The world of work is evolving and the nature of relationships between knowledge workers and their employers has changed distinctly, leading to a change in the type of rewards they prefer. The nature of these preferences in the South African, industry-specific context is poorly understood. The purpose of this study was to deepen understanding of the reward preferences of Information technology (IT) knowledge workers in South Africa, specifically as these relate to the attraction, retention and motivation of knowledge workers.
The research design included a quantitative, empirical and descriptive study of reward preferences, measured with a self-administered survey and analysed using non-parametric tests for variance between dependent and independent groups and non-parametric analysis of variance.
This study found that there are specific reward preferences in knowledge workers in the IT sector in South Africa and that these preferences apply differently when related to the attraction, retention and motivation of employees. It identified the most important reward components in the competition for knowledge workers and also demonstrated that demographic characteristics play a statistically significant role in determining reward preferences.
The study’s findings show that a holistic approach to total rewards is required, failing which, companies will find themselves facing increased turnover and job-hopping. Importantly, the study also highlights that different rewards need to form part of knowledge workers’ relationship with their employer in three different scenarios: attraction, retention and motivation.
This study investigated the reward preferences of knowledge workers in the IT industry in South Africa and explored the relationship between these reward preferences and attraction, motivation and retention.
The modern workplace is changing rapidly, with advances in technology changing the nature of the world's economy from being predominantly product based towards a new, knowledge-based paradigm. The most valuable assets we create are increasingly intangible and competitive advantage lies within the unique knowledge and experience of a company's most talented and skilled employees (Beechler & Woodward
The problem is that there simply are not enough knowledge workers to meet global demand. It has been suggested that by 2020, there may be as much as a 13% shortage of highly skilled and university-educated workers worldwide (Dewhurst, Hancock & Ellsworth
In addition to companies competing for scarce skills, there is significant cost attached to losing existing knowledge workers to voluntary turnover. These costs include decreased productivity and the direct costs of recruiting and training replacements. There are also the less quantifiable costs involved in losing employees who carry significant intellectual capital with them and the disruption in organisational processes experienced by the employer when these workers leave (Dess & Shaw
Technology drives not only a major shift in the source of value generation for companies, but also the evolution of the workplace and, subsequently, the relationship and psychological contract between employer and employee (Sutherland & Jordaan
As technology is a major driver of the changes to the psychological contract, authors writing in business publications, such as Johns and Gratton (
The changing expectations of employees, particularly in the IT sector, coupled with the evolution of the workplace, present a challenge in understanding the reward preferences of these employees and how these preferences might be changing.
Effective talent attraction, retention and motivation are critical for companies in the IT sector, as is avoiding the impact of employee turnover on their performance. It is hypothesised that developing a nuanced understanding of reward preferences will enable IT companies to better meet the needs of their employees and subsequently lead to higher levels of attraction, retention and motivation.
Whilst there is a plethora of studies in developed markets, like the United States, there is a lack of understanding of knowledge worker reward preferences in the South African context, particularly as these relate to the IT sector, which is likely to exhibit preferences particular to that industry if international trends hold true (Horwitz, Heng & Quazi
Knowledge workers are said to be those who create intangible assets by using specialised knowledge and who, due to the changing nature of the knowledge economy in which they operate, need to continuously enhance, upgrade and refresh their knowledge (Sutherland & Jordaan
Nowhere is the importance of knowledge workers as evident as in IT industries, where these workers are at the forefront of the knowledge economy. In IT, knowledge workers expect to harness technology in the workplace to provide flexibility in their working arrangements (Johns & Gratton
This illustrates the demanding nature of knowledge workers and presents employers competing for their skills with the challenge of finding a suitable frame of reference for defining exactly what it is that these highly mobile resources will expect before they will join and stay with a company.
The concept of total rewards includes everything that employees value as part of their relationship with an employer (Medcof & Rumpel
Hlalethoa (
WorldatWork total rewards model.
