1 Introduction

Industry 4.0 – enabled by digital technology and accelerated by governmental initiatives, industrial plans, and research programs (Büchi et al., 2020) – has been increasing the pace of innovation and digital transformation in manufacturing companies (Huber et al., 2022; Margherita & Braccini, 2020). The resulting boost in production capacity and flexibility at its core sets new requirements for all dimensions of Industry 4.0 (Lu, 2021; Meindl et al., 2021). As such, logistics and supply chain management (SCM) are becoming a leading-edge information systems (IS) application domain while undergoing rapid transformations to support the horizontal integration of production systems with suppliers and customers through digital technologies (Gupta et al., 2019).

In this frontline industrial development of Logistics 4.0 (Strandhagen et al., 2017a), Smart Logistics (Lee et al., 2016), or Smart Supply Chain (Frank et al., 2019a), digital technologies such as artificial intelligence (AI), blockchain, cloud computing, and the internet of things (IoT) are driving automation and planning from the execution of programmed tasks to a level where functions are (partially) autonomously performed and information is shared in a cross-organizational environment of cyber-physical processes (Klumpp & Zijm, 2019; Sigov et al., 2022). Yet, organizations are only able to benefit from such integrated cyber-physical systems when aligning their internal technology implementation efforts with the pace of external stakeholders (i.e., customers and suppliers) (Gupta et al., 2019; Shao et al., 2021). Consequently, organizations need to respond with investments in emerging technologies to redesign their logistics structures and practices (Frank et al., 2019b). For this transformation toward Logistics 4.0 to succeed, the adoption of digital technology needs to be closely governed and managed (Margherita & Braccini, 2021). So far, very few companies have successfully completed this transformation (Zhang et al., 2021). To do so, corporate decision-makers need to understand the opportunities brought about by digital technologies for as well as their interplay with diverse tasks in logistics processes (Yang et al., 2021).

In the literature, research on the systematic application of digital technologies in the context of Logistics 4.0 – contrary to extensive work on other Industry 4.0 dimensions like smart products (Kahle et al., 2020) or smart manufacturing (Tabim et al., 2021) – is limited. The existing body of knowledge covers important insights on global supply chains in Industry 4.0 (Han et al., 2021; Shao et al., 2021; von der Gracht & Stillings, 2013) as well as on organizational drivers for leveraging smart supply chains (Gupta et al., 2019; Yang et al., 2021). Other studies indicate the enormous potential of individual technologies in Logistics 4.0 (Fescioglu-Unver et al., 2015; Kopyto et al., 2020; Leofante et al., 2019) and provide knowledge on their positive impacts like increased decision-making efficiency (Barreto et al., 2017), supply chain transparency (Zhu et al., 2021), or resilience (Rajesh, 2017). Only very few studies adopt a holistic perspective on digital technologies in Logistics 4.0 by classifying Industry 4.0 applications (Strandhagen et al., 2017b), defining base technologies (Frank et al., 2019a), and identifying technological building blocks (Winkelhaus & Grosse, 2020) of Logistics 4.0. Although these studies provide a good understanding of the technological advances of Logistics 4.0, a cross-technology perspective on the systematic opportunities digital technologies provide for logistics processes is needed to form a more robust basis for them to be implemented, combined, and leveraged to improve logistics workflows and management (Shao et al., 2021; Winkelhaus & Grosse, 2020). In this regard, intralogistics is currently seen as the area of Logistics 4.0 that will benefit the most from this transformation (Winkelhaus et al., 2022).

Against this backdrop, this study aims to identify and systemize the opportunities digital technologies afford for logistics processes by adopting an intra-organizational perspective as a central starting point for digitalization initiatives toward Logistics 4.0. To this end, it means to synthesize current academic research and frontline industrial insights through the lens of affordance theory (Gibson, 1986; Majchrzak & Markus, 2013), which has gained momentum in IS research for developing a fine-grained understanding of the action potential of digital technologies in different organizational environments (Islam et al., 2020; Seidel et al., 2013; Wendt et al., 2021).

To pursue this research objective, we draw on a two-phase research approach. First, we perform a systematic literature review (Webster & Watson, 2002; Wolfswinkel et al., 2013) to rigorously develop a catalog and conceptual framework of digital technology affordances in intralogistics (DTAILs) and associated practical manifestations as cues for potential use. Second, we conduct a qualitative interview study with ten subject matter experts from academia and industry to evaluate, expand, and refine the results (Bettis et al., 2015; Goldkuhl, 2012). Thereby, the key contribution of our work is a theoretically well-founded and practically relevant catalog and conceptual framework of DTAILs in the context of Logistics 4.0 as a frontline industrial development.

