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IoT adoption in agriculture: the role of trust, perceived value and risk

Article in Journal of Business & Industrial Marketing · September 2018 DOI: 10.1108/JBIM-01-2018-0023

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The user has requested enhancement of the downloaded file. Journal of Business & Industrial Marketing IoT adoption in agriculture: the role of trust, perceived value and risk Priyanka Jayashankar, Sree Nilakanta, Wesley J. Johnston, Pushpinder Gill, Reed Burres, Article information: To cite this document: Priyanka Jayashankar, Sree Nilakanta, Wesley J. Johnston, Pushpinder Gill, Reed Burres, (2018) "IoT adoption in agriculture: the role of trust, perceived value and risk", Journal of Business & Industrial Marketing, https://doi.org/10.1108/ JBIM-01-2018-0023 Permanent link to this document: https://doi.org/10.1108/JBIM-01-2018-0023 Downloaded on: 21 September 2018, At: 10:12 (PT) References: this document contains references to 141 other documents. To copy this document: [email protected] Access to this document was granted through an Emerald subscription provided by Token:Eprints:4VZZYWKHERQZCAHDW8VC: For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

*Related content and download information correct at time of download. Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) IoT adoption in agriculture: the role of trust, perceived value and risk Priyanka Jayashankar Department of Management, Iowa State University, Ames, Iowa, USA Sree Nilakanta Department of Supply Chain and Information Ssystems, Iowa State University, Ames, Iowa, USA Wesley J. Johnston Department of Marketing, Georgia State University, Atlanta, Georgia, USA Pushpinder Gill Department of Marketing, Iowa State University, Ames, Iowa, USA, and Reed Burres Agriperil Insurance and Risk Management, Humboldt, Iowa, USA

Abstract Purpose – This paper aims to study the antecedents of Internet of Things (IoT) adoption among farmers and determine how trust in the technology influences its adoption when mediated by perceived value and risk. Through the conceptualization of trust and perceived risk, the authors factor in farmers’ perceptions of agricultural technology providers and discuss different forms of perceived value, spanning economic, green and epistemic value. Design/methodology/approach – This paper develops a distinctive research design, drawing on elements of the value-based adoption and technology acceptance models. By linking different elements of perceived value with IoT technology, the authors also apply the service-dominant logic to this study. They study how trust affects perceived value and risk and then determine how perceived value and risk, in turn, affect IoT adoption. The authors test the hypotheses by developing a structural equation model to analyze the results of a survey, wherein 492 farmers from Iowa, the USA, participated. Findings – The results show a positive relationship between trust and perceived value and a negative relationship between trust and perceived risk. Perceived value had a positive impact on IoT adoption, whereas perceived risk had a negative impact on IoT adoption. Practical implications – The research findings on trust and perceived value and risk are timely and relevant for business-to-business (B2B) marketing practitioners and agricultural stakeholders, especially in an era where farmers are expressing growing concerns about data handling risk posed by IoT technology adoption. Originality/value – The research findings signal a transition in focus from the goods-dominant logic to the service-dominant logic in agriculture, whereby farmers are drawn to IoT technology because of perceived economic, green and epistemic value and as a result, can differentiate themselves on how well they deploy operant resources. This paper not only provides a unique conceptualization of perceived value but also pave the way for a richer conceptualization of IoT core functions that enable farmers to fulfill green and epistemic goals. This is the first B2B marketing paper discussing the antecedents of IoT adoption in agriculture, such as farmers’ perceptions of both monetary and non-monetary forms of value and perceived data handling risk. Keywords Agriculture, Value, United States of America, IT, Trust, Business-to-Business Marketing Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) Paper type Research paper

Introduction Reports suggest that the number of connected agricultural devices are growing from 13 million in 2014 to 225 million in Internet of things (IoT) technology and big data applications are 2024 and the installation of IoT devices is rising at a poised to play a key role in ramping up global food production to compounded annual growth rate of 20 per cent worldwide feed billions in the coming decades. Experts are envisioning a (Machina Research, 2016; Meola, 2016). Accounts from the data-driven future, wherein IoT-based technology ranging from popular press allude to the advent of “agriculture 3.0,” which sensors on farm equipment, self-driving tractors, drones and entails exploiting data from many sources, such as sensors on GPS imaging to weather tracking would not only enable farmers farm equipment and plants, satellite images and weather tracking to feed the world but also cope better with the limited supply of fossil fuel, water and arable land (Ray, 2017; Lohr, 2015). The authors would like to thank the Iowa State University PIIR group for funding this research, as well as the various farmers who participated in the The current issue and full text archive of this journal is available on survey. The authors also thank Dr Joe Colletti, Dr Manjit Mishra, Emerald Insight at: www.emeraldinsight.com/0885-8624.htm Dr Asheesh Singh, Dr Carolyn Lawrence-Dill, Scott Zarecor, Dr Mark Rasmussen and Dr J. Arbuckle and the Iowa Farm Bureau Federation for their feedback and support.

