Smart Urban Agriculture

Christmann, Anne-Sophie; Graf-Drasch, Valerie; Schäfer, Ricarda Article — Published Version Smart Urban Agriculture Business & Information Systems Engineering Provided in Cooperation with: Springer Nature Suggested Citation: Christmann, Anne-Sophie; Graf-Drasch, Valerie; Schäfer, Ricarda (2024) : Smart Urban Agriculture, Business & Information Systems Engineering, ISSN 1867-0202, Springer Fachmedien Wiesbaden GmbH, Wiesbaden, Vol. 67, Iss. 2, pp. 247-264, https://doi.org/10.1007/s12599-024-00863-w This Version is available at: https://hdl.handle.net/10419/323643 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH PAPER Smart Urban Agriculture A Study of Digital Opportunities to Feed City Dwellers Anne-Sophie Christmann •Valerie Graf-Drasch •Ricarda Scha ¨fer Received: 3 July 2022 / Accepted: 5 December 2023 / Published online: 30 July 2024 The Author(s) 2024 Abstract Given cities’ rising environmental problems and increasing food insecurity, innovative organizational endeavors such as urban agriculture present a chance for additional ecosystem services and food production. However, urban spaces are hostile as they jeopardize the availability of air, water, or soil. While digital innovations enable the management of scarce resources in traditional agricultural contexts, little is known about their applicability in urban agriculture endeavors. This study proposes a multi-layer taxonomy focusing on digital technologies, data, and different approaches in urban agriculture, as well as 20 organizational readiness factors derived with academics and practitioners from the smart urban agriculture domain. Combining both perspectives, the study sheds light on the nature of smart urban agriculture and ways to leverage its economic, ecological, and social value. Keywords Digital opportunities Smart urban agriculture Taxonomy Readiness 1 Introduction Cities are the main contributor to global energy demand and carbon emissions (World Economic Forum 2020), making them a key lever for addressing the climate crisis (Corbett and Mellouli 2017; Gimpel et al. 2020). The rising concentration of people in cities brings up the question of how these populations can best be provided with food – especially in times of uncertain global events such as pandemics or wars. Although worrisome, these challenges constitute a ‘‘window of opportunity’’ for innovative organizations. Researchers and practitioners are increasingly pointing to ‘‘urban agriculture’’ – a promising complement to traditional rural agriculture that involves growing crops and raising livestock in cities (Carolan 2020; Langemeyer et al. 2021). However, significant hindrances exacerbate realizing the potential benefits of urban agriculture: First, urban spaces – characterized by sealed surfaces and heat stress – are a hostile habitat for many species, creating challenging environmental conditions leading to a high energy and material usage (Gimpel et al. 2021;Lu ¨ttge and Buckeridge 2020). Second, urban agriculture often fails to compete economically due to high investment costs in prime city land and required workforce (Chang and Morel 2018; Azunre et al. 2019) or governments restricting land use to very particular areas (Diehl et al. 2020). When turning toward traditional rural agriculture, digital innovation – i.e., ‘‘the creation of (and consequent change in) market offerings, business processes, or models that result from the use of digital technology’’ (Nambisan et al. 2017, p. 224) – has proven to be Accepted after three revisions by Jens Dibbern. A.-S. Christmann (&)V. Graf-Drasch R. Scha ¨fer FIM Research Center for Information Management, Augsburg, Germany e-mail: [email protected] V. Graf-Drasch e-mail: [email protected] R. Scha ¨fer e-mail: [email protected] A.-S. Christmann V. Graf-Drasch University of Hohenheim, Stuttgart, Germany A.-S. Christmann V. Graf-Drasch Branch Business & Information Systems Engineering of the Fraunhofer FIT, Augsburg, Germany R. Scha ¨fer University of Augsburg, Augsburg, Germany 123 Bus Inf Syst Eng 67(2):247–264 (2025) https://doi.org/10.1007/s12599-024-00863-w a significant value lever for driving economic and resource efficiency (Steininger et al. 2022). In urban agriculture, however, how to leverage the potential of digital technologies (i.e., smart urban agriculture) is less clear (O’Sullivan et al. 2019). So far, researchers have primarily focused on understanding individual business use-cases of smart urban agriculture (SUA). Thereby, they specifically examined SUA’s digital infrastructure and its economic and environmental impact (O’Sullivan et al. 2019; Weidner et al. 2022), as well as the social acceptance of the digital technologies used (Specht et al. 2016; Broad et al. 2021). However, many emerging SUA endeavors fail economically because of high