# Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical Farming Adoption Among Food Retailers in France

Marie Asma Ben Othmen, Cyprien Julienne, Fahim Shaikh

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## To cite this version:

Marie Asma Ben Othmen, Cyprien Julienne, Fahim Shaikh. Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical Farming Adoption Among Food Retailers in France. Journal of International Food and Agribusiness Marketing, 2024, pp.1-25. 10.1080/08974438.2024.2409109. hal-04839289

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1 Ben-Othmen, M. A., Julienne, C., & Shaikh, F. (2024). Transcending Food-Miles:

2 Unpacking the Drivers of In-Store Vertical Farming Adoption Among Food Retailers in

3 France. *Journal of International Food & Agribusiness Marketing*, 1–25.

4 https://doi.org/10.1080/08974438.2024.2409109

5

6 Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical

7 Farming Adoption among Food Retailers in France

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10 Marie Asma Ben-Othmen*

11 marie-asma.benothmen@unilasalle.fr

12 INTERACT Research Unit–Innovation, Land Management, Agriculture, Agro-Industries,

13 Knowledge, and Technology, UniLaSalle-France, Mont-Saint-Aignan, France

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15 Cyprien Julienne

16 Cyprien.JULIENNE@etu.unilasalle.fr

17 MSc Urban Agriculture & Green Cities, UniLaSalle-France, Mont-Saint-Aignan, France

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19 Fahim SHAIKH

20 Fahim.SHAIKH@etu.unilasalle.fr

21 MSc Urban Agriculture & Green Cities, UniLaSalle-France, Mont-Saint-Aignan, France

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# Transcending Food-Miles: Unpacking the Drivers of In-Store Vertical Farming Adoption

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## among Food Retailers in France

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### Abstract

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This paper examines the potential of in-store vertical farming technologies in urban food retail

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as a solution to the increasing demand for fresh, locally sourced food. By enabling a "sell where

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you produce" model, vertical farming can reduce food miles, enhance traceability, and lower

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carbon emissions. The study applies the Unified Theory of Acceptance and Use of Technology

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(UTAUT) to analyze factors influencing the adoption of these technologies among food

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retailers in Rouen, France, with a particular focus on the role of risk aversion in shaping

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attitudes and decisions.

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The findings highlight that performance expectancy and facilitating conditions are the key

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drivers of adoption intentions among retail store managers. However, perceived risks, such as

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high investments and operational costs, pose significant barriers, while social influence appears

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to have little impact on their decisions. The study underscores the importance of conducting

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economic evaluations and risk assessments of vertical farming practices to ensure their long-

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term viability. As vertical farming holds the potential to boost fresh food production in urban

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areas, reduce food miles, and create sustainable supply chains, it is crucial to carefully weigh

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its benefits and risks to support its successful adoption in urban food retail.

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Keywords local food systems, food supply chains, technology adoption, users' acceptance,

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vertical farming, urban agriculture

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Word count (excluding references): 7,202 words.

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# 1 Introduction

As urbanization accelerates and population density rises globally, urban designers are increasingly considering cities as potential hubs for local food production (Al-Chalabi, 2015a; Al-Kodmany, 2024). Concurrently, consumers are becoming more disillusioned with large-scale, industrialized food production and distribution systems dominated by multinational corporations (Birch et al., 2018). Concerns about the negative impacts and lack of transparency in global food systems, particularly concerning environmental harm, sustainability issues, health risks, and food safety, are becoming pronounced (Ofori-Parku, 2024).

In response to these concerns, urban agriculture, particularly vertical farming technology, has emerged as a promising solution that optimizes land use, incorporates advanced technologies, and supports local food production within urban environments (Benke & Tomkins, 2017). This approach to local food production ensures enhanced efficiency and sustainability and supports access to fresh and locally sourced food in urban areas (Graamans et al., 2018).

The interest in vertical farming extends beyond urban farmers to include key players in the food retail sector, such as supermarkets, grocery stores, and food cooperatives that are integral entities to the complex process of delivering food from producers to consumers, influencing distribution, marketing strategies, and food accessibility (La Londe & Masters, 1994). However, many supermarkets, particularly in France, still rely heavily on global supply chains to offer seasonal products at competitive prices. This reliance on international suppliers has led to what Thorsøe and Kjeldsen (2016) describe as "food from nowhere" which Konefal and Hatanaka (2014) argue results in the loss of the subtle qualities inherent in locally produced food. Amidst escalating apprehensions regarding climate change and the urgent need for the food and agricultural systems to adapt in ways that mitigate its effects, it has become imperative to transcend food miles not only to confront greenhouse gas emissions originating from
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worldwide supply chains but also to minimize the resource wastage attributed to the global sourcing and shipping practices that carry high environmental and social costs (Albizzati et al., 2019). Recognizing and addressing these interconnected issues is paramount, prompting local governments and civil society organizations to advocate for implementing local food systems to create more inclusive, resilient, and sustainable ones (Suhartanto et al., 2023). In response, the European Commission took a pivotal step in this direction by launching the "Farm to Fork Strategy" in 2020. This initiative is designed to support the establishment of shorter supply chains, aiming to reduce reliance on long-haul transportation and thereby enhance the resilience of regional and local food systems (Liu, 2020).

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As Trivette (2019) brings attention to the transformative potential of food retail actors in connecting urban residents with their food sources, a closer examination of the literature on this topic reveals a significant impact resulting from an integrated approach. This perspective is particularly interesting for comprehending the broader environmental and societal implications and the potential for in-store vertical farming adoption by food retail actors, thereby establishing a more interconnected and sustainable relationship between city dwellers and their food supply.

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Transitioning from this conceptual understanding, a practical manifestation of this integrated approach is observable in European food retail. In order to meet growing consumer demand for fresh, sustainable produce with low ecological impact, several food retail players are spearheading in-store vertical farming initiatives. Exemplified by successful projects in major cities like Berlin and Paris, these endeavors underscore the commitment of retail and wholesale companies to piloting on-site cultivation of herbs and vegetables within their stores (Butturini & Marcelis, 2020). However, as we delve deeper into the discussion, it becomes apparent that unanswered questions persist regarding these retail actors' pivotal role in adopting in-store vertical farming technologies and expanding local food activities. It is important to note that while extensive research has been conducted on the significant changes in retail due to recent
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technological advancements, the focus has primarily been on the Internet and other emerging technologies like robotics, the Internet of Things (IoT), virtual reality, and augmented reality in the retail industry (Shankar et al., 2021). There is limited understanding of how emerging in-store vertical farming technologies influence the retail sector's potential transformation and ecological transition, as well as their contribution to building local food systems.

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In France, while industry giants such as Casino and Auchan embraced this concept in 2018, mid-scale retail actors have yet to fully incorporate in-store vertical farms into their operational frameworks. This discrepancy raises pertinent questions about the retail landscape's broader acceptance and adoption trends and challenges.

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This article aims to fill this knowledge gap by analyzing the drivers of adoption of in-store vertical farming technologies among food retailers in France. Abundant literature underlines the importance of the behavioral intention concept in explaining the decision-making processes and procedures in the context of technology use (Mukherjee et al., 2023a; Shankar et al., 2021; Venkatesh et al., 2012; Venkatesh & Zhang, 2010) Nonetheless, although technology is often viewed as a solution to address the challenges of food supply chains and can contribute to a more sustainable food system, it comes with risks and uncertainties that may hinder its acceptance and widespread use. This research explores retailers' attitudes and decision-making processes regarding adopting in-store vertical farming technologies.

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Based on data analysis from a survey of 109 food retailers in Rouen City, France, including supermarkets, grocery stores, specialized food shops, and food cooperatives mainly serving urban residents, we develop and apply a Structural Equation Model (SEM). This model explores how retailers' perceived risk, facilitating conditions, availability, and anticipated performance impact their decisions. Furthermore, the research examines the influence of social factors on retailers' adoption of in-store vertical farming technologies. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT), a well-established framework for predicting
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and explaining technology adoption behavior (Venkatesh et al., 2003), is considered as a
theoretical reference.

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This paper is structured as follows: the initial section offers a concise overview of vertical
farming technologies and their potential contributions to achieving sustainability in the food
retail sector. Section 2 provides the theoretical foundation of the Unified Theory of Acceptance
and Use of Technology, offering insights into variables that directly predict the behavioral
intention of vertical farming technology adoption and outlining the hypotheses and the survey
design. Section 3 details the results, leading to the discussion (section 4) and the conclusion
 session 5).

