E-ISSN: x-x
ARTICLE
Artificial Intelligence and Emerging Technology Integration:
Setting a Research Agenda in Operations Management Muhamad Asif Maulana Akbar 1.
Muhammad Gilang Maulana Azka* 2 Economics and Business Faculty Universitas Sains Al-Qur'an.
Wonosobo.
Indonesia,Asif@unsiq.
Economics and Business Faculty Universitas Sains Al-Qur'an.
Wonosobo.
Indonesia,Gilangmaulanaazka@unsiq.
*Correspondence Author: gilangmaulanaazka@unsiq.
Abstract Artificial intelligence (AI) has emerged as a key driver behind numerous technological innovations and is increasingly shaping practices in supply chain and operations management.
This paper outlines a research agenda that explores how both scholars and practitioners can investigate the adoption of AI-based solutions to enhance supply chain performance.
Drawing upon the unified theory of acceptance and use of technology, we propose several avenues for inquiry that emphasize the use of mixed-methods research designs.
Given that these technologies remain in an early yet rapidly advancing stage, mixed-methods approaches provide a suitable framework to carefully evaluate their features and to identify pathways that may encourage effective adoption, with particular benefits for operations in mind.
Accordingly, we highlight three research directions grounded in the developmental, integrative, and expansion-oriented purposes of mixed-methods Keywords: artificial intelligence.
supply chain management.
operations management.
technology adoption.
mixed-methods research Academic Editor: Hayley Williams Received: September 04,2025 | Revised: September 15,2025 | Accepted: October 9,2025 | Published: October 15,2025 Citation: Akbar.
, & Azka.
Artificial intelligence and emerging technology integration: Setting a research agenda in operations management.
Future Economics and Business Studies, 1.
, 26Ae33.
https://doi.
org/10.
3390/x Introduction Artificial intelligence (AI) and machine learning have generated significant advances across diverse sectors, including healthcare, finance, transportation, and security.
For example, autonomous vehicles employ AI to enable automated navigation, lane changing, and collision avoidance using cameras and sensors.
Despite these innovations, unresolved issues remain, particularly related to ethical concerns raised by the opacity of AI decisionmaking (Wamba-Taguimdje et al.
, 2.
Within supply chain and operations management.
AI is already reshaping functions such as demand forecasting, inventory tracking, and predictive maintenance.
The combination of AI with blockchain technologies further Future Economics and Business studies 2025.
Vol 1 .
, 26-33 https://doi.
org/10.
3390/x Future Economics and Business studies 2025.
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, 26-33 strengthens decision-making through large-scale data analysis, enhancing automation, connectivity, and traceability across supply chain networks (Schuetz & Venkatesh, 2020a.
Karakas.
Acar, & Kucukaltan, 2.
Since AI depends heavily on data reliability, blockchain provides assurance by securing and validating transactions across distributed nodes (Dwivedi et al.
, 2.
Equally, the Internet of Things (IoT) supplies vast real-time datasets that enable faster decision-making (Ben-Daya.
Hassini, & Bahroun, 2.
Together, these emerging technologiesAiAI, blockchain, and IoTAiare expected to transform production and service operations in fundamental ways (Kumar.
Mookerjee, & Shubham, 2.
Nonetheless, adoption challenges remain.
StakeholdersAo willingness to embrace such innovations continues to be a critical issue, and research must examine how adoption can be encouraged in supply chain and operations contexts (Akter et al.
, 2022.
Fosso Wamba et al.
, 2.
Although prior studies provide useful insights, the rapid evolution of these technologies makes it unclear how best to integrate them into organizational settings.
The unified theory of acceptance and use of technology (UTAUT) offers a robust foundation for investigating these issues (Venkatesh et al.
, 2003.
Venkatesh.
Thong, & Xu, 2.
Contextualizing such theories not only yields practical guidance but also contributes to theory-building (Hong et al.
, 2.
While AI has existed since the 1950s, recent advances in computing power and data availability have unlocked its business potential.
Yet, concerns such as model bias and transparency persist (Venkatesh, 2.
Earlier studies have suggested several research directions in AI adoption, including individual, technological, and environmental determinants (Sharma et al.
, 2022.
Venkatesh, 2.
We extend this line of inquiry by focusing on technology characteristics, a determinant widely acknowledged as central to adoption (Zhang & Venkatesh, 2018.
Sodhi et al.
, 2.
Given the novelty of these technologies, we argue for a mixed-methods research approach, which has been recognized as especially valuable in contexts where limited knowledge exists and predictors must be identified (Venkatesh.
Brown, & Bala, 2.
Mixed-methods designs provide richer insights into complex, evolving phenomena than single-method approaches (Golicic & Davis, 2012.
Cadden et al.
, 2.
In summary, this paper contributes by framing AI alongside other emerging technologies in operations management, applying UTAUT as the theoretical foundation, and advancing a mixed-methods research agenda.
Through this perspective, we identify three research directions that can guide both scholars and practitioners in understanding and fostering the adoption of AI and related technologies within operations and supply Materials and Methods To ground our study, we adopt the Unified Theory of Acceptance and Use of Technology (UTAUT), originally proposed by Venkatesh et al.
This framework synthesizes eight earlier models of technology adoptionAiincluding TRA.
TAM.
TPB.
SCT, and DOIAiand identifies four primary determinants of behavioral intention and system use:
performance expectancy, effort expectancy, social influence, and facilitating conditions.
These relationships are moderated by demographic and contextual factors such as gender, age, prior experience, and voluntariness of use (Morris & Venkatesh, 2000.
Morris.
Venkatesh, & Ackerman, 2.