investment costs, lacking skills, a wrong selection of technologies during implementation, or the inability to materialize the expected financial, ecological, and social benefits in the long run (Langendahl 2021). What organizations urgently need to master the challenges of innovation development and deployment in SUA and better prepare themselves for launching an endeavor in this field, is a clear understanding of the concept of smart urban agriculture and the associated digital innovation process. Hence our research question reads: What is smart urban agriculture and how can its value be leveraged? To capture the essence of SUA across development, implementation, and scaling, we take two different perspectives. First, there is no shared understanding of which digital technologies, data, and functionalities SUA constitutes. In perspective 1, we thus conceptualize the phenomenon of SUA as a taxonomy by analyzing the relevant dimensions and characteristics of SUA. Second, to leverage the value of SUA, a sound understanding of the organizational requirements and prerequisites for leveraging SUA – a concept called organizational readiness – is critical (Lokuge et al. 2019). In perspective 2, we thus develop 20 organizational readiness factors for SUA by conducting semi-structured interviews with 9 research scholars and 16 smart urban farmers. Inspired by research combining different methods in one work (Venkatesh et al. 2016a), we combine perspective 1 and 2 to gain completeness: Both perspectives aim to deliver a more complete picture than one isolated approach by complementing the insights of each other. This yields so-called ‘‘meta-inferences’’ – a key result of this research. Meta-inferences depict interpretations of our findings in an integrative view. They provide the opportunity to look beyond the limitations of a single perspective and take a stance in conceptualizing SUA holistically. Our work presents three overarching implications: First, our taxonomy offers a common ground for conceptualizing the scattered nature of SUA by uniting terms from various domains and generalizing subtypes. Second, we deliver insights on organizations’ readiness to leverage digital technologies to address environmental, economic, and regulatory challenges in urban agriculture. Third, the integrative view of our findings from both perspectives, instantiated as meta-inferences, provides an opportunity for the IS discipline to capture the nature of SUA along the digital innovation process. 2 Theoretical Background 2.1 Smart Urban Agriculture SUA has links to two research streams: 1) urban agriculture and 2) smart farming. First, urban agriculture refers to the food production, processing, and marketing in urban ecosystems (Smit et al. 2001; De Bon et al. 2010). It can range from private gardens for self-consumption to sophisticated concepts such as commercially oriented, high-tech indoor farms, mostly producing vegetables, fish, and meat (Wood et al. 2020). As free space for traditional ground-based agricultural practices is scarce in most cities, some of these systems are aligned with growing food on housing facades, rooftops, or indoor greenhouses (Specht et al. 2016; Dorr et al. 2021). Second, smart farming refers to using digital technologies to optimize agricultural production in terms of efficiency, quality, and sustainability (Ko ¨ksal and Tekinerdogan 2019; Balafoutis et al. 2017). Key technologies implemented include cloud computing, the Internet of Things, or robotics (El Bilali and Allahyari 2018). Robotics, for example, can control tractors, perform planting and mechanical weeding, sort and harvest fruits, or feed animals automatically (Nair et al. 2021). In synthesizing the understanding of urban agriculture and the description of smart farming, we define SUA as the use of modern digital technologies to optimize food production in urban ecosystems in terms of efficiency, quality, and sustainability. SUA is promising for tackling the challenges of urban agriculture, by, for example, providing tools to automatically control environmental parameters (e.g., temperature, humidity) of cities (Goldstein et al. 2016; O’Sullivan et al. 2019). In addition, SUA enables the creation of entirely new concepts, such as closed-fielded vertical farming (Maye 2019). 