1. Drivers of technology implementation of
agricultural small and medium companies in
Europe
Author: Gabriella Pimpao (Student number: 437021)
MSc programme: Business Information Management
Coach: Dr. Koen Dittrich
Co-reader: Dr. Rodrigo Belo
Date: 20st
of June 2016
2. 1
Executive summary
Technological advancements of the last decades have changed every industry and created
massive amount of new opportunities to firms but despite all the potential that IT created for
businesses, enterprises still find it challenging both to adopt new information systems and to
align these systems with their business needs. Although almost every company struggles with
this challenge small and medium companies face more difficulties than their more sizeable
counter partners when implementing information technology. As SMEs are a major part of
industrial economies, their success can significantly increase their country’s competitiveness,
while their unsuccessfulness can endanger their national growth. Their importance for the global
and national economies makes them an interesting and relevant topic for research especially as
they have been relatively unsuccessful in implementing IT. Small companies often suffer rather
than profit from the impact of globalization and digitalization due to their resource poverty both
in finances and knowledge. But even though they face harsh challenges many SMEs still decide
to start IT projects, leaving the question: what prompts them to do so? During my thesis
research a conceptual model was created based on the related literature to outline which
internal and external drivers stimulate small companies to implement modern technology. This
conceptual model describes four main internal motivators (owner’s perception of IT, internal
technical knowledge, direct organizational benefits and indirect organizational benefits) and four
external motivators (customer pressure, government pressure, allied companies and suppliers
pressure and competitive pressure) that lead to the adoption of IT projects in the case of SMEs.
In the second step of this research the mentioned conceptual model was tested in one of the
biggest and most traditional industries: agriculture. This sector is under growing pressure due to
the growing population of the World, decreasing arable land, increasing prices of energy and
arising extreme weather conditions. These trends call every agricultural corporate from large to
small to transform the industry into a less labor intensive, more productive, fast growing sector.
One of the ways to achieve this is through implementing information technology on farms. To
make the implementation process smoother it is becoming more and more important to
understand how small farms make decisions on a daily basis. As a step to understand their
decision making better, in this research I studied what drives small and medium companies in
agriculture to adopt technology. Based on qualitative research I found that the internal and
external drivers in case of farms shows some differences compared to SMEs in other sector.
Three internal drivers were found to significantly influence farmers in IT adoption: direct
3. 2
organizational benefits, owner’s perception of IT and innovativeness, and the transition between
generations. While externally they are influenced by governmental initiatives, competitive
pressure from other farmers and pressure from their main clients, the producing companies.
This study therefore explains the differences between the motivation of farmers compared to
other SME owners to implement technology and helps to better understand their decision
making process.
4. 3
Preface
The author declares that the text and work presented in this master thesis is original and
that no other sources than those mentioned in the text and its references have been
used in creating the master thesis.
The copyright of this Master thesis rests with the author. The author is responsible for its
contents. RSM is only responsible for the educational coaching and cannot be held liable for the
content.
Acknowledgements
I would like to express my honest gratitude to everyone who supported me on my way to the
finalization of my master studies and thesis. This thesis would have never become a reality
without the guidance, support, help, advice and incredible patience of my coach, Koen Dittrich. I
would also like to thank my co-reader, Rodrigo Belo.
A very special thank you goes to everyone who contributed to this research by giving an
interview and sharing his or her thoughts around technology in agriculture, namely: Andrea
Beltrami, Anton Bartelen, Corné Kempenaar, Derk Gesink, Destiny Bradley, Frank Berkers,
Herman Krebbers, Leon Noordam and Peter Kooman. I am very grateful to Koen Dittrich, Corné
Kempenaar, Goran Dominion and Leon Haanstra for connecting me with agricultural experts
and farmers.
And finally I would like to thank Máté Scharnitzky for his support during the whole process and
insightful comments and suggestions about my thesis.
7. 6
Introduction
In the last few decades the developments in information technology created immense amount of
new opportunities for businesses: it is now faster and easier to have bilateral interactions with
customers in both the B2B and B2C sectors, companies are able to raise their productivity by
automating and integrating their processes while accessing the global marketplace also became
a possibility to every firm regardless of timing or location. But despite all the advantages that IT
can bring to an organization, enterprises still find it challenging both to adopt new information
systems and to align these systems with their business needs (Luftman, 2014). Although these
challenges are present in all types of corporations their severity differs greatly depending on
size or industry that a company is present in.
“And we didn’t look at start-ups and other small companies (either), because their tech-related
opportunities and challenges are quite different from those faced by large enterprises.”
(Westerman, Bonnet and McAfee, 2014).
The quote above from Westerman, Bonnet and McAfee’s book ‘Leading Digital’ shows us two
important -and often ignored- facts. Firstly, that usually studies on IT adoption focus on large
firms rather than small enterprises. And secondly that their way of and difficulties in
implementing IT are different than the ones big firms experience.
SMEs are a major part of industrial economies, the European Union calls small companies the
“backbone of European economy” (Muller et al., 2015). In the EU, 99% of the businesses are
small and medium enterprises, they “employ 2 in every 3 employees and produce 58 cents in
every euro of value added” (Muller et al., 2015). Due to their number, if SMEs are able to grow
and innovate they can significantly increase their country’s competitiveness while their
unsuccessfulness can endanger their national growth. Their importance for economy makes
them an interesting and relevant topic for research especially as they have been relatively
unsuccessful in implementing IT (Eikebrokk & Olsen, 2007) and often ignored in information
management literature.
Most small companies outside the IT sector find it hard to seize the opportunities originating
from global trends. Due to their resource poverty both in finances and knowledge SMEs often
suffer rather than profit from the impact of globalization and digitalization. Beside limited
8. 7
resources and limited expertise Salmeron and Bueno (2006) describe the hampering factors of
small firms as: greater uncertainty towards IT and lack of vision. SMEs usually have a smaller
organizational structure, making the attitude, knowledge and personality of the owner much
more influential in the organization’s life than in bigger companies. Most small firms have less
customers leading to a higher bargaining power of clients and suppliers than in the case of big
companies which increases the competitive pressure on SMEs. So both internal and external
factors can have a more dominant influence on small companies, including their decision to
implement information systems. In my research I focused on both the internal and external
influencers of IT adoption in SMEs in order to understand better the particularities that these
type of companies experience when faced with digitalization.
Although small and medium enterprises exist in all economic sectors, in my thesis I am going to
focus on one of the most traditional sectors for SMEs, agriculture. This sector represents 8.5%
of the global GDP and gives employment to 1.3 billion people worldwide, leading to agriculture
being one of the biggest global sectors (Tilney, Lecrec, & Demarest, 2015). It’s size is not the
only factor that gives importance to agriculture, but also the role it plays in feeding the growing
population of planet Earth. According to the UN’s Food and Agriculture Organization, based on
current growth the World’s population will reach 8 billion by 2025 and 9.6 billion by 2050,
increasing the demand for agricultural output by 70% (Beecham Research, 2014). These figures
are putting great pressure on the agricultural system that is also struggling with the impact of
climate change, decreasing fuel and mineral resources along with growing demand for
biological production and dwindling agricultural land due to urban development projects
(“Precision Agriculture Steering Us into the Future,” 2015). Furthermore, the agricultural
landscape is also changing as more and more farmers experience labor shortages and stricter
environmental regulations (Grant, 2012). All these new developments are calling for every
agricultural corporate from large to small to transform the industry into a less labour intensive,
more productive, fast growing sector. One of the ways to achieve this is through implementing
information technology on farms.
Although IT implementation is one of the most important sources of innovation, achieving
success through IT is neither easy nor obvious for big companies and even less for small firms,
in any sector, including agriculture. SMEs are less eager to start digitalization and face failure in
IT adoption more often than other companies (Eikebrokk & Olsen, 2007), despite these
challenges there are many small companies that still start IT projects and achieve success.
9. 8
Based on previous research we know that these projects can originate from a variety of internal
and external reasons but we are still unclear on which drivers are the most significant in
prompting an SME to implement a successful ICT solution.
Therefore, in my thesis I evaluate which are the most important motivators that trigger
investment into a new information system in small companies (excluding start-ups and
companies in the high-technology and IT sector). To get a better understanding about SME’s IT
adoption patterns, in my research first I group drivers into two categories: external and internal,
based on previous academic advancements and related literature. Following this division, I test
both the external and internal drivers through expert interviews to deduce which motivators are
the most significant in driving information technology adoption in SMEs. The conceptual model
of the thesis will be tested in the agricultural sector within Europe. Agriculture was chosen on
one hand based on personal curiosity and interest as this sector is not the first that one thinks of
when speaking of information technology and on the other hand due to the importance of the
sector in feeding the World’s growing population.