Our results are novel as they are the first to systematically illustrate the opportunities provided by a comprehensive spectrum of digital technologies to logistics processes. While previous research focuses on the main Industry 4.0 technologies (e.g., AI, blockchain, cloud computing, IoT), this study’s perspective makes it a proper basis for further theorizing on Logistics 4.0. From an IS perspective, this work advances the understanding of the opportunities provided by digital technologies as the interplay of actors and a leading-edge application domain by analyzing and emphasizing the interrelation of tasks and technology through process orientation. Apart from its theoretical contribution, the catalog and conceptual framework of DTAILs support practitioners not only in understanding the areas of opportunity in Logistics 4.0 but also stimulate structured discussions on the development of their portfolio of digital technologies in specific intralogistics processes.

The remainder of this paper is structured as follows. In Section 2, we provide relevant background on Logistics 4.0, intralogistics, and affordance theory. We present our two-phase research approach in Section 3. Section 4 illustrates the results and discusses them against the background of the theoretical foundation. Section 5 amplifies theoretical and managerial implications before Section 6 concludes by pointing out limitations and indicating avenues for future research.

2 Theoretical Background

2.1 A Task-Technology Perspective on Logistics 4.0

Logistics 4.0 is the logistical system that enables the sustainable and cost-effective satisfaction of new market demands of customer-oriented, individualized, and more responsive supply chains and logistics based on emerging digital technologies (Strandhagen et al., 2017a; Winkelhaus & Grosse, 2020). As part of SCM, logistics encompasses the management of planning, implementing, and controlling procedures for the efficient movement and storage of materials and related information from the point of origin to the point of consumption (CSCMP, 2013). Logistics 4.0 affects the key logistics activities of transport, inventory management, material handling, supply chain structure, and information flow. It facilitates improvements in areas such as sustainability, traceability, efficiency, and responsiveness to customers (Strandhagen et al., 2017a) and makes the physical supply chain dimension and the digital data value chain dimension converge to cyber-physical processes (Hofmann & Rüsch, 2017). The resulting combination of the inevitable involvement of the physical movement of goods and the high dependency on accurate and up-to-date data in the rapid environment of Industry 4.0 (Lai et al., 2018) makes logistics a key domain for digital technology integration into business operations (Gunasekaran & Ngai, 2004). Technological capabilities and targeted technology investments are found to have a significant impact on firm performance in Logistics 4.0 (Bag et al., 2020).

In the literature, the potential of a variety of individual technologies like AI (Chung, 2021; Toorajipour et al., 2021), blockchain (Chen et al., 2022; Kopyto et al., 2020; Pournader et al., 2020), and IoT (Ben-Daya et al., 2019; Wang et al., 2020; Zhu et al., 2021) as well as 5G (Dolgui & Ivanov, 2022), augmented reality (AR) (Rejeb et al., 2021), cloud computing (Xu et al., 2018), and radio-frequency identification (RFID) (Fosso Wamba & Chatfield, 2011) for different logistics activities such as real-time tracking of material flows, improved transport handling as well as smart procurement and risk management has been investigated (Hofmann & Rüsch, 2017). However, Logistics 4.0 is premised on the systematic interconnection of intelligent process elements, each relying on a spectrum of digital technologies and their interplay (Delfmann et al., 2018). Strandhagen et al. (2017b) made a step toward a more holistic perspective by classifying Industry 4.0 applications in logistics into the categories of decision support and decision-making, identification and interconnectivity, seamless information flow as well as automation, robots, and new production technology. In the recent literature, Winkelhaus and Grosse (2020) identify technological building blocks of Logistics 4.0 and describe groups of technologies to generate, handle, and use information. Frank et al. (2019a) define base technologies (i.e., IoT, cloud, big data, analytics) providing connectivity and intelligence to smart supply chains in Industry 4.0, while Koh et al. (2019) find IoT, big data analytics, cloud, 3D printing, and robotic systems to be the disruptive Industry 4.0 technologies for SCM. Balouei Jamkhaneh et al. (2022) identify AI, advanced robotics, blockchain, and additive manufacturing as the most important enablers for Logistics 4.0 service quality from a perspective of sustainability and value creation. Further, Kayikci (2018) examines enablers of digital logistics ecosystems and exemplary applications (e.g., AR, big data, IoT). So far, the analysis of digital technologies in Logistics 4.0 is primarily centered around the main Industry 4.0 technologies originating from a smart production perspective. In this connection, Winkelhaus and Grosse (2020) as well as Shao et al. (2021) point out the need to systematically consider the application of a more comprehensive spectrum of technologies and to relate their potential to specific logistics processes and tasks.

Logistics 4.0 is found to have a particularly strong impact on intralogistics as the central component of most distribution networks (Winkelhaus & Grosse, 2020). Intralogistics processes concentrate many of the typical characteristics of Logistics 4.0, such as the automation, digital assistance, or autonomous execution of physical and cognitive tasks (Fragapane et al., 2020; Winkelhaus et al., 2022), the real-time exchange of information among different (digital) actors (Strandhagen et al., 2017a), and the overall fusion of physical and digital operations in cyber-physical processes (Coelho et al., 2021). In addition, intralogistics is highly affected by Industry 4.0’s shift in production from standardization to flexibility and variability (Ivanov et al., 2021; Scholz et al., 2016). However, intralogistics (also referred to as internal or in-house logistics) exceeds the context of manufacturing companies (Fragapane et al., 2021). It comprises organizing, controlling, implementing, and optimizing the internal flow of information and material as well as the handling of goods in industry, trade, and public institutions (Arnold, 2006; VDMA, 2003). In contrast to inter-organizational logistics, its focus is on managing in-house goods and information flow activities as well as on the efficient interaction of all (technological) entities involved in its processes (Ballou, 2007). Table 1 summarizes the main process elements of intralogistics derived from existing definitions of pertinent academic literature.