2.2 Digital Innovations in Smart Urban Agriculture To leverage SUA’s sustainability potential, it is necessary to detect, implement, and scale associated innovations. While SUA research has not yet conceptualized these necessary phases, digital innovation research has already done so. SUA qualifies as digital innovations, as it uses digital technologies to transform market offerings or business processes in the urban agriculture realm. To 123 248 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) conceptualize digital innovation, Kohli and Melville (2019) propose a model with four key innovation phases organizations undergo when creating new digital innovations: initiation, development, implementation, and exploitation. While the initiation phase describes opportunity detection, the development phase includes creating, customizing, and adopting respective innovations. In the subsequent phases, implementation incorporates the deployment and maintenance of the innovation, and lastly, the exploitation phase focuses on the ongoing value creation of existing solutions (Kohli and Melville 2019). The key phases above are meant to apply to all digital innovations (Kohli and Melville 2019), including SUA. However, context is a critical factor in the exact execution of these phases, impacting innovation success (Kohli and Melville 2019; Nambisan et al. 2017). Therein, a deep understanding of the respective context (in our case: SUA) and its influence on the digital innovation process is indispensable for leveraging SUA’s value. 2.3 A Taxonomy for Smart Urban Agriculture During the four phases, high levels of knowledge regarding the specific contexts’ possibilities for applying digital technologies are required (Kohli and Melville 2019). As application knowledge is highly context-specific, a closer analysis of SUA use cases is essential to drive SUA innovation. Understanding the different dimensions on which SUA can differ is needed to master opportunity detection and development. To address this need, developing a taxonomy pinpointing dimensions of SUA deems a promising approach (Nickerson et al. 2013). In SUA, digital technologies, referring to the combination of information, computing, communication, and connectivity technologies, including the related hardand software, are at the core (Bharadwaj et al. 2013). Understanding SUA applications thus requires three components: 1) the digital technology (hardand software) itself (Bharadwaj et al. 2013), 2) the data these technologies work with to generate meaningful insights (Zhang et al. 2019), and lastly 3), the specification of the context, namely the selected SUA approach in which the digital solution is applied (Hong et al. 2014). In line with our understanding of BISE research covering the interaction between information technology, information, and people, the three presented components are suitable building blocks in the taxonomy and serve as a structuring tool for the different dimensions (Lee 2010). Table 1summarizes the building blocks. 2.4 Organizational Readiness for SUA Many innovation endeavors fail during the implementation and exploitation phases, for reasons such as financial challenges or difficulties with scaling (Roundy 2017; Battistella et al. 2021; Deserti and Rizzo 2020). Addressing this challenge, the concept of organizational readiness factors as necessary prerequisites to successfully implement and exploit the potential of an opportunity is emerging (Lokuge et al. 2019; Molla and Licker 2005). In the context of SUA, the underlying ‘‘organization’’ can range from large-scale professional firms, communitybased public endeavors up until private household projects (Wood et al. 2020). As the readiness to innovate with digital technologies is associated with seizing business opportunities (Walczuch et al. 2007), Lokuge et al. (2019) propose an organizational readiness model for digital innovation in general. The model identified seven possible areas of organizational readiness for digital innovation (namely resource readiness, IT readiness, cognitive readiness, partnership readiness, innovation valance, cultural readiness, and strategic readiness). However, as readiness often includes psychological and structural factors, such as the commitment and capability to change, readiness models must be tailored to account for the attributes of the specific technology or context (Molla and Licker 2005). 3 Study Design In analyzing the contextual implications of SUA on the general digital innovation process, we apply taxonomy research (perspective 1) and the development of organizational readiness (perspective 2). Inspired by meta-inferences in mixed-methods research (Venkatesh et al. 2016a), we derived an integrative view of both perspectives, which provides a fuller picture of the phenomenon under investigation and links both perspectives. The meta-inferences were developed in an inductive process of combining insights from all combinations of SUA’s taxonomy dimensions and SUA’s readiness categories to form broader generalizations. Thereby, we aimed at identifying causal mechanisms between both perspectives (Venkatesh et al. 2016a). Starting with perspective 1, we built the taxonomy following Kundisch et al. (2022) who align with but extend Nickerson et al. (2013) (see Appendix A (online) for an overview of the taxonomy design’s