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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,
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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
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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
Paper type Research paper
Introduction
Internet of things (IoT) technology and big data applications are
poised to play a key role in ramping up global food production to
feed billions in the coming decades. Experts are envisioning a
data-driven future, wherein IoT-based technology ranging from
sensors on farm equipment, self-driving tractors, drones and
GPS imaging to weather tracking would not only enable farmers
to feed the world but also cope better with the limited supply of
fossil fuel, water and arable land (Ray, 2017;Lohr, 2015).
Reports suggest that the number of connected agricultural
devices are growing from 13 million in 2014 to 225 million in
2024 and the installation of IoT devices is rising at a
compounded annual growth rate of 20 per cent worldwide
(Machina Research, 2016;Meola, 2016). Accounts from the
popular press allude to the advent of “agriculture 3.0,”which
entails exploiting data from many sources, such as sensors on
farm equipment and plants, satellite images and weather tracking
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/0885-8624.htm
Journal of Business & Industrial Marketing
© Emerald Publishing Limited [ISSN 0885-8624]
[DOI 10.1108/JBIM-01-2018-0023]
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
survey. The authors also thank Dr Joe Colletti, Dr Manjit Mishra,
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.
Received 9 January 2018
Revised 5 February 2018
Accepted 12 April 2018
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(Lohr, 2015). Following the advent of big data analytics in
agriculture, large volumes of data, which could not be
quantifiable in the past, can now be analyzed through statistical
models and algorithms, as a result of which farmers can monitor
what they are planting and where their seeds are placed on a real-
time basis (Johnston, 2014; Pattinson and Johnston, 2016;
Cukier and Mayer-Schoenberger, 2013;Noyes, 2014). IoT in
agriculture not only helps improve productivity and profitability
but also paves the way for drastic changes in farm management
and sustainable agriculture practices (Kite-Powell, 2016). For
instance, smart farming tools for site-specific applications of
fertilizers, GPS mapping, and accurate yield predictions have the
potential to boost sustainable farming practices and enhance
profitability (Walter et al., 2017).
In the midst of rapid population growth, dietary shifts,
resource constraints and dietary changes, there is a growing
emphasis on efficient management and optimal usage of inputs
such as fertilizers through data-driven farming decisions (Lee
and Choudhury, 2017). Such trends are compelling more
farmers to use versatile IoT tools improve crop output, lower
livestock losses and reducewater usage across a diverse range of
agricultural operations (Guerra, 2017). Cloud-based IoT tools
and sensors help livestock farmers monitor swine, cattle, broiler
and milk production.
For instance, collar units and ear tags provide almost real-time
insight into animal behavior, herd location, walking time, grazing
time, resting time and water consumption (Lee and Choudhury,
2017). Crop farmers can take smarter decisions through data
relayed via IoT sensors on weather, soil, air quality and crop
maturity (Guerra, 2017). For example, sensors deployed on the
ground or in water or in vehicles collect data on soil moisture and
crop health, which can be stored wirelessly on a server or a cloud-
based system and can be accessed by farmers through tablets and
mobile phones (Lee and Choudhury, 2017).
Autosteer technology, which is the use of a global positioning
system (GPS to guide agricultural equipment (Shockley et al.,
2011), is also an integral part of IoT in agriculture. It enables
farmers to reduce human errors in spraying insecticides, reduce
overlaps and skips in managing crop rows and reduce
machinery costs Shockley et al.,2011;Guerra, 2017). While
the above examples from popular press as well as agricultural
economics literature attest to the practical benefits of IoT
technology in agriculture, business-to-business (B2B) scholars
are yet to explore how perceived value of IoT influences
farmers’technology adoption decisions.
Agriculture technology providers (ATPs) have an important
stake in B2B markets by providing IoT devices and big data-
enabled advisory services on inputs such as seeds and fertilizers
and crop management to farmers. Information gleaned through
farm-level data can become valuable in the form of field
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
risk of misuse of data for commodity market speculation and
sale of data to third parties, which can make some farmers
averse to adopt big data tools agriculture ((Haire, 2014;Porter
and Heppelmann, 2014;Economist,2014a, 2014b). Hence,
some policymakers are expressing concern about the rise in data
asymmetry in the B2B digital agriculture market, wherein
farmers have to divulge personal farm management data to
ATPs, who in turn, reveal very little as to how data will bestored
or used or to what extant farmers can own their data (Carbonell,
2016). Farmers’concerns about the ownership and privacy of
their farm-level data have drawn the attention of key agricultural
stakeholders in the USA. From an ethical and legal standpoint,
further inquiry is warranted to determine whether farmers only
have the right to use their data in terms of access, modification
and standardization, or whether they own the data by
determining others’privileges to use the data (Van Alstyne et al,
1995). Hence, this calls for further research on how perceived
forms of risk can affect farmers’adoption of IoT technology.
