945.
Thank you for your participation
How to help
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319
Appendix 2. Online survey instrument
Understanding hospitality companies perceptions and expectations of career service provided by
hospitality schools
This survey is being conducted to obtain hospitality company recruiters/human resources
managers/representatives opinions about how important various aspects of career services
provided by hospitality schools. Your assistance in completing this survey is crucial. The survey
will not take more than three minutes of your time.
The following items are about how important
various aspects of career services provided by
hospitality schools. Please read each item
carefully and indicate how important/not
important each item is for the success of career
services provided by a hospitality school by
marking the appropriate response category.
Not
important
Somewhat
important Important
Very
Important Essential
1. Importance attached to Career Service
activities by Hospitality Dean or Director
2. Importance of relationship Hospitality Dean
or Director has with company recruiting.
representatives
3. Importance of quality of services offered by
University Career Service Office.
4. Importance of quality of Career Service
Office Facilities and Interview Rooms.
5. Importance of Hospitality School
scheduling annual Career Days or Nights.
6. Importance of Hospitality School requiring
work experience hours prior to graduation.
7. Importance of Hospitality School offering a
separate course in hospitality careers and
interview skill development.
8. Importance of Hospitality School
employing full-time Career Counselor or
academic adviser.
9. Importance of Hospitality School using
student career or internship coordinators.
10. Importance of Hospitality School attracting
a core group of hospitality recruiting
organizations.
11. Importance of hospitality recruiting
representatives visiting classes when
making on-campus recruiting trips.
(continued)
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22. What is the name of the company you work for?
23. What is your current position?
24. How long have you worked for this company? (years and months)?
12. Importance of hospitality recruiting
representatives establishing relationships
with hospitality school faculty members.
13. Importance of Hospitality School arranging
and scheduling evening presentations for
Career Previews.
14. Importance of quality of student preparation
for full-time job interviews and internships.
15. Importance of quality of student’s
educational curriculum and courses taught
in hospitality/tourism management.
16. Importance of annual placement rate of
hospitality school graduating seniors.
17. Importance of visits to hospitality company
headquarters by hospitality school
administrators and faculty.
18. Importance of Hospitality School
administrators and faculty involvement in
corporate hospitality company retreats and
training programs.
19. Importance of Hospitality School
distribution of company recruiting materials
and schedules.
20. Importance of industry experience of
hospitality school faculty.
21. Importance of the reputation of Hospitality
School.
How to help
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321
Corresponding author
Christina G. Chi can be contacted at: cgengqi@wsu.edu
25. Please enter your age in the space provided
Age:
26. Sex: (select only one)
Male Female
27. Race: (select only one)
American Indian/Alaskan Native
African-American/Black
Asian/Pacific Islander
White
Multi-racial (please specify)
Other (please specify)
Hispanic Origin
Hispanic Origin
Not of Hispanic Origin
28. What is your highest level of education?
Some High School
High School Graduate
Some College
College Graduate
Some Graduate School
Graduate Degree
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Drivers of freelance career success
ARJAN VAN DEN BORN1
* AND ARJEN VAN WITTELOOSTUIJN1,2,3
1
School of Economics, Utrecht University, Utrecht, The Netherlands
2
University of Antwerp, Antwerp, Belgium
3
Tilburg University, Tilburg, The Netherlands
Summary Recent evidence shows that the frequently proclaimed collapse of the traditional career model is actually
not supported by job tenure data. This paper argues that the observed stability of job tenure might be
explained by an increasing number of shamrock organizations. This organizational form has three types
of workers: core employees, professional freelancers, and routine workers. In such an organization, two
very different career models coexist. The organization largely determines the career of the core employee,
whereas the individual essentially shapes that of the professional freelancer. This paper studies extensively
the career of this second group: the professional freelancer, a growing phenomenon in many developed
countries but not yet the focus of many career studies. We develop a freelance career success model
on basis of the intelligent career framework augmented by insights from literature on entrepreneurship.
Data are from a web survey with responses from about 1600 independent professionals in the
Netherlands, in combination with 51 in-depth interviews. We provide two main contributions. First, we
report findings from the first large-scale quantitative study into freelance career success. Second, this study
enhances our understanding of the success of the modern career by building bridges between career and
entrepreneurship literatures. We conclude that the external environment in which an individual freelancer
operates is the most important factor determining career success. The study therefore suggests that more
work needs to be performed on the relationship between the environment and individual career success.
Copyright © 2012 John Wiley & Sons, Ltd.
Keywords: freelancers; self-employed; career success; portfolio workers
Introduction
Is the traditional career of the 20th century becoming rare? Since the 1980s, many authors have argued that the
responsibility for the career increasingly resides in the individual (Arnold, 2001; Raabe, Frese, & Beehr, 2006)
and transcends any employer (Arthur, Khapova, & Wilderom, 2005). But recently, Rodrigues and Guest (2010)
showed for multiple countries that the collapse of the traditional career model is not supported at all by actual job
tenure data. Does this imply that the commonly accepted view of people having increasingly multiple-organization
careers is a myth? This paper argues that the observed stability of job tenure may be caused by the emergence of a
new type of worker: the skilled independent professional. This new type of worker contracts out her or his skills to
various organizations (Barley & Kunda, 2004; Barley, Kunda, & Evans, 2002; Connelly & Gallagher, 2004;
Kirkpatrick & Hoque, 2006).
Avant la lettre, Handy (1985) coined the term “portfolio worker” for those workers who create a portfolio of work
for themselves. In current times, terms such as freelancer, independent professional, or contractor are more frequently
used. Although it is hard to estimate the exact number of these workers, it is clear that their incidence is growing since
the 1980s. According to Arum and Müller (2004, p. 1), “[s]elf-employment can no longer be dismissed as an economic
*Correspondence to: Jan van den Born, School of Economics, Utrecht University, Utrecht, The Netherlands. E-mail: j.a.vandenborn@uu.nl
Copyright © 2012 John Wiley & Sons, Ltd.
Received 07 December 2010
Revised 18 January 2012, Accepted 22 January 2012
Journal of Organizational Behavior, J. Organiz. Behav. 34, 24–46 (2013)
Published online 9 March 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.1786
Research Article
activity on the verge of withering away in response to processes of capital accumulation or in competition
with large firms.” Marler, Barringer, and Milkovic (2002) showed that these boundaryless workers can be
distinguished from traditional “temps” by their preference for temporary work in combination with their high
level of skill and experience. The claim is that these “contractors of choice” are especially likely to report
positive outcomes about job and career satisfaction (Anderson, 2008; Ajayi-Obe & Parker, 2005; Benz &
Frey, 2008; Guest, 2004; Guest & Clinton, 2006). As Blanchflower (2004, p. 21) summarized this argument,
“These self-employed work under a lot of pressure, find their work stressful and come home exhausted. However, they are especially likely to say they have control over their lives as well as being highly satisfied with
their lives.”
The increase of the number of independent knowledge workers fits nicely with a prediction of Handy
(1989), who argued that the organizations of the future will have three types of workers: (i) professional
employees representing the core competencies of the organization; (ii) professional freelancers contractually
hired on a project-by-project basis; and (iii) a contingent workforce doing routine jobs. Handy introduced
the term shamrock organization for this new mode of organizing. Growth in the density of this shamrock
type of organizations could explain why overall job tenure does not decrease, because the freelancer is
officially not an employee of the organization but is self-employed. Although these free agents hop from
one organization to the next, they never officially change their employer. In a way, they are employees turned
into entrepreneurs.
The shamrock organization model is associated with the coexistence of two career types. On the one hand, the
organization largely determines the career of the professional core. Hence, Lips-Wiersma and Hall (2007) correctly
argued that the role of the organization is not over. On the other hand, the individual largely determines the career of
the growing number of independent professionals—or freelancers. Consequently, modern career concepts, such as
the protean career model (Hall, 1976, 2002) and the intelligent career framework (Parker, Khapova, & Arthur,
2009), apply nicely to this new type of workers. This paper studies extensively the determinants of the career success
of this second group: the professional freelancer, a growing phenomenon in many developed countries but not yet
the focus of many career studies.
Building a Freelance Career Success Model
Process
Freelancers can be considered as a hybrid of employees and entrepreneurs. On the one hand, they are employees
because they are almost always hired by (large) firms to work for a period selling nothing else but their intangible
professional knowledge, which is different from other entrepreneurs and self-employed selling tangible products to
customers. On the other hand, they are entrepreneurs because they work for their own risk and reward without any
organizational guarantee or support. Any freelance career success model should therefore build on insights from
both the modern career literature and the literature on entrepreneurship. To develop a testable model, we first created
a draft career success model based on the available academic literature. This first draft model consisted of a number
of building blocks that included variables that might predict freelance career success, with five to 10 variables
for every building block. Second, we piloted this first conceptual model with experts from the field by interviewing 51 practitioners (i.e., 33 freelancers and 18 outside experts from intermediary agencies and professional
associations). On the basis of their comments, we created a final freelance career success model in our third step
by (i) adding groups of variables that were considered to be missing and by (ii) converting our variables into
operationalized measures. In the fourth step, we developed an online survey and administered it to test this final
freelance career success model.
DRIVERS OF FREELANCE CAREER SUCCESS 25
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Individual career perspective
Our first draft model was based both on the career and entrepreneurship literatures, starting from the intelligent
career framework (Parker et al., 2009) as the core. We consulted studies in the entrepreneurship literature to add
variables to our core model that may be related to entrepreneurial success. The freelance career can be seen as quite
similar to the boundaryless career. DeFillippi and Arthur (1996) defined boundaryless careers as “sequences of job
opportunities that go beyond the boundaries of single employment setting.” A freelancer is probably the archetypical
job hopper going from one project and employer to the next, never staying for very long in a single organization.
Therefore, the boundaryless career concept provides a good starting point to build a freelance career success model.
The boundaryless career concept was already developed in the 1970s at the Massachusetts Institute of Technology
(Tams & Arthur, 2010). However, the boundaryless career perspective was really developed further in the 1990s,
especially through the efforts of DeFillippi and Arthur (1994, 1996) and Arthur and Rousseau (1996). In the
mid-1990s, Arthur, Claman, and DeFillippi (1995) pointed to strong links between Quinn’s (1992) intelligent
enterprise and the boundaryless career concept. They argue that the creation of the intelligent enterprise requires
intelligent careers, which are built upon principles from the boundaryless career concept. This intelligent career
concept evolved into the intelligent career framework (Parker et al., 2009).
The intelligent career framework includes three interrelated classes of variables, referred to as “ways of knowing,”
that are argued to predict career development. The first way of knowing is knowing why, which involves career
motivation, personal meaning, identity, and personality. Knowing why is associated with an individual’s capability
to understand herself or himself, to explore different possibilities, and to adapt to constantly changing work
circumstances. The second way of knowing is knowing how, which reflects career-relevant skills and job-related
knowledge. Knowing how is closely related to established ideas on individual knowledge, skills, and abilities
(Schneider & Konz, 1989). The third way of knowing, knowing whom, involves relevant personal and business
networks. This has to do with career-related networks and contacts, including business relationships and personal
connections (Parker & Arthur, 2000). These three classes of variables are not independent; there are strong links
between these concepts, with a range of different theories explaining these linkages (Parker et al., 2009).
Despite the theoretical attractiveness and practical relevance of the intelligent career framework, much empirical
work continues to focus on career success as evaluated from an organizational perspective (e.g., in terms of
organizational position and promotion). This seems outdated because hierarchies are continuously flattening (Littler,
Wiesner, & Dunford, 2003) and because external labor markets generate an increasing influence over today’s
employment landscape (Cappelli, 1999). Arthur et al. (2005) therefore called for further rapprochement between
career theory and empirical research. As the employment landscape is changing, career research should reflect
the “new deal” that views the career actor as concerned more with individual rather than organizational goals and
which involves the kind of “meta-competencies” that allow for easier mobility between successive employers or
temporary contracts.
Entrepreneurial perspective
The intelligent career framework is originally based on organizational models of firm competencies and firm success
(DeFillipi & Arthur, 1994, p. 308). This is why the intelligent career framework offers an appropriate platform for
developing our draft freelance career success model. We cross the same bridge as DeFillipi and Arthur did and review
empirical results reported in studies of firm success in search for variables to be added to the model. Adding such
business aspects to our freelance career success model will create a better fit with freelancing challenges and dilemmas.
After all, a modern freelancer is a self-employed entrepreneur as well and not only a “boundaryless employee.” Hence,
we are especially interested in research into small entrepreneurs such as the self-employed and small businesses and not
so much in large businesses where the founder influence is limited (Boone, De Brabander, & van Witteloostuijn, 1996)
and other qualities are needed to be successful (Scott & Bruce, 1987).
26 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
In the succeeding texts, we highlight the core drivers of entrepreneurial success as identified by empirical research
into the self-employed and small businesses (see Parker, 2004, for an overview). First, the human capital of the
entrepreneurial founder has a strong positive impact on firm performance (e.g., Bosma, van Praag, Thurik, &
de Wit, 2004; Pennings, Lee, & van Witteloostuijn, 1998). Being well educated or highly experienced, for instance,
contributes to the success of entrepreneurial ventures. Second, much evidence relates to the positive effect of social
capital on firm success (e.g., Bosma et al., 2004; Chiesi, 2007; Hoanga & Antoncic, 2003; Witt, 2004). Inherently,
entrepreneurship is a process of building bridges in a network, and hence of developing and maintaining social
capital, which provides broad and early access to information, and offers control over the distribution and
interpretation of information. In the freelancing context, social capital can be expected to (i) generate a broad base
of referrals; (ii) help the freelancer identify promising opportunities; and (iii) increase the probability that the
freelancer knows how to pitch a project. Third, the study of the impact of what may be coined personal capital
on entrepreneurial success has a long tradition. Entrepreneurs are argued to “engage the energies of everyone,”
“involve many people inside and outside the organization,” “create and sustain networks of relationships,” and
“make the most of the intellectual and other resources people have to offer” while “helping those people to achieve
their goals as well” (McMillan & Gunther-McGrath, 2000, p. 3). These qualities highlight the desirability of a
specific type of personality or other personal features, such as self-insight and leadership style.
In their overview of the literature on entrepreneurial personality, Amit, Glosten, and Muller (1993) suggested
that the four personality traits most commonly associated with self-employment are as follows: (i) need for
achievement (McClelland, 1965); (ii) internal locus of control (Sexton & Bowman, 1986); (iii) above-average
risk-taking propensity (Brockhaus, 1980); and (iv) tolerance for ambiguity (Frenkel-Bruswik, 1948). Note,
however, that although research has shown that personality traits are important for entrepreneurial success,
personality traits have typically produced very weak relationships with entrepreneurial performance (Begley &
Boyd, 1987; Low & MacMillan, 1988; Parker, 2004; Stam et al., 2012). The search for a psychological
explanation for business success has led to the development of tailor-made multifaceted “entrepreneurial”
personality traits, such as Chen, Greene, & Crick’s (1998) entrepreneurial self-efficacy.
Expert piloting
After the creation of our draft freelance career success model, on the basis of the review of the literature, we piloted
this draft in interviews with practitioners. These interviews were semi-structured. They started with open-ended
questions (e.g., How would you define freelance career success? and What are the key factors that determine
freelance success?), followed by a series of partly closed and partly open questions on the overall draft model
and each variable in the model (e.g., Do you think this is an important determinant of freelance career success or
not, and why do you think so?). Our interviewees defined two major topics that they considered to be missing.
Additionally, they expressed a variety of smaller suggestions about missing variables and suggested many improvements in defining variables and operationalizing measures. One example is partner support. We included this
variable in the final model, as many interviewees convincingly argued that a freelancer could not be successful
without the proper support of her or his partner.
The first and largest deficiency in our draft model was the lack of reference to the external market. All 51
interviewees noticed that we ignored market factors in our initial model design. This is especially important because
we know that the environment is crucial for firm performance (Porter, 1980). A stylized fact in the business literature
is that organizational and industry variables dominate over individual-level variables as drivers of venture success
(e.g., McGahan & Porter, 1997; Sandberg & Hofer, 1987). In contrast, the external product or service market in
which the employer organization operates often plays a minor role in the employee career literature. In fact, career
studies generally neglect such forces. The studies that link career outcomes with characteristics of internal labor
markets, which were quite popular in the 1980s (e.g., Baron, Davis-Blake, & Bielby, 1986), are perhaps the most
relevant for our study. However, internal labor markets are very different from their external product or service
DRIVERS OF FREELANCE CAREER SUCCESS 27
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DOI: 10.1002/job
market counterparts. In recent years, the issue of context has gained, again, prominence in career research
(Arnold & Cohen, 2008; Cohen & Mallon, 1999; Mayrhofer, Meyer, & Steyrer, 2007). But as far as we know,
quantitative empirical studies that estimate the impact of the external product or service market on career factors
or outcomes are absent.
Clearly, our expert and freelance interviewees all consider the external market to be a very important driver of
freelance career success. Cyclical and structural market conditions of demand and supply, transparency, industry
structure, and industry institutional arrangements are likely to largely determine career outcomes and may codetermine key success factors. For instance, in the Netherlands, the traditional free professions (i.e., accounting,
law, notary, and medicine) are associated with strict education and learning requirements that effectively regulate
and limit supply and necessitate pre-entry and post-entry investments in human capital for all independent
professionals in the market (for an example, see Maijoor & van Witteloostuijn, 1996). In other markets (e.g.,
interpreters and some technical occupations), there are only a limited number of companies that employ the
specialized services of freelancers, implying an oligopsony that drives fees down (Bhaskar & To, 2003). Our
interviewees pointed out that some markets had only a limited number of potential clients; in these markets,
managing reputation was crucially important to retain business. In other markets, the number of potential clients
was much larger; in such markets, creating visibility by branding, marketing, and networking was said to be of much
greater importance than reputation management.
