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Farmers’ use of mobile phone applications in Abia state, Nigeria : a thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce (Agricultural) at Lincoln University


Academic year: 2022

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Farmers’ use of mobile phone applications in Abia state, Nigeria

A thesis

submitted in partial fulfilment of the requirements for the Degree of

Master of Commerce (Agricultural)


Lincoln University by

Victor Chimaobi Okoroji

Lincoln University




Abstract of a thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce (Agricultural).

Farmers’ use of mobile phone applications in Abia state, Nigeria


Victor Chimaobi Okoroji

In developing countries such as Nigeria, agriculture is the main source of livelihood where over 70 percent of the population engage in farming. They are mostly smallholders who are often subsistence farmers with minimal use of technology and low productivity. The use of mobile applications in agriculture can help smallholders access agricultural information and financial services, improve access to markets and enhance visibility for supply chain efficiency. Unfortunately, most farmers have not fully exploited these benefits because of lack of uptake in the use of mobile application technology.

This study seeks to explore and examine the current level of use of mobile applications for agriculture in Abia State, Nigeria and the factors that affect the uptake of this technology.

A conceptual model which builds on the extended Technology Adoption Model (TAM2) was empirically estimated using Structural Equation Modelling (SEM) to examine the factors that influence the adoption of mobile applications. Primary data were collected from a sample of approximately 260 farmers. Data were analysed using descriptive statistics and SEM with the help of IBM SPSS and IBM AMOS software.

The study results revealed the current state of mobile application use and the factors that affect the adoption of these applications by farmers. The structural model showed that seven of the direct hypothesised relationships in the research model were supported. Social influence (SI), Perceived usefulness (PU), Information/awareness (IA) and Intention to use (ITU) affected the adoption of mobile


iii applications positively, while perceived risk (PR) and Perceived cost had a negative impact on their adoption.

This study contributed extensively to farmers’ technology usage literature through its findings. It proved that extended TAM is a suitable model to explain the factors that influence mobile application adoption behaviour. It helped in bridging the information gap between agricultural application developers and farmers by revealing some important demographic information of farmers such as their age, gender, educational level, the type of farming carried out and most importantly, the factors that affected the adoption and continuing use of mobile applications by farmers.

Keywords: Mobile applications, smartphone, smallholders, ICT adoption, Structural Equation Modelling, Extended Technology Adoption Model, TAM2, SEM




I am most grateful to God Almighty who made my dream of achieving a Master’s degree a possibility.

Without Him, I would not have made it this far in life.

My heartfelt gratitude goes to my wonderful supervisors, Dr Nic Lees and Dr Sharon Lucock for their immense support throughout the process of my Master’s degree journey. Their timely support and guidance led me through this process successfully. I would also like to thank Asso. Prof. Michael Lyne for his support and counselling.

I am also grateful to Dr Jeff Heyl and Dr Carl Rich who gave me the academic references that helped me secure a Master’s study admission at Lincoln University. I would like to thank Dr Ani Kartikasari for her spiritual support and guidance and Joshua Abboa for being an excellent mentor, brother and friend who strove to see me succeed. To my wonderful family back home in Nigeria, thank you for the love, support and prayers that you have given me. To all my friends in New Zealand and abroad, you all have contributed to making this journey a success. I pray that God Almighty will grant you all your heart desires.

Finally, I would like to thank MFAT New Zealand for their financial support throughout the period of my study at Lincoln University. I also appreciate not only the scholarship coordinators at Lincoln University for their support and guidance but also Lincoln University for being a great citadel of learning with a highly qualified academic team and a good learning environment with up to date facilities.