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Background: the potential of in-store vertical farms in crafting sustainability in the food retail
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Businesses aiming for sustainability are often measured by what is known and referred to in the
literature as the triple bottom line or the 3P model (Elkington, 1994), highlighting that
companies must consider not only their economic performance (profit) but also their impact on
the environment (planet) and society (people). In this context, retailers have a crucial role to
play in promoting sustainability since they serve as a vital link between the various upstream
(i.e., natural resources, resources producers, suppliers) and downstream (i.e., consumers,
households and communities) actors involved in the food supply chain (Grewal & Levy, 2007).

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Previous research has underscored the significance of integrating sustainability into the food
retail industry, as evidenced by the development of the concept of environmental marketing
strategy, which addresses both environmental and social considerations in shaping corporate
strategies (Claro et al., 2013). More recently, there has been a notable surge in the eagerness of
certain food retailers to engage in local food initiatives (Aschemann-Witzel et al., 2016). To
carve out their unique market niche, these food outlets have initiated locally sourced programs,
introducing a broader range of seasonal and local products (Dunne et al., 2011; Lähteenmäki-

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1 Uutela, 2014). This strategic shift aims to offer consumers alternatives regarding product offerings and the overall shopping experience (Fischer, 2006; Konefal & Hatanaka, 2014). On the other hand, some food retailers have embraced in-store vertical farming technology, a form of high-yield controlled environment agriculture staked in multiple layers that promises to produce leafy greens and vegetables within cities, with the potential to reduce the resource intensity use attributed to urban food production and consumption (Petrovics & Giezen, 2022).
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7 This strategy enables more local food production, addressing sustainability challenges inherent in operational models by promoting a "sell where you produce" approach and catering to customer needs, drawing in a clientele keen on supporting local food production.
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10 In these settings, vertical farms act as self-contained entities within urban areas and are deemed "ecosystemic" as they operate as a unified entity, cultivating and selling produce within the same structure, circumventing, thereby, carbon emissions associated with transportation and enhancing the traceability of food items (Al-Chalabi, 2015b). Beyond these advantages, vertical farming offers retail companies a distinctive system for product offerings. Utilizing cutting-edge production methods that involve multiple layers, techniques, and integrated organizational interactions, in-store vertical farming technologies simplify the entire food production process from planting to retail. This not only creates a promising sustainability potential for organizations but also significantly reduces the ecological footprint of food production and ensures that densely populated urban areas maintain consistent access to fresh, high-quality food.
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21 While there is not much research on the advantages of in-store vertical farming and the challenges that retail companies may encounter when implementing it, past studies on the factors that influence innovation adoption by food retailers, indicate that they always prioritize consumers' interests (Esbjerg et al., 2016). These studies have also indicated that adopting new technologies and their resultant products must demonstrate clear consumer benefits and pose minimal risk for retailers. On the other hand, in their research on the impact of supermarket 22
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credibility on the purchase intention of novel food, Jung, Sharma, and Mattila (2022) state that both retailers and consumers must comprehend these advantages in terms of business instead of technical jargon. Clark et al. (2022) examined the drivers behind vertical farming's adoption by members of the catering industry. They found that although chefs believe that vertical farms can significantly reduce food miles and produce high-quality, fresh, and visually appealing produce, they have concerns about whether the taste of vertical farm produce can match that of conventional crops. Overall, the quality and taste perceptions of vertical farm produce are the main factors that influence chefs' willingness to use or reject these production techniques.

Even though research on vertical farming in the retail sector is sparse, insights from consumer behavior literature can help retailers understand consumer perceptions of produce grown using in-store vertical farming technologies (Adams et al., 2024). For instance, Ares et al. (2021) found predominantly positive attitudes toward vertically farmed produce in several countries, particularly China and Singapore. The study emphasized aligning vertical farming with the United Nations Sustainable Development Goals - mainly SDG 11. Sustainable Cities and Communities, and SDG 12. Sustainable consumption and production - which promote reducing carbon emissions and ensuring reliable food access drive these positive perceptions. However, concerns about high energy consumption and pricing associated with vertical farms also influence consumer attitudes. These findings highlight the complex considerations that shape consumers' perceptions of this innovative food production technology, providing valuable insights for retail stakeholders.

Despite positive strides, incorporating in-store vertical farming technologies within the retail industry remains limited, and a noticeable gap exists in the available literature explaining this trend. Similar to Trivette's (2019) argument, this article emphasizes the need to move beyond a primarily direct-to-consumer approach and consider retailers' contributions when promoting and supporting local food. We specifically explore the potential of in-store vertical farming as
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part of the solution to build sustainability in the food retail sector and, more generally, urban

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food supply chain strategies. The idea is that in-store vertical farming technologies can

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potentially reduce our dependence on global supply chains by shifting the focus and operations

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and offering direct configurations that allow consumers to learn more about the origins of their

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food and the innovative technology of its production. In this article, we argue that attitudes and

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perspectives of food retail actors are essential, given their influence on consumers' purchase

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and eating habits, and, therefore, assessed retailers' behavioral intentions to adopt in-store

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vertical farming technologies.

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## II. Material and Methods

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### 1. Conceptual framework and hypothesis

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Initially proposed to provide a more accurate explanation and prediction of technology

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acceptance behavior, the Unified Theory of Acceptance and Use of Technology (UTAUT) is

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now recognized as one of the most contemporary models for studying technology

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acceptance(Venkatesh et al., 2003). It was developed from the Technology Acceptance Model

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(TAM), introduced by Davis (1989), one of the most widely known frameworks for exploring

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how new users adopt and accept innovative technologies. TAM itself evolved from the Theory

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of Reasoned Actions (Ajzen & Fishbein, 1980a) and the Theory of Planned Behavior (Ajzen,

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1988).

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Today, the UTAUT is recognized to be an empirically validated model that is also parsimonious

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and effective for explaining and testing a wide range of behaviors. However, while it has been

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applied in various technology acceptance fields, including healthcare (Ben Arfi et al., 2021),

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education and learning (Chauhan, 2016), and agriculture (Li et al., 2020), its use in food retail

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research is still limited with previous research essentially focusing on the adoption of

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blockchain technology (Mukherjee et al., 2023b; Sheel & Nath, 2020) to improve supply chain
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sustainability, as well as exploring technological progress in designing innovative strategies for e-grocery (Dirsehan & Cankat, 2021).

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The UTAUT model includes four key antecedents (i.e., latent variables) that explain users' acceptance of new technologies: performance expectancy, effort expectancy, social influence, and facilitating conditions. These factors consider the inner characteristics of technology and the impact of external support and social influence on users' perceptions of technology. On the other hand, theories on attitudes conceptualize behavioral intention into two main stages, which are the association of 1. beliefs and attitudes on the one hand and 2. attitudes and intentions on the other (Ajzen & Fishbein, 1980). In the first stage, individuals form their attitudes based on their beliefs and cognitive processes. In the second stage, these attitudes shape their behavior and determine how they express themselves. We hypothesize that the four UTAUT variables represent the beliefs of food retailers toward vertical farming technologies, which affect their attitudes (Hypothesis H 1-5). Attitudes, in turn, will lead to the expression of behavioral intentions (Hypothesis H 6).

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Furthermore, risk perception is also included in the conceptual model to extend our understanding of its relative influence on food retailers' behavioral intention to adopt in-store vertical farming technologies. Indeed, in-store vertical farming presents several risks for retail managers, including high initial investment and operating costs, technological complexity, and uncertain consumer acceptance. Additional challenges include supply chain integration, regulatory compliance, and market competition. Managers must navigate these risks through careful planning and management to ensure the technology's successful and sustainable adoption. Accordingly, risk perception may also directly affect attitudes toward adopting in-store vertical farming technologies. Hence, we include a risk component in the original UTAUT to explain the complex process that yields acceptance.

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The following paragraphs describe the theoretical definitions of the different UTAUT components concerned by this study as well as the underlying hypothesis of this research.