In my thesis I address the following points 1) designing a conceptual model to categorize the
internal and external motivators of IT adoption in SMEs 2) testing which motivators are the
strongest for new IT systems. The latter is done through qualitative research in the agricultural
sector. The goal of my work is to:
1) Identify the most important internal and external drivers of IT adoption in SMEs
2) Test which of those drivers are the most significant in triggering the implementation of
new information systems in agricultural SMEs in Europe.
The research questions that this thesis aims to answer are:
1) Which internal drivers are the most significant for new IT projects in agricultural SMEs in
Europe?
2) Which external drivers are the most significant for new IT projects in agricultural SMEs in
Europe?
10. 9
Throughout the paper I am going to use the words motivators and drivers interchangeably as
synonyms, meaning the reason that triggers the start of new IT project. Based on this definition
the driver or motivators should be present before the start of the ICT project.
IT, ICT, information systems and digital technology will all be used as synonyms. Meaning any
information technology innovation with the aim to add business value to a firm
This thesis adds to the current literature first of all by collecting and analyzing the literature on
the motivators of IT adoption in SMEs and creating a conceptual model based on previous
academic work. Secondly by testing those drivers and identifying the most significant internal
and external ones, as such research has not been done before. And thirdly by conducting
research in the agricultural sector, which is often considered to be behind technologically and
rarely tackled in information management literature. My work’s academic relevance also
emerges from the fact that currently the amount of literature about IT adoption drivers of farms
and farmers is very limited. According to a study about precision farming ran by the European
Commision a better understanding needs to be developed on how farmers make decisions on a
day-to-day basis, in order to create systems that can achieve higher level of IT adoption in
agriculture (EIP-Agri Focus group, 2015). My work aims to help that goal by giving a better
understanding about decision making of small farmers. This thesis is also useful for
governmental organizations to understand how impactful governmental pressure can be for ICT
adoption in SMEs.
In this thesis first I introduce the topic and research question I am investigating. The second
chapter summarizes the theoretical background of my research, followed by the presentation of
my thesis’ methodology. In the fourth chapter I discuss the findings of my research and in the
last chapter I conclude my findings.
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Theory
In the next part of my thesis I am going to firstly introduce the context of my research, by
presenting the following themes:
Figure 1: Context of thesis
Followed by a literature review detailing the internal and external drivers of IT adoption in SMEs.
In the closing section of this chapter the conceptual model of the study will be presented.
SME definition and characteristics
Both the U.S. Small Business Administration and the European Union (EU) uses similar
measures to define what small and medium enterprises are, as both determine SMEs according
to their total turnover and number of employees. Despite using similar metrics the definitions of
the two organizations diverge as the American classification uses different figures based on
industry (“Table of Small Business Size Standards,” 2014), while the EU has uniform numbers
for all industries. As my research will be carried out in Europe (Netherlands, Italy and UK), in my
thesis I will use the EU’s common SME definition. According to this terminology a firm is
considered an SME if the number of employees does not exceed 250 and the turnover is less
than 40 million euros or the total assets are below 27 million euros (“User guide to the SME
definition,” 2015). Additionally, these firms should not be owned by another company or
companies to qualify as independent enterprises. (Eikebrokk and Olsen, 2007).
12. 11
Oftentimes theories or practices that prove to be useful in big companies fail to demonstrate the
same results in their smaller counter partners. This is due to the fact that SMEs cannot be
considered just the “little twin sisters” of sizable companies. As a matter of fact small and
medium companies demonstrate various characteristics that set them apart from large
organizations. Most of the differences in characteristics come from the fact that SMEs have less
funds and that roles and responsibilities are much less separate within the firm. Ballantine et al.
describes the six distinct attributes of small companies as: diminished information skills,
increased influence of and therefore dependence on main clients, scarcity of resources,
absence of business and IT strategy and inferiority in terms of information skills. Adding to these
properties SMEs also tend to employ more generalists (often family members) rather than
professional or functional experts, which leads to lower level internal knowledge and less
developed processes and techniques both functionally and in general management (Caldeira
and Ward, 2003). Due to these factors and the lack of extensive business network, smaller firms
in traditional industries have restricted access to market information and are strongly affected by
the constraints of globalization (Ghobakhloo et al., 2012). SMEs in many cases have been the
victims rather than the beneficiaries of digitization and the technological advances of the past
decades. With fewer resources and less awareness over market trends little firms find it harder
to keep up with growing customer expectations and an extremely fast changing environment,
where the traditional ways of doing business hardly work anymore. It doesn’t help their
adaptation that IT usage remains relatively low amongst them compared to big companies (Pool
et al., 2006): “Large organizations have noticeably profited more than SMEs in both IT-enabled
improved sales and cost savings.” (Ghobakhloo et al., 2012).
This research excludes SMEs and start-ups in the high-technology and IT sector due to their
more advanced technical knowledge and evident advantage in ICT adoption.
SMEs and information technology adoption
Several of the previously mentioned internal and external properties of SMEs contribute in
bigger or smaller extent to their lower success rate in ICT adoption, but based on the vast
majority of the reviewed literature there is one attribute that stands out as a hindering factor.
Small companies lack an IT or digital vision, the impact of which can be seen in the way they
invest in IT and their difficulties during the development and implementation process. An even
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more apparent consequence of the unclear IT goals is the fact that SMEs rarely implement
strategic sophisticated systems (Harindranath et al., 2008).
IT can bring different type of benefits to organizations: strategic, informational or transactional
(Mirani and Lederer, 1998). Companies need to implement completely different systems when
they want to achieve strategic advantages from when they aim for transactional gains. SMEs
rarely implement changes that move beyond the transactional category although the depth of
business impact can be much higher when implementing more complex systems (Caldeira and
Ward, 2003). Strategic systems add to the competitive advantage of the firm by contributing to
goals like: integrating core internal and external processes, improving customer satisfaction or
increasing management control (Caldeira and Ward, 2003; Torkzadeh and Doll, 1999). As
highlighted in Levy et al.’s (1999) research conducted with small and medium sized
manufacturing companies “a low priority is accorded to the use of IS or IT for management
information. While all adopted IT to aid day-to-day production and stock management, none has
realised the potential of connecting this data to overall strategic and competitive analysis.” In a
research with SMEs in the UK Harindranath et al. (2008) found that those who implemented
higher level strategic information systems were predominantly motivated by compliance needs
with the government or EU regulations. The inclination towards primarily adopting operational or
efficiency focused IT systems can be explained by SMEs limited financial resources which
makes experimentation with innovative IS solutions perilous. Although the price of both software
and hardware have been decreasing steadily in the last decades the implementation costs of an
IT system remain significant compared to SME budgets. An unsuccessful ICT project could
even risk the whole organization’s existence which makes small firm owners understandably
more cautious (Salmeron and Bueno, 2006).
It is not just financial obstacles that hinder digitization, smaller firm’s structures are mostly
smaller, simpler and usually without formal IT or IS departments. This lack of internal expertise
leads to inferior information technology knowledge (Cragg et al., 2011). Bringing in external
knowledge is a feasible solution to overcome internal knowledge gaps but as reported by SMEs
frequently even consultants and IT vendors are unaware of the specific characteristics and
challenges faced by these type of companies when adopting ICT.
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The agricultural sector
Agriculture is one of the biggest and most essential sectors in the World, the industry represents
8.5% of the global GDP and gives employment to 1.3 billion people (Tilney, Lecrec, &
Demarest, 2015). The sector is constantly growing, for example worldwide cereal production is
forecasted to increase with 350 million tonnes by 2023, which is a 15% growth compared to
2013 (Corsini, Wagner, Gocke, & Kurth, 2015). The present and future of the industry are
influenced by social, economical, political and environmental trends.
Agriculture will need to adapt itself to the growing population of the planet. Based on the
prediction of the Food and Agriculture Organization of the United Nations the number of
inhabitant of the planet will reach 8 billion by 2025 and 9.6 billion by 2050 (Beecham Research,
2014). This change is anticipated to increase the demand for agricultural output by 70%. It is not
just the overall population growth that is putting agriculture under pressure but also a growing
middle class -especially in Asia and Latin-America- that will demand more diverse and higher
quality products (Corsini et al., 2015). Parallelly to this, in the developed countries the “back to
the roots” movement is becoming more and more popular with customers expecting healthier
and more environmentally conscious options. This will not only force the sector to produce more
organic but also to become more customer savvy as health conscious clients demand more
information and thus they become a more influential part of the agricultural value chain (Grant,
2012).
