Original Research

Exploration of the reward preferences of generational groups in a fast-moving consumer goods organisation

Dale Fobian, Frans Maloa

Received: 14 June 2019; Accepted: 14 July 2020; Published: 31 Aug. 2020

Copyright: © 2020. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Orientation: The generational diversity of employees evident in today’s workforce and the important role of reward in meeting a wide variety of needs to attract, motivate and retain employees for the organisation are a key strategic contribution.

Research purpose: The purpose of this study was to explore how, whether and to what degree employees from different generational groups differ about preferences on total reward components in the fast-moving consumer goods industry, for purposes of attraction, retention and motivation.

Motivation for the study: The rationale for this study was to explore and improve the understanding of reward preferences of different generation groups.

Research design and method: The research was a quantitative, empirical and descriptive study of reward preferences in an industry-specific context. A self-administered survey instrument was used and analysed using tests for internal consistency and scale reliability, various measures for factor analysis and a general linear model, involving a multivariate analysis of variance (MANOVA), to test for significant differences between independent and dependent variables.

Main findings: Baby Boomers, Xers and Millennials did not differ significantly about preferences regarding financial and non-financial rewards. Millennials do not prefer non-financial rewards to financial rewards. The variance, however, was not large.

Practical or managerial implications: The research results provide management with informed knowledge of the types of rewards that can be administered to employees of different generational groups to attract, retain and motivate them.

Contribution and value add: The research has added insight into the reward preferences of generational groups and made recommendations for improving reward strategy for the attraction, retention and motivation of employees in the fast-moving consumer goods industry.

Keywords: generational theory; generational differences; total rewards; total reward strategy; compensation.

Introduction

Orientation

‘Everyone is different and everyone brings value to the organisation’ stated Rubin, as cited by Rose (2013, p. 31). This quotation succinctly captures the essence of this study, namely the diversity of employees present in today’s workforce and the important role of reward in meeting a wide variety of needs to attract, motivate and retain talent for the organisation and its sustained competitiveness (Brown & Reilly, 2013).

Research purpose and objectives

This study seeks to understand the reward preferences of diverse generational groups in today’s workforce. It is expected that the results of this study will inform remuneration strategies to improve attraction, retention and motivation of employees in a fast-moving consumer goods organisation.

With the research purpose, several research objectives were identified for the literature review and empirical study. First, the literature review aimed at conceptualising different generation cohorts and their reward preferences, as well as describing different components of total reward. Second, the empirical study aimed at establishing how, whether and to what degree employees from different generational groups differ about preferences on total reward components. Third, it aimed at making recommendations to the organisation informing employee reward strategies of reward preferences of different generation cohorts within the context of a fast-moving consumer goods company. Lastly, it aimed at making recommendations for future research for the field of Industrial and Organisational Psychology regarding reward strategy and reward preferences from different generational groups.

Literature review

Generations in the workplace

The workplace is becoming increasingly diverse with five generations of employees estimated to be at work by 2020 (Meister & Willyerd, 2010). The concept of generations has been defined extensively in the literature. According to Snelgar, Renard and Venter (2013), the concept of generation can be broadly defined in terms of cohorts, life experiences or historical experiences. Mannheim, as cited by Cogin (2012), defined generations as ‘a group that shares both a particular span of birth years and a set of world views grounded in the defining social or historical events that have occurred during the generations’ formative development years’. Similarly, the different generations find expression in the concept of cohorts, which refers to generations as groups of people born in a specified period (Lyons & Kuron, 2014).

The key concepts of cohorts and generations as groups of people are discussed in the literature and categorised accordingly. For example, Smit, Stanz and Bussin (2015) describe Baby Boomers as the generation that places a high value on hard work, obeying rules, dedication and military principles. The Xers are a generation that develops their skills, take on challenges and are perceived to be very adaptive in the changing business world, while the Millennials are a generation that favours teamwork, choose to follow orders and prefer to work flexible hours to successfully complete their tasks in their own ways.