The WorldatWork (
Compensation: any remuneration in the form of fixed remuneration (also referred to as ‘base pay’), which is mandatory compensation that does not vary and is not tied to performance, variable pay, which is compensation that may depend on performance, and short-term and long-term incentives.
Benefits, which are ancillary, such as medical or retirement benefits.
Work life, which is the structure, processes and environment put in place to support employees to do their jobs.
The terms performance and recognition refer to the perception that performance is being measured correctly and is in alignment with the organisation's goals. The terms also refer to the employee's duties, coupled with the employee receiving acknowledgement for helping the organisation achieve its goals.
Development opportunities refer to initiatives put in place to upgrade or enhance an employee's skills, whilst career opportunities refer to all factors that contribute to a clear career path and career planning being in place (Hlalethoa
Van Blerck (
These contradictory findings show that there is no definitive correct or incorrect model for defining reward categories and classifying the underlying components. Research by Medcof and Rumpel (
With the WorldatWork (
Understanding which rewards are preferred by employees is vital for any organisation as a starting point in developing methods of attracting and retaining top talent. Studies undertaken in an effort to deepen this understanding have suggested that reward preferences might differ based on a variety of factors (Bussin
Nienaber
Other studies have highlighted the difference in reward preferences between industries, with Medcof and Rumpel (
Moore and Bussin (
It is clear from the literature that demographic and industry-specific factors influence reward preferences; however, the difficulty lies in reliably correlating these factors with certain reward preferences, especially when studies examine employees from different sectors and types of companies. This is further complicated by reward preferences, even for a single employee, varying between those preferences that would encourage them to take up employment with an employer, those that they evaluate when deciding to stay with a current employer and those that motivate them to perform (Snelgar
Studies on reward preferences appear to indicate that they may differ based on three broad scenarios: being initially attracted to a new employer, deciding whether to remain with an existing employer or feeling motivated to perform (attraction, retention and motivation, respectively). Examples in the local context include findings by Snelgar
Having established that reward preferences may differ between these scenarios, it is imperative to understand the nature of these differences. In most cases, a competitive total compensation package forms the basis for attracting and retaining top talent (Horwitz
When examining the reward categories, as defined previously in the total rewards model, studies by Nienaber
Findings on how reward preferences differ between the three scenarios are not consistent in different studies. This appears to be based on a variety of factors, the most apparent of which are: the measuring instrument used, the categorisation of reward preferences and their components, the target population and the industry concerned. For example, Nienaber
In somewhat dissimilar findings, Bhengu and Bussin (
It follows from these findings that companies competing for talent on the basis of money alone are likely to be faced with the phenomenon of employees job-hopping, as these companies are simply competing on price. In order to gain a competitive advantage in the war for talent, there is consensus that competitive pay is only a base requirement and those companies wishing to retain top talent need to ensure that their talent management practices follow a holistic total rewards approach (Stahl
The primary objective of this study was to investigate the reward preferences of knowledge workers in the IT industry in South Africa and the influence of demographic factors on these preferences. The secondary objective was to explore the relationship between these reward preferences and attraction, motivation and retention.
The research design included a quantitative, empirical and descriptive study of reward preferences. Research was conducted in the form of primary data-gathering, which was done using a survey, consisting of a three-part questionnaire.
Part 1 of the questionnaire collected demographic information from respondents, namely age, gender, race, type of position occupied, length of service with current organisation, level of qualification and type of organisation.
Part 2 was constructed to measure reward preferences. The five categories of rewards defined by the WorldatWork (
Part 3 of the questionnaire consisted of three rank order questions. The aim of this part of the questionnaire was twofold. Firstly, it aimed to verify the overall reward preferences of respondents. Secondly, it served to determine whether respondents had significantly different reward preferences in each of three different scenarios related to an employer's rewards strategy: attracting new employees, retaining existing employees and motivating employees to perform at their peak. The components selected to comprise each of the five categories are listed in
Total rewards components.
