In public administration, the issue of personal perceptions of staff may not have been a front-stage discussion but has always been an inherent part of major discussion. The subjective perception issues of administration officials have long been an intrinsic part of the main arguments in public administration studies. For example, M. Weber's discussion of formal and substantive rationality was also about recognising the value of substance. Simon's issue of bounded rationality is also a matter of discussion about the boundaries of human perception. Lipsky's discussion of street-level bureaucrats is also an issue of cognition, in which street-level bureaucrats make decisions and carry out actions based on perception. Even in the New Public Management and Public Service Motivation arguments, it is also considered that the content of the work and how the staff perspective of work influences their behaviour. Thus their subjective perceptions have a consequential impact on policy formation and implementation.

　The Q Methodology has recently come to be used in public administration as a method for elucidating such subjective perceptions. This methodology is an analytical method derived from psychology, which attempts to capture the subjectivity of individuals that have not been adequately captured by the commonly used Likert scale method of surveying attitudes.

　In this study, the Q methodology is used to ask public servants about what they think about when they are at work. By doing so, this study aims to clarify public servants' perspectives on their work.

　The survey was conducted during the COVID-19 disaster, but data was collected using an internet survey. The data is from local Japanese public servants. Japan has a three-tier administrative structure of national, regional and basic government, and the regional and basic government employees are local public servants. A factor analysis was conducted using 241 samples, 121 from public servants at the prefectures, the regional governments, and 120 from municipal governments. Five factors were found: 1. Formal Rationalists; are diligent and precise and work like cogs in an organisation; 2. Practitioners deal with the boundaries of problem-solving in front of them; they are flexible to the problems at hand and try to lead in problem-solving; 3. Righteous leader thinkers with the porcupine dilemma; often in the middle class, they prioritise high reputation and decisiveness but have a contradictory reluctance to take on job responsibilities; 4. Ambitious Challengers; value taking risks and originality; 5. Generous work-life balance-oriented workers; are calm and problem-free in the organisation and value their private time.

　Although this study found these typologies of Japanese local public servants, it is possible to compare with other countries, for example, by using the same items, leading to the discovery of country-specific public servant characteristics. It also has many practical implications, such as improving administrative efficiency and providing basic information for type-specific human resource development and staffing strategies.

A survey of London Borough staff on the same items is currently underway, and comparisons with this should be included in the presentation where possible.

Organizational identification (OI) refers to the extent to which individuals define themselves in terms of their membership in an organization and experience a sense of belonging and attachment to it. Both employees and their organizations aspire to achieve high levels of OI, as it is related to a series of positive outcomes such as increased engagement, commitment, performance, and adaptability.

However, theoretical propositions and empirical results remain puzzling regarding the effect of OI on employee stress. On the one hand, OI is regarded as an effective stress buffer by offering a basis of social support and control. On the other hand, high OI could also increase stress by promoting strain-inducing behaviors like working long hours and presenteeism. In short, OI can be both a demand and a resource and the specific guise of OI will likely depend on elements of context.

Considering the increasing turbulence surrounding public organizations, uncertainty about changes in employees’ work environment have become one of the most salient sources of stress for public sector employees. In this article, we look at the potential of organizational identification to influence the effect of change uncertainty in public employees’ work environment on their chronic stress levels. While short, acute stress reactions can be considered harmless and natural, chronic exposure to stress is linked to negative health and performance outcomes, including burnout and long-term sickness. Research on antecedents and outcomes of employee stress should therefore distinguish between these types of stress. To answer this call, we introduce an innovative measure of chronic stress. By assessing levels of cortisol in respondents’ hair samples, we obtain an objective biological indicator of stress levels over several months.

Applications based on artificial intelligence (AI applications) play an increasing role in the public sector decision making. They have the potential to fundamentally affect public personnel management, for example by affecting the selection of job candidates. While some research argues in favor of deploying AI applications to enhance efficiency and impersonality of recruitment decisions, other studies point to challenges of algorithmic decision-making and risks of increased discrimination. This study analyzes these positions based on two studies - data of a field experiment (Keppeler, 2023) and a follow-up pre-registered survey experiment (n>500 public recruiters, data collection ends in August 2023). The field experimental results show that both, human raters and AI applications, might discriminate against women, depending on the specific position. The survey experiment applies a 2x2 factorial design to understand the extent to which recruiters rely on algorithmic (compared to human) advice determining person-job fit, and in how far a debiasing intervention (compared to no intervention) can reduce potential biases against job candidates differing in both gender and ethnicity. The study contributes to the understanding of how AI applications shape decisions comparing public and private sector recruiters to foster the understanding of human-machine interaction and design policies preserving representative bureaucracies.