Table of Contents

Abstract ... ii

Acknowledgements ... iv

List of Tables ... viii

List of Figures ... ix

List of Abbreviations ... x


Introduction ... 12

1.1 Background ... 12

1.2 Smartphone and mobile applications ... 14

1.3 Use of Mobile Applications for Agriculture in Developing Countries ... 17

1.4 Adoption of Mobile Applications by Farmers in Nigeria ... 20

1.5 Research Problem ... 21

1.6 Research Objectives ... 24


Literature Review ... 26

2.1 Theories of Technology Adoption ... 26

2.1.1 Diffusion of Innovation Theory (DIT) ... 26

2.1.2 Theory of Reasoned Action (TRA) ... 27

2.1.3 Theory of Planned Behaviour (TPB) ... 28

2.1.4 Technology Acceptance Model (TAM) ... 29

2.1.5 Extended Technology Acceptance Model (TAM2) ... 30

2.1.6 Unified Theory of Acceptance and Use of Technology (UTAUT) ... 31

2.2 Proposed Extended Technology Adoption Model TAM2 for the Adoption of Mobile Applications by Farmers ... 35

2.2.1 Theoretical Model ... 36


Methodology ... 43

3.1 Introduction ... 43

3.2 Research Model and Hypotheses ... 43

3.2.1 Data Analysis ... 44

3.3 Data Collection ... 45

3.3.1 Study area ... 46

3.3.2 Development of the Survey Instrument ... 47

3.3.3 Sample and Procedure ... 48


Data Analysis - Descriptive Statistics ... 50



4.1 Introduction ... 50

4.2 Demographic Characteristics ... 52

4.2.1 Gender and Age ... 52

4.2.2 Marital Status and Family Size ... 53

4.2.3 Educational Level ... 53

4.3 Farm Enterprise ... 54

4.3.1 Farm Size, Farm Type and Years of Experience ... 54

4.3.2 Extension Meeting Attendance and Level of Produce Consumed ... 55

4.4 Mobile Technology Preferences ... 56

4.4.1 Smartphone Ownership, Operating System and Mobile App Use for Farm Activities ... 56

4.4.2 Mobile Applications Used by Farmers. ... 57

4.4.3 Mobile Application Level of Usage ... 58

4.5. Relationships between Demographic Variables, Smartphone Ownership and Usage for Farming Activities. ... 59

4.5.1 Gender ... 59

4.5.2. Age ... 60

4.5.3 Educational Level ... 61

4.6 Farmers’ Interest in and Willingness to Use Mobile Applications ... 62

4.6.1 Perceived Usefulness (PU) ... 62

4.6.2 Intention to Use (ITU) ... 63

4.6.3 Social Influence (SI) ... 64

4.7 Summary ... 65


Data Analysis: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) ... 67

5.1 Introduction ... 67

5.2 Data Screening and Normality test ... 67

5.3 Exploratory Factor Analysis (EFA) ... 69

5.4 Confirmatory Factor Analysis (CFA) ... 76

5.4.1 Testing of Measurement Model Validity ... 78

5.4.2 Construct Validity and Construct Reliability... 79

5.4.3 Convergent Validity ... 80

5.4.4 Discriminant Validity ... 80

5.4.5 Model Fit for the Measurement Model ... 81

5.5 Conclusion: Exploratory Factor Analysis and Confirmatory Factor Analysis ... 83

5.6 Structural Equation Modelling (SEM) ... 84

5.6.1 Hypotheses Results Testing ... 85



5.7 Conclusion: Structural Equation Modelling ... 88


6.1 Discussion and Conclusions ... 89

6.2 Theoretical Implications ... 93

6.3 Practical Implications ... 95

6.4 Conclusion... 97

6.5 Limitations of the Study ... 98

6.6 Future Study Directions ... 99

References: ... 100

Appendices ... 111

Appendix B: QUESTIONNAIRE ... 113



List of Tables

Table 2.1 Theories used in ICT adoption studies and their findings ... 32

Table 3.1 Hypotheses formulation ... 44

Table 3.2 Respondents from three agricultural zones in Abia ... 49

Table 4.1 Descriptive statistics ... 51

Table 4.2 Gender and age of farmers in Abia State ... 52

Table 4.3 Marital status and family size ... 53

Table 4.4 Educational levels of farmers ... 53

Table 4.5 Farm size, farm type and years of experience ... 54

Table 4.6 Extension meeting and level of produce consumed ... 55

Table 4.7 Smartphone ownership, operating system and apps usage ... 56

Table 4.8 Mobile apps used by farmers in Abia State ... 57

Table 4.9 Mobile applications level of usage ... 58

Table 4.10 Farmers’ responses to questions regarding how they perceive mobile apps... 63

Table 4.11 Farmers’ responses to questions regarding their intention to use mobile apps ... 64

Table 4.12 Farmers’ responses to questions about Social Influence. ... 65

Table 5.1 Normality of data assessment ... 68

Table 5.2 KMO and Barlett’s test ... 70

Table 5.3 Total variance explained ... 72

Table 5.4 Pattern matrix ... 73

Table 5.5 Reliability of total questions ... 74

Table 5.6 Extracted factors, measurement variables and their Cronbach’s Alpha ... 75