phases. The onlineappendices are available via http://link.springer.com). After specifying the taxonomy’s purpose in the Introduction and Theoretical Background sections of this study (Phase 1: Identify the problem and motivate), we proceeded to define the taxonomy’s meta-characteristics, 123 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) 249 ending conditions, and evaluation goal (Phase 2: Define objectives of a solution). Meta-characteristic and ending conditions: Following Nickerson et al. (2013) advice, we defined the meta-characteristic of our taxonomy as Characteristics of digital technologies in urban agriculture. To iteratively evaluate whether our taxonomy had reached quality saturation, we chose a set of objective ‘ending conditions’ (Nickerson et al. 2013): a) no new dimensions or characteristics were added in the last iteration, b) no dimensions or characteristics were merged or split in the last iteration, and c) every dimension is unique and not repeated, d) at least one object is classified under every characteristic of every dimension. In addition, we applied the subjective ending conditions proposed by Nickerson et al. (2013), which require a taxonomy to be concise,robust,comprehensive,extendible, and explanatory. Overall, we conducted six iterations (Phase 3: Design and Development). Iterations 1, 2, and 3 followed a conceptual-to-empirical approach (deductive reasoning), and iterations 4, 5, and 6 took an empirical-toconceptual approach (inductive reasoning) (Kundisch et al. 2022). Table 2details the six iterations. Iterations 1, 2, and 3 (conceptual-to-empirical): During iteration 1, we conducted a systematic literature review (Boell and Cecez-Kecmanovic 2015) of English-language research papers with the database Web of Science covering broad terminology in the SUA realm. Appendix B (online) summarizes the search protocol. For title, abstract, and fulltext screening, we specified the following inclusion criteria: Papers addressing (1) urban contexts, (2) digital technologies, and (3) the production phase of agriculture. Two authors screened each of the resulting papers and extensively discussed their inclusion. After both screening iterations, 53 were considered. Additional studies were identified in the second iteration via a search of the AIS eLibrary, yielding eight more studies. The full list of 61 studies can be found in Appendix C online. We drew on Wolfswinkel et al. (2013) approach to qualitative data analysis and coded all studies (see exemplary coding scheme in Appendix D online). Specifically, during open coding, we first highlighted information on SUA Table 1 Building blocks of smart urban agriculture Building Block Description Source Digital technology All softand hardware of the solution and its combination of information, computing, communication, and connectivity technologies (Bharadwaj et al. 2013) Data Types, handling, and interaction of and with data (Pu ¨schel et al. 2020) Approach The type of urban agriculture applied both with respect to end products and growing and harvesting methods (Li et al. 2020) Table 2 Iterations of taxonomy development and evaluation *c-econceptual-to-empirical; ecempirical-to-conceptual # Approach* Basis # of Changes in last iteration Taxonomy 1 c-e WoS literature 8 dimensions, 27 characteristics 8 dimensions, 27 characteristics 2 c-e AIS eLibrary literature 4 dimensions, 15 characteristics 11 dimensions, 36 characteristics 3 c-e Interviews with 9 researchers 2 dimensions, 4 characteristics 10 dimensions, 33 characteristics 4 e-c 10 real-life examples 1 dimension, 9 characteristics 9 dimensions, 25 characteristics 5 e-c 32 real-life examples No changes 9 dimensions, 25 characteristics 6 e-c Interviews with 16 practitioners Renaming of 1 dimension 9 dimensions, 25 characteristics 123 250 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) technologies’ characteristics. For axial coding, we then grouped the characteristics into dimensions. Afterward, we assigned the dimensions to the three building blocks digital technology,data, and urban agriculture approach. For selective coding, we reduced and refined the characteristics in each dimension. Iteration 3 was based on interviews with nine scholars (see Appendix E online), whose feedback we used to refine the dimensions and characteristics. More information on the expert interviews, sampling strategies, and coding procedure is presented at the end of this section. To assess the real-life fit of the taxonomy, we created a set of ten SUA technologies (Appendix F online). Five of these digital technologies were identified from the literature, and the other five from real-life industry products. We built the sample based on three criteria: 1) technical aspects of digital technology described in detail, 2) advanced stage of development, 3) diverse