Journal of Business & Industrial Marketing Received 9 January 2018 © Emerald Publishing Limited [ISSN 0885-8624] Revised 5 February 2018 [DOI 10.1108/JBIM-01-2018-0023] Accepted 12 April 2018 IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

(Lohr, 2015). Following the advent of big data analytics in ATPs, who in turn, reveal very little as to how data will be stored agriculture, large volumes of data, which could not be or used or to what extant farmers can own their data (Carbonell, quantifiable in the past, can now be analyzed through statistical 2016). Farmers’ concerns about the ownership and privacy of models and algorithms, as a result of which farmers can monitor their farm-level data have drawn the attention of key agricultural what they are planting and where their seeds are placed on a real- stakeholders in the USA. From an ethical and legal standpoint, time basis (Johnston, 2014; Pattinson and Johnston, 2016; further inquiry is warranted to determine whether farmers only Cukier and Mayer-Schoenberger, 2013; Noyes, 2014). IoT in have the right to use their data in terms of access, modification agriculture not only helps improve productivity and profitability and standardization, or whether they own the data by but also paves the way for drastic changes in farm management determining others’ privileges to use the data (Van Alstyne et al, and sustainable agriculture practices (Kite-Powell, 2016). For 1995). Hence, this calls for further research on how perceived instance, smart farming tools for site-specific applications of forms of risk can affect farmers’ adoption of IoT technology. fertilizers, GPS mapping, and accurate yield predictions have the Kannan (2017) set an agenda for research in digital potential to boost sustainable farming practices and enhance marketing, wherein the interaction of digital technologies such profitability (Walter et al., 2017). as IoT with customers, context, competitors and collaborators In the midst of rapid population growth, dietary shifts, and the impact of digital technologies on value (from the resource constraints and dietary changes, there is a growing standpoint of customers and firm performance) are among the emphasis on efficient management and optimal usage of inputs core focus areas. such as fertilizers through data-driven farming decisions (Lee While scholars such as Falkenreck and Wagner (2017) and Choudhury, 2017). Such trends are compelling more recently contrasted reciprocity and trust among B2B buyers of farmers to use versatile IoT tools improve crop output, lower IoT-based engineering products across international markets, livestock losses and reduce water usage across a diverse range of there is yet to be a rich B2B narrative of IoT clients’ perceived agricultural operations (Guerra, 2017). Cloud-based IoT tools value and risk, especially in the context of agriculture. Prior and sensors help livestock farmers monitor swine, cattle, broiler research has discussed farmers’ persuasion to adopt precision and milk production. agriculture (Adrian et al., 2005). However, B2B marketing For instance, collar units and ear tags provide almost real-time scholars have not explored the digital agriculture market[1], insight into animal behavior, herd location, walking time, grazing especially in the context of IoT. time, resting time and water consumption (Lee and Choudhury, In our study, we integrate elements of the value-based 2017). Crop farmers can take smarter decisions through data adoption model, the diffusion of innovation theory, technology relayed via IoT sensors on weather, soil, air quality and crop acceptance model and the service-dominant logic to analyze maturity (Guerra, 2017). For example, sensors deployed on the how perceived value, risk and trust affect IoT adoption. We ground or in water or in vehicles collect data on soil moisture and partially draw upon Pavlou’s (2003) extended technology crop health, which can be stored wirelessly on a server or a cloud- acceptance model by incorporating perceived risk and trust as based system and can be accessed by farmers through tablets and the drivers of IoT adoption. mobile phones (Lee and Choudhury, 2017). While scholars such as Kim et al. (2007) and Ko et al. (2009) Autosteer technology, which is the use of a global positioning applied the value-based adoption model in a B2C context, we system (GPS to guide agricultural equipment (Shockley et al., integrate the concept of trust with the value-based adoption 2011), is also an integral part of IoT in agriculture. It enables model in a B2B context. Also, we conceptualize the process of farmers to reduce human errors in spraying insecticides, reduce B2B IoT adoption as a dichotomized decision process, whereby

Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) overlaps and skips in managing crop rows and reduce perceived attributes of the innovation determine the rate of its machinery costs Shockley et al.,2011; Guerra, 2017). While adoption (Rogers, 2003; MohamedSamirHusseinandMourad, the above examples from popular press as well as agricultural 2014). As IoT in agriculture is distinct in that the technology is economics literature attest to the practical benefits of IoT associated with specialized knowledge and operant resources, we technology in agriculture, business-to-business (B2B) scholars also take a cue from the service-dominant logic (Lusch et al., are yet to explore how perceived value of IoT influences 2007) and conceptualize different forms of perceived value that farmers’ technology adoption decisions. drive IoT adoption. We pose the following research questions: Agriculture technology providers (ATPs) have an important stake in B2B markets by providing IoT devices and big data- RQ1. How do perceived value and risk affect IoT adoption enabled advisory services on inputs such as seeds and fertilizers among farmers? and crop management to farmers. Information gleaned through RQ2. How does trust affect the perceived value and risk of farm-level data can become valuable in the form of field adopting IoT technology? prescriptions given by ATPs, which would enable farmers to make informed crop management decisions (Haire, 2014). However, when aggregated at a regional/country level, there is a Theoretical background and hypotheses risk of misuse of data for commodity market speculation and sale of data to third parties, which can make some farmers formulation averse to adopt big data tools agriculture ((Haire, 2014; Porter Perceived value and risk of IoT for business-to-business and Heppelmann, 2014; Economist, 2014a, 2014b). Hence, clients some policymakers are expressing concern about the rise in data Technological changes have considerably affected agriculture for asymmetry in the B2B digital agriculture market, wherein over 100 years, during which there has been a dramatic rise in farmers have to divulge personal farm management data to innovations to increase yield, reduce costs and enhance product IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

