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, positioning emerging technologies within a broader retail ecosystem in which these three dimensions form a self-reinforcing cycle that spans multiple stakeholders and can be interpreted through a range of complementary theoretical lenses.

The value of emerging technologies can only be realized through successful user adoption. Yet adopting new technologies often involves substantial costs, uncertainty and risk. Consequently, understanding the drivers of technology adoption has remained a central concern in the literature.

At the most granular level, technology adoption is rooted in users’ technology-related cognitive beliefs, with consumers, managers and employees representing the most extensively studied stakeholder groups. For consumers, perceived usefulness and perceived ease of use consistently emerge as key drivers of adoption across a range of customer-facing technologies, including contactless payment systems, IoT applications, augmented reality/virtual reality (AR/VR) technologies, parcel lockers, autonomous vehicles, delivery and service robots and other contactless technologies (Guida et al., 2023; Encarnación and Amaya, 2025; Kasilingam and Krishna, 2022; Ratchford et al., 2023; Shankar et al., 2021; Shin et al., 2026). Extending this stream of research, Garg et al. (2026) in this special issue examine the antecedents of motivation that both precede and sustain intention to use. For managers and employees, additional cognitive and evaluative beliefs shape adoption decisions. Prior research highlights the importance of perceived competence, or confidence in a technology’s ability to perform its intended functions, as well as perceived distributive justice, referring to the fairness of outcome distributions associated with technology use (Falcone et al., 2021). Similarly, valence, or the perception that benefits outweigh associated costs and appropriateness, reflecting the fit between technological features and task requirements, are critical determinants of successful adoption of technologies such as blockchain and IoT (Sharma et al., 2026). Social influence and anthropomorphism have also been identified as important drivers of employees’ willingness to work with autonomous delivery robot technologies, with these relationships mediated by perceptions of procedural fairness (Shin et al., 2026).

At the organizational level, a broad stream of research has focused on firm readiness to adopt emerging technologies, often emphasizing organizational capabilities and perceptions of expected benefits such as improved forecasting, enhanced visibility and greater operational efficiency and effectiveness (Modgil et al., 2021; Chou et al., 2023). Angelopoulos et al. (2023) and Xu and Pero (2024) further demonstrate that retailers characterized by greater digital maturity, stronger leadership support and stronger innovation orientation are more likely to invest in and successfully deploy emerging technologies. Other organizational drivers identified in the literature include organizational agility, inertia, complacency, top management support, mergers, environmental, social and governance (ESG) orientation and stakeholder priorities (Shankar et al., 2021). Park et al. (2026), featured in this special issue, add channel integration as another important determinant of retail technology application. Broader market and environmental forces have also been identified as important drivers of technology adoption. The rise of omnichannel retailing and e-commerce has intensified competitive pressure on retailers to pursue technological innovation (Jin et al., 2023; Aithal et al., 2023). At the same time, institutional pressures influence retailers to adopt technologies by imitating competitors and responding to industry norms and expectations (Hartley et al., 2022). Supply chain and macro-level disruptions, such as the COVID-19 pandemic, regulatory compliance requirements and technological progress, have also created a broader “technology push” that progressively increases the feasibility and attractiveness of adoption (Shankar et al., 2021).

Beyond cognitive and cost-benefit-related determinants, relational drivers are also becoming increasingly important, particularly with the emergence of AI-enabled technologies that exhibit human-like characteristics. Garg et al. (2026) in this special issue identify consumer engagement with retailers as an important factor influencing motivation to adopt drone technologies. Likewise, anthropomorphism–the tendency to attribute human characteristics to nonhuman entities–has been shown to influence employees’ willingness to work with autonomous delivery robots (Shin et al., 2026). Future research should further explore these relational dimensions of technology adoption. Several promising research directions also emerge from the current literature. First, existing studies often examine adoption drivers in isolation, focusing separately on consumers, employees, managers, or firms. However, technology adoption in retail ecosystems is inherently interconnected, with adoption decisions shaped by the alignment or misalignment of motivations, incentives and perceptions across multiple actors. Thus, more research is needed to examine the interaction and synergy among multiple adoption drivers across different stakeholders and supply chain actors. Second, while existing studies predominantly emphasize positive drivers, “negative antecedents” warrant equal attention. Future research should investigate how failed technological implementations, intrusive surveillance practices, or poorly designed applications may destroy value for both retailers and customers. Third, much of the literature remains concentrated in the United States, Europe and China, highlighting the need for additional research in emerging markets and across diverse cultural contexts. Fourth, rather than treating technologies such as IoT or AI as monolithic constructs, future studies should decompose them into application-level features, such as smart sensing, predictive analytics, or real-time control, to identify which functionalities most strongly influence adoption outcomes. Finally, the retail implications of emerging infrastructures and platforms, including fifth-generation (5G), micro-cloud computing and the metaverse, remain largely speculative and require longitudinal investigation. More broadly, research on technology adoption is increasingly shifting beyond antecedents and drivers toward questions surrounding post-adoption behavior, strategic operational reconfiguration, the “dark side” of technology and the broader systemic impact of emerging technologies on the retail ecosystem.