Kannan (2017) set an agenda for research in digital
marketing, wherein the interaction of digital technologies such
as IoT with customers, context, competitors and collaborators
and the impact of digital technologies on value (from the
standpoint of customers and firm performance) are among the
core focus areas.
While scholars such as Falkenreck and Wagner (2017)
recently contrasted reciprocity and trust among B2B buyers of
IoT-based engineering products across international markets,
there is yet to be a rich B2B narrative of IoT clients’perceived
value and risk, especially in the context of agriculture. Prior
research has discussed farmers’persuasion to adopt precision
agriculture (Adrian et al., 2005). However, B2B marketing
scholars have not explored the digital agriculture market[1],
especially in the context of IoT.
In our study, we integrate elements of the value-based
adoption model, the diffusion of innovation theory, technology
acceptance model and the service-dominant logic to analyze
how perceived value, risk and trust affect IoT adoption. We
partially draw upon Pavlou’s (2003) extended technology
acceptance model by incorporating perceived risk and trust as
the drivers of IoT adoption.
While scholars such as Kim et al. (2007) and Ko et al. (2009)
applied the value-based adoption model in a B2C context, we
integrate the concept of trust with the value-based adoption
model in a B2B context. Also, we conceptualize the process of
B2B IoT adoption as a dichotomized decision process, whereby
perceived attributes of the innovation determine the rate of its
adoption (Rogers, 2003;MohamedSamirHusseinandMourad,
2014). As IoT in agriculture is distinct in that the technology is
associated with specialized knowledge and operant resources, we
also take a cue from the service-dominant logic (Lusch et al.,
2007) and conceptualize different forms of perceived value that
drive IoT adoption. We pose the following research questions:
RQ1. How do perceived value and risk affect IoT adoption
among farmers?
RQ2. How does trust affect the perceived value and risk of
adopting IoT technology?
Theoretical background and hypotheses
formulation
Perceived value and risk of IoT for business-to-business
clients
Technological changes have considerably affected agriculture for
over 100 years, during which there has been a dramatic rise in
innovations to increase yield, reduce costs and enhance product
IoT adoption
Priyanka Jayashankar et al.
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quality (Schultz, 1964,Cochrane, 1993;Sunding and
Zilberman, 2001). Marketing scholars have applied B2B
marketing concepts to agriculture, as documented in a study by
Foxall (1979) who drew parallels between farmers’buying
decisions for tractors and the buying behavior of professional
buyers in manufacturing and service industries. B2B agricultural
marketing in the past century focused on the efficiency of
marketing channels and the role of distributors (Weld,1917)and
the goods-dominant logic, which pervaded much of marketing
theory in the past, primarily focusing on agricultural products as
units of exchange (Vargo and Lusch, 2004).
However, in the current day and age, the goods-dominant
logic has made way for the service dominant logic, wherein B2B
buyers in the agriculture sector have a more proactive role in
assessing the potential value of IoT as co-producers, as they can
generate real-time data through IoT on crop productivity and
weather forecasts, which ultimately would help enhance their
profitability (Mehta, 2017;Vargo and Lusch, 2004.) Also, IoT
leads to a shift in focus from operand (agricultural goods) to
operant resources such as predictions on crop output and
weather patterns (Vargo and Lusch, 2004). The value-based
adoption model, which discussed the overall benefits and
sacrificesofthetechnology,canserveasthebasisofdetermining
the antecedents of IoT adoption (Kim et al.,2007).
We have conceptualized IoT adoption as the actual usage of
the innovation by farmers and we also concur with diverse
scholars that the adoption process is not instantaneous, but
consists of several stages (Woodside and Biemans, 2005),
wherein perceived value as a cognitive variable affects
behavioral outcomes in a B2B context (Eggert and Ulaga,
2002). The value-based adoption model ties in with the
concept of perceived value, according to which consumer’s
perceptions of what is given and what is received determines the
utility of a product (Kim et al., 2007;Zeithaml, 1988).
Gao and Bai (2014) integrated the concepts of perceived
usefulness, the unified theory of technology acceptance and
usage of technology and the diffusion of innovation to discuss
how consumers’feelings of potential improvement in
performance leads to IoT adoption.