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1 Performance expectancy (PE) refers to the scope to which an individual thinks that technology use could be beneficial to them to reach a higher performance (Venkatesh et al., 2003) and is usually the strongest predictor of behavioral intention. Accordingly, the greater the perceived value of these technologies to food retailers, the more favorable their attitudes toward them will likely be. In this study, vertical farming technologies are anticipated to offer significant benefits to food retailers, such as ensuring a sustainable supply of fresh, locally produced food and minimizing food waste by better aligning supply with demand. Here, perceived effectiveness (PE) is defined as the retailers' recognition of the usefulness and advantages of vertical farming technology. Therefore, the following hypothesis was proposed:

10 H1. Performance expectancy directly, positively, and significantly affects retailers' attitudes toward vertical farming technologies.

12 Effort expectancy (EE) refers to the users' perception of how difficult or easy it is to use new technology (Venkatesh et al., 2003). Many studies consider effort expectancy the most influential factor in determining users' behavioral intention to adopt new technologies (Sun et al., 2013). In this study, EE refers to the extent to which retailers believe it is easy to learn and use vertical farming technologies and include them in their daily in-store logistics. We establish the following hypothesis based on previous studies by Chung & Jung (2019) and Martins et al. (2014) who found that the more convenient and easier to use a new technology is, the higher the behavioral intention to use it:

20 H2. Effort expectancy directly, positively, and significantly affects retailers' attitudes toward vertical farming technologies.

22 Social influence (SE) refers to the importance and impact of people close to or essential to a person, such as colleagues, friends, and family members, who can convince him or her to accept new technologies. Studies have shown the importance of SE in explaining individuals' intention to adopt and their effective adoption of new technologies (Sun et al., 2013b). In our model, the
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following hypothesis was established based on the results of studies by Venkatesh et al. (2012)

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and Chung & Jung (2019), which have shown that people's intention to use a new technology

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increases when influenced by people around them.

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H3: Social influence directly, positively, and significantly affects retailers' attitudes toward

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vertical farming technologies.

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Facilitating conditions (FC) include users' perception of the extent to which the organizational

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financial and technical infrastructure support the use of the new technology presented to them

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(Venkatesh et al., 2003). Chen and Chang (2013) have shown that facilitating conditions are

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significant factors in determining behavioral intention in adopting technologies. In this study,

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FC refers to resources and facilities that make the adoption process of vertical farming

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technologies more accessible to food retailers.

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H4: Facilitating conditions (FC) directly, positively, and significantly affect retailers'

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behavioral intention to adopt vertical farming technologies.

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Perceived risk (PR) refers to uncertainty impacting people's confidence in their decisions. Risky

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situations may occur when the probabilities of particular outcomes are unknown (Im et al.,

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2008). Moreover, perceived risk refers to users' attitude toward potential risks that may arise in

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various areas (financial, technical, production, management-related, etc.) due to the adoption

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of new technology. (Venkatesh et al., 2003). In this study, perceived risk refers to the risk that

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retailers assume by adopting vertical farming technologies in their stores and its impact on them

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and their businesses. These could include high initial capital costs, operational tasks, scale-up

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challenges, market competition, etc; we therefore hypothesize the following:

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H5. Perceived risk negatively affects retailers' attitudes toward vertical farming technology

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adoption.
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Attitudes: In this study, retailers' attitudes are expected to play a role in understanding how they

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prefer or do not prefer in-store vertical farming technologies. While the study by Rana et al.

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(2017) used attitudes as a mediating variable for PE and EE, others have found substantial
relationships between attitudes and behavioral intentions to use technology (Dwivedi et al.,

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2019). In our research, we expect attitudes to mediate the UTAUT, along with the risk

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perception construct and retailers' intentions to use vertical farming technologies in their stores,

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and we suggest the following hypothesis:

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H6. Attitudes positively affect retailers' behavioral intention to use in-store vertical farming

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technologies.

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Behavioral intention (BI) refers to the likelihood that a person will engage in specific behaviors

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under certain conditions in the future. It plays a crucial role in determining an individual's

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acceptance of new technologies (Venkatesh, 2003). In this study, BI is a critical concept in the

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model and specifically relates to the intention of food retailers to adopt in-store vertical farming

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technologies.

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Based on this hypothesis, we developed our theoretical model (Fig.1) to determine the factors

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influencing retailers' behavioral intention to embrace in-store vertical farming.

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Insert Figure 1 here

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2. Survey design and measurement

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The survey used in this study was designed based on the conceptual model presented earlier

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(Fig. 1) and consisted of three sections. The first section gathered information about the

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characteristics of the food retail business, such as total area, annual customer count, year of

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establishment, and the main fresh products sold in the store. The second section focused on

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attitudes and behavioral factors that could influence retailers' adoption of in-store vertical
1 farming technologies. Respondents were first shown images explaining vertical farming
2 technologies, followed by questions to measure the latent variables discussed earlier. These
3 variables were evaluated using a five-point Likert scale, ranging from "strongly agree" to
4 "strongly disagree." The specific questions used for this purpose are listed in Table 1. Questions
5 designed to measure Performance Expectancy (PE), Effort Expectancy (EE), Social Influence
6 (SE), and Facilitating Conditions (FC) were adapted from the existing scales proposed by
7 Venkatesh and Zhang (2010) and further supported by Sok Foon and Chan Yin Fah (2011). The
8 perceived risk was assessed using a scale developed by Stone and Grønhaug (1993). Behavioral
9 intention was measured with a single item adapted from Venkatesh and Zhang (2010). The third
10 section of the survey included questions evaluating the sustainability practices of food retailers
11 and the in-store experience they provide to customers. It also assessed their commitment to
12 supply chain transparency and their self-perception relative to their competitors.

13 *Insert Table 1 here*

14 The study employed both in-person and online surveys to engage a wide range of food retailers
15 in Rouen City, located in northwestern France. Although the findings may not fully represent
16 the entire food retail sector in urban France, Rouen—home to 110,000 residents—is a leading
17 city in promoting sustainable food systems through an innovative urban food strategy. This
18 strategy focuses on increasing the availability of fresh food, reducing the ecological footprint
19 of food, and fostering urban farming as a response to climate change. The survey campaign,
20 conducted between February and July 2020, resulted in 109 completed questionnaires. After
21 careful review, 94 were selected as usable for further analysis.

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23 3. Data analysis procedure
1 The data collected from the online survey was analyzed using STATA software version 16.
2 Most retail managers who responded were male (80%), with an average age of 34, and had been
3 operating their stores for an average of six years. While 11% of respondents reported having a
4 business degree, only 8% had food or agricultural sciences training. Regarding store
5 characteristics, the average store size was 750 square meters, though there was significant
6 variation within the sample (s.d. = 118.95). Food supermarkets were the most represented in
7 the sample (45%), followed by food co-ops (28%), greengrocers (23%), and hard discount
8 stores (18%). Notably, 90% of respondents were familiar with vertical farming and had a basic
9 understanding of its techniques, but only 19% regularly sourced fresh products from vertical
10 farms. There was strong agreement among respondents on the potential of vertical farming
11 technology to secure access to fresh food for city dwellers (81% fully agreed) and help create a
12 better customer experience in their stores (69% fully agreed) (Fig. 2).

13 *Insert Figure 2 here.*

14 Figure 3 displays the responses to questions that focused on how food retailers perceive and
15 articulate the risks associated with integrating vertical farming into their stores. Most
16 respondents identified the high initial investments and significant ongoing operational costs as
17 the main risks that could deter them from adopting vertical farming. However, over half of the
18 participants believe these risks may be mitigated over time as technology and processes
19 advance.

20 *Insert figure 3 here.*

21 To explore the relationships between the latent variables described earlier, we employed
22 Structural Equation Modeling (SEM). This robust statistical method accurately measures and
23 describes complex correlations among various factors (Hair, Ringle, & Sarstedt, 2012). SEM
24 was conducted using the Maximum Likelihood method within STATA's command language.
1 The STATA builder facilitated the creation of both path analysis and model parameters. The
2 analysis comprised two main steps. First, we assessed the measurement model by evaluating
3 Cronbach's alpha, a non-parametric indicator of internal consistency, with a threshold of >0.7
4 (Bagozzi et al., 1991). If Cronbach's alpha fell within the acceptable range, it was included in
5 the structural model. The next step involved performing confirmatory factor analysis and
6 examining the results of the structural models to ensure the measures' internal consistency and
7 improve model fit. To address potential non-normality in the measures, we primarily used the
8 Satorra-Bentler Estimator. The goodness of fit was assessed using several indices: the Root
9 Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI), the Tucker-
10 Lewis Index (TLI), and the Standardized Root Mean Squared Residual (SRMR).

