Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework…

By Peter Gordon Roetzel. 2018

In the light of the information age, information overload research in new areas (e.g., social media, virtual collaboration) rises rapidly in many fields of research in business administration with a variety of methods and subjects. This review article analyzes the development of information overload literature in business administration and related interdisciplinary fields and provides a comprehensive and overarching overview using a bibliometric literature analysis combined with a snowball sampling approach. For the last decade, this article reveals research directions and bridges of literature in a wide range of fields of business administration (e.g., accounting, finance, health management, human resources, innovation management, international management, information systems, marketing, manufacturing, or organizational science). This review article identifies the major papers of various research streams to capture the pulse of the information overload-related research and suggest new questions that could be addressed in the future and identifies concrete open gaps for further research. Furthermore, this article presents a new framework for structuring information overload issues which extends our understanding of influence factors and effects of information overload in the decision-making process.

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Citations in the corpus

Dynamic Dialogue Policy for Continual Reinforcement Learning (2022, methodology): “We stick to the above reward formulation with one exception: Instead of the turn level reward of −1, we propose to use information overload (Roetzel, 2019).”
Tackling threats to informed decision-making in democratic societies: Promoting epistemic security in a technologically-advanced world (2020, background): “However in more recent times the emergence of information technologies, particularly internet services, have exacerbated the issue, making larger quantities of additional information more easily accessible to larger populations of decision-makers than ever before (Roetzel 2019).”
Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning (2022, methodology): “We stick to the above reward formulation with one exception: Instead of the turn level reward of − 1 , we propose to use information overload (Roetzel, 2019).”
From information seeking to information avoidance: Understanding the health information behavior during a global health crisis (2020, background): “However, recently, it has received a lot of attention, especially in social media and virtual collaborations (Roetzel, 2019).”