Agriculture is also facing a so called ‘image problem’ (Guerrini, 2015) -meaning that family
members of agricultural families are more and more pursuing other careers - causing labour
shortages in the industry. Meanwhile the sizes of farms are increasing especially in Europe
forcing farms to produce higher yield and use more machinery (Corsini et al., 2015). Some
argue that these changes will turn agriculture from a labour intensive into a capital and
technology intensive sector (Grant, 2012).
Avoiding food crises, increasing food security and controlling agriculture's impact on the
environment are growing priorities on governmental agendas leading to stricter regulation for
farms. Meanwhile the industry is also facing environmental challenges with the availability of
arable land becoming limited, extreme weather conditions becoming more common due to
climate change and the need for fresh water increasing -agriculture uses 70% of the Earth’s
fresh water supplies - (Beecham Research, 2014). Our fossil fuel and mineral resources are
15. 14
also dwindling and becoming increasingly expensive (“Precision Agriculture Steering Us into the
Future,” 2015). Despite these factors pushing agriculture to innovate, the sector’s low margins
and the high volatility in commodity prices makes innovation hard to attain (Guerrini, 2015).
Based on a study conducted by the Boston Consulting Group farmers expect the following five
trends to impact agriculture’s structure and practices the most in the next 15 years: precision
farming, automation, consolidation (increasing the size of farms), professionalism and labour
shortage. So it is visible that those working in the sector also expect significant changes to
occur in the future.
Information technology in agriculture
“The use of chemical fertilizers, biological innovations, harvesting and threshing machines, and
mechanical technology mainly caused the increase in agricultural productivity per worker three folds
between 1970 and the 2000s. Over the past 15 years however, farmers started using computers and
software systems to organize their financial data and keep track of their transactions with third
parties” (Batte, 2005). And although agriculture started implementing various management systems
from the 1990s the opportunity for technology to modernize agriculture is still tremendous (Tilney et
al., 2015).
Apart from the already mentioned management systems IT has two main branches in agriculture:
precision framing and smart farming. Although the two are different - or as some say smart farming
is precision farming 2.0 - the two expressions are often used as synonyms: “make farms more
“intelligent” and more connected through the so-called “precision agriculture” also known as ‘smart
farming’.” (Guerrini, 2015). Precision farming is the technology and management approach based on
real-time observation and measurement of animals, crops or the field in a farm (EIP-Agri Focus
group, 2015), one of its main aims is to allow farming to happen on a different scale: rather than
managing the whole field of a farm, managing every square meter in the most adequate way for that
area or rather than overseeing the whole herd, taking care of each animal separately but yet
efficiently through data. Smart farming goes one step further by connecting different data sources
and forecasting based on the aggregated information.
The widespread adoption of ICT in agriculture has several barriers that have to be tackled before the
sector can become truly digital. Some of these barriers are the investment risk, the perceived
complexity of IT in agriculture and the difficulties in determining the exact benefits for a particular
16. 15
farm. As farmers are often unfamiliar with the new technologies and have insufficient knowledge
about their advantages, the fear of arising additional costs, complexities and technical problems is
high and a significant impediment to the adoption of precision and smart farming (EIP-Agri Focus
group, 2015). From a technical point of view uneven rural wireless and broadband coverage, no
standards for agricultural sensors, compatibility issues and poor user friendliness of IT tools are
problems that could be resolved for example by in involving farmers in the design of agro-technology
(Beecham Research, 2014). Agricultural companies are also reluctant to implement IT as the
ownership of the data (captured through sensors) is still a question, which is reinforcing fears
towards big suppliers and the government using the collected information.
Information technology in agricultural SMEs
Small and medium companies in the agricultural sector are influenced more by the challenges
impacting the sector than others. Due to their size and the fact that most of them are family
based, staying competitive and having sufficient labour force is even harder for them than for
bigger agro-firms. Because of the perception of high costs and complexity farmers tend to
identify technology in agriculture as a set of tools that only big firms can benefit from despite the
fact that with the lowering prices of IT SMEs can also implement such systems and achieve
significant benefits for their companies. To overcome the barrier of small farms implementing
ICT experts suggest that tools specifically for SME farms should be developed and implemented
in steps with the help of specialized advisers to help reduce complexity and decrease the risks
of a big one of investment (EIP-Agri Focus group, 2015).
Drivers of ICT adoption in SMEs
Research about IT adoption have mostly been focused on the factors that influence
implementation and the process of implementation itself. Although literature has defined the
potential drivers from various different angles, it still remains unclear which are the most
significant or most important motivators leading to new IT projects. The most significant drivers
in agriculture have also been rarely researched. In this thesis I am aiming to clarify which are
the most important external and internal IT project motivators in SMEs and test these drivers in
the agricultural sector.
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Academics over the last decades have identified the “whys” of ICT adoption decisions from
numerous different angles and with focus on different technologies (EDI - Iacovou et al., 1995;
Internet - Lee and Runge, 2001; electronic business - Zhu et al, 2003). Literature uses various
categorizations and descriptions for IT project drivers. In fact the definitions are so diverse that
even after going through previous research it remains unclear which drivers are the most
significant or which are the most important for starting IT projects. With my thesis one of my
aims is to identify the most important motivators. In order to make the findings more
straightforward I classify motivators into two subclasses: externals and internals. I consider
internal motivators those that originate from within the company (as for example from the owner,
employees or internal goals and characteristics of the organization). External motivators are
present when an ICT system adoption was triggered by entities outside the company (like
government, competitors or customers). In the second step of my research I analyze which
drivers are present most strongly in the case of farms and agricultural SMEs.
Table 1 summarizes the ICT adoption drivers in small and medium companies from previous
research. The table also includes the categorization of the drivers into internal and external
drivers based on the description in the paragraph above.
Author Adoption drivers Driver type
Dittrich et al, 2014 knowledge internal
personal internal
social external
contextual external
Windrum and
Berranger, 2003
the perceived importance of e-business by
managers
internal
the expansion of national market share internal
the expansion of global market share internal
improving company image internal
a logistical progression of past investments internal
integration of IT operations internal
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customer pressure on the firm external
competitor pressure in the industry external
pressure from key suppliers external
pressure from allied companies external
Zhu et al., 2003 technology competence internal
firm scope and size internal
consumer readiness external
competitive pressure external
Kuan and Chau, 2001 direct benefit internal
indirect benefit internal
cost internal
technical competence internal
industry pressure external
government pressure external
Lee and Runge, 2001 owner’s perception of the relative advantage internal
social expectations external
owner’s innovativeness internal
Mehrtens et al., 2001 perceived benefit internal
organizational readiness internal
external pressure external
Harrison et al., 1997 attitude internal
subjective norms external
perceived control internal
Iacovou et al., 1995 technological reason internal
organizational reason internal
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external reason external
Rogers, 1983 leader characteristics internal
internal characteristics internal
external characteristics external
technical characteristics internal
Table 1: Summary of literature on drivers of ICT adoption in SMEs
Internal drivers
The internally sourced drivers can originate both from firm or owner perspectives and
characteristics. Within the reviewed literature we can find broader and more specific definitions
and divisions of the internal motivators. Mehrtens et al. (2001) name organizational readiness
as one of the ICT adoption decision antecedents, this subclass stands for both the technical
capabilities and available financial resources of the firm. Others define more specific categories
such as: technical characteristics (Rogers, 1983), technical/technology competence (Kuan and
Chau, 2001; Zhu et al., 2003) or knowledge (Dittrich et al., 2014). Cost (Kuan and Chau, 2001)
and perceived control (meaning the available resources to prevail despite potential challenges)
are also two of the internal factors that lead to information systems adoption.
Although a few papers (Iacovou et al, 1995; Zhu et al., 2003) don’t consider the owner or
manager’s characteristics as a separate driver, most analyses see the perceptions, personality,
technical knowledge and attitude of the SME leader as an independent motivator category. Lee
and Runge describe the characteristics of the owner as the most crucial factor influencing the
decision on whether or not to adopt ICT due to the central role of the owner in small companies.
In their paper they differentiate between the ‘owner’s perception of the relative advantage of
using IT’ and ‘the owner’s innovativeness in managing their own business’ and thus defining two
of their three drivers to be directly connected to the company’s manager.
