Paths for Future Research

This research extends what is known about young worker’s commitment profiles

but there is still more to be discovered. Paths for future research include comparison research, possible methodology changes,

This thesis examined only young workers and this sample was defined as being Millennials and Generation Z workers whom are aged less than 30 years. Future research may be able to extend this research by comparing our findings to older generations or using it as a starting point to compare the two younger generations. Comparing generations could increase the current understanding of differences between generations, and if there are any. Differences between the amount of profiles found for each generation, what the profile types are, and how these profiles interact with constructs would be a massive opportunity to produce valuable data that may be used in the professional environment. Another approach could be to analyse Millennial and Generation Z commitment profiles when first entering the workforce, and again in ten years. This research could bring forth points regarding change in age and profile type, or generational change in values and desires from a workplace as the generation changes.

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In the research environment, methodological changes to this research could produce interesting results. In this thesis the behaviour of individuals was examined and so quantitative methods were used to determine the types of organisational commitment there is within young employees. A mixed methods approach could possibly be more suitable to better examining these behaviours as well as mindsets for a better understanding about the profiles found. However, as this thesis is the first of the

organisational commitment literature to look at young persons’ commitment to their

organisation, an initial step to discover if the profiles even differed from literature is needed. Future research could use a mixed methods approach for better understanding of the relationships between commitment profile groups and latent constructs.

It is recommended that future studies examining commitment profiles use a latent profile or class analysis to analyse their data set. Historically, most empirical literature regarding commitment profiles use k-cluster means analysis to great effect. In this thesis, this preference is changed, and latent profile analysis models were used to determine latent profiles of employees based on their organisational commitment levels. Similar to k-cluster means analysis, a latent profile analysis allows the data to be interpreted and labeled from the patterns of means across each profile group (Vermunt & Magidson, 2013). The use of latent profile analysis in this thesis over k-cluster means analysis is because latent profile analysis is an updated and modern version of k-cluster means analysis. k-cluster means analysis has the purpose to find groups within a dataset, with the number of groups being represented by the variable k (Jain, 2010). Each individual data point is clustered according to similarity of the features they share. The output of k-cluster means analysis results in the mean points of the k-clusters and from these means, labels can be created for the clustered data (Jain, 2010). This analysis works by not labelling groups before they are analysed and instead makes it able for the researcher to discover groups that form organically from the dataset (Jain, 2010). The

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advantage of using this technique is that this analysis finds groups or clusters that are not labelled in the data. This gives the benefit that groups do not need to be clearly labelled or predetermined in order to be examined. Within the latent analyses there are two main categories, being latent profile analysis as used in this thesis, and the more commonly known latent class analysis. The main difference is the data each analysis uses, latent profile analysis is used for continuous variables, whereas latent class analysis is used to analyse categorical data. This thesis gathered ordinal and continuous data, thus it is more appropriate to use a latent profile analysis to analysis the dataset.

Findings of this thesis indicated a large group of CC-Dominant and it was suggested that these Millennials and Generation Zers are feeling trapped in their workplace due to lack of options to leave in a current intensely competitive job market. These members could have large student loans to pay as a result of having these generations having the highest percentage of individuals to go to university. Should this be the case, the CC-Dominant group could increase in members within New

Zealand’s population after the New Zealand government Labour government’s

announced gradual free university education system. This policy commences in 2018 and begins with one the first year of university free; and will continue the next year

adding the second year free, up to three years of paid university fees (“Details of fees

free teriary,” 2017). This policy stipulates that if you leave university without

completing the degree, the fees must be paid back. 18.4 per cent of this thesis’

participants comprised of Millennials and Generation Zers whom had some level of university participation but no completed degrees. If more young people are going to university then this figure could rise as the overall rate of individuals going to university rises. This thesis suggests a further investigation into this potentially significant area and to look further into the characteristics of individuals found within

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