quality (Schultz, 1964, Cochrane, 1993; Sunding and Chang (2012) and Patterson and Spreng (1997) associated green Zilberman, 2001). Marketing scholars have applied B2B perceived value with consumer’s environmental desires, marketing concepts to agriculture, as documented in a study by sustainable expectations and green needs. There are several Foxall (1979) who drew parallels between farmers’ buying opportunities to enhance green value through IoT in agriculture. decisions for tractors and the buying behavior of professional For example, IoT facilitates the site-specific application of inputs buyers in manufacturing and service industries. B2B agricultural such as fertilizers and pesticides, which, in turn, mitigates marketing in the past century focused on the efficiency of greenhouse gas emissions (Walter et al.,2017). To determine marketing channels and the role of distributors (Weld,1917)and whether farmers are motivated to adopt IoT technology due to the goods-dominant logic, which pervaded much of marketing the potential opportunity of enhancing ecological stewardship, theory in the past, primarily focusing on agricultural products as we also factor in perceived green value into our study. units of exchange (Vargo and Lusch, 2004). The consumption value theory posits that epistemic value However, in the current day and age, the goods-dominant emanates from the desire for knowledge and intellectual curiosity logic has made way for the service dominant logic, wherein B2B (Sheth et al., 1991a, 1991b; Sánchez-Fernández and Iniesta- buyers in the agriculture sector have a more proactive role in Bonillo, 2007). Prior research indicates that epistemic value is assessing the potential value of IoT as co-producers, as they can positively associated with the intent to adopt different forms of generate real-time data through IoT on crop productivity and technology (Bhatti, 1970; Pihlström and Brush, 2008; Rouibah weather forecasts, which ultimately would help enhance their and Hamdy, 2009.) Value in virtual markets (including IoT) profitability (Mehta, 2017; Vargo and Lusch, 2004.) Also, IoT would stem from the combination of information, physical leads to a shift in focus from operand (agricultural goods) to products and services and reconfiguration and integration of operant resources such as predictions on crop output and resources and capabilities and roles among suppliers, partners and weather patterns (Vargo and Lusch, 2004). The value-based customers (Amit and Zott, 2001) and consequently farmers using adoption model, which discussed the overall benefits and IoT can tap their creativity and problem-solving skills as co-creators sacrificesofthetechnology,canserveasthebasisofdetermining of value. The preceding literature on diverse forms of perceived the antecedents of IoT adoption (Kim et al.,2007). valueformsthebasisforustoformulatethefollowinghypothesis: We have conceptualized IoT adoption as the actual usage of the innovation by farmers and we also concur with diverse H1. Perceived value has a positive association with IoT scholars that the adoption process is not instantaneous, but adoption. consists of several stages (Woodside and Biemans, 2005), wherein perceived value as a cognitive variable affects In line with the value adoption model (Kim et al.,2007), we also behavioral outcomes in a B2B context (Eggert and Ulaga, evaluate the perceived risk associated with IoT technology 2002). The value-based adoption model ties in with the adoption. Chaudhuri (1997), Mitchell (1992) and Featherman concept of perceived value, according to which consumer’s and Pavlou (2003) established a relationship between perceived perceptions of what is given and what is received determines the risk and technology adoption and purchase decisions and utility of a product (Kim et al., 2007; Zeithaml, 1988). behaviors and the relevance of privacy risks in B2B contexts was Gao and Bai (2014) integrated the concepts of perceived validated by Paluch and Wünderlich (2016).Information usefulness, the unified theory of technology acceptance and asymmetry between sellers and buyers can impinge on the usage of technology and the diffusion of innovation to discuss functioning of economically efficient, neutral B2B exchanges how consumers’ feelings of potential improvement in (Pavlou and El Sawy, 2002). Giddens’ structuration framework of significance (what Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) performance leads to IoT adoption. New combinations of IoT technologies and digital capabilities individuals interpret during social interactions) forms the basis can enable end users to boost their productivity for both for us to incorporate farmers’ perceptions of data handling risks businesses and customers (Baird and Riggins, 2016; Brynjolfsson (posed by ATPs) into our model (Edvardsson et al.,2011; and McAfee, 2015). Prior research has emphasized the role of IT Giddens, 1984). Data handling risk can distort digital agriculture in improving organizational performance (Brynjolfsson and Hitt, markets, as ATPs can have more access to large volumes of farm- 1996; Devaraj and Kohli, 2003; Mukhopadhyay et al.,1995)and level data, which farmers are not privy to, giving rise to the business value of IT can be computed based on efficiency (for information asymmetry. For instance, farmers’ groups such as example, cost reduction and productivity), as well as effectiveness the American Farm Bureau Federation have warned farmers (competitive advantage; Drucker, 1964 and Melville et al.,2004.) about their farm data being leaked to rival farmers or being For instance, the adoption of Web-based B2B procurement misused for commodity market speculation by ATPs (Charles, can enhance transaction cost savings and increase competitive 2014). This motivates us to formulate the following hypothesis: sourcing opportunities for the buyer organization (Subramanian and Shaw, 2002). IoT tools deployed in agriculture such as GPS- H2. Perceived risk has a negative relationship with IoT based mapping, field-level weather forecasting and variable rate adoption. technologies have capabilities of monitoring, control and optimization (Porter and Heppelmann, 2014; Baird and Riggins, The role of trust 2016).This forms the basis for us to factor in the perceived economic value of IoT adoption into our study. Our research lies at the intersection between information The perceived impact of IoT on the environment can be systems and B2B marketing literature. Taking a call from associated with more abstract and ethical characteristics of the Budunchi (2008), we integrate a transaction cost and social technology (Mattson, 1991, Hartman, 1967, 1973). Chen and exchange approach to develop an integrative framework for IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