Table 1 Main process elements in intralogistics
Full size table

While many tasks like order picking or internal transportation are still labor-intensive, intralogistics is currently seen as the area of Logistics 4.0 that will benefit the most from the implementation of emerging digital technologies (Winkelhaus et al., 2022). These advances toward Intralogistics 4.0 are found to enhance the organizational base of precise process-related data and act as a condition for effective supply chain integration (Zhang et al., 2016). In the current literature, only individual technological solutions to intralogistics challenges like autonomous mobile robots (Fragapane et al., 2021), digital twins (Coelho et al., 2021), or intelligent bin systems (Schuhmacher et al., 2017) are investigated. Further, Winkelhaus et al. (2022) explore changes of work characteristics of employees in Intralogistics 4.0. Based on the review of related literature, we infer that the work that has so far been done on the role of digital technologies in Logistics 4.0 provides a useful overview of predominant Industry 4.0 applications and detailed insights into some specific use cases. However, a comprehensive and theoretically well-founded cross-technology perspective on the systematic opportunities digital technologies afford in logistics processes is still required. To achieve such a conceptualization, this paper takes an IS perspective on Logistics 4.0.

In the IS discipline, the relation of tasks and technology is a pervasive subject of research that ranges from the fundamental question of the nature of digital technology (Faulkner & Runde, 2019; Orlikowski & Iacono, 2001) to the concept of task-technology fit (Goodhue & Thompson, 1995; Howard & Rose, 2019) and its implications for, e.g., technology acceptance (Dishaw & Strong, 1999) or business value of IT (Kohli & Grover, 2008). Addressing the matter of what constitutes technology, Kline (1985) points out that understanding technology depends on its intended purpose and how it functions in different contexts, particularly as an element of sociotechnical systems. In this connection, digital objects become digital technologies when they are assigned a meaning for application by actors (Hund et al., 2021). That is, the potential of digital technologies is closely related to the purpose it provides within a social context which in turn is determined by the actors using them (Baier et al., 2023; Faulkner & Runde, 2019). This sociotechnical approach is also included in work system theory by Alter (2003), which suggests that IS research should consider work systems rather than IT artifacts, where human participants and/or machines perform work (i.e., tasks and processes) using information, technology, and other resources. Instead of just focusing on technological progress, it includes a dynamic view of how work systems change over time through a combination of planned and unplanned change that can be driven by all system elements (Alter, 2015). This view corroborates the implication that a shift of the functions and tasks of actors caused by frontline industrial developments such as Logistics 4.0 also brings about new opportunities digital technologies may afford. This notion is substantiated by IS research on task-technology fit that is concerned with the extent to which a given technology assists an individual in performing a portfolio of tasks (Goodhue & Thompson, 1995; Howard & Rose, 2019). As such, task-technology fit theory underlines the interlinkage of technologies and the context they are applied in (Teo & Men, 2008). In this respect, the interactions between the individual, task, and technology are considered as the antecedents of task-technology fit (Muchenje & Seppänen, 2023). Consequently, the potential that can be realized through the application of digital technologies depends on their alignment with the processes they are supposed to support (Zigurs & Khazanchi, 2008). Hence, exploring the opportunities digital technologies afford in leading-edge application domains with transforming tasks and processes is a recurring challenge at the frontiers of IS research.

In line with the IS perspective on the strong relation of tasks and technology, this research aims to act on the suggestion to relate the opportunities digital technologies afford to specific logistics processes and tasks. To do so, this study adopts an Intralogistics 4.0 perspective as the central starting point for organizational digitalization initiatives in logistics and employs a domain-independent classification scheme of technology patterns suited to serve as a basis for examining the affordances of digital technologies. Berger et al. (2018) present an empirically validated multi-layer taxonomy of 45 real-world digital technologies resulting in seven purpose-related archetypes that deliberately abstract from granular features of individual technologies (Table 2). Instead, each archetype reflects a typical combination of digital technology characteristics (e.g., data treatment, human involvement, multiplicity, role) occurring in practice. In this paper, the archetypes enable us to achieve our research goal by arranging digital technologies as the artifact of analysis in a systematic and extendable scheme of technology patterns. Given the fast-moving nature of digital technologies and their application areas in Industry 4.0 (Lu, 2021), it presents a sound basis for research in Logistics 4.0.