Category | Components |
---|---|
Compensation (pay) | Fixed pay |
Variable pay (commission, etc.) | |
Incentives (bonuses) | |
Share options | |
Benefits | Medical |
Leave (maternity, study, annual, family responsibility, etc.) | |
Retirement | |
Work life (work environment) | Organisational structure and processes |
Tools for the job (systems, technology) | |
Access to latest technology | |
Work-life balance and flexible working arrangements | |
Office environment (facilities and support) | |
Leadership | |
Organisational climate and stability | |
Career, learning and development | Opportunities for self-directed learning and development |
Having a clear career path and planning | |
Employer-selected training programmes | |
Performance and recognition | Correctly measured and rewarded performance |
Acknowledgement for achieving organisational goals |
Due to issues with internal consistency of reward components and their categories cited in other studies (Moore & Bussin
A new set of questions was designed to measure the respondents’ preference for each of the 19 components. The questionnaire used a five-point Likert-type scale, presenting respondents with hypothetical scenarios or statements concerning each reward component. Respondents were asked to evaluate each statement and indicate whether they considered the component unimportant, of little importance, moderately important, important or very important.
The target population consisted of employees of South African IT companies who fitted the definition of knowledge workers (Sutherland & Jordaan
The research required a good probability of selecting a sample that was representative of most knowledge workers in South African IT companies. These two organisations were chosen as they had workforces that represented a diverse range of knowledge workers with varied demographics and job functions, ranging from sales to technical experts.
Data were collected by distributing an electronic version of the survey to respondents via selected HR and line managers, using Survey Monkey. Participation of the target population was voluntary, subject to informed consent and kept completely confidential by not collecting personal identifiers as part of the survey.
The second and third parts of the questionnaire in the present study contained the bulk of the information to be collected and consisted of continuous ordinal-type data measuring respondent's agreement on a five-point scale, as well as rank order data. As the research propositions were chiefly concerned with variance in this continuous data, the reference table provided by Bartlett, Kotrlik and Higgins (
Descriptive statistics (mean and median) were generated for the purposes of understanding the relative importance of reward preferences to respondents on the component level. In order for results to assist employers in tailoring their reward strategies in line with the components selected, it was necessary to determine which rewards were favoured by respondents, ranking them by median and then mean to determine this.
The ranking derived needed to be verified to determine whether differences in medians were statistically significant, thereby validating the ranking of overall reward preferences. In order to test the differences between reward component median ratings, pair-wise Wilcoxon signed rank tests were executed on all pairs of reward preferences.
The data was investigated for variance attributable to certain demographic variables. When conducting analysis of variance (ANOVA), a single dependent variable was used: the reward component rating.
General descriptive statistics and histograms were generated for responses based on each of the independent variables of interest. It was determined that their distribution violated the assumption of normality, which is essential in parametric ANOVA. In addition, the dependent variable data were either ordinal (Likert-type scale) or rank order. De Winter and Dodou (
The Kruskal-Wallis test was thus conducted by grouping responses into samples based on each of the independent variables and comparing them to detect whether samples may or may not be from the same population (indicating the probability that their variance was statistically significant).
This test was followed by pair-wise testing to determine which group in the sample (based on the independent variable) was responsible for the variance. This was done using the Mann-Whitney
In each of the three scenarios presented to respondents, corresponding to preference for attraction, retention and motivation respectively, the data contained the top ten preferred components selected by each respondent. This data were transformed into rank scores according to the ranks assigned to them by respondents.
Descriptive statistics were generated for each of the three scenarios (attraction, retention and motivation) to illustrate the overall rank scores achieved by the 19 reward components in each scenario and to allow comparison to determine where possible differences in preference might be between the scenarios.
