Guest editorial: Advancing the future of retail operations with cutting-edge technologies

The retail industry is fundamentally changing due to emerging technologies (Morenza-Cinos et al., 2019; Ta et al., 2023). Machine learning (ML) and artificial intelligence (AI) for example, can help retailers improve demand forecasting accuracy (Modgil et al., 2021; Chou et al., 2023) and analyze data in real time, enabling them to better align their inventory decisions with their logistics network capabilities (Sodero et al., 2019). Other technologies such as blockchain, the Internet of Things (IoT) and augmented reality (AR) allow retailers to support product tracking and authenticity verification, real-time data accessibility and product visualization for customers, as well as the automation of processing product returns (Hartley et al., 2021). Furthermore, these technologies have also contributed to the rise of new retail formats and fulfillment models, such as reserve/buy online pick up/return in store, webrooming and buy in store/ship-to-home, which allow retailers to offer a more seamless customer experience across retail channels (Jin et al., 2023). Additionally, last-mile delivery is being reshaped by new delivery modes, including drones and autonomous delivery (Merkert et al., 2022).While these technological innovations offer many opportunities for retailers to improve their operational efficiency, their implementation often poses significant challenges for retailers (Angelopoulos et al., 2023). Adopting new technologies requires substantial financial investments, cross-functional coordination and changes to legacy systems. For example, while over 90% of retailers have adopted AI in some shape or form, often as pilots, most retailers struggle with rolling out this technology on a larger scale [1].Research on omnichannel retailing has shown that integrating physical and digital retail channels can improve customer service experience and convenience. However, it can also create tensions related to inventory visibility, order orchestration and last-mile cost management (Hubner et al., 2016; Melacini et al., 2018). Other technologies, such as IoT and blockchain, promise greater supply chain traceability and transparency. Yet these benefits often depend on system-wide participation and sustained investments from supply chain partners (Hartley et al., 2022). Moreover, work on data-driven retail processes suggests that these tools are only as effective as the underlying data quality and managerial capabilities that support their use (Sodero et al., 2019; Modgil et al., 2021). As such, the successful implementation of these technologies is highly dependent on foundational organizational processes and managerial capabilities.In addition to implementation issues, retailers struggle with fully realizing the promised and expected benefits associated with these emerging technologies. Research suggests that the performance outcomes of these emerging technologies are contingent on contextual factors such as retail format, product and service characteristics, channel configuration, customer expectations and retailers’ digital maturity (Gauri et al., 2021; Castelo-Branco et al., 2022). For instance, technologies designed to enrich customer experience, such as AR and other digital engagement tools, may improve customers’ purchase confidence and product evaluation. However, they might also generate limited value or even friction if they are poorly aligned with customer needs or retail channel design (Grewal and Roggeveen, 2020; Hilken et al., 2017; Poushneh and Vasquez-Parraga, 2017). Technologies designed to enhance fulfillment, such as automated delivery systems or autonomous solutions, can reduce lead time and expand service options, but they also introduce operational complexities, regulatory concerns and cost tradeoffs that might offset their benefits (Merkert et al., 2022). While prior work has enhanced our understanding of how emerging technologies have the potential to continue to transform retail operations and the customer experience, much of this work remains conceptual in nature or limited in scope (Guida et al., 2023; Modgil et al., 2022).The understanding of drivers, implementation issues and outcomes of technology adoption in retail operations varies significantly depending on the technology under investigation. For example, implementation issues related to AI or big data vary greatly from those related to IoT technologies which focus on enabling real-time monitoring and connectivity across supply chain and in-store operations. Similarly, the discussion of automation versus human input varies tremendously across these technologies. Thus, the diversity of technological contexts underscores the need for context-specific research investigations in order to offer in-depth insights into how specific technologies are implemented in the retail industry.Against this backdrop this special issue is positioned at the intersection of retail operations and emerging technologies. It seeks to explore the drivers to the adoption of these technologies, the considerations that shape their implementation and the ways in which the combination of these elements can redefine customer experience and, ultimately, reshape the industry.In this editorial we first introduce the six manuscripts included in the special issue, highlighting how each manuscript