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EMPLOYEE-RECRUITER
MATCHING
A quantitative study about students’ perceptions and recruiters’ wants
_________________________________________________________________
______ Authors: Emelie Lindwall
Marketing Programme
Johanna Gustafsson
Marketing Programme
Martin Stadig
Marketing Programme
Examiner: Dr. Setayesh Sattari
Tutor: Dr. Martin Amsteus
Bachelor Thesis
Spring 2013
ABSTRACT
Recruiters are hiring people with the right set of skills and attributes in order to fit the demands
of the company. Simultaneously, students who decide to invest in a university education are most
likely doing it for one major reason – to become more attractive in the labor market. A problem
arose concerning whether there is a discrepancy between students’ perceptions of sought
employee attributes and wanted attributes by recruiters, or not. Therefore, the current study
aimed at assessing the discrepancy between the employee attributes that employers want, and
students’ perceptions of sought employee attributes.
Available literature within the field was reviewed, resulting in an identified research gap which
led to a research question as well as four stated hypotheses.
Methodology wise, a pre-study was conducted in the current research which had a qualitative
approach in order to construct the questionnaires for the main part of the study, which had a
quantitative approach. The questionnaires were answered by 83 students and 126 recruiters.
The results from the study showed that discrepancies exist concerning 10 of 26 measured
attributes. Students perceived interpersonal skills, teamwork, knowledge about the market,
gender, and well formulated CV and personal letter to be more important than recruiter
considered them to be. Moreover, recruiters considered self-management, commitment,
responsibility, self-awareness, and physical well-being to be more important than students
perceived them to be.
ACKNOWLEDGEMENTS
The following study was performed as our bachelor thesis during our last semester at the
Marketing Programme at Linnaeus University. The process of writing the thesis has not only
developed us as individuals and as a team, but has also broadened our knowledge of the chosen
subject. This thesis is valuable, not only for us, but for all students studying at marketing
programs throughout Sweden, who sooner or later will face the reality outside the walls of the
universities. Our journey of writing this thesis has come to an end, and with the final draft in our
hands we would like to conclude by thanking the people who have been important for us, as well
as for the result of the thesis.
First of all we would like to thank our examiner Dr. Setayesh Sattari who has always been there
to support us with our ongoing thesis work. The feedback has not only been invaluable, but also
crucial to how our final product is presented. We would also like to thank our tutor Dr. Martin
Amsteus, who week after week has given us invaluable comments and feedback. Thanks to Dr.
Magnus Hultman who gave us not only valuable feedback on our thesis, but who has also
broadened our knowledge and insight in how to work with the methodological tools. We would
also like to thank our fellow students who throughout the whole process of writing this thesis
have given us valuable comments.
Linnaeus University
May 2013
Emelie Lindwall Johanna Gustafsson Martin Stadig
TABLE OF CONTENTS
1. INTRODUCTION ............................................................................................ 1
1.1 BACKGROUND ................................................................................................................... 1
1.2 PROBLEM DISCUSSION ................................................................................................... 2
1.3 DELIMITATIONS ................................................................................................................ 3
1.4 OUTLINE OF THE PAPER .................................................................................................. 3
2. THEORETICAL BACKGROUND ................................................................ 5
2.1 SKILLS ................................................................................................................................. 5
2.2 EMPLOYEE ATTRIBUTES ................................................................................................. 6
2.2.1 From a Recruiter’s Point of View ................................................................................................ 6
2.2.2 From an Employee’s Point of View ............................................................................................. 7
2.3 STUDENTS’ PERCEPTIONS .............................................................................................. 8
2.4 RECRUITERS’ WANTS ....................................................................................................... 9
2.5 EVOLUTIONARY PERSPECTIVE AND MATCHING ..................................................... 9
2.6 CHAPTER SUMMARY ..................................................................................................... 10
3. RESARCH GAP AND HYPOTHESES ....................................................... 11
3.1 RESEARCH GAP ................................................................................................................ 11
3.2 RESEARCH QUESTION AND HYPOTHESES ................................................................ 11
3.3 CHAPTER SUMMARY ..................................................................................................... 12
4. METHODOLOGY.........................................................................................13
4.1 RESEARCH APPROACHES ............................................................................................. 13
4.1.1 Inductive vs. Deductive Research .............................................................................................. 13
4.1.2 Dyadic Research ........................................................................................................................ 14
4.1.3 Quantitative vs. Qualitative Research ........................................................................................ 14
4.2 RESEARCH DESIGNS ...................................................................................................... 14
4.3 DATA SOURCES ................................................................................................................ 16
4.4 RESEARCH STRATEGIES ............................................................................................... 16
4.5 DATA COLLECTION METHODS .................................................................................... 16
4.5.1 Focus Groups ............................................................................................................................. 17
4.5.2 Interviews ................................................................................................................................... 17
4.5.3 Questionnaires ............................................................................................................................ 18
4.6 DATA COLLECTION INSTRUMENTS ............................................................................ 18
4.6.1 Operationalization ...................................................................................................................... 19
4.6.2 Interview Guide/Questionnaire Design ...................................................................................... 20
4.6.3 Pretesting .................................................................................................................................... 22
4.7 SAMPLING ........................................................................................................................ 24
4.7.1 Sampling Frame ......................................................................................................................... 24
4.7.2 Sample Selection ........................................................................................................................ 24
4.8 DATA ANALYSIS ............................................................................................................... 26
4.8.1 Qualitative Data Analysis .......................................................................................................... 26
4.8.2 Quantitative Data Analysis ........................................................................................................ 27
4.9 QUALITY CRITERIA ........................................................................................................ 29
4.9.1 Quality Criteria for Qualitative Research .................................................................................. 29
4.9.2 Quality Criteria for Quantitative Research ................................................................................ 30
4.10 CHAPTER SUMMARY ................................................................................................... 31
5. EMPIRICAL DATA .......................................................................................33
5.1 EMPIRICAL DATA – FOCUS GROUPS ........................................................................... 33
5.2 EMPIRICAL DATA - INTERVIEWS ................................................................................. 34
5.3 CHAPTER SUMMARY ..................................................................................................... 35
6. RESULTS ........................................................................................................36
6.1 QUALITATIVE RESEARCH ............................................................................................. 36
6.1.1 Qualitative Reliability and Validity ........................................................................................... 36
6.1.2 Qualitative Results ..................................................................................................................... 36
6.2 QUANTITATIVE RESEARCH .......................................................................................... 39
6.2.1 Quantitative Reliability and Validity ......................................................................................... 39
6.2.2 Descriptive Statistics .................................................................................................................. 40
6.2.3 Hypotheses ................................................................................................................................. 42
6.2.4 Additional Results ...................................................................................................................... 45
6.3 CHAPTER SUMMARY ..................................................................................................... 47
7. DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS &
SUGGESTIONS FOR FUTURE RESEARCH ...............................................48
7.1. DISCUSSIONS .................................................................................................................. 48
7.1.1 Discussion of Research Question and Hypotheses .................................................................... 48
7.1.2 Discussion of Additional Results ............................................................................................... 49
7.2 CONCLUSION ................................................................................................................... 51
7.3 IMPLICATIONS ................................................................................................................. 52
7.3.1 Theoretical Implications ............................................................................................................ 52
7.3.2 Managerial Implications ............................................................................................................ 53
7.4 LIMITATIONS .................................................................................................................... 53
7.5 SUGGESTIONS FOR FUTURE RESEARCH .................................................................. 54
7.6 CHAPTER SUMMERY ...................................................................................................... 55
REFERENCE LIST ...........................................................................................56
APPENDIX A: METHOD .................................................................................63
APPENDIX B: RESULTS .................................................................................73
APPENDIX C: QUESTIONNAIRE DESIGN ................................................85
LIST OF FIGURES
Figure 3.1: Research Question .................................................................................................... 12
Figure 4.1: Inductive and Deductive Research ........................................................................... 13
Figure 4.2: Research Design ....................................................................................................... 15
Figure 4.3: Data Collection Methods .......................................................................................... 17
LIST OF TABLES
Table 4.1: Method Summary ....................................................................................................... 31
Table 5.1: Empirical Data – Focus Groups ................................................................................. 33
Table 5.2: Empirical Data – Interviews ....................................................................................... 34
Table 6.1: Soft Skills ................................................................................................................... 37
Table 6.2: Hard Skills .................................................................................................................. 38
Table 6.3: Other Attributes .......................................................................................................... 39
Table 6.4: Descriptive Statistics Students ................................................................................... 41
Table 6.5: Descriptive Statistics Recruiters................................................................................. 42
Table 6.6: Hypotheses ................................................................................................................. 43
CHAPTER ONE - INTRODUCTION
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1
. INTRODUCTION
In this chapter, the background of the study is presented. This is followed by a problem
discussion of the field, which leads to the purpose of the study. Finally, the delimitations
of the study are stated followed by an outline of the paper.
1.1 BACKGROUND
“Too many leave university without the right skills” (Daily Mail, 2012) and the numbers of
students graduating from universities in Sweden are increasing (HSV, 2011). However, it is
getting harder for students with a university degree to find a job directly after graduation (SCB,
2011). The aim for these students is often to collect knowledge and skills to become attractive in
the labor market. For these students to be attractive to companies, they have to show proof of
having the skills required by the employers (Raybould & Sheedy, 2005). Employers are hiring
employees with the right knowledge and skills to fit the culture in the company (Adkins et al,
1994). Recruitment from a company perspective can be defined as a process of attracting the
right persons into a company (Gatewood et al, 1993).
It is important for companies to find the right applicants during the recruitment process, to
sustain competitive advantage against rivals in the market (Pfeffer, 1994). Burack and Singh
(1995) demonstrate the importance of hiring people that will be adjustable for rapid changes in
the market field. Pfeffer (1994) writes that it is the individual worker who is the source for
companies’ competitive advantages. With this in mind, the human resources department has an
important role to maintain this competitive advantage by recruiting the right people that fit the
company culture. Attracting the right people for the jobs is of big interest for employers, to
minimize the risk of employing wrong personnel (Kazlauskaité & Bučiūnienė, 2008).
According to Hutchinson and Brefka (1997), job objective, academic background and work
experience are important hard skills companies look for when they are looking for new
employees. Hutchinson and Brefka (1997) also argue that personal and social skills, also known
as soft skills, are not as important when employing new personnel. On the other hand, Nealy
(2005) argues that soft skills are more important in today’s labor market for productive
1
CHAPTER ONE - INTRODUCTION
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2
performance and that current and future business leaders are looking more at these skills than
hard ones when employing new personnel.
Hesketh (2000) conducted a study which showed that employers prefer to recruit students from
specific universities, more specifically the universities that had “high status” in the university
market. A study conducted by Salas Velasco (2012), demonstrates that students with higher
average grades have a better chance of finding a job after graduation. However, Branine (2008)
found that recruitment of students have become more about finding people with the right
attitudes and personality.
1.2 PROBLEM DISCUSSION
Employers may have specific opinions about which attributes an employee should possess. For
instance, when an organization looks at hiring newly graduated students, employers look at
grades, personality as well as other personal qualities that an organization may have use for in
newly graduated students, according to Salas Velasco (2012). Branine (2008) highlights the
importance of applicant attitudes and personality, which he says are more important to employers
than the type or level of qualification acquired. However, Behrenz (2001) states that it is not until
in the second round of the recruitment process that employers start to look at soft skills and that
job seekers often are eliminated in the first round due to lack of experience or education. Hence,
the opinions regarding which attributes that are attractive to possess vary across available
literature.
Salas Velasco (2012) highlights that a students’ human resources and intangible assets are two of
the main things that will lead to competitive advantages for an organization. Salas Velasco
(2012) also contends that the hiring process within the graduate labor market is poorly
understood and hardly studied at all.
Students may have perceptions of what employers are looking for. What students perceive is
what employers will target in an applicant does not usually match the organization’s perception
of the subject (Salas Velasco, 2012). Moreover, according to Salas Velasco (2012), sought
employee attributes may differ depending on what kind of job and position or in what kind of
CHAPTER ONE - INTRODUCTION
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3
market the applicant is applying for. If there is a mismatch, there is no adaption to the
environment. Hence, more knowledge is needed concerning whether students’ perceptions of
wanted employee attributes match the employee attributes that employers want.
Consequently, a problem arises concerning whether there is a discrepancy between students’
perceptions of sought employee attributes and wanted attributes by employers or not. Hence, a
dyadic research, which will be further explained in chapter 4, is needed to see whether there is a
mismatch or not.
Purpose: To assess the discrepancy between the employee attributes that employers want, and
students’ perceptions of sought employee attributes.
1.3 DELIMITATIONS
The study focuses exclusively on undergraduate marketing students at Linnaeus University, a
public university situated in Växjö in the south of Sweden. This delimitation is made since the
authors themselves are graduating from the marketing program at Linnaeus University 2013, but
also since the graduated labor market hardly is studied at all (Salas Velasco, 2012).
Delimitations are also made concerning which companies to conduct the study on. The study will
be conducted on manufacturing companies that were the largest employers in Sweden during
year 2012, based on a list from allabolag.se (Allabolag, 2013). Focus will exclusively lie on the
attributes perceived as important, and wanted, for employment at the marketing department in
the manufacturing industry.
1.4 OUTLINE OF THE PAPER
The current study is divided into seven chapters, containing the following structure.
Chapter 1 presents a short background within the field of recruitment. This is followed by a
problem discussion that leads to the purpose of the study. The chapter ends with the delimitations
and outline of the thesis.
CHAPTER ONE - INTRODUCTION
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4
Chapter 2 presents the theoretical background including concepts, definitions and available
literature within the field.
Chapter 3 presents the research gap, research question and the hypotheses of the study.
Chapter 4 presents and justifies the choices of research approach, research design, data sources,
research strategy, data collection instruments, sampling, data analysis and quality criteria.
Chapter 5 presents the empirical data, collected through focus groups and interviews.
Chapter 6 presents the results from the qualitative data analysis followed by the result from the
quantitative data analysis.
Chapter 7 presents discussions around the finding from the data analysis and a conclusion,
which answers the purpose and research question. The chapter also presents the theoretical and
managerial implications of the study, limitations and suggestions for future research.
CHAPTER TWO – THEORETICAL BACKGROUND
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5
. THEORETICAL BACKGROUND
This chapter presents relevant theories from available literature, including
definitions and discussions around them. First, skills are presented followed by
employee attributes seen from two perspectives. This is followed by a discussion
about perceptions, wants, and finally matching and evolutionary perspective.
2.1 SKILLS
Skills can be defined as the certain personal abilities that an individual possesses (Raybould &
Sheedy, 2005). Skills can also be divided into hard skills and soft skills. Hutchinson and Brefka
(1997) define hard skills as job objectives, work experience and academic background.
Hutchinson and Brefka (1997) further define soft skills as the personal and social skill that a
person possesses. McCorkle et al (2003) have a discussion about discipline related and support
skills. McCorkle et al (2003) say that discipline skills are knowledge gathered from school, and
support skills are the same as soft skills, and further mentions communication skills,
interpersonal skills and creativity as examples of soft skills.
Salas Velasco (2012) divides skills into four different categorizations: hard skills, practical
experience, master/languages/study abroad, and soft skills. Salas Velasco (2012) refers to hard
skills as academic ability, knowledge and computer skills. Furthermore, Salas Velasco (2012)
says that soft skills are linked to the personality of a person and examples of soft skills could be
communication, teamwork and leadership. Sharma (2009, p.19) defines soft skills as “the extra
edge that set apart the leader from the followers”. Sharma (2009) also describes soft skills as the
basic life skills or survival skills, which helps people to polish their outer veneer. Moreover,
Sharma (2009) mentions examples of soft skills such as communication skills, interpersonal
skills, negotiation skills, emotional intelligence, teamwork and cooperation.
Another author who discusses the differences between hard skills and soft skills is Robles
(2012). Robles (2012) describes hard skills as the technical expertise and knowledge needed to
find a job, and soft skills as interpersonal qualities. Interpersonal qualities are the people skills
and personal attributes that someone has. Hurrell et al (2012) define a soft skill as a non-
2
CHAPTER TWO – THEORETICAL BACKGROUND
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6
technical skill that involves interpersonal and intrapersonal capabilities to handle performance in
different contexts.
2.2 EMPLOYEE ATTRIBUTES
Two perspectives were found to have been taken upon the attributes necessary for employees to
possess in available literature – from a recruiter’s point of view, and from an employee’s point of
view. These two perspectives are further discussed below.
2.2.1 From a Recruiter’s Point of View
Results regarding which employee attributes that are attractive in new employees, such as
graduates, vary between different articles within the field, but communication is seen as a vital
attribute in most articles. For instance, Raymond and McNabb (1993), highlight that both
employers and students regard communication skills as important to possess. Also in a study by
Robles (2012), a finding was that executives think that employees should be able to
communicate effectively. The same applies in Sharma’s study (2009), where communication
skills were voted as the most important soft skill necessary to possess to be able to succeed at the
workplace.
Authors highlight the importance of other soft skills, rather than communication skills. For
instance, Anderson and Shackleton (1990, p.69) says that “the ideal graduate for all occupational
groups was perceived as interesting, relaxed, strong, successful in life, active, mature,
enthusiastic, sensitive, pleasant, honest and dominant”. Branine (2008) highlights the importance
of soft skills and says that employers look beyond hard skills, and instead look for employees
who are motivated, responsible, and are able to work both independently and in teams. As
mentioned before, Robles (2012) discusses the importance of being able to communicate
effectively, but Robles (2012) also mentions getting along with co-workers, teamwork, initiative
taking, work ethics and professionalism as important. In the study by Sharma (2009),
communication skills were voted as most important as mentioned before, but also teamwork and
time management were mentioned as the second and third most important soft skills to possess.