New combinations of IoT technologies and digital capabilities
can enable end users to boost their productivity for both
businesses and customers (Baird and Riggins, 2016;Brynjolfsson
and McAfee, 2015). Prior research has emphasized the role of IT
in improving organizational performance (Brynjolfsson and Hitt,
1996;Devaraj and Kohli, 2003;Mukhopadhyay et al.,1995)and
the business value of IT can be computed based on efficiency (for
example, cost reduction and productivity), as well as effectiveness
(competitive advantage; Drucker, 1964 and Melville et al.,2004.)
For instance, the adoption of Web-based B2B procurement
can enhance transaction cost savings and increase competitive
sourcing opportunities for the buyer organization (Subramanian
and Shaw, 2002). IoT tools deployed in agriculture such as GPS-
based mapping, field-level weather forecasting and variable rate
technologies have capabilities of monitoring, control and
optimization (Porter and Heppelmann, 2014;Baird and Riggins,
2016).This forms the basis for us to factor in the perceived
economic value of IoT adoption into our study.
The perceived impact of IoT on the environment can be
associated with more abstract and ethical characteristics of the
technology (Mattson, 1991,Hartman,1967, 1973). Chen and
Chang (2012) and Patterson and Spreng (1997) associated green
perceived value with consumer’s environmental desires,
sustainable expectations and green needs. There are several
opportunities to enhance green value through IoT in agriculture.
For example, IoT facilitates the site-specific application of inputs
such as fertilizers and pesticides, which, in turn, mitigates
greenhouse gas emissions (Walter et al.,2017). To determine
whether farmers are motivated to adopt IoT technology due to
the potential opportunity of enhancing ecological stewardship,
we also factor in perceived green value into our study.
The consumption value theory posits that epistemic value
emanates from the desire for knowledge and intellectual curiosity
(Sheth et al., 1991a,1991b;Sánchez-Fernández and Iniesta-
Bonillo, 2007). Prior research indicates that epistemic value is
positively associated with the intent to adopt different forms of
technology (Bhatti, 1970;Pihlström and Brush, 2008;Rouibah
and Hamdy, 2009.) Value in virtual markets (including IoT)
would stem from the combination of information, physical
products and services and reconfiguration and integration of
resources and capabilities and roles among suppliers, partners and
customers (Amit and Zott, 2001) and consequently farmers using
IoT can tap their creativity and problem-solving skills as co-creators
of value. The preceding literature on diverse forms of perceived
valueformsthebasisforustoformulatethefollowinghypothesis:
H1. Perceived value has a positive association with IoT
adoption.
In line with the value adoption model (Kim et al.,2007), we also
evaluate the perceived risk associated with IoT technology
adoption. Chaudhuri (1997),Mitchell (1992) and Featherman
and Pavlou (2003) established a relationship between perceived
risk and technology adoption and purchase decisions and
behaviors and the relevance of privacy risks in B2Bcontexts was
validated by Paluch and Wünderlich (2016).Information
asymmetry between sellers and buyers can impinge on the
functioning of economically efficient, neutral B2B exchanges
(Pavlou and ElSawy, 2002).
Giddens’structuration framework of significance (what
individuals interpret during social interactions) forms the basis
for us to incorporate farmers’perceptions of data handling risks
(posed by ATPs) into our model (Edvardsson et al.,2011;
Giddens, 1984). Data handling risk can distort digital agriculture
markets, as ATPs can have more access to large volumes of farm-
level data, which farmers are not privy to, giving rise to
information asymmetry. For instance, farmers’groups such as
the American Farm Bureau Federation have warned farmers
about their farm data being leaked to rival farmers or being
misused for commodity market speculation by ATPs (Charles,
2014). This motivates us to formulate the following hypothesis:
H2. Perceived risk has a negative relationship with IoT
adoption.
The role of trust
Our research lies at the intersection between information
systems and B2B marketing literature. Taking a call from
Budunchi (2008), we integrate a transaction cost and social
exchange approach to develop an integrative framework for
IoT adoption
Priyanka Jayashankar et al.
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B2B innovation adoption and we discuss the role of
characteristic, processed-based and institutional trust in IoT
adoption (Luo, 2002). Trust has been extensively used in
examining the role of IT in B2B relationships (Allen et al.,
2000;Hart and Estrin, 1991;Pavlou, 2002;Ratnasingam,
2005;Webster, 1995;Budunchi, 2008;Luo, 2002). Trust has
a more pivotal role in informational technology services
including IoT, as opposed to the brick-and-mortar sector,
because of unique characteristics such as the intangibility of
certain IoT services and the absence of face-to-face interactions
between farmers and ATPs (Ha and Stoel, 2009).
