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To achieve their goals, organizations execute business processes, which require effective allocation of resources to process activities. This results in the decision-making problem: Which resources should be allocated to which process activities? This problem significantly impacts both process efficiency and effectiveness. Over the past decades, various system-initiated (largely automated) resource allocation approaches have been developed. This study presents a comprehensive overview of this field by analyzing 61 primary studies identified through a rigorous, structured literature review covering publications from 1995 to 2023. We investigate resource allocation goals and cardinalities and describe how process models, execution data, and task attributes, as well as resource attributes, are used to specify the resource allocation problem. Additionally, the type of algorithmic solution and evaluation methods are discussed. This study shows that most approaches support 1-to-1 allocation cardinalities only, specify process-oriented goals, focus on process models, and utilize rule-based methods. Based on the results, we call for future research to define common terminology, support evidence-oriented resource allocation and adaptability, and improve reproducibility and comparability by performing benchmarking studies.

Abstract Large language models (LLMs) have been touted to enable increased productivity in many areas of today's work life. Scientific research as an area of work is no exception: The potential of LLM‐based tools to assist in the daily work of scientists has become a highly discussed topic across disciplines. However, we are only at the very onset of this subject of study. It is still unclear how the potential of LLMs will materialize in research practice. With this study, we give first empirical evidence on the use of LLMs in the research process. We have investigated a set of use cases for LLM‐based tools in scientific research and conducted a first study to assess to which degree current tools are helpful. In this position paper, we report specifically on use cases related to software engineering, specifically, on generating application code and developing scripts for data analytics and visualization. While we studied seemingly simple use cases, results across tools differ significantly. Our results highlight the promise of LLM‐based tools in general, yet we also observe various issues, particularly regarding the integrity of the output these tools provide.