The second major aspect that was lacking in our initial freelance career success draft framework, according to our
interviewees, was business strategy. The essence of the strategic management literature is the assumption, for which
there is ample evidence (e.g., McGahan & Porter, 1997), that strategy matters, which is also echoed in the entrepreneurship tradition (e.g., Parker, Storey, & van Witteloostuijn, 2010). Hence, we decided to add a number of business
strategy variables in our model to reflect the fact that a freelancer is both an individual employee and an entrepreneurial “organization,” in the latter capacity pursuing business strategies just like any other business venture.
Crafting a Testable Freelance Career Success Model
Model
In Figure 1, we summarize our final freelance career success research model. The central concepts of the intelligent
career framework remain largely in place, where human capital can arguably be seen as representing knowing how,
(Individual)
career
literature
Draft freelance
career model
Entrepreneurship
literature
Interviews
(51)
Final
detailed
model
Online
survey
I II III IV
Analysis
V
Figure 1. Stepwise approach of this study
28 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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social capital as reflecting knowing whom, and both motivation and personality measures, which can jointly be
coined personal capital, as mirroring knowing why. However, compared with the intelligent career framework,
our freelance career success model is extended by adding two clusters of variables that represent the external
environment and business strategy. This is precisely the consequence of the hybrid nature of the freelance status,
implying that the individual operates as both an employee and an entrepreneur.
Of course, an empirical model is never complete and is therefore always restricted in one way or the other.
Inevitably, developing a measurement model, even one that is designed to be so comprehensive as ours, implies that
decisions have to be made about what not to take on board. For instance, not taking any variables into consideration
that refer to organizational support—a decision based on the observation that, in the end, freelancers are not
employees linked to a single organization—means that hypotheses regarding this aspect cannot be tested. As a rule,
the selection of variables in our final model is based on the academic literature and suggestions from practitioners,
bounded by practical considerations about the maximum length of a survey. In this context, the exploratory
nature of our study is key. That is, the series of hypotheses introduced in the succeeding texts is very broad
in an attempt to assess which potential drivers of career success are more important than others. Our aim is
not to test any specific theory but rather to broadly explore what does and what does not matter in this new
freelance world. In so doing, for instance, our study offers the opportunity to evaluate the value added of
variables derived from the entrepreneurship literature, such as the market environment and business strategy,
relative to those inspired by the career literature.
A final remark relates to our choice of career success measures. We used two measures of success: objective
career success (OCS) and subjective career success (SCS). Moreover, our model explicitly incorporates the
interrelationship between OCS and SCS. The distinction between OCS and SCS is important in the freelancer
context because independent professionals self-select into self-employment for a variety of reasons: not only
monetary motives but also arguments relating to autonomy, flexibility, and work–life balance are potentially important. Indeed, the extant literature emphasizes this reinforcing feedback loop, where career success not only produces
happiness but happiness also enhances further career success (Boehm & Lyubomirsky, 2008). Where we believe
that we can expect different effects of our independent variable upon OCS vis-à-vis SCS, we will develop two
separate sub-hypotheses; if we see no reason to expect a differential effect, we simply refer to freelance career
success in our hypotheses.
Hypotheses
We introduce our hypotheses by systematically discussing all Figure 1’s clusters of variables. We start with
the well-established standard human capital (knowing how) determinant of success, as this set of capabilities
is strongly supported by the literature on career success as well as that on entrepreneurial performance.
Moreover, on the basis of our interviews with experts and freelancers, we have learned that freelancers are
very aware of the need to continuously develop their human capital. They are always scanning knowledge
sources (e.g., internet, books, and magazines) to be up-to-date on the latest industry trends (Barley &
Kunda, 2004).
Hypothesis 1: Human capital is positively related to freelance career success.
Social capital (knowing why) is another cluster of variables that figures prominently in both the career and
entrepreneurship literatures. In traditional career research, measures of organizational sponsorship, which reflects
social capital in the context of a traditional career, often represent social capital. The meta-analysis of Ng, Lillian,
Eby, Sorensen, and Feldman (2005) shows that organizational sponsorship variables (e.g., career support, training
and skill development opportunities, and mentoring) demonstrate a weak relationship with salary and promotion
and a strong relationship with career satisfaction. As organizational support is largely absent for freelancers, with
DRIVERS OF FREELANCE CAREER SUCCESS 29
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DOI: 10.1002/job
the notable exception of support of former employers (Cohen & Mallon, 1999), most organizational sponsorship
measures are not very applicable to freelancers. One concept that is closely related to organizational sponsorship
is applicable to freelancers, though: agency sponsorship. Support of staffing agencies is very important for
freelancers (Barley & Kunda, 2004). Many freelancers develop strong relations with external staffing agencies,
which are important partners for freelancers in their search for new assignments.
In the entrepreneurship literature, network characteristics commonly measure social capital (e.g., Chiesi, 2007;
Witt, 2004). Such measures of social capital are applicable in the freelancer context, too, as a strong network is
required to provide a steady flow of assignments, especially in the absence of supporting agencies. Hence, the
characteristics of these networks, such as network size and tie strength (Granovetter, 1973), offer a second
perspective to measure the social capital of freelancers. A third perspective regarding social capital inspired by
the entrepreneurship literature involves the amount of support that an entrepreneur receives. Such support could
come not only from a business club or business network, such as Lions and Rotary (Barbieri, 2003; Davidson &
Honig, 2003), but also from the freelancer’s partner. Our interviewees strongly argued that freelancing is stressful
and that partner support is therefore critical.
Hypothesis 2: Social capital is positively related to freelance career success.
To measure knowing why, we focus on personality traits and motivational drivers. In selecting personalityrelated variables that can represent this personal capital of freelancers, we largely followed the example of
Eby, Butts, and Lockwood’s (2003) and used career insight, pro-activeness, and openness to represent the
freelancers’ personal capital (Figure 2). These measures of personal capital are distinct from the traditional
entrepreneurial personality measures such as internal locus of control, risk-taking propensity, and need for
achievement. We have three main reasons to do so. First, we needed to limit the number of personality-related
variables to a maximum of three because personality measures often encompass lengthy item lists that are very
time-consuming to complete. Second, these traditional entrepreneurial personality measures have never been
shown to be very powerful predictors of performance in the entrepreneurship literature (Stam et al., 2012),
and there is no reason to assume that these personality characteristics are more important in a freelance context,
where professional skills are arguably at least as important as entrepreneurial capabilities. Third, our interviewees
Subjective
Succes
(Satisfaction)
Objective
Succes
(Revenue)
Business
strategies
Market
factors
Social
Capital
(Knowing
-whom)
Human
Capital
(Knowing
-how)
Personal Cap
& Motivation
(Knowing
-why)
Figure 2. The final freelance career model
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emphasized the importance of career insight, pro-activeness, and openness. For instance, career insight involves
knowing which assignments to accept and which assignments to reject to build a clear profile and a strong
résumé, which was frequently reported as crucial to career success.
Another important aspect of knowing why is motivation. Not all freelancers pursue a freelance career because
of the same underlying motivational driver. Monetary reasons reflect, of course, a viable and often expressed
argument, but intrinsic motivation as reflected in the wish for increased autonomy, flexibility, and work–life
balance may well be even more important. It is highly likely that the type of motivation influences the outcome
of a career. Heslin (2003) suggested that career motivations and goals are the main yardsticks against which to
evaluate career success. From our interviews with freelancers, we learned that a trade-off might be at play here.
That is, those freelancers motivated by non-financial objectives related to autonomy, flexibility, and work–life
balance emphasize SCS over OCS yardsticks and vice versa, where subjective yardsticks involve “soft” nonmonetary satisfaction and objective yardsticks “hard” economic achievements.
Hypothesis 3: Career insight, pro-activeness, and openness are positively related to OCS and SCS.
Hypothesis 4a: Autonomy, flexibility, and work–life balance are negatively related to OCS.
Hypothesis 4b: Autonomy, flexibility, and work–life balance are positively related to SCS.
As said, we added business strategy to our freelance career success model because of the suggestions made
by our interviewees and for theoretical reasons as strategy is the linking pin between the external environment
and internal capabilities (Venkatraman & Camillus, 1984). As far as we know, studies on the business
strategies of freelancers are missing altogether. We decided to apply the well-established resource-based view of the
firm so as to develop a typology of freelance business strategies (Barney, 1991; Maijoor & van Witteloostuijn,
1996). The resource-based view argues that sustainable competitive advantage is derived from resources that are
valuable and rare and which are protected from imitation, transfer, or substitution. This implies that freelancers
must try to construct a rare, valuable, and protected combination of resources (knowledge, skills, abilities,
networks, etc.), which together create a sustainable competitive advantage. Such a strategy is very hard to pursue,
as even complex combinations of resources are subject to imitation and substitution in the low barrier world of
freelancing. Therefore, freelancers tend to reveal a strong tendency to continuously develop themselves to stay
ahead of competition.
We built our business strategies upon the insights of Ostgaard and Birley (1996), who distinguish the following
six strategies for businesses with less than 50 employees: marketing differentiation, product innovation, broad
market/product range, many distribution channels, growth through capital, and differentiation through quality.
These small-business strategies were discussed with our interviewees, which resulted in a somewhat adopted set
of seven freelance business strategies. In our final set of freelance business strategies, Ostgaard and Birley’s
(1996) small-business strategies of many distribution channels and growth through capital were replaced by
(i) price leadership, (ii) industry specialization, and (iii) product specialization. Subsequently, on the basis of the
theories of Porter (1980), Baum, Locke, and Smith (2001), and Boone and van Witteloostuijn (2004), not all these
resulting seven freelance business strategies can be expected to improve freelance career success. That is, only
those business strategies that aim at low cost, focus, or differentiation are hypothesized to lead to freelance career
success by developing a competitive position in a freelancer’s niche. The other strategies are “stuck-in-the-middle”
and doomed to produce failure (Porter, 1980, p. 42).
Hypothesis 5: Low-cost, focus, and differentiation strategies are positively related to freelance career success.
On the basis of insights from the management literature, we hypothesize that features of the external market are
the most important determinant of OCS. In contrast, on the basis of earlier empirical results from the traditional
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career literature (Ng et al., 2005) and related to the arguments earlier, we hypothesize that personal capital has the
strongest impact on SCS.
Hypothesis 6a: Market features dominate all other factors in explaining OCS.
Hypothesis 6b: Personal capital dominates all other factors in explaining SCS.
Method
Participants
We collected data using a customized Internet survey in the Netherlands, which ran in the period from 1 January to 1
April 2008. At the time of the research, the Netherlands is still experiencing an extensive period of robust economic
growth, although doubts about the economic outlook were starting to emerge in the financial press as the sub-prime
banking crises in the U.S. already started to affect consumer confidence and the stock exchange, albeit not yet in the
Netherlands. A variety of professional associations that represent freelancers in a range of occupations or professions
supported the Internet survey. In total, 19 professional associations agreed to inform their members about the
Internet survey and to send their membership a link to the online survey. As a result, a total of about 40 000
independent professionals received an email with information on the survey, including a hyperlink to the survey.
Of this population, 3146 persons clicked on this hyperlink to an introduction page explaining the goal of the research
and the time needed to complete the questionnaire. From this number, 1981 individuals actually started to work on
the online survey. In the end, 1612 respondents (88 per cent of those who started) finished the entire survey of 24
pages and completed 99 questions.
Our sample might reflect an overrepresentation of individuals who are members of the sponsoring professional organizations. However, the large number of respondents who copied the web link of the questionnaire
so as to forward this link to their friends and colleagues somewhat mitigated this effect. Moreover, a few very
broad Internet sites offered their services by providing information to unspecified independent professionals,
putting the link to the online questionnaire high up on their Internet pages. All in all, less than 50 per cent
of the respondents in this study are members of a professional organization. Moreover, we cannot exclude
the distinct possibility that some professions are more eager to respond to this type of requests than other
professions, which implies another source of possible overrepresentation of specific professions. In the succeeding
texts, we present the occupational distribution of our sample. Of course, this distribution must be kept in mind while
interpreting the evidence.
Variables and measures
We have two measures of career success. We assessed SCS using a slightly modified version of the widely used
(e.g., Boudreau, Boswell, & Judge, 2001; Judge & Ferris, 1993; Seibert, Kraimer, & Liden, 2001) career satisfaction scale of Greenhaus, Parasuraman, and Wormley (1990). Heslin (2005) earlier used this 6-item scale. Item
examples are How satisfied are you with the income you have attained, relative to your career aspirations? and
How satisfied are you with the autonomy you have attained, relative to your career aspirations? We measured
OCS using revenue (logged, to produce normality). In most research into firm success, a definition of income is
used instead of revenue. In the freelancer context, however, revenue is superior to income as an OCS measure
for two critical reasons. On the one hand, income is subject to tax manipulation. As a consequence, although in
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cross-industry entrepreneurial analysis income is often used, this is arguably a very noisy measure in practice.
On the other hand, usually, revenue cannot be used as a measure for entrepreneurial success, because the cost
structure of one entrepreneur (e.g., a car dealer with a large stock) can be totally different for another entrepreneur (e.g., an owner of an industrial company). However, because all respondents of our survey are highly
educated knowledge professionals, they all have similar (and relatively limited) costs. In the Netherlands, they
all need a laptop, a (mobile) telephone, an Internet connection, and a car to perform their profession. They
do not have cost of capital or extreme procurement costs that are dominant in companies dealing with tangible
goods. Of course, up-market professionals have higher absolute costs than professionals working in down-market
segments. In our sample, however, the costs reported by the respondent-freelancers are consistently between 20
and 30 per cent of total revenue. Our interviewees confirmed this percentage, and it is in line with the figures
noted in more popular research on the Internet. We do not think revenue that is a perfect measure, as some
professionals may have higher relative costs than others, but we do believe that, in the case of the independent
professionals, revenue is an appropriate measure of monetary success that is less biased than income.
To measure human capital, we used a number of variables. First, to measure experience, we included total work
experience and total freelance experience. We measured these by using the same scale, with eight answer categories:
0–6 months, 7–12 months, 1–2 years, 3–5 years, 6–10 years, 11–15 years, 16–20 years, and 21 years or more.
Second, we asked for the highest educational level of the freelancer, including the year of graduation, which is a
scale running from primary school (“Lagere school”) to a post-university degree (“post-doctoral”). Third, on the
basis of van der Heijden (2006), we asked the respondents to estimate the number of training days in the last two
years. We split this up in three types of training: training in core skills, training in developing new skills, and training
in adjacent or supporting skills (e.g., administration or personal effectiveness).
To measure social capital, we used network characteristics and a few proxies for support. First, to measure
the network characteristics, we first asked respondents to estimate the size of their personal and business
networks. The network definition given follows Witt’s (2004). We defined strong contacts in line with
Granovetter (1973) as those personal contacts that are family or friends. On the basis of Barbieri (2003)
and Davidson and Honig (2003), we divided the network in higher and lower value contacts. We defined
higher value network ties as contacts at the senior management level or above (i.e., director, vice-president,
senior vice-president, or CEO). Second, we included a question about how much time the respondent invested
in networking activities. On the basis of Aldrich and Reese (1993), we asked respondents to indicate the time
per week (in hours) that they engaged, on average, in networking, with reference to Forret and Dougherty’s
(2001) list of networking activities. Specifically, we asked individuals to rate how frequently they engaged,
on a 6-point scale ranging from never to almost every day, in giving out business cards; sending thank you
notes or gifts to people who have helped you in your work or career; sending cards, newspaper clippings,
faxes, or emails to keep in touch; phoning business contacts to keep in touch; and having lunch with business
relations. Third, to measure the amount of support the freelancer received, we asked whether the freelancer
was a member of a business club or business network such as Lions and Rotary (Barbieri, 2003; Davidson
& Honig, 2003). Fourth, we measured the influence of agency support by asking for the number of employment agencies a freelancer was registered with as well as the frequency of their communications with these
agencies. Fifth and finally, we assessed the support of the partner by using Greenhaus and Friedman’s
(2000) 4-item measure, using a 5-point Likert-type scale.
To measure personal capital, we included personality as well as motivational factors. In selecting variables,
we largely followed the example of Eby et al. (2003), using their measures of career insight, pro-activeness,
and openness. We included Noe, Noe, and Bachhuber’s (1990) 6-item measure to assess career insight. We
measured the pro-active personality trait by using Kickul and Gundry’s (2002) version of the 5-item scale
of Bateman and Crant (1993). We assessed openness to experience through Saucier’s (1994) so-called
Mini-Markers Set. We captured motivation by asking respondents to indicate the reasons why they had opted
for a freelance career. We formulated eight predefined answers in our survey on the basis of our interview
results: “more autonomy,” “increased professionalism,” “more work variety,” “more money,” “more flexibility
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in time management,” “better work–life balance,” “more challenge,” and “I had no choice.” Of course,
alternatively, we could have asked them about their current instead of their past motivations, but only the
former captures career choice motives. Moreover, we feel that the measure of past motivation is probably
highly correlated with current motivation because most respondents started their freelance career only three
to five years ago.
To measure business strategy, we included two variables. A first variable measures the distinctiveness of the
respondent-freelancer’s business proposition vis-à-vis the competition (using a 7-point Likert-type scale). A
second set of measures assesses the business strategy of the freelancer by using a number of statements based
on our seven freelance business strategies: that is, “I focus on a single industry or a single organization,” “I
focus on a single product/service,” “I offer better service,” “I offer my products/services at lower costs than
the competition,” “I have a broad product/service portfolio,” and “I offer innovative products/services.”
