Abstract
Orientation: Employee retention remains an ongoing challenge for the hospitality industry, especially given the nature of the industry (an often-hectic work environment, long-working hours and perceived poor compensation). The industry also fails to retain millennial employees, which is a grave concern.
Research purpose: The investigation studied the impact of job characteristics on the motivation of millennial hospitality employees. The job characteristics model of Hackman and Oldham served as the theoretical guide.
Motivation for the study: In addressing the retention issues that the hospitality industry faces, the study provides theoretical and empirical insight on the impact that job characteristics have on the motivation of millennial hospitality employees.
Research approach/design: The study employed a quantitative research approach. A structured questionnaire was administered to 96 millennial hospitality employees using QuestionPro. Targeted snowball sampling was used to identify respondents. Partial least squares (PLS) structured equation modelling (SEM) was applied to determine the relationship between the variables.
Main findings: The empirical findings revealed a positive relationship amongst task variety, task significance and feedback towards motivation, but a negative relationship between autonomy and task identity towards motivation.
Practical/managerial implications: Prospective hospitality employees need to be well informed about the challenges of working in the hospitality industry.
Contribution/value added: Autonomy and task identity emerged as factors that do not contribute to the motivation of hospitality employees. Millennial hospitality employees were, however, motivated by task significance, task variety and feedback.
Keywords: job characteristics; motivation; millennials; generational cohorts; hospitality industry; retention; partial least squares (PLS) structured equation modelling (SEM).
Introduction
With the advent of the 21st century, the world of work has experienced drastic changes, mainly because of the advances of all forms of information communication technology (ICT) and the Internet (FadTech4U Admin, 2018; Natter, 2018; Zappa, 2014). Advances in digital technology, together with globalisation, have created a highly competitive work environment that is increasingly becoming digital and virtual (Padhye, 2018). Digitalisation has not only introduced an influx of new job opportunities (e.g. social media managers, digital marketers, data specialists, application developers), but has also increased the demand for new competencies. Increased digitalisation and virtualisation have created a new world of work that emphasises on both technical skills (e.g. data analysis, technical writing) and soft skills (e.g. communication, intercultural understanding).
Apart from the challenges posed by increased digitalisation and virtualisation, another defining characteristic of the new world of work is increased workforce diversity. Workforce diversity implies that individuals with different characteristics (such as gender, age, language, sexuality, education and work background) are employed in organisations (Samuels, 2018). Contemporary organisations also consist of individuals from different generational cohorts, notably baby boomers (individuals born roughly between 1945 and 1965), generation X (born roughly between 1964 and 1980), millennials (born roughly between 1980 and 1995), and generation Z (born roughly between 1996 and 2010) (Francis & Hoefel, 2018). Millennials currently constitute more than 50% of the global workforce (Deloitte, 2018). This is likely to increase in the future.
Millennials have brought a new dynamic to the workplace (Kane, 2018). Millennials are comfortable using various forms of digital technology, and they generally expect good work–life balance, good pay and benefits, opportunities for advancement, meaningful work experiences and a nurturing work environment. They are often impatient to wait for promotions, and want more than just a job (Ng, Schweitzer, & Lyons, 2010). An organisation’s mission, vision and values are very important to them, as they want to know where they fit into the bigger picture. Monetary compensation is not the only reward for millennials, as they generally value good working relations, a flexible working environment and recognition from supervisors and or managers. Millennials are generally peer oriented and need to be treated as individuals (Hewlett, Sherbin, & Sumberg, 2009).