A further frontier concerns the rise of generative and agentic artificial intelligence, which differs qualitatively from the narrower technologies examined above. Whereas most retail applications of AI to date have automated discrete tasks such as forecasting or routing, generative systems increasingly mediate the consumer interface itself through conversational shopping assistants, synthetic product content and, most recently, autonomous agents that search, compare and transact on a consumer's behalf. These capabilities intensify the relational and anthropomorphic dynamics identified above, since consumers and employees now negotiate adoption with systems that appear to reason and converse. They also raise novel antecedents that the existing literature has scarcely theorized, including trust in machine-generated recommendations, the delegation of decision authority to autonomous agents and retailers' willingness to cede parts of the customer relationship to third-party AI intermediaries. Future research should examine how generative and agentic AI reshape adoption at the consumer, employee and firm levels and whether established acceptance models extend to settings in which the technology acts as an autonomous economic actor rather than a passive tool.

Although emerging technologies offer substantial promise for improving retail operations and performance, their implementation is often accompanied by considerable challenges. These challenges stem from multiple sources and frequently vary according to the technology being adopted, which underscores the importance of a nuanced, context-specific understanding of the implementation process. First and foremost, implementing these technologies may require substantial capital investment and adjustments to the physical infrastructure and human capital (Neumann et al., 2021; Solem et al., 2023). Narang and Shankar (2019), for example, show that in returns management, the lack of mature reverse logistics systems further complicates the implementation of advanced technological solutions.

One of the most prominent issues relates to the integration of new technologies with existing legacy systems. Many retailers operate within established infrastructures that are not readily compatible with advanced digital solutions, necessitating substantial investments in system redesign and process reconfiguration (Angelopoulos et al., 2023; Guida et al., 2023). Similarly, technologies such as IoT and blockchain promise greater traceability and transparency, but these benefits frequently depend on ecosystem-wide participation and sustained investments from supply chain partners (Hartley et al., 2022).

The nature and quality of the data needed to successfully implement these technologies also pose significant challenges. For example, technologies like AI and ML rely on large volumes of high-quality data and many retailers struggle with issues related to data availability, accuracy and governance (Xu and Pero, 2023). The effectiveness of data-driven decision-making is contingent on the availability of high-quality data and solid data management practices. Furthermore, the implementation of data-intensive technologies, such as IoT and big data and predictive analytics, necessitates massive information technology (IT) governance and data architecture overhauls (Sodero et al., 2019). Increasing digitalization exposes the supply chain to cyber risks (Richey et al., 2023), meaning that secure implementation is not merely an IT concern but a foundational operational requirement. Additionally, technologies such as drones and autonomous delivery systems face regulatory uncertainty (Alizadeh et al., 2026), while the use of customer data raises critical questions regarding privacy and ethical governance (Shankar et al., 2021).

Organizational and human factors may also create implementation issues. The introduction of advanced technologies often requires managers to develop new skillsets, particularly in contexts in which human-machine collaboration becomes central (van Hoek et al., 2022; Brau et al., 2023, 2024). Resistance to change, lack of technical expertise, or even simply human bias or cultural barriers may hinder technology implementation (Akkermans et al., 2024). In the context of AI in decision-making, for example, Zerilli et al. (2022) argue that managers’ attitudes towards AI algorithm recommendations range from passive acceptance (loafing) to rejection (opposition). Other studies, like McKinley et al. (2026), Cui et al. (2022) and Hou et al. (2024) demonstrate how users change their behaviors when using AI-based systems in demand forecasting, procurement and healthcare recommendations, respectively.

However, implementation challenges extend beyond internal personnel to external end-users. Just as managers require specific capabilities to use new systems, consumers must also be equipped to navigate them. For example, while online fulfillment strategies like buy-online-pickup-in-store, reserve-online pickup-in-store, buy-online-curbside-pickup and buy-online return-in-store are implemented with the main goal of empowering consumers with the choice of fulfillment method to align with their preferences (Hsia et al., 2020), the impact of these strategies might not be uniform across consumer populations. Effective implementation requires inclusive service design that accounts for the so-called “digital divide”, the reality that many consumer populations might not possess digital literacy or wide access to communication technologies (Paraskevas et al., 2025). This divide limits consumers’ skills, access and motivation to use a retailer’s online channels (Sunar and Swaminathan, 2022). Therefore, retailers face the complex implementation challenge of designing user interfaces and physical infrastructures that accommodate diverse consumer capabilities rather than alienating them, for example, by addressing consumer resistance to AR/VR or IoT and understanding how consumer preferences shape crowdsourcing delivery effectiveness.