Table 5.7 Re-specified hypotheses ... 76

Table 5.8 Selected fit indices and general rule for acceptable fit ... 79

Table 5.9 CFA Model fit criteria (before item deletion) ... 82

Table 5.10 Measurement statistics (before item deletion) ... 82

Table 5.11 Validity concerns in the CFA model ... 82

Table 5.12 Measurement statistics (after item deletion) ... 82

Table 5.13 CFA Model fit criteria (after item deletion) ... 83

Table 5.14 Model fit criteria for the structural model ... 85

Table 5.15 The estimation for regression weights of the hypothesized model ... 85

Table 5.16 Standardised regression coefficient ... 87



List of Figures

Figure 1.1 Results generated by mobile applications for agricultural and rural development ... 18

Figure 2.1 Innovation adoption curve ... 27

Figure 2.2 Theory of Reasoned Action (TRA) ... 28

Figure 2.3 Theory of Planned Behaviour (TPB) ... 29

Figure 2.4 Technology Acceptance Model (TAM) ... 30

Figure 2.5 Extended Technology Acceptance Model (TAM2) ... 30

Figure 2.6 Unified Theory of Acceptance and Use Theory (UTAUT) ... 32

Figure 2.7 Research Model ... 42

Figure 3.1 Abia State agricultural zones ... 47

Figure 4.1 Mobile operating systems used by farmers in Abia state. (%)... 57

Figure 4.2 Mobile apps used by farmers in Abia State. ... 58

Figure 4.3 Mobile apps level of usage ... 59

Figure 4.4 Gender, smartphone ownership and usage ... 60

Figure 4.5 Age, smartphone ownership and usage... 61

Figure 4.6 Education, smartphone ownership and usage ... 62

Figure 5.1 Research model based on pattern matrix ... 74

Figure 5.2 Re-specified research model ... 76

Figure 5.3 Seven factor CFA model with standardized estimates ... 77

Figure 5.4 Hypothesised structural model for the adoption of mobile phone applications ... 84

Figure 5.5 Empirical results of the structural model for factors affecting the adoption of mobile applications ... 86



List of Abbreviations

AGFI Adjusted Goodness of Fit Index AMOS Analysis of Moment Structure

AU Actual Usage

AVE Average Variance Extracted CFA Confirmatory Factor Analysis CFI Comparative Fit Index COM Compatibility

CR Composite Reliability

DIT Diffusion of Innovation Theory EFA Exploratory Factor Analysis

FAO Food and Agricultural Organisation GDP Gross Domestic Product

GES E-wallet Growth Enhancement Support Electronic wallet GFI Goodness of Fit Index

GPS Global Positioning System IA Information/Awareness

ICT Information Communication Technology IDC International Data Corporation

IFPRI International Food Policy Research Institute iOS iPhone Operating System

ITU Intention to Use

KACE Kenyan Agricultural Commodity Exchange KMO Kaiser-Meyer-Olkin

MSV Maximum Shared Square Variance NBS National Bureau of Statistics PC Perceived Cost

PE Performance Expectancy PEOU Perceived Ease of Use PU Perceived Usefulness

PR Perceived Risk

RMSEA Root Mean Square Error of Approximation SAP System Application Products


xi SE Satisfaction/Experience

SEM Structural Equation Modelling SI Social Influence

SMC Squared Multiple Correlations SMS Short Message Service

SPSS Statistical Package for the Social Science SRMR Standardized Root Mean Square Residual TAM Technology Acceptance Model

TAM2 Extended Technology Acceptance Model TPB Theory of Planned Behaviour

TRA Theory of Reasoned Action UN United Nations

USAID US Agency for International Development

UTAUT Unified Theory of Acceptance and Use of Technology



CHAPTER ONE Introduction

1.1 Background

Nigeria is a developing country in West Africa with a population of 195 million (World Population Review, 2018). Agriculture is the base of the country’s economy and has remained the main source of livelihood for most inhabitants (FAO, 2017). It has also contributed significantly to the improvement of Nigeria’s Gross Domestic Product (GDP) over the last 10 years (Sertoglu, Ugural, & Bekun, 2017). In 2016 and 2017, agriculture dominated the non-oil sector of the economy, contributing 21.26 percent in 2016 and 24.44 percent in 2017 to Nigeria’s nominal GDP (National Bureau of Statistics, 2017).

According to the National Bureau of Statistics (NBS) in 2005, as cited in Ogunniyi and Ojebuyi (2016, p.

