Analyzing the Antecedents and Consequences of Artificial Intelligence Technologies Adoption in supply chain

EXTENDED ABSTRACT
Introduction: The supply chain is the backbone of global commerce, ensuring the smooth and continuous flow of products and services between producers and domestic and industrial customers. An efficient supply chain enables businesses to meet customer needs promptly, reduce operational costs, and enhance their competitive edge effectively. Also, artificial intelligence (AI) and its subset technologies (such as machine learning and big data) have empowered businesses to make more accurate data-driven decisions regarding inventory levels, production, and delivery planning, thus optimizing route planning, and reducing costs and delivery times. Over the past decade, the issue of AI adoption in the supply chain and its potential implications has attracted the attention of both businesses and researchers in the field of supply chain and logistics management. Despite numerous studies conducted over the past decade, the diversity of these studies in the supply chains of various industries and production sectors, along with a wide range of identified factors and the lack of a comprehensive classification of these factors, have led to a lack of clarity in the findings regarding the antecedents and outcomes of AI adoption in the supply chain. Therefore, the main objective of this research is to create transparency in the results obtained from previous studies in this field.
Methodology: The research method employed in the present study is qualitative and of the meta-synthesis type. This method is utilized to identify the antecedents and consequences of AI technology adoption in the improvement and strengthening of the distribution network, aiming to integrate multiple studies to create comprehensive and interpretive findings. The method proposed by Sandelowski and Barroso (2007) is one of the most prominent methods for conducting meta-synthesis and provides better results. Thus, this seven-step method has been used in this research. The research falls under applied research, and the data collection approach is based on library research. Data gathered from papers indexed in the research database Scopus and Web of Science, as well as two Persian databases of Comprehensive Portal of Humanities and the Scientific Information Center of Jihad Daneshgahi. Also, the time period related to English articles was 2013 to 2023 and the time period related to Persian articles was 1392 to 1402.
Results and Discussion: By examining the factors leading to and resulting from the adoption of artificial intelligence (AI) technology, a conceptual framework was established that classifies these elements into two main categories, with 16 factors and 121 components. The antecedents consist of organizational, environmental, technological, human, institutional, and economic factors. On the other hand, the consequences are divided into communication, knowledge, experiential, financial, product, workforce, organizational, supply chain, and environmental outcomes. The innovation of this research lies in the comprehensive identification and categorization of the diverse antecedents and consequences associated with AI adoption in the supply chain, addressing gaps left by previous studies. These studies often lacked clarity and coherence in their findings due to the absence of a unified classification. The results from the meta-synthesis indicate that organizational and environmental factors are the most frequently cited antecedents, while organizational and supply chain consequences are the predominant outcomes. This suggests that in order to foster AI adoption in the supply chain, special emphasis should be placed on organizational and human factors, as they are pivotal in shaping organizational systems. Additionally, the research highlights a notable gap in the literature concerning economic antecedents and knowledge-related outcomes, as well as workforce-related consequences, indicating limited existing knowledge in these areas.
Conclusion: the research provides a comprehensive framework for understanding the diverse antecedents and consequences of adopting artificial intelligence (AI) technology within the supply chain. By categorizing these elements into a structured model, the study highlights the significance of organizational and environmental factors as primary antecedents, while organizational and supply chain outcomes emerge as the most frequent consequences. This comprehensive approach addresses the gaps and inconsistencies found in previous studies, offering a clearer understanding of the dynamics at play in AI adoption within supply chains. Emphasizing the role of organizational and human factors, the research underscores their fundamental importance in shaping effective organizational systems. Additionally, the study identifies a notable lack of research in areas such as economic antecedents and knowledge-related outcomes, as well as workforce-related consequences. This gap in the existing literature points to the need for further investigation to enhance the current understanding and knowledge base in these fields. Overall, the research contributes to a more transparent and cohesive body of knowledge, facilitating better decision-making and strategic planning for businesses looking to integrate AI technology into their supply chain operations. This study not only addresses the gaps and inconsistencies in previous research but also paves the way for more informed decision-making and strategic planning, ultimately enhancing the integration of AI technology in supply chain operations.