Table 2 Archetypes of digital technologies based on Berger et al. (2018)
Full size table

2.2 Affordance Lens

We draw on affordance theory to develop a fine-grained understanding of the opportunities digital technologies provide in intralogistics processes. In IS research, the concept of affordances aims to capture an action potential that originates from the relation between a technology with certain features and a user (i.e., an individual or organization) with a particular purpose in a specific environment (Majchrzak et al., 2016; Nambisan et al., 2017). Recent work has successfully applied affordance theory in various IS contexts to build theory about digital technology use (Islam et al., 2020; Pal et al., 2021; Seidel et al., 2013; Wendt et al., 2021).

Gibson (1986) first introduced the term affordance as part of his research in influential ecological psychology to express that actors initially do not perceive the physical properties of an object (e.g., a bed made of wood) but rather what objects offer or afford to them (e.g., a bed to lie on) (Gibson, 1986). Thus, the action potential of an object is relative to the observer and its context. Gibson’s work has been most notably applied to discussions of technology by Norman (1990, 1999), who argued that affordances are properties of designed artifacts that are built-in to be perceived by the users. Today, many scholars seek a balance between these two concepts and embrace affordances as bidirectional relations between actors and technological artifacts to study their action potential in a given context (Hutchby, 2001; Leonardi, 2011; Majchrzak et al., 2013). Yet, varying uses and interpretations of the term affordance can be observed in current literature (for further review, e.g., see Burlamaqui & Dong, 2015; Markus & Silver, 2008; Volkoff & Strong, 2017; Zammuto et al., 2007). Hereafter, we understand affordances as action potentials emerging from the multifaceted relation between digital technologies with certain features and a user with a particular purpose in a specific environment (Faraj & Azad, 2012; Majchrzak & Markus, 2013; Nambisan et al., 2017).

In IS research, affordance theory has attracted significant attention, as it has proven useful in understanding how a technology affords different ways for reciprocal actions to a goal-oriented actor (Lehrer et al., 2018). Following this logic, affordances are not properties of a technology or an actor alone but rather result from their interaction (Majchrzak & Markus, 2013). Thus, as relational concepts, affordances are not necessarily identical for different actors or contexts (Leonardi, 2011; Seidel et al., 2013). Further, goal-oriented properties of a technology as perceived by humans may not only offer action potentials but also constrain specific uses (Majchrzak & Markus, 2013). Recent studies take up this perspective of affordances and constraints to investigate beneficial and hindering factors for the use of digital technologies (e.g., Benbunan-Fich, 2019; Pal et al., 2021). Following the opportunity-led research focus of this study, we concentrate on affordances and do not further elaborate on constraints.

In this paper, affordance theory serves as a theoretical lens to investigate the contextual and user-specific action potential emerging from the interplay between archetypes of digital technologies and intralogistics processes at the center of the frontline industrial development of Logistics 4.0. Thereby, we aim to identify patterns that exist through the symbiotic relationship of a technological artifact (i.e., digital technologies) and an actor’s set of actions in a leading-edge process environment (i.e., intralogistics) instead of focusing on latent technology capabilities (Leidner et al., 2018; Majchrzak et al., 2013).

3 Research Design

We investigate the affordances of digital technologies for intralogistics processes including the perspectives of current research and practitioners. To account for the interdisciplinary nature of the frontline industrial development of Logistics 4.0, we followed an iterative research approach (Fig. 1).

Fig. 1
figure 1

Iterative research process

Full size image

First, we performed a systematic literature review to rigorously develop an initial catalog and conceptual framework of DTAILs and associated practical manifestations based on the insights from current research (Webster & Watson, 2002; Wolfswinkel et al., 2013). We then evaluated the preliminary results by conducting a qualitative interview study with ten logistics experts from academia and industry (Bettis et al., 2015; Goldkuhl, 2012). We continuously synthesized the theoretical and practical insights in workshops and discussions of the author team to finally obtain a catalog and conceptual framework of ten coherent DTAILs and 46 manifestations that substantiate their relevance to industry.

For the initial version of DTAILs, we conducted a systematic literature review consistent with the well-established recommendations of Webster and Watson (2002) and Wolfswinkel et al. (2013). Accordingly, we followed the five-stage process: define, search, select, analyze, and present. Figure 2 illustrates the key steps of the process – excluding present (i.e., the communication and presentation of results that is provided by this paper) – and specifies relevant criteria we applied.

Fig. 2
figure 2

Systematic literature review process based on Wolfswinkel et al. (2013)

Full size image

The define stage aims to determine the scope of the review by developing the set of search criteria (Wolfswinkel et al., 2013). To this end, we take a multidisciplinary approach and review literature from the fields of both IS and logistics research (see Fig. 2). As digital technologies are a fast-moving topic, we included high-impact journal publications as well as conferences from both fields, which feature short review cycles and report on the latest technological developments (vom Brocke et al., 2015). We searched for relevant articles using four scientific databases. In an iterative process of refining search terms and reviewing random samples of the search results, we carefully developed a single search string including synonyms for digital technologies as well as terms narrowing down their application to SCM, logistics, and intralogistics processes.