types of urban agriculture (e.g., aquaponics, greenhouses). In each of the iterations, we classified the ten technologies using our taxonomy (Oberla ¨nder et al. 2019; Nickerson et al. 2013). Iterations 4, 5, and 6 (empirical-to-conceptual): We compared real-life examples of SUA and identified similarities and differences (Nickerson et al. 2013). Iteration 4 leveraged the set of ten technologies which we extensively discussed and compared in light of our taxonomy. For iteration 5, we composed a more detailed list of 32 SUA technologies as an information source through a structured web search and classified these technologies using the taxonomy (Appendix G and H online). Following Amalia et al. (2020), we conducted our web search as a two-phased approach: 1) Website identification and 2) content analysis. For website identification, we searched the internet with keywords related to ‘‘smart urban agriculture,’’ ‘‘smart urban farming,’’ and the individual SUA approaches (e.g., ‘‘vertical farming’’ or ‘‘hydroponics’’). For the content analysis, we analyzed respective websites concerning the taxonomy’s dimensions and characteristics by screening for any information indicating an assignment to the characteristics of the taxonomy. This final classification also served as a tool to objective ending conditions c) and d) (Phase 4: Demonstration). In iteration 6, we evaluated the taxonomy (Phase 5: Evaluation) with 16 semi-structured interviews with practitioners to assess the attainment of the subjective ending conditions (see Appendix I online for the interview guideline protocol). We ensured the highest ethical standards by having our research approved by the University of Hohenheim Ethics Committee. We recruited our initial participants via personal networks and continued via snowball sampling. We stopped data collection after a total of 25 interviews as no significant new topics were brought up. Individual interviews lasted between 11 and 57 min (12.6 h in aggregate). The interviewees resided in several countries, including Austria, Germany, Israel, and the Netherlands. We recorded and transcribed each interview. We coded the interview data with reference to Wolfswinkel et al. (2013), applying a pattern-inducing technique by gathering qualitative data and clustering text segments into concepts. We compared new categories as they emerged and discussed their connection. While going back and forth between data and descriptive codes, we systematically distilled readiness factors of SUA. The coding process was divided into three stages: open coding, axial coding, and selective coding (Wolfswinkel et al. 2013; Corbin and Strauss 1990). An exemplary coding scheme outlining the coding process is part of Appendix J online. Due to the explorative nature of our research, one author started by thoroughly reading the interview transcripts and highlighting important text passages. This way, text passages on factors that could contribute to or prevent SUA organizations from using digital technologies were highlighted. During this open coding, we relied on informant terms close to the original interview data. For axial coding, we used a workshop to paraphrase and group the identified text passages, searching for similarities and differences among the codes (Corbin and Strauss 1990). If agreements about certain codes were low, we revisited the transcripts, engaged in discussions, and developed mutual understanding and consensual decision rules. For selective coding the grouping was redefined, and categories of SUA readiness were built. By integrating existing literature (Lokuge et al. (2019) digital readiness categories of Resource Readiness, Cultural Readiness, Strategic Readiness, Innovation Valence, Cognitive Readiness, and Partnership Readiness), we evaluated our data asking whether the emerging categories help us to describe and explain the phenomena we were observing. Although presented linearly above, our analysis was dynamic and iterative. We continued coding new data and refining our findings until we reached theoretical saturation, where additional interviews did not yield any change in the readiness factors. 4 Results of Perspective 1: Taxonomy Development and Evaluation We build on literature, real-life examples, and interviews throughout the six iterations to derive our taxonomy, as shown in Fig. 1. As outlined in the Theoretical Background, we use the tailored building blocks of Digital technology,Data, and Approach to structure SUA and cluster dimensions and characteristics in the three blocks (Pu ¨schel et al. 2020; Bharadwaj et al. 2013). 