Multiple Case Study Research Articles

Digital transformation of the agricultural industry: Behavioral decision-making, influencing factors, and simulation practices in the Yunnan highlands

Motivating the agricultural industry to engage in digital transformation is a challenge academically and socially. It is of great significance to study the choice of digital transformation mode of agricultural industrial organization and analyze its driving factors for promoting the sustainable development of agricultural industrial organization. This study adopts a bilateral evolutionary game to construct a decision-making model for behavioral decision-making during the digital transformation of the agricultural industry. The contingent-actual logical framework and multiple case studies of Yunnan highland agriculture are used to explore the impact of various factors on behavioral decision-making during the digital transformation of the agricultural industry. Additionally, a simulation analysis is used to verify the validity of the bilateral evolutionary game model. The results demonstrate that: (1) When the agricultural industry chooses “active transformation,” behavioral decision-making during the digital transformation of the agricultural industry reaches a Nash equilibrium; (2) transformation costs, industry revenue, and reward and penalty mechanisms are the main driving factors for whether or not the agricultural industry chooses to actively engage in digital transformation; and (3) the probability of active digital transformation increases when agricultural industry organizations obtain higher returns at lower costs. Simultaneously, the higher the government's incentives, the greater the enthusiasm. However, when the penalty is excessive, the digital transformation takes the shape of either passive transformation or forced active transformation. Subsequently, it is necessary to improve the digital transformation planning of the agricultural industry, strengthen this field's cooperation mechanism, and formulate a reasonable reward and penalty system for digital transformation.

“Above all, there’s our humanity”: Teachers’ intertextual responses to reading an ancient Hebrew text

This study examines the role played by intertextual connections suggested by teachers engaged in an interpretive dialogue on a sixth-century Hebrew text. This is a multiple-case study based on intertextual conversation research. The participants—26 Hebrew-as-L1 teachers in secular schools in Israel—were asked to study the text in havruta, a traditional Jewish approach to studying sacred texts, involving a dyad of learners who debate their meanings. Every havruta conversation was considered a case and was compared to the others. After the conversations, interviews were conducted with some of the teachers to learn more about the intertextual links that emerged. The findings suggested significant variance in the number and content of intertextual connections between the groups, given the teachers’ religiosity or previous experience with traditional Jewish texts. The connections suggested shaped the processing of the text, the teachers’ attitudes thereto, and their willingness to teach it. The main conclusion is that studying in multicultural havruta groups increases intertextual connections emerge and helps teachers to interpret the text.

A Methodological Presentation of How the Implementation of the Pregnancy Re-entry Policy among Stakeholders at Primary School Level in Chongwe District, Zambia Was Explored for a Phd Study

Research methodology is considered to be the backbone in addressing a research question. The research aimed at exploring how stakeholders perceive the implementation of the Re-entry policy. Critical theory was used as the theoretical framework in applied research. The study used qualitative methodology. The paradigm was mainly interpretive making use of multiple site case studies of stakeholders in its research design. Data was collected through desk review and individual in-depth semi-structured interviews. The data was analysed using Qualitative Content Analysis (QCA) and Reflexive Thematic analysis (RTA). Research methodology needs to be coherent in line with the research question, researcher capabilities and the context within which the study is being conducted.

Dynamic Capability Framework for Indonesian School Adaptation in Unstable Public Policy Times

ABSTRACT This research endeavors to disclose the key organizational components of three selected schools and their responses to considerable public policy flux. It aims at integrating key aspects of learning from change situations into systemic approaches to the organizational design of schools in Indonesia. Using dynamic capability framework, this research delves into an overarching question of how Indonesian schools adjust its internal and external context to unstable public policy times. Using a qualitative multiple case study approach, this research gathered data through interviews and focus group discussions. The analysis is structured in three stages namely in case analysis, cross case analysis, and finding formulation. The findings suggested that the area of adaptation could be formulated within the following phases: a) questioning and understanding the situation; b) identifying and defining school identity to guide adjustment efforts; c) maximizing communication, network, and school resources; and d) insights into the school structure. The important actions implemented by the schools correspond with dimensions of dynamic capability namely sensing, seizing, and transforming. This study also highlights the significance of a particular approach to organizational design that features dynamic capabilities and substantive capability to help schools manage their resources to successfully adapt within unstable environmental periods.