Trust has been extensively researched in business disciplines,
spanning industrial marketing (Vlachos et al.,2010),
relationship marketing (Morgan and Hunt, 1994) and social
psychology (Blau, 1964). Trust has been considered a key
component of the technology acceptance model (Pavlou, 2003;
Wu et al.,2011;Gefen et al.,2003). We also consider trust as an
antecedent to IoT adoption; scholars in the past have
established a direct, positive relationship between trust and the
adoption information technology such as cloud computing
(Akinwunmi et al., 2015).
Prior B2B research reveals a positive relationship between
trust and perceived value of adopting new technologies (Obal,
2013). In B2C marketing literature, Chen and Chang (2012)
established a positive association between green value and
green trust and perceived quality has been proven to enhance
trust in mobile financial services (Chemingui and Ben
Lallouna, 2013). In a study on global B2B services, Doney et al.
(2007) found a positive relationship between perceived value
and trust. Thus, we propose the following hypothesis:
H3. Trust has a positive relationship with perceived value of
adopting IoT technology.
Much research has been devoted to the role of trust in
e-commerce (Pavlou, 2003) and IT artifacts (Vance et al.,
2008). Agricultural stakeholders are alluding to the absence of
trust and rising concerns about information privacy of farm-
level data among farmers, who are purchasing or considering
purchasing data analytic services from ATPs (Economist,
2014a, 2014b).
While industry reports point to issues of trust being central to
concerns of ownership and transparency, which emanate from
farmers’perceptions of uneven distribution of benefits of digital
agriculture being skewed in favor of input suppliers (Hale
Group, 2014), B2B research on trust and IoT in agriculture
remains scant. Liu et al. (2008) brought to light how industrial
buyers’goodwill trust has a significantly negative impact on
perceived risk. Studies by Yousafzai et al. (2010) and
Kesharwani and Singh Bisht (2012) also indicate that trust has
a significantly negative impact on perceived risk to adopt
information technology tools. This leads to the following
hypothesis formulation:
H4. Trust has a negative relationship with perceived risk of
adopting IoT technology.
Our conceptual model has been illustrated in Figure 1.
Research design
We conceptualized IoT in agriculture based on Porter and
Heppelmann’s (2014) IoT typology, technical reports on IoT,
as well as discussions with farmers. Porter and Heppelmann’s
(2014) typology of the core functions of smart, connected
products consists of:
monitoring through sensors and external data sources
control through software embedded in the product;
optimization to product performance and enhance
predictive diagnostics; and
autonomy in product operation and enhancement.
IoT usage in conventional agriculture (which was the context in
which we conducted our study in Iowa) primarily facilitates
sensing and monitoring of production, better understanding of
specific farming conditions (the weather, environmental
conditions and pest management), precise and remote control
over farm operations such as the application of fertilizers and
pesticide and automatic weeding (Sundmaeker et al.,2016;
Verdouw et al., 2016).
Conversations with a farmer in Iowa indicated that IoT
technology encompassed: “Real time connectivity that
producers can use to manage or track machinery and
productivity (through mobile devices and websites”as well as
“analysis of planting/harvesting sent to computers.”Another
farmer pointed out that:
IOT means to me the capability to connect, share, and manage various
aspects of the farming operation. It is taking the work of hard iron and
bringing it to the 21
st
century. It is a fast growing trend that farmers in the
future will need to grasp quickly. Like all industries, many new technologies
and startup companies enter the market, making it challenging to know
which one works for you. IOTs are changing the game on how we farm
[...][...] farmers just need to understand its capability and how it can add
profitability to their bottom line.
The above discussions with farmers, as well as IoT literature,
served as the basis for us to include the following IoT-based
technologies in our survey:
Figure 1 Conceptual model
IoT adoption
Priyanka Jayashankar et al.
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yield data analysis, wherein multiple years of yield data are
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
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.
Vargo and Lusch (2008) and Vargo (2009) considered
value to be contextual, meaning-laden and idiosyncratic,
and it can be embedded in multiple contexts for consumers
(Voima et al., 2010;Mejtoft, 2011). This motivated us to
conceptualize perceived value in economic, environmental
and epistemic forms. Services and products are becoming
more entwined, especially in the case of IoT (Hallikas et al.,
2014) and traditional management theories and methods
cannot always serve as the basis for conceptualization of
value (Lusch et al., 2010;Pynnönen et al., 2011).
