Gender Influences on Factors Affecting Career Decision Making within ...

Built Environment Education Conference

Gender Influences on Factors Affecting Career Decision Making within the South African Construction Industry Dr Nicholas Chileshe Senior Lecturer in Construction Management, Faculty of Development and Society, Built Environment Division, Sheffield Hallam University, City Campus, Howard Street, Sheffield, S1 1WB, UK Corresponding Author: [email protected]

Dr Theo C Haupt Research Coordinator, South African Built Environment Research Center (SABERC), Faculty of Engineering, Cape Peninsula University of Technology, P.O Box 1906, Bellville 7535, South Africa

Abstract Against a backdrop of skills shortage and lack of career women in the South African construction industry and reducing numbers of new entrants, this study present several challenges and opportunities to the sector. For undecided students to include construction which has been identified by the South African government as the vehicle for job creation, poverty alleviation and infrastructure delivery, in their choice of future career demands that the industry market itself. This paper aims to report the findings of research into the impact of gender on the factors impacting the career decisions within the construction industry. The research uses a postal survey questionnaire technique for primary data collection. Literature review is used to identify relevant practices, which are then incorporated into the design of the survey instrument. Survey response data is subjected to descriptive statistical analysis and subsequently Analysis of Variance. Using a data triangulation and a KAP (Knowledge, Attitudes and Perceptions) approach, information was collected from a survey of 599 male and 491 female high school students in the Western Cape Province. The findings indicate that salary, working conditions, opportunities for promotion and lifelong learning opportunities were reported by both male and female students as the most important factors impacting career decisions whereas family tradition was the least important factor according to the male, and peers for female students. The findings are of particular importance as gender issues need to take into account the female career aspirations. The identification of factors enables the development of viable strategies and balance the social dynamics of the male dominated environment.

Keywords: Career Decisions, Training, Gender, Construction Industry, Analysis of Variance, South Africa.

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Dr N. Chileshe: Gender Influences on Factors Affecting Career Decision Making within the South African Industry

Introduction The main objectives of this paper are to investigate the perceptions of high school students on the factors impacting their career decisions within the South African Construction industry and to explore the influence of gender career decision making process. While there have been several career decision factor studies carried out in various countries, a few of them are within the African context. The majority of the published literature presented by Dainty et al (2004a,b); Fielden et al (2001); Gale (1994); Ling and Poh (2004) among others indicates that research has mostly been conducted in developed countries. Some research within the African context, in particular Nigeria, has explored the under-representation of women in construction (Adeyemi et al, 2006). However, this study did not explore perceptions of high school students at entry level. Furthermore, despite the existence of a range of studies on equality; gender differences in ethical decision making and women’s careers in construction, little research has been undertaken to examine if these interventions can be applied to the South African Construction Industry. Based on the existing studies there has emerged two significant gaps in the knowledge that exists regarding the factors that impact of career decisions by high school students. Firstly, there is a distinct lack of information specific to the South African context, particularly the South African context. Secondly, the past studies have focussed on service and academia related organisations. This study overcomes these two gaps and meets the objectives stated at the start of this paper. Against a backdrop of skills shortages in the South African construction industry and a reducing number of new entrants, this study presents several challenges and opportunities for the construction sector to address these issues. Opportunity exists for education officers to directly promote the construction industry in high school classrooms and use forums such as career exhibitions at schools in this effort. Such an intervention becomes more critical considering the evident lack of knowledge about what construction in fact includes. It is imperative that students recognize that the construction industry encompasses more than the delivery of houses and schools. Learned societies such as the Chartered Institute of Building – Africa (CIOB) and Association of South African Quantity Surveyors (ASAQS) have much to do to promote their particular disciplines. Consideration for choosing contracting as a career presents a particular challenge to the industry since it ranks poorly on the list of careers that construction management graduates consider for themselves (REF). Perceptions that construction is dangerous, hard, physically demanding, dirty, experiences “bad times,” and require long working hours for little money exacerbate the challenges facing the industry.