173), over 80 percent of the population lived in rural areas. About 70 percent of the population engaged in agriculture and they are made up of smallholders who cultivate or own farmland less than five hectares (Ofana, Efefiom, & Omini, 2016). These smallholders produce over 80 percent of all agricultural produce in the country.

According to Nwajiuba (2012), Nigeria has about 79 million hectares of arable land, and over 32 million hectares are cultivated for both crop and livestock production. But the current production rate has been unable to feed Nigerian’s growing population leading to food security issues and high food import bills. As at April 2018, the population of the country has increased by 1.6 million with an annual growth rate of 2.63 percent (World Population Review, 2018). A review of agricultural productivity in Nigeria by the International Food Policy Research Institute IFPRI (2009) showed that inconsistent provision of farm inputs and services by government, marketing of farm commodities, use of traditional management practices, absence of GPS for livestock productivity, inadequate information on the use of modern technology and practices, as well as poor extension service delivery were among the factors that hindered agricultural productivity. IFPRI findings revealed that these challenges were more pronounced because the majority (80 percent) of farmers in Nigeria are smallholders who cannot afford the cost of using modern technologies and farm practices.


13 Although agriculture in Nigeria has been of great importance to the economy, it is confronted by most of the challenges that hamper agricultural improvement in developing countries and include: a low level of mechanisation in agriculture, high illiteracy levels among the farmers, lack of credit facilities for farmers, weather vagaries, low technology diffusion, poor infrastructure, inadequate access to markets, defective research and extension services, implementation inefficiency, practice of tenure ownership, pests and diseases and imperfect information (Abutu, 2014; Aker, 2010, 2011; Ofana et al., 2016; UN, 2013). According to Aker, Ghosh, and Burrell (2016), most of the the agricultural problems as described by Abutu (2014), Ofana et al. (2016) and the United Nations (UN) (2013) that hamper agricultural productivity and development in developing countries could be addressed or managed effectively through the use of mobile phone applications by the farmers.

The use of mobile phones has been identified as one of the existing forms of Information Communication Technologies (ICTs) that can improve agricultural productivity and accelerate rural development processes (Asa & Uwem, 2017; Nyamba & Mlozi, 2012; Qiang, Kuek, Dymond, & Esselaar, 2012). Similarly, Chukwunonso and Tukur (2012), in their study carried out in Nigeria, recognised the mobile phone as a form of ICT that can contribute to poverty reduction and socio-economic development through its application in agriculture. In 2006, Torero and Von Braun predicted that mobile phones would be the ICT that will have the greatest diffusion and impact on the poor masses which include rural smallholders. They contend that mobile phones will help to reduce the marginalisation of the poor by promoting communication that is not restricted by time, distance, volume and medium, thereby surmounting the obstacles created by territory and social standing.

There has been a growing awareness of the usefulness of mobile phones and this has drawn the attention of individuals, businesses, governmental and non-governmental organisations to the myriad of purposes a mobile phone can serve in various sectors such as agriculture, health, business, education and the entertainment sector (Baumüller, 2012, 2015). In the agricultural sector, Costopoulou, Ntaliani, and Karetsos (2016) highlighted some of the important services that could be achieved through the use of mobile phones which include weather forecasting for farmers, agricultural product market prices, information for agricultural machinery and equipment, agricultural business


14 news, management of irrigation systems, yield forecasting and monitoring, dairy farming, management of agricultural products and crop sensors and registration of soil types. These services can be achieved through smartphones as a smartphone is needed to provide a platform where various mobile applications can be installed before usage.

1.2 Smartphone and mobile applications

Within the last decade, smartphones and mobile applications have become part of peoples’ daily lives and most essential in how they carry out their daily activities. It has helped in real-time information acquisition, communication, entertainment and for productive purposes. According to the Pew Research Center (2016), smartphones are mobile phones that can access the internet and support application installation. Qiang et al. (2012) identified some advantages of smartphones over low-end mobile devices which include a touchscreen, a wider user base, delivery of instant information conveniently, affordability, the ability to deliver personalized information to owners, and voice communication support.

Smartphones have witnessed a high rate of adoption worldwide because of their affordability and continuing improvement in functionality (Richard, 2015). The International Data Corporation (IDC) study on smartphone shipments worldwide showed that shipments would reach 1.77 billion units in 2021 from 1.53 billion shipped in 2017, which will result in a compound annual growth rate (CAGR) of 3.8 percent (Scarsella & Stofega, 2017). Similar research by the Pew Research Center (2016) shows that internet usage in developing countries increased from 45 percent in 2013 to 54 percent in 2015 just as smartphone usage increased from 21 percent in 2013 to 37 percent in 2015. It is worth noting that smartphones require internet connectivity for installed applications to function effectively.