The role of trade associations in promoting corporate sustainability transition and reporting: A case study in the food supply chain

AbstractThis study seeks to examine the interconnections and potential influencing factors between firms' sustainability initiatives and the pressures exerted by industry associations for sustainability reporting. Given the reluctance of many firms to disclose their sustainability efforts, particular attention is directed toward the Italian meat and cured meat industry, with a focus on uncovering the drivers that link firms' sustainability actions to the level of engagement by industry associations in promoting sustainability reporting. Employing a multiple case study design, this research adopts a qualitative and interpretative methodology, leveraging semi‐structured interviews to elucidate pertinent strengths and weaknesses associated with both external and internal factors, as well as managerial strategies. Encompassing various stages of the supply chain, from genetic selection and animal husbandry to processing and distribution, this study aims to bridge a gap in the existing literature on sustainability reporting by shedding light on firms' approaches to both reporting and action. Furthermore, it offers novel insights into the often‐overlooked meso‐level factors influencing firms' propensity for sustainability reporting, including the role played by trade associations.

Entrepreneurial migrations and diffusion of innovations: the case of Apulia in the first half of the 19th century

Purpose Through the reconstruction of the events of some foreign entrepreneurs who worked in the territory of the Italian city of Bari in the first half of the 19th century, this paper aims to analyze the role of entrepreneurial migration in the economic development of Apulia land in this period. Design/methodology/approach This study adopts a theoretical framework that combines the concept of mixed embeddedness in a multifocal perspective, with the model of the diffusion of innovation focusing on the role of the so-called agency of actors, and of the network, in the dissemination of innovation. The theoretical framework is applied to multiple case studies to compare the evidence that emerged from the simultaneous analysis of several situations. Findings By analyzing how innovations have spread within the network of entrepreneurs of that time, it is possible to identify some relevant aspects related to the mechanisms of dissemination of innovations in the context of entrepreneurial migration. Specifically, the opportunity structure is intended in an even broader sense than indicated in the classic approach to mixed embeddedness: it is considered as the result of the joint interaction of the political, institutional and economic context of several places, and the behavioral dynamics of several groups. Research limitations/implications Due to the specific method chosen, the outcomes of the research might apply to a narrow context. Therefore, the results need to be tested and confirmed in further empirical studies, and by applying multiple research methods. Practical implications Findings are useful and significant in the analysis of the link that exists between the diffusion of innovations and migrant entrepreneurship, and then the conclusions can be applied and extended to the current phenomenon of migration-related innovations, with specific reference to developing countries. Social implications Findings can be applied and extended to the current phenomenon of migration-related innovations and highly skilled migration, with specific reference to developing countries. Originality/value This paper contributes to shed new light on the contextual and multifocal factors that influence the development of innovations in the networks of migrant entrepreneurship, in a specific historical period and a specific context. Combining social, human and financial capital with the wider opportunity structure, this study also provides a comprehensive understanding of the modalities through which migrant and high-skilled entrepreneurs could innovate.

Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms

Drug discovery is a crucial element of biomedical research, with the goal of finding and creating new medical treatments for a variety of illnesses. Yet, the conventional process of finding new drugs is frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, and poor success rates in trials with patients. Recently, the incorporation of machine learning (ML) algorithms has become a revolutionary method to streamline and improve different phases of drug discovery. This summary offers a glimpse into the rapidly growing area of drug discovery using machine learning algorithms, emphasizing its potential to transform the process of developing treatments. The usual process of discovering drugs involves various stages such as identifying the target, finding lead compounds, conducting preclinical tests, undergoing clinical trials, and obtaining regulatory approval. All these phases require a lot of labor, time, and resources, leading to high attrition rates and limited success in turning potential compounds into approved therapies. Nevertheless, researchers can enhance and speed up crucial parts of the drug discovery process by using ML algorithms. ML algorithms use data to aid in drug discovery by utilizing computational models to examine large quantities of biological, chemical, and clinical data. These algorithms can learn from various types of data, such as genomic data, chemical structures, protein interactions, and clinical outcomes, to discover hidden patterns, find new targets for drugs, and forecast the effectiveness and safety of potential treatments. Moreover, machine learning algorithms allow for the investigation of intricate connections between molecular structures and biological effects, making it easier to create improved drug candidates with better effectiveness and specificity. Important uses of machine learning in pharmaceutical research involve finding and confirming targets, screening compounds and improving leads, repurposing drugs, and tailoring treatments for individuals. Commonly used for classification and regression tasks, supervised learning algorithms like support vector machines and random forests predict compound activity, toxicity, and pharmacokinetic properties. Clustering and dimensionality reduction techniques utilized in unsupervised learning algorithms help analyze vast datasets and discover new drug-target interactions. Advanced abilities for analyzing molecular structures, virtual screening, and designing new drugs are provided by deep learning models like convolutional neural networks and recurrent neural networks. Multiple case studies demonstrate how ML algorithms can significantly impact drug discovery. Collaboration among academia, industry, and research institutions has resulted in the creation of new ML-based methods for drug development, identifying targets, and categorizing patients. Nevertheless, there are challenges accompanying the widespread use of ML in drug discovery. In healthcare, it is crucial to address ethical considerations, regulatory hurdles, and data privacy concerns to ensure the responsible and ethical use of ML algorithms. The potential for transforming drug discovery and therapeutic development is immense with the incorporation of machine learning algorithms. Through utilizing data-driven methods, researchers can speed up the discovery of new potential drugs, enhance the effectiveness of treatments, and ultimately enhance the results for patients. Ongoing innovation, teamwork, and cross-disciplinary studies are crucial to fully leverage the potential of ML in revolutionizing drug discovery and precision medicine.

Exploring industrial symbiosis for circular economy: investigating and comparing the anatomy and development strategies in Italy

PurposeThe purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.Design/methodology/approachThe current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.FindingsThis research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.Originality/valueCurrent literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).

Collaborative Spaces as Intermediaries for Small Business Innovation

The rapid pace of technological change has been pushing firms to make their innovation processes more open. However, both inbound and outbound processes are complex to manage, especially for small businesses. These firms have their innovation activities constrained by missing capabilities, financial resources, and market reach. However, the existence of innovation intermediaries may overcome these issues by fostering collaboration among different stakeholders and thus bridging the gap between technological opportunities and user needs. Therefore, the aim of this paper is to analyze how collaborative spaces intermediate small business innovation. A multiple case study comprising five collaborative spaces was carried out. Results indicate how each type of collaborative space may facilitate innovation according to their intermediation roles, type of governance, main members/users, type of knowledge produced, and the core elements of attraction they offer.

Unveiling maker mindsets: a journey of formation and transformation through design thinking-making pedagogy within a lesson study context