Although as mentioned earlier SMEs often lack strategic goals when implementing IT two pair of
authors: Kuan and Chan (2001) and Windrum and Berranger (2003) mention several strategic
motivators between the drivers of IT adoption in SMEs. Slightly differently from the rest of the
literature Windrum and Berranger (2003) describe four specific motivators that incorporate the
desired goal in the motivator itself, these are: improving company image, integration of IT into
20. 19
operations, expansion of national or global market share. Together with direct and indirect
benefit defined by Kuan and Chau (2001) these can be considered as strategic internal
motivators.
Although internal motivators are very diverse, based on the literature I categorized them into
four categories:
● Internal technical knowledge: the available technical competence in the organization
● Owner perception on IT: the owner’s attitude towards IT and innovation, his/her
perception of the positive consequences of new ICT implementation
● Direct organizational benefit from IT: short term benefits expected from IT (for example
efficiency gains)
● Indirect organizational benefit from IT: long term improvements expected from IT (for
example company image improvement)
Figure 2: Categories of internal drivers
Other than being diverse internal motivators also leave the question open about which of them
is the most significant. Therefore, in my research I am going to study which of these internal
drivers is the most important for the implementation of new IT projects in agricultural firms. This
will be done through expert interviews.
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External drivers
It is not only individuals who often base their decisions on expectations of others, IT adoption
can also be motivated by social pressure or other external expectations (Lee and Runge, 2001;
Dittrich et al, 2014; Harrison et al, 1997). External motivators can originate from pressure by
customers, suppliers, allied companies, the industry or the government (Mehrtens et al, 2001;
Windrum and Berranger, 2003). Ghobakhloo et al. (2012) suggest that small firms face pressure
to keep up with the competition and that their desire and need to stay competitive affect their
ICT decisions hugely. Due to their smaller client base SMEs are more exposed to the needs
and demands of their customers around IT system integration, digital innovation or online
services (Zhu et al., 2003). The bargaining power of both buyers and suppliers is strong against
small companies, which also contributes to the high competitive pressure weighing on them
(Carr et al., 2003). Resulting from the points mentioned before the desire to stay innovative and
grow, forces many SMEs to adopt new IT solutions and strive for high customer satisfaction.
Apart from the market, government initiatives and policies can also prompt SMEs to opt for
implementing or innovating their IT solutions. Although literature report that the government’s
incentives are not unanimously advantageous (Ghobakhloo et al., 2012) Harindranath et al.’s
research with UK SMEs suggests that often government or supranational (EU) regulations are
the only incentives for the implementation of strategic and complex systems.
Although external motivators are also diverse, they are less extensive than internal drivers,
making their grouping more straightforward. Based on previous research I divided the external
motivators into four subclasses:
● Customer pressure: intention from customers to be connected through information
systems and the threat of them leaving for more IT savvy companies
● Competitive pressure: the level of ICT usage in the industry
● Allied companies and suppliers’ pressure: need to satisfy information system needs and
requirements of partner companies
● Government pressure: compliance need with authority regulations
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Figure 3: Categories of external drivers
In my research I am going to test which of these four external drivers is the most significant for
the start of IT projects in farms.
Differences in drivers
The types of motivation can be divided in several different way, in the following section I am
going to present two categorizations. Based on existing motivational theories Carsud and
Brännback divided motivators into two categories: push factor dominant and pull factor
dominant motivators, and although these two categories were first used to describe the behavior
of individuals they argue that most patterns are also applicable in the entrepreneurial context.
The push factor dominant or drive motivational theory can be observed when the motivation
activates an action to reduce tension or avoid a threat, this type of motivation is very similar to
the defensive approach described by Westerman, Bonnet and McAfee (2014) in their book
‘Leading digital’. They define companies, who take a defensive approach as: “these companies
are under threat and need to focus on their long-term survival” (Westerman, Bonnet and
McAfee, 2014). The drivers described above are both due to a threat or pressure mostly
originating from outside the company, meaning they are external motivators. These external
motivators although don’t come from within the organization need to be dealt with in order for
the organization to survive.
Motivational theories where the pull factor dominates (Carsrud & Brännback, 2011) are the
other side of the coin, in these cases it is rather the goal or desired end point that motivates or
23. 22
in other words “pulls” decision and action. This theory closely corresponds with Westerman,
Bonnet and McAfee’s (2014) description of the offensive approach in companies regarding
digitization. In ‘Leading digital’ the expression offensive approach is used when a company aims
to get ahead of competition by implementing digital strategies without an apparent threat
pressuring them to do so. The drivers described in this paragraph originate internally so they
can be considered internal motivators.
This further categorization of drivers helps to understand the differences of internal and external
drivers better and the reason why they should be evaluated separately.
Conceptual model
The following conceptual model was created through literature review and was used as the
basis for the expert interviews - further described in the next chapter. Through qualitative
research my aim was on one hand to understand which of the four internal drivers (internal
technical knowledge and need for IT implementation; owner perception of IT; direct
organizational benefit; indirect organizational benefit) are the most important for IT adoption in
SMEs. And on the other hand to be able to deduce the same about the four external drivers
(consumer, competitive, government, allied companies and suppliers pressure)
Figure 4: Conceptual model
24. 23
Methodology
Research method
My thesis aims to understand which internal and external drivers are the most important for new
IT projects. I chose to conduct qualitative research for my thesis due to the following reasons:
Motivators of an IT project can be subjective and different for each stakeholder involved that is
why it is important to capture opinions and individual thoughts along with diverse perspectives.
Qualitative research makes it possible to get deeper insight into people’s opinions and also
understand the context in which they were when made a certain decision. Qualitative research
also allows participants to respond more elaborately, explain important details that could be
disregarded in quantitative research. Additionally, qualitative research by allowing for more
interaction between the stakeholders of the study which also gives more flexibility and
spontaneity that could result in unexpected point of views and unforeseen perspectives.
Within the types of qualitative research, for my thesis I used: expert interviews. Expert
interviews are a specific form of semi structured interviews, where interviewees represent a
group (Flick, 2009). The methodology’s weaknesses are the low precision of measurement, little
basis for generalizability and lack of rigor of research. Expert interview can also be challenged if
they turn into “rhetoric interviews” where the interviewee gives a lecture of his or her area rather
than answering the asked questions or if the expert switches between his/her professional and
personal role. (Flick, 2009). On the other hand, it’s strength is how much it can take into
consideration the context of the topic. This method is useful when trying to answer questions
like ‘How’ or ‘Why’. As in my thesis my goal is not to predict future decisions or happenings but
rather to understand and explain why SMEs decide to start new IT projects (which internal and
external drivers are the most important for this decision), interviews are an appropriate method
for the research.
Data collection
Data for this research was collected by conducting 10 semi-structured interviews with 4 SME
farmers, 5 researchers in the area of smart/precision farming and 1 agricultural consultant. All
the 4 farmers are part of innovational groups and are frontrunners in adopting technology in
25. 24
agriculture thus can be considered as experts. Based on Flick: “We can label those people as
experts who are particularly competent as authorities on a certain matter of facts”. The
interviewees were found based on references and recommendations from agricultural
professionals. The farmer participants were selected based on whether their company qualified
or not as small or medium. For this thesis the following people were interviewed:
Name Abbreviation used* Role Country
Andrea Beltrami AB1 Farmer Italy
Anton Bartelen AB2 Farmer Netherlands
Corné Kempenaar CK Researcher Netherlands
Derk Gesink DG Farmer Netherlands
Destiny Bradley DB Researcher United Kingdom
Frank Berkers FB Researcher Netherlands
Herman Krebbers HK Consultant Netherlands
Koen Dittrich KD Researcher Netherlands
Leon Noordam LN Farmer Netherlands
Peter Kooman PK Researcher Netherlands
*In the Findings chapter interviewees will be referred to by their abbreviations
Table 2: List of interviewed experts
The interviews were 30-50 minutes long and took place either personally (3) or through Skype
(7) and except for one were all recorded and transcribed (with the help of the software program:
VoiceBase). One interview was not recorded but notes were taken during the conversation.
During the interviews the interviewees were first shortly introduced to the purpose of the
interview and at that point it was also clarified that all questions throughout the interview (unless
specified otherwise in a question) are about small and medium agricultural firms (emphasizing
that big companies were not part of the research). After that I explained them the format and
planned duration of the interview and finally asked their permission to record the conversation.
Two set of questions were used to conduct the research: one specifically for the researchers
and the consultant (Appendix 1) and another for the farmer participants (Appendix 2). Although
26. 25
there was a pre-prepared set of questions as the interviews were semi-structured each interview
was adapted (by adding more or taking away some questions) to the flow of the discussion.