Although our interviewees strongly suggested to incorporate market factors into our measurement model,
this was not easy because objective information on these freelance markets and their many niches is
completely missing in the Netherlands. Regrettably, reliable information on market factors such as numbers
of suppliers, number of competitors, market transparency, and intellectual property rights is simply not available. However, if we would not take account of the environment, this is likely to produce an omitted-variable
bias. The potentially strong impact of the market combined with the adverse effect of not including market
factors made us decide to use the profession of the freelancer [interim manager, information technology
(IT) professional, etc.] to act as a dummy proxy for all market factors. It is debatable whether this occupational dummy can act as a market dummy. Alternatively, this dummy can simply be taken as a control
variable (or even a human capital variable, as occupation tends to be highly correlated with certain specific
skills). However, our interviewees made strongly arguments in favor of a direct link between their occupation
or profession and the market they operate in.
That is, they convincingly argued that the characteristics of the market they operate in are very much
defined by their occupation or profession. In some professions, there is excess supply, being associated with
low and declining fees; in other professions, there is a shortage of skilled labor, resulting in high and increasing fees. Therefore, we decided to follow our interviewees by assuming that the occupational or professional
dummy signals market conditions more than anything else, although this dummy also captures other aspects
such as specific intangible skills. Additionally, influence of the external market was also measured with a
second indicator: the geographical location of the freelancer based on postal code. We translated the latter
into a binary variable that distinguished between the most urbanized part of the Netherlands—“Randstad,”
which is the heavily populated area in the Amsterdam–The Hague–Rotterdam–Utrecht triangle—and the rest
of the country.
Finally, we added a number of control variables to our freelance career success model, such as age (in years),
gender (with female coded as 0 and male coded as 1), and health status. We measured health status using a
self-assessed Likert-type scale ranging from 1 = poor health condition to 7 = excellent health condition. To conclude,
we included a dummy variable that indicated whether the freelancer had another source of income (e.g., through a
pension or a part-time job).
Designing the survey
We started with converting our final research model, variables, and corresponding measures into an Internet
survey. Sixty freelancers extensively tested this survey to ensure that all questions were clear and easy to
answer and that ambiguous and vague terms were avoided. The Internet survey was in Dutch. We first translated validated scales adopted from the international literature from the original English into Dutch and then
another person back-translated it from Dutch into English. If there were differences between the original
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English text and the English text that was twice translated, both translators and a third independent person (all
fluent in Dutch and English) decided on the final text in Dutch.
Common-method variance (CMV) has been identified as a major problem in organizational research on the
basis of self-reports (Chang, van Witteloostuijn, & Eden, 2010; Podsakoff & Organ, 1986; Spector, 2006). Our
data are mostly self-reports of objective data and therefore only minimally affected by CMV issues (Spector,
2006, p. 229). Nevertheless, we adopted the procedural remedies suggested by Podsakoff, MacKenzie, Lee,
and Podsakoff (2003) to overcome any remaining CMV issues. We assured respondents of anonymity and
confidentiality and asked them to answer honestly. We assured individuals that right and wrong answers did
not exist. Also, the fact that the survey was sponsored by a total of 19 professional freelance organizations
that were all considered very trustworthy decreased the tendency of respondents to give socially desirable
answers. Moreover, we promised all respondents a benchmark report on their position in relation to other
independent professionals, which motivated respondents to give the “true” score, as they were anxious to
see how they performed within their peer group of freelancers, given that wrong answers would distort their
own benchmark report. We further designed the survey tool in a way to prevent CMV by adopting a random
order of questions, including different types of scales, and by providing clear information on the survey
completion process to prevent boredom.
Data Reduction and Analysis
Given the large number of survey questions, we performed data reduction analyses. For every cluster of multiitem independent variables (i.e., human capital, social capital, personality, motivation, and business strategy),
we performed a factor analysis on all variables to reduce data complexity. Multicolinearity was not an issue
in our data set (all variance inflation factor scores are lower than 1.6), with one exception: the colinearity
between age, year of graduation, and total work experience is too high. This is why we excluded both total
work experience and year of graduation from the list of human capital variables (but we kept age as a control
variable) on which the factor analysis was performed. The factor analysis generated four human capital
variables with (a) eigenvalues larger than 1 and (b) an appropriate pattern of loadings: (i) total freelance
experience; (ii) higher education level; (iii) university education level; and (iv) recent training (the sum of
training days in core professional, new professional, and supporting and adjacent skills in 2006 and 2007).
The original survey includes 18 items that attempt to measure social capital. We measured not only network size
but also the total number of weak and strong links, the number of persons new in the network (refreshment), and the
seniority or quality of the network. In a factor analysis, the original and validated scales of network activity (the five
questions from Forret & Dougherty, 2001) and partner support (the four questions from Greenhaus & Friedman,
2000) came out as separate variables, given the eigenvalues and loading pattern. This is why we performed a second
factor analysis without the nine network activity and partner support questions. This resulted in a clear 3-factor
outcome, applying the usual eigenvalue threshold (>1) and clean loading pattern criterion: size of the network,
business club membership, and actively managing employment agencies.
The survey includes 15 personality questions, relating to three validated scales: four on openness, five on proactivity, and six on career insight. From the factor analysis, all three scales emerged as specific factors with eigenvalues larger than 1 and a clean factor loading pattern. We thus decided to work with the original test scores
rather than the factor loading sum. On the basis of the usual eigenvalue and loading pattern criteria, three motivational factors emerged from the factor analysis with the eight motivation-related items: (i) motivated
by work–life balance and flexibility; (ii) motivated by professionalism and autonomy; and (iii) motivated by
challenge, variety, and money. This is a somewhat peculiar finding. It seems logical that work–life balance
and flexibility are in the same grouping, and the grouping of professionalism and autonomy is also
DRIVERS OF FREELANCE CAREER SUCCESS 35
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
understandable because a high level of professionalism might also require a high level of autonomy. It is less
understandable that variety loads on the same factor as challenge and money. Apparently, the same underlying motivational factor, which may be related to ambition and the need to feel challenged, drives these three
aspects.
We factor analyzed 11 business strategy items. On the basis of the eigenvalues and the loading pattern, four business strategy variables emerged: (i) freelancers who carry innovative and/or differentiating products or services;
(ii) freelancers who specialize in one industry; (iii) freelancers who offer a broad product/service range; and
(iv) freelancers whose value proposition is based on providing better service and/or lower cost.
Finally, we used correlations and logical reasoning to create a limited set of occupational dummies as proxies
for the external market, next to the geographical location dummy distinguishing the urbanized Randstad from
the rest of the country. In our data set, it was possible for freelancers to have more than one profession. So,
a freelancer could be a coach and a trainer or a software programmer and a functional analyst. This
allowed us to see which professions are very much linked. We used this information about overlap between
professionals together with logical reasoning to create six occupational variables. First, an interim manager
is someone who temporarily heads an organization or department frequently to turn this organization around.
Second, an interim professional is someone with specific knowledge of supporting business processes (e.g.,
finance, human resource management, legal, or IT). Third, a journalist or media professional is someone
working in the media industry (e.g., editor or journalist). Fourth, a technical professional is someone who
works as an engineer in electronics, construction, and so on. Fifth, coaches and trainers are involved in educating and coaching individuals and groups in certain skills. The sixth and last group defined was called other free
agents but consisted primarily out of persons working in facility management. This last group was the only
group with significant lower education levels with only 34 per cent having a master’s degree (overall, this
is 44 per cent).
The control variables included in this study are age, gender, having another source of income (e.g., a pension
scheme or a long-term employment contract), and health status. We excluded from further analysis a number of
other variables that are available from the survey because they are endogenous to freelance income (e.g., percentage
of freelance income of total income and size of the financial buffer in months). We added age and age squared to
explore the potentially parabolic relationship between age and freelance revenue, following standard labor economics. We included SCS in the OCS equation, and vice versa, to estimate the interrelationship between both measures
of freelance career success.
To investigate whether, despite all the preventive measures, CMV in the data might bias the estimates as reported
in the succeeding texts, we conducted a single overarching Harmon one-factor test. On the basis of these results,
there is no evidence that CMV poses a problem. The factor with the largest eigenvalue explains less than eight
per cent of the total variance, and more than 20 factors had eigenvalues above 1. In our study, we included six scales
that are based on multiple items: openness, pro-activeness, career insight, network activity, partner support, and
SCS. We checked the internal consistency of these scales. All tests proved reliable with satisfactory Cronbach’s
alphas (a > 0.7), and all reflected a single factor with an eigenvalue above 1.
Results
We show means and standard deviations of the study variables in Table 1. Tables 2 and 3 give the estimation
results of the freelance career success model. We estimated both equations using maximum likelihood with
robust variance estimators to tackle heterogeneity. We also applied a number of other estimators (such as an
instrumental variables estimation to deal with endogeneity issues), but all these robustness analyses generated
very comparable results (available upon request).
36 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Table 1. Means, standard deviation, and correlations of dependent and independent variables.
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1 Objective career success
(log)
10.77 1.15 1 .34 .12 .20 .05 .03 .20 .01 .12 .25 .05 .09 .11 .07 .04 .05 .08 .12 .15 .00 .04 .32 .27 .15 .08 .04 .24 .03 .16 .27 .34
2 Subjective career
success
31.96 6.50
— 1 .04 .08 .03 .10 .19 .18 .10 .05 .06 .25 .23 .29 .06 .06 .13 .28 .05 .04 .07 .04 .02 .04 .02 .02 .06 .02 .23 .02 .15
3 Bachelor’s degree 0.41 0.49 — — 1 .74 .03 .04 .03 .04 .03 .04 .03 .07 .03 .04 .05 .03 .01 .06 .06 .07 .04 .02 .08 .02 .08 .00 .01 .01 .06 .05 .05
4 Master’s degree 0.44 0.50 —— — 1 .00 .02 .00 .08 .08 .02 .00 .07 .02 .04 .07 .03 .06 .03 .10 .10 .06 .04 .15 .03 .01 .09 .08 .07 .08 .00 .06
5 Freelance experience
(years)
18.80 6.87 —— — — 1 .04 .10 .05 .00 .11 .00 .05 .06 .14 .07 .16 .03 .19 .07 .04 .03 .10 .13 .15 .06 .04 .15 .41 .01 .01 .05
6 Training days (per year) 5.87 5.48 —— — — — 1 .20 .02 .12 .01 .11 .09 .15 .23 .02 .02 .07 .13 .00 .02 .01 .05 .04 .13 .27 .05 .06 .02 .01 .07 .04
7 Network activity (score) 16.08 3.79 —— — — — — 1 .08 .31 .11 .25 .29 .24 .33 .00 .03 .05 .25 .02 .05 .03 .17 .13 .01 .12 .11 .14 .01 .08 .02 .06
8 Partner support (score) 14.94 3.99 —— — — — — — 1 .01 .04 .05 .14 .14 .19 .06 .01 .06 .10 .01 .01 .04 .03 .04 .04 .03 .04 .05 .10 .11 .16 .03
9 Size of network (factor) 0.00 1.00 —— — — — — — — 1 .00 .00 .15 .14 .11 .01 .01 .06 .19 .03 .04 .04 .11 .08 .01 .08 .04 .04 .04 .05 .03 .03
10 Managing agencies (factor) 0.00 1.00 —— — — — — — — — 1 .00 .06 .07 .12 .00 .11 .03 .12 .11 .08 .04 .41 .20 .14 .06 .10 .10 .01 .07 .18 .12
11 Business club membership
(factor)
0.00 1.00 —— — — — — — — — — 1 .11 .10 .15 .02 .00 .02 .09 .01 .01 .01 .06 .07 .05 .06 .08 .03 .07 .04 .08 .03
12 Openness (score) 19.07 3.21 —— — — — — — — — — — 1 .46 .33 .02 .03 .10 .21 .01 .06 .04 .16 .05 .05 .11 .01 .05 .01 .19 .09 .04
13 Pro-activeness (score) 21.91 4.00 —— — — — — — — — — — — 1 .34 .01 .06 .15 .35 .02 .07 .04 .22 .11 .04 .05 .03 .11 .02 .12 .07 .01
14 Career insight (score) 21.85 3.99 —— — — — — — — — — — — — 1 .01 .09 .06 .29 .06 .00 .04 .12 .04 .07 .14 .00 .06 .02 .12 .06 .01
15 Work–life balance (score) 0.00 1.00 —— — — — — — — — — — — — — 1 .00 .00 .03 .05 .02 .05 .07 .04 .07 .00 .05 .01 .18 .03 .10 .06
16 Challenge and profit
(factor)
0.00 1.00 —— — — — — — — — — — — — — — 1 .00 .03 .03 .06 .08 .10 .02 .00 .03 .05 .07 .13 .06 .02 .01
17 Autonomy and
mastery (factor)
0.00 1.00 —— — — — — — — — — — — — — — — 1 .16 .01 .02 .05 .01 .05 .02 .13 .09 .06 .07 .00 .06 .05
18 Differentiate and innovate
(factor)
0.00 1.00 —— — — — — — — — — — — — — — — — 1 .00 .00 .00 .00 .02 .05 .08 .03 .00 .18 .07 .03 .03
19 Industry specialization
(factor)
0.00 1.00 —— — — — — — — — — — — — — — — — — 1 .00 .00 .12 .12 .25 .02 .06 .02 .03 .03 .08 .03
20 Product range (factor) 0.00 1.00 —— — — — — — — — — — — — — — — — — — 1 .00 .09 .06 .01 .06 .03 .02 .05 .01 .02 .01
21 Price leadership (factor) 0.00 1.00 —— — — — — — — — — — — — — — — — — — — 1 .09 .00 .10 .00 .02 .02 .06 .01 .03 .03
22 Interim manager 0.27 0.45 —— — — — — — — — — — — — — — — — — — — — 1 .26 .20 .06 .03 .21 .12 .10 .28 .04
23 Interim professional 0.46 0.50 —— — — — — — — — — — — — — — — — — — — — — 1 .33 .02 .12 .29 .01 .04 .30 .03
24 Journalist 0.19 0.39 —— — — — — — — — — — — — — — — — — — — — — — 1 .23 .12 .08 .09 .05 .16 .04
25 Trainer/coach 0.36 0.48 —— — — — — — — — — — — — — — — — — — — — — — — 1 .14 .18 .14 .00 .11 .09
26 Technical 0.06 0.25 —— — — — — — — — — — — — — — — — — — — — — — — — 1 .08 .05 .02 .21 .02
27 Other freelancer 0.15 0.35 —— — — — — — — — — — — — — — — — — — — — — — — — — 1 .04 .05 .19 .03
28 Age 46.29 8.71 —— — — — — — — — — — — — — — — — — — — — — — — — — — 1 .00 .19 .15
29 Health status 6.07 0.89 —— — — — — — — — — — — — — — — — — — — — — — — — — — — 1 .04 .07
30 Gender 0.50 0.50 —— — — — — — — — — — — — — — — — — — — — — — — — — — — — 1 .00
31 Other income 0.24 0.43 —— — — — — — — — — — — — — — — — — — — — — — — — — — — — — 1
DRIVERS OF FREELANCE CAREER SUCCESS 37
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Objective career success
Table 2 shows that the adjusted R2 is 44 per cent. This implies a very good model fit for this type of analyses, given
the stylized fact in the entrepreneurship literature that an (adjusted) R2 of 15 per cent is already exceptional in studies
of small-firm performance (Parker et al., 2010). Almost all control variables are significant and have the expected
sign. This is also the case for most external market and human capital variables, with only one remarkable exception
within the group of human capital variables: the significantly negative relationship between recent training effort and
OCS. This may indicate reverse causality. First, a freelancer engaging in training cannot charge clients for services.
Second, independent professionals without a current assignment have much more spare time to invest in training.
Within the group personality variables of the personal capital cluster, none of the variables are significant. Neither
career insight nor openness nor a pro-active personality seems to be related to OCS. A number of motivation
Table 2. Testing the freelance career model—objective career success.
Factor Coefficient SE z-statistic
Control variables Constant 6.980609 0.7493958 9.311
Age 0.0812761 0.0286227 2.841
Age2 0.0009651 0.0003046 3.171
Gender (male = 1) 0.4200948 0.0580151 7.241
Health (self-assessment score) 0.0472644 0.0286808 1.65†
Receiving other income (e.g., regular job) 0.589795 0.0666048 8.861
Market factors Living in Randstad area (Randstad = 1) 0.0799059 0.0480397 1.66†
Interim manager (dummy) 0.402606 0.0606881 6.631
Interim professional (dummy) 0.1432689 0.0589014 2.43*
Journalist and/or media professional (dummy) 0.3227554 0.0780846 4.131
Technical professional (dummy) 0.1039439 0.1071783 0.97
Trainer and/or coach (dummy) 0.2152659 0.0572763 3.761
Other free agent (dummy) 0.461215 0.0811657 5.681
Human capital University education (dummy) 0.3556629 0.0790103 4.501
“HBO” education (dummy) 0.1380998 0.0805207 1.72†
Freelance experience (log) 0.1622809 0.0229957 7.061
Recent training participation (sum) 0.0073522 0.0025106 2.931
Motivation capital Motivated by autonomy and professionalism 0.0415792 0.0244645 1.70†
Motivated by challenge and money 0.0196196 0.0237121 0.83
Motivated by work–life balance and flexibility 0.0535313 0.0235548 2.27*
Personality capital Insight in career (sum) 0.0042271 0.0074761 0.57
Open personality (sum) 0.0034226 0.0070108 0.49
Pro-active personality (sum) 0.0149058 0.0092242 1.62
Social capital Business club member (factor) 0.0149219 0.024884 0.60
Managing the agent (factor) 0.0838912 0.0248262 3.381
Size of network (factor) 0.0338625 0.0267804 1.26
Network activity (sum) 0.029306 0.0074711 3.921
Partner support (sum) 0.0018221 0.0058532 0.31
Business strategy Better service or low-cost strategy (factor) 0.0101224 0.0236016 0.43
Innovative and differentiation strategy (factor) 0.02476 0.0282662 0.88
Industry specialization strategy (factor) 0.0562731 0.0235155 2.39*
Broad product range (factor) 0.0468089 0.0247748 1.89†
Subjective career success (sum) 0.0441134 0.004511 9.781
Adjusted R2 0.4393 Est. method ML (robust)
Log likelihood 1754.97 Observations (n) 1385
Akaike information criterion 2.581907
†
significant at 10% level, *significant at 5% level; and **significant at 1% level.
