Business Model Data Tools and Artificial intelligence (AI): Current State and Future Challenges

Business Model Data Tools and Artificial intelligence (AI): Current State and Future Challenges

Basma Hamrouni (Kasdi Merbah Ouargla University, Algeria), Abdelhabib Bourouis (University of Oum El Bouaghi, Algeria), Ahmed Korichi (Kasdi Merbah Ouargla University, Algeria), and Brahmi Mohsen (University of Sfax, Tunisia)
DOI: 10.4018/978-1-6684-6766-4.ch007
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Abstract

An important axis of the contribution of business models is building a base for computer-assisted management tools. Management scholars are important actors that generate models and concepts. Nevertheless, these concepts are rarely translated into software-based tools, which could deliver huge value to management. However, the highly creative task of business model innovation is not effectively supported by these studied tools. Also, trust is not offered by these AI-based DSS systems to allow extremely accurate decisional guidance. To address these issues, the authors combine findings with studying 18 software tools from research and practice from scholars on software tools for business model design to address these problems. First, the authors provide a comprehensive taxonomy that identifies five novel categories of software-based business model development tools. Second, they provide a new generation of tools using the AI for Business Model that can give explanations in order to increase trust in suggested solutions. The performance results provided demonstrate the advantages of the suggested method.
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Introduction

The business model framework is the basis for the decision-making process (Hamrouni et al., 2021). It is considered by entrepreneurship as a cognitive device. The integration and organization of strategic objectives are done by this cognitive device to remain profitable in the long run and benefit from the business opportunities (Baden-Fuller &Morgan, 2010; Chesbrough &Rosenbloom, 2002; Hacklin & Wallnöfer, 2012). Due to the fast transformation in the business environment, companies are required to assess their Business Model (BM) in order to stay competitive (Henry Chesbrough, 2006), since the BM is essential to the success of any organization (Magretta, 2002). It offers a solid framework for comprehending, analyzing, communicating, and managing strategic decisions (Al-Debei &Avison, 2010; Osterwalder et al., 2005; Shafer et al., 2004). Business model research is a rapidly developing field that is still in itsearly stages and in need of a conceptual underpinning. The literature on BM originates in awide variety of study areas, including information systems (Gordijn & Akkermans, 2001), technology and innovation management (Chesbrough, 2007) and strategic management (Amit & Zott, 2001). The business model construct has the ability to add useful tools and theoretical insights to the strategy literature. Contrary to what adherents of the business model construct claim, we contend that the current business model literature has not yet completely tapped into the potential for theoretical contributions (Lanzolla et al., 2021).

However, several scholars have made contributions to the field of business model design (Bouwman et al., 2008; Gordijn & Akkermans, 2003), and innovation (Sinkovics et al., 2021; López-Nicolás et al., 2021). In the literature, there are many calls for additional tools to assist in the development and assessment of BM. As a result, computer-based tools were developed. Research in the Information Systems (IS) discipline and adjunct disciplines concluded the necessity of developing business model development tools (BMDT) (Spieth & Schneider, 2016), that contribute to the development of strategies and products. During the process of ideating, software-based tools allow the integration and documentation of information from various sources easily (Szopinskiet et al., 2017). According to academics, a software-based business model data tool (BMDT) is useful for designing and innovating business models, particularly when using AI techniques and machine learning (Ebel et al., 2016; Veit et al., 2014; Athanasopoulou & De Reuver, 2020). However, these AI-based DSS systems do not offer enough trust to allow extremely accurate decisional guidance. The majority of the business model innovation (BMI) tools documented in the academic literature are used to support design processes rather than for testing or implementation. The most of BMI tools are designed for and are most effective when used to experiment with the creation or redesign of a business model and integrate it into a value. chain and value network ecosystem, which has implications for managers according to (Munir et al., 2022). Athanasopoulou et al. (2020) has argued that the existent business model tools enable the building of a business model, but they typically do not support evaluating several business models and selecting the best one. He proposed that future business model tools should include functions that facilitate choosing amongst different business model options.

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