## III. Results

### 1. Test of the model: the exploratory factor analysis

13 The exploratory factor analysis was conducted to investigate whether there is a relationship
14 between the items used in the questionnaire to assess food retailers' behavioral intentions
15 regarding adopting on-store vertical farming technologies. Table 2 displays the correlation
16 matrix for the items, revealing that the data are not highly correlated. Additionally, Bartlett's
17 Test of Sphericity was performed, and its significant result ($\chi^2 = 3052.31$, p-value < 0.001)
18 confirms that adequate correlations exist among the measurements. The Kaiser-Meyer-Olkin
19 (KMO) measure of sampling adequacy was 0.922, well above the threshold of 0.5, indicating
20 substantial intercorrelations among the data and justifying the use of factor analysis.

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22 *Insert Table 2 here*
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Table 3 presents the results of the exploratory factor analysis conducted for the measurement scale. To verify the reliability and validity of the items, we followed the guidelines established by Hair, Howard, and Nitzl (2020). Both Cronbach's alpha and Dillon-Goldstein's rho were used, with values exceeding 0.7 and 0.8, respectively, confirming the reliability of the items.

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Items with factor loadings below 0.7 were removed, which led to excluding the items related to social influence (SI4) and performance expectancy (PE3). Additionally, the factor analysis indicated that the FC4 item should be categorized under effort expectancy rather than the facilitating conditions group, as initially planned.

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Insert Table 3 here

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## 2. Confirmatory Factor Analysis

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Confirmatory Factor Analysis (CFA) was conducted to validate the reliability of the measures selected for the structural model, building on the results from the previously performed Exploratory Factor Analysis (EFA). While the factorial structure of constructs such as Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Facilitating Conditions (FC) is well-established in the literature on technology use (e.g., UTAUT), the EFA was instrumental in refining and adapting the measurement items to fit the specific context of the food retail sector. This refinement included the introduction of the perceived risk (PR) variable.

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The Root Mean Square Error of Approximation (RMSEA) values were below 0.05, indicating a good fit (Pituch & Stevens, 2016). The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI), which are incremental fit indices, both achieved values above 0.95, reflecting a perfect fit. However, the Standardized Root Mean Square Residual (SRMR) values exceeded 0.10 for the Social Influence variable, which did not meet the acceptable fit criteria and led to its removal
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from the model. Consequently, the baseline Structural Equation Model (SEM) includes only

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four latent variables: PE, EE, FC, and PR.

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### 3. The Structural Equation Model

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The Structural Equation Model (SEM) was utilized to examine the relationships among

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Performance Expectancy (PE), Effort Expectancy (EE), Facilitating Conditions (FC), Perceived

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Risk (PR), attitudes, and behavioral intention to adopt in-store vertical farming technologies.

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Based on the Exploratory Factor Analysis (EFA) results, one item related to Effort Expectancy

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was replaced by a Facilitating Conditions item (i.e., FC4, which translates to help availability).

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Consequently, only three items were included in the Facilitating Conditions latent variable

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(FC1, FC2, and FC3). Additionally, PE3 was removed from the Performance Expectancy

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construct due to its low factor loading (0.53, below the acceptable threshold of 0.7).

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Initially, the model fit was unsatisfactory, as indicated by the fit indices: RMSEA = 0.148, CFI

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= 0.898, TLI = 0.891, SRMR = 0.328, and CD = 1.000. This poor fit might be due to

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specification errors. MacCallum (1986) suggests that model fit can often be improved by

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allowing covariances between omitted latent variables. Modification indices for path

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coefficients and covariances indicated that adding a covariance between PE and EE would

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improve the model fit. After these adjustments, the model fit indices improved to RMSEA =

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0.084, CFI = 0.927, TLI = 0.906, SRMR = 0.081, and CD = 0.999. Table 4 presents the

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standardized estimates for the different paths of the model and the corresponding critical ratio.

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*Insert Table 4 here.*

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The findings indicate that the variable performance expectancy (PE) (B= 0.471, t = 5.365) and

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facilitating conditions (FC) (B = 0.21, t= 5.032) made the most significant contribution to

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retailers' attitudes toward in-store vertical farming technologies. As expected, the perceived
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risks associated with vertical farming technology in retail settings have a negative and

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significant coefficient path (B = -0.155, t=0.751). Finally, as predicted, the coefficient path

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from attitudes to behavioral intention is positive and statistically significant (Table 5)

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Insert Table 5 here

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Discussion

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Today's food retail businesses must prioritize sustainability in their core strategies due to society's constantly evolving attitudes and norms (Naidoo & Gasparatos, 2018). At the same time, the consumption of locally produced food, sourced with minimal food miles, is gaining increased attention (Megicks et al., 2012; Memery et al., 2015). As customer-facing entities, food retailers have a unique position in the supply chain to influence both their suppliers and customers. This study focuses on the adoption of in-store vertical farming technologies by food retailers in highly dense urban areas in Rouen City, France, as a way to address sustainability in food supply chains.

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This study is the first to utilize the Unified Theory of Acceptance and Use of Technology to investigate the factors influencing the adoption of in-store vertical farming technology. The research examines the influence of performance expectancy, effort expectancy, facilitating conditions, and social influence on attitudes toward adopting in-store vertical farming. Additionally, the study introduces a new concept of perceived risk, which was not part of the original UTAUT but was developed specifically for this research to provide a more comprehensive understanding of the complexities involved in the empirical data analysis, considering various aspects of the reality of vertical farming technologies adoption by the retail actors.

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The final model from the data analysis revealed that performance expectancy, facilitating conditions, and effort expectancy were significant predictors of the outcome variable. However,
social influence was excluded from the final model as it did not contribute to its goodness of
fit. The findings underscore a noteworthy relationship between performance expectancy and
food retailers' attitudes toward vertical in-store farming activities. Specifically, the results
demonstrate that performance expectancy exerts a positive and highly significant influence. It
is noteworthy that getting fresh food products made and delivered in time to city dwellers at the
lowest cost possible while ensuring that the product remains intact during transport is an
ongoing challenge for urban fresh food retailers, which may explain why performance
expectancy proved to have the most potent direct effect on retailers' attitudes toward vertical
farming technologies. As food retailers in our sample increasingly perceive the efficacy of
vertical farming technologies in quality, cost reduction, risk mitigation, and sustainability, their
inclination and motivation to integrate these technologies into their operations intensify. This
aligns with Kshetri's (2018) perspective, which posits that performance expectancy
encompasses various dimensions crucial to retailers, and a heightened expectation of superior
performance across these dimensions correlates with a heightened intention to adopt innovative
technologies within the retail and overall supply chain sector (Wong et al., 2020).

Our findings reveal that a significant portion of our sample strongly emphasizes sustainability
within their businesses, indicating the importance of this dimension to them. Most respondents
believe vertical farming could play a crucial role in protecting biodiversity, managing water
resources more effectively, and mitigating the impacts of climate change on agriculture.
Additionally, 82% of respondents agreed that adopting vertical farming technologies in their
stores would significantly reduce fresh food waste. Vertical farming allows for the precise
production of food quantities tailored to customer demand while minimizing transportation
losses—addressing a critical social, nutritional, and environmental challenge in the supply
chain (Parfitt et al., 2010). While food waste is recognized to be generated at different stages
of the supply chain, a meta-analysis of the studies dealing with retail food waste quantification
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by Cicatiello et al. (2017) underscores that, unlike most other stakeholders in the food chain,
retailers are eager to implement actions aimed at reducing food waste as it represents a loss in
their business and poses a risk to their profit margins.

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Beyond sustainability, most of our respondents agreed that integrating vertical farming into their stores would enhance the overall customer experience. This finding is consistent with research on retail environments and customer experience paradigms, which shows that merely attracting customers to the store is no longer sufficient (Eroglu et al., 2001; Seock Marjorie, 2015). Retail success today hinges less on the products being sold and more on how they are presented and experienced. As a result, retailers are increasingly focused on transforming the store experience into something more engaging, educational, and memorable for shoppers.