38 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
variables are significant, though: freelancers primarily driven by flexibility and/or work–life balance motives have
lower revenue than professionals who are in freelancing for reasons other than that.
Two social capital variables proved to be significant: managing agencies, and the network activity score of
Forret and Dougherty (2001). Actively managing employment agencies (i.e., being registered with employment
agencies and actively visiting them) adds to the OCS of the independent professional. The positive and
significant estimate of network activity suggests that being an active networker is important. Calling people,
visiting business contacts, handing out business cards, and sending mails, cards, and gifts to individuals in
the network contribute to financial success. Neither the size of the network nor whether an individual is a
member of a business club adds to OCS. Also, partner support proved not to be related to OCS. Finally, the
business strategy variables also seem to add to OCS. Industry specialization is important for freelancers,
Table 3. Testing the freelance career model—subjective career success.
Factor Coefficient SE z-statistic
Control variables Constant 1.356558 4.407242 0.31
Age 0.2052013 0.173482 1.18
Age squared 0.002439 0.001864 1.31
Gender (male = 1) 0.799514 0.3471066 2.30*
Health (self-assessment score) 0.984084 0.1931404 5.10**
Receiving other income (e.g., regular job) 0.4763586 0.4079105 1.17
Market factors Living in Randstad area (Randstad = 1) 0.0150572 0.306105 0.05
Interim manager (dummy) 1.173891 0.398087 2.95**
Interim professional (dummy) 0.7440611 0.3739605 1.99*
Journalist and/or media professional (dummy) 0.5157591 0.5009407 1.03
Technical professional (dummy) 0.434244 0.6271601 0.69
Trainer and/or coach (dummy) 0.2692279 0.3636077 0.74
Other free agent (dummy) 0.0336539 0.5867987 0.06
Human capital University education (dummy) 0.7539263 0.4749756 1.59
“HBO” education (dummy) 0.4834854 0.4773371 1.01
Freelance experience (log) 0.003937 0.141182 0.03
Recent training participation (sum) 0.0242919 0.0153892 1.58
Motivation capital Motivated by autonomy and professionalism 0.3427641 0.1552853 2.21*
Motivated by challenge and money 0.1804451 0.1503154 1.20
Motivated by work–life balance and flexibility 0.3705541 0.1561106 2.37*
Personality Capital Insight in career (sum) 0.225339 0.0479622 4.70**
Open personality (sum) 0.1392696 0.049208 2.83**
Pro-active personality (sum) 0.130006 0.0616591 2.11*
Social capital Business club member (factor) 0.1613818 0.1536743 1.05
Managing the agent (factor) 0.0023039 0.1864347 0.01
Size of network (factor) 0.0089879 0.1558321 0.06
Network activity (sum) 0.0369041 0.0511501 0.72
Partner support (sum) 0.1274405 0.0467177 2.73**
Business strategy Better service or low-cost strategy (factor) 0.2922321 0.1466776 1.99*
Innovative and differentiation strategy (factor) 0.9313536 0.1882798 4.95**
Industry specialization strategy (factor) 0.0075344 0.1495288 0.05
Broad product range (factor) 0.4305484 0.162117 2.66**
Objective career success (log) 1.769593 0.1813919 9.76**
Adjusted R2 0.2868 Est. method ML (robust)
Log likelihood 4311.5 Observations (n) 1385
Akaike information criterion 6.273649
†
significant at 10% level;
*significant at 5% level; and
**significant at 1% level.
DRIVERS OF FREELANCE CAREER SUCCESS 39
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
significantly improving OCS. A broad product/service range is almost significant, leading to less OCS. This suggests that focuses on an industry and on a small range of products/services are important financial success
factors.
Subjective career success
Looking at the results in Tables 2 and 3, we observe that the adjusted R2 is considerably lower for the SCS (0.29)
than for OCS (0.44), albeit still far above the top level of 0.15 in the small-business literature. This suggests that
our model is better in predicting OCS than SCS. Moreover, the estimates reveal that a set of very different variables
is important in determining SCS when compared with OCS. In effect, the variables that are important for SCS are
those variables that are irrelevant to OCS and vice versa. Among the control variables, only self-assessed health
and gender are significantly predicting SCS, in the expected direction. Men are less satisfied with their career than
women, and freelancers with good health are more satisfied with their career than those struggling with their health.
By and large, external market and human capital indicators, although crucial for OCS, fail to affect SCS. Only the
dummies for interim managers and professionals are significant. Freelancers in these occupations are not as satisfied
with their career as one would expect. All personality variables from the personal capital cluster are strongly significant. Especially, career insight stands out, but the personality traits of openness to experience and pro-activeness are
essential drivers of SCS as well. Motivation is important for SCS, too: professionals who freelance for reasons of
flexibility and work–life balance and professionals who freelance because of autonomy and professionalism are
more satisfied with their career. Only one social capital variable has a positive impact on SCS: partner support.
Although not hypothesized about, for lack of theory on this, we explore the link between business strategy
variables and SCS. Indeed, business strategy turns out to be very important for SCS. Especially, independent
professionals who distinguish themselves through a strategy that is innovative and independent professionals
with a broad product/service range have highly satisfying careers (i.e., high SCS scores). It might be that a large
variety of different assignments and being innovative provides intrinsic utility to independent professionals,
which subsequently increases their SCS. Independent professionals with a “low-cost” or “better-service” strategy
have lower SCS levels. In the last row of Tables 2 and 3, the positive cross-relationship between OCS and SCS
is evident. Although causality cannot be established in cross-sectional analysis, OCS and SCS are definitely positively
related (r = 0.34, with p < 0.001). This correlation is somewhat higher than the average correlation of 0.30 between
salary and career satisfaction (with a 95 per cent confidence interval of 0.28–0.32) as reported by Ng et al. (2005) in their
meta-analysis of OCS and SCS for employees. The fact that some freelancers in our sample are largely motivated
by monetary rewards may perhaps explain the somewhat higher correlation between OCS and SCS when compared
with employees.
In Table 4, an overview is given of our findings about the first five hypotheses. For the most part, empirical
evidence supported these hypotheses. Nevertheless, there are some interesting observations. Human capital is
important for OCS but not for SCS. In contrast, personal capital is important for SCS but not for OCS. Only social
capital is important for both OCS and SCS. All in all, our hypotheses are fully or partly supported. Our hypotheses
are arguably not very detailed, but we feel that these broadly formulated hypotheses are appropriate in the context of
exploratory analyses of a relatively new and unexplored subject.
Our final two hypotheses (6a and 6b) are not listed in Table 4, as we need complementary analyses for this pair of
hypotheses. We applied Budescu’s (1993) dominance analysis, based on generalized least squares estimation, in this
respect. Although dominance analysis is a very labor-intensive method, it is very much suited to explore our two
remaining hypotheses. For every possible combination of variable classes, we calculated the R2 measure. In total,
this generates 64 measures of fit for OCS and SCS. For all combinations, we calculated the extra fit of adding a
new class of variables. Subsequently, we computed the average extra fit. We reported the outcomes of this exercise
in Tables 5 and 6. K is the number of variable classes already in the equation. In the cells, the extra R2 measured the
40 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
effect on fit. For example, if an equation includes only one class of variables (K = 1)—that is, human capital, social
capital, personality measures, motivational variables, or business strategy—the addition of the external market
proxies will increase the fit of that equation (i.e., R2
) with, on average, 0.0819. Similarly, if we add the external
market dummies to an equation with two other classes of variables included (K = 2)—for example, human and social
capital—the fit implied by the extra R2 will increase with an average of 0.0744.
Table 6. Dominance analysis of subjective career success.
External market Human capital Personality Motivation Social capital Business strategy
K= 0 0.0070 0.0104 0.0720 0.0155 0.0410 0.0614
K= 1 0.0081 0.0076 0.0613 0.0121 0.0358 0.0503
K= 2 0.0086 0.0055 0.0519 0.0097 0.0315 0.0406
K= 3 0.0087 0.0001 0.0437 0.0078 0.0220 0.0240
K= 4 0.0085 0.0032 0.0346 0.0063 0.0254 0.0256
K= 5 0.0083 0.0028 0.0311 0.0052 0.0236 0.0202
Mean 0.0085 0.0038 0.0481 0.0090 0.0283 0.0346
Relative percentage 6.4 2.9 36.4 6.8 21.4 26.2
Table 4. Overview of hypotheses.
Hypothesis Outcome
1. Human capital is positively related to OCS
and SCS
Partly accepted. Yes, for OCS (p < 0.001); no for SCS (p = 0.271)
2. Social capital is positively related to OCS
and SCS
Completely accepted. Yes, for OCS (p < 0.001); yes for SCS (p = 0.034)
3. Career insight, pro-activeness, and openness
are positively related to OCS and SCS
Partly accepted. No, for OCS (p = 0.18); yes for SCS (p < 0.001)
4a: Autonomy, flexibility, and work–life balance
are negatively related to OCS
Partly accepted. Only flexibility and work–life balance are motivations
that negatively affect OCS (p = 0.02). Autonomy is not negatively related
to OCS
4b: Autonomy, flexibility, and work–life balance
are positively related to SCS
Fully accepted. Flexibility and work–life balance are motivations that
positively affect OCS (p = 0.02). Autonomy is also positively related to
SCS (p = 0.03)
5. Low-cost, focus, and differentiation strategies
are positively related to OCS and SCS
Partly accepted. Focus strategies [i.e., product/service focus (p = 0.06) and
industry focus (p = 0.02)] are positively related to OCS. Differentiation
leads to SCS (p < 0.001). Low-cost strategy leads to less SCS (p = 0.04)
OCS, objective career success; SCS, subjective career success.
Table 5. Dominance analysis of objective career success.
External market Human capital Personality Motivation Social capital Business strategy
K= 0 0.0895 0.0368 0.0000 0.0059 0.0465 0.0134
K= 1 0.0819 0.0391 0.0011 0.0053 0.0411 0.0110
K= 2 0.0744 0.0405 0.0018 0.0047 0.0356 0.0090
K= 3 0.0669 0.0411 0.0020 0.0042 0.0298 0.0073
K= 4 0.0598 0.0413 0.0023 0.0038 0.0248 0.0055
K= 5 0.0531 0.0412 0.0014 0.0035 0.0200 0.0040
Mean 0.0707 0.0405 0.0018 0.0045 0.0328 0.0082
Relative percentage 44.6 25.6 1.1 2.8 20.7 5.2
DRIVERS OF FREELANCE CAREER SUCCESS 41
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
By inspecting all possible combinations, we can conclude that the average extra fit of adding external market
dummies is 0.0707. This is higher than the average extra fit of human capital (0.0405), personality (0.0018),
motivation (0.0045), social capital (0.0328), and business strategy (0.0082) variables. External market features
are, therefore, the most important determinant of OCS (44.6 per cent of explained variance), followed by human
capital (25.6 per cent) and social capital (20.7 per cent), respectively. Business strategy (5.2 per cent), motivation
(2.8 per cent), and personality (1.1 per cent) are all of minor importance in driving OCS.
We followed the same procedure to determine the relative importance of factors determining SCS. We summarized the findings in Table 6. Personality (0.0481; 36.4 per cent of explained variance) is the most important driver
of SCS, followed by business strategy (0.0346; 26.2 per cent) and social capital (0.0283; 21.4 per cent), in that order.
The other success drivers are all less important. The average extra fit of market features is 0.0085 or 6.4 per cent, of
motivation 0.0090 or 6.8 per cent, and of human capital 0.0038 or 2.9 per cent. These drivers are arguably very
important in determining OCS, but they are almost irrelevant in affecting SCS. Social capital is the only factor that
has a considerably positive impact on both OCS and SCS. On the basis of the evidence provided by the dominance
analysis, we accept our final two Hypotheses 6a and 6b.
Discussion
We would like to start with emphasizing the two major benefits of this study. First, it is one of the first large-scale
quantitative studies that specifically targets freelancers as the sample group. If Handy’s (1989) assessment about
modern organizations is correct, the number of freelancers will continue to increase, as current evidence strongly
suggests indeed. Second, as the freelance career is the quintessence of the individual career, as is argued in this
paper, freelance careers provide a great opportunity to test the individual career concept. We believe that this
empirical study of freelancers provided perhaps one of the best settings for testing insights from the individual
career perspective.
A first important finding of this study is that a model based on the intelligent career framework augmented with
factors from the entrepreneurship literature is largely suited to explain freelance career success, as is demonstrated
by the support for many of our hypotheses. Hence, this study confirms the usefulness of individual career models. A
second finding of this study is that subjective and objective career yardsticks are characterized by very different
underlying processes. Individual career makers continuously make trade-offs between SCS, on the one hand, and
OCS, on the other hand. More research into the trade-off between SCS and OCS is needed, to deepen our
understanding of the options open to freelancers. Additionally, a third key result of this study is that business aspects
such as the external market and business strategy are critical elements driving the freelance career. This confirms that
freelancers reflect a hybrid, with elements from the small-business world and aspects from individual career theory.
A better understanding of the external market and its implications for individual careers is needed if we want to
make better career models. Here, we find that career models can learn from strategic management, as both
perspectives address the question about what drives competitive advantage, but strategic management especially
addresses the environment as an element in the vector of variables that determine success. In the current study, only
a first step is taken.
We also think that there is much practical merit in all the small insights provided by this empirical research that is
relevant to freelancers. This paper shows that there is a parabolic influence of age on OCS and that the maximum
revenue of freelancers lies between 45 and 50 years of age; after which, utilization rates and professional fees
diminish. With respect to freelance networks, the study shows that network size is not important, but that building
strong relations with agents and putting substantial effort in building and maintaining a network are essential
elements that drive both OCS and SCS. Moreover, the paper demonstrates clearly the importance of a focus strategy,
where the freelancer specializes in a product/service or sector, as well as the adverse effects of a low-cost strategy.
These and other insights are interesting for academics but of great practical importance to freelancers, too.
42 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
As any study, ours suffers from drawbacks, which leave room for further study. We would like to emphasize seven
of these. First, our final model as depicted in Figure 1 does not incorporate the concept of strategic fit (Parker &
van Witteloostuijn, 2010). Since the 1960s, contingency theory has argued convincingly that there is no universally
best way to achieve organizational success. Instead, the performance-maximizing course of action is contingent upon
the organization’s internal context and external environment. The concept of strategic fit is part of the intelligent career
framework, too, as interrelationships between the three ways of knowing may affect career outcomes. Although
modeling these interrelations is probably warranted from a theoretical perspective, we have not modeled these complex
interrelations at this exploratory stage of our research (see also Zajac, Kraatz, & Bresser, 2000) but set this aside as a
task for future work.
Second, this study is cross-sectional, which implies that we cannot study career changes over lifetimes. As the
modern career becomes more fluid, individuals leap increasingly from one assignment to another and from a
freelance contract to a traditional employment job and back. Third, our cross-section only includes freelancers,
implying that we cannot investigate any selection effect. In future work, it would be interesting to see what causes
an individual to become a freelancer, instead of an employee or an employer. Fourth, this study’s design does not
include signaling (Jones, 2002) and stretch work (O’Mahony & Bechky, 2006), although we know that these
strategies are crucial to the success of freelancers. A future study might—for instance, by studying the curriculum
vitae of freelancers—focus on such signaling and stretch work behavior, to determine how these strategies impact
the success rate of freelancers. Fifth, this study focuses on the Netherlands. A comparative international study of
freelancers, either generally or focusing on a number of specific freelance markets (e.g., IT and media), is needed
to see whether our findings are generalizable to the economies of other developed and non-developed societies. Last
but not least, this study was restricted to occupational dummies as proxies for market factors. This is far from ideal,
as we discussed at length earlier. We should try to include direct measures of market features (size, transparency,
seller versus buyer markets, etc.) in future work. Additionally, as career choices are always shaped by the whole
context in which the individual resides, we should look not only into developments in the marketplace but also at
other aspects that may guide the interests, goals, and actions of freelancers (Lent, Brown, & Hackett, 2000).
Author biographies
Arjan van de Born is assistant professor in Economics and Management at the Universities of Antwerpen and
Utrecht. His research interests range from entrepreneurship, network organizations and organizational change.
Arjen van Witteloostuijn is full professor in Economics and Management at the Universities of Antwerpen,
Tilburg and Utrecht. His research interests range from the effect of language on individual decision-making to the
evolution of political party systems. He has published widely in such journals as the Academy of Management Journal, Academy of Management Review, American Journal of Political Science, American Sociological Review,
Journal of International Business Studies, Management Science, Organization Science and Strategic Management
Journal.