Data analysis
The transcribed interviews were analyzed in an iterative process with a software tool for
qualitative research, Atlas.ti. As a first step open coding was used to identify patterns in the data
explaining the reasons of IT adoption along with other phenomena with the potential to add
value to the thesis (such as ‘definition of precision farming’ or ‘difference between pioneer
farmers and followers’). During this first step of analysis 68 categories were identified. To
illustrate how codes emerged from the interview transcripts, observe the following example: “I
studied plant breeding in Wageningen it is the agricultural university (code: education of farmer)
so i saw in a quite early stage the possibilities of these systems, and that convinced me that it
might work (code: believe in technology)” - (see other examples in Appendix 3). The next step -
following axial coding principles -was to group the emerged categories into families (Corbin &
Strauss, 1998) and also examine their relationship with each other - such as one code being
cause of another or a certain code being part of a bigger code. In this part of the analysis
process four families or overall categories emerged: internal drivers (33 codes), external drivers
(10 codes), influencers (10 codes) and other relevant concepts (18 codes) - (see also Appendix
4). Later on these codes were further categorized into the concepts described in the next,
‘Findings’ chapter. Following these processes, the data was also analyzed by using the
previously defined drivers from the conceptual model as predefined codes. These two streams
of analysis (one originating from open coding, another based on predefined codes) gave the
basis for the findings of this research.
27. 26
Research results and findings
In the following chapter I will present the findings of my qualitative research conducted through
expert interviews. The findings will be presented in two ways following my data analysis method,
firstly the drivers will be presented according to categories that emerged through open coding
and divided into external and internal drivers. In this part I aim to present the drivers the closest
possible to the wording and context in which they were mentioned throughout the interviews.
Secondly the data will be categorized in comparison to the previously defined conceptual model.
Internal drivers
During the coding process 33 internal drivers emerged that were grouped into seven sub-
categories in the second step of the data analysis. In this subchapter all seven sub-categories
will be presented.
Financial driver
When analysing the conducted interviews three driver categories emerged that although named
in various ways by the research participants all have the underlying reason to reach better
financial results for small and medium agricultural businesses. One of these sub-drivers is
financial gain meaning when an interviewee explicitly describes a financial term as motivator
such as profitability or return on investment - amongst others-. The second of these sub-drivers
is more efficient use of input, whose end goal is similarly, to achieve better financial results
(“Reason two is using less input so this reduces costs” (CK)). The third one is improving
production - for example by increasing yield or animal fertility - which like the previous sub-driver
also aims to earn better monetary results as an end goal (“have the same yield or higher and
then it is a better returns” (CK)). In the following subsections, I will explain each of these sub-
drivers in the closest possible way to how they were mentioned by the interviewed experts.
Financial gain
Achieving financial benefits is one of the first motivators that comes forth as a reason for IT
implementations and can take many different forms. Throughout the interviews concrete
financial reasons were mentioned by six of the ten interviewees taking part in the research, in
five different ways: improve income, profitability, return on investment, reduced costs and cost
efficiency. Financial gains can be also considered as the only reason ("Profitability, that is the
28. 27
only reason” (PK)) or most important reason ("At the end it is of course increasing the farm
income the strongest reason” (CK)) for implementing IT on farms. Getting better financial results
were found to be not only a motivator for information technology investment but also a
precondition to be met by the chosen technology (“they want to try new things but it needs to be
cost effective” (KD)), meaning that farmers expect a proof of positive return on investment
before starting an IT project (“They should earn money on it, they should earn more than it
costs” (PK)). Financial gains are between the most mentioned internal motivators driving
farming companies to implement ICT also due to the fact that improving financial results is a key
to success and survival in any economic sector including agriculture (“After all they are
entrepreneurs who run a business so they want to know that in a certain time horizon they can
get back their investments” (KD)).
More efficient use of input
Through analysing the conducted interviews, I found that interviewees were implying the same
core rationale by mentioning both “using less input” or “efficiency”. From the interviewed ten
experts nine mentioned “using less input” or “efficiency” as internal motivators for ICT
implementation in farms, making this the second most grounded of all drivers. Input was
specified as “water, seed, chemicals, fertilizers” (AB2), “energy” (KD) and other “raw materials”
(AB1). Both from the context in which the sub-driver was mentioned ("most farms and farmers
are mostly driven by efficiency” (KD)) and from the groundedness (9:10) we can convey that
using input more efficiently is strongly present and an important driver of IT usage in agriculture.
Other than the previously mentioned financial reason behind using less input another motive
was also mentioned: environmental regulations (“we have laws for environment, and they
become more and more strict, so you have to you use inputs as efficient as possible because
we are not to allowed to spray more fertilizer” (DG)), this driver will be discussed in more detail
in the external motivators section.
Improvement of production
Throughout the research three main sub-domains were referred to, as elements improving
production, depending on the type of farm the expert was most knowledgeable about: “higher
crop yield” (LN), “improving health of the animals” (AB1), “improving the fertility of the animals”
(DB). Increased production was described as a method to “improve results” (HK) and was
referred to by one interviewee as the leading reason for IT implementation (“it’s purely a
production thing” (DB)).
29. 28
All in all although both more efficient use of input and improvement of production were
mentioned as individual reasons and often as main drivers of IT implementation it can be
deduced from the data that their underlying reason is the achievement of better financial results.
And that is why improvement of production, more efficient use of input and financial gain is
categorized together into ‘financial motive’.
Work simplification
When asked about the motive to implement information technology the interviewees mentioned
“making the work easier and simpler” with similar frequency as the different financial motives. All
the interviewed farmers gave reasons connected to this driver when asked about what led them
to implement technology:
● "...minimizing the work of the employees, the effort of the employees” (AB1)
● “Does it make my work easier or less exhausting when I have invested in new
technologies?” (AB2)
● “The first goal was to make the work easier and simpler” (DG)
● “First with the technology to drive straight everyone saw the opportunity to work less
time with less tiredness” (LN)
Although only two of the non-farmer interviewees mentioned this driver based on the farmers
who implement IT systems, technology making their work simpler is a significant (“The first goal
was to make the work easier and simpler” (DG)) driver to start IT projects.
Emotional driver
While analyzing the conducted interviews another pattern was also recognized: emotional
reasons. As - results of IT systems but mostly - simplification of work is very effortful to quantify
and measure most often it is not the economic incentive that drives these type of technology
implementation projects. Both farmers ("because we hate to have curves in our fields” (LN)) and
agricultural researchers (“the farmers they like it because it's easy to use” (HK)) suggested that
emotion plays an important role in deciding on starting certain IT projects. Interviewed
researchers even questioned the financial utility of some of these investments, arguing that the
main reason for them is purely based on emotions:
30. 29
● “Maybe not, because it was so economic, this equipment was like 40 000 euros but
simply how things were done on the field were in straight lines, they like that” (CK)
● “...it's not done because it's giving you much benefits, you can doubt about if the cost is
lower than the extra income from this kind of technology but the farmers they like it”
(HK)
In conclusion - based on the conducted interviews and their analysis - we can imply that
emotions are important motives for new IT systems.
Innovativeness and technology interest
From the people who participated in the research for this thesis everyone mentioned either
innovativeness, future orientation or being tech savvy as drivers of IT implementation, making
this driver similarly grounded as the financial motives. As the examined SMEs all had less than
20 employees these characteristics mostly needed to be present in the owner in order to start
introducing new technology. Being interested in technology ("these farmers, they are interested
in technology” (HK), “you have to be very interested in technology” (LN)) was named not only as
a driver but also as a condition that farmers who want to successfully implement technology
need to fulfill. Another motivator that was brought up during the interviews was future orientation
(‘they think it can be beneficial for the future if they learn to use these data and use this
technology” (HK)) and believe in technology (“Personally I think technology will be the future for
farming” (AB1)), both of which even motivated farmers to implement new solutions when the
economic groundedness was questionable - similarly to the emotional driver -: “Even some
applications are not economically viable people still use it because they think it will be the
future” (PK). Seven interviewees named the owner’s innovativeness as a motive for IT
implementation:
● “farmers who decided to invest in precision agriculture are more open for innovations”
(AB2)
● “interested in innovation” (CK)
● “use all these new tools and tricks and if it doesn't work they don't really mind, they tried
it.” (DB)
● “we have a number of farms that are really experimental with these technologies” (FB)
● “I want to to experience this new technology on my own farm” (HK)
● “many of them really like to explore a new way, a bit of out of the box thinking” (KD)
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● “they always want to use the new thing, their motivation is if it is new I will try” (PK)
Several interviewees mentioned that tech savviness and innovation are mostly characteristics of
the more educated and younger farmers. Based on the section above it is visible that curiosity
and the willingness to try out new methods is a considerable driver of IT implementation.