In order to identify where statistically significant reward preferences might exist across the three scenarios and across all reward components, a Friedman ANOVA was conducted, with each of the scenarios being regarded as a dependent sample, as they were rated by the same respondents.
Where possibly significant differences were indicated by the Friedman ANOVA (
The primary objective of this study was to investigate the reward preferences of knowledge workers in the IT industry in South Africa. The secondary objective was to explore the relationship between these reward preferences and attraction, motivation and retention.
South African IT knowledge workers have overall reward preferences, which show significant differences as they relate to attraction, retention and motivation respectively.
Demographic characteristics play a significant role in determining the reward preferences for South African IT knowledge workers.
The main research limitation was that the sampling technique used could not guarantee adequate representation of all demographic characteristics intended to be measured and compared. In addition, because two large multinationals were targeted, the findings may apply mostly to corporate IT companies and may not be generalisable to all companies operating in the IT sector, particularly smaller, niche environments.
The research aimed to develop a better understanding of reward preferences and their relationship to attraction, retention and motivation and did not explore any causal relationships in differing reward preferences. Whilst this still provides valuable insight into what reward preferences actually are in the local context, there may be complex reasons for differences in such preferences across different demographics, which were not evaluated.
The survey was distributed to a total of 563 potential respondents; 135 completed questionnaires were returned. Of these responses, 14 were incomplete or unusable, providing 121 usable responses. This signified a response rate of 23.9%. The demographic characteristics of the sample are described in
Demographic characteristics of the sample (
Demographic characteristic | Frequency | Percentage |
---|---|---|
< 30 years | 30 | 24.79 |
30–40 years | 57 | 47.11 |
40 + years | 34 | 28.10 |
Male | 77 | 63.64 |
Female | 44 | 36.36 |
White people | 55 | 45.45 |
Indian | 18 | 14.87 |
Asian | 1 | 0.82 |
Mixed race | 21 | 17.35 |
Black African | 26 | 21.48 |
< 2 years | 35 | 28.92 |
2–5 years | 43 | 35.54 |
5 + years | 43 | 35.54 |
High school | 27 | 22.31 |
Diploma | 42 | 34.71 |
Degree | 52 | 42.97 |
Sales | 29 | 23.97 |
Technical specialist | 27 | 22.31 |
Management and executive | 25 | 20.66 |
Operations and technical support | 21 | 17.36 |
Consulting | 9 | 7.43 |
Enabling functions | 10 | 8.26 |
Internal consistency, measured by calculating the Cronbach's alpha of reward categories (compensation, benefits, work life, career, learning and development and performance and recognition) was found to be low (smaller than 0.6) for all categories set out in
Overall preference for different reward components was measured on the central tendency of their scores on the five-point Likert-type scale. A summary of these measures is presented in
Summary of overall reward preferences sorted by median and mean.
Importance | Reward component | Mean | Median | Upward range | Downward range |
---|---|---|---|---|---|
Very important | Quality of leadership | 4.686 | 5 | 0 | 4 |
Base pay | 4.653 | 5 | 1 | 4 | |
Incentives and bonuses | 4.620 | 5 | 2 | 4 | |
Correctly measured performance | 4.587 | 5 | 3 | 3 | |
Flexible working and work-life balance | 4.562 | 5 | 4 | 3 | |
Retirement benefit | 4.496 | 5 | 4 | 5 | |
Acknowledgement and recognition | 4.488 | 5 | 4 | 4 | |
Self-directed learning and development | 4.388 | 5 | 3 | 3 | |
Tools and systems | 4.339 | 5 | 3 | 2 | |
Medical | 4.322 | 5 | 4 | 1 | |
Clear career path | 4.314 | 5 | 5 | 1 | |
Not important | Climate and stability | 4.149 | 4 | 1 | 4 |
Organisational structure and processes | 4.058 | 4 | 1 | 3 | |
Access to latest technology | 4.041 | 4 | 2 | 2 | |
Amount of leave | 4.017 | 4 | 3 | 1 | |
Training from employer | 3.983 | 4 | 4 | 0 | |
Office environment | 3.545 | 4 | 0 | 2 | |
Moderately important | Shares | 3.438 | 3 | 1 | 1 |
Variable pay | 3.372 | 3 | 2 | 0 |
The variance of ratings was such that it was only possible to rank ratings into three major categories of importance. However, results of the Wilcoxon matched-pairs tests between each item and the remaining items showed some significant differences. These are expressed in
Respondents showed statistically similar preferences for the first 11 items shown in
The upward and downward range numbers show that the approximate ranking of items in
The influence of demographics on overall reward preference ratings was measured by conducting a Kruskal-Wallis ANOVA and controlling for each demographic as the independent variable.