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46 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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DOI: 10.1002/job
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ARJAN VAN DEN BORN1
* AND ARJEN VAN WITTELOOSTUIJN1,2,3
1
School of Economics, Utrecht University, Utrecht, The Netherlands
2
University of Antwerp, Antwerp, Belgium
3
Tilburg University, Tilburg, The Netherlands
Summary Recent evidence shows that the frequently proclaimed collapse of the traditional career model is actually
not supported by job tenure data. This paper argues that the observed stability of job tenure might be
explained by an increasing number of shamrock organizations. This organizational form has three types
of workers: core employees, professional freelancers, and routine workers. In such an organization, two
very different career models coexist. The organization largely determines the career of the core employee,
whereas the individual essentially shapes that of the professional freelancer. This paper studies extensively
the career of this second group: the professional freelancer, a growing phenomenon in many developed
countries but not yet the focus of many career studies. We develop a freelance career success model
on basis of the intelligent career framework augmented by insights from literature on entrepreneurship.
Data are from a web survey with responses from about 1600 independent professionals in the
Netherlands, in combination with 51 in-depth interviews. We provide two main contributions. First, we
report findings from the first large-scale quantitative study into freelance career success. Second, this study
enhances our understanding of the success of the modern career by building bridges between career and
entrepreneurship literatures. We conclude that the external environment in which an individual freelancer
operates is the most important factor determining career success. The study therefore suggests that more
work needs to be performed on the relationship between the environment and individual career success.
Copyright © 2012 John Wiley & Sons, Ltd.
Keywords: freelancers; self-employed; career success; portfolio workers
Introduction
Is the traditional career of the 20th century becoming rare? Since the 1980s, many authors have argued that the
responsibility for the career increasingly resides in the individual (Arnold, 2001; Raabe, Frese, & Beehr, 2006)
and transcends any employer (Arthur, Khapova, & Wilderom, 2005). But recently, Rodrigues and Guest (2010)
showed for multiple countries that the collapse of the traditional career model is not supported at all by actual job
tenure data. Does this imply that the commonly accepted view of people having increasingly multiple-organization
careers is a myth? This paper argues that the observed stability of job tenure may be caused by the emergence of a
new type of worker: the skilled independent professional. This new type of worker contracts out her or his skills to
various organizations (Barley & Kunda, 2004; Barley, Kunda, & Evans, 2002; Connelly & Gallagher, 2004;
Kirkpatrick & Hoque, 2006).
Avant la lettre, Handy (1985) coined the term “portfolio worker” for those workers who create a portfolio of work
for themselves. In current times, terms such as freelancer, independent professional, or contractor are more frequently
used. Although it is hard to estimate the exact number of these workers, it is clear that their incidence is growing since
the 1980s. According to Arum and Müller (2004, p. 1), “[s]elf-employment can no longer be dismissed as an economic
*Correspondence to: Jan van den Born, School of Economics, Utrecht University, Utrecht, The Netherlands. E-mail: j.a.vandenborn@uu.nl
Copyright © 2012 John Wiley & Sons, Ltd.
Received 07 December 2010
Revised 18 January 2012, Accepted 22 January 2012
Journal of Organizational Behavior, J. Organiz. Behav. 34, 24–46 (2013)
Published online 9 March 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.1786
Research Article
activity on the verge of withering away in response to processes of capital accumulation or in competition
with large firms.” Marler, Barringer, and Milkovic (2002) showed that these boundaryless workers can be
distinguished from traditional “temps” by their preference for temporary work in combination with their high
level of skill and experience. The claim is that these “contractors of choice” are especially likely to report
positive outcomes about job and career satisfaction (Anderson, 2008; Ajayi-Obe & Parker, 2005; Benz &
Frey, 2008; Guest, 2004; Guest & Clinton, 2006). As Blanchflower (2004, p. 21) summarized this argument,
“These self-employed work under a lot of pressure, find their work stressful and come home exhausted. However, they are especially likely to say they have control over their lives as well as being highly satisfied with
their lives.”
The increase of the number of independent knowledge workers fits nicely with a prediction of Handy
(1989), who argued that the organizations of the future will have three types of workers: (i) professional
employees representing the core competencies of the organization; (ii) professional freelancers contractually
hired on a project-by-project basis; and (iii) a contingent workforce doing routine jobs. Handy introduced
the term shamrock organization for this new mode of organizing. Growth in the density of this shamrock
type of organizations could explain why overall job tenure does not decrease, because the freelancer is
officially not an employee of the organization but is self-employed. Although these free agents hop from
one organization to the next, they never officially change their employer. In a way, they are employees turned
into entrepreneurs.
The shamrock organization model is associated with the coexistence of two career types. On the one hand, the
organization largely determines the career of the professional core. Hence, Lips-Wiersma and Hall (2007) correctly
argued that the role of the organization is not over. On the other hand, the individual largely determines the career of
the growing number of independent professionals—or freelancers. Consequently, modern career concepts, such as
the protean career model (Hall, 1976, 2002) and the intelligent career framework (Parker, Khapova, & Arthur,
2009), apply nicely to this new type of workers. This paper studies extensively the determinants of the career success
of this second group: the professional freelancer, a growing phenomenon in many developed countries but not yet
the focus of many career studies.
Building a Freelance Career Success Model
Process
Freelancers can be considered as a hybrid of employees and entrepreneurs. On the one hand, they are employees
because they are almost always hired by (large) firms to work for a period selling nothing else but their intangible
professional knowledge, which is different from other entrepreneurs and self-employed selling tangible products to
customers. On the other hand, they are entrepreneurs because they work for their own risk and reward without any
organizational guarantee or support. Any freelance career success model should therefore build on insights from
both the modern career literature and the literature on entrepreneurship. To develop a testable model, we first created
a draft career success model based on the available academic literature. This first draft model consisted of a number
of building blocks that included variables that might predict freelance career success, with five to 10 variables
for every building block. Second, we piloted this first conceptual model with experts from the field by interviewing 51 practitioners (i.e., 33 freelancers and 18 outside experts from intermediary agencies and professional
associations). On the basis of their comments, we created a final freelance career success model in our third step
by (i) adding groups of variables that were considered to be missing and by (ii) converting our variables into
operationalized measures. In the fourth step, we developed an online survey and administered it to test this final
freelance career success model.
DRIVERS OF FREELANCE CAREER SUCCESS 25
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Individual career perspective
Our first draft model was based both on the career and entrepreneurship literatures, starting from the intelligent
career framework (Parker et al., 2009) as the core. We consulted studies in the entrepreneurship literature to add
variables to our core model that may be related to entrepreneurial success. The freelance career can be seen as quite
similar to the boundaryless career. DeFillippi and Arthur (1996) defined boundaryless careers as “sequences of job
opportunities that go beyond the boundaries of single employment setting.” A freelancer is probably the archetypical
job hopper going from one project and employer to the next, never staying for very long in a single organization.
Therefore, the boundaryless career concept provides a good starting point to build a freelance career success model.
The boundaryless career concept was already developed in the 1970s at the Massachusetts Institute of Technology
(Tams & Arthur, 2010). However, the boundaryless career perspective was really developed further in the 1990s,
especially through the efforts of DeFillippi and Arthur (1994, 1996) and Arthur and Rousseau (1996). In the
mid-1990s, Arthur, Claman, and DeFillippi (1995) pointed to strong links between Quinn’s (1992) intelligent
enterprise and the boundaryless career concept. They argue that the creation of the intelligent enterprise requires
intelligent careers, which are built upon principles from the boundaryless career concept. This intelligent career
concept evolved into the intelligent career framework (Parker et al., 2009).
The intelligent career framework includes three interrelated classes of variables, referred to as “ways of knowing,”
that are argued to predict career development. The first way of knowing is knowing why, which involves career
motivation, personal meaning, identity, and personality. Knowing why is associated with an individual’s capability
to understand herself or himself, to explore different possibilities, and to adapt to constantly changing work
circumstances. The second way of knowing is knowing how, which reflects career-relevant skills and job-related
knowledge. Knowing how is closely related to established ideas on individual knowledge, skills, and abilities
(Schneider & Konz, 1989). The third way of knowing, knowing whom, involves relevant personal and business
networks. This has to do with career-related networks and contacts, including business relationships and personal
connections (Parker & Arthur, 2000). These three classes of variables are not independent; there are strong links
between these concepts, with a range of different theories explaining these linkages (Parker et al., 2009).
Despite the theoretical attractiveness and practical relevance of the intelligent career framework, much empirical
work continues to focus on career success as evaluated from an organizational perspective (e.g., in terms of
organizational position and promotion). This seems outdated because hierarchies are continuously flattening (Littler,
Wiesner, & Dunford, 2003) and because external labor markets generate an increasing influence over today’s
employment landscape (Cappelli, 1999). Arthur et al. (2005) therefore called for further rapprochement between
career theory and empirical research. As the employment landscape is changing, career research should reflect
the “new deal” that views the career actor as concerned more with individual rather than organizational goals and
which involves the kind of “meta-competencies” that allow for easier mobility between successive employers or
temporary contracts.
Entrepreneurial perspective
The intelligent career framework is originally based on organizational models of firm competencies and firm success
(DeFillipi & Arthur, 1994, p. 308). This is why the intelligent career framework offers an appropriate platform for
developing our draft freelance career success model. We cross the same bridge as DeFillipi and Arthur did and review
empirical results reported in studies of firm success in search for variables to be added to the model. Adding such
business aspects to our freelance career success model will create a better fit with freelancing challenges and dilemmas.
After all, a modern freelancer is a self-employed entrepreneur as well and not only a “boundaryless employee.” Hence,
we are especially interested in research into small entrepreneurs such as the self-employed and small businesses and not
so much in large businesses where the founder influence is limited (Boone, De Brabander, & van Witteloostuijn, 1996)
and other qualities are needed to be successful (Scott & Bruce, 1987).
26 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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DOI: 10.1002/job
In the succeeding texts, we highlight the core drivers of entrepreneurial success as identified by empirical research
into the self-employed and small businesses (see Parker, 2004, for an overview). First, the human capital of the
entrepreneurial founder has a strong positive impact on firm performance (e.g., Bosma, van Praag, Thurik, &
de Wit, 2004; Pennings, Lee, & van Witteloostuijn, 1998). Being well educated or highly experienced, for instance,
contributes to the success of entrepreneurial ventures. Second, much evidence relates to the positive effect of social
capital on firm success (e.g., Bosma et al., 2004; Chiesi, 2007; Hoanga & Antoncic, 2003; Witt, 2004). Inherently,
entrepreneurship is a process of building bridges in a network, and hence of developing and maintaining social
capital, which provides broad and early access to information, and offers control over the distribution and
interpretation of information. In the freelancing context, social capital can be expected to (i) generate a broad base
of referrals; (ii) help the freelancer identify promising opportunities; and (iii) increase the probability that the
freelancer knows how to pitch a project. Third, the study of the impact of what may be coined personal capital
on entrepreneurial success has a long tradition. Entrepreneurs are argued to “engage the energies of everyone,”
“involve many people inside and outside the organization,” “create and sustain networks of relationships,” and
“make the most of the intellectual and other resources people have to offer” while “helping those people to achieve
their goals as well” (McMillan & Gunther-McGrath, 2000, p. 3). These qualities highlight the desirability of a
specific type of personality or other personal features, such as self-insight and leadership style.
In their overview of the literature on entrepreneurial personality, Amit, Glosten, and Muller (1993) suggested
that the four personality traits most commonly associated with self-employment are as follows: (i) need for
achievement (McClelland, 1965); (ii) internal locus of control (Sexton & Bowman, 1986); (iii) above-average
risk-taking propensity (Brockhaus, 1980); and (iv) tolerance for ambiguity (Frenkel-Bruswik, 1948). Note,
however, that although research has shown that personality traits are important for entrepreneurial success,
personality traits have typically produced very weak relationships with entrepreneurial performance (Begley &
Boyd, 1987; Low & MacMillan, 1988; Parker, 2004; Stam et al., 2012). The search for a psychological
explanation for business success has led to the development of tailor-made multifaceted “entrepreneurial”
personality traits, such as Chen, Greene, & Crick’s (1998) entrepreneurial self-efficacy.
Expert piloting
After the creation of our draft freelance career success model, on the basis of the review of the literature, we piloted
this draft in interviews with practitioners. These interviews were semi-structured. They started with open-ended
questions (e.g., How would you define freelance career success? and What are the key factors that determine
freelance success?), followed by a series of partly closed and partly open questions on the overall draft model
and each variable in the model (e.g., Do you think this is an important determinant of freelance career success or
not, and why do you think so?). Our interviewees defined two major topics that they considered to be missing.
Additionally, they expressed a variety of smaller suggestions about missing variables and suggested many improvements in defining variables and operationalizing measures. One example is partner support. We included this
variable in the final model, as many interviewees convincingly argued that a freelancer could not be successful
without the proper support of her or his partner.
The first and largest deficiency in our draft model was the lack of reference to the external market. All 51
interviewees noticed that we ignored market factors in our initial model design. This is especially important because
we know that the environment is crucial for firm performance (Porter, 1980). A stylized fact in the business literature
is that organizational and industry variables dominate over individual-level variables as drivers of venture success
(e.g., McGahan & Porter, 1997; Sandberg & Hofer, 1987). In contrast, the external product or service market in
which the employer organization operates often plays a minor role in the employee career literature. In fact, career
studies generally neglect such forces. The studies that link career outcomes with characteristics of internal labor
markets, which were quite popular in the 1980s (e.g., Baron, Davis-Blake, & Bielby, 1986), are perhaps the most
relevant for our study. However, internal labor markets are very different from their external product or service
DRIVERS OF FREELANCE CAREER SUCCESS 27
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DOI: 10.1002/job
market counterparts. In recent years, the issue of context has gained, again, prominence in career research
(Arnold & Cohen, 2008; Cohen & Mallon, 1999; Mayrhofer, Meyer, & Steyrer, 2007). But as far as we know,
quantitative empirical studies that estimate the impact of the external product or service market on career factors
or outcomes are absent.
Clearly, our expert and freelance interviewees all consider the external market to be a very important driver of
freelance career success. Cyclical and structural market conditions of demand and supply, transparency, industry
structure, and industry institutional arrangements are likely to largely determine career outcomes and may codetermine key success factors. For instance, in the Netherlands, the traditional free professions (i.e., accounting,
law, notary, and medicine) are associated with strict education and learning requirements that effectively regulate
and limit supply and necessitate pre-entry and post-entry investments in human capital for all independent
professionals in the market (for an example, see Maijoor & van Witteloostuijn, 1996). In other markets (e.g.,
interpreters and some technical occupations), there are only a limited number of companies that employ the
specialized services of freelancers, implying an oligopsony that drives fees down (Bhaskar & To, 2003). Our
interviewees pointed out that some markets had only a limited number of potential clients; in these markets,
managing reputation was crucially important to retain business. In other markets, the number of potential clients
was much larger; in such markets, creating visibility by branding, marketing, and networking was said to be of much
greater importance than reputation management.
The second major aspect that was lacking in our initial freelance career success draft framework, according to our
interviewees, was business strategy. The essence of the strategic management literature is the assumption, for which
there is ample evidence (e.g., McGahan & Porter, 1997), that strategy matters, which is also echoed in the entrepreneurship tradition (e.g., Parker, Storey, & van Witteloostuijn, 2010). Hence, we decided to add a number of business
strategy variables in our model to reflect the fact that a freelancer is both an individual employee and an entrepreneurial “organization,” in the latter capacity pursuing business strategies just like any other business venture.
Crafting a Testable Freelance Career Success Model
Model
In Figure 1, we summarize our final freelance career success research model. The central concepts of the intelligent
career framework remain largely in place, where human capital can arguably be seen as representing knowing how,
(Individual)
career
literature
Draft freelance
career model
Entrepreneurship
literature
Interviews
(51)
Final
detailed
model
Online
survey
I II III IV
Analysis
V
Figure 1. Stepwise approach of this study
28 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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DOI: 10.1002/job
social capital as reflecting knowing whom, and both motivation and personality measures, which can jointly be
coined personal capital, as mirroring knowing why. However, compared with the intelligent career framework,
our freelance career success model is extended by adding two clusters of variables that represent the external
environment and business strategy. This is precisely the consequence of the hybrid nature of the freelance status,
implying that the individual operates as both an employee and an entrepreneur.
Of course, an empirical model is never complete and is therefore always restricted in one way or the other.
Inevitably, developing a measurement model, even one that is designed to be so comprehensive as ours, implies that
decisions have to be made about what not to take on board. For instance, not taking any variables into consideration
that refer to organizational support—a decision based on the observation that, in the end, freelancers are not
employees linked to a single organization—means that hypotheses regarding this aspect cannot be tested. As a rule,
the selection of variables in our final model is based on the academic literature and suggestions from practitioners,
bounded by practical considerations about the maximum length of a survey. In this context, the exploratory
nature of our study is key. That is, the series of hypotheses introduced in the succeeding texts is very broad
in an attempt to assess which potential drivers of career success are more important than others. Our aim is
not to test any specific theory but rather to broadly explore what does and what does not matter in this new
freelance world. In so doing, for instance, our study offers the opportunity to evaluate the value added of
variables derived from the entrepreneurship literature, such as the market environment and business strategy,
relative to those inspired by the career literature.
A final remark relates to our choice of career success measures. We used two measures of success: objective
career success (OCS) and subjective career success (SCS). Moreover, our model explicitly incorporates the
interrelationship between OCS and SCS. The distinction between OCS and SCS is important in the freelancer
context because independent professionals self-select into self-employment for a variety of reasons: not only
monetary motives but also arguments relating to autonomy, flexibility, and work–life balance are potentially important. Indeed, the extant literature emphasizes this reinforcing feedback loop, where career success not only produces
happiness but happiness also enhances further career success (Boehm & Lyubomirsky, 2008). Where we believe
that we can expect different effects of our independent variable upon OCS vis-à-vis SCS, we will develop two
separate sub-hypotheses; if we see no reason to expect a differential effect, we simply refer to freelance career
success in our hypotheses.