Maker Education (ME) focuses on fostering students’ Maker Mindsets (MM). However, prevailing ME practices often lack a structured approach, that impedes the effective development of students’ MM. To address this challenge, a Design Thinking-making pedagogy (DTMP) is used to design ME lessons through a three-month Lesson Study (LS) cycle. Employing a qualitative multiple case study methodology, we studied the development and transformation of MM in three elementary school students. Diverse data were collected, including student artifacts, video recordings of teacher discussions, and audio recordings of the interviews. The findings revealed common cognitive, self-efficacy, and collaboration improvements among the three students. Unique changes in individual cognitive skills, motivational patterns, and obstacles to collaboration were also observed. Furthermore, key factors influencing MM development were identified across individual, social, and pedagogical dimensions. Individual factors included prior knowledge, maker skills, and motivation, whereas social factors included teachers’ attitudes, recognition, feedback, and peer collaboration. Pedagogical practices, such as project difficulty, structure, and authenticity, play a crucial role in MM development. This study not only contributes valuable insights into MM development, but also underscores the pivotal role of tailored, personal real-life experiences in ME pedagogy. Furthermore, our study suggests that teachers can enhance ME pedagogy by intentionally designing it to facilitate MM in students, using the LS approach.

An implementation model for socio-technical digital tools

PurposeWhile Digitalisation is gaining momentum among practitioners and the scientific world, there is still a struggle to embark on the digitalisation journey successfully. The struggles are more significant for SMEs compared to large companies. Such transformation could face internal resistance, which evokes the need to put it into a socio-technical perspective such as lean. This paper investigates how SMEs could implement digital tools and technologies in their operations.Design/methodology/approachWe relied on a multiple case study design in three SME manufacturing companies in Italy. Based on the experience of those companies, the struggles in the implementation and the lessons learned, we formulate an implementation model of digital tools driven by lean thinking.FindingsCompanies tend to implement first digital tools that help with real-time data collection and stress that introducing digital tools becomes challenging without reducing waste in production. The model stresses top management commitment, middle-line involvement and operator training to resist change. All these factors coincide with socio-technical lean bundles developed by seminal works. In addition, the study highlights that financial incentives are not necessarily the common barrier to digital tools implementation in SMEs but rather the cultural aspect.Originality/valueOur paper enriches the extant body of knowledge by deriving knowledge around digitalisation implementation through lessons learned and corrective actions. It allows managers to benchmark and compare the current state of the implementation process with that of other companies and the one proposed to make corrective actions when necessary.

What Extraordinary Times Tell Us About Ordinary Ones: A Multiple Case Study of Precariously Employed Food Retail and Service Workers in Two U.S. State Contexts during the COVID-19 Pandemic

Disease prevention relies on a complex interplay between social context and individual behaviors. Work and the employment conditions that shape it are key domains where this interplay occurs, a reality highlighted by the COVID-19 pandemic. To explore the links between employment quality and social context as drivers of disease prevention, we conducted a multiple case study of food retail and services workers during COVID-19 in two U.S. states – Indiana and Washington – with differing norms and policy landscape. We drew on public health surveillance data, government/NGO documents, and media sources to contextualize in-depth interviews with 26 precariously employed food workers.  Analysis consisted of a within-case and a cross-case phase, each drawing on state contextual and interview data. Precariously employed food workers in Indiana and Washington had contrasting expectations of employers, government, and public health that paralleled policies and norms in their respective states. Ultimately, though reasons for it differed, workers in both states made choices about preventive behaviors in an individualized way that was shaped by their employment circumstances.  Explicit consideration of employment and the social safety net in public health would better prepare us for the next crisis and ameliorate health inequities under ordinary circumstances. 

Pesantren Resistance to Indonesia's National Curriculum to Defend Its Curriculum Model