Moreover, as literature on perceived value of IoT in B2B
contexts is limited, we developed survey items on perceived
economic and green value based on industry reports from the
Hale Group (2014) and the American Farm Bureau
Federation. We analyzed perceived economic value with
respect to enhancing profits and yield, lowering input costs,
managing time better, being led to new farming techniques and
dealing better with production-related issues through the use of
technology. We discussed perceived green value in the context
of farmers’ability to avoid nutrient loss and excessive pesticide
and fertilizer usage and promote better environment
stewardship.
Scholars have linked information and communication
technology (ICT) with the development of knowledge
capabilities, such as creativity, problem-solving and
argumentation and improved social and psychological
capabilities, which include higher levels of program
management skills (Johnstone, 2007;Gigler, 2011). Hence, we
used the ICT-based capabilities narrative to assess epistemic
Figure 2 Forms of IoT technology in agriculture
IoT adoption
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value and developed survey items to determine farmers’ability
to solve problems, make informed decisions, ask the right
questions to extension specialists and innovate more with
respect to crop management.
The research context: technology adoption in
agriculture
The agriculture sector in the USA has witnessed the growth of
high-tech, large-scale farms producing food, which is affordable
for the masses, as well as plant-based fuels such as ethanol and
chemicals (The Economist,2014a, 2014b). At the same time,
large-scale ATPs, as well as start-up companies, are reaching
out to younger, technologically savvy farmers across the USA to
promote IoT technology.
Farmers specializing in growing conventional crops such as
corn and soy in the USA have historically been at the forefront
of adopting new technology (Hale Group, 2014). For instance,
the diffusion of innovation theory, which discusses the spread
of ideas and technology (Rogers, 2010), has its roots in a rural
sociological study conducted by Ryan and Gross (1943) on the
adoption of hybrid corn varieties by farmers in the USA.
Anecdotal evidence also points to the popularity of
genetically modified organisms (GMO) technology among
farmers in the USA.
For example, a researcher narrated how GMO technology
has led to more cost-savings for farmers in his hometown in
Iowa due to which they can spend more time with theirfamilies
and also focus on non-farm jobs (Riesselman, 2015). Over the
past two decades, the adoption of precision agriculture tools by
farmers has produced mixed results, as some tools have been
considered highly valuable, whereas others have provided
minimum value to farmers (Hale Group, 2014).
A study conducted by the Hale Group (2014) indicated that
conventional agriculture farmers in the Midwestern state of
Iowa need digital agriculture tools with a clear value
proposition and farmers are also fearful about the misuse of
data by ATPs, activist groups and hackers. Hence, we
considered the conventional agriculture sector in the USA a
suitable context for us to examine farmers perceive the value
and risk of adopting IoT technology.
Data collection
To gather data for this study, we administered a survey across
Iowa during the summer of 2017. We limited our study to Iowa
to confine our analysis to solely conventional crop growers. A
brief overview of ATPs and various forms of big data
technology was incorporated into the survey. We initially pre-
tested our survey via Qualtrics and made further revisions
based on initial findings, as well as feedback from respondents
and agricultural community leaders.
We later disseminated our survey via Qualtrics through the
Iowa Farm Bureau Federation and also reached out to farmers
through available databases. Most of the questions in our
survey were close-ended, except for a few open-ended
questions on farm activity (such as sales and operation size).
Out of 1,544 farmers who were contacted, we received 492
fully completed surveys. The perceptual survey items were
mostly based on a five-point Likert scale, whereas a few were
based on dichotomous scales. We also incentivized farmers’
participation in the study by providing online gift cards.
Findings
The mean age of the farmers interviewed was 52 and 86 per
cent of the farmers were male. Among the farmers surveyed,
over 49 per cent reported selling their produce to local growth
co-ops, whereas 37 per cent reported selling their produce to
processing plants. Nearly 20 per cent of the farmers reported
gross annual sales in the range of $50,000 and less than
$150,000, whereas around 49 per cent reported income in the
range of $150,000 and less than $1m.
A little over 68 per cent of the farmers were running their
farms as sole proprietorship firms, whereas the rest of the farms
were established as partnerships, limited liability companies, S-
corporations or other forms of corporations. Theaverage size of
owned farmland in which corn and soy were grown was 225
acres and 158 acres, respectively, whereas average size of rented
farmland was 314 acres (corn) and 237 acres (soy).
As we have a prior theoretical justification for developing our
conceptual model and hypotheses, we tested out the overall fitof
our model through a structural equation modeling (SEM)
technique (Phillips and Pugh,1994;Perry et al.,2002). We tested
our model using the SEM approach on Stata. We controlled for
farm size and the farmer’s age. The measurement model, which
is shown in Figure 3,showsthecoefficients estimated for the
model, as well as the error variance for each equation.
In addition, we present the correlation between variables in
our model in Table I.