B2B innovation adoption and we discuss the role of perceived risk. Studies by Yousafzai et al. (2010) and characteristic, processed-based and institutional trust in IoT Kesharwani and Singh Bisht (2012) also indicate that trust has adoption (Luo, 2002). Trust has been extensively used in a significantly negative impact on perceived risk to adopt examining the role of IT in B2B relationships (Allen et al., information technology tools. This leads to the following 2000; Hart and Estrin, 1991; Pavlou, 2002; Ratnasingam, hypothesis formulation: 2005; Webster, 1995; Budunchi, 2008; Luo, 2002). Trust has a more pivotal role in informational technology services H4. Trust has a negative relationship with perceived risk of including IoT, as opposed to the brick-and-mortar sector, adopting IoT technology. because of unique characteristics such as the intangibility of certain IoT services and the absence of face-to-face interactions Our conceptual model has been illustrated in Figure 1. between farmers and ATPs (Ha and Stoel, 2009). Trust has been extensively researched in business disciplines, Research design spanning industrial marketing (Vlachos et al.,2010), We conceptualized IoT in agriculture based on Porter and relationship marketing (Morgan and Hunt, 1994) and social Heppelmann’s (2014) IoT typology, technical reports on IoT, psychology (Blau, 1964). Trust has been considered a key as well as discussions with farmers. Porter and Heppelmann’s component of the technology acceptance model (Pavlou, 2003; (2014) typology of the core functions of smart, connected Wu et al.,2011; Gefen et al.,2003). We also consider trust as an products consists of: antecedent to IoT adoption; scholars in the past have  monitoring through sensors and external data sources established a direct, positive relationship between trust and the  control through software embedded in the product; adoption information technology such as cloud computing  optimization to product performance and enhance (Akinwunmi et al., 2015). predictive diagnostics; and Prior B2B research reveals a positive relationship between  autonomy in product operation and enhancement. trust and perceived value of adopting new technologies (Obal, 2013). In B2C marketing literature, Chen and Chang (2012) IoT usage in conventional agriculture (which was the context in established a positive association between green value and which we conducted our study in Iowa) primarily facilitates green trust and perceived quality has been proven to enhance sensing and monitoring of production, better understanding of trust in mobile financial services (Chemingui and Ben specific farming conditions (the weather, environmental Lallouna, 2013). In a study on global B2B services, Doney et al. conditions and pest management), precise and remote control (2007) found a positive relationship between perceived value over farm operations such as the application of fertilizers and and trust. Thus, we propose the following hypothesis: pesticide and automatic weeding (Sundmaeker et al.,2016; Verdouw et al., 2016). H3. Trust has a positive relationship with perceived value of Conversations with a farmer in Iowa indicated that IoT adopting IoT technology. technology encompassed: “Real time connectivity that Much research has been devoted to the role of trust in producers can use to manage or track machinery and e-commerce (Pavlou, 2003) and IT artifacts (Vance et al., productivity (through mobile devices and websites” as well as 2008). Agricultural stakeholders are alluding to the absence of “analysis of planting/harvesting sent to computers.” Another trust and rising concerns about information privacy of farm- farmer pointed out that: level data among farmers, who are purchasing or considering IOT means to me the capability to connect, share, and manage various Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) purchasing data analytic services from ATPs (Economist, aspects of the farming operation. It is taking the work of hard iron and 2014a, 2014b). bringing it to the 21st century. It is a fast growing trend that farmers in the future will need to grasp quickly. Like all industries, many new technologies While industry reports point to issues of trust being central to and startup companies enter the market, making it challenging to know concerns of ownership and transparency, which emanate from which one works for you. IOTs are changing the game on how we farm farmers’ perceptions of uneven distribution of benefits of digital [...][...] farmers just need to understand its capability and how it can add fi agriculture being skewed in favor of input suppliers (Hale pro tability to their bottom line. Group, 2014), B2B research on trust and IoT in agriculture The above discussions with farmers, as well as IoT literature, remains scant. Liu et al. (2008) brought to light how industrial served as the basis for us to include the following IoT-based buyers’ goodwill trust has a significantly negative impact on technologies in our survey:

Figure 1 Conceptual model IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

 yield data analysis, wherein multiple years of yield data are Figure 2 Forms of IoT technology in agriculture condensed into a single composite layer;  GPS-based field mapping, which allows farmers to create maps with precise acreage for field areas and road locations (Gps.gov, 2016);  variable rate technologies, which allow the site-specific application of farm inputs;  field-level weather forecasts, wherein sensors help determine local weather and precipitation conditions (Mehta, 2015);  autosteer technology, which enables precision agriculture machinery to function on an autopilot mode to enhance operational accuracy and productivity for farmers (Cozzens, 2017);  services that measure productivity such as yield monitoring systems, which enable farmers to gather information on grain yields through harvesting vehicles as well as sensor data on soil conditions, moisture and crop yields (Puri, 2016; Scriber, 2017); and  machine optimization tools such as high-precision satellite positioning systems and sensors that record farm operations, optimize yields and reduce the use of agriculture inputs (CEMA, 2017). Figure 2 illustrates the various forms of IoT technology discussed above. Drawing upon Luo’s (2002) trust framework, which is derived from the social exchange theory and relationship marketing, we have factored in characteristic (community- based), process-based (linked to prior purchasing experience, service provider’s reputation and value-added services) and institutional trust (certification through third parties). Here, we discuss trust in the context of farmers’ overall trust in ATPs, the influence of stakeholders such as fellow farmers and extension specialists, as well as privacy agreements on enhancing trust in ATPs and the role of ATPs’ discounts and free advisory services in building up farmers’ relationship with ATPs. As limited literature was available on a very nascent B2B industry such as agricultural IoT, we referred to industry Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) reports (Hale Group, 2014 and the American Farm Bureau Federation) on agricultural IoT (wherein farmers had been interviewed) to develop survey items (see Appendix)on perceived risk of data handling. Here, we discussed the risk of ATPs sharing raw data with neighboring farmers and real estate speculators and also how raw data could be used for commodity price speculation and making decisions for farmers. Federation. We analyzed perceived economic value with Vargo and Lusch (2008) and Vargo (2009) considered respect to enhancing profits and yield, lowering input costs, value to be contextual, meaning-laden and idiosyncratic, managing time better, being led to new farming techniques and and it can be embedded in multiple contexts for consumers dealing better with production-related issues through the use of (Voima et al., 2010; Mejtoft, 2011). This motivated us to technology. We discussed perceived green value in the context conceptualize perceived value in economic, environmental of farmers’ ability to avoid nutrient loss and excessive pesticide and epistemic forms. Services and products are becoming and fertilizer usage and promote better environment more entwined, especially in the case of IoT (Hallikas et al., stewardship. 2014) and traditional management theories and methods Scholars have linked information and communication cannot always serve as the basis for conceptualization of technology (ICT) with the development of knowledge value (Lusch et al., 2010; Pynnönen et al., 2011). capabilities, such as creativity, problem-solving and Moreover, as literature on perceived value of IoT in B2B argumentation and improved social and psychological contexts is limited, we developed survey items on perceived capabilities, which include higher levels of program economic and green value based on industry reports from the management skills (Johnstone, 2007; Gigler, 2011). Hence, we Hale Group (2014) and the American Farm Bureau used the ICT-based capabilities narrative to assess epistemic IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