Junek et al (2009) says that employers think that students who are being employed perform well
in the areas accountability, cooperation, and productivity, to mention a few. Cook and Finch
CHAPTER TWO – THEORETICAL BACKGROUND
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7
(1994) also emphasize soft skills as more important, rather than hard skills. Anderson and
Shackleton (1990) describe the ideal graduate for all occupational groups and only mention soft
skills such as interesting, enthusiastic and honest, while Junek et al (2009) say that students are
accountable and productive. However, Behrenz (2001) highlights personal engagement and
social competence, but also professional knowledge. Cook and Finch (1994) looked at whether
educational background, prior work experience, or training potential was viewed as most
important to recruiters. The result showed that training potential was the most important of the
three, hence, the soft skills overweight the hard skills (Ibid).
Not only soft skills are highlighted as important to possess. For instance, Sharma (2009) both
mentions the soft skills that are necessary to have to succeed at the workplace as well as that
employers also want experienced staff. Similarly, Behrenz (2001) says that job seekers are often
eliminated in the first round of the recruitment process due to the fact that they lack the hard
skills needed such as experience or education, and that it is first in the second round that
employers start to look at the soft skills such as personal engagement and social competence.
However, Behrenz (2001) also says that results show that only somewhat half of those getting
hired fulfilled the demands for experience and/or education completely.
2.2.2 From an Employee’s Point of View
McCorkle et al (2003) found that students with a higher GPA score are better prepared for the
job market. This can be contrasted by Salas Velasco’s article (2012), where the author found that
grades do not matter when applying for a job, and instead discusses that the most important skills
are the soft ones. Junek et al (2009) found that students consider communication skill to be the
most important skill to possess to get employed after graduation. Raymond and McNabb (1993)
came up with the same results. They found that both students and employers have the same
opinions about social skills and communication skills – that they are the most essential skills for
companies when recruiting students (Ibid). DuPre and Williams’ study (2011), concerning the
understanding of students’ perceptions, found that students rank communication skill high, right
below work ethic. Work ethic was considered to be the most important skill (Ibid). All of the
above mentioned studies were conducted within a time span of 18 years and they all got similar
results: from a student’s point of view, communication skill is an important attribute to possess
CHAPTER TWO – THEORETICAL BACKGROUND
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8
in order to be employed by a company. Other important attributes that students consider essential
to possess are work ethics, teamwork skills and to have a good personality (Raymond &
McNabb, 1993; DuPre & Williams, 2011; Salas Velasco, 2012). Attributes that students consider
less important include previous work experience and level of university degree (Raymond &
McNabb, 1993; Salas Velasco, 2012).
Singer and Bruhns (1991) investigated which hard skills that were important to possess from a
student’s perspective, and highlight that work experience is the most essential one. Work
experience is followed by type of education and academic achievements (Ibid). Salas Velasco’s
study (2012) shows that students think good grades are more important than previous working
experience, in the discussion concerning hard skills. The two studies are conducted with a
difference of 21 years and also in different countries, which might explain the differences in the
result. The articles that investigated both soft skills and hard skills (Raymond & McNabb, 1993;
Salas Velasco, 2012) show that students consider soft skills to be more important than hard
skills. The articles highlight the importance of applicants telling the organization or employer
that they possess both the skills and the experience that the organization is demanding.
Graduating students most often have neither experience nor specific skills, but they may have
personal interests or hobbies that in the end can turn into relevant skills. If this is the case,
applicants need to demonstrate that the skills are both transferable and important for the job they
are applying for (Dacre Pool & Sewall, 2007; Breaugh, 2008).
2.3 STUDENTS’ PERCEPTIONS
Perception is defined as the process by which stimuli are selected, organized and interpreted
(Solomon et al, 2010). Applicants who are applying for a job often have inaccurate perceptions
of the position that they are applying for, and often applicants get wrong expectations from the
employers of what the job will give them in return (Breaugh, 2008). If an applicant gets hired
with wrong expectations it will most likely lead to dissatisfaction, and subsequently quitting
within a near future, than applicants who get a more accurate perception (Ibid). Dacre Pool and
Sewall (2007) highlight the importance of aligning students’ perceptions with the industry’s
expectations over how to perform to get a job. Moreover, Dacre Pool and Sewall (2007) say that
as it is now, students’ perceptions do not match the industry’s expectations of performance.
CHAPTER TWO – THEORETICAL BACKGROUND
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9
According to DuPre and Williams (2011) students’ perceptions of what an employer seeks in
new coworkers are: work ethic, communication skills, teamwork, analytical skills, and technical
skills.
2.4 RECRUITERS’ WANTS
Wants is defined as a need that must be satisfied, which can be both individually and culturally
determined (Solomon et al, 2010). Marketing is a broad field when it comes to applying for jobs,
and recruiters want employees to possess both soft and hard skills (Salas Velasco, 2012).
Specific jobs demand specific attributes, as well as small employers versus large employers
demand different attributes (Ibid). According to Kelley and Gaedeke (1990), the most wanted
attributes by employers who are recruiting people for marketing positions are: oral
communication skills, interpersonal skills, enthusiasm/motivation, written communication skills,
as well as work experience. Floyd and Gordon (1998), among others, also highlight the value of
problem-solving skills, while Salas Velasco (2012) highlights the importance of personal
characteristics. Employers are less concerned about numerical and information technology skills
of graduating students and instead appreciate skill such as self-management and teamwork
(Ibid). Behrenz (2001) however, highlights that there is an interaction between wanted hard and
soft skills. Behrenz (2001) says that employers mainly want job applicants with a good education
and work experience in the first round of the recruitment process, but that they in the second
round prefer skills such as professional knowledge, personal engagement as well as social
competence.
2.5 EVOLUTIONARY PERSPECTIVE AND MATCHING
Evolutionary perspective has its roots in Charles Darwin’s famous theory Darwinism (Hodgson,
2005). Darwin said that the species that best fit the environment would survive and be able to
pass on their positive attributes, while the species with non-surviving attributes will die out
(Ibid). Darwinism principles about variation, replication and selection can also be applied outside
the biological area and is then called universal Darwinism (Dawkins, 1976). Universal
Darwinism can, and has been, applied to other evolving and open systems such as social
evolution (Ibid). When general evolutionary concepts are applied to economic phenomena, this is
referred to as evolutionary economics (Powell & Wakeley, 2003). According to Powell and
CHAPTER TWO – THEORETICAL BACKGROUND
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10
Wakeley (2003) evolutionary perspective aims to explain the process of change within a system.
If there is a change in the system, only those who best fit the environment will succeed in the
future, and the others will fail and gradually disappear (Ibid).
The importance of matching has been discussed for decades, according to Salas Velasco (2012),
for instance cornering matching the right person to the right job. Both employers and job seekers
put a lot of time and resources into the process of job search (Salas Velasco, 2012). For instance,
a conducted study in the North East showed that there was an acute mismatch between supply
and demand of individuals that possessed the wanted set of attributes in sectors such as business-
to-business services (Hartshorn & Sear, 2005). To avoid these happenings, Breaugh (2008)
mentions that organizations must provide realistic information of their wants during the
recruitment process, in order to improve the person-organization fit. Moreover, Breaugh (2008)
says that applicants need to have self-insight to be able to identify a person-organization fit.
Therefore, the process of employee recruitment should be seen as one of the biggest challenges
for organizations (Punia & Sharma, 2008). There is an ongoing uncertainty both within the
economy and the competitive marketplace, why it is crucial for companies to find employees
with the right set of attributes (Ibid). This is important since it might increase the chances for
productivity which affects a company’s earnings, why matching employees to the demands of
the organization becomes vital (Ibid).
2.6 CHAPTER SUMMARY
This chapter has gone through relevant theories from available literature. First, skills were
discussed, where the main discussion concerned the division of skills into categories. The main
focus was on the division into soft skills such as communication skills and personality, and hard
skills, such as experience and academic background. The second theory discussed employee
attributes, seen from two perspectives. Here, researchers’ opinions vary concerning which
attributes employees see as important, and which employee attributes employers find attractive.
The two following theories discussed were perceptions and wants, in relation to the current field
of study. Finally, evolutionary perspective and matching was discussed.
CHAPTER THREE – RESEARCH GAP AND HYPOTHESES
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11
. RESARCH GAP AND HYPOTHESES
Through the literature review in the previous chapter, it became clear that a research
gap exists. This gap is further discussed in this chapter, followed by the stated
research question and hypotheses of the study.
3.1 RESEARCH GAP
The results from available literature vary, both from recruiters’ and employees’ point of view on
attractive employee attributes. Hence, an updated research was considered necessary. Available
studies have also been conducted in different countries, on different target groups as well as on
slightly or majorly differing subjects. Thus, it was considered necessary to conduct the current
research since the two major viewpoints in available literature were taken together, and research
was conducted on students as well as on recruiters at a possible job market for those students.
Through the literature review, it became clear that earlier researches concerned either which
attributes students think are important to possess for an employee, i.e. from an employee’s point
of view, or concerned which attributes personnel managers want when recruiting new
employees, i.e. from a recruiter’s point of view. Hence, it became clear that no research
compared these two outcomes, which showed a research gap within the field of the study. The
authors therefore concluded that there might not be a match between students’ perceptions of
wanted employee attributes and actually wanted employee attributes, hence this is what is
covered in the current study.
3.2 RESEARCH QUESTION AND HYPOTHESES
The above identified research gap lead to the following research question:
RQ1: Is there a discrepancy between the attributes students perceive as wanted, and the
attributes personnel managers actually want? (shown in figure 3.1)
Moreover, four hypotheses were stated:
● H1: There is a mismatch between the employee attributes that students perceive recruiters
want, and the recruiters’ wants regarding employee attributes.
3
CHAPTER THREE – RESEARCH GAP AND HYPOTHESES
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12
● H2: There is a mismatch between how important recruiters consider soft skills to be, and
how important students perceive soft skills to be.
● H3: There is a mismatch between how important recruiters consider hard skills to be, and
how important students perceive hard skills to be.
● H4: There is a mismatch between how important recruiters consider other attributes to be,
and how important students perceive other attributes to be.
Figure 3.1: Research Question
3.3 CHAPTER SUMMARY
This chapter has gone through the identified research gap of the study. For instance, an updated
research was considered necessary since the results in available literature vary. Moreover, most
available researches have had either one or the other of two identified viewpoints why it was
considered necessary to combine the two viewpoints in the same research. Furthermore, a
research question was stated, as well as four hypotheses.
CHAPTER FOUR – METHODOLOGY
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13
. METHODOLOGY
The following chapter includes descriptions and justifications of choices of research
approach, research design, data sources, research strategies, data collection
methods and data collection instruments. The chapter also describes the process of
sampling and data analysis, and finishes of by describing the quality criteria of the study.
4.1 RESEARCH APPROACHES
This subchapter presents inductive and deductive research, dyadic research, as well as
quantitative and qualitative research, and justifies the approaches chosen for the current study.
4.1.1 Inductive vs. Deductive Research
Inductive and deductive theory are two philosophical approaches in research methodology, in
which valid conclusions can be made (Bryman & Bell, 2011). An inductive approach is mainly
the outcome of research, where conclusions are drawn from collected data and then developed
into new theoretical frameworks (Ibid). According to Bryman and Bell (2011), deductive
research is the most common type of research approach of the two, and represents the
relationship between theory and research (Ibid). When creating hypotheses or research questions,
only accessible theories within the domain are to be used (Ibid). The two approaches are
illustrated in figure 4.1. The current study was exclusively deductive since the purpose, research
question, and hypotheses were based on already existing theories. More information about
inductive and deductive research can be found in Appendix A: Method, A1.
Figure 4.1: Inductive and Deductive Research (adopted from Bryman and Bell, 2011)
4
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4.1.2 Dyadic Research
A dyad is described as when different individuals or organizations interact or work together over
a length of time (Thompson & Walker, 1982; Medlin, 2003). Thompson and Walker (1982) list
three different interpretations a dyad should have to be able to exist: consisting in time, mutual
actions, as well as engage personnel elements from the two objectives in question. When
conducting a dyadic research, conceptualizing the pattern between two individuals or
organizations is essential (Ibid). This may take many forms due to different conceptual models of
relationships and interactions, but can for instance be direction of interaction (Ibid). A dyadic
research do not only reflect one individual or organization, but the relationship and interaction
between them, hence a reflection from two perspectives (Ibid).
Since the current study combined two perspectives, which have been taken in already existing
research, the research was dyadic.
4.1.3 Quantitative vs. Qualitative Research
Research can be divided into a quantitative and/or qualitative research approach (Bryman & Bell,
2011). According to Bryman and Bell (2011), the most obvious distinction is the fact that
quantitative researchers use measurements, while qualitative researchers do not. As mentioned
by Bryman and Bell (2011), there is also a possibility to successfully combine the two
approaches into a mixed methods research approach.
For the current research, it was chosen to use a mixed methods approach, meaning that both
qualitative and quantitative research methods were used. This was chosen since both words and
quantifications were emphasized as important for the study. The qualitative research was used to
collect data for the development of the quantitative research. The main conclusions of the study
were drawn from the quantitative research. More information about quantitative and qualitative
research can be found in Appendix A: Method, A2.
4.2 RESEARCH DESIGNS
A research design is the intended plan to be followed to be able to answer research aims and
objectives, hence it gives a structure to be able to solve the actual problem (Bryman & Bell,
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2011; Hair et al, 2003). There are several types of research designs, as can be seen in figure 4.2,
but mainly three types are discussed by Bryman and Bell (2011): exploratory, descriptive, and
causal research design. Further information about these research designs can be found in
Appendix A: Method, A3.
When selecting which research design to use it is essential to look deeper into factors such as
available resources, previous research, amount of control over variables as well as the purpose
and research questions (Bryman & Bell, 2011). Two research designs were used for the current
study: exploratory research design and descriptive research design. This was due to that both
focus groups and interviews were used to come up with attributes for the questionnaires as well
as a literature review to investigate both recruiters’ and students’ beliefs and opinions concerning
recruitment. Moreover, the resources and time were limited for the current research.
Descriptive research design can be further divided into either longitudinal design or cross-
sectional design, and the choice affect time and resource dimensions (Zikmund et al, 2010;
Bryman & Bell, 2011). Dependent on the previous decision of using a descriptive research
design, a cross-sectional design was chosen thus it preferably can be used when working with
quantitative studies, but also due to the time dimensions and resource constraints. Furthermore, it
was chosen to work with multiple cross-sectional design since the study focuses on more than
one section, i.e. recruiters and students. Figure 4.2 states the choices made regarding the research
design. More information about longitudinal design and cross-sectional design can be found in
Appendix A: Method, A3.
Figure 4.2: Research Design (adopted from Bryman and Bell, 2011)
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4.3 DATA SOURCES
Primary data and secondary data is either qualitative or quantitative data that is collected to
uncover the purpose of a study (Bryman & Bell, 2011). The difference between primary and
secondary data is that the authors themselves collect primary data, while secondary data has been
collected by another researcher and for another purpose (Ghauri & Grønhaug, 2005).
The current study used both primary and secondary data. Due to the lack of previous studies
within the chosen field, the study first and foremost relied on primary data collected through
focus groups, interviews and questionnaires. Further information explaining data sources can be
found in Appendix A: Method, A4.
4.4 RESEARCH STRATEGIES
There are several different research strategies to use when conducting a research, and all
strategies are different when it comes to how to collect and analyze the empirical data (Yin,
2009). Yin (2009) presents five of these research strategies: experiment, survey, archival
analysis, history, and case study.
The research strategy experiment was dismissed since the current study had no interest in having
control over behavioral events. Since the research questions in the current study did not focus on
how and why questions, the strategies history and case study could be excluded. Archival
analysis was dismissed, due to the fact that the research was not conducted on observations and
analyzes of documents or archives. The research strategy survey was considered most
appropriate for the current study, since primary data was collected and focus was not on
collecting and analyzing secondary data. Survey was also considered appropriate since the
current study focuses on creating statistical inferences from the collected data. More information
explaining the different research strategies can be found in Appendix A: Method, A5.
4.5 DATA COLLECTION METHODS
This subchapter presents different research methods to choose from when collecting data.
Bryman and Bell (2003) points out the following five main methods that can be used to collect
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primary data: interviews, questionnaires, focus groups, observations and content analysis. The
three methods used in the current study as can be seen in figure 4.3 – focus groups, interviews
and questionnaires – are further explained in this chapter.
Figure 4.3: Data Collection Methods (adopted from Hair et al, 2003)
4.5.1 Focus Groups
According to Bryman and Bell (2003), focus group is a qualitative data collection method, which
can be described as interviewing several people in a group at the same time, concentrating on
one specific topic. Focus groups was considered to be the most appropriate data collection
instrument to use for the first phase of the data collection, since the aim was to gain greater
knowledge of the respondents’, i.e. students’, perceptions and opinions concerning a specific
topic. More information about focus groups can be found in Appendix A: Method, A6.
4.5.2 Interviews
According to Bryman and Bell (2003) and Hair et al (2003), interviews can be both qualitative
and quantitative and the goal with interviews is to collect information from respondents
concerning their true opinions regarding a complex topic. In a simple way, interviews can be
described as when a person, the interviewer, asks questions to another person, the interviewee
(Hair et al, 2003; Eriksson & Wiedersheim-Paul, 2011). According to Ghauri and Grønhaug
(2005), there are three different types of interviews: structured, unstructured, and semi-
structured. More information about interviews can be found in Appendix A: Method, A7.
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Due to the fact that semi-structured interviews give the interviewer flexibility, e.g. to change the
order as well as ask follow-up questions concerning a specific topic, this approach was
considered the most appropriate one to use. Telephone interviews were used in the current study
for the second phase of the data collection. This was seen as appropriate since it was considered
the easiest and least time consuming way to reach out to the respondents, i.e. personnel managers
at manufacturing companies, and at the same time reach the desired amount of understanding.