The overall Chi-square statistic generated by the SEM
output was significant at
x
2
= 13.52, Prob >
x
2
= 0.0012.
Other fit indices such as the comparative fit index and the root
mean squared residual were also significant. We also ran the
robust standards errors test, according to which there was no
heteroscedasticity in the data. The endogenous variables in the
SEM were perceived value, perceived risk and IoT adoption,
whereas trust was considered an exogenous variable. The
findings for the hypotheses are summarized in Table II.
We established a direct, positive association between
perceived value and IoT adoption, which indicates that a
combination of perceived economic, environmental and
epistemic value motivates farmers to adopt IoT technology and
hence, we were able to support our first hypothesis. We attribute
the direct positive association between perceived value and IoT
technology adoption to the fact that farmers in Iowa, especially
those specializing in large-scale, conventional agriculture are
more historically predisposed to adopt new technologies.
The direct, positive relationship between perceived value and
IoT adoption resonates well with prior research on value-based
adoption of technology such as the work of Kim et al. (2005).
This implies that ATPs would benefit more by focusing on
enhancing perceived value (economic, environmental and
epistemic) to enhance IoT adoption among farmers. Also, the
results imply that farmers adopt IoT technology due to their
understanding of the economic benefits, as well as the ethical
and abstract characteristics of IoT technology (Mattson, 1991,
Hartman,1967, 1973).
Clearly, farmers are not merely drawn to IoT technology by
potential economic benefits, but also its ecological and
IoT adoption
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knowledge-enhancing features, which could pave the way for a
richer conceptualization of IoT technology in agriculture in the
future as well as the buying decisions of B2B clients. We could
establish a direct, negative relationship between perceived risk
and IoT adoption (for which the coefficient was negative due to
which we support the second hypothesis). This finding further
reinforces the importance of privacy-related risk in industrial
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
can enhance process-based, characteristic and institutional
trust among farmers to enhance perceived value, which
ultimately would increase IoT adoption. The findings resonate
with B2C and B2B research by Sirdeshmukh et al. (2002) and
Doney et al. (2007), respectively, who could establish a positive
association between trust and perceived value.
Finally, H4, whereby trust negatively affects perceived risk, is
also validated. Consequently, ATPs can enhance farmers’trust
to mitigate perceived risk and hence boost IoT adoption. Our
findings reveal that the process of IoT technology adoption is
staggered, whereby behavioral outcomes are determined by a
series of cognitive processes spanning trust, and the
conceptualization of perceived value and risk. Although
perceived value can boost IoT adoption, there is likelihood that
perceived risks can hold back more cautious farmers from
adopting or even later continuing the usage of IoT technology.
We also notice that older farmers are less likely to adopt IoT.
Theoretical implications
Our B2B scholarship on IoT adoption in agriculture is timely
and conceptually relevant, as our research findings on IoT
adoption in a B2B agricultural setting resonate with the shift in
focus from operand to operant resources in service-dominant
logic literature (Vargo and Lusch, 2004).
Our conceptualization of how perceived economic,
environmental and epistemic values collectively enhance IoT
adoption indicates that B2B technology adopters, especially in
the context of IoT, are more drawn to potential capabilities,
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 0.06 0.03 * Support
H3 Trust Perceived value 0.81 0.12 *** Support
H4 Trust Perceived risk 0.12 0.05 ** Support
Notes: ***Significance level lower 0.001; **significance level less than 0.05; *significance equal to 0.05
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 0.12 1.00
Perceived value 0.35 0.24 1.00
IoT adoption 0.07 0.15 0.33 1.00
Farm size 0.03 0.05 0.08 0.38 1.00
Age 0.14 0.04 0.17 0.34 0.01 1.00
IoT adoption
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stemming from a unique combination of services and physical
products (Amit and Zott, 2001).
Thus far, marketing scholars are yet to extend this service-
dominant perspective to perceived value of IoT adoption in
agriculture. Much research in the past has been devoted to
perceived customer value in industrial contexts with a strong
thrust on linking customer value with competitive advantage
(Lapierre, 2000;Woodruff, 1997;Parasuraman, 1997).
Perceived value in B2B contexts has been measured based on
value for money and overall product benefits (Dodds et al.,
1991;Obal, 2013). However, B2B research with a more
holistic conceptualization of perceived value, spanning
sustainability and knowledge creation remainslimited.