Owing to its broad scope and proven applicability.
UTAUT has been employed across different countries, user groups, and technological domains (Maruping et al.
, 2017.
Chipeva et al.
, 2018.
Martins.
Oliveira, & Popovis, 2.
In addition, we examine the integration of emerging technologiesAiAI with blockchain and AI with IoTAiwithin supply chain management.
Blockchain, a decentralized ledger system, ensures secure, transparent, and tamper-resistant recording of transactions (Wamba & Queiroz, 2.
When paired with AI, blockchain systems enable predictive analytics, improved traceability, error reduction, and automated decision-making (Cammarano et al.
, 2022.
Tian et al.
, 2.
Similarly.
IoT devices generate real-time operational Future Economics and Business studies 2025.
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, 26-33 data that, once processed by AI algorithms, allow for the development of digital twins, predictive maintenance, demand forecasting, and optimized inventory management (Ivanov et al.
, 2.
To analyze adoption in this evolving landscape, we propose a mixed-methods design (Venkatesh.
Brown, & Bala, 2.
Three complementary purposes guide this approach:
developmental studies, where qualitative insights help identify relevant technological .
completeness studies, which combine quantitative validation with qualitative and .
expansion studies, which use sequential designs to refine adoption strategies and remove barriers.
Linking these studies to UTAUT ensures a structured lens through which adoption drivers, feature utilization, and consequent benefits can be assessed.
Figure 1.
UTAUT
Result The review highlights a range of technological features that showcase the potential of AI integration within supply chains.
For blockchain-enabled applications, notable outcomes include enhanced transparency, secure auditing, reliable traceability, and smart contracts that automate key business transactions (Pun.
Swaminathan, & Hou, 2021.
Wong et al.
, 2.
The synergy of AI with blockchain further allows machine learning algorithms to detect inefficiencies, forecast demand, improve production quality, and optimize risk management processes (Lohmer & Lasch, 2.
For AIAeIoT applications, the findings point to real-time monitoring, the creation of digital twin models of supply chain networks, and advanced predictive analytics that inform strategies to minimize costs, reduce environmental impacts, and manage risks (BenDaya.
Hassini, & Bahroun, 2019.
Ivanov et al.
, 2.
Practical implementations, such as IBMAos AI modules, demonstrate capabilities in product recalls, demand spike prediction, and cost-efficient logistics planning.
These results collectively reveal that the integration of AI with blockchain and IoT significantly enhances decision-making, efficiency, and resilience in supply chain operations.
Discussion Despite the clear benefits, several challenges hinder the widespread adoption of AIdriven technologies.
Concerns include reluctance among stakeholders to share sensitive data (Queiroz & Fosso Wamba, 2.
, regulatory ambiguities (Mathivathanan et al.
, 2.
the inherent Aublack boxAy effect of AI models, and the high costs of system integration (Sodhi et al.
, 2.
Such barriers underscore the need for further research into adoption determinants and strategies that can alleviate organizational resistance.
Future Economics and Business studies 2025.
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, 26-33 From a theoretical standpoint.
UTAUT remains the most comprehensive lens for analyzing adoption behavior, but recent meta-analyses suggest opportunities to refine the model by incorporating new predictors and contextual adaptations (Blut et al.
, 2.
Mixed-methods research offers an avenue for achieving this, as it provides not only statistical validation but also qualitative explanations and corrective feedback.
By sequentially or concurrently combining methodologies, researchers can better capture how features influence adoption, identify unanticipated barriers, and propose targeted interventions.
Ultimately, this research agenda extends prior work by framing AI alongside blockchain and IoT within supply chain management, applying UTAUT as a guiding theory, and advancing a structured mixed-methods pathway.
The implications are twofold: for scholars, it enriches the literature on technology adoption in dynamic contexts.
for practitioners, it provides actionable guidance on how to integrate emerging technologies to achieve efficiency, transparency, and sustainable outcomes.
Figure 2.
Future directions rooted in UTAUT and mixed-methods research Conclusions Artificial intelligence (AI)-enabled technologies, such as blockchain-based solutions, are increasingly becoming integral to supply chain operations.
Nonetheless, these technologies remain in a developmental phase, and their full potential has yet to be fully realized.
This study proposed a research agenda that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) with mixed-methods research, emphasizing three complementary purposesAidevelopmental, completeness, and expansion.
Through this integration, we outlined how scholars and practitioners can better examine adoption processes, usage patterns, and the resulting benefits.
As these technologies continue to evolve and new functionalities emerge, ongoing research that fuses UTAUT with mixedmethods approaches will be critical to advancing understanding in this domain.
Although our examples primarily drew on AIAeblockchain integration, the framework presented here is equally applicable to other AI-driven innovations in supply chain and operations From a theoretical standpoint, this work contributes to the information systems and operations management literature by providing one of the earliest comprehensive research agendas addressing AI and emerging technology adoption.
Specifically, the proposed directions enhance the UTAUT framework by: .
expanding the understanding of AI-specific features and their influence on user perceptions, .
clarifying how and why these features shape adoption intentions and technology use, and .
identifying key enablers and barriers within organizational contexts.
For practitioners, the agenda offers actionable insights into leveraging AI-based technologies in supply chains.
By following these three research directions, decision-makers Future Economics and Business studies 2025.
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, 26-33 can gain a clearer view of adoption drivers, anticipate challenges during implementation, and design strategies to mitigate potential obstacles.
Ultimately, this dual contributionAi advancing theory while offering practical guidanceAistrengthens the foundation for both academic inquiry and managerial practice in the era of AI-powered operations management.
References