4.5.3 Questionnaires
Questionnaire is a quantitative data collection method that preferably can be used if the aim is to
collect data from a large amount of respondents (Hair et al, 2003; Bryman & Bell, 2011). In a
questionnaire, respondents answer pre-determined questions of key characteristics of individuals,
companies, events or other phenomena (Ibid).
For the third and last steps of the data collection, the quantitative research method questionnaire
was used, since it was considered the best way to collect a large amount of data (Bryman & Bell,
2003). Online questionnaire was considered to be the least costly quantitative method to reach
out to the desired large number of respondents, thus it was used (Ibid). More information about
questionnaires as a data collection method can be found in Appendix A: Method, A8.
4.6 DATA COLLECTION INSTRUMENTS
Two different qualitative research methods, focus groups and interviews, were used to create the
basis for the main method of the current study, which was the quantitative research method
questionnaire. Through three focus groups, a list of the employee attributes that students perceive
recruiters look for when hiring employees was created. Through five interviews with personnel
managers, a list of the employee attributes that recruiters want when hiring new employees was
created. A merger of the outcomes was developed into the foundation of the final questionnaires
which were sent to both students and personnel managers. This subchapter goes through the data
collection instruments used for the different data collection methods.
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4.6.1 Operationalization
Operationalization is the process of turning relevant concepts into something measureable, i.e.
developing questions from theories (Bryman & Bell, 2011). The following chapter includes
operationalization for each data collection method used.
4.6.1.1 Operationalization – Focus Groups
Through an operationalization, the following question was developed to be used in the focus
groups with students: “Which employee attributes do you think recruiters consider important
when hiring employees for the marketing department at a manufacturing company?”. The
operationalization can be seen in Appendix A: Method, A9.
4.6.1.2 Operationalization – Interviews
Through an operationalization, the following question was developed to be used in the interviews
with personnel managers: “Which employee attributes do you consider important when hiring
employees for the marketing department at your company?”. The operationalization can be seen
in Appendix A: Method, A10.
4.6.1.3 Operationalization – Questionnaires
Through an operationalization, the following question was developed to be used in the
questionnaire to students: “How important do you think the following employee attributes are for
recruiters, when hiring new employees to the marketing department at a manufacturing
company?”. The attributes are the ones developed through focus groups and interviews.
Through an operationalization, the following question was developed to be used in the
questionnaire to recruiters: “How important do you consider the following employee attributes to
be, when hiring new employees to the marketing department?”. The attributes are the ones
developed through focus groups and interviews.
The operationalization for the two different questionnaires can be seen in Appendix A: Method,
A11.
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4.6.2 Interview Guide/Questionnaire Design
Before a focus group or interview is conducted, there is a need to have a brief list of the topics or
areas that wants to be covered during the focus group or interview (Bryman & Bell, 2011). If
semi-structured interviews are to be conducted, there is a need to have a somewhat more
structured list of issues to be addressed or questions to be asked (Ibid). When it comes to a
questionnaire, the process of designing the questionnaire is rather crucial, due to the well-known
problem of low response rates connected to questionnaires (Ibid).
The interview guides for the focus groups and interviews are presented below, followed by the
questionnaire designs.
4.6.2.1 Interview Guide – Focus Groups
One question was stated during the focus groups:
Which employee attributes do you think recruiters consider important when hiring
employees for the marketing department at a manufacturing company?
This question was lead by a discussion around the specific topic. The students got time to list the
most important attributes individually on a paper, before the group discussion begun. The
answers were compared and discussed between the respondents. The question was asked to find
out which attributes students consider important to possess, and worked as a foundation when
developing the questionnaires.
4.6.2.2 Interview Guide – Interviews
Five interviews with personnel managers were conducted to find out which employee attributes
they consider important when hiring employees to their marketing department. During the
interviews one question was asked:
Which employee attributes do you consider important when hiring employees for the
marketing department at your company?
The aim with the interviews was similar to the aim with the focus groups. Since it was
considered problematic to conduct focus groups with personnel managers, it was decided to
conduct interviews instead. However, the question remained similar to the one asked during the
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focus groups, but was developed to aim at personnel managers’ wants instead of students’
perceptions. Hence, the current question was not developed from other researchers’ interview
questions dealing with similar topics. The aim with this question was to find out which employee
attributes recruiters consider important when hiring new employees, and as with the outcome
from the focus groups, this worked as a foundation when developing the questionnaires.
4.6.2.3 Questionnaire Design
Conducting an online questionnaire was acknowledged as the most appropriate way to reach the
targeted population. The questionnaires were designed via Google Form and a total number of 31
questions were asked in the questionnaires. Two different questionnaires were constructed, due
to that there were two different types of respondents – personnel managers and students. The
questionnaires were designed in a way that they appeared important, where the questions, layout,
color and length of the questionnaire easily could be changed to fit the wanted appeal (Hair et al,
2003). In the questionnaires, closed questions were used since questionnaires with many open
questions have a tendency to lower the response rate (Bryman & Bell, 2011). Closed questions
were also used because they are easier to answer from a respondent’s perspective and are not that
time consuming to analyze (Eliasson, 2010).
The questionnaire designs were developed through a study by Robles (2012). The first part of the
questionnaires contained a descriptive section, with an introduction where it was stated
approximately how long time the questionnaire would take to answer, the purpose of the study,
and who was responsible for it. The cover letters can be seen in Appendix A: Method, A12-A13
The questions were designed in a Likert type scale, where the respondents ranked their answer
on a 1-7 scale depending on how “Not at all important” (1) or “Extremely important” (7) they
considered the stated attribute to be (Bryman & Bell, 2011). This scale was applied on all items.
In the questionnaire sent to student the following question was asked:
How important do you think the following employee attributes are for recruiters, when
hiring new employees to the marketing department at a manufacturing company?
In the questionnaire sent to personnel manager the following questions was asked:
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How important do you consider the following employee attributes to be, when hiring new
employees to the marketing department?
The items that were measured were divided into three categories: soft skills, hard skills, and
other attributes. These categories were however not separated visibly in the questionnaires sent
to the respondents. To see the categories used and the items connected to each category, see table
6.1, 6.2 and 6.3 in chapter 6.
Questions of demography were added at the end of the questionnaires. The following questions
were asked to the students:
Your gender?
Numbers of years you have been studying at the marketing program?
Are you actively searching for a job?
Dream employer?
E-mail address (optional)?
The following questions of demography were asked to the personnel managers:
Your gender?
Your position at the company?
Number of years at current position?
Number of employees at your company?
Industry your company is in?
To see the final drafts of the questionnaires, see Appendix C: Questionnaire Design, C1 for the
questionnaire to students and Appendix C: Questionnaire Design, C2 for the questionnaire to
recruiters.
4.6.3 Pretesting
According to Ghauri and Grønhaug (2005), a pilot study should be considered before conducting
an interview, a focus group or sending out a questionnaire. The pretest should be conducted on a
sample, preferably 3-5 people from the targeted population or with experts within the field, to
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make sure that concepts and questions are understandable (Ibid). Pretesting gives the researchers
a first hint of the responses (Ibid). All together, this makes it easier to prepare the final draft of
questions, intended for the respondents (Ibid). Below, the pretesting for each data collection
instrument is further explained.
4.6.3.1 Pretesting – Focus Groups
For the current study, the question for the focus groups was shown to one academic. It was
considered hard to conduct a more thorough pretest of the intended question for the focus group,
due to the data collection method’s flexibility (Bryman & Bell, 2011).
4.6.3.2 Pretesting – Interviews
For the current study, the interview question was pretested through a review by two academics
(cf. Czaja, 1998; Ghauri & Grønhaug, 2005). Sending the questions to an academic or expert
within the field is a good way of pretesting, since academics and experts easily can detect
problems not found through other techniques (Czaja, 1998; Ghauri & Grønhaug, 2005). This
approach is rather inexpensive, and you will get critique from multiple perspectives (Czaja,
1998). The pretesting of the interview questions was done to ease the understanding of certain
words, terms, and concepts as well as to look deeper into the structure of the sentences and the
possibility of probing (cf. Czaja, 1998). The main goal of the pretest was to receive immediate
thoughts and reactions of the question, as well as to make sure that the question was well
formulated (Czaja, 1998).
4.6.3.3 Pretesting – Questionnaires
According to Czaja (1998), the importance of pretesting a questionnaire first and foremost is
about uncovering whether respondents understand the words, terms and concepts used as well as
how the questions are asked (Ibid). Other crucial factors to uncover are if the respondents
understand the answer format as well as interpret the questions as intended, otherwise the
sentence structure might be too hard and complex (Ibid). If the respondents feel comfortable with
the different response categories, and if they provide complete answers, are other aspects to look
into (Ibid). Last but not least, it is important to look deeper into the respondents attentiveness and
interest of the questionnaire which might give an overall indicator of how easy it is for the
respondents to complete the questionnaire with correct answers (Ibid). Factors affecting this are
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the overall logical flow of the questionnaire and lack of instructions (Ibid). One preferable way
of pretesting a questionnaire is to send it to an academic or expert within the field (Czaja, 1998;
Ghauri & Grønhaug, 2005).
In the current study, the two questionnaires were first sent to three academics. Once feedback
had been given and the questionnaires had been revised, they were sent to 10 potential
respondents – five students and five personnel managers (cf. Ghauri & Grønhaug, 2005).
4.7 SAMPLING
This subchapter presents the sampling frames followed by the sample selection made for each
data collection instrument used. Sample, in this sense, is defined as “the segment of the
population that is selected for an investigation” (Bryman & Bell, 2011, p.176).
4.7.1 Sampling Frame
According to Ghauri and Grønhaug (2005), a sampling frame needs to be used when sampling. A
sampling frame can be described as a list where the whole population is listed, and it is from this
list that the sample will be drawn (Hair et al, 2003; Ghauri & Grønhaug, 2005). The current
study aimed at two populations. The first population was all undergraduate students who were
studying at the marketing program at Linnaeus University in Växjö. The sampling frame for the
students hence was a list of all of these students from which the sample for the focus group could
be drawn. The sampling frame contained 229 units. The second population was personnel
managers at the manufacturing companies which were the largest employers in Sweden during
the year 2012, based on a list from the website allabolag.se. This list contained 687 units. Hence,
this list was the sampling frame for the second population, from which the sample for the
interviews could be drawn.
4.7.2 Sample Selection
When collecting data from one or more elements, there are two different methods to use (Ghauri
& Grønhaug, 2005). One method is to collect data from all people within the population, and the
second one is to collect data from a representative sample of people from the population (Ibid).
The latter one is referred to as sampling (Ibid). There are many different reasons why sampling
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should be used when conducting a research, but the two main reasons are that sampling is both
less costly and less time consuming than collecting data from whole populations (Ibid). The
sample selection for focus groups, interviews and questionnaires are further discussed below.
4.7.2.1 Sample Selection for Focus Groups
The sample for a focus group should be able to represent the population, meaning that the
respondents should be able to represent a specific group (Bryman & Bell, 2011). Opinions
concerning whether or not to select participants who are known to each other differ among
researchers (Ibid). Bryman and Bell (2011) say that some researchers prefer to exclude people
who are known to each other since their existing relationships may contaminate the session.
However, some researchers prefer to select natural groups whenever possible (Ibid). If a focus
group is meant to explore a collective understanding of a topic within a specific group, this can
be achieved more easily by selecting participants who are all members of the same group (Ibid).
The samples for the focus groups were drawn from the aforementioned sampling frame of
students. 18 people accepted the invitations to participate in the focus groups, hence giving six
participants in each of the three focus groups held.
4.7.2.2 Sample Selection for Interviews
The sample of interviewees should be able to give an in-depth analysis, and be able to represent
the targeted population. According to Bryman and Bell (2011), it is more or less impossible for
researchers to explain why the interviewees were selected. Most of the times, the interviewees
are selected randomly from the targeted population by convenience, opportunity or occasion, and
it is also widely known and accepted by the researchers (Ibid).
The sample for the interviews was drawn from the aforementioned sampling frame of personnel
managers. The first five personnel managers from the sampling frame who accepted to
participate in an interview were chosen.
4.7.2.3 Sample Selection for Questionnaires
According to Bryman and Bell (2011), there is almost always a need for sampling when
conducting quantitative research. The validity and reliability, which will be discussed later in this
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chapter, are to an extent affected by the chosen sample, according to Uprichard (2013). Bryman
and Bell (2011) state that there are two different kinds of samples, defined by the way the sample
was selected: probability sample and non-probability sample. If the sample is a probability
sample, each unit in the population has a chance of being selected (Hair et al, 2003; Bryman &
Bell, 2011). In a non-probability sample, the sample has not been selected randomly, meaning
some units are more likely to be selected than others (Ibid). As mentioned by Bryman and Bell
(2011), a probability sample is more likely to generate a representative sample as well as
minimizing the risk of sampling errors. A sampling error is by the same authors defined as “the
difference between a sample and the population from which it is selected” (Bryman & Bell,
2011, p.176).
The sampling made for the questionnaires, both the one to students and the one for personnel
managers, were considered to be probability samples. The questionnaires were sent out to each
unit of the sampling frames, why all units of the populations had the same chance to participate
in the study. Hence, the sample of students contained 229 units and the sample of personnel
managers contained 687 units, i.e. both populations.
4.8 DATA ANALYSIS
This subchapter covers techniques for analyzing qualitative and quantitative data. Analyzing
data, both qualitative and quantitative, is something that should be considered in an early stage
when conducting a research (Bryman & Bell, 2011). However, a common error is that no
concern is taken on how to analyze the collected data until later in the research process (Ibid).
Therefore, the authors should in an early stage be aware of what techniques to use to be able to
analyze collected data (Ibid).
4.8.1 Qualitative Data Analysis
In a research study, the analysis of data is made to gain knowledge and understanding of the
collected data (Ghauri & Grønhaug, 2005). Marshall and Rossman (1999) define data analysis as
when the researchers structure, order and bring meaning to the data that has been collected. The
data analysis helps the researchers to dived and reduce data, clarify problems as well as test
hypotheses (Ghauri & Grønhaug, 2005).
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Miles and Huberman (1994) present three components to use as guidance when conducting the
data analysis. The three components are: data reduction, data display, and conclusion
drawings/verification (Ibid). Data reduction is the process of simplifying, selecting, focusing,
abstracting and transforming the collected data (Ibid). During this step, the researchers categorize
the data and indentify themes and patterns (Ibid). Data display is when the relevant data is being
presented together in some context, which can be in the form of a text document, charts or
matrices (Ibid). The last step, conclusion drawing/verification, is the process of finding patterns
and to draw conclusions from the collected data (Ibid).
In the current research, the aforementioned data analysis components that Miles and Huberman
(1994) present were used to analyze the data collected from the focus groups and interviews.
When the focus groups and interviews had been conducted, the employee attributes mentioned
were transcribed in a document, which became the foundation of the empirical data. The next
step was to simplify and transform the collected data through reduction of data that was
considered irrelevant for the study. Through a table, similar attributes were identified and put
together into broader concepts that were to be used in the quantitative data collection instrument.
This process is further explained in chapter 6.1.
4.8.2 Quantitative Data Analysis
In a quantitative research, statistical analysis is conducted to test hypotheses and draw statistical
inferences (Krishnaswami & Satyaprasad, 2010). It is also conducted to determine the value of
unknown characteristics of the population investigated (Ibid). Before quantitative data is
processed and analyzed it is rather meaningless and to make quantitative data meaningful it
needs to be processed (Saunders et al, 2009). By processing data into graphs, charts, and
statistics, it becomes possible to explore, present and examine relationships and trends within the
data (Saunders et al, 2009).
4.8.2.1 Data Preparation
According to Hair et al (2003), researchers must examine data when it has been collected to
ensure its validity, before it can be analyzed. Editing data involves for instance missing data,
coding, as well as entering data (Hair et al, 2003). More information about data preparation can
be found in Appendix A: Method, A14.
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In the current study, the data was edited once it had been collected, which means that
questionnaires with missing data were eliminated. The data from the collected questionnaires
was later on entered into the system SPSS, which is a program developed for the whole process
of planning, data collection, analysis, reporting and deployment (IBM, 2013).
4.8.2.2 Descriptive Statistics
According to Befring (1994), descriptive data includes principles, methods and techniques to be
able to compile, present, identify and interpret empirical data. The descriptive data is all about
organizing and identifying different patterns from collected data (Ibid).
The questionnaire sent to students included the following descriptive items: gender, number of
years you have been studying at the marketing program, if you are actively searching for a job,
and what dream employer you have. In the questionnaire to recruiters, the following descriptive
items was included: gender, position at the company, number of the years working at the current
position, number of employees working at the company, and what industry the company are in.
Further information about descriptive statistics can be seen in Appendix A: Method, A15.
4.8.2.3 Mann-Whitney U test
The most frequently asked question within business research is whether or not the means
between two groups of respondents are significantly different (Hair et al 2003; Saunders et al,
2009). To be able to see whether or not there is a difference, a null hypothesis as well as an
alternative hypothesis needs to be stated (Hair et al, 2003). Secondly, the significance level when
testing the null hypothesis needs to be selected, where the traditional significance level (α) is
0.05 (Hair et al, 2003; Pallant, 2010). Last, an appropriate statistical test needs to be selected
(Hair et al, 2003).
A Mann-Whitney U test can be used to test the difference between two independent groups on a
single, ordinal variable, and demands no specific distribution (Weiner & Craighead, 2010). A
Mann-Whitney U test can preferably be used when required assumptions for a t-test are not
achieved (Ibid). For instance, when conducting a t-test, it is required that the items are measured
at a interval or ratio level, but a Mann-Whitney U test only requires the variables to be measured
at an ordinal level (Ibid). Moreover, a Mann-Whitney U test does not require the variables to be
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normally distributed, which a t-test does (Ibid). A Mann-Whitney U test only requires the
assumptions of random samples and independent observations (Pallant, 2010). Mann-Whitney U
test belongs to the family of non-parametric tests, which concludes that no assumptions of the
underlying population distribution are drawn, also called distribution-free tests (Weiner &
Craighead, 2010; Pallant, 2010).