However, the generation and cohort concept received wide criticism in the literature, primarily owing to the assertion that preferences change during life stages. It is also difficult to distinguish the influence of three factors: age, period and cohort on generational development. In addition, there is also a challenge with methodological alternatives to researching generations which are believed to narrow research outcomes to generational influence (Campbell, Campbell, Siedor, & Twenge, 2015; Zacher, 2015) and the acknowledgement that different generations can overlap, with the latter referred to as cusp generations (Becton, Walker, & Jones-Farmer, 2014; Van der Walt, 2010). Moreover, there are significant differences and inconsistencies with cohort periods in the literature.

However, sound counter-arguments to the criticism are also offered in the literature. For example, there is an argument that values (key to generational distinctiveness) do not necessarily change as people age (Cogin, 2012). In the context of rewards, Schullery (2013) found Xers and Millennials to be significantly more interested in extrinsic and leisure rewards than Baby Boomers. Furthermore, Chen and Lian (2015) also found in their study, conducted in China, that Millennials preferred extrinsic rewards, more so than other generations. The findings by Schullery (2013) and Chen and Lian (2015) stimulate further debate and create interest in understanding the generational differences and their preferences in total rewards.

However, the study of the rewards leads to the question: ‘How can reward strategies address different preferences for types of reward structures by different generations’ groups in an organisation?’ Pregnolato, Bussin and Schlechter (2017, p. 1) contend that total reward and reward mix overall were the desired reward strategy in the organisation. Their study concluded that in order of preference, remuneration and benefits were being rated most preferred; performance and recognition were rated second; work–life balance practices, learning and career advancement were rated similarly in overall preference; and the work–life balance climate was rated lowest overall in terms of its importance in retention.

Nevertheless, Xavier (2014) asserted that a World at Work survey found that only 26% of the respondents reported having an integrated approach to total rewards. As an alternative, Brown (2014) proposed ‘smart rewards’. Part of the smart rewards approach involves employers mining their engagement data to identify the various generational groupings and their preferences in the workforce. However, it was established that the segmentation of employees, according to their cohort grouping, enables employees to easily select a rewards package from the wide choice available that best meets their personal needs and stage in their lives (Brown, 2014). Nonetheless, an exploration of the rewards preference is necessary to understand if there are differences between the different generational cohorts.

Generational preferences and rewards

Research on generational preferences and rewards has been conducted in different industries. For instance, in the Information, Computing and Technology (ICT) industry, research by Moore and Bussin (2012) showed that generation theory did not have a bearing on reward preferences. In this research, all the generations, except for the Baby Boomers, rated their compensation package as the most important aspect of rewards and variable pay as the lowest aspect of reward. However, Bussin and Van Rooy (2014)’s research in the finance sector found that generations valued different types of financial compensation. Boomers valued reward aspects such as fixed and long-term compensation. Xers valued a balance between fixed and variable compensation. Millennials not only valued a balance between fixed and variable compensation but also demonstrated some indication that they valued higher variable pay. However, Sillery, as cited by Risher (2014), states that compensation best practices show that key drivers of Millennials commitment were related to perceptions of their careers (current and future opportunity for pay, growth and job enrichment).

Furthermore, a study by Smit et al. (2015) in South African organisations showed significant differences in preferences for rewards by generations in that Millennials (who favour teamwork, choose to follow orders and prefer to work flexible hours to successfully complete their tasks in their own ways) scored the top three most important total reward components as performance management, development, and benefits and safety. For Xers (who develop their skills, take on challenges and are perceived to be very adaptive in the changing business world), performance management, development opportunities and compensation were important to retain them, while for Baby Boomers and Veterans (who place high value on hard work, obeying rules, dedication and military principles) the top three total reward components were compensation, performance management, and benefits and safety.

However, Pregnolato et al. (2017, p. 1) found limited differences between generations. According to the study, Millennials assigned a slightly lower level of importance to remuneration versus Xers and Baby Boomers. Millennials specified work–life balance as being slightly more important in their retention compared to Xers and Baby Boomers. However, Bussin and Van Rooy (2014)’s research contested that monetary aspects of reward strongly influenced all decisions made, irrespective of generation. In contrast, research reported by Brown and Reilly (2013) indicated that career opportunities and organisational reputation were the most important drivers for all generations, with this being attributed to the difficult economic climate.