The focus of this study is on the hospitality industry as one of the largest industries in the world. Hospitality is the largest sector within the tourism industry and the term ‘tourism and hospitality industry’ applies widely (WTTC, 2017). Because of its potential for economic growth and job creation, the hospitality industry is a crucial part of any economy (Fredericks, 2018). The outbreak of Covid-19 2019/2020 has, however, had a devastating impact on the hospitality industry globally, as countries closed their international borders and all forms of travel and tourism ceased. Before the outbreak of the pandemic, the hospitality industry accounted for one out of every 10 jobs worldwide. In South Africa, the industry provided about 740 000 direct and over 1.5 million indirect job opportunities across the economy (VoyagesAfrica, 2020). Although many students pursue careers in the hospitality industry, they typically leave the industry within a period of 6 years (Giang, 2013). Therefore, the industry experiences persistent turnover and retention challenges.
Purpose
Given the retention challenges the hospitality industry faces, owners or managers of accommodation establishments need to be aware of ways in which millennial employees can be motivated. This article investigated the extent to which job characteristics influence the motivation of millennial hospitality employees.
Literature review
The job characteristics model and millennial hospitality employees
The workplace of the 1960s was characterised by repetitive processes that often resulted in demotivation and monotony amongst employees (Young, 2018). It is for this reason that Hackman and Oldham developed and introduced the Job Characteristics Model (JCM). The model was based on the idea that the key to maintaining motivation is the job itself (Luenendonk, 2017). The JCM was verified and tested on 658 employees who worked in 62 different jobs in 7 organisations, with reliable and conclusive results. The researchers found that less interesting jobs reduced the motivation and productivity of employees. They further noted that varied tasks improved employee motivation and increased productivity. According to them, with meaningful tasks and effective communication, employees are likely to be more engaged with their roles and have an increased sense of responsibility (Hackman & Oldham, 1976). Because the JCM has been scientifically validated, even in the South African context (Steyn & Vawda, 2014), its five job characteristics could be used as a checklist for job creation or job review. This model has universal applicability and can be applied to any role (Jed, Li, & Brokkshire, 2007; Westerman, 2007).
The JCM posits five core dimensions, namely, skills variety (the required skills and abilities expected from job holders), task identity (the completion of a whole or identifiable piece of work, not just a small part), task significance (the perceivable impact of the job on the lives of other people), autonomy (the extent of a job holder can decide how jobs can be performed) and feedback (information about job performance). These dimensions influence three critical psychological states (meaningfulness, responsibility and knowledge of results) which, in turn, influence some personal and work outcomes (such as motivation, job satisfaction, growth satisfaction, lower absenteeism, lower turnover and work effectiveness).
As human capital forms the backbone of all organisations in the new world of work, the recruitment and retention of millennials challenge both governments and private sector institutions in their ongoing quest to secure suitably skilled human capital (Hussein, 2018). Millennials are the future managers and business leaders; therefore, it is imperative to attract and retain them in the hospitality industry. Diamandis (2015) reckoned that, irrespective of the sector, millennials are the most important demographic group to consider in the workforce today. Currently, organisations mostly employ four generational cohorts, namely baby boomers, generation X, millennials and generation Z. Each of these cohorts has different perspectives of work and life in general shaped by the environments and historical timeframes they were exposed to when growing up.
According to Dimock (2019), millennials grew up in the shadow of the Iraq and Afghanistan wars, which contributed to the intense polarisation that have shaped the prevailing global political environment. In South Africa, most millennials experienced the 2008 elections in which their vote became part of the political conversation (Johnstone, 2018; Main & Writer, 2017). Ruiz and Davis (2017) confirmed that employee turnover is a pressing challenge for the hospitality industry. Some of the reasons for the high employee turnover in the hospitality industry are long hours, challenges in work–life balance and poor compensation (Van Zyl, 2011). Mbane (2017) found a strong correlation between fair compensation and employee retention in the hospitality industry. Mbane (2017) and Ali and Stafford (2017) also found that job satisfaction factors in the hospitality industry, like relationships with colleagues, meaningful work, professional development, work engagement, fair working hours and health care, had a positive impact on millennial employees’ intentions to quit.