The results of these tests per demographic variable, in the form of the relevant
Summary of reward preference comparisons by demographics.
Reward category | Reward component | Gender | Race | Age group | Tenure | Educational level | Job role |
---|---|---|---|---|---|---|---|
Compensation | Base pay | 0.3335 | 0.0035 | 0.2578 | 0.0943 | 0.2657 | 0.5694 |
Variable pay | 0.9558 | 0.5370 | 0.9732 | 0.1242 | 0.4027 | 0.0001 | |
Incentives and bonuses | 0.3242 | 0.6165 | 0.0679 | 0.1754 | 0.1716 | 0.0550 | |
Shares | 0.8863 | 0.2721 | 0.9993 | 0.295 | 0.0328 | 0.3936 | |
Benefits | Medical | 0.1647 | 0.7397 | 0.144 | 0.3893 | 0.1183 | 0.2074 |
Amount of leave | 0.7647 | 0.6321 | 0.8543 | 0.0512 | 0.0439 | 0.0232 | |
Retirement benefit | 0.4910 | 0.4284 | 0.6882 | 0.0072 | 0.0895 | 0.0083 | |
Work life (work environment) | Organisational structure and processes | 0.1404 | 0.6258 | 0.2554 | 0.5850 | 0.0124 | 0.0333 |
Tools and systems | 0.0447 | 0.0630 | 0.1403 | 0.3196 | 0.0003 | 0.0975 | |
Access to latest technology | 0.3536 | 0.4807 | 0.7852 | 0.3741 | 0.0084 | 0.1235 | |
Flexible working and work-life balance | 0.3750 | 0.2664 | 0.0646 | 0.0098 | 0.9961 | 0.5096 | |
Office environment | 0.4756 | 0.0323 | 0.5208 | 0.5990 | 0.0681 | 0.0701 | |
Quality of leadership | 0.8445 | 0.8609 | 0.7028 | 0.5780 | 0.5223 | 0.5402 | |
Climate and stability | 0.6723 | 0.8783 | 0.97 | 0.9812 | 0.0649 | 0.3325 | |
Career, learning and development | Self-directed learning and development | 0.2182 | 0.4890 | 0.0413 | 0.6995 | 0.6690 | 0.0844 |
Clear career path | 0.9268 | 0.0789 | 0.6495 | 0.1530 | 0.9492 | 0.6690 | |
Training from employer | 0.4510 | 0.0001 | 0.0029 | 0.0253 | 0.0254 | 0.0067 | |
Performance and recognition | Correctly measured performance | 0.0119 | 0.3665 | 0.7019 | 0.5027 | 0.7225 | 0.1351 |
Acknowledgement and recognition | 0.1415 | 0.2199 | 0.9565 | 0.1889 | 0.7357 | 0.1040 |
Summary of significant differences found in the impact of demographic characteristics on reward preferences.