123 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) 251 Digital Technology: The first four dimensions relate to aspects of digital technology applied in the context of urban agriculture. Firstly, the Role of Technology indicates whether digital technology is a Supporter or an Enabler of the urban agriculture solution (Benbasat and Zmud 2003; Hanelt et al. 2017). Supporters are digital technologies that improve existing solutions, for example, by reducing water resources required in a rooftop garden (Harada et al. 2018). By contrast, enablers are central components of urban agriculture solutions and are required for the solution to work at all. Examples include all types of highly automized robotic vertical farms, where operations would completely still stand if the underlying digital technologies (such as robotics algorithms) stopped working. Secondly, the dimension Functionality Level indicates the highest functionality level sorted from least to most advanced functionality. Therein, the higher functionality levels such as adaptation also include the lower levels such as monitoring. The characteristic Monitoring describes all activities (such as data collection and analyses) related to measuring and tracking parameters during operation without actively changing them (e.g., nutrient levels, sunlight) (Fe ´lix et al. 2018). The characteristic Recommendations goes one step further and involve actively recommending specific alternatives (e.g., adding more water) (Galdon et al. 2021). Adaptation either refers to a) environmental adaptation by actively controlling and changing environmental parameters such as light, irrigation, and nutrients (e.g., by modifying the light intensity) (Gravalos et al. 2019), or b) production adaption by performing actions directly on the product, such as smart harvesting or weed management (Ampatzidis et al. 2017; Farhangi et al. 2020; Ofori and El-Gayar 2021). Those characteristics give insights into the smartness of SUA technologies. According to Alter (2020), the smartness of SUA technology depends on the technology’s ability to use automated capabilities and physical, informational, technical, and intellectual resources to process, interpret and/or learn from information. Thus, the smartness of technologies classified in Monitoring is comparably lower than those in Recommendations which, in turn, has a lower level of smartness than Adaption. Thirdly, Support in Urban Agriculture Planning involves using digital technology to find suitable spaces for urban agriculture or simulate different urban agriculture set-ups (Khan and Ahmed 2017; Ghandar et al. 2021). SUA technologies either support this planning process (i.e., YES characteristic) or do not (i.e., No characteristic). Lastly, the dimension Interface describes where humans and machines interact. An interface can be either directly integrated with the digital technology itself (Solution-integrated), through, for example, displays on the technology, or through External Devices such as wearables (Niemo ¨ller et al. 2019). Data: The next two dimensions refer to the data collected, analyzed, and acted upon. This building block includes data collected directly by the operator or externals (e.g., weather data). Firstly, the Source refers to the location of data collection (Pu ¨schel et al. 2020). Aerial Remote Sensing describes data collected from drones and satellites in the air, for example, aerial images (Egerer et al. 2020). Fig. 1 Taxonomy of smart urban agriculture technologies 123 252 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) Ground-based Sensing refers to all ground-based data sources collecting and acting upon information such as temperature, humidity, or nutrient levels (Surantha and Surantha 2020). Lastly, Agent-related Sensing refers to data collected directly at and by human agents (e.g., farmers, technicians) within the SUA solution, such as activities and movements tracked by smart watches, mobile phones, or smart glasses (Niemo ¨ller et al. 2019). Secondly, Content specifies what data are collected and used by the digital technology and differentiates between Process Data,Environmental Data, and their combination (Process and Environmental Data). Process data includes data on a machine’s operations (e.g., irrigation performed, robotic movements), refers to data on the actual product (e.g., growth level or health status information Ofori and El-Gayar 2021), or includes interactional data that involves communication between humans and machines, such as human-generated input to the system (Nadal et al. 2017). Conversely, Environmental Data comprises information on the surrounding environment (e.g., CO 2 levels, topography) (Nadal et al. 2017). SUA endeavors operating on comprehensive data types include Process and Environmental Data. Approach: The third set of dimensions relates to the underlying urban agriculture approach. Each urban agriculture solution can be classified according to its basic Type. Urban agriculture solutions are either classified as Ground Indoor (e.g., greenhouses, hydroponic