Hypotheses
We introduce our hypotheses by systematically discussing all Figure 1’s clusters of variables. We start with
the well-established standard human capital (knowing how) determinant of success, as this set of capabilities
is strongly supported by the literature on career success as well as that on entrepreneurial performance.
Moreover, on the basis of our interviews with experts and freelancers, we have learned that freelancers are
very aware of the need to continuously develop their human capital. They are always scanning knowledge
sources (e.g., internet, books, and magazines) to be up-to-date on the latest industry trends (Barley &
Kunda, 2004).
Hypothesis 1: Human capital is positively related to freelance career success.
Social capital (knowing why) is another cluster of variables that figures prominently in both the career and
entrepreneurship literatures. In traditional career research, measures of organizational sponsorship, which reflects
social capital in the context of a traditional career, often represent social capital. The meta-analysis of Ng, Lillian,
Eby, Sorensen, and Feldman (2005) shows that organizational sponsorship variables (e.g., career support, training
and skill development opportunities, and mentoring) demonstrate a weak relationship with salary and promotion
and a strong relationship with career satisfaction. As organizational support is largely absent for freelancers, with
DRIVERS OF FREELANCE CAREER SUCCESS 29
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DOI: 10.1002/job
the notable exception of support of former employers (Cohen & Mallon, 1999), most organizational sponsorship
measures are not very applicable to freelancers. One concept that is closely related to organizational sponsorship
is applicable to freelancers, though: agency sponsorship. Support of staffing agencies is very important for
freelancers (Barley & Kunda, 2004). Many freelancers develop strong relations with external staffing agencies,
which are important partners for freelancers in their search for new assignments.
In the entrepreneurship literature, network characteristics commonly measure social capital (e.g., Chiesi, 2007;
Witt, 2004). Such measures of social capital are applicable in the freelancer context, too, as a strong network is
required to provide a steady flow of assignments, especially in the absence of supporting agencies. Hence, the
characteristics of these networks, such as network size and tie strength (Granovetter, 1973), offer a second
perspective to measure the social capital of freelancers. A third perspective regarding social capital inspired by
the entrepreneurship literature involves the amount of support that an entrepreneur receives. Such support could
come not only from a business club or business network, such as Lions and Rotary (Barbieri, 2003; Davidson &
Honig, 2003), but also from the freelancer’s partner. Our interviewees strongly argued that freelancing is stressful
and that partner support is therefore critical.
Hypothesis 2: Social capital is positively related to freelance career success.
To measure knowing why, we focus on personality traits and motivational drivers. In selecting personalityrelated variables that can represent this personal capital of freelancers, we largely followed the example of
Eby, Butts, and Lockwood’s (2003) and used career insight, pro-activeness, and openness to represent the
freelancers’ personal capital (Figure 2). These measures of personal capital are distinct from the traditional
entrepreneurial personality measures such as internal locus of control, risk-taking propensity, and need for
achievement. We have three main reasons to do so. First, we needed to limit the number of personality-related
variables to a maximum of three because personality measures often encompass lengthy item lists that are very
time-consuming to complete. Second, these traditional entrepreneurial personality measures have never been
shown to be very powerful predictors of performance in the entrepreneurship literature (Stam et al., 2012),
and there is no reason to assume that these personality characteristics are more important in a freelance context,
where professional skills are arguably at least as important as entrepreneurial capabilities. Third, our interviewees
Subjective
Succes
(Satisfaction)
Objective
Succes
(Revenue)
Business
strategies
Market
factors
Social
Capital
(Knowing
-whom)
Human
Capital
(Knowing
-how)
Personal Cap
& Motivation
(Knowing
-why)
Figure 2. The final freelance career model
30 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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DOI: 10.1002/job
emphasized the importance of career insight, pro-activeness, and openness. For instance, career insight involves
knowing which assignments to accept and which assignments to reject to build a clear profile and a strong
résumé, which was frequently reported as crucial to career success.
Another important aspect of knowing why is motivation. Not all freelancers pursue a freelance career because
of the same underlying motivational driver. Monetary reasons reflect, of course, a viable and often expressed
argument, but intrinsic motivation as reflected in the wish for increased autonomy, flexibility, and work–life
balance may well be even more important. It is highly likely that the type of motivation influences the outcome
of a career. Heslin (2003) suggested that career motivations and goals are the main yardsticks against which to
evaluate career success. From our interviews with freelancers, we learned that a trade-off might be at play here.
That is, those freelancers motivated by non-financial objectives related to autonomy, flexibility, and work–life
balance emphasize SCS over OCS yardsticks and vice versa, where subjective yardsticks involve “soft” nonmonetary satisfaction and objective yardsticks “hard” economic achievements.
Hypothesis 3: Career insight, pro-activeness, and openness are positively related to OCS and SCS.
Hypothesis 4a: Autonomy, flexibility, and work–life balance are negatively related to OCS.
Hypothesis 4b: Autonomy, flexibility, and work–life balance are positively related to SCS.
As said, we added business strategy to our freelance career success model because of the suggestions made
by our interviewees and for theoretical reasons as strategy is the linking pin between the external environment
and internal capabilities (Venkatraman & Camillus, 1984). As far as we know, studies on the business
strategies of freelancers are missing altogether. We decided to apply the well-established resource-based view of the
firm so as to develop a typology of freelance business strategies (Barney, 1991; Maijoor & van Witteloostuijn,
1996). The resource-based view argues that sustainable competitive advantage is derived from resources that are
valuable and rare and which are protected from imitation, transfer, or substitution. This implies that freelancers
must try to construct a rare, valuable, and protected combination of resources (knowledge, skills, abilities,
networks, etc.), which together create a sustainable competitive advantage. Such a strategy is very hard to pursue,
as even complex combinations of resources are subject to imitation and substitution in the low barrier world of
freelancing. Therefore, freelancers tend to reveal a strong tendency to continuously develop themselves to stay
ahead of competition.
We built our business strategies upon the insights of Ostgaard and Birley (1996), who distinguish the following
six strategies for businesses with less than 50 employees: marketing differentiation, product innovation, broad
market/product range, many distribution channels, growth through capital, and differentiation through quality.
These small-business strategies were discussed with our interviewees, which resulted in a somewhat adopted set
of seven freelance business strategies. In our final set of freelance business strategies, Ostgaard and Birley’s
(1996) small-business strategies of many distribution channels and growth through capital were replaced by
(i) price leadership, (ii) industry specialization, and (iii) product specialization. Subsequently, on the basis of the
theories of Porter (1980), Baum, Locke, and Smith (2001), and Boone and van Witteloostuijn (2004), not all these
resulting seven freelance business strategies can be expected to improve freelance career success. That is, only
those business strategies that aim at low cost, focus, or differentiation are hypothesized to lead to freelance career
success by developing a competitive position in a freelancer’s niche. The other strategies are “stuck-in-the-middle”
and doomed to produce failure (Porter, 1980, p. 42).
Hypothesis 5: Low-cost, focus, and differentiation strategies are positively related to freelance career success.
On the basis of insights from the management literature, we hypothesize that features of the external market are
the most important determinant of OCS. In contrast, on the basis of earlier empirical results from the traditional
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career literature (Ng et al., 2005) and related to the arguments earlier, we hypothesize that personal capital has the
strongest impact on SCS.
Hypothesis 6a: Market features dominate all other factors in explaining OCS.
Hypothesis 6b: Personal capital dominates all other factors in explaining SCS.
Method
Participants
We collected data using a customized Internet survey in the Netherlands, which ran in the period from 1 January to 1
April 2008. At the time of the research, the Netherlands is still experiencing an extensive period of robust economic
growth, although doubts about the economic outlook were starting to emerge in the financial press as the sub-prime
banking crises in the U.S. already started to affect consumer confidence and the stock exchange, albeit not yet in the
Netherlands. A variety of professional associations that represent freelancers in a range of occupations or professions
supported the Internet survey. In total, 19 professional associations agreed to inform their members about the
Internet survey and to send their membership a link to the online survey. As a result, a total of about 40 000
independent professionals received an email with information on the survey, including a hyperlink to the survey.
Of this population, 3146 persons clicked on this hyperlink to an introduction page explaining the goal of the research
and the time needed to complete the questionnaire. From this number, 1981 individuals actually started to work on
the online survey. In the end, 1612 respondents (88 per cent of those who started) finished the entire survey of 24
pages and completed 99 questions.
Our sample might reflect an overrepresentation of individuals who are members of the sponsoring professional organizations. However, the large number of respondents who copied the web link of the questionnaire
so as to forward this link to their friends and colleagues somewhat mitigated this effect. Moreover, a few very
broad Internet sites offered their services by providing information to unspecified independent professionals,
putting the link to the online questionnaire high up on their Internet pages. All in all, less than 50 per cent
of the respondents in this study are members of a professional organization. Moreover, we cannot exclude
the distinct possibility that some professions are more eager to respond to this type of requests than other
professions, which implies another source of possible overrepresentation of specific professions. In the succeeding
texts, we present the occupational distribution of our sample. Of course, this distribution must be kept in mind while
interpreting the evidence.
Variables and measures
We have two measures of career success. We assessed SCS using a slightly modified version of the widely used
(e.g., Boudreau, Boswell, & Judge, 2001; Judge & Ferris, 1993; Seibert, Kraimer, & Liden, 2001) career satisfaction scale of Greenhaus, Parasuraman, and Wormley (1990). Heslin (2005) earlier used this 6-item scale. Item
examples are How satisfied are you with the income you have attained, relative to your career aspirations? and
How satisfied are you with the autonomy you have attained, relative to your career aspirations? We measured
OCS using revenue (logged, to produce normality). In most research into firm success, a definition of income is
used instead of revenue. In the freelancer context, however, revenue is superior to income as an OCS measure
for two critical reasons. On the one hand, income is subject to tax manipulation. As a consequence, although in
32 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
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cross-industry entrepreneurial analysis income is often used, this is arguably a very noisy measure in practice.
On the other hand, usually, revenue cannot be used as a measure for entrepreneurial success, because the cost
structure of one entrepreneur (e.g., a car dealer with a large stock) can be totally different for another entrepreneur (e.g., an owner of an industrial company). However, because all respondents of our survey are highly
educated knowledge professionals, they all have similar (and relatively limited) costs. In the Netherlands, they
all need a laptop, a (mobile) telephone, an Internet connection, and a car to perform their profession. They
do not have cost of capital or extreme procurement costs that are dominant in companies dealing with tangible
goods. Of course, up-market professionals have higher absolute costs than professionals working in down-market
segments. In our sample, however, the costs reported by the respondent-freelancers are consistently between 20
and 30 per cent of total revenue. Our interviewees confirmed this percentage, and it is in line with the figures
noted in more popular research on the Internet. We do not think revenue that is a perfect measure, as some
professionals may have higher relative costs than others, but we do believe that, in the case of the independent
professionals, revenue is an appropriate measure of monetary success that is less biased than income.
To measure human capital, we used a number of variables. First, to measure experience, we included total work
experience and total freelance experience. We measured these by using the same scale, with eight answer categories:
0–6 months, 7–12 months, 1–2 years, 3–5 years, 6–10 years, 11–15 years, 16–20 years, and 21 years or more.
Second, we asked for the highest educational level of the freelancer, including the year of graduation, which is a
scale running from primary school (“Lagere school”) to a post-university degree (“post-doctoral”). Third, on the
basis of van der Heijden (2006), we asked the respondents to estimate the number of training days in the last two
years. We split this up in three types of training: training in core skills, training in developing new skills, and training
in adjacent or supporting skills (e.g., administration or personal effectiveness).
To measure social capital, we used network characteristics and a few proxies for support. First, to measure
the network characteristics, we first asked respondents to estimate the size of their personal and business
networks. The network definition given follows Witt’s (2004). We defined strong contacts in line with
Granovetter (1973) as those personal contacts that are family or friends. On the basis of Barbieri (2003)
and Davidson and Honig (2003), we divided the network in higher and lower value contacts. We defined
higher value network ties as contacts at the senior management level or above (i.e., director, vice-president,
senior vice-president, or CEO). Second, we included a question about how much time the respondent invested
in networking activities. On the basis of Aldrich and Reese (1993), we asked respondents to indicate the time
per week (in hours) that they engaged, on average, in networking, with reference to Forret and Dougherty’s
(2001) list of networking activities. Specifically, we asked individuals to rate how frequently they engaged,
on a 6-point scale ranging from never to almost every day, in giving out business cards; sending thank you
notes or gifts to people who have helped you in your work or career; sending cards, newspaper clippings,
faxes, or emails to keep in touch; phoning business contacts to keep in touch; and having lunch with business
relations. Third, to measure the amount of support the freelancer received, we asked whether the freelancer
was a member of a business club or business network such as Lions and Rotary (Barbieri, 2003; Davidson
& Honig, 2003). Fourth, we measured the influence of agency support by asking for the number of employment agencies a freelancer was registered with as well as the frequency of their communications with these
agencies. Fifth and finally, we assessed the support of the partner by using Greenhaus and Friedman’s
(2000) 4-item measure, using a 5-point Likert-type scale.
To measure personal capital, we included personality as well as motivational factors. In selecting variables,
we largely followed the example of Eby et al. (2003), using their measures of career insight, pro-activeness,
and openness. We included Noe, Noe, and Bachhuber’s (1990) 6-item measure to assess career insight. We
measured the pro-active personality trait by using Kickul and Gundry’s (2002) version of the 5-item scale
of Bateman and Crant (1993). We assessed openness to experience through Saucier’s (1994) so-called
Mini-Markers Set. We captured motivation by asking respondents to indicate the reasons why they had opted
for a freelance career. We formulated eight predefined answers in our survey on the basis of our interview
results: “more autonomy,” “increased professionalism,” “more work variety,” “more money,” “more flexibility
DRIVERS OF FREELANCE CAREER SUCCESS 33
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in time management,” “better work–life balance,” “more challenge,” and “I had no choice.” Of course,
alternatively, we could have asked them about their current instead of their past motivations, but only the
former captures career choice motives. Moreover, we feel that the measure of past motivation is probably
highly correlated with current motivation because most respondents started their freelance career only three
to five years ago.
To measure business strategy, we included two variables. A first variable measures the distinctiveness of the
respondent-freelancer’s business proposition vis-à-vis the competition (using a 7-point Likert-type scale). A
second set of measures assesses the business strategy of the freelancer by using a number of statements based
on our seven freelance business strategies: that is, “I focus on a single industry or a single organization,” “I
focus on a single product/service,” “I offer better service,” “I offer my products/services at lower costs than
the competition,” “I have a broad product/service portfolio,” and “I offer innovative products/services.”
Although our interviewees strongly suggested to incorporate market factors into our measurement model,
this was not easy because objective information on these freelance markets and their many niches is
completely missing in the Netherlands. Regrettably, reliable information on market factors such as numbers
of suppliers, number of competitors, market transparency, and intellectual property rights is simply not available. However, if we would not take account of the environment, this is likely to produce an omitted-variable
bias. The potentially strong impact of the market combined with the adverse effect of not including market
factors made us decide to use the profession of the freelancer [interim manager, information technology
(IT) professional, etc.] to act as a dummy proxy for all market factors. It is debatable whether this occupational dummy can act as a market dummy. Alternatively, this dummy can simply be taken as a control
variable (or even a human capital variable, as occupation tends to be highly correlated with certain specific
skills). However, our interviewees made strongly arguments in favor of a direct link between their occupation
or profession and the market they operate in.
That is, they convincingly argued that the characteristics of the market they operate in are very much
defined by their occupation or profession. In some professions, there is excess supply, being associated with
low and declining fees; in other professions, there is a shortage of skilled labor, resulting in high and increasing fees. Therefore, we decided to follow our interviewees by assuming that the occupational or professional
dummy signals market conditions more than anything else, although this dummy also captures other aspects
such as specific intangible skills. Additionally, influence of the external market was also measured with a
second indicator: the geographical location of the freelancer based on postal code. We translated the latter
into a binary variable that distinguished between the most urbanized part of the Netherlands—“Randstad,”
which is the heavily populated area in the Amsterdam–The Hague–Rotterdam–Utrecht triangle—and the rest
of the country.
Finally, we added a number of control variables to our freelance career success model, such as age (in years),
gender (with female coded as 0 and male coded as 1), and health status. We measured health status using a
self-assessed Likert-type scale ranging from 1 = poor health condition to 7 = excellent health condition. To conclude,
we included a dummy variable that indicated whether the freelancer had another source of income (e.g., through a
pension or a part-time job).
Designing the survey
We started with converting our final research model, variables, and corresponding measures into an Internet
survey. Sixty freelancers extensively tested this survey to ensure that all questions were clear and easy to
answer and that ambiguous and vague terms were avoided. The Internet survey was in Dutch. We first translated validated scales adopted from the international literature from the original English into Dutch and then
another person back-translated it from Dutch into English. If there were differences between the original
34 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
English text and the English text that was twice translated, both translators and a third independent person (all
fluent in Dutch and English) decided on the final text in Dutch.
