Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives

Annals of Operations Research https://doi.org/10.1007/s10479-022-05159-4 ORIGINAL RESEARCH Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives Sara I. C. Lemos1 · Fernando A. F. Ferreira2,3 · Constantin Zopounidis4,5 Emilios Galariotis5 · Neuza C. M. Q. F. Ferreira6 · Accepted: 21 December 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Given the increasingly significant role of small and medium-sized enterprises (SMEs) in the global economy and the ever more competitive markets in which these companies operate, SMEs’ ability to adopt artificial intelligence (AI) technologies is of utmost importance. Due to constantly evolving social, environmental, and technological scenarios, the managers of these firms must increasingly focus on incorporating new tools such as AI into SME operations in order to enjoy their benefits. However, the subjectivity and complexity of this adaptation process makes integrated analyses of key factors challenging. The present study sought to develop a multi-criteria decision-support system that applies cognitive mapping and the decision-making trial and evaluation laboratory technique in a neutrosophic context. The main objective is to overcome the limitations of previous studies and models by structuring the decision problem and identifying and understanding which factors should be central to adaptation initiative analyses. A panel of experts in AI were recruited to facilitate the construction of an analysis system that takes into account indeterminacy in decision-making processes. The results were validated by both the panel members and project managers at COTEC Portugal—a leading think-and-action network that seeks to advance technology diffusion and business innovation cooperation. The proposed system’s practical implications and benefits are also analyzed. Keywords Artificial intelligence · Cognitive mapping · Decision-MAking Trial and Evaluation Laboratory (DEMATEL) · Neutrosophic logic · Small and medium-sized enterprise (SME) 1 Introduction The transformation started by the industrial revolution has forced all companies— regardless of their size, industry, or location—to embark on the digitalization process. However, B Constantin Zopounidis ; Extended author information available on the last page of the article 123 Annals of Operations Research small and medium-sized enterprises (SMEs) have been especially slow to integrate digital technologies, with only one in five SMEs in the European Union currently running highly digitalized operations (Bettoni et al., 2021). As a result, these companies are under increasing pressure to implement complex growth plans that strengthen their competitiveness and help them stay abreast with constantly evolving technological and social innovations (De Marco et al., 2020; Falahat et al., 2020; Jung et al., 2018). For example, SMEs need to adapt to advances based on artificial intelligence (AI), which, according to Magistretti et al. (2019), is expected to become a complementary tool for SME decision-making processes. These firms’ ability to adopt new technologies is, nevertheless, often restricted by SMEs’ lack of resources and limited awareness of technological and social changes (Bettoni et al., 2021; Strotmann, 2007). Although various authors have studied the basic nature of adaptation to new technologies, little is known about the actual impact of innovative tools on SMEs (cf. Mittal et al., 2018). The extant literature on this topic has limitations regarding: (1) the identification of evaluation and decision criteria; (2) definition of these criteria’s relative importance; and (3) analysis of the dynamics of the criteria’s causal interrelationships (Freire et al., 2021). To fill these significant gaps, the present research first applied the jointly understanding, reflecting, and negotiating strategy (JOURNEY) making approach via cognitive mapping techniques. The second phase then applied the decision-making trial and evaluation laboratory (DEMATEL) technique to process data in a neutrosophic context. This combination of methodologies facilitated both analyses of the dynamics of cause-and-effect relationships between the decision criteria identified and the incorporation of indeterminacy into the decision-making process. With a view to increasing complementarity, two research questions were addressed: • How can decision makers identify key initiatives that SMEs need to implement in order to manage change during adaptations to AI and how are these initiatives interrelated? • Which drivers of adaptation have significant enough impacts that they should be given priority in order to facilitate SME adoption of AI tools? The selected methodologies were implemented during two group work sessions with a panel of specialists (i.e., professionals with practical knowledge about SME adaptation to AI technologies). Both meetings were held online due to coronavirus disease-19 (COVID19) pandemic restrictions. These sessions comprised open discussions of how to structure the decision problem, which enabled the expert panel to identify the most relevant criteria and create a group cognitive map. The DEMATEL technique then helped the panel members examine the cause-and-effect relationships related to SME-AI adaptation processes and complete the necessary neutrosophic evaluations. This study is the first to combine the DEMATEL technique and neutrosophic logic in order to conduct research on how SMEs can best adapt to AI tools, thereby contributing to the literature on this topic and generating opportunities for future investigations on related subjects. This paper’s remaining sections are as follows. The next section presents a literature review focused on AI and change management. Section three explains the methodologies applied, while section four covers the methodological application and main results. The final section offers conclusions, summarizes the insights gained, and makes recommendations for future research. 123 Annals of Operations Research 2 Literature review and research GAP AI as a concept can be traced back to 1950, when the British mathematician Turing (1950) posed the following question: “Can machines think?”. The cited author states that, for a machine to be intelligent, it needs to “learn from experience” that is, the stimuli to which the machine is exposed. Nilsson (1984, p. 5) asserts that the term AI refers to a “different class of machines […] that can perform tasks requiring reasoning, judgment, and perception that previously could be done only by humans”. In 1989, McCarthy (1989) used this term to describe computers that process large amounts of data in sophisticated ways. According to Ayedee and Kumar (2020), SMEs’ biggest challenges when adopting AI include, among others, their employees’ less extensive training a (...truncated)