From the different perspectives on rewards, the preferences for generations seem to be conflicting regarding how, whether and to what degree generations have reward preferences or not. To address the issue, a few research hypotheses were formulated for empirical testing as follows:

H01: Generational groups do not prefer financial rewards to non-financial rewards.

H1: Generational groups prefer financial rewards to non-financial rewards.

H02: Millennials do not prefer non-financial rewards to financial rewards.

H2: Millennials prefer non-financial rewards above financial rewards.

H03: Generational groups’ preferences in terms of reward, if any, will not be large.

H3: Generational groups’ preferences in terms of reward, if any, will be large.

This study therefore aims at benefiting Industrial Organisational Psychologists and Reward Practitioners in understanding reward preferences of different generation groups in the fast-moving consumer goods sector.

Research design

A quantitative survey research design was chosen in this study to reach a large portion of participants in a fast-moving consumer goods organisation within a set period. The design was cross-sectional to include a view of all generational cohorts at one point in time (Bussin & Van Rooy, 2014; Bussin & Thabethe, 2018; Moore & Bussin, 2012).

The independent variable in the research design is the generational groups as defined in the different cohorts, namely Baby Boomers, Xers and Millennials. The dependent variables include employees’ perceptions and preferences for components of the total rewards.

This study therefore aimed at improving the understanding of the reward preferences amongst different generational groups. It is expected that the results of this study will inform remuneration strategies and highlight how different generational groups prefer financial to non-financial rewards as well as different preferences within an organisation.

Research method

Research participants

The participants in this study were from a large fast-moving consumer goods organisation in South Africa. Participants were identified through convenience non-probability sampling. The organisation employed approximately 2800 non-bargaining unit employees across South Africa. The payroll department of the organisation made available all permanent, non-bargaining unit employees in South Africa, with more than 12 months of service and with organisation email addresses, which came to 2139 employees in the survey population.

Moore and Bussin (2012) South African generational cohorts were used to categorise participants. Baby Boomers were employees born between 1941 and 1960. Xers were employees born between 1961 and 1980. The Millennials were employees born between 1981 and 2007. Of the 2139 employees, 107 (5%) were Baby Boomers, 1241 (58%) Xers and 791 (37%) Millennials.

A random sample of 60 participants by generation cohort was invited to participate in a pilot survey. Nineteen (32%) of the participants responded to the electronic survey questionnaire, and one participant retracted, resulting in 18 (30%) consenting respondents for the pilot survey.

A total of 2079 employees were invited to participate in the main survey population. However, 6 months expired from the time of receiving the original list of employee email addresses from the organisation’s payroll department, to when the survey was implemented. As a result, 85 employees who were originally invited left the organisation during the period. Of the remaining 1994 employees, 608 (30.5%) employees responded to the electronic survey questionnaire, while three retracted, leaving 605 (30.3%) consenting respondents for the main survey. The sample size was adequate to continue with the study, as Sekaran and Bougie (2010) state that the sample size between 30 and 500 is suitable for most research. With a 95% confidence level and a total population of 1 million, the sample size of 384 is desired. Thus, the sample size of 605 for this study was more than adequate.

The demographic characteristics of the main survey participants were determined from the survey questionnaire. Given a strong culture of confidentiality in the organisation regarding remuneration information, a minimalist approach was adopted to ensure maximum confidentiality of participants and maximum participation rate. Pseudonyms were used to hide the identity of the participants.

Tables 1–4 outline the demographic characteristics of the participants. Table 3 highlights the participant’s generation cohort demographic of the sample versus the actual profile of the organisation. Baby Boomers made up 4.8% versus 5%, Xers 63.5% versus 58% and Millennials 31.7% versus 37%.

Table 1 indicates the gender composition of the sample with males constituting the majority at 56.2% compared with females at 43.8%.

Table 2 indicates the race composition of the sample with the mixed race in the majority constituting 38.5% followed by the African race at 20.5%. The least represented race was Indian at 6.4%.

Table 3 indicates that the generation cohort composition of the sample, the generation X (Xers), was in the majority constituting 63.5% followed by the Millennials at 31.7%. The least represented are the Baby Boomers at 4.8%.