Taking a cue from how Grönroos and Helle (2012) used
financial returns on dyadic business relationships as metrics for
B2B value creation and how Biggemann et al. (2014)
incorporated ecological, social and financial dimensions of
sustainability into B2B value creation, we incorporated a
sustainability-based as well as economic narrative of perceived
value into our study. The value adoption model has been used in
B2C contexts (Kim et al.,2007) and we have modified the model
for a B2B agricultural context. From a value maximization
standpoint (Kim et al., 2007), our research shows how both
monetary and non-monetary forms of perceived value drive IoT
adoption in an industrial context, which is an under-researched
theme in value adoption literature. While Kim et al’s (2007)
integrative value adoption model and Ziethaml’s (1988) research
discuss perceived value in the context of what is given (costs) and
what is received (benefits), our research is unique in that we
provide a holistic narrative of perceived value and also integrate
the concepts of perceived risk and trust with IoT technology
adoption.
We conducted our research in a unique B2B context,
wherein perceived risks of technology adopters are yet to be
fully addressed. We examine the phenomenological context
that farmers experience with respect to perceived data handling
risk, stemming from raw farm-level data being leaked to
neighboring farms and real estate speculators by technology
providers and link intra-firm technology adoption to a wider
societal context (Cortez and Johnston, 2017;Sakari Makkonen
and J. Johnston, 2014).
We draw a parallel between perceived risk and the concept of
significance in the structuration theory (Giddens, 1984), as
perceived risk is determined by what IoT technology adopters
would interpret during their interactions with technology
providers. While much research has been conducted on how
consumers’privacy concerns impact product/service usage and
purchase (Korgaonkar and Wolin, 1999;Herschel and
Andrews, 1997), B2B scholarship on data handling risk
affecting information technology adoption, especially in sectors
such as agriculture, is still limited.
While we acknowledge that trust has been extensively used as
an antecedent of technology adoption in both B2B and B2C
literature, one of our research findings, which is relevant to B2B
scholars, is that both perceived value and risk mediate the
relationship between trust and IoT adoption. Moreover, as we
have included perceived epistemic value (such as enhancing
knowledge and problem-solving abilities) as part of perceived
value, there is also potential for farmers to explore possibilities
of value co-creation, which is an integral part of the service-
dominant logic (Vargo and Lusch, 2004.)
The direct, positive relationship between perceived value and
IoT also paves the way for developing a richer typology for IoT
functions, especially in a B2B agricultural context. While
Porter and Heppelmann (2014) emphasized on how IoT
technology helps fulfill operational goals such as control,
monitoring and autonomy, our findings indicate that potential
knowledge creation and sustainable agricultural practices also
motivate farmers to adopt IoT technology. Thus, IoT
technology can be conceptualized on the basis of epistemic and
sustainability-related features.
Managerial implications
Our research clearly indicates that farmers are motivated by
both economic and non-economic forms of perceived value to
adopt IoT technology. Thus, B2B marketers and policymakers
would need to determine how to enhance perceived economic,
environmental and epistemic value through their IoT offerings.
Farmers, for their part, would benefit by assessing how to
maximize perceived economic, environmental and epistemic
value while adopting technological solutions. IoT technology
adopters could be segmented into different groups based on
how they rank different forms of perceived value.
Also, B2B marketers and policymakers could assess how
different demographic groups of IoT adopters assess perceived
value. While in our study, the average age of survey respondents
was 52, B2B marketers and other agricultural stakeholders
would benefit by studying what forms of perceived value
motivate tech-savvy, millennial farmers to adopt IoT
technology. This would also pave the way for determining how
a spectrum of IoT adopters, spanning innovators, early
adopters, the late majority and laggards perceive the value of
IoT technology. As the perceived environmental value
contributed to IoT technology adoption, B2B marketers could
explore incorporating a green marketing narrative into their
dialogue with farming communities while promoting IoT
technology.
As epistemic value also played a role in enhancing IoT
adoption, we concur that farmers are focusing on operant
resources that enhance knowledge and innovation. As the
operand resources such as corn and soybean produced by
conventional farmers are homogenous, we posit that farmers
will differentiate themselves and enhance their competitive
advantage based on how well they leverage operant resources,
such as predictions of crop output and weather conditions.
Thus, ATPs may consider incorporating knowledge creation
and innovation into their marketing narrative for IoT
technology. The growing importance of epistemic value could
also give rise to co-creation of value by B2B IoT adopters in
agriculture.
The agriculture sector can be transformed by new business
models that give rise to co-creation, wherein B2B customers
influence suppliers’resources, processes, products, services
and solutions (Kohtamäki and Rajala, 2016). Marketing
scholars have called for a paradigm shift from a transactional
approach toward managing clients to value creation and value
chain development (Webster, 1997;Vargo and Lusch, 2004;
Tretyak and Sloev, 2013). The service-dominant logic paves
IoT adoption
Priyanka Jayashankar et al.