Moreover, modern stores are evolving beyond their traditional role of simply selling products; they now function as showrooms, museums, warehouses, and fulfillment centers (Mohan et al., 2013). In-store vertical farms have the potential to enhance the customer experience by offering fresh, high-quality produce directly from the source, creating a more engaging and educational shopping environment. In addition to visually demonstrating sustainability efforts, these farms appeal to eco-conscious consumers and add a unique, innovative element that differentiates the store from competitors. Greenery also improves the store's ambiance, ultimately creating a more personalized and memorable shopping experience.

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Our results highlight that facilitating conditions are crucial in determining retailers' behavioral intention to adopt vertical farming. Even though this finding contradicts the results of a recent study by Mukherjee et al. (2023), putting forth that the facilitating conditions did not promote blockchain technology adoption in the food retail sector, it is consistent with the results of a survey conducted by Shao et al. (2022) on the factors influencing public intention to use micro-vertical farming. The study found that people's perception of the availability of resources
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needed to grow plants through vertical farming (i.e., facilitating conditions) was a significant
motivator in determining their intention to adopt it.

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The results show that the ease of putting effort into vertical farming (i.e., effort expectancy) positively impacts retail store managers' intention to participate. However, this influence is not as strong as the impact of facilitating conditions, such as having the necessary resources, knowledge, and investments and ensuring that vertical farming aligns with the retail store's business strategy. This suggests that facilitating conditions significantly influence attitudes toward in-store vertical farming technology more than ease of use. This paper also contributes to understanding the role of risk perception by food retailers in shaping attitudes towards in-store vertical farming and behavioral intention. According to our findings, food retailers' decision to adopt in-store vertical farming hinges on their perception of the risks and benefits rather than the actual ones. This perception is crucial in determining whether the technology is adequate for their business performance. The term perceived risk has been commonly defined in the literature as a combination of uncertainty and the seriousness of the outcomes involved (Featherman & Pavlou, 2003). Our research highlights a clear pattern where the negative impact of perceived risk on food retailers' inclination to adopt vertical farming is outweighed by the performance expectancy coefficient in absolute terms. This suggests that retailers foresee more significant performance benefits from in-store vertical farming than the associated risks would indicate. Contrary to our initial assumptions, a high perception of risk does not necessarily correlate with a low perception of benefits. It is important to note that 20% of the respondents already have experience using vertical farming technology or sourcing food from vertical farms. Previous experiences can significantly influence food retailers' perceptions of their ability to manage the risks of adopting vertical farming technologies in their stores. This aligns with the conclusions of Ellis et al. (2008), who emphasize that individuals tend to perceive fewer risks as the level of experience increases.

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1 Our findings highlight the apprehensions voiced by retailers concerning the substantial
2 investment and operational costs linked to the implementation of vertical farms. Additionally,
3 over half of the respondents acknowledged that in-store vertical farming would involve a
4 considerable energy dependency to secure a significant power supply for continuous year-round
5 operation. These perceptions align with those reported by Oliveira, Ferson and Dyer (2021) and
6 outline three key challenges facing vertical farm businesses: environmental sustainability (i.e.,
7 energy consumption), standardization, and profitability. Mainly, high failure stems from the
8 increased number of premature business decisions about location, choice, pricing strategy,
9 system design, and other critical issues (Baumont de Oliveira et al., 2022).

10 Drawing insights from the outcomes of the confirmatory factor analysis, the final structural
11 model excluded social influence. Although similar findings on the adoption of in-store vertical
12 farming technologies are nonexistent, our results align with prior research conducted in the
13 agricultural field, which has shown that social influence has little impact on farmers' willingness
14 to adopt innovative technologies. Moreover, according to Li et al. (2020), although the social
15 impact may motivate farmers to adopt new technologies, actual adoption is more probable when
16 there are no hindrances to accessing the necessary resources and knowledge and when the
17 perceived financial risks are low. On the other hand, previous studies suggest social influence
18 could influence but not determine whether people would use vertical farms to grow their food,
19 as shown by Shao et al. (2022).

20 Conclusion

21 With the mounting demand for fresh, locally sourced food in urban areas, it becomes essential
22 to examine how various players in the food supply chain can contribute to the success and
23 sustainability of both the local food system and the overall supply chain. In this paper, we
24 suggest that the adoption of in-store vertical farming by food retailers presents a transformative
25 opportunity to transcend traditional limitations associated with food miles through several
mechanisms, including a localized production, “sell where you produce” model, enhancing thereby traceability, continued access to fresh produce and reduced carbon emissions.

Moreover, we hypothesize that since food retailers form greater interconnectivity in the local food system than farms, their motivations for adopting vertical farming technologies to secure fresh and local food access to city dwellers may differ from those of traditional farmers.

The theoretical contribution of this paper lies in exploring food retail actors operating in urban areas, which contributes to the body of research on the use of technology to promote sustainable urban food supply chains and systems while providing additional UTAUT empirical applications and evidence. Previous research has focused directly on rational behavioral intention to adopt technologies while ignoring the effects of less rational factors such as attitudes. By including the attitudes variable in the UTAUT model, this research results provide insights on the relevance of including rational and irrational factors to improve the model specification quality.

Even if performance expectancy and facilitating conditions significantly shape attitudes toward in-store vertical farming adoption, risk aversion improved the model's quality. Hence, our findings enrich the original research on using UTAUT and the critical insights that risk aversion could bring to understanding the behavioral intention of adopting technologies.

Vertical farming has undergone extensive scrutiny regarding technological and agricultural advancements, yet economic evaluation and risk assessment associated with these practices are still relatively nascent areas of focus within the fields of environment, natural resources, food, and economics. Despite vertical farming's positioning as an innovative method leveraging technology to enhance yields per unit area, it remains an emerging sector facing significant challenges in profitability and a notable failure rate. While both practitioners and academics emphasize the need for comprehensive economic analyses of vertical farming, progress has been hampered by a lack of valid and easily accessible data. Existing studies often fall short in
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addressing the critical risks and uncertainties essential for enabling informed and empowered business planning in this field.

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To address these gaps, effective risk management strategies must be developed to mitigate profit fluctuations and enhance investment security, thereby increasing the profitability margins for food retailers. However, the sector continues to struggle with a lack of adequate knowledge and accessible education concerning the integration and operation of vertical farming systems, including aspects like irrigation, lighting, plant science, and manufacturing systems. These deficiencies contribute significantly to the failure of such systems. Moreover, a scarcity of capital investment sources and misaligned support and expectations from funders further exacerbate these challenges. Failures are often rooted in poor early decisions related to pricing, crop selection, and location.

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While our research employed a quantitative approach to explore these issues, it would greatly benefit from a complementary qualitative approach, such as semi-structured interviews, to gain deeper insights into how retailers perceive and experience the risks associated with adopting in-store vertical farming. This combined methodology would provide a more nuanced understanding of the challenges faced by retailers, particularly in navigating the uncertainties and risk manifestations that are critical to the successful adoption and integration of vertical farming systems.

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Ultimately, it is essential to acknowledge the potential challenges associated with directly applying the findings of this research to alternative urban or geographical settings, even within France. Rouen, as the administrative capital of Normandy, represents a unique context as the primary agricultural region in France. Notably, a mere 2% of the food supplied to Rouen city residents originates from local sources. Despite ongoing efforts by policymakers to establish a local food plan, the supply chains for fresh produce continue to rely heavily on extensive networks. Consequently, future research endeavors should aim to collect data from diverse regional and urban settings to ensure the validation of the model proposed in this study. This

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broader approach will enhance the generalizability and applicability of the research findings

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across various contexts.

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Declarations

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Conflict of interest: The authors declare no conflict of interest. Competing interest: The authors

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declare they have no relevant or nonfinancial interests to disclose.

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Ethics approval/declarations: Does not apply.

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Consent to participate (include appropriate statements): Respondents involved in this study

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have all consented.

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Consent for publication: The authors declare that this is an original work that has not previously

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been considered for publication.

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Availability of data and material/ Data availability: The datasets generated during and/or

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analyzed during the current study are available from the corresponding author on reasonable

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request.

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Code availability (software application or custom code): The codes generated during and/or

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analyzed during the current study are available from the corresponding author on reasonable

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request.

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References

19

Adams, F., Etuah, S., Appiah, G. B., Aidoo, R., Mensah, J. O., Nyekyeyel, J., Appiah, F. N.,

20

Serebour, M., Kwarteng, N., & Asare, P. (2024). Do Consumer Opinions Matter?

21

Consumer Perception and Purchasing Decisions of Greenhouse Vegetables in Ghana.