According to Pallant (2010), there are six variables to take into consideration when conducting a
Mann-Whitney U test: (1) The first one to look deeper into is the median, since it is through the
median that the two groups are compared and ranked, (2) The N-value shows the respondents’
total size, (3) The median is there after converted into mean ranks, however the actual
distribution of the scores does not matter, (4) The U value shows how much one of the groups
differ from the expected, (5) The z-value shows the correction for ties in the data, (6) And last,
the two-tailed sigma (Asymp. Sig.) should correspond with the α value and in this case be less
than 0.05 to show a statistically significant difference.
Since the current study aimed at comparing the difference between two groups, from a random
sample, and with independent observations, Mann-Whitney U tests were conducted.
4.9 QUALITY CRITERIA
In the current study, multiple methods were used to guarantee the quality of the outcome. Two
qualitative researches were conducted as a pre-study for the quantitative research. The purpose
was to increase the quality of the content in the questionnaires, hence increasing the reliability
and validity of the outcome of the study. Reliability refers to the consistency of a measure of a
concept, while validity has to do with whether or not a measure of a concept is really measuring
that concept (Bryman & Bell, 2011). The quality of the study will be further discussed below.
4.9.1 Quality Criteria for Qualitative Research
According to Bryman and Bell (2011), there have been discussions among qualitative
researchers regarding the relevance of reliability and validity measures for qualitative research
such as focus groups and interviews. Bryman and Bell (2011) say that reliability and validity are
more relevant when conducting quantitative research, since both concerns measurements, which
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is more connected to quantifications. However, as mentioned by Ali and Yusof (2011), quality is
an integral part of qualitative research why there is still an explicit need to highlight the
reliability and validity of qualitative research. The reliability and validity of the qualitative
research will be further discussed below.
As mentioned before, reliability refers to the consistency of a measure of a concept, and
moreover whether the results of a study are repeatable (Bryman & Bell, 2011). Unlike reliability,
validity has to do with whether or not a measure of a concept is really measuring that concept
(Bryman & Bell, 2011). There are several different types of validity that can be considered when
conducting qualitative research (Ibid). For the current study, three types of validity were chosen
to focus on: content validity, construct validity, and external validity. Content validity, or face
validity, is that the measure should reflect the concept in questions (Bryman & Bell, 2011). One
way to seek content validity is by consulting a small sample of typical respondents and/or
experts (Hair et al, 2003). Construct validity is concerned with minimizing the risks of errors, i.e.
random error and method variance (Bagozzi et al, 1991), while external validity is concerned
with whether the results can be generalized or not (Bryman & Bell, 2011). How the reliability
and the validity of the qualitative research were assessed can be seen in chapter 6.1.1.
4.9.2 Quality Criteria for Quantitative Research
As mentioned before, Bryman and Bell (2011) say that reliability and validity is particularly an
issue when conducting quantitative research. In quantitative research, particularly three factors
should be considered when analyzing whether a measure is reliable or not: stability, internal
reliability, and inter-observation consistency (Bryman & Bell, 2011). Stability refers to whether
or not a measure is stable over time, meaning that there will be little variation over time in the
results obtained, according to Bryman and Bell (2011). The same authors state that to be able to
measure the stability of quantitative research, longitudinal research needs to be appointed.
Internal reliability is whether or not the indicators that make up a scale is consistent, meaning
whether or not a respondent’s score on one indicator seem to be related to their score on the other
indicators (Bryman & Bell, 2011). Bryman and Bell (2011), state that a Cronbach Alpha
coefficient over 0.8 shows high reliability. Inter-observation consistency refers to the subjective
CHAPTER FOUR – METHODOLOGY
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31
decisions when more than one observer is involved in activities such as categorizing open-ended
questions in a content analysis (Bryman & Bell, 2011).
In connection to quantitative research, validity is often referred to as measurement validity
(Bryman & Bell, 2011). As with the validity of qualitative research, there are several different
types of validity that can be considered when conducting quantitative research (Ibid). Two types
of validity mentioned by Hair et al (2003) were chosen to focus on in the current study: content
validity, and construct validity. Content validity, or face validity, is as mentioned before that the
measure reflects the concept in questions (Bryman & Bell, 2011). According to Hair et al (2003)
this can be assured through pretesting. Construct validity is, also as mentioned before, concerned
with minimizing the risks of errors, i.e. random error and method variance (Bagozzi et al, 1991).
How the reliability and the validity for the quantitative research were assessed can be seen in
chapter 6.2.1.
4.10 CHAPTER SUMMARY
The methodology chapter has contained discussions around different method approaches,
designs, strategies, etc. to use and justifications for the chosen ones. Table 4.1 shows a summary
of the methods chosen in the current study.
Table 4.1: Method Summary
Method Summary
Method chapter Chosen method
Research approach Deductive
Dyadic
Qualitative and quantitative
Research design Exploratory
Descriptive – multiple cross-sectional
Data sources Primary data
Secondary data
Research Strategy Survey
Data collection methods/instruments Focus groups
Telephone interviews
Online questionnaires
Sampling Probability samples
Data analysis method Qualitative: Miles and Huberman’s (1994) three
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32
components
Quantitative: Descriptive statistics and Mann-
Whitney U test
Quality criteria Reliability
Validity
CHAPTER FIVE – EMPIRICAL DATA
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33
. EMPIRICAL DATA
The following chapter presents the empirical data collected through qualitative
research. First, the empirical data gathered from focus groups are presented
followed by the empirical data collected through interviews.
5.1 EMPIRICAL DATA – FOCUS GROUPS
Three focus groups were conducted with six participants (both males and females) in each focus
group, which gave a total of 18 participants. Several attributes that students perceive employers
want were mentioned during the three focus groups. The stated attributes were divided into three
categories – soft skills, hard skills, and other attributes – and can be seen in table 5.1.
Table 5.1: Empirical Data – Focus Groups
Empirical Data – Focus Groups
Soft skills Hard skills Other attributes
Outgoing
Connection between applicant and
interviewer
Humorous
Patience
Relationship building skills
Be able to manage all sort of people
Communication skills
Team work
Fit into a group fast
Independent
Self-management
Loyal
Initiative
Motivated
Motivated to develop within the
company
Future visions
Ambitious
Commitment
Engagement
Analytical
Leadership skills
Strategic thinking
Creativity
Responsibility
Work experience
Internships
Language skills
Knowledge about the market
Knowledge about the industry
Knowledge about the company
Education
University
Grades
Practical skills
Reference of skills
Look healthy
Age
Gender
Well formulated CV and personal
letter
5
CHAPTER FIVE – EMPIRICAL DATA
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34
Problem solver
Conflict solver
Attitude
Enthusiasm
Flexibility
Willing to move to another
city/country
Self-awareness
Clothing
Appearance
Cultural knowledge
Exchange semester
5.2 EMPIRICAL DATA - INTERVIEWS
Five interviews with recruiters were conducted. A compilation was made of the attributes
employers want employees to possess when hiring, which were mentioned during the conducted
interviews. The stated attributes were divided into three categories – soft skills, hard skills, and
other attributes – and can be seen in table 5.2.
Table 5.2: Empirical Data – Interviews
Empirical Data – Interviews
Soft skills Hard skills Other attributes
Relationship building skills
Ability to maintain relationships
Ability to create value for the
customer
Service-oriented
Social ability
Chemistry between applicant and
interviewer
Oral communication skills
Written communication skills
Ability to cooperate with other
departments within the organization
Ability to work independently
Motivated
Strong driving force
Real interest in the business
Passion for sales
Creative
Problem solver
Be able to travel a lot
Flexible in actions and behavior
Experience from similar work
Linguist
Language skills
Relevant education
Practical skills
No other attributes but hard and soft
skills were mentioned during the
interviews.
CHAPTER FIVE – EMPIRICAL DATA
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35
Professional
Businesslike
5.3 CHAPTER SUMMARY
This chapter has gone through the empirical data collected through qualitative data collection
methods. First, the data collected from the focus groups with students were stated, organized into
three categories of attributes, namely soft skills, hard skill, and other attributes. This was
followed by the attributes mentioned during the conducted interviews with personnel managers,
which were categorized in the same manner as with the attributes from the focus groups. The
attributes stated in table 5.1 and 5.2 were further analyzed as can be seen in chapter 6.1.
CHAPTER SIX – RESULTS
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36
. RESULTS
In this chapter, the results of the study are presented. First, the qualitative results
from the focus groups and interviews are presented which worked as the basis when
developing the questionnaires. This is followed by the quantitative results from the
questionnaires.
6.1 QUALITATIVE RESEARCH
First, this subchapter presents how the validity and reliability of the qualitative part of the study
were sought. This is followed by a presentation of the processed empirical data presented in
chapter 5.
6.1.1 Qualitative Reliability and Validity
For the conducted qualitative research, the reliability was sought through the use of multiple
focus groups and interviews, to ensure that the results were consistent. The reliability was also
sought through detailed descriptions of the data collection procedure, as can be seen in Appendix
B: Results, B1-B2.
To seek content validity of the qualitative research, pretesting was relied on to a great extent.
Moreover, the development of questions for the focus groups and interviews from available
theories was considered as increasing the content validity of the qualitative research. The
qualitative research was conducted through both focus groups and interviews, which helps to
seek construct validity for the qualitative research. Also, proper referencing, interview guides
and saved original transcripts from focus groups and interviews add on the construct validity of
the research. The construct validity was also sought during the focus groups, from the fact that
the main things mentioned during the discussions were written down on a white board for all
members to review. To seek the external validity of the qualitative research, data was gathered
through focus groups and interviews until the point of saturation.
6.1.2 Qualitative Results
The attributes collected from the focus groups and interviews were transcribed and compiled into
a list with three categories of attributes: soft skills, hard skills, and other attributes. This was the
6
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37
empirical data from the focus groups and interviews, and can be seen in chapter 5. Through this
list, it became clear that many attributes had similar meanings and could be compounded into
attributes with broader meanings, why this was done.
As can be seen in table 6.1, 6.2 and 6.3, all attributes mentioned during the focus groups and
interviews compose the left column. These were further compounded into broader attributes
which were to be used in the questionnaires, and can be seen in the second column of the tables.
The third column from the left, in table 6.1 and 6.2, consist of the name of the author(s) claiming
that the attribute is a soft skill (table 6.1) or a hard skill (table 6.2). To enhance the respondents
understanding of the concepts that were to be used in the questionnaires, definitions of the
concepts were added for the soft skills and hard skills, as can be seen in the right column of the
tables.
Table 6.1: Soft Skills
Soft Skills
Attributes from focus
groups/interviews
Attributes used in
questionnaires
Soft skill
according to
Definition of
attributes Outgoing Interpersonal skills Verma & Bedi (2008);
Robles (2012); Culpin
& Scott (2012)
Nice, personable, sense of
humor, friendly, nurturing,
empathetic, has self-control,
patient, sociability, warmth,
social skills (Robles, 2012,
p.455), build/maintain
relationships (Soon et al, 2010)
Connection between applicant and
interviewer
Humorous
Patience
Relationship building skills
Be able to manage all sort of people
Ability to maintain relationships
Ability to create value for the customer
Service-oriented
Oral communication skills Communication skills Verma & Bedi (2008);
Lear (2011); Robles
(2012); Culpin & Scott
(2012)
Oral, speaking capability,
written, presenting, listening
(Robles, 2012, p.455)
Written communication skills
Team work Teamwork Verma & Bedi (2008);
Robles (2012); Culpin
& Scott (2012)
Cooperative, gets along with
others, agreeable, supportive,
helpful, collaborative (Robles,
2012, p.455)
Fit into a group fast
Ability to cooperate with other departments
within the organization
Independent Self-management
Shulz (2008); Andrews
& Higson (2008)
Control of one’s own behavior,
monitoring and managing one’s
own work (Manz & Sims, 1980)
Self-management
Loyal Work ethic Robles (2012) Hard working, willing to work,
loyal, initiative, self-motivated,
on time, good attendance
(Robles, 2012, p.455)
Initiative
Motivated
Motivated to develop within the company
Future visions
Ambitious
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38
Strong driving force
Commitment Commitment
Lear (2011)
Workplace bonds, commitment
to the employing organization,
dedication (Klein et al, 2012)
Engagement
Real interest in the business
Passion for sales
Analytical Analytical skills Culpin & Scott (2012) Ability to analyze abstract
phenomena (NE, 2013)
Leadership skills Leadership skills Verma & Bedi (2008);
Weber et al (2009)
Lead by example, sharing
information, understanding the
needs of the team, counseling,
management skills (Verma &
Bedi, 2008)
Strategic thinking Strategic thinking Ferling (2000);
Andrews & Higson
(2008)
“A way of solving strategic
problems that combines a
rational and convergent approach
with creative and divergent
thought processes” (Bonn, 2005,
p.337)
Creativity Creativity Andrews & Higson
(2008); Thammineni
(2012);
Originality, imagination, goal-
direction, problem-solving (El-
Murad & West, 2004)
Responsibility Responsibility Robles (2012) Accountable, reliable, gets the
job done, resourceful, self-
disciplined, wants to do well,
conscientious, common sense
(Robles, 2012, p.455)
Problem solver Problem solver
Verma & Bedi (2008) Selecting the most effective
alternative when dealing with
problems (D’Zurilla &
Goldfried, 1971)
Conflict solver
Attitude Positive attitude Robles (2012) Optimistic, enthusiastic,
encouraging, happy, confident
(Robles, 2012, p.455)
Enthusiasm
Flexibility Flexibility Robles (2012) Adaptability, willing to change,
lifelong learner, accepts new
things, adjusts, teachable
(Robles, 2012, p.455)
Willing to move to another city/country
Be able to travel a lot
Be flexible in actions and behavior
Self-awareness Self-awareness Soon et al (2010) Is a state in which people attend
to their own consciousness,
body, personal history, or some
other aspect of themselves
(Duval & Wicklund, 1972)
Clothing Professionalism Andrews & Higson
(2008); Robles (2012)
Businesslike, well-dressed,
appearance, poised (Robles,
2012, p.455)
Appearance
Professional
Businesslike
Cultural knowledge Cultural experience Baker (2004) Experience from other countries,
through travelling, exchange
semesters, etc (From left
column)
Exchange semester
Table 6.2: Hard Skills
Hard Skills
Attributes from focus
groups/interviews
Attributes used in
questionnaires
Hard skill
according to
Definition of
attributes Work experience Work experience Robles (2012) Experience from working,
experience from similar work Internships
CHAPTER SIX – RESULTS
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39
Experience from similar work (From left column)
Language skills Multilingual Shulz (2008) Ability to communicate in
various languages (Beeth, 1997) Linguist
Knowledge about the market Knowledge about the
market
Robles (2012); Salas
Velasco (2012)
Knowledge about the industry
and market, including
customers, competitors etc
(From left column)
Knowledge about the industry
Knowledge about the company
Relevant education
Education
Robles (2012) Relevant education for the job,
grades, university (From left
column)
University
Grades
Practical skills Practical skills Robles (2012); Salas
Velasco (2012)
Ability to use software
programs (Robles, 2012) Reference of skills
Table 6.3: Other Attributes
Other Attributes
Attributes from focus groups/
interviews
Attributes used in questionnaires
Look healthy Physical well-being
Age Age
Gender Gender
Well formulated CV and personal letter Well formulated CV and personal letter
6.2 QUANTITATIVE RESEARCH
This subchapter starts by describing how the validity and reliability of the quantitative part of the
study were sought. This is followed by a presentation of the data collected from the two
questionnaires. The descriptive statistics are presented next, followed by the results from the
hypotheses tests, which were conducted through Mann-Whitney U tests in SPSS. Moreover,
additional results are presented.
6.2.1 Quantitative Reliability and Validity
The reliability of the quantitative research was as mentioned before assessed through three
factors: stability, internal reliability, and inter-observation consistency. Time-constraint limited
the ability to measure the stability of the current study, why the stability of the results cannot be
sought. The internal reliability of the questionnaires sent out, were measured according to the
scale of Bryman and Bell (2011), which state that a Cronbach Alpha coefficient over 0.8 show
high reliability. Due to the fact that two questionnaires were sent out, two reliability tests were
conducted. The reliability test of the questionnaires to students, showed a Cronbach Alpha
CHAPTER SIX – RESULTS
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40
coefficient of 0.881 and the reliability test of the questionnaires to recruiters showed a Cronbach
Alpha coefficient of 0.842. This indicates that the study has high reliability. The inter-
observation consistency was not regarded as a problem in the current study. The authors have
been working together several times, conducting qualitative and quantitative researches and
analyses. Moreover, all authors participated and contributed during the process of analysis to
minimize the risk of inconsistency.
As with the content validity of the qualitative research, the quantitative content validity was
sought through pretesting the questionnaires before sending them out to respondents. This is
considered a way to measure content validity according to Hair et al (2003). The questionnaires
were sent to academics for judgment before sending them to a small representative sample of the
population prior the final distribution to all respondents. The construct validity of the quantitative
research was sought by conducting a correlation analysis between the different attributes and
between the different indexes. According to Katz (2006), a correlation greater than 0.9 will cause
problem in the analysis, while those above 0.8 are in the gray area. No correlation in either the
analysis of the questionnaire to students or the questionnaire to recruiters exceeded 0.8. Hence,
the construct validity of the quantitative analysis was sought. See Appendix B: Results, B3 for
the results from the correlation analysis of indexes, and Appendix B: Results, B4 for the results
from the correlation analysis of single attributes.