advances our understanding of how retailers engage in the adoption of these technologies, technology-specific implementation opportunities and challenges and retail, consumer and supply chain outcomes. Second, we identify avenues for future research structured around the three key dimensions of antecedents, implementation opportunities and challenges and outcomes. In doing so, this editorial provides a starting point for additional research to further explore the impacts of emerging technologies in retail operations while also encouraging research that is theoretically grounded and empirically rigorous.Employing a diverse set of empirical methods ranging from qualitative and survey to simulation and quantitative techniques, the six papers in this special issue collectively portray retail technology not as a set of isolated innovations, but as an interconnected ecosystem in which operational design, consumer behavior, organizational capabilities and channel integration jointly determine value creation. Across the studies, a common theme emerges: the success of emerging retail technologies depends not merely on technical feasibility, but on how effectively technologies are embedded within broader omnichannel systems and aligned with consumer expectations, organizational capabilities and strategic objectives. Yuan et al. (2026) challenge the assumption that omnichannel return strategies such as buy-online-return-in-store (BORIS) automatically create value, demonstrating that their benefits are highly conditional and dependent on complementary operational and marketing decisions. Garg et al. (2026) similarly show that the viability of drone delivery hinges not only on operational efficiency, but also on consumer motivation, perceived value and retailer-consumer engagement. Complementarily, Merkert et al. (2026) explore how drones can enhance warehousing and last-mile delivery from an organizational perspective and highlight barriers as well as opportunities for the implementation of this technology. Park et al. (2026) further extend this ecosystem perspective by illustrating how third-party delivery platforms can complement, rather than cannibalize, retailers’ proprietary channels when channel integration is strategically managed. Oliveira et al. (2026) shift attention toward the information-processing capabilities required to coordinate increasingly decentralized and consumer-centric fulfillment systems, while Bianco et al. (2026) demonstrate that warehouse automation creates value only when developed as a dynamic organizational capability aligned with evolving retail complexity. Collectively, these studies build a broader picture of modern retail transformation in which technology adoption, channel coordination, organizational adaptability and consumer engagement operate as mutually reinforcing components of a highly integrated retail ecosystem. A summary of the selected papers is presented in Table 1.Yuan et al. (2026) examine the role of BORIS as a popular strategy to attract consumers. In this study, the authors analyze data gathered for North America’s Top 1,000 e-retailers (2013–2019). They reveal that BORIS’s operational challenges often outweigh its benefits. Analyzing website sales, conversion rates, order value and traffic, this study shows that BORIS offers negligible direct benefits for pure e-retailers. For omnichannel “bricks-and-clicks” retailers, BORIS only weakly boosts traffic and order value when paired with free return shipping or low sponsored search spend, though it can drive incremental traffic when synchronized with high-spend search campaigns. Ultimately, their findings suggest that while BORIS can offer highly specific, conditional advantages, its overall impact on e-retailer performance is weak, challenging the conventional wisdom of its effectiveness.Garg et al. (2026) shift the conversation on drone-enabled retail operations away from purely technical and operational feasibility toward a deeper understanding of the demand side. The research offers a timely and theoretically rigorous examination of a critical bottleneck in retail innovation: the formation of consumer motivation. Drawing on expectancy theory and integrating partial least squares structural equation modeling (PLS-SEM) with Necessary Condition Analysis (NCA), the research provides a dual perspective on what is “sufficient” to drive motivation and what is “indispensable” for it to exist. The findings show that unless consumers perceive meaningful personal benefit, improvements in technical reliability (expectancy) or outcome utility (instrumentality) are unlikely to generate the necessary motivational force for adoption. The paper further enriches the retail conversation by introducing Consumer Engagement with Retailers (CER) as a critical relational mechanism in technology acceptance.Merkert et al. (2026) adopt a qualitative research approach to shed light on the technological, organizational and environmental factors that impact drone operations for warehousing and last-mile delivery. The authors conduct semi-structured interviews with 38 warehouse and drone delivery managers across the globe to explore the organizational challenges and limitations associated with the implementation of this new technology for fulfillment operations. Analyzing the data through the lens of Technology–Organization–Environment (TOE), the findings