Generational driver
As farming companies are predominantly family businesses one driver of new ICT systems are
the transitions between generations (“In my company recently me and my brother started to
work (...) we made the choice to implement information technology” (AB1)). Half of the research
participants stated in some way that a new generation of the family taking over the farm is a
significant source of innovation (“the new generation also brings new ideas new technologies”
(KD), “kids of farmers mostly, they are the ones who start new technologies on the farms at
home” (PK)). This is also due to the fact that farming “is a family business you don't have many
new employees coming in so only new generations can bring in new technology” (PK).
Size growth and decreasing workforce
Although farms becoming bigger in terms of area is also a macro trend it affects agricultural
companies from the inside as they need to adapt to the new organizational size. Bigger farms
don’t necessarily mean more workforce which results in the need for new solutions to manage
the farm’s changing size, for example through IT implementation: “the area per farm increases,
with a bigger farm of 3-4 hundred hectares it is harder to know all parts of your farm so then we
need to go to data-driven farming” (PK).
The amount of employees working on a farm mostly did not grow apace with the growth of the
farms’ lands (“growing farms and less people” (LN)), resulting in the need to do more labour per
capita (“with one person we have to do more and more” (LN)). This phenomenon pushes
agricultural businesses to digitize more and use more technology (“they need big machines to
work with only a few people on a farm” (CK)).
New business opportunities
IT as a way of creating new business opportunities was mentioned by two interviewees with one
of them stating it as one of the main reasons for technology implementation (“fourth objective is
that it will give new business opportunities” (CK)).
32. 31
External drivers
In the process of open coding 10 external drivers emerged which were merged into 3 sub-
categories.
Government
Governmental initiatives as drivers have been mentioned by six of the interviewees in two
forms: environmental regulations and financial grants. The two are fundamentally different as
financial grants are opportunities that companies can chose to utilize to grow or innovate their
business while environmental regulations “are basically a license to produce and so they have
to do” (CK), meaning that SMEs have no choice but to comply with the rules. Despite their
differences both can trigger information technology innovation in farms.
With environmental regulations shifting towards a higher level of rigor - especially in Europe -
(“laws for environment, (...) become more and more strict” (DK)) farmers are forced to apply
reduced amounts of agro chemicals on their fields (“with the governmental rules we are not
allowed to use that much fertiliser“ (LN)). This change can pressure farmers to start using
precision farming and implement technology on their farms to make the use of chemicals more
efficient and traceable (“environmental regulations that oblige farmers to apply less agro
chemicals or not treat some parts of the field which makes that they have to have some
technology to deal with it that.” (CK)).
Governmental or intergovernmental (EU) grants (“there has been awful lot of government
grants” (DB)) and subsidies (“lot of possibility to get subsidy” (HK)) can also incentives
businesses in the agricultural sector to adopt new technologies (“Europe usually gives some
money incentives for the implementation of technology so maybe in the in one or two years we
are choosing another technology.” (AB1)). All in all governmental initiatives can trigger ICT
implementation both by pressuring farms and by providing opportunities.
Other farmers
The interview data reveals that whether or not farmers implement technology is highly
influenced by their peers (“they talk to each other and learn from each other and that is how new
techniques are spreading” (PK)). Other farmers can influence both whether a farm implements
technology and also exactly which technology it will start using (“in two or three years almost
every farmer here in the area bought the same system” (DG)). Some interview participants
mentioned that the wish of ‘keeping up with the Joneses’ can play a significant role in IT projects
33. 32
implementation (“more "your neighbor is also watching at your field" thing then economic” (CK),
“the neighbor has bought one, so we should also invest in such a machine” (AB2)). Farmers
also influence each other through innovation groups (“I am part of an innovational group of
farmers. With those people we are always trying to find new technology to improve the farming.
We stimulate each other to look for new opportunities” (LN)), although this influencer is more
typical for pioneer farms.
Customer demand
Farms principally interact with processing companies who buy their products but with the clean
and healthy eating movement on the rise the end customers are also becoming a factor that
agricultural firms need to account for. In areas “where biological food is a trend (...) there you
also need to show your customers that you have a high quality biological product and you need
to show the content of your product” (KD) which is a driver for the implementation of IT systems
that can facilitate farms monitoring the content of their products. Due to the fact that agricultural
SMEs have low bargaining power towards the processing companies (“over your producer you
you don't have much power because if they don't buy from you then they will buy from your
neighbor” (KD)) the demands voiced by producers need to be fulfilled by farms (“if they say well
we want to know beforehand what kind product you have so you have to come with a report
than you need to be able to measure it yourself” (KD)). These demands are one of the reasons
that can drive farming companies to introduce new IT systems.
Other influencers
In the course of the interview data analysis I found elements that were not specific drivers but
were still influencing the decision on whether to adopt information system in an a small and
medium farm. The difference between drivers and influencers is that influencers don’t directly
impact the decision making about whether or not to implement technology but still influences by
providing information or opening up new perspectives. Influencers also have a more general
impact on farmers not necessary connected to IT.
Education of farmers
“The education level of farmers have gone up quite substantially based on studies when
compared to 10-15 years ago. So well education is an important factor as they learn about new
technologies at universities” (KD). Out of the ten experts I interviewed four mentioned that the
34. 33
increase in education level of farmers has a significant impact on IT implementation in the
agricultural sector as young farmers can see the effect of modern technology first hand (“I
studied plant breeding in Wageningen it is the agricultural university so I saw in a quite early
stage the possibilities of these systems, and that convinced me that it might work.” (DG)).
Available financial resources
The money availability to invest in IT came up more as a ‘precondition’ of technology
implementation (“a good financial year and then there's much more opportunities invest” (CK)).
According to the interviews some farmers had recently good incomes (“some of them had a
rather good income because of the good prices of product” (HK)) which made them more willing
to invest (“I am in discussion group with some farmers and they have money so that not the
issue” (KD))
Price of technology
Based on the interview the lowering price of technology (“robots (...) those sort of things are
becoming increasingly cheaper” (DB), “the technology also became cheaper that is also
important” (PK)) also influences IT investments. With the costs of smart and precision farming
decreasing farmers tend to chose modern technology more compared to the more traditional
way (“they want to a new tractor or a new machine and when you want to buy it then the extra
cost of this precision farming it's not show much anymore compared” (HK))
Consultants & academic research
Consultancies and academic research projects allow farm owners to examine and understand
better new technologies before implementing them (“We do experiments at the university, the
farmers come and see. And there are some organizations like Delphy, consultancy service they
do experiments and farmers can come to look at those experiments too.” (PK)). Advisory
services also give suggestions regarding grants to farmers and with that helping new
investments come to live (“Advisory companies who are informing farmers about possibilities for
subsidies and ‘good, modern investments’.” (AB2))
Press & events
Big agricultural exhibitions (“In September there's a big event called agricultural show (...) there
we demo different technologies” (PK)), smaller farmer meetings (“in the winter you have all
these meetings of farmers, when they talk to each other and learn from each other” (PK)) and
35. 34
the agricultural press were all mentioned as sources of information for farmers about smart
farming. They also play a role in introducing and spreading the word about the new technologies
(“The agricultural press knows of those new techniques they want to know and show does it
work does not work” (PK))
Suppliers
Suppliers were portrayed as a factor that pushes and tries to convince farmer to adopt new IT
systems (“it is pushed by the companies who have the technology” (PK)) regardless of the need
for the technology.
Comparison of results and conceptual model
Based on the data collected through qualitative research, the following drivers emerged:
Driver from
research
Connected category
from conceptual
model
Groundedness*
Nr. of
mentions
Internal
drivers
Financial driver
Direct organizational
benefit
10 40
Innovativeness and
technology interest
Owner’s perception of IT 10 31
Work simplification
Direct organizational
benefit
6 24
Size growth and
decreasing workforce
Direct organizational
benefit
6 13
Generational driver - 5 10
Emotional driver Owner’s perception of IT 3 6
New business
opportunities
Indirect organizational
benefit
2 2
External
drivers
Government Government pressure 6 15
Other farmers Competitive pressure 6 13
Customers Customer pressure 4 16
(*number of interviewees mentioning the driver (out of 10))
Table 3: Summary of internal and external drivers based on qualitative research
36. 35
The assignment of the conceptual model categories to the emerged drivers was done by
comparing the conceptual model category’s definitions to the description of the emerged drivers.