Demographic characteristic | Significant results |
---|---|
Gender | Female respondents assigned a higher mean rank to the reward components |
Race | White or Caucasian respondents showing the lowest preference for |
Age | |
Tenure | Respondents who had been with their employer for longer (more than five years) showed significantly greater preferences for |
Level of education | Respondents who had no tertiary education showed less preference for the components |
Job role | Respondents in sales showed a significant preference for the component |
The component |
|
Operations and sales respondents were both found to have assigned relatively high mean ranks to the component |
|
The component |
Respondents showed similar preferences for the components base pay and incentives and bonuses across all three scenarios. Similarly, the component flexible working and work-life balance was found to be highly preferable in all three scenarios. It is notable that components categorised as benefits in the total rewards model were found to be more preferable in the scenario of attracting and retaining, whilst they were not preferred in the motivation scenario.
A summary of the median and mean rank scores of all components in the attraction scenario is shown in
Summary of rank scores for attraction.
Variable | Mean | Median |
---|---|---|
Base pay | 8.3058 | 10 |
Incentives and bonuses | 4.6198 | 6 |
Medical | 4.5620 | 6 |
Flexible working and work-life balance | 4.4545 | 5 |
Retirement benefit | 3.8595 | 5 |
Quality of leadership | 3.5537 | 3 |
Climate and stability | 3.3554 | 3 |
Self-directed learning and development | 2.8595 | 2 |
Clear career path | 2.6364 | 2 |
Acknowledgement and recognition | 2.0413 | 1 |
Variable pay | 2.8017 | 0 |
Amount of leave | 2.1653 | 0 |
Shares | 1.7438 | 0 |
Correctly measured performance | 1.7025 | 0 |
Organisational structure and processes | 1.6860 | 0 |
Tools and systems | 1.4463 | 0 |
Training from employer | 1.2562 | 0 |
Office environment | 0.8926 | 0 |
Access to latest technology | 0.7438 | 0 |
The top ten reward components preferred by respondents for the attraction scenario are shown above the line, with less important components shaded.
In the retention scenario, reward components were found to be similarly preferred, though in a slightly different order than in the attraction scenario. A summary of mean and median rank scores for reward components in the retention scenario is shown in
Summary of rank scores for retention.
Variable | Mean | Median |
---|---|---|
Base pay | 7.95041 | 10 |
Incentives and bonuses | 4.87603 | 6 |
Flexible working and work-life balance | 5.07438 | 5 |
Medical | 4.40496 | 5 |
Retirement benefit | 3.48760 | 3 |
Acknowledgement and recognition | 3.11570 | 3 |
Quality of leadership | 3.38843 | 2 |
Self-directed learning and development | 3.32231 | 2 |
Clear career path | 2.69421 | 2 |
Correctly measured performance | 2.38017 | 1 |
Climate and stability | 2.18182 | 1 |
Variable pay | 2.13223 | 0 |
Amount of leave | 1.97521 | 0 |
Tools and systems | 1.85124 | 0 |
Organisational structure and processes | 1.74380 | 0 |
Shares | 1.59504 | 0 |
Training from employer | 1.07438 | 0 |
Access to latest technology | 0.92562 | 0 |
Office environment | 0.58678 | 0 |
In the motivation scenario, respondents showed similar preferences for the components base pay, incentives and bonuses and flexible working and work-life balance, whilst preferences for components relating to the reward categories of career, learning and development and performance and recognition featured prominently.
A summary of mean and median rank scores for reward components in the motivation scenario, with the top ten reward components shown above the line and less important components shaded, is shown in
Summary of rank scores for motivation.
Variable | Mean | Median |
---|---|---|
Base pay | 5.76033 | 7 |
Incentives and bonuses | 4.66942 | 6 |
Flexible working and work-life balance | 4.78512 | 5 |
Acknowledgement and recognition | 4.50413 | 4 |
Self-directed learning and development | 3.60331 | 4 |
Correctly measured performance | 3.52066 | 3 |
Clear career path | 2.98347 | 2 |
Quality of leadership | 2.95041 | 1 |
Tools and systems | 2.68595 | 1 |
Climate and stability | 2.46281 | 1 |
Variable pay | 2.38843 | 0 |
Organisational structure and processes | 2.28926 | 0 |
Access to latest technology | 2.16529 | 0 |
Medical | 2.08264 | 0 |
Office environment | 1.74380 | 0 |
Training from employer | 1.71074 | 0 |
Amount of leave | 1.43802 | 0 |
Retirement benefit | 1.34711 | 0 |
Shares | 1.33884 | 0 |
Respondents showed significantly different preferences for many components across the three scenarios. An illustration of differences in median rank scores for all reward components is shown in
Different reward preferences for attract, retain and motivate.
