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Purpose Human lab experiments have become an established method in information systems research for investigating user behavior, perception and even neurophysiology. The purpose of this paper is to facilitate experimental research by providing a practical guide on how to implement and conduct lab experiments in the freely available experimental platform Brownie. Design/methodology/approach Laying the groundwork of the tutorial, the paper first provides a brief overview of common design considerations for lab experiments and a generic session framework. Building on the use case of the widely used trust game, the paper then covers the different stages involved in running an experimental session and maps the conceptual elements of the study design to the implementation of the experimental software. Findings The paper generates findings on how computerized lab experiments can be designed and implemented. Furthermore, it maps out the design considerations an experimenter may take into account when implementing an experiment and organizing it along a session structure (e.g. participant instructions, individual and group interaction, state and trait questionnaires). Originality/value The paper reduces barriers for researchers to engage in experiment implementation and replication by providing a step-by-step tutorial for the design and implementation of human lab experiments.
In the NeuroIS field, experimental software needs to simultaneously present experimental stimuli to participants while recording, analyzing, or displaying neurophysiological measures. For example, a researcher might record a user’s heart beat (neurophysiological measure) as the user interacts with an e-commerce website (stimulus) to track changes in user arousal or show a user’s changing arousal levels during an exciting game. In this paper, we identify requirements for a NeuroIS experimental platform that we call Brownie and present its architecture and functionality. We then evaluate Brownie via a literature review and a case study that demonstrates Brownie’s capability to meet the requirements in a complex research context. We also verify Brownie’s usability via a quantitative study with prospective experimenters who implemented a test experiment in Brownie and an alternative software. We summarize the salient features of Brownie as follows: 1) it integrates neurophysiological measurements, 2) it incorporates real-time processing of neurophysiological data, 3) it facilitates research on individual and group behavior in the lab, 4) it offers a large variety of options for presenting experimental stimuli, and 5) it is open source and easily extensible with open source libraries. In summary, we conclude that Brownie is innovative in its potential to reduce barriers for IS researchers by fostering replicability and research collaboration and to support NeuroIS and interdisciplinary research in cognate areas, such as management, economics, or human-computer interaction.