Mobile applications (mobile apps) are software programmes designed to run on mobile devices like smartphones and tablets (Costopoulou et al., 2016). They are mostly built to provide users with similar services to those accessible on desktop and laptop computers (PCs). The functions they perform are essential and specific, ranging from productivity, entertainment, and access to information. They are


15 designed to be interactive and easy to use and also provide users with mobile contents such as text, audio, recordings, images, graphics and videos.

Notably, there are six categories of mobile apps which are utility mobile apps, lifestyle mobile apps, games/entertainment mobile apps, social media mobile apps, productivity mobile apps and news/information channel mobile apps (Lane & Manner, 2012; Matteo, 2018). These six categories cover virtually all human activities from business, health, agriculture, entertainment, sports, travel, tourism, education and production to finance (Costopoulou et al., 2016). Most mobile apps designed to aid farmers and agribusiness stakeholders fall under productivity mobile apps, news/information mobile apps and social media mobile apps.

There are various types of operating systems that can be found on a smartphone, and this determines the type of mobile application that could be compatible with or installed in them (Divya & Kumar, 2016). Some of the notable mobile operating systems that run on most smartphones include Android, iOS, Windows and Blackberry. The operating system on a mobile phone has been identified as one of the factors that affect the use of mobile applications by an individual (Lim, Bentley, Kanakam, Ishikawa,

& Honiden, 2014). A user with an Android-based smartphone can only install Android-based applications; the same applies to iOS, Windows and Blackberry-based smartphones. In the same manner, an iOS user cannot use an Android mobile application if they cannot find the iOS version of such a mobile application. Studies have shown that Android is the most popular and the most used mobile operating system followed by iOS, Windows and Blackberry (Costopoulou et al., 2016; Divya &

Kumar, 2016; Joseph & Shinto Kurian, 2013). According to Divya and Kumar (2016, p. 438), “Android gets 80.7 percent, and it is the best smartphone operating system in the world” because it has an open source operating system which makes it possible for users to install third-party applications from apps stores.

Mobile application developers strive to develop each mobile app on various smartphone operating systems with the aim of reaching a wider user base. Because each operating system has its own distinctiveness, this becomes a challenge to developers because each operating system has a unique


16 coding stream. Therefore technical issues related to mobile operating systems’ continuous support, update and design have to be dealt with (Pastore, 2013). When mobile app developers are unable to replicate an app on various mobile operating systems, this becomes an issue that the end users have to deal with because of incompatibility of the operating system. Studies have identified lack of compatibility as one of the main reasons for not adopting or using a mobile application despite its perceived benefits (Al-Jabri & Sohail, 2012; Shaikh & Karjaluoto, 2015).

Studies on the use of mobile applications by farmers is an aspect of technological innovation that has received much attention, but most of these studies tend to generalise the term “mobile phone use”

and “mobile phone adoption” without taking into consideration the difference between using a mobile phone and using a mobile phone application (Baumüller, 2015; Richard, 2015). To use a mobile application for any purpose, a person must have a smartphone or a tablet (a mini computer with a mobile operating system). This serves as a platform where applications can be installed before use.

Although the smartphone evolved from mobile phones because of advances in technology, a mobile phone is simply a phone used for the primary aim of making and receiving calls and sending text messages. Other useful features found in a mobile phone include a calendar, calculator, alarm, clock, radio, and touch light. But a smartphone offers a wide range of additional services, some of which may be obtained on a desktop or laptop computer. Smartphones are affordable, very portable and easy to operate, with the ability to deliver instant and convenient services (Qiang et al., 2012). This makes them an ideal tool for smallholders. The ability of a smartphone to support mobile applications has made it easier for individuals and businesses to get things done easily and in a timely manner, thereby making them more productive. According to Pastore (2013), companies and businesses that make use of apps on smartphones have been able to stay close to their customers and remain active in their competitive environment. It has also created economic opportunities for employment, learning a new skill, receiving information or medical treatment and even starting a new business (Aker & Mbiti, 2010).