6.2.2 Descriptive Statistics
Students:
With 229 questionnaires sent out to students, a total of 83 completed questionnaires were
received, which gave a total response rate of 36.2%. An outlier test was conducted and outliers
were detected. However, when they were removed the Cronbach Alpha coefficient did not
increased as expected and hence, the outliers were not removed. None of the questionnaires were
excluded due to the fact of not fully completed. The generated data resulted in 48 female
respondents (57.8%) and 34 male respondents (41.0%), while one person did not specify gender
(1.2%). See table 6.4.
CHAPTER SIX – RESULTS
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41
The generated data regarding how many years the students had been studying at the marketing
program showed the following result: 21 1st year students (25.3%), 18 2
nd year students (21.7%)
and 44 3rd
year students (53.0%). Furthermore, 36 students answered that they were actively
searching for a job (43.4%), and 47 students (56.6%) did not look for a job at the moment. See
table 6.4. As a future dream employer the most frequent answers from the students were IKEA
followed by Apple and Nike.
Table 6.4: Descriptive Statistics Students
Students
Total Response Rate Gender Number of Years Studying: Actively searching for a job?
Male Female 1 2 3 Yes No
36.2% 41.0% 57.8% 25.3% 21.7% 52.0% 43.4% 56.6%
The means and standard deviations retrieved from the students are presented in three different
ways. First, the mean and standard deviation of all attributes together. Secondly, the means and
standard deviations for the different indexes (soft skills/hard skills/other attributes), and last, the
means and standard deviations for all 26 attributes separated. The tables are shown in Appendix
B: Results, B5-B7.
Recruiters:
With 687 questionnaires sent out to recruiters, a total of 129 completed questionnaires were
received which gave a response rate of 18.8%. Three of the questionnaires were excluded due to
the fact of not fully completed. As with the students, an outlier test was conducted. The test
revealed some outliers, but also here the removal of the outliers did not generate a higher
Cronbach Alpha coefficient. Hence, no outliers were removed. The generated data resulted in 53
female respondents (42.1%), 72 male respondents (57.1%), and one person did not specify
gender (0.8%). See table 6.5.
The result from the question regarding how many employees the company had where the
respondents were working, showed that most of the recruiters worked for companies with 200-
499 employees (36.5 %). This was followed by more than 500 employees (27.8%) and 100-199
employees (24.6%). See table 6.5.
CHAPTER SIX – RESULTS
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42
Generated data concerning how many years the specific recruiter had been working at the current
position, showed that most of them had been working for 6-10 years at the current position
(27%). This was closely followed by recruiters who had been working at the current position for
3-5 years (26.2%). 23.8% had been working at the current position for 1-2 years. See table 6.5.
The most common positions at the company of the respondents were human resource manager,
CEO and marketing manager.
Table 6.5: Descriptive Statistics Recruiters
Recruiters
Total
Respon
se Rate
Gender No. of Employees at Your Company No. of Years at Current Position
Male Femal
e
0-49 50-
99
100-
199
200-
499
500+ 1-2 3-5 6-10 11-
15
16-
20
21+
18.8% 57.1% 42.1% 2.4
%
6.3
%
24.6
%
36.5
%
27.8
%
23.8
%
26.2
%
27.0
%
7.9
%
4.0
%
4.0
%
The means and standard deviations retrieved from the recruiters are presented in three different
ways. First, the mean and standard deviation of all attributes together. Secondly, the means and
standard deviations for the different indexes (soft skills/hard skills/other attributes), and last, the
means and standard deviations for all 26 attributes separated. The tables are shown in Appendix
B: Results, B5-B7.
6.2.3 Hypotheses
There are different methods to use when comparing two means between different groups, e.g. a t-
test (Pallant, 2010), but the Mann-Whitney U test was considered the most appropriate test to use
to test the four stated hypotheses of the current study. To know which test to use the collected
data have to meet specific assumptions. The data in the current study violated the assumptions
required to conduct a t-test. According to Sawilowsky and Blair (1992) and Stonehouse and
Forrester (1998), a non-parametric test is more powerful to use than a t-test when these
assumptions are violated. Hence, a Mann-Whitney U test, which is a non-parametric test, was
considered the most appropriate test in relation to these assumptions. The first assumption
required for a Mann-Whitney U test is that the research has to use random samples (Pallant,
2010). This assumption was achieved since all units of the populations had equal chances of
participating in the study. The second assumption for a Mann-Whitney U test is that the research
CHAPTER SIX – RESULTS
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43
has independent observations (Pallant, 2010). The current study had independent observations
since each respondent could only be counted once, and the same respondent did not appear in
more than one group. Moreover, the results from a conducted Shapiro-Wilks test, which is
sensitive to normality, showed that the data had non-normal distribution, hence a α level under
.05 (Pett, 1997). The result for the Shapiro-Wilks test can be seen in Appendix B: Results, B8.
Furthermore, a test for both skewness, which indicates the symmetry of the distribution, as well
as kurtosis, which provides information of the “peakedness” of the distribution was conducted
(Pallent 2010). According to Hair et al (2007), normally distributed data has skewness values
within a threshold of -1 to +1 and kurtosis values within -3 to +3. The test showed that some
attributes were skewed and some attributes did not pass the level for kurtosis. The results for this
test can be seen in Appendix B: Results, B9. Hence, a Mann-Whitney U test was considered
appropriate since the data does not have to be normally distributed in order to conduct this test
unlike a t-test (Weiner & Craighead, 2010). Furthermore, the data in the study was measured on
ordinal scales, which fits well when using non-parametric techniques (Pallant, 2010).
The alpha level (α) used in the current study was 0.05 (cf. Hair et al, 2003; Saunders et al, 2009),
meaning that the z-value should be less than -1.96 or greater than +1.96 in order to reject the null
hypotheses. The results from the Mann-Whitney U test for hypothesis 1 can be seen in Appendix
B: Results, B10, and the results for hypotheses 2, 3 and 4 can be seen in Appendix B: Results,
B11. Moreover, H1 was rejected in relation to hypothesis 1, 2, 3 and 4, as can be seen in table
6.6. The results are also further presented below.
Table 6.6: Hypotheses
Hypotheses
H1 was rejected H0 was rejected
Hypothesis 1 X
Hypothesis 2 X
Hypothesis 3 X
Hypothesis 4 X
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44
6.2.3.1 Hypothesis 1
H1: There is a mismatch between the employee attributes that students perceive employers want,
and the employers’ wants regarding employee attributes.
The result of this test show as follow:
All attributes: There was no statistically significant difference in the scores from students
(Md = 5.04, n = 83) and recruiters (Md = 5.15, n = 126), U = 4917.5, z = -.728, p = .466.
These results suggest that there is no mismatch between employee attributes that students
perceive employers want, and the employers’ wants regarding employee attributes.
Hence, H1 was rejected in relation to hypothesis 1.
6.2.3.2 Hypothesis 2
H2: There is a mismatch between how important recruiters consider soft skills to be, and how
important students perceive soft skills to be.
The result of this test show as follows:
Soft skills: There was no statistically significant difference in the scores from students (Md =
5.53, n = 83) and recruiters (Md = 5.71, n = 126), U = 4509.5, z = -1.683, p = .092. These
results suggest that there is a mismatch between how important students perceive soft skills
to be, and how important recruiters consider soft skills to be.
Hence, H1 was rejected in relation to hypothesis 2.
6.2.3.3 Hypothesis 3
H3: There is a mismatch between how important recruiters consider hard skills to be, and how
important students perceive hard skills to be.
The result of this index is as follows:
Hard skills: There was no statistically significant difference in the scores from students (Md
= 5.00, n = 83) and recruiters (Md = 4.80, n = 126), U = 4571.5, z = -1.543, p = .123. These
CHAPTER SIX – RESULTS
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45
results suggest that there is no mismatch between how important students perceive hard
skills to be, and how important recruiters consider hard skills to be.
Hence, H1 was rejected in relation to hypothesis 3.
6.2.3.4 Hypothesis 4
H4: There is a mismatch between how important recruiters consider other attributes to be, and
how important students perceive other attributes to be.
The results of this index is as follows:
Other attributes: There was no statistically significant difference in the scores from students
(Md = 3.75, n = 83) and recruiters (Md = 3.50, n = 126), U = 4703, z = -1.235, p = .217.
These results suggest that there is no mismatch between how important student perceive
other attributes to be, and how important recruiters consider other attributes to be.
Hence, H1 was rejected in relation to hypothesis 4.
6.2.4 Additional Results
One Mann-Whitney U test was also conducted where the medians of each single attribute was
compared. The results from this Mann-Whitney U test show that discrepancies exist between
students’ perceptions of important employee attributes to possess, and the employee attributes
recruiters consider important. The results are presented below, and can also be seen in Appendix
B: Results, B12.
At α = 0.05, there is a mismatch between the perceived/considered importance of interpersonal
skills, teamwork, self-management, commitment, responsibility, self-awareness, knowledge
about the market, physical well-being, gender, and well formulated CV and personal letter. The
results of these attributes are as follows:
Interpersonal skills: There was a statistically significant in the scores from students (Md =
6.00, n = 83) and recruiters (Md = 6.00, n = 126), U = 4371.5, z = -2.141, p = .032. These
results suggest that there is a mismatch between how important students perceive
interpersonal skills to be, and how important recruiters consider interpersonal skills to be.
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46
Teamwork: There was a statistically significant difference in the scores from students (Md =
6.00, n = 83) and recruiters (Md = 6.00, n = 126), U = 4404.5, z = -2.071, p = .038. These
results suggest that there is a mismatch between how important students perceive teamwork
to be, and how important recruiters consider teamwork to be.
Self-management: There was a statistically significant difference in the scores from students
(Md = 5.00, n = 83) and recruiters (Md = 6.00, n = 126), U = 3750, z = -3.656, p = .000.
These results suggest that there is a mismatch between how important students perceive self-
management to be, and how important recruiters consider self-management to be.
Commitment: There was a statistically significant difference in the scores from students (Md
= 5.00, n = 83) and recruiters (Md = 6.00, n = 126), U = 3914.5, z = -3.206, p = .001. These
results suggest that there is a mismatch between how important students perceive
commitment to be, and how important recruiters consider commitment to be.
Responsibility: There was a statistically significant difference in the scores from students
(Md = 6.00, n = 83) and recruiters (Md = 6.00, n = 126), U = 4273, z = -2.401, p = .016.
These results suggest that there is a mismatch between how important students perceive
responsibility to be, and how important recruiters consider responsibility to be.
Self-awareness: There was a statistically significant difference in the scores from students
(Md = 5.00, n = 83) and recruiters (Md = 5.00, n = 126), U = 4246, z = -2.731, p = .018.
These results suggest that there is a mismatch between how important students perceive self-
awareness to be, and how important recruiters consider self-awareness to be.
Knowledge about the market: There was a statistically significant difference in the scores
from students (Md = 5.00, n = 83) and recruiters (Md = 5.00, n = 126), U = 4051, z = -2.840,
p = .005. These results suggest that there is a mismatch between how important students
perceive knowledge about the market to be, and how important recruiters consider
knowledge about the market to be.
Physical well-being: There was a statistically significant difference in the scores from
students (Md = 5.00, n = 83) and recruiters (Md = 5.00, n = 126), U = 3701, z = -3.689, p =
.000. These results suggest that there is a mismatch between how important students
perceive physical well-being to be, and how important recruiters consider physical well-
being to be.
CHAPTER SIX – RESULTS
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47
Gender: There was a statistically significant difference in the scores from students (Md =
2.00, n = 83) and recruiters (Md = 1.00, n = 126), U = 4188.5, z = -2.752, p = .006. These
results suggest that there is a mismatch between how important students perceive gender to
be, and how important recruiters consider gender to be.
Well formulated CV and personal letter: There was a statistically significant difference in
the scores from students (Md = 5.00, n = 83) and recruiters (Md = 4.70, n = 126), U = 4349,
z = -2.111, p = .035. These results suggest that there is a mismatch between how important
students perceive well formulated CV and personal letter to be, and how important recruiters
consider well formulated CV and personal letter to be.
6.3 CHAPTER SUMMARY
Chapter 6 analyzed not only the results from the qualitative studies, but first and foremost the
quantitative results, since that was the main data collection of the research.
The reliability of the qualitative part of the study was sought through conduction of multiple
focus groups and interviews, as well as detailed descriptions on the data collection procedures.
Moreover, the validity of the qualitative research was also sought. When data from the
qualitative research was analyzed, it became clear that the attributes had similar meaning and
could be compounded into broader attributes. These were further developed into the attributes
stated in the quantitative study, i.e. the questionnaires.
The reliability was also sought for the quantitative part of the study, through stability (which
showed a minor problem with time-constraint), internal reliability (the Cronbach Alpha
coefficient showed high reliability) and inter-observation consistency (all authors participated
when analyzing to minimize the risk of inconsistency). Validity was also sought for the
quantitative research. For instance, the construct validity was tested through a correlation
analysis. The results from the quantitative research resulted in rejection of H1 in regard to
hypothesis 1, 2 3 and 4. However, the results indicate that there are still discrepancies between
the perceived/considered importance of 10 of the 26 measured attributes.
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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48
. DISCUSSIONS, CONCLUSION, IMPLICATIONS,
LIMITATIONS & SUGGESTIONS FOR FUTURE
RESEARCH
This chapter begins with a discussion around the findings, which consequently answers the
purpose of the study. The chapter continues with a discussion about the theoretical and
managerial implications of the study, followed by the limitations of the study and suggestions for
future research.
7.1. DISCUSSIONS
From the statistics mentioned in the previous chapter, it could be stated that there might be
discrepancies between the employee attributes that employers want, and students’ perceptions of
sought employee attributes. When measuring the attributes indexed into categories, it could be
stated that there are no statistically significant discrepancies concerning soft skills, hard skills, or
other attributes. However, results indicate that there is a discrepancy concerning soft skills, just
not statistically significant. When measuring all 26 attributes by themselves, the discrepancies
concern the following 10 attributes: interpersonal skills, teamwork, self-management,
commitment, responsibility, self-awareness, knowledge about the market, physical well-being,
gender, and well formulated CV and personal letter. The findings are further discussed below.
7.1.1 Discussion of Research Question and Hypotheses
The results of the study show that H1 in regard to all hypotheses was rejected. Hence, there is no
proof that there are statistically significant discrepancies between the perceived/considered
importance of employee attributes. Moreover, there is no proof that there are statistically
significant discrepancies between the perceived/considered importance of soft skills, hard skills,
or other attributes. However, the results show that even though there is no statistically significant
discrepancy between perceived/considered importance of soft skills, the figures suggest that a
discrepancy can exist, but with a certainty level of 90%. Soft skills: Results show that recruiters
considered soft skills to be more important than students perceived soft skills to be. This is in
line with many previous researches, which state that soft skills are becoming more and more
7
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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49
important for an employee to possess. For instance, Nealy (2005) argues that business leaders are
looking more at the soft skills than the hard skills, since it positively affect the productive
performance. Also Branine (2008) highlights the importance of soft skills, and says that
recruiters look beyond the hard skills. Cook and Finch (1994) had the same conclusion – soft
skills are emphasized as more important, rather than hard skills.
As the results show, H1 was rejected in regard to all hypotheses. But to answer the research
question of the study, there is a discrepancy between some of the attributes students perceive as
wanted and the attributes personnel managers actually want. These finding are further discussed
below.
7.1.2 Discussion of Additional Results
Two of the attributes, where discrepancies between perceived/considered importance were found
and where students perceived them to be more important than recruiters considered them to be,
were soft skills. These two attributes were: interpersonal skills, and teamwork. Interpersonal
skills: Interpersonal skill is a soft skill according to Verma and Bedi (2008), Robles (2012) and
Culpin and Scott (2012), and students perceived interpersonal skills to be more important than
recruiters considered interpersonal skills to be. However, Kelley and Gaedeke (1990) say that
one of the most wanted employee attributes when recruiters are hiring people for marketing
positions are interpersonal skills. Teamwork: Also teamwork is a soft skill according to Verma
and Bedi (2008), Robles (2012) and Culpin and Scott (2012). Branine (2008) highlighted that
recruiters look for employees who are able to work in teams, but as the results show, students
perceived teamwork to be more important than recruiters considered it to be. Also Robles (2012)
says that teamwork is an important employee attribute for recruiters, as well as Sharma (2009)
who says that teamwork is the second most important soft skill to possess. Other studies have
also shown that students perceive teamwork as essential to possess (Raymond & McNabb, 1993;
DuPre & Williams, 2011; Salas Velasco, 2012).
Four of the attributes, where discrepancies between the perceived/considered importance were
found, were soft skills and where recruiters considered the attributes to be more important than
students. These attributes were: self-management, commitment, responsibility, and self-
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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50
awareness. Self-management: According to Shulz (2008) and Andrews and Higson (2008), self-
management is a soft skill. The results from the current study show that recruiters considered
self-management to be more important to possess as an employee, than students perceived it to
be. This is supported by Branine (2008) who state that recruiters are looking for employees who
are able to work independently. Also Salas Velasco (2012) states that recruiters appreciate skills
such as self-management. Commitment: Also commitment is a soft skill according to Lear
(2011). As with self-management, recruiters considered commitment to be more important than
students perceived it to be. Engagement is something that Behrenz (2001) states that recruiters
look for during the process of recruitment. Moreover, Behrenz (2001) states that personal
engagement within a company is crucial for the success of the company. Due to this fact, it is
understandable that recruiters considered commitment to be of greater importance.
Responsibility: Another soft skill is responsibility (Robles, 2012), and also here recruiters
considered the attribute to be more important than students perceived it to be. As with self-
management, Branine (2008) states that responsibility is something that recruiters are looking for
when hiring new employees. According to Robles (2012), accountability is a one of the
definitions of responsibility. Moreover, Junek et al (2009) say that students are often perceived
as accountable, why it can be considered positive to have been a student when looking for a job
since recruiters considered responsibility important. Self-awareness: Another attribute
considered as more important by recruiters is self-awareness, which is a soft skill according to
Soon et al (2010). Self-awareness was mentioned during the focus groups as important to
possess, and this is in line with previous research. For instance, Goleman (1998) revealed data
from a study which showed that factors such as self-awareness does not only affect the
successfulness of the employees, but does also lead to more successful companies. Hence, it is
understandable that recruiters considered self-awareness to be of greater importance.