Ruiz and Davis (2017) further reported that millennials in full-service restaurants require engaged learning, positive working conditions, including managers who can encourage good working relationships and the availability of growth opportunities. Similarly, Elsbury (2018) argued that millennials can be retained by providing a positive company culture, showing trust in them and offering them professional development opportunities. The 2018 Deloitte report stated that 38% of millennials are likely to stay with the organisation if their employer supports the local community. This can involve projects such as recycling programmes, volunteer opportunities and community outreaches.
Millennials often seek advice from peers and mentors. According to Asghar (2014), millennials can work hard and display good work ethics, but they do not appreciate blind orders. They regard the best person for the job as the one who does it best, not necessarily the most senior person in the workplace. Therefore, millennials often question the ‘starting at the bottom’ approach that applies in many organisations. Millennials generally believe that respect should be earned and not demanded (Hannus, 2016). Research by Diamandis (2015) revealed that millennials are motivated by continuous training and development, flexibility and remuneration. Apart from this, millennial employees value a fulfilling job and work–life balance (Walters & Ford, 2019).
Motivation
The term ‘motivation’ derived from its Latin root word movere meaning ‘to stimulate’. Behind every human action, there is a motive, which is why it is important for management to provide incentives for people to work hard and stay with the organisation. According to Pakdel (2013), the origin of motivation can be viewed as a singular determinant of human thoughts, feelings and actions. Motivation works with other determinants such as cognition, emotions and habits, and it has both extrinsic (involving external rewards like money, bonuses, gifts, etc.) and intrinsic (when individuals are internally motivated about something) components (Chand, 2018; Hannus, 2016). Being motivated can energise individuals to perform well in their jobs, thus producing behaviour that supports the organisation in reaching its goals (Pakdel, 2013). Motivated individuals are more likely to take initiative, apply their skills and put in extra effort into achieving goals (Nel et al., 2014). Enhancing individual motivation should be part of planned managerial processes where organisations support individual development and advancement (Chand, 2018).
In conceptualising motivation in the work context, Hertzberg’s two-factor theory identifies two factors, namely, hygiene factors and motivators. Hygiene factors are aspects that need to be in place for individuals to not be dissatisfied (e.g. working conditions) and motivators elicit performance from employees such as recognition and praise (Hertzberg, 1968). Hertzberg’s two-factor theory supports the notion that attitude towards work can determine success or failure, hence its applicability to this study (Nel et al., 2014). For the sake of this study, the motivational factors were grouped into dissatisfiers and satisfiers. Dissatisfiers included aspects such as working conditions, salary and benefits and supervision, whilst satisfiers included recognition, responsibility, interesting work, growth, challenging work, achievement and continuous learning (Thurston, 2013).
Data and methods
Research approach
This study adhered to objectivism as ontological stance as the researchers believed that employees exist in an independent reality. This aligns with the epistemological stance of positivism which views the world in an objective way through data collection and interpretation (Cheung, 2019). Positivism asserts that only knowledge confirmed through observation can be considered genuine as such knowledge is based on verifiable facts (Bless et al., 2016). Positivism was, therefore, regarded as an appropriate paradigm for this study. Considering the focus of the study, a quantitative research approach was adopted (Kumar, 2014).
Measures
The data were gathered using a structured questionnaire, and partial least square (PLS)-structured equation modelling (SEM) was applied to determine the relationships between the variables.
Participants
As a result of the devastating impacts of the Covid-19 pandemic, many hospitality establishments had to either close down or place employees on temporary leave. This added to the challenges of gathering the data for this investigation. Targeted snowball sampling was thus used to reach millennial hospitality employees. Snowball sampling is a non-probability sampling technique in which existing acquaintances are used to recruit new respondents (Leedy & Ormrod, 2015). Existing acquaintances (specifically alumni) from the Hospitality Management Department of the Central University of Technology (CUT), Free State were targeted for data collection. The researchers obtained access to the names and contact numbers of all the hospitality management alumni since 2014 as these records were up to date. It was confirmed that all respondents were millennial employess of the hospitality sector before disseminating the questionnaire. QuestionPro was used to administer the structured questionnaire. Respondents were approached via email and WhatsApp with the link as attachment. The questionnaire was disseminated to 136 millennial hospitality employees, of which 96 responded with completed questionnaires. This yielded a response rate of 71%.