Table 4 indicates the composition of the sample with the non-management group in the majority (64.8%) compared with the management group (35.2%).

The management and non-management scales were determined through the Paterson job grading system which is a popular grading instrument that categorises jobs according to their level of complexity within the organisational structure. Thus, the emphasis and use of the grading measure are on face validity as the instrument measures what it purports to measure and has been used extensively in practice. The reliability of Paterson is its consistency and its stability over time as the preferred grading system in practice.

Measuring instrument

To establish how, whether and to what degree employees from different generational groups differed about preferences on total reward components, an electronic survey questionnaire was used, namely the Rewards Preference Questionnaire (Bussin & Thabethe, 2018; Nienaber & Bussin, 2009). The electronic survey questionnaire comprised the following sections (Bussin & Thabethe, 2018):

Section 1: Demographic factors; gender, race group, age group and seniority

Section 2: Reward preferences; questions derived from items extracted from the World at Work (2010) total reward model

Section 3: The testing of reward preferences of generational groups.

A five-point Likert scale in Section 2 of the questionnaire was used to measure reward preferences on each reward item ranging from 1 (least important/totally disagree) to 5 (extremely important/ fully agree). The measuring instrument was piloted on a small group of 18 consenting respondents to determine the time taken for completing the survey and to identify any ambiguities or difficulties with the survey (Bussin & Van Rooy, 2014). Consequently, the initial response time of 20–30 min was changed to 15–20 min, and the term ‘medical aide’ was changed to ‘medical aid’ owing to feedback from participants. A Cronbach’s alpha score of reliability measured 0.859 on the measuring instrument, above the acceptable level of 0.70 (Bussin & Van Rooy, 2014; DeVellis, 2016; Pallant, 2013). This was indicative of internal consistency and scale reliability of the Rewards Preference Questionnaire.

Research procedure

The Rewards Preference Questionnaire was disseminated to survey participants via a link in an email to participants’ organisational email addresses provided by the payroll department of the organisation. For the main survey, this included 2079 survey participants. Survey participants were given 2 weeks to complete the self-administered web-based questionnaire hosted by Google Forms. A reminder email was sent to survey participants 1 week after the survey was initiated and 2 days before the survey was closed, to encourage participation. Participation was voluntary and no pressure was placed on participants to complete the survey. Participant responses were recorded anonymously to ensure confidentiality. Pseudonyms were used to hide the identity of the participants. After 2 weeks, the survey was closed, and the survey data were exported from Google Forms host to a Microsoft Excel spreadsheet.

Statistical analysis

Analysis: The data collected in the Microsoft Excel spreadsheet were analysed using IBM Statistical Package for the Social Science (IBM SPSS v24). The services of a university statistician were procured to assist with the data analysis. The data were analysed using a three-stage data analysis process (Bussin & Thabethe, 2018).

Stage 1 involved Cronbach’s alpha reliability measure to confirm the internal consistency and scale reliability of the overall Rewards Preference Questionnaire applied in the main survey.

Stage 2 involved a Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy, a Bartlett’s test of sphericity for factor analysis suitability, a principal axis factoring (PAF) and Oblimin with Kaiser normalisation rotation method for factor analysis. After going through the results on the emerging factors, Cronbach’s alpha reliability measures were calculated on composite scores to confirm the internal consistency and scale reliability of the new factors.

Stage 3 involved a general linear model – MANOVA, partial eta squared for effect size to test for significant differences between independent variables (generations: Baby Boomers, Xers and Millennials) and dependent variables (reward factors) to test the formulated research hypothesis.

Measures taken to protect confidentiality included the survey questionnaire distributed by email to employee organisation email addresses with an embedded link to an electronic form developed using electronic Google Forms. Both employee email access and the electronic form were password protected; data were non-identifiable information direct from respondents with consent collected by means of electronic Google Forms, electronically and automatically recorded in an electronic spreadsheet developed using Google Sheets; records will be stored on a centralised electronic password secured database on Google drive for 5 years. Only the researcher and statistician had access to the recorded data. In addition, the statistician signed a confidentiality form.

Ethical consideration

Permission to conduct the research was granted by the organisation and the Research and Ethics Committee of the University of South Africa, approval certificate 2018_CEMS/IOP_018.