22

*Journal of International Food & Agribusiness Marketing*, 36(3), 424–446.

23

https://doi.org/10.1080/08974438.2022.2145536

24

Ajzen, I. (1988). *Attitudes, Personality and Behaviour* (D. Press, Ed.).

25

Ajzen, I., & Fishbein, M. (1980). *Understanding attitudes and predicting social behaviour*.

26

Albizzati, P. F., Tonini, D., Chammard, C. B., & Astrup, T. F. (2019). Valorisation of surplus

27

food in the French retail sector: Environmental and economic impacts. *Waste*

28

*Management*, 90, 141–151. https://doi.org/https://doi.org/10.1016/j.wasman.2019.04.034

29

Al-Chalabi, M. (2015). Vertical farming: Skyscraper sustainability? *Sustainable Cities and*

30

*Society*, 18, 74–77. https://doi.org/10.1016/j.scs.2015.06.003
1

Al-Kodmany, K. (2024). *Vertical Farms for Future Cities BT - Digital Agriculture: A Solution for Sustainable Food and Nutritional Security* (P. M. Priyadarshan, S. M. Jain,

2

S. Penna, & J. M. Al-Khayri, Eds.; pp. 179–197). Springer International Publishing.

3

4

https://doi.org/10.1007/978-3-031-43548-5_6

5

Aschemann-Witzel, J., de Hooge, I., & Normann, A. (2016). Consumer-Related Food Waste:

6

Role of Food Marketing and Retailers and Potential for Action. *Journal of International Food & Agribusiness Marketing*, 28(3), 271–285.

7

8

https://doi.org/10.1080/08974438.2015.1110549

9

Baumont de Oliveira, F. J., Ferson, S., Dyer, R. A. D., Thomas, J. M. H., Myers, P. D., &

10

Gray, N. G. (2022). How High Is High Enough? Assessing Financial Risk for Vertical

11

Farms Using Imprecise Probability. *Sustainability (Switzerland)*, 14(9), 1–29.

12

https://doi.org/10.3390/su14095676

13

Benke, K., & Tomkins, B. (2017). Future food-production systems: Vertical farming and

14

controlled-environment agriculture. *Sustainability: Science, Practice, and Policy*, 13(1),

15

13–26. https://doi.org/10.1080/15487733.2017.1394054

16

Birch, D., Memery, J., & De Silva Kanakaratne, M. (2018). The mindful consumer: Balancing

17

egoistic and altruistic motivations to purchase local food. *Journal of Retailing and*

18

*Consumer Services*, 40(October 2017), 221–228.

19

https://doi.org/10.1016/j.jretconser.2017.10.013

20

Butturini, M., & Marcelis, L. F. M. (2020). Chapter 4 - Vertical farming in Europe: Present

21

status and outlook. In T. Kozai, G. Niu, & M. B. T.-P. F. (Second E. Takagaki (Eds.),

22

*Plant Factory* (pp. 77–91). Academic Press. https://doi.org/https://doi.org/10.1016/B978-

23

0-12-816691-8.00004-2

24

Chen, K.-Y., & Chang, M.-L. (2013). User acceptance of ‘near field communication’ mobile

25

phone service: an investigation based on the ‘unified theory of acceptance and use of

26

technology’ model. *The Service Industries Journal*, 33(6), 609–623.

27

https://doi.org/10.1080/02642069.2011.622369

28

Chung, Y.-S., & Jung, C.-H. (2019). A Study on the Factors Affecting Acceptance of Easy

29

Payment Services Using Extended UTAUT Model. *Journal of Information Technology*

30

*Applications and Management*, 26(2), 1–11.

31

Clark, A., Ellison, M., Robinson, G., & Hill, T. R. (2022). Consumer perceptions of vertical

32

farming with a focus on the catering industry. *Proceedings of the Nutrition Society*,

33

81(OCE5), 2022. https://doi.org/10.1017/s0029665122002506

34

Claro, D. P., Laban Neto, S. A., & de Oliveira Claro, P. B. (2013). Sustainability drivers in

35

food retail. *Journal of Retailing and Consumer Services*, 20(3), 365–371.

36

https://doi.org/10.1016/j.jretconser.2013.02.003

37

Dirsehan, T., & Cankat, E. (2021). Role of mobile food-ordering applications in developing

38

restaurants’ brand satisfaction and loyalty in the pandemic period. *Journal of Retailing*

39

*and Consumer Services*, 62, 102608.

40

Dunne, J. B., Chambers, K. J., Giombolini, K. J., & Schlegel, S. A. (2011). What does ‘local’

41

mean in the grocery store? Multiplicity in food retailers’ perspectives on sourcing and
1 marketing local foods. *Renewable Agriculture and Food Systems*, 26(1), 46-59.

2 https://doi.org/DOI: 10.1017/S1742170510000402

3 Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-

4 examining the Unified Theory of Acceptance and Use of Technology (UTAUT):

5 Towards a Revised Theoretical Model. *Information Systems Frontiers*, 21(3), 719-734.

6 https://doi.org/10.1007/s10796-017-9774-y

7 Elkington, J. (1994). Towards the Sustainable Corporation: Win-Win-Win Business Strategies

8 for Sustainable Development. *California Management Review*, 36(2), 90-100.

9 https://doi.org/10.2307/41165746

10 Ellis, S., Ganzach, Y., Pazy, A., & Ricci-Siag, T. (2008). On the perception and

11 operationalization of risk perception. *Judgment and Decision Making*, 3(4), 317-324.

12 https://doi.org/DOI: 10.1017/S1930297500000887

13 Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online

14 retailing: A conceptual model and implications. *Journal of Business Research*, 54(2),

15 177-184.

16 Esbjerg, L., Burt, S., & Pearse, H. (2016). Retailers and technology-driven innovation in the

17 food sector: caretakers of consumer interests or barriers to innovation? *British Food

18 Journal*, 1-15.

19 Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk

20 facets perspective. *International Journal of Human Computer Studies*, 59(4), 451-474.

21 https://doi.org/10.1016/S1071-5819(03)00111-3

22 Fischer, C. (2006). The Complexities of Modern Food Consumption and Implications for

23 International Food Product Marketers. *Journal of International Food & Agribusiness*

24 *Marketing*, 19(1), 7-35. https://doi.org/10.1300/J047v19n01_02

25 Graamans, L., Baeza, E., van den Dobbelsteen, A., Tsafaras, I., & Stanghellini, C. (2018).

26 Plant factories versus greenhouses: Comparison of resource use efficiency. *Agricultural*

27 Systems, 160(November 2017), 31-43. https://doi.org/10.1016/j.agsy.2017.11.003

28 Grewal, D., & Levy, M. (2007). Retailing research: Past, present, and future. *Journal of*

29 *Retailing*, 83(4), 447-464. https://doi.org/https://doi.org/10.1016/j.jretai.2007.09.003

30 Im, I., Kim, Y., & Han, H.-J. (2008). The effects of perceived risk and technology type on

31 users' acceptance of technologies. *Information & Management*, 45(1), 1-9.

32 Jung, I. N., Sharma, A., & Mattila, A. S. (2022). The impact of supermarket credibility on

33 purchase intention of novel food. *Journal of Retailing and Consumer Services*, 64,

34 102754. https://doi.org/https://doi.org/10.1016/j.jretconser.2021.102754

35 Konefal, J., & Hatanaka, M. (2014). Producing and consuming food: Justice and sustainability

36 in a globalized world? *Twenty Lessons in Environmental Sociology*, 191-208.

37 Kshetri, N. (2018). 1 Blockchain's roles in meeting key supply chain management objectives.

38 *International Journal of Information Management*, 39(December 2017), 80-89.

39 https://doi.org/10.1016/j.ijinfomgt.2017.12.005
1

2

3

La Londe, B. J., & Masters, J. M. (1994). Emerging logistics strategies: blueprints for the next century. *International Journal of Physical Distribution & Logistics Management, 24*(7), 35-47.

4

5

6

Lähteenmäki-Uutela, A. (2014). Legal, Ethical, or Responsible Food. *Journal of International Food & Agribusiness Marketing, 26*(4), 235-257.
https://doi.org/10.1080/08974438.2013.833565

7

8

9

Liu, S. (2020). Food supply pressure in France and Germany during COVID-19: Causes from manufacturing. *Journal of Agriculture, Food Systems, and Community Development, 9*(4), 139-142. https://doi.org/10.5304/jafscd.2020.094.007

10

11

12

Martins, C., Oliveira, T., & Popović, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. *International Journal of Information Management, 34*(1), 1-13.