Measuring instrument
Consistent with a quantitative research approach, a structured questionnaire was used to gather data from participants. A structured questionnaire is a primary measuring instrument with which data in survey research were collected (Cheung, 2019). The questionnaire for this study consisted of two sections:
Section A captured the perceived job characteristics of millennials based on the JCM designed by Hackman and Oldham (Hackman & Oldham, 1976; Luenendonk, 2017). Section B measured the level of motivation of millennial employees based on Herzberg’s (1968) two-factor theory.
Design
The study followed a survey research design and only millennial hospitality employees were included. As the records were available, the hopsitality management alumni of CUT that completed their qualifications (either diploma or BTech) since 2014 were included in the study.
Analysis
As indicated before, PLS-SEM was used to determine the relationship between the variables.
Ethical considerations
This study adhered to all relevant ethical research standards. Participants were informed that their participation was voluntary and that all information provided would be treated as anonymous and confidential.
Results
Because all data variables significantly deviated from a normal distribution, the use of PLS-SEM was justified. The PLS-SEM analysis was selected instead of regression analysis as normality of constructs is an assumption for conducting regression analysis but not an assumption for conducting PLS-SEM. The statistical package, SmartPLS version 3.0, was employed to conduct the PLS-SEM analysis. The study adhered to the minimum sample size for conducting a PLS-SEM analysis, namely equal to the larger of the following: (1) 10 times the largest number of formative indicators used to measure one construct, or (2) 10 times the largest number of structural paths directed at a particular construct in the structural model (Hair, Hult, Ringle, & Sarstedt, 2017). The research model in Figure 1 shows the largest number of structural paths directed at the construct. Therefore, the minimum required sample size for the PLS-SEM analysis was 50 (Hair et al., 2017).
The hypotheses for the study were formulated as follows:
H1: There is a statistically significant relationship between autonomy and the motivation of millennial hospitality employees.
H2: There is a statistically significant relationship between providing feedback and the motivation of millennial hospitality employees.
H3: There is a statistically significant relationship between skills variety and the motivation of millennial hospitality employees.
H4: There is a statistically significant relationship between task identity and the motivation of millennial hospitality employees.
H5: There is a statistically significant relationship between task significance and the motivation of millennial hospitality employees.
The evaluation of the PLS-SEM model for the study was done in a two-stage process, which included assessing both the outer and the inner model.
Assessing the outer model
Before testing for a significant relationship in the structural model, the measurement model must be evaluated first (Fornell & Larcker, 1981). This involves ascertaining reliability and validity (Ramayah, Lee, & In, 2011). Reliability tests aim to find stability and the consistency of the measuring instrument, whereas validity tests assess how accurate the instrument measures a particular concept (Janadari, Subramaniam Ramalu, Wei, & Abdullah, 2016). In order to assess the reliability and validity of the measurement model, the following were assessed: indicator reliability; convergent validity; internal consistency reliability and discriminant validity.
Reliability and validity
Indicator reliability
According to Hulland (1999), reflective indicator loadings of > 0.5 show that the item is a good measurement of a latent construct. Therefore, all items with indicator loadings lower than 0.5 were removed namely: (Feedback) FD3, (task identity) TI4, (task significance) TS1, (task variety) SV2, SV5, (motivation) MOT3, MOT7, MOT9, MOT10, MOT12, MOT14, MOT15, MOT16, MOT17, MOT18, MOT19, MOT20, MOT22. The only indicator not meeting the 0.5 threshold was (feedback) FD4 with a value of 0.497 only slightly below the threshold value of 0.5. The FD4 indicator was not removed as this would have led to the feedback construct having only two indicators. According to Ringle, Sarstedt and Straub (2012), a minimum number of three indicators is necessary for the measurement of each construct in order to ensure content validity. Because of all indicators exceeding the 0.5 loading value (except one), it can, therefore, be concluded that all constructs exhibit indicator reliability. The factor loadings are presented in Table 1.
