Recommender systems increasingly depend on explanations to convey the importance and relevance of the options recommended to their users. Previous research on explanations has predominantly concentrated on developing new explanation methods and studying how explanations can enhance recommendation effectiveness and understandability. However, limited attention has been given to understanding the persuasiveness of recommendations, particularly whether manipulating explanations to be persuasive can influence users’ choices and preferences for options. Here, we report a study (N=231) in which participants were required to make choices among recommended options across three tasks based on the provided explanations. These explanations were presented in a form of natural language. To study the effect of persuasiveness, the explanations were systematically manipulated by incorporating principles of reciprocity, commitment, authority, social proof, liking, and scarcity. In addition, explanations were manipulated for their rationality. The results reveal that the persuasiveness of explanations significantly impacts participants’ recommendation choice behavior, with persuasive explanations being preferred over non-persuasive ones. Moreover, persuasiveness also significantly increased participants’ tendencies toward irrational choices: an increased proportion of selecting a lower utility option when accompanied by a persuasive explanation. The results demonstrate that explanations substantially influence user choice behavior in recommender systems and that users’ choices, as well as the resulting user profiles, can be intentionally or unintentionally shaped by the introduction of persuasive elements in explanations. This could have unprecedented and cumulative effects on the bias of recommendations and can raise ethical concerns about the effect of persuasiveness on users’ long-term behavior in systems integrated with recommendation support. • Examines the influence of persuasive principles on users’ choice-behavior in recommender systems. • Provides empirical evidence on how explanation types affect rational and irrational choices. • Demonstrates the impact of language manipulation in recommendation explanations.

Abstract The lack of core theories is a challenge for the whole software engineering (SE) discipline, particularly crucial for the agile software development (ASD) field, which is largely practice-driven. Without solid and continuous theoretical development glued by core theories, ASD risks repeating wrong practices and oversimplifying real-world phenomena. To address this issue and foster a strong link between empirical evidence and theoretical development, we conduct this critical review using the Complex Network Analysis (CNA) approach, in response to the editors’ call on the XP2020 conference. Based on 83 selected articles and 88 identified theories, our analysis traced the originating disciplines of these theories and synthesized 3 key theory communities. We position ASD core theories between empirical generalization and middle-range theories in the SE theory spectrum and offer practical guidelines for researchers to use, borrow, and generate ASD theories. It is further recommended that new theory development be aligned with the theory of coordination and control theory while employing Complex Adaptive Systems (CAS) theory as a theoretical lens when borrowing theories to ASD.

Individuals tend to hold back their ideas because they feel concerned about being evaluated. This leads to the untapped creative potential for organisations that depend on the creative abilities and ideas of their employees, as idea evaluation is essential for further developing and assessing creative ideas that inhibit the potential to turn into innovative products or services. In my research, I propose the use of AI-based computer systems for idea evaluation to address evaluation apprehension. With the help of an experiment (n = 228), I test whether individuals feel concerned about evaluation when a computer evaluates their idea. The results show that people do not feel evaluation apprehension when they present their idea to an AI-based system, but in contrast, feel concerned when they present their idea to a human. These findings contribute to the theory of evaluation apprehension but also to theories of human-computer collaboration and hold potential for companies to increase their creative outcome.

Due to significant technological advances in the field of artificial intelligence (AI), which are driven by increased computing power, the ubiquitous availability of data, as well as new algorithms...

Recently, blockchain and DevOps have received attention from the software industry as both offer number of benefits individually. Automation, measurement, and sharing are the core pillars of DevOps, and to securely manage these; blockchain can play an important role. This study aims to understand the role of blockchain technology in the DevOps paradigm. This study presents factors that influence the adoption of blockchain in DevOps. Furthermore, this study suggests a framework that assists in merging the characteristics of blockchain in the DevOps paradigm. The findings of this study give a knowledge base for industry experts and the research community to develop the roadmap and guidelines for the adoption of blockchain in the DevOps paradigm.