13

14

15

Megicks, P., Memery, J., & Angell, R. J. (2012). Understanding local food shopping: Unpacking the ethical dimension. *Journal of Marketing Management, 28*(3-4), 264-289.
https://doi.org/10.1080/0267257X.2012.658838

16

17

18

Memery, J., Angell, R., Megicks, P., & Lindgreen, A. (2015). Unpicking motives to purchase locally-produced food: Analysis of direct and moderation effects. *European Journal of Marketing, 49*(7-8), 1207-1233. https://doi.org/10.1108/EJM-02-2014-0075

19

20

Mohan, G., Sivakumaran, B., & Sharma, P. (2013). Impact of store environment on impulse buying behavior. *European Journal of Marketing, 47*(10), 1711-1732.

21

22

23

Mukherjee, S., Baral, M. M., Lavanya, B. L., Nagariya, R., Singh Patel, B., & Chittipaka, V. (2023a). Intentions to adopt the blockchain: investigation of the retail supply chain. *Management Decision, 61*(5), 1320-1351. https://doi.org/10.1108/MD-03-2022-0369

24

25

26

Mukherjee, S., Baral, M. M., Lavanya, B. L., Nagariya, R., Singh Patel, B., & Chittipaka, V. (2023b). Intentions to adopt the blockchain: investigation of the retail supply chain. *Management Decision, 61*(5), 1320-1351. https://doi.org/10.1108/MD-03-2022-0369

27

28

29

Naidoo, M., & Gasparatos, A. (2018). Corporate environmental sustainability in the retail sector: Drivers, strategies and performance measurement. *Journal of Cleaner Production, 203*, 125-142. https://doi.org/10.1016/j.jclepro.2018.08.253

30

31

32

Oliveira, F. J. B. De, Ferson, S., & Dyer, R. (2021). System Framework for Vertical Farming Business Developments. *International Journal of Decision Support System Technolog, 13*(1), 34-66. https://doi.org/10.4018/IJDSST.2021010103

33

34

35

36

Parfitt, J., Barthel, M., & MacNaughton, S. (2010). Food waste within food supply chains: Quantification and potential for change to 2050. *Philosophical Transactions of the Royal Society B: Biological Sciences, 365*(1554), 3065-3081.
https://doi.org/10.1098/rstb.2010.0126

37

38

39

Petrovics, D., & Giezen, M. (2022). Planning for sustainable urban food systems: an analysis of the up-scaling potential of vertical farming. *Journal of Environmental Planning and Management, 65*(5), 785-808. https://doi.org/10.1080/09640568.2021.1903404
1

2

3

Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens' adoption of an electronic government system: towards a unified view. *Information Systems Frontiers, 19*(3), 549-568. https://doi.org/10.1007/s10796-015-9613-y

4

5

Seock Marjorie, Y.-K. N. (2015). Retail store environment and customer experience: a paradigm. *Journal of Fashion Marketing and Management, 11*(4), 571-586.

6

7

8

Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J., Bull, J. S., & Waddoups, R. (2021). How Technology is Changing Retail. *Journal of Retailing, 97*(1), 13-27. https://doi.org/10.1016/j.jretai.2020.10.006

9

10

11

Shao, Y., Wang, Z., Zhou, Z., Chen, H., Cui, Y., & Zhou, Z. (2022). Determinants Affecting Public Intention to Use Micro-Vertical Farming: A Survey Investigation. *Sustainability (Switzerland), 14*(15), 1-26. https://doi.org/10.3390/su14159114

12

13

14

Sheel, A., & Nath, V. (2020). Antecedents of blockchain technology adoption intentions in the supply chain. *International Journal of Business Innovation and Research, 21*(4), 564-584. https://doi.org/10.1504/IJBIR.2020.106011

15

16

17

18

Suhartanto, D., Dean, D., Edi Arsawan, I. W., Pradana, M., & Farhani, I. (2023). Eating Good Food and Helping the Community: The Drivers of Online Local Food Purchasing. *Journal of International Food & Agribusiness Marketing, 1-21*. https://doi.org/10.1080/08974438.2023.2190737

19

20

21

22

Sun, Y., Wang, N., Xitong, G., & Zeyu, P. (2013a). UNDERSTANDING THE ACCEPTANCE OF MOBILE HEALTH SERVICES: A COMPARISON AND INTEGRATION OF ALTERNATIVE MODELS. *Journal of Electronic Commerce, 9*(1), 179-185.

23

24

25

26

Sun, Y., Wang, N., Xitong, G., & Zeyu, P. (2013b). UNDERSTANDING THE ACCEPTANCE OF MOBILE HEALTH SERVICES: A COMPARISON AND INTEGRATION OF ALTERNATIVE MODELS. *Journal of Electronic Commerce, 9*(1), 179-185.

27

28

29

Thorsøe, M., & Kjeldsen, C. (2016). The Constitution of Trust: Function, Configuration and Generation of Trust in Alternative Food Networks. *Sociologia Ruralis, 56*(2), 157-175. https://doi.org/10.1111/soru.12082

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Trivette, S. A. (2019). The importance of food retailers: applying network analysis techniques to the study of local food systems. *Agriculture and Human Values, 36*(1), 77-90. https://doi.org/10.1007/s10460-018-9885-1

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Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. *MIS Quarterly: Management Information Systems, 27*(3), 425-478. https://doi.org/10.2307/30036540

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Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. *MIS Quarterly, 15*(7), 157-178.

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Venkatesh, V., & Zhang, X. (2010). Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. *Journal of Global Information Technology Management, 13*(1), 5-27. https://doi.org/10.1080/1097198X.2010.10856507
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Wong, L. W., Tan, G. W. H., Lee, V. H., Ooi, K. B., & Sohal, A. (2020). Unearthing the determinants of Blockchain adoption in supply chain management. *International Journal of Production Research*, 58(7), 2100–2123.
https://doi.org/10.1080/00207543.2020.1730463

<figure><img src="image_2.png" /><figcaption>Figure 1. Research conceptual model and hypothesis</figcaption></figure>
<table><caption>Table 1 – Measurement items included in the survey</caption><thead><tr><td>Variables</td><td>Measurement items</td></tr></thead><tbody><tr><td>Performance expectancy [PE]</td><td>[PE1] Using vertical farming technology would improve my business performance.<br/>[PE2] Using vertical farming technology would save me time.<br/>[PE3] I find vertical farming technology useful</td></tr><tr><td>Effort expectancy [EE]</td><td>[EE1] It is easy for me to learn vertical farming technologies.<br/>[EE2] I can easily become skillful in using vertical farming technologies.<br/>[EE3] I can easily interact with vertical farming technologies.<br/>[EE4] I would find vertical farming technologies easy to use.</td></tr><tr><td>Social Influence [SI]</td><td>[SI1] People I consider important think I should use vertical farming technologies in my store.<br/>[SI2] People I am familiar with think I should use vertical farming technologies as part of my business strategy.<br/>[SI3] People who influence my behavior think I should use vertical farming technologies as part of my business strategy.<br/>[SI4] Most people around me think I should use vertical farming technologies in my business strategy.</td></tr><tr><td>Perceived risk [PR]</td><td>[PR1] Using vertical farming in my store is risky.<br/>[PR2] I feel that using vertical farming technologies would cause me much trouble if something went wrong.<br/>[PR3] I am sure I would make a mistake if I used vertical farming technologies in my store.</td></tr><tr><td>Facilitating Condition [FC]</td><td>[FC1] My living environment encourages me to use vertical farming technologies.<br/>[FC2] My business environment encourages me to use vertical farming technologies.<br/>[FC3] Using vertical farming technologies is compatible with my business strategy.<br/>[FC4] Help is available if I encounter any problems related to my use of vertical farming technologies.</td></tr><tr><td>Attitudes [AT]</td><td>[AT1] I appreciate using in-store vertical farming to produce fresh food.<br/>[AT2] I prefer using in-store vertical farming technology to produce fresh food.<br/>[AT3] I am glad I can use vertical farming technologies in my store to increase the availability of fresh produce to my customers.</td></tr><tr><td>Behavioral Intention [BI]</td><td>[BI] I intend to use vertical farming technologies in my store.</td></tr></tbody></table>
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<figure><img src="image_3.png" /></figure>