Literature Review Several studies on the factors impacting the career decision making process have being conducted. For example Agapiou (2002) conducted an empirical review of the attitudes of school-age girls, their parents and educators about career prospects in construction. His study found some reservations held by the girls are mostly to do with issues such as the 2 Built Environment Education Conference Copyright © CEBE 2007


physical nature of the work, the social dynamics of working in a male-dominated environment, and the availability of career paths of completion of apprenticeship training. Some of the factors impacting the career decisions were identified as follows: other family members, peers, exposure to experiences; recognition of their own aptitudes and preferences; and exposure to role models. However the approach adopted in this study was a qualitative one and no descriptive or statistical analysis were conducted which would have enabled the ranking of the factors impacting on the career decisions. The identification of the importance of factors through the ranking analysis would enable the development of viable strategies. Dainty et al (2000) investigated the women's career under-achievement in large UK construction companies. They found that the poor image of the industry adversely affects popularity as a career choice. Moreover, Dainty et al (2000) found that a lack of a dedicated curriculum and partnership centres impacted on the career choices of school children. Bennett at al (1999) investigated whether the career aspirations of women, in comparison to men changed once they worked in the construction industry. They found those women's choices to be influenced by family background factors. They further identified these factors to be more complex and inter-related classified them into the following factors: facultative or women's career development; individual, background, educational and adult lifestyle. Court and Moralee's (1995) suggest that family and friends influence female students on whether to enter the construction industry or not. Ling and Poh (2004) conducted a literature search in their study which investigated the barriers that were preventing female undergraduates, who majored in quantity surveying from entering the construction industry. They operationalised the variables of these potential situations into internal and external factors. Citing Dainty et al (2000) study, Ling and Poh (2004) they defined the internal factors as relating to personal attributes, circumstances, characteristics and abilities. External factors were deemed to include the nature of the industry, working conditions, and the sexists attitudes among the industry players. According to a report commissioned by the DTI (Department of Trade and Industry) (2006, Review of Sustainable Construction 2006 – A Summary) a positive image of the industry as well as positive advice and guidance given by parents, peers and career teachers were found to influence career decisions amongst school children. Other studies such as that carried out by Wilkinson (1996) examined the factors affecting the career choice of male and female civil engineering students in the UK. The findings suggested that although they were significant differences exist between male and female civil engineering students in the factors impacting career choice, these factors ranked low in their importance. Whereas females considered "location of organisation nearer to family and friends" as the most important, the men were significantly influenced by “salary”. Some factors such as “opportunity for overseas travel” and “benefits like pensions” were found to be equally important for both male and female high students.

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Research Conceptual Framework The conceptual framework in this study was developed by drawing data from current and key concepts on factors impacting career decisions from literature, research papers and examples from other successful industries. Culminating from this, two key elements which underlie the career decision making within the South African construction industry were translated into the following research questions to operationlise and contextualise the research. These were: (i)

Does gender have an influence on the factors impacting career decisions within the South African construction industry?

(ii)

How significant (if any) are these differences?





Research Methodology To investigate the influence of gender on the factors impacting the career decisions of high school students in South Africa, the following research methodology was employed in the study. Sample This study issued 1500 questionnaires to randomly selected high school students within the Western Cape province in three different grades; 10; 11; and 12. A total of 1121 were returned. This produced an effective response rate of 75% which was considered very high despite the difficulties encountered in engaging the Grade 12’s relative to the participation in the study. The response rate was therefore deemed adequate for the purpose of data analysis. Of the 1121 usable response, 407 (36.3 per cent) were from Grade 10’s, 576 (51.3 per cent) were from Grade 11’s and 138 (12.3 per cent) were from Grade 12’s. Males made up the larger proportion of the sample, namely 55.1%. The schools who participated in the survey are listed in Table 1.0 together with the distribution of the sample. Table 1 Frequency of Respondents by Socio-Economic Location School

Number of Participants

Percentage

Kasselsvlei High

120

10.9%

Elsies River High

108

9.8%

Oval North

77

7.0%

Tafelsig High

183

16.6%

Joe Slovo High

123

11.2%

Bellville Technical HS

100

9.1%

Masiyile Senior Secondary

130

11.8%

Symphony High

50

4.5%

Perseverence Secondary

14

1.3%

Oude Molen Technical HS

138

12.5%

Total

1043

94.7%



Other schools (57) in the survey with less than 10 respondents are not shown in Table 1.0. It is evident that the schools are located across the full socio-economic profile of the region being situated inter alia, in Khayelitsha, Guguletu, Kasselsvlei, Bellville, Mitchells Plain, and Pinelands.