Purpose: This article explores the challenges faced by pesantren (Islamic boarding schools) in Indonesia in reconciling the national curriculum with their traditional religious education while preserving their identity.   Methods: The research employed a qualitative approach, utilizing multiple case studies conducted in two pesantren in Indonesia. The study involved observations, interviews, and documentation collected from January to December 2023.   Result and Conclusion: The findings reveal a contestation between the national curriculum and the curriculum offered by pesantren. While many pesantren have integrated formal education, they exhibit resistance by focusing on Islamic teachings and values. Pesantren often modify the national curriculum to align with their religious identity. This study underscores the complexity of integrating formal education with pesantren's values and emphasizes the necessity of an inclusive approach from the government to ensure the relevance of education. Furthermore, it highlights pesantren's resistance to the dominance of the national curriculum, reflecting tensions between the government's modernization agenda and the traditional values upheld by pesantren.   Research Implications: This research contributes to understanding the dynamics between formal education and traditional Islamic education within Indonesia's pesantren context. It sheds light on the challenges pesantren face in maintaining their identity while conforming to national educational standards. The findings have implications for policymakers, educators, and stakeholders involved in shaping the educational landscape of Indonesia.   Originality/ Value: This study offers original insights into the interaction between the national curriculum and pesantren's curriculum, which have not been extensively explored in existing literature. This research contributes to a deeper understanding of educational dynamics in Indonesia by examining pesantren's responses to government policies and state control. Additionally, it underscores the significance of preserving the unique identity of pesantren amidst efforts to modernize the educational system.

Adopting Critical Success Factors (CSF) Methods to Achieve High Management Performance on Dairy Farms

It is known that the agribusiness sector in Brazil is made up of several companies operating in different business models in their environment. They operate in production side as no cyclical and the market side as cyclical, such as milk and dairy products. It is, however, characterized by dynamic and complex processes and competitive events, applied technologies, specialized labor problems, with risks and uncertainties inherent to the environment. Dairy farming is a relevant segment of this sector, both in terms of representativeness and wealth generation. Therefore, dairy farms need to be aware of innovations, and be effective in the pursuit of productive performance and profitability. The present study aims to evaluate the Critical Success Factors (CSF) involved in the search for high productive performance and profitability in dairy farms assisted by technical and managerial consulting of an agribusiness alpha company. The research has a qualitative approach and a descriptive conclusive character. Multiple case studies were carried out, and the units of analysis are 6 farms, their respective leaders (owners and managers), and consultants specialized in dairy farming from the company Alfa de Consultoria. The results demonstrate that the CSFs that promote high performance in dairy farms are: financial management; leadership and people management; process management; employee training and development; adequate sizing of the project; search for continuous improvement; management of indices and technical indicators; ensure animal comfort; technical assistance and consultancy.

The evolution of the trust relationship between the family and its external advisors throughout generations: A family business perspective

AbstractThis study delves into the evolving dynamics of the trust relationship between the family and its external advisors across generations in family firms, a crucial, yet underexplored topic in the family firm literature. Anchored in the resource‐based view, family firms leverage external advisors to compensate for internal resource deficits. Employing a qualitative multiple case study approach, our research unveils the nuanced evolution of trust between the family and these external advisors across generations. The findings reveal a distinctive trust evolution, transitioning from interpersonal trust in the first generation to a more sophisticated reliance on the advisor's competencies and trust in the processes of the advisor's firm in subsequent generations. This transformation in the trust relationship is marked by a shift from emotionally charged relationships to more business‐oriented relationships, openness becoming less self‐evident but more calculated, neutrality assuming importance where it was initially absent, and competence being subjected to critical assessment rather than presumed. With this, our study makes a notable contribution to the family firm literature by providing a dynamic perspective on the trust relationship between the family and its external advisors. It also addresses whether family firm decision makers prioritize interpersonal trust in the advisor over trust in competencies or the system, providing valuable insights for practitioners and scholars alike. Overall, this study enriches our understanding of the sustained and evolving nature of trust as a critical resource for competitive advantage in family firms.