In the following section I will present each category of the previously defined conceptual model
and evaluate their significance based on the conducted research. I consider a driver to be
significant if it’ groundedness is at least 3 (at least 3 research participants talked about it) and if
it was mentioned a minimum of 5 times.
Internal technical knowledge
None of the interviewees mentioned technical knowledge as a needed criteria or driver of IT
implementation. The education of the owner, which resembles some similarity with the above
mentioned driver appeared only as an influencer. Although by education of the owner, research
participants mentioned a more general higher education knowledge. This way internal technical
knowledge is not significant and thus not carried as a driver for SMEs in the agricultural sector.
Owner’s perception of IT
Innovativeness and technology interest became the second strongest internal driver. This driver
is slightly broader than owner’s perception of IT which only conveys the farmer’s attitude
towards information technology. Innovativeness emerged as a very important driver which
needs to be captured to have an applicable conceptual model describing the drivers of IT
adoption in agricultural SMEs. The existence of an emotional driver also emerged from the
interviews which is strongly connected to the attitude of the owner towards technology.
Direct organizational benefit
Although none of the interviewees mentioned this exact wording but both the financial driver,
work simplification and finding new solution to manage growing land area with decreasing
workforce aim at achieving direct organizational benefits. As all of the mentioned three emerged
drivers are significant, direct organizational benefit can be considered a significant driver.
Indirect organizational benefit
From the emerged drivers only ‘new business opportunities’ -which is not significant - aims at
reaching indirect organizational benefit. So based on my research no evidence was found that
achieving indirect organizational benefit strongly influences farmers’ decision to implement IT.
37. 36
Customer pressure
Despite the fact that farms have little interaction with their end customers, the processing
companies buying the farm products have a considerable influence on their IT systems, mostly
due to the low bargaining power of SMEs.
Competitive pressure
Competitive pressure was earlier described as: “the level of ICT usage in the industry”.
Agricultural SMEs have a specific form of competitive pressure as farms strongly influence each
other’s willingness to implement ICT and they also impact the type of technology their
“neighbors” are going to use. According to the interviewees “other farmers” were the second
most important external driver.
Allied companies and suppliers pressure
Based on the interviewed experts technology suppliers rarely drive technology implementation
although they attempt to put pressure on farmers to adopt their IT solutions. The supplier’s
impact was defined as an influencer rather than a driver as according to the interviewees they
don’t directly drive adoption. Allied companies were never mentioned throughout the research.
Government pressure
Governmental initiatives were the most grounded and mentioned external driver. The only
difference between the conceptual model description and the research results is that our
interviewees mentioned not only pressure (environmental regulations) but also opportunities
(grants) that governments provide.
Additional driver
Based on the qualitative research a new internal driver emerged: the generational driver. The
generational driver comes from the fact that most farms are family businesses and that new
technology or ideas are often brought in by the transition to a younger generation.
Discussion
Based on the findings of the research and the section above, I suggest the following adapted
conceptual model to describe the most significant internal and external drivers of IT adoption in
agricultural SMEs.
38. 37
Figure 5: Adapted conceptual model
Internal drivers
● Owner’s perception of IT and innovation: the owner’s emotions, attitude and interest
towards IT and innovation, his/her perception of the positive consequences of new ICT
implementation.
● Direct organizational benefit from IT: benefits that are expected to get realized shortly
(within 1, 5 years) after implementation (specifically: financial benefits, efficiency gains,
production improvement and work becoming simpler) of a new IT solution.
● Generational driver: new IT systems becoming implemented due to the generational shift
in the leadership of the farm.
External drivers
● Customer pressure: the pressure of agricultural processing companies to implement IT
systems and the threat of them leaving for more IT savvy companies.
● Competitive pressure: the level of ICT usage and type of technology used in the network
of the company.
● Government initiatives: compliance need with authority regulations and financial aid
opportunities provided by the government.
The description of the drivers have been adapted based on the interview-data analysis, also
three previous drivers were taken out of the model (internal technical knowledge, indirect
39. 38
organizational benefit, allied companies and supplier pressure) as they were found not to be
significant in motivating IT implementation in agriculture. The new conceptual model only
contains the three most important drivers both on the external and internal side. The
descriptions were adjusted to more precisely depict the drivers in the agricultural sector (rather
than SMEs in general).
40. 39
Conclusions
Summary of outcomes
Small and medium companies are distinct from large corporations in several different ways.
Their bargaining power is lower, their workforce is small and often unqualified, they lack
financial resources and the influence of the owner is much higher than in the case of big
companies. Due to these reasons small and medium companies face tremendous challenges
when implementing IT so despite the technological advancements and digital boom of the last
decades their level of IT adoption is still low. In order to understand this issue better the first aim
of this study was to define which drivers prompt small companies to implement information
systems. By better understanding the decision making of SME owners, we can tackle better the
challenges they face when faced with information technology.
Based on previous academic advancements throughout my research I found that the IT drivers
of small and medium companies can be divided into two categories: internal and external
drivers. Internally on one hand the IT adoption of SMEs is highly influenced by the owner’s
perception of information technology, which is due to the small size of the workforce in such
companies and also the fact that often the company owner is the one personally driving the
firm’s strategy without it being formalized. On the other hand the aim of direct and indirect
impact also drives small companies to start new IT projects, the types of direct and indirect
impact can be diverse ranging from improvements in the company’s image to financial benefits.
Lastly the internal IT knowledge also influences technology adoption. Externally SMEs face
pressure from many different sides: the government, clients, allied companies and suppliers.
Their exposure to pressure is greater than in case of the more sizable counter partners due to
more limited resources, a weaker network and smaller financial reserves. These internal and
external drivers were summarized in the conceptual model of my research.
Other than defining the drivers of IT adoption, the second aim of this study was also to test
whether these drivers are applicable for SMEs in the agricultural industry. Small companies in
the agricultural sector other than having the general characteristics of SMEs are distinct mostly
in two ways: on one hand the percentage of family businesses is even higher than in other
industries and on the other hand farmers have a greater impact on each other than in other
41. 40
sectors. These traits are all visible in the drivers that lead small agricultural companies to
implement technology.
The first research question that this thesis set to address was: Which internal drivers are the
most significant for new IT projects in agricultural SMEs in Europe? And secondly: Which
external drivers are the most significant for new IT projects in agricultural SMEs in Europe?
These two questions were answered through the findings of a qualitative research ran by
interviewing ten farming experts in Europe.
During my research I found that in fact the drivers of IT adoption in case of farms is different
than the drivers of SMEs in other sectors. The findings of this study showed that the most
important internal drivers for farms to implement IT are the direct and short term benefits they
anticipate to gain through IT and the perception and emotions that farmers have towards IT
along with their innovativeness. Thirdly I also found that it is typical for farms to switch to new
technologies when a new generation starts working on the field.
The short term benefits that farmers aim to gain through IT implementation prompting them to
start such projects can be diverse ranging from financial benefits to improving the routine work
on the farm or adapting to the changing size of farms and decreasing available workforce.
These reasons originate both from macro factors impacting the industry, both from the fact that
farmers are entrepreneurs aiming to have higher results from their business. The owner’s
perception of IT and innovativeness is a slightly different motivator than in case of other SMEs
as it also contains the ‘innovativeness’ of the owner. This driver doesn’t necessarily mean a well
formed innovation strategy in the farm but rather the curiosity of the farmers and his believe in
the technology leading to experiment with new technology. Lastly internally the farms are also
influenced by new generations who take on the management of the farm. This driver is very
typical in the sector as to date most farms are still family businesses.
When testing external drivers, I found that although agro SMEs do face pressure for IT
implementation from processing companies and the government, they can also use
governmental initiatives (such as grants) as opportunities and the pressure from IT suppliers
and allied companies is not present in their case. The demands of the processing companies
(as clients of farms) can strongly prompt farmers to implement technology as elseway these
customers can easily turn to other small farms for products. As already mentioned before the
42. 41
government impacts agricultural companies in two main ways: on one hand they can pressure
through environmental regulations prompting farms to make more use of sensors in order to
apply less chemicals, while on the other hand they also provide subsidies and grants to
positively incentives IT adoption. And lastly competitive pressure has a very specific form in
case of small agricultural firms as based on the interviewed experts farmers have a significant
impact on whether or not their peers implement technology and also on what type of technology
they chose.
Interestingly both when talking about external both when talking about internal drivers the
emotional reasons to start IT projects were mentioned as often and strongly as the business
reasons (such as financial benefits). Although I found three significant drivers both internally and
externally based on the interviews internal drivers appeared to be stronger.