not only highlight the benefits and barriers of drone delivery adoption but also the importance of inter-organizational collaboration and different governance mechanisms for successful implementation of the technology.Park et al. (2026) examine the synergy between consumer-facing delivery platforms and omnichannel retail operations. Drawing on channel capabilities theory and leveraging a proprietary dataset, the authors show that platform partnerships can complement rather than cannibalize retailers’ direct channels. Specifically, each platform partnership increases physical store sales by 1.36% and direct online sales by 42.6%, suggesting that third-party platforms function as effective customer acquisition and discovery mechanisms that redirect traffic to higher-margin proprietary channels. The benefits are particularly pronounced for restaurant chains with sparse physical networks and for firms with deeper channel integration, where customers order through the retailer’s own interface while the platform handles fulfillment. Overall, the study demonstrates that strategic collaboration with delivery platforms can expand, rather than substitute for, direct-channel sales.Oliveira et al. (2026) focus on how omnichannel retailers can shift away from centralized fulfillment models toward distribution networks that are both adaptive and consumer-centric. Drawing on Organizational Information Processing Theory (OIPT), the authors argue that coordinating multiple delivery modes (quick commerce, scheduled delivery and in-store pickup) across varied consumer demands generates considerable task uncertainty and information load. This challenge is amplified in emerging markets, where security concerns make attended home deliveries the norm and tight time-window coordination a common operational constraint. Using data from a Brazilian omnichannel retailer and a consumer survey, they conduct simulation analyses to test whether an adaptive network that allocates orders in real time according to inventory availability and consumer preferences can mitigate this task uncertainty. Their results show that no single facility type can effectively serve such diverse demands. A decentralized, hybrid configuration that postpones fulfillment decisions through real-time data instead reduces task uncertainty and lowers failed delivery attempts, while also cutting quick-commerce fulfillment times. Ultimately, the study illustrates how information-processing capabilities help empower omnichannel retailers to manage operational tradeoffs in increasingly consumer-centric environments.Finally, Bianco et al. (2026) examine how grocery retailers implement warehouse automation to transform logistics processes in response to operational complexity. They argue that warehouse automation is a dynamic capability rather than a technical investment. Specifically, the authors analyze core distribution processes by conducting semi-structured interviews with eight automation providers and six grocery retailers, complemented by site visits. Their results unveil five strategic dimensions that guide warehouse automation decisions: selectivity, accessibility, expandability, scalability and resilience. Interpreted through the lens of Dynamic Capabilities theory (Teece et al., 1997), their framework demonstrates that selectivity and accessibility strengthen a firm’s sensing capabilities for effective decision-making, while expandability and scalability facilitate seizing new business opportunities. They conclude that successful automation depends on managerial and organizational logic that aligns technological design with evolving retail demands, bridging the gap between functional intralogistics research and capability-based strategic perspectives.This section develops an integrative research agenda organized around three interrelated dimensions: the antecedents of technology adoption, the implementation and operational integration of these technologies, and their outcomes and systemic implications. Figure 1 summarizes this organizing logic, emerging technologies within a broader retail ecosystem in which these three dimensions a that multiple and can through a of complementary value of emerging technologies can only through successful adoption. Yet new technologies often substantial uncertainty and understanding the drivers of technology adoption has a in the the most technology adoption is in with managers and the most For perceived and perceived of use as key drivers of adoption across a of technologies, including systems, IoT augmented reality technologies, autonomous delivery and service and other technologies (Guida et al., 2023; and and et al., 2023; et al., 2021; et al., this of Garg et al. 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Park et al. in this special issue, channel integration as of retail technology and environmental have also as drivers of technology adoption. The rise of omnichannel retailing and has on retailers to technological (Jin et al., 2023; et al., 2023). the time, retailers to adopt technologies by and to industry and expectations (Hartley et al., 2022). chain and such as the regulatory and technological have also a broader that increases the feasibility and of adoption et al., and relational drivers are also increasingly particularly with the of technologies that Garg et al. 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