Convergent validity
Convergent validity is the extent to which a measure correlates positively with alternative measures of the same construct (Hair et al., 2017). Convergent validity is assessed using the average variance extracted (AVE), which is the average variance shared between a construct and its measures (Janadari et al., 2016). According to Bagozzi and Yi (1988), as well as Fornell and Larcker (1981), the AVE should be greater than 0.5. For autonomy (0.662), feedback (0.612) and skills variety (0.540), the AVEs were above the 0.5 threshold, whilst for the other three constructs, namely motivation (0.414), task identity (0.445) and task significance (0.490), they were only slightly below 0.5.
Internal consistency reliability
Internal consistency reliability can be assessed using the composite reliability (CR) of a construct. Gefen, Straub and Boudreau (2000) explained that the CR should be greater than 0.7 in order to indicate adequate internal consistency reliability. The composite reliability constructs were as follows: autonomy 0.852; feedback 0.817; motivation 0.884; task variety 0.777; task identity 0.700 and task significance 0.742. As all the CR scores were above 0.70, good internal consistency was achieved.
Discriminant validity
Discriminant validity is the extent to which a construct is truly distinct from other constructs by empirical standards. According to Hair et al. (2017), discriminant validity implies that a ‘construct is unique and captures phenomena not represented by other constructs in the model’. To assess the discriminant validity of the measurement in the study, the Fornell-Larcker criterion was used. This criterion compares the square root of the AVE values with the latent variable correlations. Specifically, the square root of each construct’s AVE should be greater than its highest correlation with any other construct (Janadari et al., 2016).
The square root of the AVE of each latent variable is shown diagonally in bold in Table 2, along with the correlations of the latent variable with other latent variables. Table 2 illustrates that the square root of the AVE of each latent variable was indeed higher than any correlation with any other latent variable, also indicating the discriminant validity of the measurement model.
Assessing the inner model
The assessment of the structural model of the study was conducted in three steps: assessing the significance and relevance of the structural model relationships, assessing the level of R2 and finding the effect size (f2).
Step 1: Assess the significance and relevance of the structural model relationship
The direct effects of all the hypothesised relationships were evaluated by making use of bootstrapping analysis. Bootstrapping is a resampling technique that draws a large number of subsamples from the original data (with replacement) and estimates models for each subsample. It is used to determine standard errors of coefficients to assess their statistical significance without relying on distributional assumptions (Hair et al., 2017). The standardised beta and t-values were calculated by the bootstrapping procedure with a resample of 5000. The results of the bootstrapping procedure are shown in Table 3.
| TABLE 3: Path model results of structured equation modelling model. |
According to Table 3, a positive statistically significant relationship was found between feedback and motivation (β = 0.255, p = 0.045). H2 of the study was, therefore, supported. A positive statistically significant relationship was also found between skills variety and motivation (β = 0.248, p = 0.045). H3 of the study was, therefore, supported. Lastly, a positive statistically significant relationship was found between task significance and motivation (β = 0.238, p = 0.026). H5 of the study was, therefore, supported.
In contrast, Table 3 shows no statistically significant relationship between autonomy and motivation (β = 0.133, p = 0.295), and task identify and motivation (β = 0.072, p = 0.537). Therefore, H1 and H4 of the study were rejected.
Step 2: Assess the level of R2
The R2 measures the proportion of variance in a latent endogenous construct that is explained by other exogenous constructs expressed as a percentage (Chin, 1988). Exogenous constructs are independent constructs in all equations in which they appear. On the other hand, endogenous constructs are dependent constructs in at least one equation, although they may be independent variables in other equations in the system.