systems), Ground Open Air (e.g., community gardens), Vertical Indoor (e.g., indoor vertical farms), Vertical Open Air (e.g., productive fac¸ades), or Rooftop Open Air (e.g., rooftop gardens) (Dorr et al. 2021). End Product comprises the agricultural products produced in the urban agriculture solution. Urban agriculture solutions can contribute to food security by producing Plants (e.g., vegetables) or Animal and Plant products (e.g., aquaponics combining plant cultivation in a hydroponic system and fish farming in an aquaculture system) (Padilla et al. 2018; Wood et al. 2020). The dimension Nutrient Medium indicates the nutrient medium used to produce the end product (Padilla et al. 2018). Growth environments include Soil, as in traditional agriculture or greenhouses, and Water or Air,asin hydroponic or aeroponic systems (Padilla et al. 2018). As detailed before, we classified 32 SUA technologies using the final taxonomy to first evaluate if all real-life examples are classifiable through our taxonomy, and second to test if every characteristic is addressable through at least one real-life example (objective ending conditions). Appendix H (online) lists the 32 technologies selected and their assignment to the different characteristics. Figure 2 shows the classification of these 32 technologies within the taxonomy. The number below the characteristics indicates how many SUA technologies were assigned to the characteristics. The classification proves that a) all objects could be classified within the dimensions and characteristics, and b) all characteristics were relevant to at least one SUA technology. 5 Results of Perspective 2: Readiness Factors in Smart Urban Agriculture After conceptualizing the phenomenon of SUA as a taxonomy, interviews with 16 SUA practitioners on requirements and prerequisites needed for leveraging SUA laid the ground for the derivation of 5 SUA readiness categories that gather 20 SUA readiness factors. As stated in the section Study Design, we referred to Lokuge et al. (2019) proposed categories of digital readiness to gather emerging SUA readiness into categories. Table 3presents our main findings: As shown in column one, most of Lokuge et al. (2019) readiness categories, namely, resource readiness, cultural readiness, strategic readiness, and partnership readiness were also relevant for SUA readiness. Further, our data revealed a new readiness category, namely regulatory readiness. Column three represents detailed descriptions of each of the 20 readiness factors named in column two. For resource readiness, the readiness factors IT infrastructure, IT expertise, and finances are in line with (Lokuge et al. 2019), whereas integration, and sustainability turned out to be specifically relevant for SUA readiness. With cultural readiness, the existing factors knowledge sharing, trial-and-error mentality, appreciation, and fun factor are complemented by culture of change and transdisciplinary mindset. Moving on to strategic readiness, only stakeholder awareness is found to be relevant for SUA readiness, expanded by scale and scale-up pace.Partnership readiness complements the existing factor personal network with high-tech supply availability, ecosystem integration, and training opportunities.Regulatory readiness categories the newly found SUA readiness factors of adherence and laws. Finally, column four provides an exemplary quote from the interviews for transparency. 6 Discussion There is substantial evidence that digital technologies are promising for mastering urban agriculture, e.g., by automizing tedious manual labor or reducing resource consumption during production (Langendahl 2021). However, in practice, their potential is not yet leveraged. The fundamental challenge is that many of the emerging SUA endeavors fail economically – may it be due to high 123 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) 253 investment costs, lacking skills and talent in workforces, a wrong selection of technologies during implementation, or the inability to materialize the expected financial, ecological, and social benefits in the long run (Langendahl 2021; Yigitcanlar et al. 2022). In respect to solving this challenge, our contribution is twofold. First, we contribute a taxonomy that enables both research and practice to better understand what application possibilities ‘‘digital technologies in urban agriculture’’ comprise, and what differences exist between various types of SUA. Therein, we make use of the value of taxonomies which research in the BISE community describes as 1) building the basis for conceptualizing a new phenomenon (i.e., SUA) and 2), a necessary step towards reducing research and practice’s overload caused by the multitude of different SUA technologies available (Kundisch et al. 2022). We build on urgent calls from