Investigating acceptance behavior in software engineering—Theoretical perspectives

Background: Software engineering research aims to establish software development practice on a scientific basis. However, the evidence of the efficacy of technology is insufficient to ensure its uptake in industry. In the absence of a theoretical frame of reference, we mainly rely on best practices and expert judgment from industry-academia collaboration and software process improvement research to improve the acceptance of the proposed technology.Objective: To identify acceptance models and theories and discuss their applicability in the research of acceptance behavior related to software development.Method: We analyzed literature reviews within an interdisciplinary team to identify models and theories relevant to software engineering research. We further discuss acceptance behavior from the human information processing perspective of automatic and affect-driven processes ("fast"system 1 thinking) and rational and rule-governed processes ("slow"system 2 thinking).Results: We identified 30 potentially relevant models and theories. Several of them have been used in researching acceptance behavior in contexts related to software development, but few have been validated in such contexts.They use constructs that capture aspects of (automatic) system 1 and (rational) system 2 oriented processes. However, their operationalizations focus on system 2 oriented processes indicating a rational view of behavior, thus overlooking important psychological processes underpinning behavior. Conclusions: Software engineering research may use acceptance behavior models and theories more extensively to understand and predict practice adoption in the industry. Such theoretical foundations will help improve the impact of software engineering research. However, more consideration should be given to their validation, overlap, construct operationalization, and employed data collection mechanisms when using these models and theories.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).