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<caption>Figure 2. How do retailers perceive the performance of vertical farming technologies?</caption>

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<figure><img src="image_4.png" /></figure>

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<caption>Figure 3. How do retailers perceive risks from embracing in-store vertical farming?</caption>

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<table><caption>Table 2 – Correlation matrix of the measures</caption><thead><tr><th></th><th>Performance<br>Expectancy</th><th>Facilitating<br>Conditions</th><th>Social<br>Influences</th><th>Risk<br>Perception</th><th>Effort<br>Expectancy</th><th>Attitudes</th><th>Behavioral<br>Intention</th></tr></thead><tbody><tr><td>PE1</td><td>0.903</td><td>0.568</td><td>0.503</td><td>-0.015</td><td>0.536</td><td>0.523</td><td>0.654</td></tr><tr><td>PE2</td><td>0.901</td><td>0.492</td><td>0.479</td><td>0.017</td><td>0.455</td><td>0.397</td><td>0.586</td></tr><tr><td>PE4</td><td>0.895</td><td>0.585</td><td>0.517</td><td>0.016</td><td>0.635</td><td>0.572</td><td>0.669</td></tr><tr><td>EE1</td><td>0.496</td><td>0.597</td><td>0.382</td><td>-0.072</td><td>0.862</td><td>0.151</td><td>0.523</td></tr><tr><td>EE2</td><td>0.505</td><td>0.638</td><td>0.467</td><td>-0.102</td><td>0.909</td><td>0.597</td><td>0.597</td></tr><tr><td>EE4</td><td>0.490</td><td>0.596</td><td>0.426</td><td>-0.039</td><td>0.888</td><td>0.638</td><td>0.572</td></tr><tr><td>SI1</td><td>0.493</td><td>0.507</td><td>0.734</td><td>0.060</td><td>0.515</td><td>0.596</td><td>0.511</td></tr><tr><td>SI2</td><td>0.500</td><td>0.501</td><td>0.750</td><td>0.024</td><td>0.484</td><td>0.047</td><td>0.493</td></tr><tr><td>SI3</td><td>0.544</td><td>0.497</td><td>0.702</td><td>-0.009</td><td>0.470</td><td>0.500</td><td>0.449</td></tr><tr><td>SI4</td><td>0.387</td><td>0.410</td><td>0.471</td><td>-0.034</td><td>0.459</td><td>0.544</td><td>0.343</td></tr><tr><td>PR1</td><td>-0.056</td><td>-0.101</td><td>-0.026</td><td>0.821</td><td>-0.075</td><td>0.387</td><td>-0.177</td></tr><tr><td>PR2</td><td>0.164</td><td>0.068</td><td>0.133</td><td>0.896</td><td>0.035</td><td>0.471</td><td>0.085</td></tr><tr><td>PR3</td><td>-0.137</td><td>-0.104</td><td>-0.077</td><td>0.892</td><td>-0.103</td><td>-0.026</td><td>-0.276</td></tr><tr><td>FC1</td><td>0.491</td><td>0.897</td><td>0.518</td><td>-0.013</td><td>0.630</td><td>0.133</td><td>0.586</td></tr><tr><td>FC2</td><td>0.472</td><td>0.898</td><td>0.443</td><td>-0.024</td><td>0.631</td><td>-0.077</td><td>0.518</td></tr><tr><td>FC3</td><td>0.627</td><td>0.847</td><td>0.469</td><td>-0.077</td><td>0.600</td><td>0.544</td><td>0.655</td></tr><tr><td>FC4</td><td>0.520</td><td>0.650</td><td>0.468</td><td>0.025</td><td>0.732</td><td>0.387</td><td>0.632</td></tr><tr><td>AT1</td><td>0.490</td><td>0.596</td><td>-0.072</td><td>0.515</td><td>0.586</td><td>0.911</td><td>0.518</td></tr><tr><td>AT2</td><td>0.493</td><td>0.507</td><td>-0.102</td><td>0.484</td><td>0.518</td><td>0.896</td><td>0.443</td></tr><tr><td>AT3</td><td>0.500</td><td>0.501</td><td>-0.039</td><td>0.470</td><td>0.655</td><td>0.892</td><td>0.469</td></tr><tr><td>BI</td><td>0.632</td><td>-0.130</td><td>0.668</td><td>0.521</td><td>0.677</td><td>0.717</td><td>1.000</td></tr></tbody></table>
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<table><caption>Table 3 – Psychometric properties of the measurement scales</caption><thead><tr><th>Latent variables</th><th>Dimensions</th><th>Factor loading</th><th>Cronbach's alpha</th><th>AVE</th></tr></thead><tbody><tr><td rowspan="3">Performance Expectancy</td><td>PE1</td><td>0,708</td><td rowspan="3">0,886</td><td rowspan="3">0,785</td></tr><tr><td>PE2</td><td>0,749</td></tr><tr><td>PE4</td><td>0,743</td></tr><tr><td rowspan="4">Effort Expectancy</td><td>EE1</td><td>0,776</td><td rowspan="4">0,877</td><td rowspan="4">0,792</td></tr><tr><td>EE2</td><td>0,822</td></tr><tr><td>EE4</td><td>0,828</td></tr><tr><td>FC4</td><td>0,496</td></tr><tr><td rowspan="3">Social Influence</td><td>SI1</td><td>0,856</td><td rowspan="3">0,852</td><td rowspan="3">0,84</td></tr><tr><td>SI2</td><td>0,879</td></tr><tr><td>SI3</td><td>0,834</td></tr><tr><td rowspan="3">Perceived Risk</td><td>PR1</td><td>0,818</td><td rowspan="3">0,713</td><td rowspan="3">0,787</td></tr><tr><td>PR2</td><td>0,772</td></tr><tr><td>PR3</td><td>0,814</td></tr><tr><td rowspan="3">Facilitating Conditions</td><td>FC1</td><td>0,724</td><td rowspan="3">0,852</td><td rowspan="3">0,744</td></tr><tr><td>FC2</td><td>0,638</td></tr><tr><td>FC3</td><td>0,737</td></tr><tr><td rowspan="3">Attitudes</td><td>AT1</td><td>0,923</td><td rowspan="3">0,923</td><td rowspan="3">0,866</td></tr><tr><td>AT2</td><td>0,982</td></tr><tr><td>AT3</td><td>0,891</td></tr></tbody></table>

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<table><caption>Table 5 – Standardized estimates</caption><thead><tr><th>Mediating and dependent variables</th><th>Independent variables</th><th>Standard Path (mean score)</th><th>Standard Error</th><th>Critical Ratio</th></tr></thead><tbody><tr><td rowspan="4">Attitudes</td><td>Performance expectancy</td><td>0,471</td><td>1,74</td><td>0,92</td></tr><tr><td>Effort expectancy</td><td>0,189</td><td>1,11</td><td>0,71</td></tr><tr><td>Facilitating conditions</td><td>0,213</td><td>1,04</td><td>0,84</td></tr><tr><td>Perceived risk</td><td>-0,15</td><td>0,87</td><td>0,76</td></tr><tr><td>Behavioral Intention</td><td>Attitudes</td><td>0,684</td><td>0,02</td><td>0,91</td></tr></tbody></table>

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<table><caption>Table 5 – Hypothesis test results</caption><thead><tr><td>Hypothesis test</td><td>Results</td></tr></thead><tbody><tr><td>H1. Performance expectancy directly, positively, and significantly affects retailers' attitudes toward vertical farming technologies.</td><td>Validated</td></tr><tr><td>H2. Effort expectancy directly, positively, and significantly affects retailers' attitudes toward vertical farming technologies.</td><td>Validated</td></tr><tr><td>H3. Social influence directly, positively, and significantly affects retailers' attitudes toward vertical farming technologies.</td><td>Rejected</td></tr><tr><td>H4. Facilitating conditions (FC) directly, positively, and significantly affect retailers' behavioral intention to adopt vertical farming technologies.</td><td>Validated</td></tr><tr><td>H5. Perceived risk negatively affects retailers' attitudes toward vertical farming technology adoption.</td><td>Validated</td></tr><tr><td>H6. Attitudes positively affect retailers' behavioral intention to use in-store vertical farming technologies.</td><td>Validated</td></tr></tbody></table>

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