When the System Does Not Fit: Coping Strategies of Employment Consultants

Abstract

Case and knowledge management systems are spread at the frontline across public agencies. However, such systems are dedicated for the collaboration within the agency rather than for the face-toface interaction with the clients. If used as a collaborative resource at the frontline, case and knowledge management systems might disturb the service provision by displaying unfiltered internal information, disclosing private data of other clients, or revealing the limits of frontline employees' competence (if they cannot explain something) or their authority (if they cannot override something). Observation in the German Public Employment Agency shows that employment consultants make use of various coping strategies during face-to-face consultations to extend existing boundaries set by the case and knowledge management systems and by the rules considering their usage. The analysis of these coping strategies unveils the forces that shape the conduct of employment consultants during their contacts with clients: the consultants' own understanding of work, the actual and the perceived needs of the clients, and the political mission as well as the internal rules of the employment agency. The findings form a twofold contribution: First, they contribute to the discourse on work in employment agencies by illustrating how the complexities of social welfare apparatus demonstrate themselves in singular behavioural patterns. Second, they contribute to the discourse on screen-level bureaucracy by depicting the consultants as active and conscious mediators rather than passive interfaces between the system and the client.

A sociotechnical view of algorithmic fairness

“…All these applications bear risks of discriminating: these systems' accuracy may be higher for some types of crimes or for some ethnic groups. Similarly, digital technology was shown to restrict freedom of public administration—the mythical ‘computer’ rather than every officer or the organization was taking decisions about what a fair welfare subsidy is (Dolata, Schenk, Fuhrer, Marti, & Schwabe, 2020; Landsbergen, 2004). Although the publicity identified humans as decision‐makers accountable for fairness, in fact, the work was and continues to be distributed between social and technical components.…”

Learning barriers at the workplace: Development and validation of a measurement instrument

“…This measurement instrument is appropriate to determine what hinders individuals from learning in their organizations. In line with Dolata et al (2021) The field of consultancy is a challenging work environment with highly complex tasks (O'Leary, 2020) and with a constant need for professional development. While studies often focus on the diverse facilitating factors for learning (Jeong et al, 2018), less is known about how to determine what hinders individuals from learning at the workplace.…”

“…The consultancy, is suitable in this regard due to its special focus on multiple, complex and challenging work environments; the complexity of tasks; the high variety of solution strategies; and its diverse career development paths (Korster, 2022). These characteristics fall in line with a constant need for professional development and lifelong learning to keep up with the magnificent and ill-structured problems occurring at the workplace (O' Leary, 2020;Dolata et al, 2021). The required competences of consultants are diverse and dynamic as well (Wißhak and Hochholdinger, 2020;Helens-Hart and Engstrom, 2021;van der Baan et al, 2022).…”

"We Would Never Write That Down"

“…AI-based decision-support tools require "considering digital data as reliable and complete representations of the phenomena" [23], however, as before mentioned, research shows that classifications of people are not stable [30,31], but human constructs that arise through interpretative work [42]. Additional value may be added in the interaction between people, which could cause problems to computers as they cannot experience the world as human beings and the dynamics involved in these contexts [28]. Examples from medical research include the development of ML tools to predict sepsis in patients.…”