1.3 Use of Mobile Applications for Agriculture in Developing Countries

In most developing countries, the use of mobile phone applications for agricultural purposes is still gaining popularity, while in some developing countries such as India, Kenya, Uganda, South Africa and Tanzania, agricultural productivity has been improved through the use of mobile applications (Qiang et al., 2012). Baumüller (2015) asserted that the use of mobile applications for agriculture has the potential to reach and assist rural smallholders. He went on to confirm that the agricultural sector of most developing countries is characterised by a greater number of smallholders who are often subsistence farmers with obsolete technology and low productivity. A review on the use of mobile applications in developing countries by Hatt, Wills, and Harris (2013) showed that mobile apps had improved health-related services in Asia, while in Africa, mobile money applications have improved financial transactions. According to the World Bank (2017), opportunities abound for agriculture to be enhanced through ICT, by improving market access and value chains, providing information on disease and climate, and facilitating extension service delivery, providing a better market link and distribution channels, as well as access to financial services which include payments, insurance and credits.

Qiang et al. (2012) carried out a study, where they investigated the impact of 92 mobile applications for agriculture and rural development in Africa, Asia, Latin America and the Caribbean (developing countries). They found that most agricultural mobile applications focused more on providing market information, facilitating market links, improving supply chain integration and increasing access to extension services. Among the uses served by the various agricultural apps, valuable information was rated the most important, because of the high level of information asymmetry affecting the rural markets in developing countries (Aker, 2010; Brown, Zelenska, & Mobarak, 2013; Qiang et al., 2012;

World Bank, 2017). The mobile applications that improved agricultural supply chain integration facilitated other social and economic benefits including value addition, job creation, reduction in product losses and strengthening of the global competitiveness of developing countries. Figure 1.1 shows the results generated from the various agricultural applications studied by Qiang et al. (2012).


18 Figure 1.1 Results generated by mobile applications for agricultural and rural development

source: (Qiang et al., 2012, p. 17)

Results generated by Qiang et al. (2012) showed that use of mobile applications helps smallholders achieve higher incomes, with lower transaction and distribution costs on output sales and input supplies. Both producers and consumers enjoyed improved traceability. Other stakeholders such as financial institutions had new opportunities to explore.

In Sub-Saharan Africa, Kenya has been recognised as the pacesetter in the development of agricultural mobile apps (Baumüller, 2015). This is evident in the number of mobile apps being used by Kenyan farmers and the extensive research that has been carried out on the impact and adoption of these mobile apps (Baumüller, 2013; Kante, Oboko, & Chepken, 2016; Kirui, Okello, Nyikal, & Njiraini, 2013;


19 Wyche & Steinfield, 2016). The findings showed that Kenyan farmers increased their farm productivity and income by using such mobile apps as Virtual City AgriManager, M-Pesa, KACE (Kenyan Agricultural Commodity Exchange), DrumNet and KilimoSalama. Brown et al. (2013, p. 20) reported that M-Pesa “is the most widely adopted mobile financial service around the world with over 14 million users by early 2011.” This represents over 70 percent of Kenya’s adult population. However, Gichamba (2015, p. 4) noted that most Kenyan farmers that benefited from these mobile apps were farmers in suburban regions and agriculture intensive areas. Similarly, farmers from Uganda witnessed a positive impact on their farming productivity by using mobile apps like Grameen (weather application), Esoko, Google Trader, WeFarm, Infotrade, Foodnet and Farmgain(Qiang et al., 2012). Evidently,Martin and Abbott (2011) found in their study on the adoption of mobile phone use in Uganda that 87 percent of the farmers used mobile apps for coordination of inputs and 70 percent used them for accessing market information. These services were considered the most important for Ugandan farmers.

Other notable Sub-Saharan African countries witnessing improvement in their agricultural sector through the adoption of mobile apps include Ghana, Tanzania, Botswana and South Africa. Esoko mobile app and Cocoa Link reduced asymmetric information faced by Ghanaian farmers (Aker et al., 2016). Modisar mobile app improved livestock production in Botswana (Chukwunonso & Tukur, 2012).

M-Kilimo helped Tanzanian farmers receive extension services and market information that ultimately increased their productivity and income (Temu, Henjewele, & Swai, 2016).

Some of the agricultural mobile apps charge subscribers (farmers) for using the services provided through the mobile apps (Qiang et al., 2012). While some of the mobile apps provide services that are subsidised, some others are completely free of charge, because the services have been paid for or subsidised by either government, donors, private companies, commercial banks or trust funds. The services or content of the mobile apps can either be provided by the government, extension workers, media, or specialised commercial units for mobile money apps, and finally through crowdsourcing where the farmers can contribute all the useful information at their disposal. Crowdsourcing is typical of such social media apps as Twitter, WhatsApp and Telegram.


