PurposeKnowledge-intensive processes (KiPs) are typically viewed as business processes performed by knowledge workers making complex decisions. Increasing digitalization of KiPs through AI and other means reduces the adequacy of that view. This conceptual contribution proposes and illustrates an updated view of KiPs based on a work system perspective (WSP) that expands the scope of BPM. Design/methodology/approachThree WSP approaches are proposed to analyze KiPs. First, work system models are used with different degrees of specificity to support different stakeholders. Second, continuous design dimensions are proposed for visualizing important KiP issues and challenges. Third, the roles and responsibilities of digital agents in relation to different facets of work are analyzed. The three approaches are introduced using a running example and are evaluated using a real-world KiP involving diagnostic radiology. FindingsThe three WSP approaches help to analyze KiPs and attain a greater understanding by offering an integrated view of KiPs that spans management and technical viewpoints. Originality/valueThis conceptual contribution shows how ideas and models related to the WSP provide paths for integrated visualization and analysis of KiPs. The paper extends Business Process Management (BPM) by providing approaches that stakeholders with different concerns and interests can use for describing and analyzing KiPs that may be automated to varying degrees.

This paper’s path toward anticipating foreseeable workarounds could help analysts and managers encourage beneficial workarounds and discourage harmful workarounds. It builds on a 2015 EMMSAD paper that suggested a less efficient path toward the same goal before LLM capabilities were available. The unique, LLM-based methodology in this research follows aspects of design science research. The goal is to create a design artifact, i.e., a substantial list of generic workarounds that might be used in IS engineering. The starting point is ten synthetic IS-related cases of around 1500 words generated by an LLM from brief situation summaries. The theoretical basis is work system theory and a theory of workarounds. The method for producing artifacts is analogous to recent developments in generative knowledge discovery. The method can be described as generative abstraction, refinement, compilation, and application (GARCA). 1) Abstraction: The LLM identified 50 foreseeable workarounds for each case and organized them using work system theory. 2) Refinement: The LLM produced lists of broadly applicable generic workarounds by eliminating situational details from situation-specific workarounds for each case. 3) Compilation (and further refinement): The LLM produced a single organized list of generic workarounds by reducing and combining the ten lists. It also added preconditions and red flags that increase the likelihood of specific generic workarounds. The main artifact produced was an organized list of 83 generic workarounds. 4) Application: The LLM applied the 83 generic workarounds to identify foreseeable situation-specific workarounds in an additional synthetic case. That illustrated the potential value of including generic workarounds in checklists for design and development teams probing the logic and details of an initial system design. Those workarounds could be combined with discipline-specific generic workarounds (e.g., generic workarounds for medical work) that could be developed similarly.

This paper demonstrates possibilities and challenges in using an LLM to create a domain knowledge graph (DKG) for the work system domain and then converting it to a situation-specific knowledge graph (SSKG) for a specific system. It starts by summarizing existing ideas about work systems and a taxonomy of knowledge objects that have been presented previously. It uses an LLM to create a DKG based on parts of the work system perspective and highlights semantic challenges in that process. It uses an LLM to apply that DKG in creating SSKGs for two case study examples, one about ride hailing and one about medical care. It uses the LLM to identify parts of the DKG that were not the involved in links to nodes containing information from each case. It uses an LLM to prune those unnecessary nodes from the SSKG but also asks the LLM to identify parts of the DKG that might be relevant even though they were not included in a pruned SSKG. The conclusion stresses both practical possibilities and semantic challenges revealed in this research and discusses next steps related to use of LLMs in conjunction with DKGs and SSKGs in systems engineering.

This research-in-progress paper presents part of an ongoing project related to using LLMs for describing, analyzing, and designing work systems (including information systems). General axioms that apply to any non-trivial WS or IS might provide a path toward new tools and methods. This paper identifies 24 work system axioms that extend earlier research. They are organized in five categories: 1) system in context, 2) system operation, 3) system goals and goal attainment, 4) system uncertainties, and 5) system-related change. The axioms potentially address the challenge of helping business and IS/IT professionals understand and collaborate around systems in organization. This preliminary research hints at the potential value of the axioms based on results when an LLM applied them to two case studies.