Results

Stage 1 – Rewards Preference Questionnaire reliability

A Cronbach’s alpha score of reliability measured 0.894 on the 45 questionnaire items instrument. This is above the acceptable level of 0.70 (Bussin & Van Rooy, 2014; DeVellis, 2016; Pallant, 2013), indicating internal consistency and scale reliability of the Rewards Preference Questionnaire applied in the main survey.

Stage 2 – Reward factor reliability

A KMO measure of sampling adequacy and a Bartlett’s test of sphericity were calculated to determine the data sets suitability for factor analysis. Kaiser–Meyer–Olkin returned values between 0 and 1, and KMO values between 0.8 and 1. This indicates that the sampling is adequate (Cerny & Kaiser, 1977). For the Bartlett’s test of sphericity, small values (fewer than 0.05) of the significance level indicate that a factor analysis may be useful with the data (IBM Knowledge Centre, Internet). The KMO measured 0.873, and the Bartlett’s test of sphericity measured 0.000, indicating that the survey data were suitable for factor analysis.

The PAF method was used to extract the factors based on the assumptions that the constructs were correlated. The Oblimin with Kaiser normalisation rotation method was used to simplify the structure. Kootstra (2004) suggests that the pattern matrix is used to interpret the factors (and structure matrix for corroboration). The author suggested, with some caution, only interpreting factor loadings with an absolute value > 0.4 (which explain around 16% of variance). The sample size of > 350 with a factor loading ≥ 0.30 was considered significant. Any loadings < 0.30 were not considered and consequently suppressed at the output. Thus, the bigger the sample and the smaller the loadings, the significant the factor. Consequently, six factors were selected for Cronbach’s alpha reliability measures on composite scores to confirm the internal consistency and scale reliability of the factors (see Table 5).

Table 5 presents the questionnaire items extracted from the World at Work Categories. A three-factor structure developed from the loaded items. An item loaded on a given factor if it was 0.30 or greater for that factor and < 0.30 for the other. Five items loaded on Factor 1 (Career learning and development), two items loaded on Factor 4 (Compensation) and four items loaded on Factor 8 (Performance and recognition).

Table 6 confirms which individual item of a factor measured the same factor or construct consistently. The higher the Cronbach’s alpha value, the ‘better’ the items or questions formed part of the construct or factor. The Cronbach’s alpha reliability measures of the three factors confirmed the internal consistency and scale reliability above the acceptable level of 0.70 (Bussin & Van Rooy, 2014; DeVellis, 2016; Pallant, 2013). The reliability estimates were 0.802, 0.773 and 0.795 for Factor 1 (Career, learning and development), Factor 4 (Compensation) and Factor 8 (Performance and recognition), respectively. This indicated good reliability.

Consequently, differences between independent variables (Baby Boomers, Xers and Millennials) and dependent variables (Factor 1: Career, learning and development; Factor 4: Compensation; Factor 8: Performance and recognition) were identified to test the formulated research hypotheses.

Stage 3 – Research hypothesis testing

The descriptive statistics for the general linear model – MANOVA are illustrated in Table 7. This table provides the mean and standard deviation for the three different dependent variables (Factor 1: Career, learning and development; Factor 4: Compensation; and Factor 8: Performance and recognition) and the independent variable groups (Baby Boomers, Xers and Millennials). In addition, the table also provides ‘Total’ rows for independent variable groups, which can be used to test the first formulated hypothesis.

Table 7 describes the mean, standard deviation and the total number of the sample used to analyse the factor structure investigated in the study. For Factor 1 (Career, learning and development), mean 4.259 with a standard deviation of 0.701 was for the Millennials who composed the second highest majority after the (Xers) of the sample. The lowest mean 3.096 with a standard deviation of 1.094 was for the Baby Boomers who composed the minority of the sample. For Factor 4 (Compensation), mean 4.724 with a standard deviation of 0.510 was high for the Baby Boomers. The lowest mean 4.53 with a standard deviation of 0.688 was for the Millennials. For Factor 8 (Performance and recognition), mean 4.448 with a standard deviation of 0.536 was high for the Baby Boomers. The lowest mean 4.391 with a standard deviation of 0.665 was for the generation X (Xers) who composed the majority representation of the sample.