Thriving In College: A Qualitative Exploration of High-Functioning Autism Spectrum Disorder in the Philippine Higher Education Landscape

This study explored the experiences of students diagnosed with High Functioning Autism Spectrum Disorder (HF ASD) in higher education. It also investigated academic challenges in the mainstream, learning the coping strategies and opportunities of the existing special support programs. Data were analyzed through the integration of Yin's five phases of analysis, ensuring that it complements the stages of the multiple case study methodology and a structured, in-depth exploration of the data. The study enlisted a diverse group of participants across the Philippines. Findings underscored the resilience and adaptability of the participants to enhance a transformative impact of education for the special student population. Relevant concerns related to social interactions, the importance of inclusive environments, and the significance of self-advocacy, empowerment, and familial and peer support for academic success are highlighted. In conclusion, the study recommends the establishment of on-campus counseling services tailored to HF ASD students and advocates for the fostering of inclusive and supportive higher education environments. This research contributes valuable insights for developing targeted interventions and support systems to enhance the overall well-being and success of HF ASD students thriving in the college setting.

An activity theory analysis of digital innovation orchestration in Industry 4.0

PurposeThis paper explores the orchestration of digital innovation in Industry 4.0 organisations.Design/methodology/approachThe study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.FindingsThe results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.Originality/valueThe study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.

Extreme Weather Disruptions and Emergency Preparedness Among Older Adults in Ohio: An Eight-County Assessment

AbstractThe disproportionate risks and impacts of climate change and extreme weather on older adults are increasingly evident. While especially true in disaster-prone areas, human-caused climate change introduces an element of uncertainty even in previously identified “safe” regions such as the Midwestern United States. Using a cumulative disadvantage and vulnerability-informed framework and descriptive statistics from multiple data sources, this article provides an overview of climate impacts, vulnerabilities, and county-level characteristics, focusing on older adults living in Central Ohio. A comparative multiple-case study methodology was used to triangulate regionally representative primary and secondary data sources to examine state and county-level measures of vulnerability, emergency preparedness, and disruptions caused by extreme weather among older adults across eight counties in Central Ohio. Seventy-eight percent of older adults in the sample reported being prepared for emergencies per Federal Emergency Management Agency guidelines. Older adults in Union County reported the highest rates of preparedness, while those in Fayette County reported the lowest. County-level rates of disruption of life activities by extreme weather ranged widely. Among the most rural in the region, Fayette County emerged as uniquely disadvantaged, with the lowest median income, the most vulnerable across multiple social vulnerability dimensions, and the most reported disruptions to life activities from extreme weather. County profiles offer a snapshot of existing vulnerabilities, socioeconomic conditions, special needs, preparedness, and current disruptions among older adults in the region and can inform resource mobilization across community and policy contexts.

Corporate Social Responsibility Practices of Global Automakers in Asia: Application of Carroll Model with Multi-Case Study Method

In spite of acknowledging the importance of sustainable and socially responsible practices; researchers have not been able to unanimously identify the priority areas of CSR for the automotive sector. This research was designed to understand the CSR activities of the global automakers in the Asian countries by applying the Carroll model wherein researchers wished to analyse the CSR programs of the global automakers, identify the priority CSR areas and assess the effect of CSR strategies on business performance of global automakers. Case study method was used in the existing study. This study utilised multiple case study research strategy whereby two automobile companies were studied in depth namely, Toyota and Volkswagen. The data for the study was secured from different- structured data sources like company reports, sustainability data records and media coverage. Data was analysed using content analysis. The study concluded that both companies are widely engaged in CSR initiatives that covered ethical, philanthropic, legal, and economic responsibilities. Toyota focused on their production process and policies that was employee centric, Volkswagen worked on industry modernization as well in their ethical practices. The philanthropic initiatives at both places worked towards education/training, employment generation, collaborations/partnerships, environmental and social welfare. Volkswagen also focused on sports and, art and cultural aspects in their humanitarian responsibilities. Both the peers were on the same page regarding economic practices where the cutting costs and improving efficiency was in focus. Both Toyota and Volkswagen gave more priority to the philanthropic and ethical aspect than the economic aspect. Both the companies experienced similar outcomes from these CSR initiatives like positive financial measures, quality metrics, brand identity improvement and differentiation, worker and consumer satisfaction, risk management and aversion, improved partnership opportunities.

Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach

Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.