value and developed survey items to determine farmers’ ability based on dichotomous scales. We also incentivized farmers’ to solve problems, make informed decisions, ask the right participation in the study by providing online gift cards. questions to extension specialists and innovate more with respect to crop management. Findings The mean age of the farmers interviewed was 52 and 86 per The research context: technology adoption in cent of the farmers were male. Among the farmers surveyed, agriculture over 49 per cent reported selling their produce to local growth The agriculture sector in the USA has witnessed the growth of co-ops, whereas 37 per cent reported selling their produce to high-tech, large-scale farms producing food, which is affordable processing plants. Nearly 20 per cent of the farmers reported for the masses, as well as plant-based fuels such as ethanol and gross annual sales in the range of $50,000 and less than chemicals (The Economist, 2014a, 2014b). At the same time, $150,000, whereas around 49 per cent reported income in the large-scale ATPs, as well as start-up companies, are reaching range of $150,000 and less than $1m. out to younger, technologically savvy farmers across the USA to A little over 68 per cent of the farmers were running their promote IoT technology. farms as sole proprietorship firms, whereas the rest of the farms Farmers specializing in growing conventional crops such as were established as partnerships, limited liability companies, S- corn and soy in the USA have historically been at the forefront corporations or other forms of corporations. The average size of of adopting new technology (Hale Group, 2014). For instance, owned farmland in which corn and soy were grown was 225 the diffusion of innovation theory, which discusses the spread acres and 158 acres, respectively, whereas average size of rented of ideas and technology (Rogers, 2010), has its roots in a rural farmland was 314 acres (corn) and 237 acres (soy). sociological study conducted by Ryan and Gross (1943) on the As we have a prior theoretical justification for developing our adoption of hybrid corn varieties by farmers in the USA. conceptual model and hypotheses, we tested out the overall fitof Anecdotal evidence also points to the popularity of our model through a structural equation modeling (SEM) genetically modified organisms (GMO) technology among technique (Phillips and Pugh,1994; Perry et al.,2002). We tested farmers in the USA. our model using the SEM approach on Stata. We controlled for For example, a researcher narrated how GMO technology farm size and the farmer’s age. The measurement model, which has led to more cost-savings for farmers in his hometown in is shown in Figure 3,showsthecoefficients estimated for the Iowa due to which they can spend more time with their families model, as well as the error variance for each equation. and also focus on non-farm jobs (Riesselman, 2015). Over the In addition, we present the correlation between variables in past two decades, the adoption of precision agriculture tools by our model in Table I. farmers has produced mixed results, as some tools have been The overall Chi-square statistic generated by the SEM fi x 2 > x 2 considered highly valuable, whereas others have provided output was signi cant at = 13.52, Prob = 0.0012. fi fi minimum value to farmers (Hale Group, 2014). Other t indices such as the comparative t index and the root fi A study conducted by the Hale Group (2014) indicated that mean squared residual were also signi cant. We also ran the conventional agriculture farmers in the Midwestern state of robust standards errors test, according to which there was no Iowa need digital agriculture tools with a clear value heteroscedasticity in the data. The endogenous variables in the proposition and farmers are also fearful about the misuse of SEM were perceived value, perceived risk and IoT adoption, data by ATPs, activist groups and hackers. Hence, we whereas trust was considered an exogenous variable. The fi Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) considered the conventional agriculture sector in the USA a ndings for the hypotheses are summarized in Table II. suitable context for us to examine farmers perceive the value We established a direct, positive association between and risk of adopting IoT technology. perceived value and IoT adoption, which indicates that a combination of perceived economic, environmental and Data collection epistemic value motivates farmers to adopt IoT technology and hence, we were able to support our first hypothesis. We attribute To gather data for this study, we administered a survey across the direct positive association between perceived value and IoT Iowa during the summer of 2017. We limited our study to Iowa technology adoption to the fact that farmers in Iowa, especially to confine our analysis to solely conventional crop growers. A those specializing in large-scale, conventional agriculture are brief overview of ATPs and various forms of big data more historically predisposed to adopt new technologies. technology was incorporated into the survey. We initially pre- The direct, positive relationship between perceived value and tested our survey via Qualtrics and made further revisions IoT adoption resonates well with prior research on value-based based on initial findings, as well as feedback from respondents adoption of technology such as the work of Kim et al. (2005). and agricultural community leaders. This implies that ATPs would benefit more by focusing on We later disseminated our survey via Qualtrics through the enhancing perceived value (economic, environmental and Iowa Farm Bureau Federation and also reached out to farmers epistemic) to enhance IoT adoption among farmers. Also, the through available databases. Most of the questions in our results imply that farmers adopt IoT technology due to their survey were close-ended, except for a few open-ended understanding of the economic benefits, as well as the ethical questions on farm activity (such as sales and operation size). and abstract characteristics of IoT technology (Mattson, 1991, Out of 1,544 farmers who were contacted, we received 492 Hartman, 1967, 1973). fully completed surveys. The perceptual survey items were Clearly, farmers are not merely drawn to IoT technology by mostly based on a five-point Likert scale, whereas a few were potential economic benefits, but also its ecological and IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

Figure 3 Measurement model

Table I Correlation among model components Model components Trust Perceived risk Perceived value IoT adoption Farm size Age Trust 1.00 Perceived risk À0.12 1.00 Perceived value 0.35 À0.24 1.00 IoT adoption 0.07 À0.15 0.33 1.00 Farm size À0.03 À0.05 0.08 0.38 1.00 Age À0.14 À0.04 À0.17 À0.34 À0.01 1.00

Table II SEM results Hypothesis Antecedent Variable Coefficient Standard error Significance Decision H1 Perceived value IoT adoption 0.06 0.01 *** Support H2 Perceived risk IoT adoption À0.06 0.03 * Support H3 Trust Perceived value 0.81 0.12 *** Support H4 Trust Perceived risk À0.12 0.05 ** Support Notes: ***Significance level lower 0.001; **significance level less than 0.05; *significance equal to 0.05 Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT)

knowledge-enhancing features, which could pave the way for a staggered, whereby behavioral outcomes are determined by a richer conceptualization of IoT technology in agriculture in the series of cognitive processes spanning trust, and the future as well as the buying decisions of B2B clients. We could conceptualization of perceived value and risk. Although establish a direct, negative relationship between perceived risk perceived value can boost IoT adoption, there is likelihood that and IoT adoption (for which the coefficient was negative due to perceived risks can hold back more cautious farmers from which we support the second hypothesis). This finding further adopting or even later continuing the usage of IoT technology. reinforces the importance of privacy-related risk in industrial We also notice that older farmers are less likely to adopt IoT. settings (Paluch and Wünderlich, 2016). Our findings validate H3, according to which higher levels of trust can help enhance perceived value. This implies that ATPs Theoretical implications can enhance process-based, characteristic and institutional trust among farmers to enhance perceived value, which Our B2B scholarship on IoT adoption in agriculture is timely ultimately would increase IoT adoption. The findings resonate and conceptually relevant, as our research findings on IoT with B2C and B2B research by Sirdeshmukh et al. (2002) and adoption in a B2B agricultural setting resonate with the shift in Doney et al. (2007), respectively, who could establish a positive focus from operand to operant resources in service-dominant association between trust and perceived value. logic literature (Vargo and Lusch, 2004). Finally, H4, whereby trust negatively affects perceived risk, is Our conceptualization of how perceived economic, also validated. Consequently, ATPs can enhance farmers’ trust environmental and epistemic values collectively enhance IoT to mitigate perceived risk and hence boost IoT adoption. Our adoption indicates that B2B technology adopters, especially in findings reveal that the process of IoT technology adoption is the context of IoT, are more drawn to potential capabilities, IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