The first research hypothesis was formulated as follows:

H01: Generational groups do not prefer financial rewards to non-financial rewards.

H1: Generational groups prefer financial rewards to non-financial rewards.

The following rules were applied to test the hypothesis: If financial rewards (Factor 4) total mean greater than non-financial rewards (Factors 1 and 8), then H01 is rejected and H1 is accepted.

An analysis of the total means in Table 7 showed that Factor 4 (Compensation) = 4.5942 was greater than Factor 1 (Career, learning and development) = 3.9848 and Factor 8 (Performance and recognition) = 4.3983. Consequently, H01 is rejected and H1 is accepted, meaning generational groups prefer financial rewards to non-financial rewards.

The second research hypothesis was formulated as:

H02: Millennials do not prefer non-financial rewards to financial rewards.

H2: Millennials prefer non-financial rewards to financial rewards.

The following rules were applied to test the hypothesis: If Millennials’ non-financial rewards (Factors 1 and 8) means greater than Millennial financial rewards (Factor 4) mean, then H02 is rejected and H2 is accepted.

An analysis of the factor means for Millennials in Table 7 showed that Factor 1 (Career, learning and development) = 4.2594 and Factor 8 (Performance and recognition) = 4.4036 were both less than Factor 4 (Compensation) = 4.5391. Consequently, H02 is accepted and H2 is rejected, meaning that Millennials do not prefer non-financial rewards to financial rewards.

To test the third formulated research hypothesis, the Tests of Between-Subjects multivariate analysis was used (see Table 8 for a detailed summary).

Table 8 shows the F-values for the independent variables in the model. Statistical Package for the Social Science (SPSS) gives a partial eta squared to calculate the F-value for MANOVA. The partial eta squared is used to interpret the F-value. The partial eta squared is similar to the R squared in the simple ANOVA analysis. In this case, the partial eta squared for Factor 1 is 0.88 with an F-value of 28.958. The partial eta squared can be interpreted as 8.8% of the variability in the dependent variables being accounted for by variability in Factor 1. The significant level at 0.01 means the p-value should be equal to or below 0.00.

From the above analysis, it is evident that Baby Boomers, Xers and Millennials do not differ significantly about preferences regarding compensation, performance and recognition. However, preferences regarding career, learning and development seem to be progressively preferred by the younger generations.

The final research hypothesis was formulated as follows:

H03: Generational groups’ preferences in terms of reward, if any, will not be large.

H3: Generational groups’ preferences in terms of reward, if any, will be large.

For measuring MANOVA, which involves non-independent or repeated measures, the partial eta squared in Table 8 was used to indicate the size effect of the variance for Factor 1 (Brown, 2007). For interpretation of the partial eta squared values, Brown (2007) suggests moving the decimal point two places to the right in each case and interpret the results as percentages of variance. The University of Cambridge suggests any partial eta squared size effect > 25% as large (converted). Consequently, the following rule was applied: partial eta squared > 25%, then H04 is rejected and H4 is accepted.

An analysis of the partial eta squared for the Factor 1 = 8.8%, which is < 25%, therefore H04 is accepted and H4 is rejected, meaning generational groups’ preferences for Factor 1 (Career, learning and development) reward, is not large.

Discussion

Outline of the results

In testing the research hypotheses, H1 was accepted meaning that generational groups preferred financial rewards to non-financial rewards. This finding proves similar to earlier findings in the literature by Pregnolato et al. (2017), who suggested that overall remuneration and benefits were rated most preferred in the total reward mix. Moore and Bussin (2012) also showed that all the generations, except for the Baby Boomers, rated their compensation package as the most important aspect of rewards.