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the way for both service providers and customers to integrate
resources and co-create value (Lusch and Vargo, 2006;Vargo,
2009).
Value co-creation has gained much resonance in marketing
and strategy literature, whereby B2B clients share experiences
and have dialogues with the company and among themselves
through formal crowdsourcing channels and social networks
(Prahalad and Ramaswamy, 2004,Füller, 2010;Lee et al.,
2012). In recent times, a few startups and non-profits are
creating crowdsourcing and open-source agronomic platforms,
wherein collaborative communities of farmers share
information with each other and can co-create value (Fast
Company, 2017). This ties in with the service-dominant logic,
which compares the customer with a co-producer (Vargo and
Lusch, 2004).
Different value propositions can be developed based on what
forms of perceived value are associated with specific IoT
applications. For instance, ATPs can explore whether they
should enhance the perceived green value of specific IoT tools,
which have more sustainability-related features.
Our study clearly indicates that perceived risk of data being
misused can adversely affect IoT adoption. The onus would be
on both ATPs and policymakers to ensure that adequate
privacy safeguards are in place to pre-empt confidential farm-
level data from being leaked. Also, ATPs need to allay farmers’
fears of data being misused for real estate speculation or being
divulged to neighboring farmers. As farmers are voicing
concerns about information asymmetry distorting the digital
agriculture market, we also foresee the advent of more farmer-
owned cooperatives or even crowdsourcing networks, which
can help mitigate theperceived risk of data misuse.
Our research brings to light how trust can play a catalyzing
role in allaying farmers’perceived risks and ultimately
enhancing IoT adoption. Thus, B2B marketers can focus on
how to restore process-based, institutional and characteristic
trust in IoT technology providers among farmers, especially in
an era during which there are higher levels of consolidation
among agri-business companies, spawning more fears about
concentration of power. ATPs can also foster higher levels of
trust among farmers so as to increase perceived value, which
ultimately would lead to higher levels of IoT adoption.
Future research directions
As our study was based on a cross-sectional research design, we
could not determine how farmers’post-adoption behavior
changed over a period of time. We suggest that scholars could
conduct further longitudinal studies to assess how perceptions
of value, risk and even value-in-use evolve across various phases
of IoT technology adoption. As our current research focuses on
adoption as an outcome, we recommend further scholarship on
the levels of IoT adoption (for instance, the number of years
IoT technology has been in use) across different demographic
groups of farmers.
We also suggest that scholars could explore adoption of
agricultural IoT innovations along the diffusion of innovation
stages articulated by Rogers (2010), namely, awareness,
initiation and implementation, where each stage can be
assessed using the length of use of the innovation. Scholars
could also examine and contrast the extant of IoT adoption
across a spectrum of innovators and the early and late majority
and laggards.
Our research is a novel attempt to assess how monetary and
non-monetary forms of perceived value can affect IoT adoption
among farmers and we encourage further scholarship on
different forms of perceived value and also how these forms of
value can affect B2B clients’buying decisions. As IoT in
agriculture is still a burgeoning market, we call for more
research on how contextual factors unique to digital agriculture
have a bearing on technology adoption. For instance, scholars
could investigate how consolidation across agribusiness firms
and value co-creation through crowdsourcing could affect IoT
adoption.
We call for further research on how trust in agricultural
technology providers, as well as farmers’perceived risk, change
from the pre-adoption to the post-adoption phase. Also, studies
incorporating narratives from both technology providers and
farmers would give more insights into how trust and perceived
value and risk affect IoT adoption.
As we could identify the role of epistemic value in
contributing to IoT adoption, B2B scholars can discuss how
B2B buyers such as conventional farmers are differentiating
themselves by shifting their focus from operand to operant
resources, which help enhance knowledge creation and
innovation. While our study has focused on farmers’
perceptions, we recommend that further research can be
conducted on how farmers’networks play a role in facilitating
technology adoption.
Note
1. At this juncture, one should draw a clear distinction
between precision agriculture and big data. According to
a National Academy Press Publication in 1997, precision
agriculture refers to the deployment of data from multiple
sources through IT tools, which facilitates better decision
making on crop management. Precision agriculture,
which has been in existence for over 20 years, is a subset
of big data. A distinguishing feature of big data is that
sophisticated analytical tools can be used to identify
complex interactions across several production factors
and multiple years (Sonka and Cheng, 2015).
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Corresponding author
Priyanka Jayashankar can be contacted at: priyanka@
iastate.edu
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