Common-method variance (CMV) has been identified as a major problem in organizational research on the
basis of self-reports (Chang, van Witteloostuijn, & Eden, 2010; Podsakoff & Organ, 1986; Spector, 2006). Our
data are mostly self-reports of objective data and therefore only minimally affected by CMV issues (Spector,
2006, p. 229). Nevertheless, we adopted the procedural remedies suggested by Podsakoff, MacKenzie, Lee,
and Podsakoff (2003) to overcome any remaining CMV issues. We assured respondents of anonymity and
confidentiality and asked them to answer honestly. We assured individuals that right and wrong answers did
not exist. Also, the fact that the survey was sponsored by a total of 19 professional freelance organizations
that were all considered very trustworthy decreased the tendency of respondents to give socially desirable
answers. Moreover, we promised all respondents a benchmark report on their position in relation to other
independent professionals, which motivated respondents to give the “true” score, as they were anxious to
see how they performed within their peer group of freelancers, given that wrong answers would distort their
own benchmark report. We further designed the survey tool in a way to prevent CMV by adopting a random
order of questions, including different types of scales, and by providing clear information on the survey
completion process to prevent boredom.
Data Reduction and Analysis
Given the large number of survey questions, we performed data reduction analyses. For every cluster of multiitem independent variables (i.e., human capital, social capital, personality, motivation, and business strategy),
we performed a factor analysis on all variables to reduce data complexity. Multicolinearity was not an issue
in our data set (all variance inflation factor scores are lower than 1.6), with one exception: the colinearity
between age, year of graduation, and total work experience is too high. This is why we excluded both total
work experience and year of graduation from the list of human capital variables (but we kept age as a control
variable) on which the factor analysis was performed. The factor analysis generated four human capital
variables with (a) eigenvalues larger than 1 and (b) an appropriate pattern of loadings: (i) total freelance
experience; (ii) higher education level; (iii) university education level; and (iv) recent training (the sum of
training days in core professional, new professional, and supporting and adjacent skills in 2006 and 2007).
The original survey includes 18 items that attempt to measure social capital. We measured not only network size
but also the total number of weak and strong links, the number of persons new in the network (refreshment), and the
seniority or quality of the network. In a factor analysis, the original and validated scales of network activity (the five
questions from Forret & Dougherty, 2001) and partner support (the four questions from Greenhaus & Friedman,
2000) came out as separate variables, given the eigenvalues and loading pattern. This is why we performed a second
factor analysis without the nine network activity and partner support questions. This resulted in a clear 3-factor
outcome, applying the usual eigenvalue threshold (>1) and clean loading pattern criterion: size of the network,
business club membership, and actively managing employment agencies.
The survey includes 15 personality questions, relating to three validated scales: four on openness, five on proactivity, and six on career insight. From the factor analysis, all three scales emerged as specific factors with eigenvalues larger than 1 and a clean factor loading pattern. We thus decided to work with the original test scores
rather than the factor loading sum. On the basis of the usual eigenvalue and loading pattern criteria, three motivational factors emerged from the factor analysis with the eight motivation-related items: (i) motivated
by work–life balance and flexibility; (ii) motivated by professionalism and autonomy; and (iii) motivated by
challenge, variety, and money. This is a somewhat peculiar finding. It seems logical that work–life balance
and flexibility are in the same grouping, and the grouping of professionalism and autonomy is also
DRIVERS OF FREELANCE CAREER SUCCESS 35
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
understandable because a high level of professionalism might also require a high level of autonomy. It is less
understandable that variety loads on the same factor as challenge and money. Apparently, the same underlying motivational factor, which may be related to ambition and the need to feel challenged, drives these three
aspects.
We factor analyzed 11 business strategy items. On the basis of the eigenvalues and the loading pattern, four business strategy variables emerged: (i) freelancers who carry innovative and/or differentiating products or services;
(ii) freelancers who specialize in one industry; (iii) freelancers who offer a broad product/service range; and
(iv) freelancers whose value proposition is based on providing better service and/or lower cost.
Finally, we used correlations and logical reasoning to create a limited set of occupational dummies as proxies
for the external market, next to the geographical location dummy distinguishing the urbanized Randstad from
the rest of the country. In our data set, it was possible for freelancers to have more than one profession. So,
a freelancer could be a coach and a trainer or a software programmer and a functional analyst. This
allowed us to see which professions are very much linked. We used this information about overlap between
professionals together with logical reasoning to create six occupational variables. First, an interim manager
is someone who temporarily heads an organization or department frequently to turn this organization around.
Second, an interim professional is someone with specific knowledge of supporting business processes (e.g.,
finance, human resource management, legal, or IT). Third, a journalist or media professional is someone
working in the media industry (e.g., editor or journalist). Fourth, a technical professional is someone who
works as an engineer in electronics, construction, and so on. Fifth, coaches and trainers are involved in educating and coaching individuals and groups in certain skills. The sixth and last group defined was called other free
agents but consisted primarily out of persons working in facility management. This last group was the only
group with significant lower education levels with only 34 per cent having a master’s degree (overall, this
is 44 per cent).
The control variables included in this study are age, gender, having another source of income (e.g., a pension
scheme or a long-term employment contract), and health status. We excluded from further analysis a number of
other variables that are available from the survey because they are endogenous to freelance income (e.g., percentage
of freelance income of total income and size of the financial buffer in months). We added age and age squared to
explore the potentially parabolic relationship between age and freelance revenue, following standard labor economics. We included SCS in the OCS equation, and vice versa, to estimate the interrelationship between both measures
of freelance career success.
To investigate whether, despite all the preventive measures, CMV in the data might bias the estimates as reported
in the succeeding texts, we conducted a single overarching Harmon one-factor test. On the basis of these results,
there is no evidence that CMV poses a problem. The factor with the largest eigenvalue explains less than eight
per cent of the total variance, and more than 20 factors had eigenvalues above 1. In our study, we included six scales
that are based on multiple items: openness, pro-activeness, career insight, network activity, partner support, and
SCS. We checked the internal consistency of these scales. All tests proved reliable with satisfactory Cronbach’s
alphas (a > 0.7), and all reflected a single factor with an eigenvalue above 1.
Results
We show means and standard deviations of the study variables in Table 1. Tables 2 and 3 give the estimation
results of the freelance career success model. We estimated both equations using maximum likelihood with
robust variance estimators to tackle heterogeneity. We also applied a number of other estimators (such as an
instrumental variables estimation to deal with endogeneity issues), but all these robustness analyses generated
very comparable results (available upon request).
36 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Table 1. Means, standard deviation, and correlations of dependent and independent variables.
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1 Objective career success
(log)
10.77 1.15 1 .34 .12 .20 .05 .03 .20 .01 .12 .25 .05 .09 .11 .07 .04 .05 .08 .12 .15 .00 .04 .32 .27 .15 .08 .04 .24 .03 .16 .27 .34
2 Subjective career
success
31.96 6.50
— 1 .04 .08 .03 .10 .19 .18 .10 .05 .06 .25 .23 .29 .06 .06 .13 .28 .05 .04 .07 .04 .02 .04 .02 .02 .06 .02 .23 .02 .15
3 Bachelor’s degree 0.41 0.49 — — 1 .74 .03 .04 .03 .04 .03 .04 .03 .07 .03 .04 .05 .03 .01 .06 .06 .07 .04 .02 .08 .02 .08 .00 .01 .01 .06 .05 .05
4 Master’s degree 0.44 0.50 —— — 1 .00 .02 .00 .08 .08 .02 .00 .07 .02 .04 .07 .03 .06 .03 .10 .10 .06 .04 .15 .03 .01 .09 .08 .07 .08 .00 .06
5 Freelance experience
(years)
18.80 6.87 —— — — 1 .04 .10 .05 .00 .11 .00 .05 .06 .14 .07 .16 .03 .19 .07 .04 .03 .10 .13 .15 .06 .04 .15 .41 .01 .01 .05
6 Training days (per year) 5.87 5.48 —— — — — 1 .20 .02 .12 .01 .11 .09 .15 .23 .02 .02 .07 .13 .00 .02 .01 .05 .04 .13 .27 .05 .06 .02 .01 .07 .04
7 Network activity (score) 16.08 3.79 —— — — — — 1 .08 .31 .11 .25 .29 .24 .33 .00 .03 .05 .25 .02 .05 .03 .17 .13 .01 .12 .11 .14 .01 .08 .02 .06
8 Partner support (score) 14.94 3.99 —— — — — — — 1 .01 .04 .05 .14 .14 .19 .06 .01 .06 .10 .01 .01 .04 .03 .04 .04 .03 .04 .05 .10 .11 .16 .03
9 Size of network (factor) 0.00 1.00 —— — — — — — — 1 .00 .00 .15 .14 .11 .01 .01 .06 .19 .03 .04 .04 .11 .08 .01 .08 .04 .04 .04 .05 .03 .03
10 Managing agencies (factor) 0.00 1.00 —— — — — — — — — 1 .00 .06 .07 .12 .00 .11 .03 .12 .11 .08 .04 .41 .20 .14 .06 .10 .10 .01 .07 .18 .12
11 Business club membership
(factor)
0.00 1.00 —— — — — — — — — — 1 .11 .10 .15 .02 .00 .02 .09 .01 .01 .01 .06 .07 .05 .06 .08 .03 .07 .04 .08 .03
12 Openness (score) 19.07 3.21 —— — — — — — — — — — 1 .46 .33 .02 .03 .10 .21 .01 .06 .04 .16 .05 .05 .11 .01 .05 .01 .19 .09 .04
13 Pro-activeness (score) 21.91 4.00 —— — — — — — — — — — — 1 .34 .01 .06 .15 .35 .02 .07 .04 .22 .11 .04 .05 .03 .11 .02 .12 .07 .01
14 Career insight (score) 21.85 3.99 —— — — — — — — — — — — — 1 .01 .09 .06 .29 .06 .00 .04 .12 .04 .07 .14 .00 .06 .02 .12 .06 .01
15 Work–life balance (score) 0.00 1.00 —— — — — — — — — — — — — — 1 .00 .00 .03 .05 .02 .05 .07 .04 .07 .00 .05 .01 .18 .03 .10 .06
16 Challenge and profit
(factor)
0.00 1.00 —— — — — — — — — — — — — — — 1 .00 .03 .03 .06 .08 .10 .02 .00 .03 .05 .07 .13 .06 .02 .01
17 Autonomy and
mastery (factor)
0.00 1.00 —— — — — — — — — — — — — — — — 1 .16 .01 .02 .05 .01 .05 .02 .13 .09 .06 .07 .00 .06 .05
18 Differentiate and innovate
(factor)
0.00 1.00 —— — — — — — — — — — — — — — — — 1 .00 .00 .00 .00 .02 .05 .08 .03 .00 .18 .07 .03 .03
19 Industry specialization
(factor)
0.00 1.00 —— — — — — — — — — — — — — — — — — 1 .00 .00 .12 .12 .25 .02 .06 .02 .03 .03 .08 .03
20 Product range (factor) 0.00 1.00 —— — — — — — — — — — — — — — — — — — 1 .00 .09 .06 .01 .06 .03 .02 .05 .01 .02 .01
21 Price leadership (factor) 0.00 1.00 —— — — — — — — — — — — — — — — — — — — 1 .09 .00 .10 .00 .02 .02 .06 .01 .03 .03
22 Interim manager 0.27 0.45 —— — — — — — — — — — — — — — — — — — — — 1 .26 .20 .06 .03 .21 .12 .10 .28 .04
23 Interim professional 0.46 0.50 —— — — — — — — — — — — — — — — — — — — — — 1 .33 .02 .12 .29 .01 .04 .30 .03
24 Journalist 0.19 0.39 —— — — — — — — — — — — — — — — — — — — — — — 1 .23 .12 .08 .09 .05 .16 .04
25 Trainer/coach 0.36 0.48 —— — — — — — — — — — — — — — — — — — — — — — — 1 .14 .18 .14 .00 .11 .09
26 Technical 0.06 0.25 —— — — — — — — — — — — — — — — — — — — — — — — — 1 .08 .05 .02 .21 .02
27 Other freelancer 0.15 0.35 —— — — — — — — — — — — — — — — — — — — — — — — — — 1 .04 .05 .19 .03
28 Age 46.29 8.71 —— — — — — — — — — — — — — — — — — — — — — — — — — — 1 .00 .19 .15
29 Health status 6.07 0.89 —— — — — — — — — — — — — — — — — — — — — — — — — — — — 1 .04 .07
30 Gender 0.50 0.50 —— — — — — — — — — — — — — — — — — — — — — — — — — — — — 1 .00
31 Other income 0.24 0.43 —— — — — — — — — — — — — — — — — — — — — — — — — — — — — — 1
DRIVERS OF FREELANCE CAREER SUCCESS 37
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
Objective career success
Table 2 shows that the adjusted R2 is 44 per cent. This implies a very good model fit for this type of analyses, given
the stylized fact in the entrepreneurship literature that an (adjusted) R2 of 15 per cent is already exceptional in studies
of small-firm performance (Parker et al., 2010). Almost all control variables are significant and have the expected
sign. This is also the case for most external market and human capital variables, with only one remarkable exception
within the group of human capital variables: the significantly negative relationship between recent training effort and
OCS. This may indicate reverse causality. First, a freelancer engaging in training cannot charge clients for services.
Second, independent professionals without a current assignment have much more spare time to invest in training.
Within the group personality variables of the personal capital cluster, none of the variables are significant. Neither
career insight nor openness nor a pro-active personality seems to be related to OCS. A number of motivation
Table 2. Testing the freelance career model—objective career success.
Factor Coefficient SE z-statistic
Control variables Constant 6.980609 0.7493958 9.311
Age 0.0812761 0.0286227 2.841
Age2 0.0009651 0.0003046 3.171
Gender (male = 1) 0.4200948 0.0580151 7.241
Health (self-assessment score) 0.0472644 0.0286808 1.65†
Receiving other income (e.g., regular job) 0.589795 0.0666048 8.861
Market factors Living in Randstad area (Randstad = 1) 0.0799059 0.0480397 1.66†
Interim manager (dummy) 0.402606 0.0606881 6.631
Interim professional (dummy) 0.1432689 0.0589014 2.43*
Journalist and/or media professional (dummy) 0.3227554 0.0780846 4.131
Technical professional (dummy) 0.1039439 0.1071783 0.97
Trainer and/or coach (dummy) 0.2152659 0.0572763 3.761
Other free agent (dummy) 0.461215 0.0811657 5.681
Human capital University education (dummy) 0.3556629 0.0790103 4.501
“HBO” education (dummy) 0.1380998 0.0805207 1.72†
Freelance experience (log) 0.1622809 0.0229957 7.061
Recent training participation (sum) 0.0073522 0.0025106 2.931
Motivation capital Motivated by autonomy and professionalism 0.0415792 0.0244645 1.70†
Motivated by challenge and money 0.0196196 0.0237121 0.83
Motivated by work–life balance and flexibility 0.0535313 0.0235548 2.27*
Personality capital Insight in career (sum) 0.0042271 0.0074761 0.57
Open personality (sum) 0.0034226 0.0070108 0.49
Pro-active personality (sum) 0.0149058 0.0092242 1.62
Social capital Business club member (factor) 0.0149219 0.024884 0.60
Managing the agent (factor) 0.0838912 0.0248262 3.381
Size of network (factor) 0.0338625 0.0267804 1.26
Network activity (sum) 0.029306 0.0074711 3.921
Partner support (sum) 0.0018221 0.0058532 0.31
Business strategy Better service or low-cost strategy (factor) 0.0101224 0.0236016 0.43
Innovative and differentiation strategy (factor) 0.02476 0.0282662 0.88
Industry specialization strategy (factor) 0.0562731 0.0235155 2.39*
Broad product range (factor) 0.0468089 0.0247748 1.89†
Subjective career success (sum) 0.0441134 0.004511 9.781
Adjusted R2 0.4393 Est. method ML (robust)
Log likelihood 1754.97 Observations (n) 1385
Akaike information criterion 2.581907
†
significant at 10% level, *significant at 5% level; and **significant at 1% level.
38 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
variables are significant, though: freelancers primarily driven by flexibility and/or work–life balance motives have
lower revenue than professionals who are in freelancing for reasons other than that.
Two social capital variables proved to be significant: managing agencies, and the network activity score of
Forret and Dougherty (2001). Actively managing employment agencies (i.e., being registered with employment
agencies and actively visiting them) adds to the OCS of the independent professional. The positive and
significant estimate of network activity suggests that being an active networker is important. Calling people,
visiting business contacts, handing out business cards, and sending mails, cards, and gifts to individuals in
the network contribute to financial success. Neither the size of the network nor whether an individual is a
member of a business club adds to OCS. Also, partner support proved not to be related to OCS. Finally, the
business strategy variables also seem to add to OCS. Industry specialization is important for freelancers,
Table 3. Testing the freelance career model—subjective career success.