The search stage refers to the application of the search terms to the identified sources (Wolfswinkel et al., 2013). We used our search string for a title, abstract, and keyword search in the selected databases. We examined extant studies between 2010 and 2022 to ensure timeliness and focus on emerging digital technologies in the recent scientific discourse. The initial search yielded 798 articles.

In the select stage, the literature sample is refined by applying the defined inclusion and exclusion criteria (Wolfswinkel et al., 2013). We first screened all titles and abstracts for relevance to determine the thematic fit of every article against the background of the research goal. For this purpose, we used a three-point Likert scale classifying each publication according to whether they displayed no focus (score = 0), a low focus (score = 1), or a high focus (score = 2) on the application of specific digital technologies in intralogistics. In doing so, we identified 121 articles worthy of a full-text screening. During this process, we further excluded articles without a clear (intra) logistics focus in accordance with our research objective (e.g., smart city concepts or theoretical models on urban city transport) as well as articles without an appropriate level of generalizability. Including a forward and backward search, we ended up with 65 articles relevant to the scope of our research. Finally, we augmented the set of academic articles by reviewing professional literature following the key principles of Garousi et al. (2019) to increase the practical relevance and timeliness of the results. We obtained an overview of current trends and developments in the industry by searching in public and industrial databases and screening appropriate professional publications in the context of applied research (e.g., Fraunhofer), whitepapers by leading consulting firms (e.g., Accenture, PwC), and reports from influential logistics providers (e.g., DHL, FedEx). The final literature sample comprised 82 academic and professional articles.

The analyze stage deals with the extraction of concepts and constructs from the selected set of literature (Wolfswinkel et al., 2013). We drew on the proposed three-step coding approach including open, axial, and selective coding. During open coding, we closely read every article to highlight and extract all statements (i.e., excerpts) related to our research goal. We structured the resulting 770 statements in a process-technology-matrix (1:n mapping) comprising the main process elements in intralogistics (Table 1) and the archetypes of digital technologies (Table 2). Continuing with axial coding, we aggregated statements within the same cell to one or more preliminary manifestations with a single-sentence definition in several joint workshops of the author team. Focusing on their interrelation and level of order, we then re-evaluated, adapted, and abstracted the results to 46 manifestations (i.e., categories). In the final coding step, we used selective coding to derive affordances from the set of practical manifestations. We shaped and refined the affordances by continuously linking and comparing the categories and their underlying statements in the author team. During the process, we repeatedly reconsidered our results to consolidate, eliminate, or shift DTAILs, and refine their names and descriptions where necessary. By cross-referencing our thoughts with established concepts of Logistics 4.0 (Winkelhaus & Grosse, 2020), we finally developed a conceptual framework as a logical structure representing the focus areas and interrelations of the DTAILs. This step resulted in an initial catalog and framework of 12 DTAILs and 46 manifestations.

To evaluate the theoretically developed results against first-hand practical experience, we conducted ten semi-structured interviews with domain experts (Table 3) following the guidelines of Myers and Newman (2007). The sample of participants is considered adequate as it provided great information value through high-quality and in-depth conversation with specialized interviewees on the basis of previously established concepts (Malterud et al., 2016). The interviews were performed by two researchers and lasted between 70 and 90 min each. Every interview was recorded and transcribed for further analysis to ensure data integrity. During the interviews, we introduced the participants to the theoretical background of our research before presenting the conceptual framework and discussing all DTAILs in terms of understandability, completeness, consistency, level of detail, and applicability (Sonnenberg & vom Brocke 2011). We also requested feedback from personal practical experience and specific use cases in connection with the affordances presented. After each interview, we compared new insights with extant literature, discussed them within the author team, and refined the preliminary findings where necessary. Ultimately, the interviews revealed new perspectives on multiple DTAILs that had not yet been sufficiently covered in the literature due to the rapid developments of the topic. Accordingly, we revised several descriptions and rearranged affordances to yield a final coherent catalog and conceptual framework of ten DTAILs specified by 46 manifestations.

Table 3 Expert interview study
Full size table

4 Results

In this section, we present the catalog and conceptual framework of DTAILs. Table 4 explains each DTAIL, indicates its associated practical manifestations, and provides concrete examples for application from literature to foster practical understanding. For a detailed explanation of every manifestation of the DTAILs and their references, please refer to the Appendix. The conceptual framework of DTAILs then illustrates their scope and interrelation in four affordance layers: Data, Manual Tasks, Goods & Assets, and Decisions & Management (Fig. 3). The affordance layers emerge from the logistics tasks and functions the DTAILs address and reflect key aspects of previous research on the impact of digital technology on logistics systems (i.e., the generation, handling, and use of information, the shift of human involvement and material movement, and the transformation of execution and management activities) (Winkelhaus & Grosse, 2020). Finally, Table 5 links the DTAILs to the archetypes of digital technologies and the intralogistics process elements they apply to (see Section 2.1) to allow deeper insights into how (technology perspective) and where (process perspective) the affordances emerge.

