1,785
Views
7
CrossRef citations to date
0
Altmetric
Research Articles

The complexities of digitization and street-level discretion: a socio-materiality perspective

ORCID Icon
Pages 25-47 | Published online: 17 Feb 2022
 

ABSTRACT

This study contributes to the debate on eGovernment and street-level discretion by using a qualitative case study of digitization at a street-level bureaucracy. This study advances this debate in three ways. First, we argue that the impact of digitization on street-level discretion can be best understood by examining the affordances and constraints that emerge relationally through the interactions between users (social) and technology (material). Second, subordinate-supervisor relations shape how street-level bureaucrats exercise discretion, and the introduction of technology reconfigures these relations. Third, system-level and street-level discretion shape rather than displace each other through a dialectic relationship.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohammad Alshallaqi

Mohammad Alshallaqi graduated in December 2019 from Lancaster University Management School with a PhD in Organization, Work and Technology (OWT) studies. He is interested in studying digital transformations in and around public sector organizations with a particular focus on how practices, structures, and technologies interrelate and shape the outcomes of digital transformation reforms.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 338.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.