The R2 value of motivation was 0.51, which implies that a combination of feedback, skills variety and task significance explains 51% of the variance in the motivation of millennial hospitality employees. According to Cohen (1992), R2 values of 0.12 or below indicate a low effect size, values between 0.13 and 0.25 indicate medium effect size and values of 0.26 or above indicate high effect size. From these guidelines, it is evident that a combination of feedback, skills variety and task significance constructs had a high predictive power towards the motivation of millennial hospitality employees.
Step 3: Assess the effect size
The assessment of the effect size of a construct evaluates whether the omitted construct has a substantive impact on the endogenous construct, which is also known as the effect size of the exogenous latent variable on the model. The assessment of this effect size follows Cohen’s (1992) guidelines, which are as follows: 0.02 < = f2 < 0.15: weak effect; 0.15 < = f2 < 0.35: moderate effect; f2 > 0.35: strong effect.
The effect sizes of the three constructs which had a statistically significant relationship with motivation are as follows: feedback (0.085), skills variety (0.077) and task significance (0.071). These constructs had a weak effect size in the prediction of motivation. This means that, in the absence of one of these constructs, the other two constructs will still be able to predict the motivation of millennial hospitality employees.
The above constitutes the PLS-SEM model for the study, which is presented above in Figure 2.
 |
FIGURE 2: The partial least squares structured equation modelling model for the impact of job characteristics on the motivation of millennial employees. |
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Discussion
Outline of the results
The findings of the study indicate a positive relationship between task variety (0.248, p = 0.045), task significance (0.238, p = 0.026) and feedback (0.255, p = 0.045) towards motivation. In contrast, the study found a negative relationship between task identity (0.072, p = 0.537) and autonomy (0.133, p = 0.295) towards motivation.
The results will be discussed taking into consideration the characteristics of millennials and the challenges of working in the hospitality industry. Working in the hospitality industry exposes employees to a variety of tasks (task variety construct). As the industry is customer-centred and people-oriented, staff are expected to perform a variety of tasks, for example, dealing with customer complaints, making reservations and performing other administration-related duties, checking that venues adhere to customer specifications and planning and managing events. This can relate to the need of millennials to perform challenging work that requires continuous personal and professional development.
Millennials are motivated by employers that offer career opportunities, leadership development programmes and job security (Matthewman, 2015). Some sections of the industry (like large hotel groups) do offer continuous training and development opportunities, as well as lucrative career opportunities, whilst smaller establishments may not be as progressive. These findings closely relate to those of Kemboi, Biwott, Chenuos and Rutto (2013) who found that task variety is positively associated with job performance in the healthcare industry.
Task significance refers to the belief that the work one performs has a positive impact on others. This directly relates to the needs of millennials to have a positive impact on society – whether through the organisations that employ them, or through self-employment. Doing a job that relates to their inner values can create a sense of purpose and meaning for millennial employees. In this sense, millennials actively seek purpose and meaning in their jobs (Walters & Ford, 2019).
Feedback is important for millennials as they continuously seek advice on their current performance and require praise for good performance. They generally want to build trust relationships with their supervisors and or managers and are open to engage in mentorship programmes (Bass, 2017). As mentioned earlier, millennials do not appreciate blind orders, and they often question the ‘starting from the bottom’ approach. They also believe that respect should be earned and the best person for the job is the one that does it well (Hannus, 2016). This can be problematic in the hospitality industry as it is often the more senior staff that plan and coordinate activities usually to the expectations of paying customers.
Autonomy relates to providing employees the freedom to decide on how and when they want to execute tasks. Working in the hospitality industry often involves a hectic work schedule and long hours (Van Zyl, 2011). Also, teamwork is often expected in performing tasks (like preparing meals, planning events and setting up venues), which implies that employees need to cultivate good interpersonal and communication skills.
