3. Results
Three approaches were adopted in answering the key questions posed by the study. First, a bibliometric analysis of the evolution of research on demographics-based impediments to professionalism in order to uncover the thematic areas and clusters. Also, an author citation map to outline key collaborative efforts in the topic area. A presentation of the document analysis followed these results on professionals’ registration amongst quantity surveyors in South Africa. This was used as a case to illustrate the issues identified. A systematic review of the documents used for the bibliometric review was then conducted to connect the issues identified with previous findings and suggest practical insights and solutions.
Evolution of Research on Demographics-Based Impediments to Professionalism
Thematic Areas and Clusters
To examine the state of the art of studies in this area, co-occurrence of keywords was examined based on all keywords [
20,
21,
22]. Using VOSViewer, a co-occurrence network map was generated to highlight the prolific extant areas examined, future areas proposed, and the extent of work covered. This gave insight into the key themes to examine in the document analysis of the study [
23]. The various colours represent clusters of closely related terms or frequently appear together. For instance, terms related to racial and ethnic categories like "race difference," "ethnicity," "ethnic group," and "minority group" are clustered together in green. Its appearance confirms the themes is a critical research examination area. Central terms like "human," "male," "female," and "demographics" indicate that these terms are pivotal in the dataset and have strong connections to various other terms [
24]. Their positioning suggests they are foundational concepts for this dataset. The blue cluster encompasses terms related to psychological well-being and mental states like "depression," "burnout," and "suicidal ideation." Terms here like "discrimination," "racism," and "workplace" suggest themes of social dynamics and discrimination. Moreover, methodologies and survey tools, as evidenced by terms like "questionnaire" and "surveys and questionnaires were also indicated. The lines connecting terms indicate relationships or co-occurrences [
25]. The thicker a line, the stronger the relationship, meaning those terms often appear together. The networks are visualized and presented in figure 2 below.
Table 2.
Thematic focus and emerging areas.
Table 2.
Thematic focus and emerging areas.
| Examined Demographic Areas |
Consequences of this Phenomenon Examined |
Potential Future Research Focus |
| Race |
depression |
Relationship between race issues and workplace depression |
| Equity |
burnout |
Relationship between Equity issues and workplace burnout/performance |
| Sex |
suicidal ideation |
Relationship between sex/gender issues and wellbeing |
| Ethnic Group/ Ethnicity |
Workplace discrimination |
Relationship between ethnicity and workplace discrimination |
Given the prominence of terms like "race," "gender," "discrimination," "depression," and "workplace," the underlying dataset or research might be exploring the relationships between demographics, psychological well-being, and social issues [
26,
27,
28].
Author Citation Network Map
Collaborative patterns or co-authorship networks generated using VOSViewer reveal the depth of ideation between authors in furthering this area of research. This reveals critical insight into the level of Urgency and importance assigned to this issue in the built Environment [
10,
29]. Each node (circle) represents an author or group of authors. The size of the node might indicate the prominence or frequency with which these authors appear in the dataset (e.g., the number of publications or collaborations they have) [
30]. The links or lines connecting different authors suggest co-authorship or collaboration between them. If two authors are connected, it likely means they have co-authored a paper or multiple papers together.
The different colours can indicate clusters or groups of authors who frequently collaborate. Each colour grouping represents a collaborative community within the dataset: For instance, the green-coloured authors might be a group that frequently collaborates on similar topics or within a particular domain. The same applies to orange, blue, and purple clusters [
31,
32,
33]. The spatial proximity between nodes can suggest the strength of collaboration. Authors placed closer together might have stronger collaborative ties compared to those further apart. This group centred around "cals i.," "wright s.," "jasri.," "dowey l.," and others indicate a dense collaboration pattern, with many authors frequently working together (red cluster). Anchored by "dodge p." and "drybye l.n.," this cluster represents another collaborative community (blue cluster). "dade l." and "day l." seem to be the focal points of this cluster, possibly indicating their prominent role in this community (Green cluster). "alk e.a." and "wang x." represents a less dense collaboration pattern, perhaps indicating a more specialized or distinct research area compared to the others. (Orange cluster). Authors such as "Schmidt s.w." are relatively isolated from the central clusters, suggesting they have fewer collaborations within this specific dataset. Their positioning on the outskirts might indicate peripheral collaboration with the main clusters. This is shown in
Figure 3 below.
Distribution of professionally registered quantity surveyors (PrQs) in South Africa based on race.
The highest proportion of PrQs identifies as White, accounting for just over 50% of the total. This is significant, especially when considering the broader demographic distribution of South Africa. The second largest group is the African demographic, though its representation is notably less than half of the White group. Both the Indian and Colored demographics have considerably lower representations, with the Indian group slightly ahead of the Colored group. The dominance of White PrQs might be rooted in historical privileges associated with apartheid, where educational and professional opportunities were skewed in favour of the White population[
6,
34]. The relatively lower representation of African, Indian, and coloured professionals could suggest the existence of barriers – either educational, socioeconomic, or systemic within the profession. Given South Africa's emphasis on transformation and diversity in the post-apartheid era, this chart might raise questions about the effectiveness of diversity initiatives within the quantity surveying profession[
35,
36,
37]. This is shown in
Figure 4.
Crosstabulation (or crosstab) of the age group distribution against the racial backgrounds of professionally registered quantity surveyors (PrQs) in South Africa.