Results of the Friedman ANOVA tests on each reward component across the three scenarios found statistically significant differences in preference for all reward components, except five. A summary of the relevant
Summary of Friedman ANOVA results of statistical significance in reward preferences for attract, retain and motivate.
Reward category | Reward component | Friedman ANOVA |
---|---|---|
Compensation | Base pay | 0 |
Variable pay | 0.0762 | |
Incentives and bonuses | 0.3509 | |
Shares | 0.3971 | |
Benefits | Medical | 0 |
Amount of leave | 0.0028 | |
Retirement benefit | 0 | |
Work-life (work environment) | Organisational structure and processes | 0.0999 |
Tools and systems | - | |
Access to latest technology | 0 | |
Flexible working and work-life balance | 0.097 | |
Office environment | 0.0001 | |
Quality of leadership | 0.4486 | |
Climate and stability | 0.0034 | |
Career, learning and development | Self-directed learning and development | 0.244 |
Clear career path | 0.4216 | |
Training from employer | 0.007 | |
Performance and recognition | Correctly measured performance | 0.0001 |
Acknowledgement and recognition | 0 |
The results show that there are overall reward preferences in knowledge workers in the IT sector in SA.
Relative importance of reward components.
Reward component | Relative rank | Importance |
---|---|---|
Quality of leadership | 1 | Very Important |
Base pay | 2 | |
Incentives and bonuses | 3 | |
Correctly measured performance | 4 | |
Flexible working and work-life balance | 5 | |
Retirement benefit | 6 | |
Acknowledgement and recognition | 7 | |
Self-directed learning and development | 8 | |
Tools and systems | 9 | |
Medical | 10 | |
Clear career path | 11 | |
Climate and stability | 12 | Important |
Organisational structure and processes | 13 | |
Access to latest technology | 14 | |
Amount of leave | 15 | |
Training from employer | 16 | |
Office environment | 17 | |
Shares | 18 | Moderately important |
Variable pay | 19 |
The findings support the literature and show that the main elements of monetary compensation are still crucially important (Bunton & Brewer
The inclusion of benefits such as medical and retirement in the factors considered very important supports the notion in contemporary business writing (such as in the work of Horwitz
The findings illustrate the relatively high importance of flexible working arrangements and work-life balance to knowledge workers in the IT industry, which is in agreement with more recent industry-specific business literature by Johns and Gratton (
Components that can be considered part of the work-life (work environment) reward category were found to be important to respondents, in line with findings in the high-technology industry by Medcof and Rumpel (
The findings regarding attraction, retention and motivation show that there are significant differences between rewards that matter to knowledge workers in these three scenarios. This supports business literature and academic studies on the subject (Bhengu & Bussin
Overall, the findings of this study show somewhat similar preferences in the scenarios of attraction and retention on most components, whilst they differ notably for motivation. This supports the work of Snelgar
Drawing together the above findings and theory, a competitive rewards model for South African IT knowledge workers is proposed. This model (illustrated in
Proposed competitive rewards model for information technology knowledge workers in South Africa.
The model does not suggest that components should be considered in isolation, or that those listed as the most important in attraction, for example, are unimportant for, say, retention. Rather, it is an attempt at a holistic structuring of the most pertinent rewards for South African IT knowledge workers.