It was found to be a discrepancy between the perceived/considered importance of one hard skill,
knowledge about the market. Knowledge about the market: This is a hard skill according to
Robles (2012) and Salas Velasco (2012), and in this case, students perceived knowledge about
the market to be more important than recruiters considered it to be. However, Behrenz (2001) for
instance, highlights the importance of possessing professional knowledge.
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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51
The results also showed that there is a discrepancy between the perceived/considered importance
of one attribute categorized as other attributes, where recruiters considered the attribute to be
more important than students perceived it to be. Physical well-being: Recruiters considered
physical well-being to be more important than students perceived it to be. This is in line with
what Hurley-Hanson and Giannantonio (2006) say: image norms, such as physical attractiveness
including weight, height, clothing, facial beauty and handicap status, play a role when recruiters
consider candidates for a job.
There were also discrepancies between the perceived/considered importance of two other
attributes, categorized as other attributes, where students perceived the attributes to be more
important than recruiters considered them to be. These two attributes were gender, and well
formulated CV and personal letter. Gender: Concerning gender, the results from the current
study indicate that students perceived gender to be more important than recruiters. Since this is a
rather sensitive question, it may be hard to get an accurate result. However, if the statistics
concerning this question is accurate, gender does not have as high impact when looking for a job
as students perceive it has. Well formulated CV and personal letter: Also concerning well
formulated CV and personal letter, students perceived it to be more important than recruiters
considered it to be. According to Osoian et al (2011), students should consider their CV or
resume as one of the most important tool for their future career. But as the results show,
recruiters did not consider a well-formulated CV and personal letter as important as students
perceived it to be.
7.2 CONCLUSION
As the results show from the analysis of the attributes indexed into categories, recruiters seem to
consider soft skills to be more important than students perceive soft skills to be. As explained
earlier in the current study, those who best fit the environment will succeed in the future if there
is a change within a system (Powell & Wakely, 2003). Students must understand the importance
of possessing soft skills in order to get hired. Those students who best fit the demands from
recruiters, will have an increased chance to succeed. And moreover, recruiters must provide clear
information of how important it is to possess these skills, to be able to match the right employees
to the organization.
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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52
The results from the analysis of each single attribute show that students seem to perceive
interpersonal skills, teamwork, knowledge about the market, gender, and well formulated CV
and personal letter to be more important than recruiters consider them to be. Moreover, recruiters
seem to consider self-management, commitment, responsibility, self-awareness, and physical
well-being to be more important than students perceive them to be.
Hence, students do not have to worry so much about possessing interpersonal skills, ability to
teamwork, knowledge about the market, to have the “right” gender, or to have a well formulated
CV and personal letter to get hired. However, students who want to get hired must understand
that self-management, commitment, responsibility, self-awareness, and physical well-being are
more important than they perceive them to be today. Since students do not have accurate
perceptions of how important these attributes are, recruiters must provide clearer information
about what they look for when hiring new employees.
7.3 IMPLICATIONS
In this subchapter, the implications of the study are discussed. First, the theoretical contributions
that the current study provides are discussed. This is followed by a discussion around the
managerial implications.
7.3.1 Theoretical Implications
Since the results from similar studies vary, an updated research was considered necessary. The
current study provides updated results concerning the importance of employee attributes.
Moreover, previous research had been conducted in different countries, and on different target
groups. The current study are limited to Sweden as well as focused on one specific target group,
and a possible job market for those students, and can hence be seen as a theoretical contribution.
Previous studies had been conducted on slightly, or majorly differing subjects than the subject in
the current study. Since no previous study is exactly like the current one, the study contributes to
available literature. Moreover, most of the available researches focused on one or the other of the
two viewpoints focused on in the current research. The current study contributes to available
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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53
literature by providing a dyadic approach, with both an exploratory research design as well as a
descriptive research design
Moreover, the current research discusses the importance of matching in the context of
evolutionary perspective, i.e. “survival of the fittest”. By applying these theories to the study,
hence in a new context, a theoretical contribution is provided to available literature.
7.3.2 Managerial Implications
In addition to already stated theoretical contributions, the current study has provided new
insights in correspondence with available studies within the field. The results of the study can
provide information to students concerning what recruiters are looking for when hiring new
employees. By focusing on the attributes that recruiter consider important, chances for getting a
job may increase. Students who possess those attributes can better highlight the possession of the
attributes, while those who did not think of the attributes as important, but who possess them,
can take them into consideration. Those who do not possess the attributes can try to develop
them.
The results of the current study can also be of importance to recruiters. Since there are some
discrepancies between students’ perceptions of sought employee attributes and the employee
attributes that recruiters actually seek, this may indicate that recruiters have to be more clear
about what they are actually looking for. If recruiters provide accurate information about what
they are looking for, they will most likely increase their chances of getting what they look for.
Universities may also find the results useful. By focusing the educations towards the attributes
highlighted by recruiters as important, students’ chances of getting a job after graduation may
increase. Hence, by focusing on what is necessary for students to get a job after graduation, these
educations may gain popularity.
7.4 LIMITATIONS
The current study had some limitations, which could have influenced the results of the study.
First of all, the time frame for the study was limited. With a longer time frame, more time could
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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54
have been spent on collecting data, as well as more time on analyzing the collected data. This
could have provided the study with higher reliability since there was no time to ensure stable
results. Moreover, a longer time frame would also mean that the respondents would have had
more time to answer the questionnaires, which could have resulted in a higher response rate.
The time constrains also affected the theoretical insight in the subject of the study. With a longer
time frame, a larger amount of time could have been spent on going through available literature
within the field. Increased understanding of the different theories used in the current research
could have increased the quality of the research, and other theories appropriate for the current
study could have been found which may have affected the results of the research. With a better
understanding of the theories used, a more thorough analysis could have been conducted, which
could have strengthened the result of the study.
Another limitation that constrained the study was the limited resources. If more resources would
have been available, the response rates could have increased for both questionnaires. This, by
sending the questionnaires via mail, calling several phone calls to the respondents as well as
providing a more tempting incentive to the respondents.
The support from organizations and students can also be seen as a limitation of the study. Some
companies and students did not want to participate in the current study. If all the students and
recruiters that the questionnaires were sent to would have participated, the response rate would
have increased and made the study more statistically reliable. Moreover, the access to
information has in some matters been a limitation in the current study. Some articles have not
been accessible, due to regulations or need for memberships and payments.
7.5 SUGGESTIONS FOR FUTURE RESEARCH
Since the stability of the results could not be ensured in the current study due to time constraints,
a suggestion for future research is to conduct a similar study in the future. This would be
appropriate since it would ensure the stability of the results, or to see how the results change over
time. Another suggestion for future research is to increase the sample size, hence increasing the
number of respondents. This could help to get a more accurate and reliable result.
CHAPTER SEVEN – DISCUSSIONS, CONCLUSION, IMPLICATIONS, LIMITATIONS
AND SUGGESTIONS FOR FUTURE RESEARCH
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55
For future research it could also be suggested to conduct a similar research but in a new context.
The new context could be to change the geographical area of the study, hence choosing to
conduct the study on marketing student in another country and recruiters in the manufacturing
industry in another country. Another new context could be to conduct the study on other students
than marketing students, as well on recruiters in an industry appropriate to those students.
Additional research could also be suggested for a similar study but on more than one university,
i.e. use a sample from multiple universities.
Since the current study focused on attributes sought when hiring employees for the marketing
department in the manufacturing, it could be of interest to just change the industry, to see which
attributes are sought when hiring new employees to the marketing department in other industries.
The current study focused on 26 specific attributes developed during the study. For future
research it could be suggested to investigate the importance of other attributes than the ones used
in the current study. Moreover, it would be interesting to investigate how universities concentrate
on teaching students the skills that are considered important to recruiters.
Finally, as the current study had a quantitative approach, it can be of interest for future studies to
conduct a qualitative study to understand the underlying reasons behind why students and
recruiters consider some skills more important than other skills.
7.6 CHAPTER SUMMERY
In this chapter, there have been discussion of the results. The chapter also includes a conclusion
that answers the research question and purpose of the study. This was followed by implications
of the study, where it was suggested that the research for instance contributed theoretically by
testing theories in a new context, and managerially the study can be useful for students, recruiters
and universities. The chapters also discusses possible limitations for the study such as time and
resource limitations. Finally, the chapter ends with suggestions for future research, such as
conducting the study on other students than marketing students.
56
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APPENDIX A: METHOD
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63
APPENDIX A: METHOD
A1. Inductive vs. Deductive Research
An inductive approach is mainly the outcome of research, where conclusions are drawn from
collected data and then developed to new theoretical frameworks (Bryman & Bell, 2011). The
inductive research should be seen as the “the systematic process of establishing a general
proposition on the basis of observation or particular facts” (Ghauri & Grønhaug, 2005, p.16).
Deductive theory is the most common view and represents the relationship between theory and
research, and when creating hypotheses or research questions only accessible theories within the
domain are to be used (Bryman & Bell, 2011). According to Saunders et al (2009), deductive
research can be described as testing the development of a theory. It can also be seen as “the
logical process of deriving a conclusion from a known premise or something known as true”
(Ghauri & Grønhaug, 2005, p.16). Both research approaches demand imagination and creativity
from the researchers, as well as that the research goes beyond statistics to data collection (Ibid).
Common characteristics are that the inductive research often is conducted on a small sample,
compared to the deductive research that often is carried out on larger samples (Saunders et al,
2009). Saunders et al (2009) also state that the deductive research often emphasizes the
collection of quantitative data and the inductive research often use qualitative data.
A2. Quantitative vs. Qualitative Research
Research can be divided into a quantitative and/or qualitative research approach (Bryman & Bell,
2011). According to Bryman and Bell (2011), the most obvious distinction is the fact that
quantitative researchers use measurements, while qualitative researchers do not. In a quantitative
research, quantifications are emphasized in the collection and analysis of data (Bryman & Bell,
2011). Similarly, Ghauri and Grønhaug (2005), highlight that the results of a quantitative
research should be presented through statistics. Moreover, Bryman and Bell (2011) say that
words are emphasized in the collection and analysis of data in a qualitative research, and Ghauri
and Grønhaug (2005) state that the results from a qualitative research should not be statistical
and no quantifications should be made.
APPENDIX A: METHOD
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64
Strauss and Corbin (2008) mention that a qualitative research is often used when doing a study in
social and behavior sciences and when researchers want to investigate human behavior. A
qualitative research is also good to use when investigating people’s attitudes towards a brand,
trend or behavior (Ghauri & Grønhaug, 2005). Strauss and Corbin (2008) identify a number of
characteristics that are often used in qualitative researches. One of them is that interviews and
observations are common data collection instruments, while another characteristic is that the
report is written or verbal (Strauss & Corbin, 2008). Common data collection methods for
quantitative research are questionnaires and structured interviews (Ghauri & Grønhaug, 2005).
A3. Research Designs
Exploratory research design: Refers to a design that is preferably used when researchers are at
an early stage of a research project (Bryman & Bell, 2011). The design aims at gaining
familiarity within a phenomenon or to create greater knowledge in a specific field with already
existing data (Hair et al 2003; Dhawan, 2010). This research design is time consuming and
therefore requires a lot of resources (Zikmund et al, 2010).
Descriptive research design: Aims to investigate and portray how a specific individual, group or
situation act or behave (Hair et al, 2003; Dhawan, 2010). When using a descriptive research
design a definition of what is investigated as well as how it is measured is needed, hence it
should answer questions of who, what, when, where and how (Dhawan, 2010; Bryman & Bell,
2011). Descriptive research design can preferably be used when creating generalization patterns
and association patterns within a specific field (Zikmund et al, 2010; Bryman & Bell, 2011).
Causal research design: Aims at testing one or more hypotheses of a causal relationship between
variables (Dhawan, 2010). Causal research design is preferably used when a researcher wants to
gain more knowledge on what impact a change in one specific variable has on another (Hair et
al, 2003; Zikmund et al, 2010). This type of research design requires the researchers to have both
control and knowledge of the different variables, hence it is both complicated to conduct as well
as time consuming (Ibid).
APPENDIX A: METHOD
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65
Longitudinal design: Is a research design that is used to map out changes in a specific business
over a long period of time and on at least two different occasions, but usually more (Hair et al,
2003; Bryman & Bell, 2011). Longitudinal design involves drawings with “phenomena at
vertical and horizontal levels” (Bryman & Bell, 2011, p.57), where the goal is to analyze
interconnections through time (Ibid). Longitudinal design is rarely used within business and
management research, due to time and resource constraints connected with this approach (Ibid).
Cross-sectional design: Is also known as social survey design, and is the “collection of data on
more than one case (usually quite lot more than one) and at a single point in time in order to
collect a body of quantitative or quantifiable data in connection with two or more variables
(usually many more than two), which are then examined to detect patterns of associations”
(Bryman & Bell, 2011, p.53). Cross-sectional design can be further divided into single cross-
sectional design and multiple cross-sectional design. Single cross-sectional design means that
data is collected from only one section, while multiple cross-sectional design means that data is
collected from more than one section (Bryman & Bell, 2011).
A4. Data Sources
There are several advantages and disadvantages with primary and secondary data sources
(Ghauri & Grønhaug, 2005). According to Ghauri and Grønhaug (2005), primary data is both
expensive and time consuming in the sense of collecting data, while secondary data is less costly
and less time consuming to collect. For this reason, secondary data can preferably be used to
lower the cost and be more time efficient (Ibid). However, according to Bryman and Bell (2011),
there might be a lack of familiarity when using secondary data and the data collected might be
too complex to understand. Moreover, the researchers do not have any control over the quality of
already collected data as well as the use of different key variables that the former researchers
used, which might be problematic (Bryman & Bell, 2011). When using secondary data, the
opportunity for cross-cultural analysis occurs (Ibid). A broader perspective is given to the
research if the authors use data not only collected by themselves or even in the same country as
where the research is being conducted (Ibid). There might thus be cultural differences between
the countries, which should be considered (Ibid). Collecting primary data is as mentioned before
time consuming and by using secondary data, more time can be spent on analyzing the data
APPENDIX A: METHOD
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66
collected (Ghauri & Grønhaug, 2005). Analyzing data collected from other researches also gives
the opportunity to re-analyze the findings that might preferably need new insights (Ibid).
A5. Research Strategies
Experiment is a methodical process where one or more variables are altered to establish different
effects (Yin, 2009). Survey can be described as when conducting a study on a specific sample of
individuals and make statistical inferences of the result (Ibid). Archival analysis is an
observational method where the researchers collect and analyze secondary data (Ibid). A history
research strategy focuses on collection and analysis of historical documents (Ibid). The last
strategy, case study, can be described as when researchers analyze an individual unit (e.g. a
person, group, or event), with a focus on developmental factors in connection to the context or
appropriate theory (Ibid).
The table below was used when deciding which strategy that was best suited for the current
study. In the table, five different research strategies are demonstrated, including the criteria from
which they were examined. The three different criteria used in the model are: (1) how the
research questions are formulated, (2) the control over behavioral event, and (3) the focus on
contemporary events.
Research Strategies (Yin, 2009, p.8)
Method Conditions
Form of research question: Requires control over
behavioral event?
Focus on
contemporary events?
Experiment How? Why? Yes No
Survey Who? What? Where? How many? How
much?
No Yes
Archival Analysis Who? What? Where? How many? How
much?
No Yes/No
History How? Why? No No
Case Study How? Why? No Yes
A6. Focus Groups
When conducting focus groups there is one person leading the focus group, often called a
moderator, with the purpose of managing the discussion without taking too much space (Bryman
APPENDIX A: METHOD
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67
& Bell, 2003). The purpose of conducting a focus group is to create understanding of group
members' perceptions, attitudes or opinions about a specific topic (Eliasson, 2010). It also helps
the researchers to gain deeper understanding and access information of a specific subject, which
becomes clearer gradually (Ibid).
The best way to collect information from a focus group is to first record the discussion and then
write down the information (Bryman & Bell, 2003). Regarding the size of the group, Morgan
(1988) writes that six to ten participants is a good number of participants. According to
Blackburn and Stokes (2000), the group should not contain more than 8 people since this could
make the group harder to maneuver. Using too large groups can make it more difficult for some
people to express their true opinions in subjects that the respondents have limited knowledge or
experience in (Bryman & Bell, 2003).
A7. Interviews
When conducting a semi-structured interview, the interviewer has a list of pre-determined topics
that are going to be covered through the interview (Bryman & Bell, 2003). The questions do not
have to follow a specific schedule and new questions can be asked during the interview
depending if new topics arise (Hair et al, 2003; Bryman & Bell, 2011). Hence, semi-structured
interviews were considered appropriate for the current study.
One way of conducting interviews is through telephone interviews, which brings several benefits
(Eriksson & Wiedersheim-Paul, 2011). Telephone interviews are cheaper, less time consuming,
have a high response rate and are easier to manage than face-to-face interviews (Ibid).
A8. Questionnaires
When conducting a questionnaire it is essential to design it in an easy way and to avoid
complicated and complex questions (Hair et al, 2003; Bryman & Bell, 2011). It is essential to
keep the questionnaires simple, hence not too long and with too many questions since the
respondents might get tired and stop answering the questionnaire (Ibid).
Questionnaire questions can be designed in two ways: open questions and/or closed questions
(Eliasson, 2010). When using closed questions, the respondent has different pre-determined
APPENDIX A: METHOD
______________________________________________________________________________
68
answers to choose from, which makes the results easier to analyze and can more easily be turned
into statistics than open questions (Ibid).