Table 4 Affordances of digital technologies in intralogistics processes
Full size table
Fig. 3
figure 3

Conceptual framework of affordance layers

Full size image
Table 5 Mapping of DTAILs across archetypes of digital technologies and intralogistics process elements
Full size table

The conceptual framework of affordance layers (Fig. 3) illustrates the interrelations and scopes of DTAILs, building on the action potential they comprise and the nature of logistics processes they concern. Overall, (1) Ubiquitous Data Availability defines the core Data layer that facilitates all other DTAILs in the Goods & Assets, Manual Tasks, and Decisions & Management layers by enabling location- and time-independent data availability through the systematic capturing of data over all logistics processes and the (horizontal and vertical) integration by means of a central data hub.

(1) Ubiquitous Data Availability supports the assistance, monitoring, and automation of Manual Tasks: While the (2) Assistance of Manual Tasks refers to digital technologies augmenting manual workflows with real-time instructions, task-related information, location- or load-based guidance, and automatic physical support through human-technology interaction, the (4) Automation of Manual Tasks describes the end-to-end execution of (formerly) manual tasks, e.g., handling activities around physical resources, quality control, and data capturing, by digital technologies resulting in digital process autonomy. At the intersection of these two DTAILs, the (3) Monitoring of Manual Tasks does not focus on the execution of tasks itself but on the control of process data and workflows to reduce or eliminate errors in manual tasks, ensure health sustainability of physical activities, and increase task efficiency.

In addition, (1) Ubiquitous Data Availability taps action potential related to physical Goods & Assets by continuously tracking and monitoring activities that directly affect the physical objects handled. While (5) Tracking of Goods and Assets relates to their autonomous identification and real-time localization, (6) Condition Monitoring of Goods and Assets comprises checking and predicting their physical condition subject to environmental factors or usage-related changes (e.g., external forces or displacements). Both DTAILs include secure documentation and real-time synchronization of the corresponding data. Up to this, all DTAILs mentioned affect the flow of goods and the flow of information on an operational level.

In contrast, the four DTAILs in the Decisions & Management layer predominantly build on (1) Ubiquitous Data Availability to handle the flow of information by processing and analyzing data for (operational) decision-making or logistics management on a (partly) strategic level. In this respect, (7) Operational Scheduling Support and (8) Operational Decision Automation facilitate operational processes and decisions through autonomation, optimization, and coordination of resources. Beyond, (9) Interruption Management is located at the intersection of supporting the management of both interruptions (operational) and risks (tactical). Finally, (10) Strategic Decision Support addresses a purely strategic level to enhance analytical and predictive capabilities. Unlike all other DTAILs closely involving the physical environment in the form of the flow of goods, (9) and (10) both relate to the digital world and its flow of information.

Building on the catalog and conceptual framework of DTAILs that provide insights into their scope and interrelation, Table 5 maps the DTAILs to their underlying archetypes of digital technologies and to the specific intralogistics process elements they concern. Adding the perspective of tasks and technology grounded in IS research enables us to take a more detailed look at the sociotechnical system building the context in which the affordances emerge.

First, the emergence of opportunities of digital technologies related to Sensor-based Data Collection (e.g., gesture recognition, IoT) in the form of (1) Ubiquitous Data Availability across all process elements is notable. In this connection, RFID sensors, for instance, capture real-world data and transmit signals to provide real-time visibility into the execution of manual tasks, the movement of goods, or current inventory levels. To provide (1) Ubiquitous Data Availability as a basis for (nearly) all following DTAILs, the extensive collection of data can be carried out via the integration of different layers of sensors and communication capabilities (i.e., the IoT paradigm) and complemented by the processing (i.e., Connectivity technologies like blockchain) and provision (i.e., Platform technologies like cloud computing) of data for further decision-support along the internal supply chain. Moreover, digital technologies associated with the archetype of Sensor-based Data Collection as well as with Analytical Insight Generation (e.g., AI and data analytics) are connected to almost all other DTAILs. This finding reflects the relevance of object-generated data and resulting analytical insights for operational and strategic decision-making in logistic processes. While (1) Ubiquitous Data Availability is enabled by the associated digital technologies across all intralogistics process elements, the remaining DTAILs relate to specific (i.e., one to three) process elements. This finding highlights the problem–solution-fit between the individual affordances identified and the diverse potential of digital technologies for various intralogistics process elements.

Second, the differences in focus on Manual Tasks (i.e., (2), (3), and (4)) or on physical Goods & Assets (i.e., (5) and (6)) between two clusters of DTAILs reconfirms the view of affordances as multifaceted relational structures of technologies with certain features and sets of actions of an actor in a specific process environment. Regarding Manual Tasks, a human actor uses digital technologies primarily associated with the archetypes of Analytical Interaction (e.g., virtual assistants) and Augmented Interaction (e.g., augmented reality devices) to support or monitor manual process steps. In contrast, when it comes to DTAILs related to the control of Goods & Assets, the actor applying the digital technology needs to be defined at a higher level. In this case, the tracking, monitoring, and controlling of goods through object-generated data can be viewed as the outcome of the goal-orientation of an entire organization with a data-based (intra)logistics system (programmed by humans) operating automated processes. In this setting, physical objects (i.e., goods and assets) moving in the context of different process elements deliver information that allows operational adaptions and, in turn, enables other DTAILs. Here, the archetypes of Platform and Connectivity are essential for the critical data infrastructure.