All in all according to the conducted qualitative research the most significant drivers for new IT
projects in agricultural SMEs in Europe are: direct organizational benefit from IT, the owner’s
perception of IT and innovation and generational switch. While the most significant external
drivers are: governmental initiatives, customer pressure and competitive pressure. While
indirect organizational benefit from IT, technical knowledge in the organization and supplier
pressure were derived as non-significant drivers for agricultural SMEs.
Implications
The findings of this research contribute both to the literature of the drivers or small and medium
companies and the IT adoption in the agricultural sector. This research is pioneer as it uses the
context of the agricultural sector for testing the validity of information technology implementation
drivers. This study adds to new conceptual model to the information management literature.
Firstly it provides a new conceptual model that summarizes the external and internal drivers that
prompt SMEs - in general - to implement modern technology and secondly it presents another
conceptual model adapted to the specificities of the agricultural sector. Through these
contributions this thesis helps the understanding of IT decisions in small and medium firms.
Growing the efficiency of agriculture is essential due to the growing population of planet Earth,
IT provides an opportunity for that. Although in Europe the digitization of farms is growing and
becoming more common other, developing parts of the World are behind in terms of agricultural
technology. As this research was conducted in Europe it’s results can be used as a basis to
43. 42
understand how to motivate farmers to start implementing modern technology in Asia, Africa or
Latin-America. By understanding the drivers of digitalization in agriculture, consultancy services
and cooperatives will be able to help farming firms to introduce ICT in a faster way.
Governments can also use the outcomes of this research to adapt their policies in a way that it
enhances digitalization in agriculture in the most valuable way possible.
Limitations and recommendations for future research
This study has a number of limitations. First of all interviewees who took part in the research
were either farmers, who successfully implemented IT systems or people who are also primarily
in touch with the successful agricultural technology projects. And in this way unsuccessful cases
were not part of the research. Further studies might also want to include businesses who were
unsuccessful in IT implementation. Secondly most farmer participants were front runners in the
industry in terms of technology implementation. Although in case of the consultant and
researcher participants several questions were asked about the motivation of those who are
rather followers than industry leaders, in the future it could be insightful to run a study that
involves pioneers, followers and laggards according to the percentage they represent in
agriculture. Thirdly I need to mention the fact that most business owners are eager to discuss
their successes and show their company in the most positive way possible which could have
lead to biased answers. The fourth limitation is due to the number of interviewees that were run,
increasing the number of cases in the future will help to expand the generalization scope of the
findings. And finally, the language of the interviewees: out of the 10 interviewees only one was a
native English speaker for all other research participants English was not their first language,
which could have limited the depth of their answers especially as discussing motivators can be
fairly personal.
Given the importance of the agricultural sector more research on its particularities when
adopting IT would be relevant. Further details are needed on each internal and external driver
mentioned in this thesis to understand their role in decision making and how they impact each
other. Another interesting approach could be to investigate frontrunner farms and the followers
separately to better understand the difference in their motivation to start ICT projects
44. 43
Appendixes
Appendix 1 - Interview protocol (researchers & consultant)
Introduction
1. Purpose of the interview
2. Terms of confidentiality
3. Format & duration of the interview
Questions about the motivation and decision to implementing technology on small and
medium farms
1. What are the internal factors that influence the decision to implement IT solutions in
small and medium farms?
2. What are the external factors that influence the decision to implement IT solutions small
and medium farms?
3. Which are the most influencing ones?
4. In your experience what are the main reason for small and medium farms to implement
technology?
5. What are the factors that small farmers take into consideration before deciding to
implement new technology?
6. Which are the reasons/factors mentioned by those small farmers who decide not to
implement modern technology?
7. What are usually small farmer’s biggest questions/fears regarding the implementation of
the technology?
8. What differentiates the farms who decide to implement smart farming from the ones that
decide not to implement it?
Questions about the implementation process of technology on small and medium farms
1. What type of goals do the IT projects have in case of small farms?
2. How much are the results the farmers are aiming for achieved?
a. Are there usually numerical goals defined? Are these measured? How?
3. What changes in the everyday operations of the small farms due to the system?
45. 44
4. In your experience are there usually any unexpected positive impact(s) of the new
system?
5. In your experience are there usually any unintended negative impact(s) of the new
system?
6. What do small farmers like the most about the new system (after implemented)?
7. What do small farmers like the least about the new IT (after implemented)?
8. What is the opinion of other employees of the small farm about the new system?
9. In your experience what stops a small farm from implementing a new IT
system/technology successfully?
10. What differentiates the small farms where implementation is successful from the one
where it is not?
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Appendix 2 - Interview protocol (farmers)
Introduction
1. Purpose of the interview
2. Terms of confidentiality
3. Format & duration of the interview
Questions about the motivation and decision to implementing technology on the farm
1. What type of precision farming solutions have you implemented on the farm? When?
2. Why have the you decided to use this new technology?
3. What internal factors influenced the decision to implement this IT solution?
4. What external factors influenced the decision to implement this IT solution?
5. How did you feel about implementing this new technology?
6. What made you want to implement the new technology the most?
7. What were your biggest questions/fears regarding the technology?
Questions about the implementation process of IT
1. What was the goal of the IT project? What were the results that you were aiming to
achieve with the project?
a. How much were these achieved?
b. Did you have numerical goals? Did you measure them?
2. What changed in your everyday operations due to the system?
3. What was/were the unexpected positive impact(s) of the system?
4. What was/were the unintentional negative impact(s) of the system?
5. What do you like the most about the new system?
6. What do you like the least about the new IT?
7. What do others in the company like the most about the new system?
8. What do they like the least about the new IT?
9. Overall how would you evaluate the new system? Why?
47. 46
Appendix 3 - Coding scheme (examples)
Code Quotes Interviewee source
Believe in technology
(internal driver)
Farming is about ‘gut feeling’,
so as an agricultural
entrepreneur you should
believe in your investments
and invest in them.
AB2
Because i was convinced of
the advantages
DG
These farmers They are
interested in technology they
think and they get convinced
for themselves
HK
I studied plant breeding in
Wageningen it is the
agricultural university so i
saw in a quite early stage the
possibilities. Of these
systems. And that convinced
me that it might work.
DG
Consultants (influencer) Advisory companies who are
informing farmers about
possibilities for subsidies and
‘good, modern investments’.
AB2
cooperatives, also
accountancies have advisory
roles and these will very often
interact with the farmer to
their calculations or have
discussions
FB
And there are some
organizations like Delphy,
consultancy service they do
experiments and farmers
PK
48. 47
come to look at those
experiments
Environmental regulations
(external driver)
laws for environment and
they become more and more
strict
DG
environmental regulations
that oblige farmers to apply
less agro chemicals or not
treat some parts of the field
which makes that they have
to have some technology to
deal with it that.
CK
with the governmental rules
we are not allowed to use
that much fertiliser
LN
Environmental regulations
are a license to produce and
so they have to do
CK
Higher yield (internal driver) increase our crop yield LN
they calculated it brings at
least 5% more yield than all
the farmers are coming and
implementing it
PK
reduces costs so that you will
have the same yield or higher
and better returns
CK
better results so you will have
higher yield
HK
49. 48
Appendix 4 - List of code groups and their members
Code group (family) Codes
External drivers financial benefits/grants/subsidies
environmental regulations
other farmers
government data request
soil price
staying competitive/in business (survival)
customers information needs
demand of processing companies
government
customer
Internal drivers believe in future of technology
improve income
size of the farms
high value crops
younger generation
difficulty with current way
improve, make easier routine work
innovativeness of owner
having access to latest technology
efficiency/less input
reduced costs
higher yield
new business opportunity
emotional
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previously implemented technology
believe in the technology
improve production
improvement in fertility of animals
keen in using technology
cost efficiency
return on investment
monitor input and output
growth
mindset, vision
ambition
profitability
more work with less people
interested in technology
improve health of animals
having a picture of the current state of the
company
owner's perception of IT & it's impact
financial gain
improve current way of working
Influencers supplier companies/tech providers
academia
events
agricultural press
consultants
price & availability of technology
education on of the farmer
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innovation groups
available financial resources
newsletters
Other concepts pioneers
majority of farmers
investment needed
effort needed
scepticism
education needed
followers
insufficient resources
age
additional technology needed
tradition
smart farming definition
precision farming
difference between precision and smart
farming
sufficient results
not seeing benefit
I think the margins on a farm are too low for
experimenting
difference between pioneers and laggards