Factor Coefficient SE z-statistic
Control variables Constant 1.356558 4.407242 0.31
Age 0.2052013 0.173482 1.18
Age squared 0.002439 0.001864 1.31
Gender (male = 1) 0.799514 0.3471066 2.30*
Health (self-assessment score) 0.984084 0.1931404 5.10**
Receiving other income (e.g., regular job) 0.4763586 0.4079105 1.17
Market factors Living in Randstad area (Randstad = 1) 0.0150572 0.306105 0.05
Interim manager (dummy) 1.173891 0.398087 2.95**
Interim professional (dummy) 0.7440611 0.3739605 1.99*
Journalist and/or media professional (dummy) 0.5157591 0.5009407 1.03
Technical professional (dummy) 0.434244 0.6271601 0.69
Trainer and/or coach (dummy) 0.2692279 0.3636077 0.74
Other free agent (dummy) 0.0336539 0.5867987 0.06
Human capital University education (dummy) 0.7539263 0.4749756 1.59
“HBO” education (dummy) 0.4834854 0.4773371 1.01
Freelance experience (log) 0.003937 0.141182 0.03
Recent training participation (sum) 0.0242919 0.0153892 1.58
Motivation capital Motivated by autonomy and professionalism 0.3427641 0.1552853 2.21*
Motivated by challenge and money 0.1804451 0.1503154 1.20
Motivated by work–life balance and flexibility 0.3705541 0.1561106 2.37*
Personality Capital Insight in career (sum) 0.225339 0.0479622 4.70**
Open personality (sum) 0.1392696 0.049208 2.83**
Pro-active personality (sum) 0.130006 0.0616591 2.11*
Social capital Business club member (factor) 0.1613818 0.1536743 1.05
Managing the agent (factor) 0.0023039 0.1864347 0.01
Size of network (factor) 0.0089879 0.1558321 0.06
Network activity (sum) 0.0369041 0.0511501 0.72
Partner support (sum) 0.1274405 0.0467177 2.73**
Business strategy Better service or low-cost strategy (factor) 0.2922321 0.1466776 1.99*
Innovative and differentiation strategy (factor) 0.9313536 0.1882798 4.95**
Industry specialization strategy (factor) 0.0075344 0.1495288 0.05
Broad product range (factor) 0.4305484 0.162117 2.66**
Objective career success (log) 1.769593 0.1813919 9.76**
Adjusted R2 0.2868 Est. method ML (robust)
Log likelihood 4311.5 Observations (n) 1385
Akaike information criterion 6.273649
†
significant at 10% level;
*significant at 5% level; and
**significant at 1% level.
DRIVERS OF FREELANCE CAREER SUCCESS 39
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
significantly improving OCS. A broad product/service range is almost significant, leading to less OCS. This suggests that focuses on an industry and on a small range of products/services are important financial success
factors.
Subjective career success
Looking at the results in Tables 2 and 3, we observe that the adjusted R2 is considerably lower for the SCS (0.29)
than for OCS (0.44), albeit still far above the top level of 0.15 in the small-business literature. This suggests that
our model is better in predicting OCS than SCS. Moreover, the estimates reveal that a set of very different variables
is important in determining SCS when compared with OCS. In effect, the variables that are important for SCS are
those variables that are irrelevant to OCS and vice versa. Among the control variables, only self-assessed health
and gender are significantly predicting SCS, in the expected direction. Men are less satisfied with their career than
women, and freelancers with good health are more satisfied with their career than those struggling with their health.
By and large, external market and human capital indicators, although crucial for OCS, fail to affect SCS. Only the
dummies for interim managers and professionals are significant. Freelancers in these occupations are not as satisfied
with their career as one would expect. All personality variables from the personal capital cluster are strongly significant. Especially, career insight stands out, but the personality traits of openness to experience and pro-activeness are
essential drivers of SCS as well. Motivation is important for SCS, too: professionals who freelance for reasons of
flexibility and work–life balance and professionals who freelance because of autonomy and professionalism are
more satisfied with their career. Only one social capital variable has a positive impact on SCS: partner support.
Although not hypothesized about, for lack of theory on this, we explore the link between business strategy
variables and SCS. Indeed, business strategy turns out to be very important for SCS. Especially, independent
professionals who distinguish themselves through a strategy that is innovative and independent professionals
with a broad product/service range have highly satisfying careers (i.e., high SCS scores). It might be that a large
variety of different assignments and being innovative provides intrinsic utility to independent professionals,
which subsequently increases their SCS. Independent professionals with a “low-cost” or “better-service” strategy
have lower SCS levels. In the last row of Tables 2 and 3, the positive cross-relationship between OCS and SCS
is evident. Although causality cannot be established in cross-sectional analysis, OCS and SCS are definitely positively
related (r = 0.34, with p < 0.001). This correlation is somewhat higher than the average correlation of 0.30 between
salary and career satisfaction (with a 95 per cent confidence interval of 0.28–0.32) as reported by Ng et al. (2005) in their
meta-analysis of OCS and SCS for employees. The fact that some freelancers in our sample are largely motivated
by monetary rewards may perhaps explain the somewhat higher correlation between OCS and SCS when compared
with employees.
In Table 4, an overview is given of our findings about the first five hypotheses. For the most part, empirical
evidence supported these hypotheses. Nevertheless, there are some interesting observations. Human capital is
important for OCS but not for SCS. In contrast, personal capital is important for SCS but not for OCS. Only social
capital is important for both OCS and SCS. All in all, our hypotheses are fully or partly supported. Our hypotheses
are arguably not very detailed, but we feel that these broadly formulated hypotheses are appropriate in the context of
exploratory analyses of a relatively new and unexplored subject.
Our final two hypotheses (6a and 6b) are not listed in Table 4, as we need complementary analyses for this pair of
hypotheses. We applied Budescu’s (1993) dominance analysis, based on generalized least squares estimation, in this
respect. Although dominance analysis is a very labor-intensive method, it is very much suited to explore our two
remaining hypotheses. For every possible combination of variable classes, we calculated the R2 measure. In total,
this generates 64 measures of fit for OCS and SCS. For all combinations, we calculated the extra fit of adding a
new class of variables. Subsequently, we computed the average extra fit. We reported the outcomes of this exercise
in Tables 5 and 6. K is the number of variable classes already in the equation. In the cells, the extra R2 measured the
40 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
effect on fit. For example, if an equation includes only one class of variables (K = 1)—that is, human capital, social
capital, personality measures, motivational variables, or business strategy—the addition of the external market
proxies will increase the fit of that equation (i.e., R2
) with, on average, 0.0819. Similarly, if we add the external
market dummies to an equation with two other classes of variables included (K = 2)—for example, human and social
capital—the fit implied by the extra R2 will increase with an average of 0.0744.
Table 6. Dominance analysis of subjective career success.
External market Human capital Personality Motivation Social capital Business strategy
K= 0 0.0070 0.0104 0.0720 0.0155 0.0410 0.0614
K= 1 0.0081 0.0076 0.0613 0.0121 0.0358 0.0503
K= 2 0.0086 0.0055 0.0519 0.0097 0.0315 0.0406
K= 3 0.0087 0.0001 0.0437 0.0078 0.0220 0.0240
K= 4 0.0085 0.0032 0.0346 0.0063 0.0254 0.0256
K= 5 0.0083 0.0028 0.0311 0.0052 0.0236 0.0202
Mean 0.0085 0.0038 0.0481 0.0090 0.0283 0.0346
Relative percentage 6.4 2.9 36.4 6.8 21.4 26.2
Table 4. Overview of hypotheses.
Hypothesis Outcome
1. Human capital is positively related to OCS
and SCS
Partly accepted. Yes, for OCS (p < 0.001); no for SCS (p = 0.271)
2. Social capital is positively related to OCS
and SCS
Completely accepted. Yes, for OCS (p < 0.001); yes for SCS (p = 0.034)
3. Career insight, pro-activeness, and openness
are positively related to OCS and SCS
Partly accepted. No, for OCS (p = 0.18); yes for SCS (p < 0.001)
4a: Autonomy, flexibility, and work–life balance
are negatively related to OCS
Partly accepted. Only flexibility and work–life balance are motivations
that negatively affect OCS (p = 0.02). Autonomy is not negatively related
to OCS
4b: Autonomy, flexibility, and work–life balance
are positively related to SCS
Fully accepted. Flexibility and work–life balance are motivations that
positively affect OCS (p = 0.02). Autonomy is also positively related to
SCS (p = 0.03)
5. Low-cost, focus, and differentiation strategies
are positively related to OCS and SCS
Partly accepted. Focus strategies [i.e., product/service focus (p = 0.06) and
industry focus (p = 0.02)] are positively related to OCS. Differentiation
leads to SCS (p < 0.001). Low-cost strategy leads to less SCS (p = 0.04)
OCS, objective career success; SCS, subjective career success.
Table 5. Dominance analysis of objective career success.
External market Human capital Personality Motivation Social capital Business strategy
K= 0 0.0895 0.0368 0.0000 0.0059 0.0465 0.0134
K= 1 0.0819 0.0391 0.0011 0.0053 0.0411 0.0110
K= 2 0.0744 0.0405 0.0018 0.0047 0.0356 0.0090
K= 3 0.0669 0.0411 0.0020 0.0042 0.0298 0.0073
K= 4 0.0598 0.0413 0.0023 0.0038 0.0248 0.0055
K= 5 0.0531 0.0412 0.0014 0.0035 0.0200 0.0040
Mean 0.0707 0.0405 0.0018 0.0045 0.0328 0.0082
Relative percentage 44.6 25.6 1.1 2.8 20.7 5.2
DRIVERS OF FREELANCE CAREER SUCCESS 41
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
By inspecting all possible combinations, we can conclude that the average extra fit of adding external market
dummies is 0.0707. This is higher than the average extra fit of human capital (0.0405), personality (0.0018),
motivation (0.0045), social capital (0.0328), and business strategy (0.0082) variables. External market features
are, therefore, the most important determinant of OCS (44.6 per cent of explained variance), followed by human
capital (25.6 per cent) and social capital (20.7 per cent), respectively. Business strategy (5.2 per cent), motivation
(2.8 per cent), and personality (1.1 per cent) are all of minor importance in driving OCS.
We followed the same procedure to determine the relative importance of factors determining SCS. We summarized the findings in Table 6. Personality (0.0481; 36.4 per cent of explained variance) is the most important driver
of SCS, followed by business strategy (0.0346; 26.2 per cent) and social capital (0.0283; 21.4 per cent), in that order.
The other success drivers are all less important. The average extra fit of market features is 0.0085 or 6.4 per cent, of
motivation 0.0090 or 6.8 per cent, and of human capital 0.0038 or 2.9 per cent. These drivers are arguably very
important in determining OCS, but they are almost irrelevant in affecting SCS. Social capital is the only factor that
has a considerably positive impact on both OCS and SCS. On the basis of the evidence provided by the dominance
analysis, we accept our final two Hypotheses 6a and 6b.
Discussion
We would like to start with emphasizing the two major benefits of this study. First, it is one of the first large-scale
quantitative studies that specifically targets freelancers as the sample group. If Handy’s (1989) assessment about
modern organizations is correct, the number of freelancers will continue to increase, as current evidence strongly
suggests indeed. Second, as the freelance career is the quintessence of the individual career, as is argued in this
paper, freelance careers provide a great opportunity to test the individual career concept. We believe that this
empirical study of freelancers provided perhaps one of the best settings for testing insights from the individual
career perspective.
A first important finding of this study is that a model based on the intelligent career framework augmented with
factors from the entrepreneurship literature is largely suited to explain freelance career success, as is demonstrated
by the support for many of our hypotheses. Hence, this study confirms the usefulness of individual career models. A
second finding of this study is that subjective and objective career yardsticks are characterized by very different
underlying processes. Individual career makers continuously make trade-offs between SCS, on the one hand, and
OCS, on the other hand. More research into the trade-off between SCS and OCS is needed, to deepen our
understanding of the options open to freelancers. Additionally, a third key result of this study is that business aspects
such as the external market and business strategy are critical elements driving the freelance career. This confirms that
freelancers reflect a hybrid, with elements from the small-business world and aspects from individual career theory.
A better understanding of the external market and its implications for individual careers is needed if we want to
make better career models. Here, we find that career models can learn from strategic management, as both
perspectives address the question about what drives competitive advantage, but strategic management especially
addresses the environment as an element in the vector of variables that determine success. In the current study, only
a first step is taken.
We also think that there is much practical merit in all the small insights provided by this empirical research that is
relevant to freelancers. This paper shows that there is a parabolic influence of age on OCS and that the maximum
revenue of freelancers lies between 45 and 50 years of age; after which, utilization rates and professional fees
diminish. With respect to freelance networks, the study shows that network size is not important, but that building
strong relations with agents and putting substantial effort in building and maintaining a network are essential
elements that drive both OCS and SCS. Moreover, the paper demonstrates clearly the importance of a focus strategy,
where the freelancer specializes in a product/service or sector, as well as the adverse effects of a low-cost strategy.
These and other insights are interesting for academics but of great practical importance to freelancers, too.
42 A. VAN DEN BORN AND A. VAN WITTELOOSTUIJN
Copyright © 2012 John Wiley & Sons, Ltd. J. Organiz. Behav. 34, 24–46 (2013)
DOI: 10.1002/job
As any study, ours suffers from drawbacks, which leave room for further study. We would like to emphasize seven
of these. First, our final model as depicted in Figure 1 does not incorporate the concept of strategic fit (Parker &
van Witteloostuijn, 2010). Since the 1960s, contingency theory has argued convincingly that there is no universally
best way to achieve organizational success. Instead, the performance-maximizing course of action is contingent upon
the organization’s internal context and external environment. The concept of strategic fit is part of the intelligent career
framework, too, as interrelationships between the three ways of knowing may affect career outcomes. Although
modeling these interrelations is probably warranted from a theoretical perspective, we have not modeled these complex
interrelations at this exploratory stage of our research (see also Zajac, Kraatz, & Bresser, 2000) but set this aside as a
task for future work.
Second, this study is cross-sectional, which implies that we cannot study career changes over lifetimes. As the
modern career becomes more fluid, individuals leap increasingly from one assignment to another and from a
freelance contract to a traditional employment job and back. Third, our cross-section only includes freelancers,
implying that we cannot investigate any selection effect. In future work, it would be interesting to see what causes
an individual to become a freelancer, instead of an employee or an employer. Fourth, this study’s design does not
include signaling (Jones, 2002) and stretch work (O’Mahony & Bechky, 2006), although we know that these
strategies are crucial to the success of freelancers. A future study might—for instance, by studying the curriculum
vitae of freelancers—focus on such signaling and stretch work behavior, to determine how these strategies impact
the success rate of freelancers. Fifth, this study focuses on the Netherlands. A comparative international study of
freelancers, either generally or focusing on a number of specific freelance markets (e.g., IT and media), is needed
to see whether our findings are generalizable to the economies of other developed and non-developed societies. Last
but not least, this study was restricted to occupational dummies as proxies for market factors. This is far from ideal,
as we discussed at length earlier. We should try to include direct measures of market features (size, transparency,
seller versus buyer markets, etc.) in future work. Additionally, as career choices are always shaped by the whole
context in which the individual resides, we should look not only into developments in the marketplace but also at
other aspects that may guide the interests, goals, and actions of freelancers (Lent, Brown, & Hackett, 2000).
Author biographies
Arjan van de Born is assistant professor in Economics and Management at the Universities of Antwerpen and
Utrecht. His research interests range from entrepreneurship, network organizations and organizational change.
Arjen van Witteloostuijn is full professor in Economics and Management at the Universities of Antwerpen,
Tilburg and Utrecht. His research interests range from the effect of language on individual decision-making to the
evolution of political party systems. He has published widely in such journals as the Academy of Management Journal, Academy of Management Review, American Journal of Political Science, American Sociological Review,
Journal of International Business Studies, Management Science, Organization Science and Strategic Management
Journal.
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10 Transferable Skills
Transferrable Skills
Business Strategy
Leadership and Team Management
Problem Solving
Teamwork Ability
Data Analysis
Communication Skills
Time Management
Work Ethic
Commercial Awareness
Listening and Providing
Transferable Skills
As part of your portfolio, you will need to look at all your work and academic experiences, and determine how to best present them to provide a comprehensive and powerful digital body of work. For this class, your portfolio should be comprised of key coursework completed during the sports management program, relevant work experience, your resume, and any other pertinent information you would like to showcase.
Additionally, your portfolio is a living document that you can use beyond this class to showcase your accomplishments, share with prospective employers as part of the interview process, and/or use to build your brand. As such, after graduation from this program, you should update your portfolio as you move throughout your career.
Your portfolio is also a great way to showcase the transferable skills you have gained through your experiences in professions other than sports, and show how they lend themselves to the sports profession. When transitioning from one profession to another, showing how your experiences fit in your new profession is a critical component to promote yourself to future employers. Transferable skills can help to show how your experiences in one profession helped to prepare you for another profession.
Skills such as teamwork, problem solving, managing, negotiation, time management, and data analysis are key components of many professions, as opposed to being exclusive to one profession or another.
For Week 2, it is suggested that you login to Portfolium and add information you feel is relevant to the “Skills” section of your portfolio. This will allow you to make consistent progress in order to complete your portfolio by the end of the course.
Whether you’re a current student, have just graduated, or graduated several years ago, Career Services is here to help you at any point in your career.
For this week’s discussion, go to the Career Services page at the APUS Library. Once there, click around and review the many different things they offer. Once you have checked things out, watched some videos, read some of the articles, or possibly even reached out to one of the career services staff members, share what you discovered with your classmates. What did you learn? What things do you think might help you in your job search? Is there anything you were looking for that you couldn’t find?
Resume red flags….not having internship….diversity in job, but not frequent…overstating experience..too many pages to resume, and not key words, too many leaderships, too much on resume….keep resume short and simple
Nonverbal co mmunication, eyes mouth, looking in other directions
Business etiquette..happy hour with co workers, put cell phone away…..how you eat
Professionalism….timeliness, assertive…respect empathic
Public Speaking – know your topic, don’t talk right away, captivate audience, energy, don’t read presentation, open body language..
Candid career.com in the Library career videos
Rockawin, D. (2012). Using Innovative Technology to Overcome Job Interview Anxiety. Australian Journal of Career Development (ACER Press), 21(2), 46–52. https://doi-org.ezproxy2.apus.edu/10.1177/103841621202100206
Chi, C. G., & Gursoy, D. (2009). How to help your graduates secure better jobs? an industry perspective. International Journal of Contemporary Hospitality Management, 21(3), 308-322. doi:http://dx.doi.org.ezproxy2.apus.edu/10.1108/09596110910948314
Born, A., & Witteloostuijn, A. (2013). Drivers of freelance career success. Journal of Organizational Behavior, 34(1), 24–46. https://doi-org.ezproxy2.apus.edu/10.1002/job.1786
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