digital transformation literature regarding the importance of enabling a conscious, well-informed adoption of digital technologies in order to reduce project failure (Hess et al. 2016; Riera and Iijima 2019). Second, discussing our results against the broader context of smart city and IT-enabled green city literature, we interpret our findings as an extension to existing studies in the field. On a general level, existing research recognizes the need for digital technologies in cities for a sustainable transformation (Dewi et al. 2018; Maye 2019). However, the main focus currently mainly lies on use cases related to democratizing governance, healthcare, sustainable housing, mobility, or education (Yigitcanlar et al. 2022; Kinelski et al. 2022). Acknowledging the importance of these areas, we argue for a stronger integration of smart urban farming within the smart city and IT-enabled green city literature streams, given the demonstrated potential of SUA. Complementing previous studies on related fields such as smart city readiness (Yigitcanlar et al. 2022; Dewi et al. 2018), the readiness factors in these studies directly address the challenge of financially failing SUA initiatives by providing a list of factors to consider during project launch. However, the contribution of both the taxonomy and the readiness factors is not isolated, which is why we provide an integrative view of the findings from both perspectives, called ‘‘meta-inferences’’. They are summarized in Table 4. The presented 15 meta-inferences allow the disclosure of interrelations and boundary conditions of SUA’s taxonomy dimensions and readiness categories. The complementary perspectives on SUA reveal relevant findings on the intersection of taxonomic research and readiness research, as presented by the meta-inferences. In addition, in order to explain how the two perspectives Fig. 2 Technology classification using the taxonomy of smart urban agriculture 123 254 A.-S. Christmann et al.: Smart Urban Agriculture, Bus Inf Syst Eng 67(2):247–264 (2025) validate our initial findings and draw conclusions on the distribution of examples across characteristics. Secondly, we interviewed practitioners from Austria, Germany, Israel, and the Netherlands. While this sample covers different regions, it does not represent the global SUA industry. It would be interesting to assess local differences in SUA readiness, such as policies and regulations, resources, and skills, via a larger sample of interviews. Our study yields interesting pathways for future research in the BISE community. Currently, the process of farmers’ evolution from urban to smart urban is unknown. Becoming a SUA champion will not be a binary process from 0 to 1 but will involve various stages of development. For research and practice to understand this process, the development of a maturity model of SUA – including the readiness factors for each stage (e.g., readiness levels) – is a research endeavor that promises to strengthen conceptual understanding of the topic (Linhart et al. 2017). Future investigations may also quantify the specific gains triggered by digital technologies in SUA contexts, to better explicate its value. Empirical research, for example, could conduct field studies that compare the outcome of different urban agriculture approaches – with and without digital technologies. Results may differ in terms of environmental sustainability (i.e., energy savings, efficiency of resources), societal (i.e., facilitation of work effort) and technical gains (i.e., creation of data sources, data transparency, monitoring and prediction options) or economic profitability. Similarly, future research can evaluate differences in gains based on the individual SUA dimensions and characteristics. 7 Conclusion Mindful of the grand sustainability challenges we grapple with, they may constitute a ‘‘window of opportunity’’ for new organizational endeavors. We propose a multi-layer taxonomy that characterizes digital technologies helping to leverage opportunities in urban agriculture and suggest 20 organizational readiness factors for smart urban agriculture as a starting aid. Our work contributes to BISE research by providing a structuring tool to guide scholars working at the intersection of BISE, sustainability, and innovative digital opportunities in the urban realm. Overall, our study sets the scene for a thorough conceptual understanding of the nature of SUA technologies and requirements for leveraging their value, and, we hope, presents a further step toward creative ideas on how to turn crises into opportunities. Supplementary InformationThe online version contains supplementary material available at https://doi.org/10.1007/s12599024-00863-w. Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/. 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