**5. Conclusions**

This paper has reviewed both organizational literature and public administration literature on turnover intention, and its relationship to both job performance and job satisfaction. It has also considered the impact of work motivations—including theoretical framework and empirical findings. The paper has o fferred a explanatory risk managemen<sup>t</sup> model which integrates work motivations (intrinsic and extrinsic motivations, and public service motivation), job satisfaction, and job performance, and the interplay between those factors in shaping public sector employee's turnover intention. Findings reveal that employee turnover is a considerable risk to manage in the public sector in many countries including developing countries in Asia.

It is evidenced in the findings that job satisfaction has a negative and significant relationship with the intention to leave an organization. Subsequently, employees' satisfaction is more likely built up from

intrinsic and extrinsic motivation factors, as positive correlations have shown in most of the findings. This relation is also found in most PSM studies. Interestingly, the relationship between job performance and intention to leave has been diversely reported, as it could be positive, negative, a non-linear relationship, or not significant. However, research generally confirms a positive relationship between job performance and work motivations, including intrinsic motivation, extrinsic motivation, and PSM.

Another key point emanating from the results is the interplay between job performance and job satisfaction. Again, mixed results are reported. Some research indicates that employee satisfaction leads to better job performance On the other hand, other research suggests that higher job satisfaction was caused by higher levels of performance. Nevertheless, both groups agree that positive correlations exist.

It is important to note that most of the research on the public sector turnover intention has been from developed-western countries (the USA and European countries); or developed and non-western countries (Australia, New Zealand, and China). Some of the studies also employed a pre-existing data set derived from governmental survey or international survey organizations, leaving a question of reliability.

In conclusion, as the paper aimed to empirically examine the nexus between three key factors and their influences on employees' turnover intention in order to e ffectively manage this organizational risk, there was no specific methodology section provided. Our proposed explanatory risk managemen<sup>t</sup> model is based on both traditional and contemporary concepts in the research though which has been descriptively validated with the existing research. Our motivation was to develop this conceptual risk managemen<sup>t</sup> model for advancing it to a further application paper which will provide details of mixed methods, i.e., both qualitative and quantitative approaches to validate our model further with application to real world data from an Asian country. Moreover, considering many mixed research outputs in the topics, the proposed model could serve as a guideline for future research in public sector research. Further research should be directed at answering the questions such as to what extent do work motivation, job performance, and job satisfaction influences the intention to leave the public service? In this setting, PSM should be considered a critical work motivation in the public sector along with intrinsic and extrinsic motivations. Future research on these topic should also take into account the context of the study, such as in a developing and non-western country, a reformed organization, and possibly experiencing a high voluntary turnover. Studies of this type should be addressed to clarify our understanding of the importance of turnover intention in the public sector.

**Author Contributions:** Conceptualization, C.P.; A.R.; Methodology, A.R.; Formal Analysis, C.P.; Data Curation, C.P.; A.R.; Writing—Early Draft Preparation, C.P.; Writing—Review and Editing, A.R.; J.H. Supervision, A.R.; J.H. Project Administration, A.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thanks the editors and three anonymous reviewers for their valuable insightful comments and stimulus which were used to form this final version. We would also like to acknowledge the editing support provided by the Data Science Research Unit at the Charles Sturt University, Australia.

**Conflicts of Interest:** The authors declare no conflict of interest.