20 Mobile applications have also created business opportunities for companies with an interest in improving agricultural productivity in developing countries (Baumüller, 2015). In India, Nokia and Reuters Thomson are providing information services to farmers (Saravanan & Bhattacharjee, 2014). In Uganda, Google is connecting producers to consumers through an internet-based platform (Ssekibuule, Quinn, & Leyton-Brown, 2013). In Ghana, a German software company “System Application Products” (SAP), is overseeing supply chain management systems for smallholders (Baumüller, 2015). In Tanzania, Vodafone Group, US Agency for International Development (USAID) and TechnoServe have partnered to boost agricultural productivity and incomes of farmers in Tanzania through the use of mobile technology (Vodafone Group, 2014).

These studies highlight the potential of mobile phone apps to improve agricultural productivity and therefore the need to understand more about the use of mobile phone application technologies by farmers in developing countries.

Looking at the impact assessment of the use of mobile apps for agriculture in developing countries, a good number of studies have been conducted on assessing the impact of mobile phones in developing countries (Aker & Mbiti, 2010; Chhachhar & Hassan, 2013; Martin & Abbott, 2011), with just a few having focused on agricultural mobile applications’ adoption and impact.

1.4 Adoption of Mobile Applications by Farmers in Nigeria

The mobile phone industry in Nigeria has played a significant role in the socio-economic development of the country by creating a platform for innovation, digital inclusion, and access to information exchange, finance, markets and governance to millions of citizens who have been excluded from these services (Brown et al., 2013; GSMA, 2016; Ogunniyi & Ojebuyi, 2016). Unfortunately, most farmers have not fully exploited these benefits because of lack of uptake in the use of mobile application technology (Chhachhar, Chen, & Jin, 2016). Mobile phone technology is a crucial factor that can contribute to poverty reduction and economic development through its application in agriculture (Baumüller, 2012). The level of internet usage in Nigerian has been increasing and according to the Pew Research Center (2016, p. 15), “in 2014, 38 percent of Nigerian internet users said they access the


21 internet several times a day. In 2015, the number increased to 58 percent.” The number of smartphone users in Nigeria is estimated to reach 23.3 million by 2019 from 11 million in 2014 (Statista, 2018). Despite this significant increase in smartphone and internet usage, there is still a prevalent digital divide in developing countries where social and economic inequalities still affect the access, use and impact of ICTs (Ohemeng & Ofosu-Adarkwa, 2014).

In Nigeria, the number of mobile apps that could aid agricultural productivity is increasing, and there are some that are still at their development stage with the web version already in existence and running. Notable mobile apps for agriculture in Nigeria include apps such as GES E-wallet, which stands for Growth Enhancement Support Electronic wallet. It was created by the Ministry of Agricultural and Rural Development in Nigeria to provide soft loans to farmers, track seed and fertiliser disbursement and educate farmers on farming methods that will improve their output (Uwalaka, 2017). Agrikore is another mobile app that connects farmers, agro-dealers, commodity traders and insurers under a platform that ensures transparency and honesty among the actors in the system. Verdant mobile app offers market information and general agricultural guidance and Agrodata is dedicated to providing agricultural information and research data. Hello Tractor app helps farmers access tractors and other farming tools. Probityfarms is used for farm management as well as to connect farmers to market and Compare-the-market is designed to compare the price of food crops and livestock in Nigeria on a daily basis. Cellulant app works in partnership with the federal government of Nigeria to help farmers redeem subsidised seed and fertiliser vouchers from designated retail outlets. Farmers use WhatsApp and Telegram to create informal groups where information and ideas are exchanged. Various Nigerian mobile banking apps are designed to ease payments and other financial transactions on-the-go.

Although there are a significant number of mobile apps that can help farmers, the use of these is low especially by small farmers.