Furthermore, this study found that H02 was accepted, meaning that Millennials did not prefer non-financial rewards to financial rewards. This is similar to earlier findings in the literature where Moore and Bussin (2012) showed that all the generations rated their compensation package as the most important aspect of rewards (except for the Baby Boomers). Also, Bussin and Van Rooy’s (2014) research study confirmed that monetary aspects of reward strongly influenced all decisions made, irrespective of generation. The findings differ from Schullery (2013) who found that Xers and Millennials were significantly more interested in extrinsic and leisure rewards than Baby Boomers. While Chen and Lian (2015)’s study found Millennials preferred extrinsic rewards, more so than other generations. In this study, the older generations progressively preferred financial rewards more than the younger generations, as demonstrated by the mean scores: Baby Boomers = 4.72, Xers = 4.61 and Millennials = 4.53.

In testing Hypothesis 3, the partial eta squared indicated a reasonably small size effect of the variance of 8.8% for Factor 1. Despite the small size effect, it is notable that Millennials scored career, learning and development (4.25) almost equal to performance and recognition (4.40) and remuneration (4.53). This is perhaps similar to an earlier finding in the literature where Sillery, as cited by Risher (2014), stated that compensation best practices show that key drivers of Millennials commitment are related to perceptions of their careers (current and future opportunity for pay, growth and job enrichment).

Practical implications

Overall, the findings in this study support the concept of total reward, and in particular, the findings by Pregnolato et al. (2017), who suggested an ideal reward mix for employees in general, where remuneration and benefits were rated most preferred, performance and recognition were rated second, and work–life balance practices, learning and career advancement were rated similarly in overall preference.

The only practical benefit for the organisation in using generational group segmentation in their total reward strategy will be in the insight that career, learning and development seem to be progressively preferred by the younger generations, where Millennials scored 4.25, Xers scored 3.91 and Baby Boomers scored 3.09 out of a total of 5. In addition, a notable insight is that Millennials scored career, learning and development (4.25) almost equal to performance and recognition (4.40), and remuneration (4.53). This has practical significance for reward strategy recommendations for motivating Millennials.

Limitations and recommendations

Limitations

The respondents surveyed in this study were located in one large organisation in the fast-moving consumer goods industry. Inferences made about generational preference for reward in the fast-moving consumer goods industry are based solely on the representation of this one organisation.

The gender demographics of the respondents, 56.2% male and 43.8% female, appeared similar to that of the national economic active population demographics on gender: 55% male and 45% female (Department of Labour, 2018). However, the remainder of the demographics appeared to reflect the specific demographics of the organisation. The generational cohorts, 31.7% Millennials, 63.5% Xers and 4.8% Baby Boomers, differed markedly from a previous study conducted by Moore and Bussin (2012) amongst Information and Communication Technology organisations where there were 23.2% Millennials, 31.7% Xers, 32.2% Baby Boomers and 12.8% Veterans. It is uncertain to what degree these differences in cohorts will impact any comparison in findings. The absence of Veterans in this study could possibly be explained by the average retirement age across industries being 65 years and the approximate 6-year gap in studies.

The Rewards Preference Questionnaire comprised questions derived from items extracted from the World at Work (2010) total rewards model with the following categories: remuneration (cash and similar), benefits remuneration (cash supplements), work–life balance (organisational practices, policies and programmes supportive of employees), performance and recognition (individual efforts towards the achievement of business goals), and development and career opportunities (learning experiences and career opportunities). Only the remuneration, performance and recognition, and development and career opportunities factors proved to be similar and had acceptable internal consistency and scale reliability in this study. Consequently, generational preferences for benefits remuneration and work–life balance could not be adequately tested against the research hypotheses. Furthermore, the remuneration factor did not clearly differentiate between fixed and variable compensation to make any reliable reward strategy recommendations to this level of detail.

Recommendations

Given the above discussion of the results of the study, and considering the above limitations, the following recommendations are made regarding the organisations reward strategy:

• Compensation (salary/guaranteed remuneration and annual bonus/performance incentive) is the highest preferred form of reward amongst all generations. Compensation influences performance via two different mechanisms: sorting effects, which is the attraction and turnover of employees, and incentive effects (Gerhart & Fang, 2014). Key principles influencing the sorting effects in reward strategy design should be the organisation’s adopted institutional and human capital frameworks that include practices of peer organisations, norms developed in professions, individual characteristics and corporate governance systems and practices (Maloa & Rajah, 2012). Key principles impacting the incentive effects in reward strategy should be process concepts of motivation: expectancy theory, equity theory and tournament theory focusing on how behaviour is energised, directed and sustained (De Vito, Brown, Bannister, Cianci, & Mujtaba, 2018). Most employers implement multiple incentive plans to motivate employees and drive diverse employer requirements (Gerhart & Fang, 2014).
• Performance and recognition (constructive and honest feedback on performance, monthly communication sessions about business progress, a balanced scorecard or performance agreement with agreed objectives) is the second highest preferred form of reward amongst all generations. Process concepts of motivation in the form of expectancy theory when contracting and giving feedback on performance are a key concept in formulating reward strategy for this component.
• Millennials rank growth and development (growth opportunities, learning and development opportunity to rotate and experience different types of jobs) as almost on a par with compensation, and performance and recognition. High investment leverage in motivating Millennials can be achieved with this component. A key set of principles influencing reward strategy design for this component should be Jacobs, Renard and Snelgar’s (2014) study demonstrating the positive correlation between intrinsic rewards (meaningfulness, choice, competence and progress) and engagement (vigour, absorption and dedication). Learning and development opportunities, career advancement opportunities, job autonomy, challenging work, individual performance appraisals and work–life balance are important reward practices to consider and encourage intrinsic rewards of employees and thus potentially work engagement for the employer.
• Benefits may appear important in the retention of all employees, including, but not limited to, retirement, medical aid, study leave and subsidised tuition benefits. Focusing on one clear differentiating benefit for the organisation amongst competitors may be able to present retention value (Kwon & Hein, 2013).

The following recommendations are made regarding future research in the field of Industrial Organisational Psychology regarding reward strategy and different generational groups:

• Literature has operationalised the concept cusp generations. The cusp generation between Baby Boomers and Xers, born between 1964 and 1969, has been labelled Generation Jones by Giancola, as cited by Taylor (2018), and more recently, the cusp generation between Xers and Millennials, born between 1978 and 1984, has been labelled the Xennial generation by Lamanga, as cited by Taylor (2018). Future research can be expanded to include these generations in exploring how, whether and to what degree these generational segments will have on reward preferences.
• For future research in the field of Industrial Organisational Psychology regarding reward strategy and different generational groups, the survey instrument can be explored to yield improved statistical reliability and validity of a broader representation or the total reward components.
• An alternative method to cross-sectional research (examining age cohorts any point in time) found in literature is cross-temporal research (examining the same age at different time periods). Campbell et al. (2015) suggested that using the latter, cross-temporal survey method, would be more favourable to control for any age effects when researching generations. This may contribute to understanding Hypothesis 3, where generational group preferences regarding career, learning and development seem to be preferred progressively more by the younger generations, with further clarity.
• To expand on practical recommendations on reward strategies for organisations and to deepen the field of Industrial Organisational Psychology, a qualitative element may be added to the quantitative design, a sequential exploratory design in future research.

Conclusion

In this study, sufficient data were collected from an adequate representation of survey participants, and the overall measuring instrument was found to be reliable for the purpose of the research. The data collection and processing were discussed in three progressive stages. Various statistical methods were implemented at each stage and ultimately the research hypothesis was tested. From the analysis, it was evident that generational groups preferred financial rewards to non-financial rewards. Millennials do not prefer non-financial rewards to financial rewards. Baby Boomers, Xers and Millennials do not differ significantly about preferences regarding compensation and performance and recognition. Preferences regarding career, learning and development are progressively preferred by the younger generations. The variance, however, is not large. To this end, various recommendations are made regarding reward strategy and future research in the field of Industrial Organisational Psychology regarding reward strategy and different generational groups.

Acknowledgements

Appreciation to Mr Phil Roux, Mr Tertius Carstens and Ms Nandi Ngumbela for access to the fast-moving consumer goods environment to conduct this research; to Professor Bussin for granting permission to use the Rewards Preference Questionnaire in this study; and to Mr Andries Masenge, Professor Sumei van Antwerpen and Mr Jan Nel for assisting in statistical analysis, technical editing and language editing, respectively.

The authors have declared that no competing interests exist.

All authors contributed equally to this work.

The research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data sharing is not applicable to this article as no new data were created or analysed in this study.

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

References