stemming from a unique combination of services and physical of value co-creation, which is an integral part of the service- products (Amit and Zott, 2001). dominant logic (Vargo and Lusch, 2004.) Thus far, marketing scholars are yet to extend this service- The direct, positive relationship between perceived value and dominant perspective to perceived value of IoT adoption in IoT also paves the way for developing a richer typology for IoT agriculture. Much research in the past has been devoted to functions, especially in a B2B agricultural context. While perceived customer value in industrial contexts with a strong Porter and Heppelmann (2014) emphasized on how IoT thrust on linking customer value with competitive advantage technology helps fulfill operational goals such as control, (Lapierre, 2000; Woodruff, 1997; Parasuraman, 1997). monitoring and autonomy, our findings indicate that potential Perceived value in B2B contexts has been measured based on knowledge creation and sustainable agricultural practices also value for money and overall product benefits (Dodds et al., motivate farmers to adopt IoT technology. Thus, IoT 1991; Obal, 2013). However, B2B research with a more technology can be conceptualized on the basis of epistemic and holistic conceptualization of perceived value, spanning sustainability-related features. sustainability and knowledge creation remains limited. Taking a cue from how Grönroos and Helle (2012) used Managerial implications financial returns on dyadic business relationships as metrics for Our research clearly indicates that farmers are motivated by B2B value creation and how Biggemann et al. (2014) both economic and non-economic forms of perceived value to incorporated ecological, social and financial dimensions of adopt IoT technology. Thus, B2B marketers and policymakers sustainability into B2B value creation, we incorporated a would need to determine how to enhance perceived economic, sustainability-based as well as economic narrative of perceived environmental and epistemic value through their IoT offerings. value into our study. The value adoption model has been used in Farmers, for their part, would benefit by assessing how to fi B2C contexts (Kim et al.,2007) and we have modi ed the model maximize perceived economic, environmental and epistemic for a B2B agricultural context. From a value maximization value while adopting technological solutions. IoT technology standpoint (Kim et al., 2007), our research shows how both adopters could be segmented into different groups based on monetary and non-monetary forms of perceived value drive IoT how they rank different forms of perceived value. adoption in an industrial context, which is an under-researched Also, B2B marketers and policymakers could assess how ’ theme in value adoption literature. While Kim et al s (2007) different demographic groups of IoT adopters assess perceived ’ integrative value adoption model and Ziethaml s (1988) research value. While in our study, the average age of survey respondents discuss perceived value in the context of what is given (costs) and was 52, B2B marketers and other agricultural stakeholders fi what is received (bene ts), our research is unique in that we would benefit by studying what forms of perceived value provide a holistic narrative of perceived value and also integrate motivate tech-savvy, millennial farmers to adopt IoT the concepts of perceived risk and trust with IoT technology technology. This would also pave the way for determining how adoption. a spectrum of IoT adopters, spanning innovators, early We conducted our research in a unique B2B context, adopters, the late majority and laggards perceive the value of wherein perceived risks of technology adopters are yet to be IoT technology. As the perceived environmental value fully addressed. We examine the phenomenological context contributed to IoT technology adoption, B2B marketers could that farmers experience with respect to perceived data handling explore incorporating a green marketing narrative into their risk, stemming from raw farm-level data being leaked to dialogue with farming communities while promoting IoT neighboring farms and real estate speculators by technology

Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) technology. providers and link intra-firm technology adoption to a wider As epistemic value also played a role in enhancing IoT societal context (Cortez and Johnston, 2017; Sakari Makkonen adoption, we concur that farmers are focusing on operant and J. Johnston, 2014). resources that enhance knowledge and innovation. As the We draw a parallel between perceived risk and the concept of operand resources such as corn and soybean produced by significance in the structuration theory (Giddens, 1984), as conventional farmers are homogenous, we posit that farmers perceived risk is determined by what IoT technology adopters will differentiate themselves and enhance their competitive would interpret during their interactions with technology advantage based on how well they leverage operant resources, providers. While much research has been conducted on how such as predictions of crop output and weather conditions. consumers’ privacy concerns impact product/service usage and Thus, ATPs may consider incorporating knowledge creation purchase (Korgaonkar and Wolin, 1999; Herschel and and innovation into their marketing narrative for IoT Andrews, 1997), B2B scholarship on data handling risk technology. The growing importance of epistemic value could affecting information technology adoption, especially in sectors also give rise to co-creation of value by B2B IoT adopters in such as agriculture, is still limited. agriculture. While we acknowledge that trust has been extensively used as The agriculture sector can be transformed by new business an antecedent of technology adoption in both B2B and B2C models that give rise to co-creation, wherein B2B customers literature, one of our research findings, which is relevant to B2B influence suppliers’ resources, processes, products, services scholars, is that both perceived value and risk mediate the and solutions (Kohtamäki and Rajala, 2016). Marketing relationship between trust and IoT adoption. Moreover, as we scholars have called for a paradigm shift from a transactional have included perceived epistemic value (such as enhancing approach toward managing clients to value creation and value knowledge and problem-solving abilities) as part of perceived chain development (Webster, 1997; Vargo and Lusch, 2004; value, there is also potential for farmers to explore possibilities Tretyak and Sloev, 2013). The service-dominant logic paves IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al.

the way for both service providers and customers to integrate across a spectrum of innovators and the early and late majority resources and co-create value (Lusch and Vargo, 2006; Vargo, and laggards. 2009). Our research is a novel attempt to assess how monetary and Value co-creation has gained much resonance in marketing non-monetary forms of perceived value can affect IoT adoption and strategy literature, whereby B2B clients share experiences among farmers and we encourage further scholarship on and have dialogues with the company and among themselves different forms of perceived value and also how these forms of through formal crowdsourcing channels and social networks value can affect B2B clients’ buying decisions. As IoT in (Prahalad and Ramaswamy, 2004, Füller, 2010; Lee et al., agriculture is still a burgeoning market, we call for more 2012). In recent times, a few startups and non-profits are research on how contextual factors unique to digital agriculture creating crowdsourcing and open-source agronomic platforms, have a bearing on technology adoption. For instance, scholars wherein collaborative communities of farmers share could investigate how consolidation across agribusiness firms information with each other and can co-create value (Fast and value co-creation through crowdsourcing could affect IoT Company, 2017). This ties in with the service-dominant logic, adoption. which compares the customer with a co-producer (Vargo and We call for further research on how trust in agricultural Lusch, 2004). technology providers, as well as farmers’ perceived risk, change Different value propositions can be developed based on what from the pre-adoption to the post-adoption phase. Also, studies forms of perceived value are associated with specific IoT incorporating narratives from both technology providers and applications. For instance, ATPs can explore whether they farmers would give more insights into how trust and perceived should enhance the perceived green value of specific IoT tools, value and risk affect IoT adoption. which have more sustainability-related features. As we could identify the role of epistemic value in Our study clearly indicates that perceived risk of data being contributing to IoT adoption, B2B scholars can discuss how misused can adversely affect IoT adoption. The onus would be B2B buyers such as conventional farmers are differentiating on both ATPs and policymakers to ensure that adequate themselves by shifting their focus from operand to operant privacy safeguards are in place to pre-empt confidential farm- resources, which help enhance knowledge creation and level data from being leaked. Also, ATPs need to allay farmers’ innovation. While our study has focused on farmers’ fears of data being misused for real estate speculation or being perceptions, we recommend that further research can be divulged to neighboring farmers. As farmers are voicing conducted on how farmers’ networks play a role in facilitating concerns about information asymmetry distorting the digital technology adoption. agriculture market, we also foresee the advent of more farmer- owned cooperatives or even crowdsourcing networks, which can help mitigate the perceived risk of data misuse. Note Our research brings to light how trust can play a catalyzing 1. At this juncture, one should draw a clear distinction role in allaying farmers’ perceived risks and ultimately between precision agriculture and big data. According to enhancing IoT adoption. Thus, B2B marketers can focus on a National Academy Press Publication in 1997, precision how to restore process-based, institutional and characteristic agriculture refers to the deployment of data from multiple trust in IoT technology providers among farmers, especially in sources through IT tools, which facilitates better decision an era during which there are higher levels of consolidation making on crop management. Precision agriculture, among agri-business companies, spawning more fears about which has been in existence for over 20 years, is a subset Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) concentration of power. ATPs can also foster higher levels of of big data. A distinguishing feature of big data is that trust among farmers so as to increase perceived value, which sophisticated analytical tools can be used to identify ultimately would lead to higher levels of IoT adoption. complex interactions across several production factors and multiple years (Sonka and Cheng, 2015). Future research directions As our study was based on a cross-sectional research design, we could not determine how farmers’ post-adoption behavior References changed over a period of time. We suggest that scholars could Adrian, A.M., Norwood, S.H. and Mask, P.L. (2005), conduct further longitudinal studies to assess how perceptions “Producers’ perceptions and attitudes toward precision of value, risk and even value-in-use evolve across various phases agriculture technologies”, Computers and Electronics in of IoT technology adoption. 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Appendix. Survey items Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al. Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al. Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT) IoT adoption Journal of Business & Industrial Marketing Priyanka Jayashankar et al. Downloaded by 173.17.62.164 At 10:12 21 September 2018 (PT)

Corresponding author Priyanka Jayashankar can be contacted at: priyanka@ iastate.edu

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