Measurement Instrument The data collection instrument was a self administered structured questionnaire. The questionnaire was pre-tested by sending it to randomly selected South African high schools and construction related organisations. Based on the feedback, the questionnaire was modified. Piloting is necessary as its very difficult to predict how respondents will interpret and react to questions (Gill and Johnson 1991), Another reason for piloting would be to estimate the probable numbers of refusals and non-contacts and compare the effectiveness of various ways of reducing non-responses. (Moser and Kalton, 1979). The final questionnaire comprised nine parts. The first part seeks background information about the respondents. The second part deals with factors impacting career decisions. The third part investigated the student knowledge of the construction products. Part four identified the construction disciplines and professions considered among the high school students. The activities included in the work of a construction manager were examined in Part five, whereas Part six investigated the pre-requisite high school subjects for construction management degree enrolment. Part seven explored the fields of employment of construction management graduates. Various levels of agreement on construction-related statements were sought in part eight. The 23 statements could be categorized into internal and external factors affecting the image of the construction industry. Finally part nine investigates the categories of construction employment that children should seek. This paper reports on the first and second parts of the survey instrument. The majority of the second part had questions confined to simple “yes”, “no” and “unsure” considering the limited time available to interact with them.

Statistical Methods The primary focus of the study presented in this paper was to determine whether differences existed in career decision making process between the females and male high school students. Statistical Package for Social Sciences (SPSS) computer program was used to analyse the data generated by the research questions. Analysis of Variance (ANOVA), and separate independent t-test were used for the analysis. According to Pallant (2005), an independent-samples t-test is used when you want to compare the mean score, on some continuous variable, for two different groups of subjects. As observed by Wetzel, (2005), descriptive statistics are concerned with taking data and turning it into useful and consumable information Forza (2002) opines that carrying out such preliminary data analysis before assessing the measurement of quality gives preliminary indication of how well the coding and entering of



data have been, how good the scales are, and whether there is suspicion of poor content validity or systematic bias. To find out whether there was a significant difference between the groups (male and female students); the Levene’s test also provided the solution in testing the equal variance assumptions (Field, 2000). As asserted by Forza (2002), the function of ttests is to see whether there is a significant difference in the means for two groups in the variable of interest. To measure the Impact of gender on career decision making in the South African Construction Industry, Independent t-tests were conducted.

Results and Discussion When asked whether they had decided on their future career already, 72.5% responded affirmatively. The predominating factors as evidenced in Table 2.0 influencing their career choices were salary (58.5%), working conditions (40.2%), opportunities for promotion (36.0%), and lifelong learning (30.4%). The choices of the few students (16.6%) had being influenced by their teachers and / or guidance counsellors suggesting that they were not major players in guiding career choices of students. As seen from the table, respondents ranked “Salary” as the most important factor impacting career decisions. “Working Conditions”, “Opportunities for Promotion” and “Lifelong Learning Opportunities” followed in order. “Family Tradition” was insignificant in this study. It ranked 10th in order of importance. •

Impact of Gender on Career Decision Making Process

In Table 2, the frequency of the responses of the 10 factors are also presented for the two groups: first males and then females. ANOVA revealed that no significant differences existed between the scores of the male and female high school students within this sample.



Table 2 Factors Impacting Career Decisions - Frequency of Respondents by Gender Total Sample

Male

Female

(N =599)

(N = 491)

Career Decision Factors (CDF)

Yes

No

Rank

Yes

No

Rank

Yes

No

Rank

CDF1= Salary

637

451

1

369

229

1

268

222

1

CDF2 = Working conditions

438

649

2

263

334

2

175

315

2

CDF3 = Opportunities for promotion

391

696

3

245

352

3

146

344

3

CDF4 = Lifelong learning opportunities

330

757

4

188

409

4

142

348

4

CDF5 = Status and prestige

185

902

5

120

477

5

65

425

6

CDF6 = Teachers and counsellors

181

906

6

80

517

6

101

389

5

CDF7 = Skills shortage

176

911

7

119

478

7

57

433

9

CDF8 = Peers

136

951

8

80

517

8

56

434

10

CDF9 = Media coverage

132

955

9

73

524

9

59

431

7

Family tradition

123

964

10

64

533

10

59

431

7

As seen from the Table 2, both male and female high school students ranked “Salary” as the most important factor impacting career decisions. “Working Conditions”, “Opportunities for Promotion” and “Lifelong Learning Opportunities” followed in order. “Family Tradition” and “Peers” were insignificant in this study for Male and Female high school students respectively. They ranked 10th in order of importance. The findings in Tables 2 and 3 contradict those of Courts and Moralee’s (1995) investigation into gender issues in the building professions who ranked family and friends. On the contrary in this study the females ranked family tradition as one of the least factors likely to influence their career decisions. However some limitations in making the comparisons are noted as Court and Moralee’s sample consisted of first and second-year under graduates from the university whereas this sample draws from high school students. It must be acknowledged that the university sample provides more meaningful responses as they are nearer to entering the profession world whereas those of high school students could still change. 9 Built Environment Education Conference Copyright © CEBE 2007


Table 3 Results of t-test comparing factors impacting career decisions scores of males and females

Career Factors

Male

Female

(N=599)

(N =491)

Mean

Rank

s.d.