One way to collect data through a questionnaire is to do it over the Internet (Evans & Marthur,
2005). Online questionnaires make it easy to reach out to a large amount of people (Ibid).
Advantages by doing the questionnaires over the Internet include that the respondents can
answer the questionnaire when they have time for it, and the cost of designing as well as
reaching out to respondents is low (Ibid).
A9. Operationalization – Focus Group
Operationalization – Focus Group
Concept Concept definition Operational
definition
Question
Perception The process by which stimuli are
selected, organized and
interpreted (Solomon et al, 2010)
Find which attributes
students perceive
recruiters want in new
employees.
Which employee attributes do you
think recruiters consider
important when hiring employees
for the marketing department at a
manufacturing company?
Employee
attributes
Skills: the certain personal
abilities that an individual
possesses (Raybould & Sheedy,
2005)
Soft skills: the personal and social
skill that a person possesses
(Hutchinson & Brefka, 1997)
Hard skills: job objectives, work
experience and academic
background (Hutchinson &
Brefka, 1997)
A10. Operationalization – Interviews
Operationalization – Interviews
Concept Concept definition Operational
definition
Questions
Wants The form needs take as shaped by
culture and individual personality
(Armstrong et al, 2009)
See if the attributes
highlighted by students
correspond to the
attributes recruiters
want when hiring new
employees.
Which employee attributes do you
consider important when hiring
employees for the marketing
department at your company? Employee
attributes
Skills: the certain personal
abilities that an individual
possesses (Raybould & Sheedy,
2005)
APPENDIX A: METHOD
______________________________________________________________________________
69
Soft skills: the personal and social
skill that a person possesses
(Hutchinson & Brefka, 1997)
Hard skills: job objectives, work
experience and academic
background (Hutchinson &
Brefka, 1997)
A11. Operationalization – Questionnaires
Operationalization – Questionnaire to students
Concept Concept definition Operational
definition
Questions
Perception The process by which stimuli are
selected, organized and
interpreted (Solomon et al, 2010)
Measure which
attributes (on a 1-7
scale) students perceive
recruiters want when
hiring new employees.
How important do you think the
following employee attributes are
for recruiters, when hiring new
employees to the marketing
department at a manufacturing
company?
(The attributes are the ones
developed from the focus groups
and interviews)
Employee
attributes
Skills: the certain personal
abilities that an individual
possesses (Raybould & Sheedy,
2005).
Soft skills: the personal and social
skill that a person possesses
(Hutchinson & Brefka, 1997)
Hard skills: job objectives, work
experience and academic
background (Hutchinson &
Brefka, 1997)
Operationalization – Questionnaire to recruiters
Concept Concept definition Operational
definition
Questions
Wants The form needs take as shaped by
culture and individual personality
(Armstrong et al, 2009)
Measure which
attributes (on a 1-7
scale) recruiters want
when hiring new
employees.
How important do you consider
the following employee attributes
to be, when hiring new employees
to the marketing department?
(The attributes are the ones
developed from the focus groups
and interviews)
Employee
attributes
Skills: the certain personal
abilities that an individual
possesses (Raybould & Sheedy,
2005).
Soft skills: the personal and social
skill that a person possesses
(Hutchinson & Brefka, 1997)
Hard skills: job objectives, work
experience and academic
background (Hutchinson &
Brefka, 1997)
APPENDIX A: METHOD
______________________________________________________________________________
70
A12. Cover Letter Questionnaire Students
Hello marketing students!
We are conducting our bachelor thesis and we would be truly grateful if you could help us by
answering a short questionnaire. If you answer the questions, you have the opportunity to win
lottery tickets with a chance of becoming a millionaire!
It is an interesting questionnaire about which attributes you as a marketing student think is
important to possess to get a future job at the marketing department in the manufacturing
industry. The questionnaire will take approximately 5-10 minutes to complete. You will be
anonymous, but to be able to win the lottery tickets you need to leave your e-mail address by the
end of the questionnaire. This is however optional.
Please click on the link below to answer the questionnaire:
https://docs.google.com/forms/d/1I_LqcGFcMAmnb49GTts35v_etVww3oI37z9OvAnY6uM/vie
wform
A13. Cover Letter Questionnaire Recruiters
Dear Mr/Mrs,
We are three students studying at the Marketing Program at Linnaeus University in Växjö,
Sweden. We are conducting our bachelor thesis and would appreciate your help by answering a
short questionnaire, which will only take approximately 5 minutes to complete.
Below, you find the link to the questionnaire:
https://docs.google.com/forms/d/1OHLBEBDL_6oqTKPXsz5VtTQ71jx0ygb4JpujZOxMm2w/v
iewform
By using the website allabolag.se we got a list of the largest employers in Sweden in the
manufacturing industry and your company was on that list. We would be truly grateful if you
could answer this questionnaire and by so helping us to reach our academic goal with this thesis.
APPENDIX A: METHOD
______________________________________________________________________________
71
The purpose of the study is to find out which employee attributes you consider important when
you are hiring new employees to your marketing department, and then compare that outcome
with the employee attributes marketing students think you are looking for.
If you feel that you are not the right person to answer this questionnaire, please forward it to the
person within your company that you think are more appropriate to answer it.
You, and the company you are working for, will of course be anonymous. If you have any
questions or concerns about the questionnaire, contact the persons responsible at:
Emelie Lindwall phone: +46 70 450 34 97 e-mail: [email protected]
Johanna Gustafsson phone: +46 73 843 90 56 e-mail: [email protected]
Martin Stadig phone: +46 70 314 38 29 e-mail: [email protected]
Thank you for your contribution to our bachelor's thesis!
Martin Stadig, Emelie Lindwall and Johanna Gustafsson
A14. Data Preparation
According to Hair et al (2003), collected data needs to be edited, which means that data must be
inspected to ensure completeness and consistency. Editing involves the level of understanding
the questions (Ibid). To make sure that a respondent understand a question correct, the researcher
can conduct a manipulation check, which means that the researcher can ask one question in two
different ways (Ibid). Last, editing might end up in eliminating certain questionnaires due to the
fact of missing data (Ibid). If the respondent did not understand certain questions correct, skipped
questions, or if the respondent did not fit the qualification criteria, are examples of missing data
(Ibid). “Coding means assigning a number to a particular response so the answer can be entered
into a database” (Hair et al, 2003, p.230). Collected data needs to be coded, preferably using
numerical codes, which will make it easier to enter data more quickly into the database, but also
to end up with fewer errors (Saunders et al, 2009). Hair et al (2003) state that data should be
coded before data is collected, e.g. by using a 7-point Likert agree-disagree scale. The decision
must be made concerning whether strongly agree will be coded 1 or 7, but the most proper way
APPENDIX A: METHOD
______________________________________________________________________________
72
would be to code the largest number to strongly agree (Ibid). Once data has been successfully
coded, it needs to be entered into a computer, which will help to find obvious errors (Ibid).
A15. Descriptive Statistics
Saunders (2009) describes descriptive statistics as a general expression for statistics that explains
different variables. The role of descriptive statistics is to summarize the large amount of data that
has been collected (Krishnaswami & Satyaprasad, 2012). Hence, by using descriptive data, the
researchers are able to organize basic characteristics, as well as summarize data in a more
straightforward and understandable way, e.g. variations between gender or age (Zikmund et al,
2010). The descriptive statistics also make it possible to explain and compare variables
numerically (Saunders, 2009).
APPENDIX B: RESULTS
______________________________________________________________________________
73
APPENDIX B: RESULTS
B1. Focus Group Procedure
The number of focus groups was decided by the consideration of two facts. Firstly, conducting
too many focus groups is often a waste of time (Bryman & Bell, 2011). Secondly, too few groups
may not be enough due to the fact that the responses might be specific to that one group (Bryman
& Bell, 2011). Hence, it was considered that three focus groups would be appropriate for the
current study.
Potential respondents were contacted personally and asked to participate in one of the three
planned focus groups. The three focus groups each contained six participants to get a good
composition, with a total of 18 respondents (cf. Bryman & Bell, 2011). The 18 participants were
announced time and place for the focus group and on the day of the event, a text message was
sent to the participants as a reminder. The focus groups were conducted in a group room at the
library of Linnaeus University in Växjö.
When the respondents had been gathered, they were informed about the purpose of the study as
well as its limitations. One main question was asked and discussed, with room for both
additional and follow-up questions (cf. Jacobsen, 1993). The question was impartial and well
thought out to encourage the respondents into a discussion and to obtain each group member’s
genuine opinions (cf. Webb, 2002). A committed moderator, whose task was to encourage, guide
and stimulate the focus groups so that the participants dared to share their true thoughts and
opinions about the chosen topic and question, led the focus groups. The moderator also made
sure that the time for the focus group was not too lengthy (cf. Greenbaum, 2000). The group
sessions lasted for 30 minutes. During the sessions, a secretary on a computer took notes.
Attributes mentioned by the respondents were also written down on a whiteboard for all
respondents to review. The third focus group differed somewhat from the first and second focus
groups. Not only did the third focus group go through the above-mentioned procedure, but it also
worked as a confirmation group to test the outcomes from the first and second focus group. They
were presented to a summary of all the attributes mentioned during all focus groups and were
APPENDIX B: RESULTS
______________________________________________________________________________
74
requested to discuss whether the list of attributes were representative of the reality or if any
attributes had to be added or removed.
After the sessions, a first draft of the answers were transcribed, interpreted and processed (cf.
Jacobsen, 1993). The answers were thereafter compiled into a more coherent structure, where the
responses were analyzed and further developed into a foundation of what was most important to
continue to work with (cf. Jacobsen, 1993).
B2. Interview Procedure
A total of five interviews were conducted with a sample of personnel managers. The
organizations on the sampling frame were contacted via telephone or e-mail, to get hold of the
right person for the interview. Later, an introduction letter was sent to the respondents including
the purpose of the study, how the findings might be useful for the authors and the respondents
themselves (cf. Bryman & Bell, 2011). Five personnel managers accepted to participate in an
interview, with a promise that the company and interviewee in question would remain
anonymous. Telephone appointments with the interviewees were booked, and a pretest of the
interview question was conducted to seek validity (cf. DiCicco-Bloom & Crabtree, 2006).
During the interview, the interviewees were first informed about the purpose of the study, the
importance of their participation and why they had been chosen to participate (cf. Bryman &
Bell, 2003). The interviews began with one open question that was followed up by
supplementary questions depending on the answer from the first question. Leading questions
were avoided during the interview to avoid controlling the response (Bryman & Bell, 2003).
During the interview everything that was said was recorded with the respondents’ permission, so
that the interviewer could concentrate on the conversation instead of taking notes (Ibid).
When the interviews had been conducted, a first draft of the answers was interpreted and
processed (cf. Jacobsen, 1993). The answers were thereafter compiled into a more coherent
structure, where the responses were analyzed and further developed into a foundation of what
was most important to continue to work with (Ibid).
APPENDIX B: RESULTS
______________________________________________________________________________
75
B3. Correlation Analysis Indexes
Students
Soft skills Hard skills Other attributes
Soft skills 1
Hard skills .477** 1
Other attributes .316** .366** 1
** Correlation is significant at the 0.01 level (2-tailed)
Recruiters
Soft skills Hard skills Other attributes
Soft skills 1
Hard skills .490** 1
Other attributes .263** .372** 1
** Correlation is significant at the 0.01 level (2-tailed)
APPENDIX B: RESULTS
______________________________________________________________________________
76
B4. Correlation Analysis Single Attributes
Students:
Inte
rpers
onal
skil
ls
Com
munic
atio
n s
kil
ls
Tea
mw
ork
Sel
f-m
anag
emen
t
Work
eth
ic
Com
mit
men
t
Anal
yti
cal
skil
ls
Lea
der
ship
skil
ls
Str
ateg
ic t
hin
kin
g
Cre
ativ
ity
Res
po
nsi
bil
ity
Pro
ble
m s
olv
er
Posi
tive
atti
tude
Fle
xib
ilit
y
Sel
f-aw
are
nes
s
Pro
fess
ional
ism
Work
exper
ience
Cult
ura
l ex
per
ience
Mult
ilin
gual
Know
ledge
about
the
mar
ket
Educa
tio
n
Pra
ctic
al s
kil
ls
Physi
cal
wel
l-bei
ng
Age
Gen
der
Wel
l fo
rmula
ted C
V a
nd p
erso
nal
let
ter
Inte
r
per
so
nal
skil
ls
1
Co
mm
u
nic
atio
n
skil
ls
.53
6*
*
1
Tea
mw
ork
.50
4*
*
.25
3*
1
Sel
f-
man
age
men
t
.23
3*
.10
3
.44
7*
*
1
Wo
rk
eth
ic
.18
7
.30
2*
*
.16
4
.34
2*
*
1
Co
mm
itm
ent
.12
3
.20
6
.27
8*
.54
9*
*
.30
0*
*
1
An
aly
tica
l
skil
ls
.00
1
.06
0
.26
5*
.28
2*
*
.20
7
.32
7*
*
1
Lea
der
ship
skil
ls
.27
4*
.30
6*
*
.28
6*
*
.36
0*
*
.29
3*
*
.41
6*
*
.37
5*
*
1
Str
ateg
ic
thin
kin
g
.193
.266*
.314**
.357**
.421**
.453**
.573**
.546**
1
Cre
ativ
ity
.293**
.288**
.411**
.356**
.263*
.281*
.346**
.451**
..565**
1
Res
ponsi
b
ilit
y
.253*
.274*
.381**
.526**
.296**
.425**
.256*
.363**
.319**
.392**
1
Pro
ble
m
solv
er
.36
6*
*
.34
3*
*
.39
5*
*