This labour-intensive and customer-oriented industry may not leave a lot of room for autonomy in performing jobs. As millennials crave autonomy and flexibility in doing their jobs, this can be at odds with the working conditions in the industry (Diamandis, 2015). This can, in part, explain why autonomy does not relate to motivation in this study. Similar findings were recorded by Sá and Moura Sá (2014), namely that autonomy scored the lowest in their research on call centre employees. Similarly, Sever and Malbašić (2019) also found that autonomy rated the lowest amongst Croatian millennial employees, which supports the fact that this aspect is important for millennials.
Task identity refers to doing tasks from beginning to end with a visible outcome. Millennials require certainty on what is expected from them in the work context and how they fit into the structure of the organisation. They also generally demand good remuneration (Walters & Ford, 2019). Because of the highly operational nature of working in the hospitality industry, not all establishments have clearly defined structures and roles. Thus, staff are often expected to perform a variety of tasks at all levels of the organisation. There is also a perception that hospitality work is often degrading and exploitative, offering low remuneration and taken as a last resort (Wood, 1997). As mentioned previously, both remuneration and recognition are important to millennial employees (Mbane, 2017; Rizzo, 2016), which can partly explain the low predictive score of task identity towards motivation.
Other research on job characteristics includes that of Johari and Yahya (2016) who found that task significance and feedback significantly influenced the job performance of public servants. Similarly, Renier and Vawda (2014) found that task variety, task significance, task identity, feedback and autonomy correlated positively to the job satisfaction of South African white-collar workers. They also determined that all five job characteristics were weak predictors of perceived stress and depression.
Practical implications
According to Montgomery and Spragg (2017), millennial employees have the highest turnover rates compared to other generational cohorts. They are also the least satisfied with their jobs. This is a cause of concern especially for the hospitality industry with its retention challenges. The empirical findings of this investigation revealed that autonomy and task identity do not contribute to the motivation of millennial hopsitality employees. The findings further show that hospitality managers and leaders also need to put effort into recruiting and selecting candidates who have realistic expectations of working in the industry. Realistic expectations also need to be cultivated at universities and all other centres that train prospective hopsitality industry employees.
Limitations and recommendations
The study focused only on millennial hospitality employees; therefore, the findings of study cannot be generalised to hospitality employees of all generational cohorts. The small sample size can be a limitation. It is further suggested that similar studies be conducted in other industries to ascertain whether similar results are reported.
The following recommendations are proposed to enhance the motivation of millennial hospitality employees:
Prospective employees (including hospitality management students) need to be informed about the expectations of working in the hospitality industry. It is imperative that they have realistic expectations upon entering the industry. Realistic expectations can be created by means of work-integrated learning and other experimental learning programmes offered by universities and other training centres, including learnerships. These programmes need to expose students and prospective hospitality employees to the nature of the job and what they can expect once they are employed in the industry. Hospitality leaders and managers have an important role to play in giving students access to their establishments to gain practical experience in the industry. As teamwork and dealing with various stakeholders are an integral part of working in the hospitality industry, it is critical that team work and interpersonal skills, including intercultural and communication skills, be developed as part of hospitality management programmes at universities and other training centres.
Conclusion
In addressing the persisting retention challenges the hospitality industry faces, the objective of this article was to investigate the impact of job characteristics on the motivation of millennial hospitality employees. The main findings indicated a positive relationship amongst task significance, task variety and feedback towards the motivation of millennial hospitality employees. However, no significant relationships were found between autonomy and task identity towards the motivation.
Acknowledgements
Competing interests
The authors have declared that no competing interests exist.
Author’s contributions
M.S. performed the literature review and conducted the data gathering. She also compiled the first draft of the article. D.K. assisted with the overall objectives of the study and refined the literature section as well as the results and conclusion.
Funding information
The study was funded by both the Central University of Technology, Free State and the National Research Foundation (NRF).
Data availability
Data sharing is not applicable to this article and no new data were created and analysed in this study.
Disclaimer
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, and the Publisher/s.
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