Third, the DTAILs focusing on (operational) decisions or logistics management and emerging in the corresponding digital process elements distinguish themselves from DTAILs associated with cyber-physical processes. Naturally, the latter primarily relate to an operational level, while the rest also includes a strategic perspective. Precisely, the DTAILs (2) to (6) originate in a cyber-physical process environment, (7) and (8) form a transition zone, where the data-based optimization of planning activities and decision-making leads to the adaption of physical processes, and (9) and (10) mainly concern digital processes linked with managing information flows.

Fourth, every DTAIL is linked with multiple archetypes of digital technologies complementing one another. Particularly, infrastructure-focused technologies (e.g., Platform or Connectivity) and specific application-oriented technologies (e.g., Sensor-based Data Collection or Analytical Insight Generation) interact (i.e., (1), (5), (7), (9)). For instance, the combination of blockchain with IoT can enable smart logistics contracts that facilitate logistics process automation by transparently and inalterably documenting micro-transactions. This finding emphasizes the increasing importance of digital technology convergence that, in logistic processes, is observed in the form of cyber-physical systems.

Finally, the above findings also indicate that the systematic interconnection of intelligent process elements and multiple archetypes of digital technologies brings about potential constraints that present avenues for future in-depth analyses. For instance, digital technologies associated with the archetypes of Sensor-based Data Collection and Analytical Insight Generation are vital for the availability and informative value of object-generated data that in turn enables other archetypes. Consequently, the interdependence of certain archetypes creates technological base requirements in Logistics 4.0 that may present constraints and market barriers for some organizations. Thus, not all technological opportunities may be readily available for all market players. Further, the development of integrated cyber-physical systems along the supply chain may constrain internal technology implementation efforts of organizations in the sense that they need to be aligned with external stakeholders in a cross-organizational environment.

5 Discussion

5.1 Theoretical Implications

Our work connects to ongoing discussions on the role of digital technologies in the concept of Logistics 4.0 (Bag et al., 2020; Shao et al., 2021; Strandhagen et al., 2017a, b; Winkelhaus & Grosse, 2020) and offers valuable implications for IS research in terms of understanding technology affordances in frontline industrial developments. Along these lines, the main theoretical implications of this study to the IS body of knowledge are twofold as they provide new insights into the opportunities digital technologies afford in Logistics 4.0 and lay the foundation for further theorizing.

First, our work contributes to a systematic understanding of the affordances of digital technologies in Logistics 4.0. The literature provides a useful overview of the predominant Industry 4.0 applications in logistics and detailed insights into individual use cases (Frank et al., 2019a; Leofante et al., 2019). However, it does not offer a comprehensive cross-technology perspective on the systematic opportunities digital technologies afford in this context. Further, previous studies do not relate their potential to specific logistics processes and tasks (Shao et al., 2021; Winkelhaus & Grosse, 2020). Consequently, this study complements existing knowledge by presenting a novel and theoretically well-founded affordance perspective on Logistics 4.0 that is in line with recent IS research drawing on affordance theory (e.g., Islam et al., 2020; Seidel et al., 2013; Wendt et al., 2021). It is the first to offer a cross-technology and process-oriented perspective on how digital technologies can be used in Logistics 4.0. To do so, it draws on archetypes of digital technologies (Berger et al., 2018) and intralogistics processes as the central starting point for digitalization initiatives toward Logistics 4.0. The presented catalog and conceptual framework of affordances establish holistic patterns of the action potential that emerges from the interplay between digital technologies with certain features and intralogistics processes as specific organizational sets of actions. To achieve a close connection between theoretical and application-oriented research, we combined our theory-based knowledge from the structured literature review with detailed expert insights on real-world examples from an interview study. Thus, the catalog of DTAILs and associated practical manifestations appreciates the need for a comprehensive understanding of how digital technologies can support organizational processes in Logistics 4.0. The conceptual framework of affordance layers adds to theory on Logistics 4.0 as a leading-edge IS application domain by systemizing the DTAILs based on their effectual focus areas and interrelations. Further, these results add a new level of granularity to the field of intralogistics and advance the existing body of knowledge on the fit of Industry 4.0 applications (Strandhagen et al., 2017b), work characteristic changes (Winkelhaus et al., 2022), and digital technology impact (Zhang et al., 2016).