This exploratory paper builds on the EMMSAD 2024 paper "Could a Large Language Model Contribute Significantly to Requirements Analysis?" Eight versions of each of three LLM prompts (for system structure, analysis, and recommendations) were applied to three 3000+ word case studies. Those versions expressed different "treatments" including a control with no RAG augmentation, a version with RAG augmentation based on an analysis template used by MBA and EMBA students, and six other versions based on theoretical approaches such as activity theory, a BPM design space, work system principles, and so on. The LLM responses were somewhat reliable for summarizing system structure, less reliable for summarizing an analysis, and often generic and impractical for recommendations because the LLM did not understand contexts. This new paper adds three new capabilities: 1) RAG augmentation using a knowledge base consisting of "knowledge objects" built on work system theory, 2) application of that knowledge base using chain-of-thought reasoning, 3) inclusion of direct feedback from analysts during an analysis process in order to correct errors and to extend the prompt in new directions. Examples are used to illustrate results from applying those capabilities to 3 disparate case studies.

This exploratory paper addresses challenges related to aspects of the theme of CAISE 2025, "bridging silos." That theme applies directly to low levels of synergy between entire academic disciplines and to similar issues involving different IS modeling traditions that encounter gaps between rigor and precision versus comprehension and usability by many IS stakeholders who lack advanced IT training. A work system perspective can address those issues by serving as an integrating nexus between disciplines and between IS modeling traditions while also bridging gaps between rigor and broad usability in IS modeling. The work system perspective provides a natural nexus at many areas of overlap between disciplines that focus on systems designed and operated to achieve goals. Other possibilities for bridging silos involving IS engineering traditions touch upon data, process, and software modeling.

The term smart is often used carelessly in relation to systems, devices, and other entities such as cities that capture or otherwise process or use information. This conceptual paper treats the idea of smartness in a way that suggests directions for making cyber-human systems smarter. Cyber-human systems can be viewed as work systems. This paper defines work system, cyber-human system, algorithmic agent, and smartness of systems and devices. It links those ideas to challenges that can be addressed by applying ideas that managers and IS designers discuss rarely, if at all, such as dimensions of smartness for devices and systems, facets of work, roles and responsibilities of algorithmic agents, different types of engagement and patterns of interaction between people and algorithmic agents, explicit use of various types of knowledge objects, and performance criteria that are often deemphasized. In combination, those ideas reveal many opportunities for IS analysis and design practice to make cyber-human systems smarter.

Many discussions of the tremendously important topic of AI are undermined by vague definitions of what AI is and by generalizations that are too distant from reality to be useful when pursuing or evaluating real world applications. Inconsistent and exaggerated definitions of AI published during 2019-2022 illustrate the need for an approach to validating current generalizations about AI and its uses and not just repeating decades-old predictions and images based on science fiction movies. Next, this paper presents a series of evaluation issues for generalizations and a series of ideas that are useful for describing AI applications as uses of algorithms based on techniques associated with AI. It incorporates those ideas into summaries of six diverse applications of algorithms associated with AI. A concluding section presents suggestions for realistic descriptions of AI applications and for generalizations about AI.

This chapter is a contribution to an anthology in honor of Ulrich Frank. It describes some of the twists and turns in my research both before and after my 2017 visits to Ulrich's Enterprise Modelling Research Group at the University of Duisburg-Essen. Those visits helped me rethink aspects of personal research goals and directions related to one of the grand challenges for IS research.

We explore a novel context where employees engage in low-code no-code work, enabled by software-based workarounds that don't comply with organizational norms and aren't endorsed by corporate management, yet are essential to organizational success. We describe how employees at a warehouse that supports a major retailer in Hong Kong engage in work practices combining low-code/no-code approaches with noncompliant workaround behaviors. We consider the practical implications and potential contributions of such noncompliant work and provide recommendations for business managers overseeing low-code/no-code efforts.

The Work System Theory (WST) is a foundation theory that enables analysis of systems in organizations. It encompasses a set of concepts that help describing, analyzing, designing and evaluating purposeful systems that perform work. A WST-based method guides a work system's analysis through the identification of problems/opportunities, summarizing the As -Is and To -Be versions of a system. Motivated by a Design Science project running in an academic institution, we explore in this paper the application of graph -based semantic technologies to specify and analyze work systems and to bridge their design -time view with run-time data found in legacy systems. Our contribution is twofold (1) we propose an ontological schema that informs RDF-based knowledge graph building with a Work System perspective; (2) we demonstrate some benefits of having work systems represented as Knowledge Graphs that are linked to operational data and further subjected to semantic queries and deductive reasoning. The Design Science artifact is iteratively developed in the host institution of the first authors and builds on previous development of a knowledge graph that has been lifted from legacy databases. The graph -based approach is viable to bridge an inherent conceptual gap between the work systems conceptualization and operational data schemas, thus adding value both to decision -making and to run-time systems that will be later built to benefit from the semantic distinctions present in the resulting graph.

