The Research Field “Modeling Business Information Systems” - Current Challenges and Elements of a Future Research Agenda

The research field “Modeling business information systems” has a long tradi- tion in the scientific discipline of Busi- ness and Information Systems Engi- neering (BISE). The present paper high- lights research shaping the research field, discusses challenges impairing the development of the research field in the coming years, and outlines ele- ments of a future research agenda

A Reference Model for Data-Driven Business Model Innovation Initiatives in Incumbent Firms

In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part of an emerging research field of data-driven business model (DDBM) innovation. Aiming to deploy DDBM innovation, companies start initiatives to either renovate their existing BM or develop a new DDBM. Responding to the recent calls for further research on design knowledge for DDBM innovation, we developed a reference model for DDBM innovation initiatives. Building upon a design science research approach and the Work System Theory as a kernel theory and a set of design principles, we propose a reference model comprising a static and a dynamic view. Our results are based on a research study with empirical insights from 18 companies, 19 cases and 16 expert interviews as well as theoretical grounding from a systematic literature research on key concepts of DDBM innovation. The developed reference model fills a gap mentioned in the DDBM innovation literature and provides practical guidance for companies

An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

This paper motivates, describes, demonstrates in use, and evaluates the Open Models Laboratory (OMiLAB)—an open digital ecosystem designed to help one conceptualize and operationalize conceptual modeling methods. The OMiLAB ecosystem, which a generalized understanding of “model value” motivates, targets research and education stakeholders who fulfill various roles in a modeling method\u27s lifecycle. While we have many reports on novel modeling methods and tools for various domains, we lack knowledge on conceptualizing such methods via a full-fledged dedicated open ecosystem and a methodology that facilitates entry points for novices and an open innovation space for experienced stakeholders. This gap continues due to the lack of an open process and platform for 1) conducting research in the field of modeling method design, 2) developing agile modeling tools and model-driven digital products, and 3) experimenting with and disseminating such methods and related prototypes. OMiLAB incorporates principles, practices, procedures, tools, and services required to address the issues above since it focuses on being the operational deployment for a conceptualization and operationalization process built on several pillars: 1) a granularly defined “modeling method” concept whose building blocks one can customize for the domain of choice, 2) an “agile modeling method engineering” framework that helps one quickly prototype modeling tools, 3) a model-aware “digital product design lab”, and 4) dissemination channels for reaching a global community. In this paper, we demonstrate and evaluate the OMiLAB in research with two selected application cases for domain- and case-specific requirements. Besides these exemplary cases, OMiLAB has proven to effectively satisfy requirements that almost 50 modeling methods raise and, thus, to support researchers in designing novel modeling methods, developing tools, and disseminating outcomes. We also measured OMiLAB’s educational impact

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda

Modeling styles in conceptual data modeling: Reflecting observations in a series of multimodal studies

A modeling style characterizes a modeler’s sequencing of processing a modeling task in terms of applying the modeling language and its language concepts while constructing a conceptual model. Presently, surprisingly little is known about the different modeling styles modelers exhibit when performing conceptual data modeling. In this research, we combine complementary modes of observation including audio-visual protocols, recorded modeler-tool interactions, and pre-/post-modeling surveys of modelers to identify modeling styles in 24 data modeling processes performed by modelers at different stages of experience in data modeling. Our study identifies and characterizes three distinct modeling styles refining our current knowledge about data modeling processes and informing design science research on style-specific, targeted modeling (software tool) support for data modelers

Full Paper: Neural Text Generators in Enterprise Modeling: Can Chatgpt be Used as Proxy Domain Expert?

Enterprise modeling is concerned with the systematic development of a comprehensive and holistic representation of an enterprise (an enterprise model) to support organisational initiatives. Domain experts have an essential role in enterprise modeling projects (EM), as they provide the required domain knowledge or specifics of the organisation under consideration. The paper investigates if neural text generators (large language models) can reduce the dependency on domain experts for certain tasks in enterprise modeling. The main contributions of this paper are (1) a systematic literature analysis on neural text generator use in EM, (2) the identification of potential for applying large language models in EM, and (3) findings from quasi-experiments comparing output of ChatGPT and domain experts for the same EM task

Responsibility Modeling for Operational Contributions of Algorithmic Agents

This paper presents an agent responsibility framework that can be used to identify, describe, and analyze many possible roles that algorithmic agents might perform for information systems and other work systems, including those involving robotic process automation. The two dimensions of the framework are 1) a spectrum of possible roles for algorithmic agents and 2) a set of facets of work to which algorithmic agents might be applied in work systems. This paper explains those ideas, applies two examples to illustrate their potential use, discusses alternative ways to use the framework, and identifies areas for future research

The Important Role of System Dynamics Investigation on Business Model, Industry and Performance Management

Purpose: This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics in modeling and the ability to measure, analyze and capture problems in business, industry, manufacturing etc. Design/Methodology/Approach: The research presented in this work is qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps, and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of system dynamics models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used. Findings: The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective, and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach. Originality/value: The significant differences between static and dynamics can be used for business research and strategic performance management. This comparative study analyses some system dynamics models from many authors worldwide. Their goals are behind their strategic business models and encounters for their respective progress. This approach may serve as a checklist for new researchers in the field

Conceptual modeling for the design of intelligent and emergent information systems

A key requirement to today's fast changing economic environment is the ability of organizations to adapt dynamically in an effective and efficient manner. Information and Communication Technologies play a crucially important role in addressing such adaptation requirements. The notion of `intelligent software' has emerged as a means by which enterprises can respond to changes in a reactive manner but also to explore, in a pro-active manner, possibilities for new business models. The development of such software systems demands analysis, design and implementation paradigms that recognize the need for ‘co-development’ of these systems with enterprise goals, processes and capabilities. The work presented in this paper is motivated by this need and to this end it proposes a paradigm that recognizes co-development as a knowledge-based activity. The proposed solution is based on a multi-perspective modeling approach that involves (i) modeling key aspects of the enterprise, (ii) reasoning about design choices and (iii) supporting strategic decision-making through simulations. The utility of the approach is demonstrated though a case study in the field of marketing for a start-up company