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The management literature has seen a surge of publications that focus on the utilisation opportunities of Artificial Intelligence (AI) technologies. This paper conducts a Systematic Literature Review (SLR) to create an in-depth understanding of this development by analysing the latest academic contributions on the impact of AI on the field of Strategic Management (SM). In so doing, it aims to provide an integrative framework that merges the current understanding in this area into suitable categories according to the two dimensions of SM and AI. The analysis revealed that: (1) almost 70% of these were published within the last six years, underlining the growing importance of this topic; (2) only 30% of the selected papers were based on empirical data; (3) the majority of the papers were categorised in the 'Strategy Formulation & Selection' stage of the SM process; and (4) machine learning is far and away the most common AI technology providing new opportunities for SM. Finally, we present further ideas and directions to advance this field of academic research.
Innovation in small and medium enterprises (SMEs) is often the result of technology-driven or market-pull entrepreneurship activities. So far, although its importance in practice, as well as in academia continues to grow, extant research exhibits little theory about the process of technology-driven entrepreneurship in SMEs. The study aims to better understand how technology-driven entrepreneurship processes transform business in SMEs in the manufacturing industry. Therefore, we developed a technological entrepreneurship (TE) process framework by utilizing the flexible pattern matching approach (FPMA). We iteratively compared a priori patterns from existing theoretical knowledge to empirical findings that emerged from in-depth interviews with corporate executives in the manufacturing industry. The framework highlights the TE process in SMEs leading to four output components: (1) corporate-function-related, (2) business-model-related, (3) competitiveness-related, and (4) customer-related. This study makes a unique contribution to academia by being the first that develops a TE process framework tailored to SMEs from the manufacturing industry. We point out that sustainable growth and competitiveness of SMEs depends on appropriate TE process management, and we underline the strategic importance of TE-driven transformation for SME managers. Our study expands the scope of TE and SME research and provides empirically grounded insights into technology-driven innovation.
Artificial intelligence-enabled business model innovation: competencies and roles of top management
(2023)
Research in artificial intelligence and business model
innovation is flourishing. Nevertheless, the current discussion lacks
an overarching understanding of, and thus has not sufficiently addressed,
the interface between artificial intelligence-enabled business
model innovation and the critical role of top management. Although
a paradigm shift affecting top management is already occurring,
extant management literature is limited, especially in terms of
primary research. Accordingly, this study explores how top management
can encourage and facilitate artificial intelligence-enabled
business model innovation. We utilized an inductive approach and
conducted semistructured interviews with 47 practitioners to develop
a grounded theory. The developed framework consists of five
top management competencies and eight top management roles.
Overall, our study contributes to research in business model innovation
theory, revealing that top management requires a specific
skill set to carry out their roles and fulfill expectations.
This publication-based dissertation analyzes the importance of artificial intelligence (AI) and technological entrepreneurship on firm level over six chapters. The essence of this dissertation consists of four independent research papers developed for publication in academic journals whose peer review process is double-blinded. The first chapter offers a general introduction to the subject matter and provides a summary of the four research papers in this dissertation. The second chapter is a systematic literature review that focuses on the importance of AI in strategic management. The third chapter is a research paper that examines the significance of technology-driven entrepreneurship activities and provides crucial lessons from small and medium-sized enterprises operating in the manufacturing industry. The fourth chapter is a research paper that empirically examines how top management can encourage and facilitate AI-enabled business model innovation. The fifth chapter comprises a teaching case study and provides and understanding of how to implement an AI-based analytical tool in a firm. The sixth chapter outlines the main findings and contributions of this dissertation.
The use of analytical tools and disruptive technologies is a strategic imperative for companies to operate successfully in global markets. Wilo, a leading premium provider of pumps and pump systems, tasked Holger Jentsch, the Vice President (VP) of the Group Sales Excellence department, with piloting initial technological transformation projects in certain sales processes. As a lighthouse project, Jentsch aimed to use an artificial intelligence (AI) based analytics tool to prevent customer churn. Therefore, the case outlines critical success factors for the implementation of data-based decision-making which are elementary for Jentsch’s digital transformation project.