Studies suggest that the adoption of emerging technologies in retail influences a wide range of operational, strategic and consumer-related outcomes. One of the most consistently observed benefits is improved operational efficiency. Automation, robotics and AI-driven systems enhance the speed and accuracy of warehousing, fulfillment and inventory management processes, resulting in cost reductions and improved service levels (Merkert et al., 2022; Modgil et al., 2021). Beyond efficiency gains, emerging technologies in retail reshape managerial decision-making. However, different technologies, such as AR, VR, AI and drone delivery to name a few, possess unique characteristics, suggesting that the mechanisms through which they influence enhanced managerial decision-making might vary. Future research should further contribute by examining how specific technologies shape managerial cognition and decision processes, thereby contributing to a more nuanced understanding of technology-enabled decision-making in retail operations.

In addition to operational efficiency, emerging technologies in retail operations have also significantly changed customer experience and outcomes. For example, retailers leveraging technologies such as AR/VR, personalization algorithms and contactless payment systems can significantly enhance the shopping experience for their customers. These technologies provide customers with more engaging, convenient and seamless interactions across retail channels and can subsequently lead to higher levels of customer satisfaction and loyalty (Ratchford et al., 2023; Guida et al., 2023). However, these technologies may introduce unintended consequences since they can be inherently biased, potentially leading to negative customer sentiments (RTTS, 2024). Thus, future research could explore the ethical, behavioral and policy outcomes of these technologies in retail operations, specifically with respect to customer perceptions of bias, fairness and trust.

Furthermore, the implementation of these technologies can lead to enhanced supply chain agility, resilience and sustainability. For example, digital solutions can lead to efficiency gains regarding resource utilization, waste reduction and environmentally-friendly practices (Belaud et al., 2019; Bai and Sarkis, 2020). Similarly, real-time data visibility and advanced analytics enable retailers to respond more effectively to supply chain disruptions and demand fluctuations (Russo et al., 2022). However, these benefits are accompanied by new risks, particularly cybersecurity threats, which highlight the double-edged sword of digital transformation (Richey et al., 2022). Given that the volume of data generated and processed through these technologies increases retailers’ exposure to cyberattacks, data breaches and ransomware attacks (Turgel et al., 2023). Future research could explore these risks more systematically to better understand the potential dark side of technology adoption for retail operations.

Lastly, an under-researched outcome refers to the potential social impact of retailer technologies. Parasksevas et al. (2025), for example, found that retailers’ focus on omnichannel and online fulfillment strategies ameliorates the negative impacts of digital divide conditions on vulnerable consumer populations and supports retailer sales. Building on this insight, future research could further contribute to this discussion by providing nuanced and in-depth insights into how technology adoption in retail affects underprivileged and disabled populations. Furthermore, researchers could explore the implementation and impact of emerging technologies across countries. Such an approach would allow scholars to shed light on the varying institutional differences, specifically with regard to the regulatory, economic and cultural conditions.

Beyond identifying empirical drivers and outcomes of technology adoption, future research would also benefit from stronger theoretical integration. The complexity of emerging retail technologies increasingly requires multi-level theoretical perspectives that can explain not only why firms adopt technologies, but also how technologies reshape operational processes, consumer experiences and broader retail ecosystems. Table 2 highlights several promising theoretical lenses that may help scholars better examine the organizational, institutional, operational and consumer-facing dimensions of technology-enabled retail transformation.

Table 3 summarizes key future research questions emerging from the theoretical and empirical gaps identified throughout this editorial. We organize the questions according to the primary phenomenon being explained and distinguish it from how that phenomenon is theoretically interpreted. Antecedents capture the individual, technological, organizational and relational conditions that shape readiness, acceptance, or resistance before adoption. Implementation issues concern the processes through which emerging technologies are integrated, governed, coordinated, adapted and scaled within retail and supply chain systems. Outcomes refer to the consequences of adoption and use for consumers, employees, managers, firms, interorganizational relationships and broader operational performance. Theory development and integration, by contrast, seeks to explain the underlying mechanisms, construct relationships, boundary conditions and conceptual extensions needed to account for these phenomena.

This distinction is important because empirical findings and theoretical implications serve different purposes. Findings establish what was observed within a particular setting, sample and research design; theoretical implications explain why those observations matter for existing concepts, assumptions and causal explanations. A statistically significant relationship, for example, does not in itself constitute a theoretical contribution unless it clarifies a mechanism, challenges an established assumption, identifies a boundary condition, or extends the explanatory reach of a theory. Accordingly, the questions in Table 3 are assigned according to their principal analytical purpose, while recognizing that strong research often connect several categories, for example, by identifying an antecedent, tracing its influence through implementation, assessing its outcomes and using those relationships to refine or integrate theory.

Technological innovations continue to redefine and transform the boundaries of the retail industry. As retailers increasingly adopt sophisticated technologies to thrive in a hypercompetitive, omnichannel environment while serving an increasingly demanding customer base, the need for rigorous research examining the elements that shape their successful implementation becomes paramount. We hope that the contributions presented in this special issue stimulate further scholarly inquiry into the role of technology in shaping the future of retail logistics and supply chain management.