Second, our work facilitates further theorizing on the role of digital technologies in frontline industrial developments. It advances the understanding of the opportunities of digital technologies as interplay of actors and a leading-edge application domain by analyzing and emphasizing the interrelation of tasks and technology through process orientation. The methodological approach to rigorous affordance development introduced in this paper presents a blueprint for investigating affordances in a broader range of domains in a cross-technology and process-oriented manner. Thereby, this study also extends affordance theory applications by underscoring the dynamic interrelation between digital technologies, processes, and the domain-specific environment (Faraj & Azad, 2012; Majchrzak & Markus, 2013). Further, the presented results lay the foundation for fellow scholars to build theory beyond the level of individual technologies and develop a holistic understanding of the concept of Logistics 4.0 as per Winkelhaus and Grosse (2020). The presented catalog and conceptual framework of DTAILs may serve as a starting point to explore the affordances of digital technologies for inter-organizational processes and at different levels of the supply chain, as requested by Shao et al. (2021). Finally, the results of this study build a sound basis for relating research on Logistics 4.0 transformations (e.g., IS capability frameworks or maturity models) to the specific affordances of digital technologies.

5.2 Managerial Implications

Our work has two main practical implications that became evident in the interviews and support managers as organizational decision-makers in Logistics 4.0 initiatives (e.g., business development representatives, digital solutions specialists, logistics executives).

First, our results support managers in assessing and actively monitoring the extent to which their own Logistics 4.0 initiatives leverage the opportunities of digital technologies in intralogistics processes. Based on our results, managers can determine the technological status quo of logistics processes in their organizations by assessing which DTAILs are currently covered and which may advance certain processes. As managers are facing a variety of individual technology solutions in Logistics 4.0 (e.g., via trend reports) (Strandhagen et al., 2017b), abstracting from technological fads and taking a process-oriented cross-technology perspective can help them identify untapped action potential. Further, managers may evaluate the progress of their transformation toward Logistics 4.0 compared to competitors or up- and downstream partners. In this way, Logistics 4.0 initiatives can be strategically and technologically aligned to promote the horizontal integration of intralogistics processes and cyber-physical systems with all levels of the supply chain (Gupta et al., 2019). Moreover, our results lay the foundation for defining suitable affordance-oriented indicators for the progress organizations make when transforming their intralogistics processes.

Second, managers should leverage our results to structure strategic discussions on the development of their portfolio of digital technologies in intralogistics processes. In Logistics 4.0, managers constantly need to make decisions related to the fit between new technological solutions and current as well as planned initiatives (Yang et al., 2021). To this end, our results tackle the main concern raised by the interviewed experts, who repeatedly described a clutter of available digital technologies that makes it difficult to determine their action potential and identify suitable areas of application (I-5, I-6, I-8, I-10). The catalog and conceptual framework of DTAILs support managers in considering all relevant opportunities provided by comprehensible technology patterns. The process orientation of this study further encourages managers to adopt a task-technology-fit perspective when discussing where to catch up or how and where to proceed when developing their technology portfolio. This helps avoid isolated solutions and offsets potential subjective biases toward certain technologies. Finally, our results should be particularly relevant for organizations with different maturity levels and technological needs (Müller et al., 2018). Especially organizations at early stages of digital technology adoption (e.g., start-ups, small and medium-sized enterprises) deal with limited financial and personnel resources to obtain the interdisciplinary competencies necessary to manage the transformation toward Logistics 4.0.

6 Limitations and Future Research

The present study has limitations that may point fellow scholars in the direction for further beneficial research. First, we established our results inductively based on a systematic literature review and a qualitative interview study. While domain experts validated the representativeness and conceptual accurateness of our findings, the literature sample is restricted to recognized outlets of the IS and logistics disciplines. Future research could expand its scope to more technical outlets, patent databases, or inter-organizational logistics processes to evaluate the transferability of the framework and catalog of affordances to related domains and expand them if required. A further limitation of this study is that it is not longitudinal in design. In this sense, it is necessary to update the results periodically as new digital technologies emerge. This is supposed to be facilitated by the scheme of archetypes of digital technologies that allows for the easy integration of new technologies. Finally, this study’s scope does not include the actualization of affordances or their interplay with broader organizational criteria for technology selection and adoption (e.g., IT business value, IS capabilities, and trust). From a theoretical perspective, future research could investigate how the actualization of the DTAILs in organizations is influenced by present sociotechnical factors on the actor level (e.g., employee expertise), the structure level (e.g., organizational structures), and the technology level (e.g., technological infrastructure). In addition, future work should explore constraints as the perceived barriers to goal achievement that hinder realizing the benefits of the use of digital technologies in Logistics 4.0 and form the counterpart to affordances. From a managerial point of view, future research could seek to provide additional methodological guidance to navigate managers through the process of transferring the DTAILs to their individual business settings. Shedding light on individual application areas, future researchers may wish to consider adding a demonstration phase to the present results and to explore different industry cases.

The above limitations notwithstanding, we are confident that the catalog and conceptual framework of ten DTAILs and 46 manifestations developed in this study offer a comprehensive IS understanding of the opportunities digital technologies provide for intralogistics processes. We expect that the results serve as a foundation for fellow researchers for further theorizing on Logistics 4.0 and practitioners for purposefully managing digitalization initiatives toward Logistics 4.0.