Studies on the influence of demographics on reward preferences appear to be largely motivated by the desire to find meaningful ways of segmenting the knowledge workforce so that more targeted, and therefore more effective, reward strategies can be designed (Du Toit, Erasmus & Strydom
This study found differences in reward preference based on several demographic characteristics, but these should be interpreted in light of the usefulness of said differences in providing meaningful segmentation variables.
Whilst it was found that some differences existed between race groups, and even though other authors have suggested investigating race as a segmentation variable (Moore & Bussin
Snelgar
The findings related to age group show that younger employees placed higher value on learning and development driven and directed by them, as well as training selected and provided by the employer. Findings related to age group are suggested to be related to life stages (Snelgar
Findings also show that employees with longer tenures have a slightly lower preference for training determined and provided by their employer, which probably stems from them being established in the employer's environment and familiar with the domain knowledge required to perform their work.
The results show that employees with higher levels of education show less preference for optimal tools and systems, organisational structure and processes in place to do their jobs. This is likely to be a symptom of employees who are engaged in more functional work relying less on their knowledge capital for the bulk of their performance. They would be more beholden to the organisation's processes and to the systems they rely on for performing their jobs. Employees with higher knowledge capital would possibly see their performance as relying more on the skilful application of said knowledge in order to succeed.
Concerning job roles, comparative studies in the local context are scarce, particularly industry-specific studies such as the present study. Findings of the present study show some differences in rewards preferred by employees with specific job roles. It found that workers in enabling functions such as marketing, HR and finance show a stronger preference for training determined and provided by their employer, whilst those in management and executive positions consider employer-provided training relatively less important. This is possibly due to those in management and executive positions requiring more self-driven development to perform in their jobs rather than domain-specific training, such as that normally provided by employers.
This research showed that there are specific reward preferences for knowledge workers in the IT sector in SA. The impact of demographic characteristics on reward preferences was also demonstrated. A model for structuring competitive total rewards in the South African IT industry was proposed that shows that there are different reward strategies that can be successfully used to attract, retain and motivate knowledge workers. The war for talent in both the local and the global marketplace will only be won by those who adopt a total reward strategy that is appropriate to the preferences of their knowledge workers and keeps pace with the evolving trends in the world in which we work.
It is recommended that managers and leaders in the South African IT sector inspect their organisations’ rewards through the lens of the total rewards concept used throughout this study and that they take stock of whether they have considered all of the aspects required to acquire and retain top talent.
The simplified list of rewards components used in this study could provide a basis for investigating whether they are meeting the preferences of their knowledge workers, or for conducting employee surveys of their own. If employers wish to know where to start and what to focus on, the relative rankings determined by this study will provide insight into the importance of different total rewards components. Leaders in the IT industry should be aware that the war for talent cannot be won on price alone.
It would be meaningful to investigate if the simplified, condensed reward components measured could be factor analysed to determine an appropriate categorisation.
One shortcoming of this study, which should be addressed in the local context, is the type of rating instrument used to measure overall reward preferences. The five-point Likert-type items ranged from unimportant to very important, but the median value, moderately important, is not truly a preference-agnostic point on the scale. Furthermore, the nature of reward preferences means that studies that ask respondents to rate their preferences are likely to be plagued by low variance and positive skewedness towards higher ratings. Realistically, people consider all rewards important to some extent.
A recommendation would be to address this shortcoming by devising a more appropriate measuring instrument, perhaps asking respondents to score reward components out of 10, by enlarging the rating scale to 7 or 10 points, and modifying the interval descriptions, or by forcing pair-wise trade-off questions, which might be more complicated, but would perhaps yield a more accurate real ranking of reward preferences.
The authors declare that they have no financial or personal relationship(s) that may have inappropriately influenced them in writing this article.
M.B. (University of Johannesburg) was the project leader and W.T. (University of Pretoria) was responsible for the experiment and project design. M.B. made conceptual contributions and wrote the manuscript.