Mean

Rank

s.d.

t-stat CDF1= Salary

1.381

1

.4894

1.450

1

.5021

-2.305


1.557

2

.5005

1.639

2

.4849

-2.750


1.587

3

.4962

1.699

3

.4638

-3.804


1.682

4

.4696

1.707

4

.4602

-.863


1.796

5

.4075

1.864

6

.3495

-2.901


1.863

6

.3491

1.790

5

.4125

3.148


1.798

7

.4062

1.879

9

.3317

-3.603

CDF8 = Peers

1.863

8

.3491

1.882

10

.3293

-.917


1.875

9

.3365

1.876

7

.3363

-0.058

Family tradition

1.889

10

.3189

1.876

7

.3363

.697

In order to ensure the correct interpretation of results from the independent-samples t-test, Levene’s test of equality of variances was conducted. This test whether the (variance) variation of scores for the two groups (males and females) is the same. This enabled the correct usage of which t-value that SPSS provides. As evident from Table 3.0 the following factors (“Life Long Learning”; “Family Tradition”; “Media Coverage”; and “ Peers”) had Sig. value large than 0.05, therefore the t-value for Equal variances assumed was used. The remaining six career decision factors (“Salary”; “Opportunities for Promotion”; “Status and Prestige”; “Working Conditions”; “Teachers/Counsellors”; and “Skills Shortage”) had sig value < .05 and equal variances was not assumed.



Table 3 also shows the rankings of the career decision factors according to gender. There were some differences in the rankings at the bottom of the factors. By far the biggest difference was in family tradition. Males considered family tradition least important (tenth overall) whereas females considered it as seventh most important. This finding is consistent with literature. For example Maringe (2006) found male students to consider parents, teachers and career guidance as relatively unimportant to their decision making compared to their female counterparts. The research argued that the reasons for this difference could be attributed to the boys desire to demonstrate greater independence whereas girls were more concerned with building and strengthening relationships (Maringe, 2006:476) Testing the Significance of the Differences by Gender on Career Decision Making Process In order to find out whether there was a significant difference between the two groups (males and females) in the scores for the career decision factors, the value of Sig. (2-tailed) from the SPSS output provided the solution where Sig. (2-tailed) is equal or less than 0.05 indicated a significant difference in the mean scores and for the value above 0.05, meant there was no significant difference between the two groups. Table 4 shows the results of the separate independent samples test (t-test) for assessing the difference between the mean scores. Separate independent t-test was carried out in order to find out the effects of gender in career decision making. The results are also shown in Table 4.



Table 4 Results of t-test comparing factors impacting career decisions scores of males and females Levene’s Test for Equality of Variances

t-test for Equality of Means

F

t

Significant Sig

df

Career Decision Factors

Sig. (2tailed)

(yes or no)

t-stat CDF1= Salary

14.981

.000

2.299

1036

.022

Yes


23.454

.000

2.759

1085

.006

Yes


51.172

.000

3.829

1067

.006

Yes


2.782

.096

-.863

1054

.389

No


34.210

.000

2.901

1085

.003

Yes


38.977

.000

3.097 8

963

.002

Yes


53.777

.000

3.603

1087

.000

Yes

CDF8 = Peers

3.306

.069

-.917

1087

.360


.012

.914

0.058

1087

.954

Family tradition

1.921

.166

.697

1087

.486

No No

No

There was no significant difference in scores for males and females of “lifelong learning”(t=.863, p> 0.05), “family tradition” (t=.697, p>0.05), “media coverage” (t=-0.058, p>0.05); and “peers” (t=-.917, p>0.05). However males reported significantly higher scores of “Salary”, “Opportunities for Promotion”, “Status and Prestige”, “Working Conditions” and “Skills Shortage” than females (t=-2.299, p