.46
8*
*
.21
0
.30
1*
*
.41
1*
*
.44
7*
*
.36
8*
*
.38
2*
*
.35
7*
*
1
APPENDIX B: RESULTS
______________________________________________________________________________
77
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Posi
tive
atti
tude
.645**
.437**
..463**
.280*
.172
.156
-.015
.358**
.318**
.371**
.259*
.471**
1
Fle
xib
ilit
y
.572**
.354**
.363**
.247*
.305**
.312**
.201
.532**
.376**
.274*
.448**
.396**
.587**
1
Sel
f-
awar
enes
s
.405**
.261
.347**
.362**
.314**
.268*
.276*
.468**
.499**
.458**
.313**
.289**
.597**
.587**
1
Pro
fess
ion
alis
m
.332**
.332**
.200
.191
.210
.336**
.247*
.335**
.415**
.456**
.242*
.275*
.362**
.345**
.479**
1
Work
exper
ienc
e .190
.200
.104
.146
.117
.136
.157
.187
.198
.214
.165
.019
-.066
.119
.117
.259*
1
Cu
ltu
ral
exp
erie
nc
e .08
6
.07
8
.01
1
.14
1
.14
8
.28
5*
*
.16
7
.34
7*
*
.33
0*
*
.28
5
.09
4
.25
1*
.19
6
.24
9*
.34
9*
*
.23
2*
.24
7*
1
Mu
ltil
ing
ual
-.0
13
-.0
01
-.1
71
-.0
45
.07
5
-.0
16
.26
9*
.27
3*
.18
2
.21
3
-.0
50
.28
0*
.04
6
.17
3
.28
5*
*
.27
6*
.15
9
.61
5*
*
1
Kn
ow
led
ge
abo
ut
the
mar
ket
.04
7
.21
5
-.1
88
.01
4
.36
2*
*
.06
7
.11
7
.11
9
.23
7*
.13
8
.03
0
.13
2
.02
3
.11
0
.19
0*
*
.20
0
.14
9
.47
2*
*
.40
3*
*
1
Ed
uca
tio
n
.10
2
.34
0*
*
.28
4*
*
.18
4
.10
7
.35
5*
*
.32
9*
*
.38
2*
*
.39
7*
*
.34
0*
*
.26
9*
.32
5
.10
8
.19
2
.24
3*
.26
9*
.46
2*
*
.25
3*
.14
9
.22
8*
1
Pra
ctic
al
skil
ls
.13
9
.10
3
.16
6
.23
9*
.00
0
.20
9
.33
3*
*
.14
5
.24
2*
.32
9*
*
.20
1
.22
8
.08
6
.12
2
.16
0*
.41
2*
.26
5*
.25
5*
.20
5
.23
3*
.23
5*
1
Ph
ysi
cal
wel
l-
bei
ng
.20
9
.15
6
.25
6*
.32
4*
*
.12
3
.20
4
.25
0*
.47
5*
*
.46
9*
*
.32
6*
*
.35
5*
*
.31
6*
*
.45
1*
*
.47
0*
*
.47
7*
*
.23
6*
.05
6
.29
6*
*
.06
8
.11
8
.18
4
.28
7*
*
1
Age
.018
.063
-.130
.145
-.032
.154
.056
.204
.159
-.012
.184
.150
.046
.030
.035
.096
.351**
.135
.145
.016
.164
.019
.131
1
Gen
der
-.021
-.104
-.068
.001
-.137
.028
-.043
-.131
-.051
-.158
-.173
.032
-.012
-.047
-.002
-.027
.126
.040
.032
-.00
6
.000
.091
.104
.469**
1
Wel
l
form
ula
ted C
V
and p
erso
nal
lett
er
.196
.359**
.058
-.015
.089
-.023
.093
.204
.163
.177
.039
.197
.258*
.109
.094
.181
.363**
.089
.199
.223*
.229*
.169
.021
.268*
.142
1
APPENDIX B: RESULTS
______________________________________________________________________________
78
Recruiters:
In
terp
ers
onal
skil
ls
Com
munic
atio
n s
kil
ls
Tea
mw
ork
Sel
f-m
anag
emen
t
Work
eth
ic
Com
mit
men
t
Anal
yti
cal
skil
ls
Lea
der
ship
skil
ls
Str
ateg
ic t
hin
kin
g
Cre
ativ
ity
Res
ponsi
bil
ity
Pro
ble
m s
olv
er
Posi
tive
atti
tude
Fle
xib
ilit
y
Sel
f-aw
are
nes
s
Pro
fess
ional
ism
Work
exper
ience
Cult
ura
l ex
per
ience
Mult
ilin
gual
Know
ledge
about
the
mar
ket
Educa
tio
n
Pra
ctic
al s
kil
ls
Physi
cal
wel
l-bei
ng
Age
Gen
der
Wel
l fo
rmula
ted C
V a
nd p
erso
nal
let
ter
Inte
r
per
so
nal
skil
ls
1
Co
mm
u
nic
atio
n
skil
ls
.35
6*
*
1
Tea
mw
ork
.33
7*
*
.13
8
1
Sel
f-
man
age
men
t
.24
2*
*
.16
2
.19
9*
1
Wo
rk
eth
ic
.22
9*
*
.25
3*
*
.18
8*
.50
3*
*
1
Co
mm
itm
ent
.27
4*
*
.21
7*
.16
3
.38
8*
*
.40
9*
*
1
An
aly
tica
l
skil
ls
.12
9
.22
0*
.33
2*
*
.33
3*
*
.31
5*
*
.25
9*
*
1
Lea
der
ship
skil
ls
.14
7
.10
9
.41
9*
*
.27
6*
*
.15
3
-26
4*
*
.38
2*
*
1
Str
ateg
ic
thin
kin
g
.091
.313**
.208*
.285**
.238**
.259**
.328**
.480**
1
Cre
ativ
ity
.224*
.421**
.093
.172
.269**
.325**
.298**
.189*
.431**
1
Res
ponsi
b
ilit
y
.195*
.097
.164
.369**
.400**
.294**
.095
.244**
.206*
.282**
1
Pro
ble
m
solv
er
.22
5*
.19
9*
.23
5*
*
.18
7*
.21
9*
.32
6*
*
.41
3*
*
.36
4*
*
.21
4*
.43
2*
*
.26
2*
*
1
APPENDIX B: RESULTS
______________________________________________________________________________
79
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Posi
tive
atti
tude
.392**
.214*
.143
.304**
.297**
.406**
.213*
.151
.059
.279**
.217*
.283**
1
Fle
xib
ilit
y
.230**
.099
.219*
.231**
.248**
.324**
.197*
.282**
.219*
.295**
.249**
.411**
.441**
1
Sel
f-
awar
enes
s
.297**
.130
.114
.403**
.186*
.319**
.157
.199*
.244**
.237**
.160
.319**
.431**
.430**
1
Pro
fess
ion
alis
m
.266**
.280**
.178*
.326**
.407**
.403**
.162
.173
.249**
.242**
.207*
.334**
.442**
.344**
.295**
1
Work
exper
ienc
e .073
.145
.039
-.042
.033
-.063
.213*
.121
.207*
.084
.037
.200*
.122
.032
.098
.051
1
Cu
ltu
ral
exp
erie
nc
e .16
6
..2
11
*
.16
1
.09
2
.12
7
.24
2*
*
.19
4*
.19
0*
.42
8*
*
.36
6*
*
.08
9
.18
2*
.18
0*
.14
3
.42
0*
*
.33
0*
*
.23
3*
*
1
Mu
ltil
ing
ual
.13
8
.18
9*
.18
9*
.15
0
.11
3
.20
3*
.10
6
.12
9
.25
8*
*
.20
3*
.23
8*
*
.05
5
.21
4*
.19
1*
.19
0*
.27
1*
*
.17
6*
.55
5*
*
1
Kn
ow
led
ge
abo
ut
the
mar
ket
-.0
74
.24
6*
*
-.0
55
.01
6
.01
7
.15
6
.18
8*
-.0
52
.27
4*
*
.26
0*
*
-.0
51
.13
7
.07
9
-.0
26
.11
7
.16
7
.28
8*
*
.29
9*
*
.20
4*
1
Ed
uca
tio
n
.11
2
.11
7
.22
3*
.12
6
-.0
33
.07
7
.22
2*
.27
1*
*
.11
0
.07
6
.06
2
.27
5*
*
.17
7*
.06
4
.22
4*
.18
7*
.37
1*
*
.18
5*
.10
6
.15
7
1
Pra
ctic
al
skil
ls
.16
1
.23
5*
*
.11
6
.24
8*
*
.24
5*
*
.16
9
.21
2*
.20
4*
.22
0*
.28
6*
*
.23
1*
*
.30
6*
*
.15
2
.27
1*
*
.13
7
.30
2*
*
.09
5
.12
7
.15
6
-.0
24
.06
6
1
Ph
ysi
cal
wel
l-
bei
ng
.32
7*
*
.08
5
.15
4
.23
6*
*
.25
4*
*
.24
4*
*
.08
0
.13
1
.10
2
.23
4*
*
.25
8*
*
.29
3*
*
.31
9*
*
.36
9*
*
.22
0*
.35
0*
*
.09
6
.10
3
.19
6*
.00
6
.09
0
.38
6*
*
1
Age
-.02
4
-.052
-.074
.099
.015
.080
.066
.047
.101
.042
-.063
.120
.072
.081
.173
.292**
.239**
.185*
.223*
.284**
.254**
.098
.146
1
Gen
der
.033
-.162
.031
.179*
.063
.126
-.002
-.057
.022
-.122
.006
-.096
.002
.083
.186*
.190*
.045
.116
.060
.171
.175
.005
.047
.419**
1
Wel
l
form
ula
ted C
V
and p
erso
nal
lett
er
.027
.051
-.017
.028
-.004
-.028
.234**
.005
-.027
.058
-.184*
.211*
.090
.205*
.019
.036
.003
.064
.002
.017
.227*
.139
.094
.223*
.054
1
APPENDIX B: RESULTS
______________________________________________________________________________
80
B5. Means and Standard Deviations – Sum All Attributes
Group N Mean Std. Deviation
Students 83 5.0943 .62320
Recruiters 126 5.1560 .44240
B6. Means and Standard Deviations – Indexes
Students Recruiters
Index N Mean Std. Deviation N Mean Std. Deviation
Soft skills 83 5.4904 .71627 126 5.6431 .49118
Hard skills 83 4.9157 .81517 126 4.8090 .63251
Other attributes 83 3.6343 .88459 126 3.5196 .69892
B7. Means and Standard Deviations – Single Attributes Students Recruiters
Attribute N Mean Std. Deviation N Mean Std. Deviation
Interpersonal skills 83 6,04 1,087 126 5,87 ,829
Communication
skills
83 6,11 1,082 126 6,17 ,728
Teamwork 83 6,07 ,908 126 5,89 ,751
Self-management 83 5,43 1,048 126 5,94 ,762
Work ethic 83 6,07 ,947 126 6,20 ,716
Commitment 83 5,41 1,166 126 5,92 ,900
Analytical skills 83 5,45 1,106 126 5,37 ,907
Leadership skills 83 5,06 1,301 126 5,10 ,978
Strategic thinking 83 5,23 1,182 126 5,28 ,932
Creativity 83 5,60 1,157 126 5,72 ,834
Responsibility 83 5,84 1,120 126 6,25 ,665
Problem solver 83 5,31 1,322 126 5,53 ,879
Positive attitude 83 5,82 1,280 126 5,91 ,877
Flexibility 83 5,59 1,200 126 5,93 ,831
Self-awareness 83 4,80 1,403 126 5,21 1,112
Professionalism 83 5,19 1,401 126 5,37 ,992
Work experience 83 4,80 1,455 126 4,64 1,082
Cultural experience 83 4,33 1,308 126 4,27 1,392
Multilingual 83 4,80 1,237 126 4,90 1,223
Knowledge about the
market
83 5,33 1,211 126 4,83 1,231
Education 83 5,00 1,325 126 4,84 ,991
Practical skills 83 4,66 1,213 126 4,83 ,961
Physical well-being 83 4,62 1,349 126 5,25 1,001
Age 83 2,92 1,399 126 2,65 1,138
APPENDIX B: RESULTS
______________________________________________________________________________
81
Gender 83 2,22 1,440 126 1,74 1,245
Well formulated CV
and personal letter
83 4,79 1,454 126 4,44 1,167
B8. Shapiro-Wilks Test
Students Recruiters
Statistic df Sig. Statistic df Sig.
Interpersonal skills ,753 83 ,000 ,856 126 ,000
Communication skills ,746 83 ,000 ,810 126 ,000
Teamwork ,826 83 ,000 ,841 126 ,000
Self-management ,903 83 ,000 ,843 126 ,000
Work ethic ,808 83 ,000 ,808 126 ,000
Commitment ,895 83 ,000 ,861 126 ,000
Analytical skills ,904 83 ,000 ,897 126 ,000
Leadership skills ,921 83 ,000 ,905 126 ,000
Strategic thinking ,919 83 ,000 ,897 126 ,000
Creativity ,880 83 ,000 ,857 126 ,000
Responsibility ,845 83 ,000 ,788 126 ,000
Problem solver ,880 83 ,000 ,859 126 ,000
Positive attitude ,827 83 ,000 ,857 126 ,000
Flexibility ,859 83 ,000 ,825 126 ,000
Self-awareness ,937 83 ,000 ,905 126 ,000
Professionalism ,911 83 ,000 ,895 126 ,000
Work experience ,940 83 ,001 ,919 126 ,000
Cultural experience ,941 83 ,001 ,924 126 ,000
Multilingual ,927 83 ,000 ,923 126 ,000
Knowledge about the
market
,904 83 ,000 ,913 126 ,000
Education ,926 83 ,000 ,898 126 ,000
Practical skills ,933 83 ,000 ,894 126 ,000
Physical well-being ,939 83 ,001 ,906 126 ,000
Age ,917 83 ,000 ,902 126 ,000
Gender ,798 83 ,000 ,641 126 ,000
Well formulated CV
and personal letter
,931 83 ,000 ,934 126 ,000
B9. Skewness and Kurtosis
Students
N Skewness Kurtosis
Statistic Statistic Std. Error Statistic Std. Error
Interpersonal skills 83 -1,941 ,264 5,731 ,523
Communication skills 83 -1,818 ,264 4,141 ,523
APPENDIX B: RESULTS
______________________________________________________________________________
82
Teamwork 83 -,947 ,264 ,796 ,523
Self-management 83 -,484 ,264 ,499 ,523
Work ethic 83 -1,294 ,264 2,970 ,523
Commitment 83 -,809 ,264 1,475 ,523
Analytical skills 83 -,319 ,264 -,122 ,523
Leadership skills 83 -,353 ,264 -,164 ,523
Strategic thinking 83 -,232 ,264 -,516 ,523
Creativity 83 -,419 ,264 -,445 ,523
Responsibility 83 -1,062 ,264 ,994 ,523
Problem solver 83 -1,017 ,264 1,117 ,523
Positive attitude 83 -1,227 ,264 1,749 ,523
Flexibility 83 -1,065 ,264 1,352 ,523
Self-awareness 83 -,140 ,264 -,466 ,523
Professionalism 83 -,326 ,264 -,812 ,523
Work experience 83 -,242 ,264 -,510 ,523
Cultural experience 83 -,227 ,264 -,456 ,523
Multilingual 83 -,113 ,264 -,161 ,523
Knowledge about the
market
83 -,318 ,264 -,680 ,523
Education 83 -,515 ,264 ,257 ,523
Practical skills 83 -,202 ,264 ,358 ,523
Physical well-being 83 -,087 ,264 -,202 ,523
Age 83 ,318 ,264 -,282 ,523
Gender 83 ,839 ,264 -,588 ,523
Well formulated CV
and personal letter
83 -,496 ,264 -,288 ,523
Recruiters
N Skewness Kurtosis
Statistic Statistic Std. Error Statistic Std. Error
Interpersonal skills 126 -,524 ,216 ,348 ,428
Communication skills 126 -,535 ,216 -,093 ,428
Teamwork 126 -,160 ,216 -,456 ,428
Self-management 126 -,233 ,216 -,468 ,428
Work ethic 126 -,453 ,216 -,456 ,428
Commitment 126 -,573 ,216 -,033 ,428
Analytical skills 126 -,174 ,216 -,009 ,428
Leadership skills 126 -,106 ,216 -,231 ,428
Strategic thinking 126 -,229 ,216 ,091 ,428
Creativity 126 -,416 ,216 -,237 ,428
Responsibility 126 -,346 ,216 -,745 ,428
Problem solver 126 -,696 ,216 ,446 ,428
Positive attitude 126 -,622 ,216 ,203 ,428
Flexibility 126 -,963 ,216 2,913 ,428
Self-awareness 126 -,569 ,216 ,527 ,428
Professionalism 126 -,451 ,216 -,083 ,428
APPENDIX B: RESULTS
______________________________________________________________________________
83
Work experience 126 ,071 ,216 ,255 ,428
Cultural experience 126 -,569 ,216 -,040 ,428
Multilingual 126 -,530 ,216 ,619 ,428
Knowledge about the
market
126 -,583 ,216 1,082 ,428
Education 126 -,221 ,216 -,516 ,428
Practical skills 126 ,017 ,216 ,276 ,428
Physical well-being 126 -,324 ,216 -,016 ,428
Age 126 ,163 ,216 -,962 ,428
Gender 126 1,493 ,216 ,946 ,428
Well formulated CV
and personal letter
126 -,277 ,216 ,311 ,428
B10. Results Hypothesis 1
Attribute Md (n) Mean rank U-value z-value Asymp.
Sig. (2-
tailed)
Students Recruiters Students Recruiters
All attributes 5.04 (83) 5.15 (126) 101.25 107.47 4917.5 -.728 .466
B11. Results Hypotheses 2, 3 and 4
Attribute Md (n) Mean rank U-value z-value Asymp.
Sig. (2-
tailed)
Students Recruiters Students Recruiters
Soft skills 5.53 (83) 5.71 (126) 96.33 110.71 4509.5 -1.683 .092
Hard skills 5.00 (83) 4.80 (126) 112.92 99.78 4571.5 -1.543 .123
Other attributes 3.75 (83) 3.50 (126) 111.34 100.83 4703 -1.235 .217
B12. Additional Results
Attribute Md (n) Mean rank U-
value
z-value Asymp.
Sig. (2-
tailed)
Students Recruiters Students Recruiters
Interpersonal skills 6.00 (83) 6.00 (126) 115.33 98.19 4371.5 -2.141 .032
Communication
skills
6.00 (83) 6.00 (126) 107.36 103.44 5033 -.497 .619
Teamwork 6.00 (83) 6.00 (126) 114.93 98.46 4404.5 -2.071 .038
Self-management 5.00 (83) 6.00 (126) 87.18 116.74 3750 -3.656 .000
Work ethic 6.00 (83) 6.00 (126) 102.07 106.93 4985.5 -.614 .539
Commitment 5.00 (83) 6.00 (126) 89.16 115.43 3914.5 -3.206 .001
Analytical skills 5.00 (83) 5.00 (126) 107.80 103.15 4996.5 -.568 .570
Leadership skills 5.00 (83) 5.00 (126) 104.61 105.26 5196.5 -.079 .937
APPENDIX B: RESULTS
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Strategic thinking 5.00 (83) 5.00 (126) 103.73 105.83 5124 -.256 .798
Creativity 6.00 (83) 6.00 (126) 102.63 106.56 5032.5 -.481 .630
Responsibility 6.00 (83) 6.00 (126) 93.48 112.59 4273 -2.401 .016
Problem solver 5.30 (83) 6.00 (126) 100.96 107.66 4893.5 -.827 .408
Positive attitude 6.00 (83) 6.00 (126) 106.52 104.00 5102.5 -.311 .756
Flexibility 6.00 (83) 6.00 (126) 96.43 110.64 4518 -1.763 .078
Self-awareness 5.00 (83) 5.00 (126) 93.16 112.80 4246 -2.371 .018
Professionalism 5.00 (83) 5.00 (126) 101.54 107.28 4941.5 -.695 .487
Work experience 5.00 (83) 5.00 (126) 110.20 101.57 4797 -1.042 .298
Cultural
experience
4.00 (83) 4.15 (126) 104.93 105.05 5223 -.014 .989
Multilingual 5.00 (83) 5.00 (126) 101.29 107.44 4921 -.742 .458
Knowledge about
the market
5.00 (83) 5.00 (126) 119.19 95.65 4051 -2.840 .005
Education 5.00 (83) 5.00 (126) 111.02 101.04 4729.5 -1.212 .225
Practical skills 5.00 (83) 5.00 (126) 100.33 108.08 4841.5 -.948 .343
Physical well-
being
5.00 (83) 5.00 (126) 86.59 117.13 3701 -3.689 .000
Age 3.00 (83) 2.70 (126) 111.36 100.81 4701 -1.267 .205
Gender 2.00 (83) 1.00 (126) 117.54 96.74 4188.5 -2.752 .006
Well formulated
CV and personal
letter
5.00 (83) 4.70 (126) 115.60 98.02 4349 -2.111 .035
APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
C1. Questionnaire Design Students
APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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C2. Questionnaire Design Recruiters
APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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APPENDIX C: QUESTIONNAIRE DESIGN
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Linnaeus University a firm focus on quality and competence
On 1 January 2010 Växjö University and the University of Kalmar merged to form Linnaeus University. This new
university is the product of a will to improve the quality, enhance the appeal and boost the development potential
of teaching and research, at the same time as it plays a prominent role in working closely together with local
society. Linnaeus University offers an attractive knowledge environment characterised by high quality and a
competitive portfolio of skills.
Linnaeus University is a modern, international university with the emphasis on the desire for knowledge, creative
thinking and practical innovations. For us, the focus is on proximity to our students, but also on the world around
us and the future ahead.
Linnæus University
SE-391 82 Kalmar/SE-351 95 Växjö
Telephone +46 772-28 80 00