Purpose The lack of conceptual approaches for organizing and expressing capabilities, usage and impact of intelligent machines (IMs) in work settings is an obstacle to moving beyond isolated case examples, domain-specific studies, 2 × 2 frameworks and expert opinion in discussions of IMs and work. This paper's purpose is to illuminate many issues that often are not addressed directly in research, practice or punditry related to IMs. It pursues that purpose by presenting an integrated approach for identifying and organizing important aspects of analysis and evaluation related to IMs in work settings. Design/methodology/approach This paper integrates previously published ideas related to work systems (WSs), smart devices and systems, facets of work, roles and responsibilities of information systems, interactions between people and machines and a range of criteria for evaluating system performance. Findings Eight principles outline a straightforward and flexible approach for analyzing and evaluating IMs and the WSs that use them. Those principles are based on the above ideas. Originality/value This paper provides a novel approach for identifying design choices for situated use of IMs. The breadth, depth and integration of this approach address a gap in existing literature, which rarely aspires to this paper’s thoroughness in combining ideas that support the description, analysis, design and evaluation of situated uses of IMs.

The increasing automation of work and increasing reliance on autonomous devices and systems calls for re-conceptualizing information system usage. Thinking of IS usage as the hands-on use of computerized devices through interfaces is increasingly inadequate where roles and responsibilities are delegated to IT-enabled devices and systems that may operate autonomously and may not interact directly with users. This paper’s new IS usage theory (ISUT) consists of three axioms and 12 underlying assumptions that apply to IS usage for nontrivial sociotechnical and totally automated work systems. A background section includes the rationale for pursuing a new view of IS usage and basic concepts related to systems, information systems, work system theory (WST) and the ISUT’s boundary conditions. A summary of selected research articles related to IS usage emphasizes how central ideas in those articles diverge from the ISUT’s underlying assumptions and may not suffice for describing increasingly automated work practices. A two-dimensional agent responsibility framework facilitates the application of the ISUT’s system-oriented view of IS usage by combining a spectrum of IS roles and a series of facets of work. Four illustrative real-world examples demonstrate the practical application of the ISUT. The conclusion emphasizes this paper’s motivation, theory creation process, theory presentation and content, and contribution.

This research-in-progress paper presents a quasi-experiment in which three different ChatGPT-4 prompts (for system structure, analysis, and recommendations) are applied in standard or augmented form to the work system in each of three case studies (automated warehouses, ride hailing platforms, and medication administration systems). The augmented forms (treatments) are based on different sets of ideas. Each case study comprises 3000+ words. The prompts are detailed requests for responses of up to 500 words related to three steps (system structure, analysis, and recommendations) related to those cases. A null treatment serving as a quasi-control uses standard prompts for each case without augmentation. The first actual treatment is a revision of an analysis template used by MBA and EMBA students; the other six are sets of questions based on activity theory, a BPM design space, system principles, and three other approaches The research questions are whether ChatGPT-4 can produce a useful first cut at system structure, analysis, and recommendations and whether various augmentations of ChatGPT-4 prompts improve or extend the outputs significantly.

This tutorial explains the most updated version of work system perspective, which extends work system theory (WST) and related ideas that originally emerged from a series of IS textbooks (1992, 1996, 1999, 2002). Those textbooks and subsequent efforts were guided by the aspiration of making analysis and design readily understandable and usable by business professionals. The textbooks articulated the beginnings of the work system method (WSM) whose conceptual basis was clarified in the form of WST in 2013. Subsequent developments articulate a broader work system perspective (WSP), which builds on WST and WSM to provide an integrated approach for describing, analyzing, designing, and evaluating IT-enabled systems that may be sociotechnical (with people performing some of the work), totally automated, or cyber-human (with extensive interaction between people and computerized devices).