There's a notably high percentage of White professionals in the 65+ age category (19.9%). This suggests that a significant portion of the older generation of PrQs in South Africa is White.
| Age Group * Race Crosstabulation |
| |
Race |
Total |
| African |
White |
Indian |
Coloured |
| Age Group |
20-25 |
Count |
1 |
0 |
1 |
0 |
2 |
| % within Race |
0.1% |
0.0% |
0.4% |
0.0% |
0.1% |
| 26-29 |
Count |
32 |
66 |
8 |
2 |
108 |
| % within Race |
4.8% |
4.3% |
3.2% |
3.1% |
4.3% |
| 30-34 |
Count |
129 |
253 |
74 |
20 |
476 |
| % within Race |
19.2% |
16.6% |
30.0% |
31.3% |
19.0% |
| 35-39 |
Count |
161 |
223 |
49 |
8 |
441 |
| % within Race |
24.0% |
14.6% |
19.8% |
12.5% |
17.6% |
| 40-44 |
Count |
140 |
146 |
27 |
12 |
325 |
| % within Race |
20.9% |
9.6% |
10.9% |
18.8% |
13.0% |
| 45-49 |
Count |
94 |
126 |
28 |
8 |
256 |
| % within Race |
14.0% |
8.3% |
11.3% |
12.5% |
10.2% |
| 50-54 |
Count |
40 |
146 |
30 |
5 |
221 |
| % within Race |
6.0% |
9.6% |
12.1% |
7.8% |
8.8% |
| 55-59 |
Count |
33 |
139 |
12 |
5 |
189 |
| % within Race |
4.9% |
9.1% |
4.9% |
7.8% |
7.5% |
| 60-64 |
Count |
22 |
123 |
8 |
1 |
154 |
| % within Race |
3.3% |
8.1% |
3.2% |
1.6% |
6.1% |
| 65+ |
Count |
19 |
303 |
10 |
3 |
335 |
| % within Race |
2.8% |
19.9% |
4.0% |
4.7% |
13.4% |
| Total |
Count |
671 |
1525 |
247 |
64 |
2507 |
| % within Race |
100.0% |
100.0% |
100.0% |
100.0% |
100.0% |
African professionals appear to be younger on average, with their highest representation in the 35-39 age category (24.0%). This might indicate a more recent influx of young African professionals into the profession. The largest proportion of Indian PrQs falls in the 30-34 age category (30.0%). This suggests that many Indian professionals are currently in their prime working years. The coloured demographic displays relatively balanced representation across the 30-34, 40-44, and 45-49 age groups, with 31.3%, 18.8%, and 12.5% respectively.
The 30-34 age group has the highest overall representation across all races (19.0% of the total). The least represented age group across all races is the 20-25 category, indicating that few professionals register at such an early age. The 65+ age group is notably significant, especially for White professionals, which can reflect either long-term career commitment or possible delays in retirement [
5,
38].
The chart shown in
Figure 5 represents the distribution of professionally registered quantity surveyors in South Africa, which suggests a measure of those in the profession who have reached a certain level of recognition or qualification. South Africa has a unique racial and socio-political history, which may have implications for professional representation across different racial groups. The significantly high number of white male registered quantity surveyors suggests they are the most represented demographic in the profession. This could be attributed to various reasons, including historical advantages in education and professional opportunities.
The "Coloured" category appears to be the least represented. Understanding the reasons behind this underrepresentation would require further investigation. This alludes to underrepresentation. In terms of gender disparity, across all racial categories, there is a higher representation of men compared to women.
Distribution of professionally registered quantity surveyors (PrQs) in South Africa based on Race and Age Group
This section, as shown in
Figure 6. illustrates "PrQs by Race and Age Group" for professionally registered quantity surveyors in South Africa. It presents a breakdown of these professionals by race and then further subdivides each racial group by age. The age groups are divided into eight categories, ranging from "20-25" to "65+". Most of the professionally registered quantity surveyors across all racial groups are within the "30-34" to "50-54" age brackets. Younger professionals ("20-25" and "26-29") are less represented across all racial categories, which might be expected given the time it takes to complete education and gain professional recognition. The "65+" age group, representing senior professionals, is also smaller in representation.
Racial breakdown revealed that a substantial representation in the "30-34" and "35-39" age groups, with a decline in the older age groups are African. For whites, There is a notable peak in the "45-49" age group. The representation remains relatively high from "30-34" to "55-59" but sees a decline in the younger and older age brackets. With regards to Indians, the largest representation is in the "35-39" age group, with a significant drop for both younger and older professionals. For coloured, The representation is relatively low across all age groups, with slight peaks in "30-34" and "40-44".
Distribution of professionally registered quantity surveyors (PrQs) in South Africa based on Gender and Age Group.
Overall gender distribution reveals that there is a pronounced difference in the number of registered Male PrQs compared to Female PrQs across all age groups. This shows that the profession has a higher male dominance. With young professionals, there's a significant representation in the "20-25" age group, which then sees a notable decline in the "26-29" age bracket amongst the female groups. Meanwhile, in the male group, The representation starts relatively lower in the "20-25" age group compared to females but then sees a continuous increase, peaking at "40-44". This is shown in figure 7
On female mid-career professionals, there is a gradual increase from the "26-29" age group, peaking at "35-39". After this peak, there's a consistent decline. Compared to their male counterpart, the representation is highest from "30-34" to "50-54", with a particularly pronounced peak at "40-44". For senior professionals, the representation diminishes considerably from the "55-59" age group onward for females. In the male group, there's a decrease post the "50-54" age group, but the representation remains relatively higher than females, even in the "65+" bracket.