1.5 Research Problem

Agriculture has been identified as the main source of livelihood for most Nigerians (FAO, 2017), where 70 percent of the population engages in agriculture, and they are mainly smallholders who produce on


22 a small scale. These smallholders produce over 80 percent of the countries’ entire agricultural output which is not enough to feed the growing population of Nigeria, leading to over-dependence on imported food (Nwajiuba, 2012). The main challenges faced by these smallholders are access to agricultural information, access to market and access to financial services (Baumüller, 2012; Nwajiuba, 2012). Studies have proven that the use of mobile application in agriculture can help smallholders access agricultural information and financial services, improve access to markets and enhance visibility for supply chain efficiency (Aker & Mbiti, 2010; Baumüller, 2015; Qiang et al., 2012; Vodafone Group and Accenture, 2011)

According to Lim et al. (2014), there has been little research carried out on the behaviours of mobile app users and their mobile devices. They found in their study that lack of user feedback and inability to understand app users’ behaviour caused many mobile apps to fail as app users seldom give user feedback or reviews irrespective of the level of satisfaction derived (McIlroy, Shang, Ali, & Hassan, 2017). Lim et al. (2014) went ahead to reveal that app developers lack important demographic information about users such as their age, gender, educational level and income level. This makes it difficult to understand the usage pattern of these apps.

Ogunniyi and Ojebuyi (2016), conducted a study on the use of mobile phones for agribusiness by farmers in Southwest Nigeria, where they found that 76 percent of farmers mostly use mobile phone radio and 83 percent use their phone just for calls. However, the study did not take into consideration the use of agricultural mobile apps by farms as they only focused on such utility tools as SMS, calls, calendar, alarm, radio etc. that have been displaced by a vast range of more advanced technological options (Richard, 2015). The factors that affect the use of agricultural mobile apps were overlooked in their study.

A study carried out by Asa and Uwem (2017) in South-south Nigeria revealed that 90 percent of the rural farmers had mobile phones and 98 percent had access to mobile phones, but the study failed to capture the type of phones that the farmers use and the operating system installed on such mobile phones. Similarly, Jaji, Abanigbe, and Abass (2017) in their study carried out in South-west Nigeria


23 revealed a 98 percent mobile phone ownership and usage, mostly for accessing information. However, their study also did not clearly capture how this information was being accessed. There is the need to understand the kind of phone and mobile apps used by farmers and the factors that influence farmers’

willingness to use mobile apps.

Chukwunonso and Tukur (2012) in their study on the adoption of ICT in agriculture in Nigeria discovered that ICT cost, lack of access and awareness, lack of end-user information exchange and trust were among the factors that affected ICT adoption. Their study looked at ICT from a broader point of view. This included computer acquisition, software installations and internet access, which the farmers considered to be expensive. Smartphones are less expensive, and mobile apps are easier to access from app stores. Studies have shown that adoption of mobile apps by consumers is mostly influenced by ease of use, trust, performance expectancy, cost and social influence (Chukwunonso &

Tukur, 2012; Malik, Suresh, & Sharma, 2017; Shaikh & Karjaluoto, 2015). On the other hand, Xu, Frey, Fleisch, and Ilic (2016) and Lane and Manner (2012) discovered in their study that mobile app adoption is influenced by the user's personality traits and not about the perceived benefits or costs of the app.

They discovered that extroverted individuals preferred gaming, entertainment and social media mobile apps. To them, productivity apps were less important, while conscientious individuals would rather go for productivity apps and information apps. Unal, Temizel, and Eren (2017) study on mobile apps adoption shows that gender influences the choice of apps downloaded by individuals. These findings show the need for a better understanding of how farmers perceive the use of mobile apps.

Most of the studies on the use of mobile phones in agriculture in Nigeria have approached the concept from a generalised point of view (Aker, 2010; Asa & Uwem, 2017; Chhachhar et al., 2016; Ogunniyi &

Ojebuyi, 2016). There has been no distinction between a mobile phone and a smartphone, between utility tools/apps and productive mobile apps, or between information mobile apps and social media mobile apps. They have all been categorised as “the use of mobile phone in agriculture”. In essence, there are clearly defined differences between these terminologies. Most agricultural mobile apps fall under the category of productivity mobile apps, information mobile apps and social media mobile apps. These three categories of mobile apps have been designed to function only on a smartphone.


24 Furthermore, existing literature on the use of mobile phones in Nigeria has not captured farmers’

perceived interest and willingness to use mobile apps. There is also the need to bridge the information gap between agricultural app developers and app users. Quantitative analysis will help to provide the farmers’ demographic and socioeconomic characteristics and how they affect farmers’ use of smartphones and mobile apps.

1.6 Research Objectives

So far, the findings from earlier studies on the use of mobile applications by farmers in Nigeria are insufficient to conclude that farmers are using mobile applications and that they have improved their productivity. To help address this gap, this research is aimed at providing a critical understanding of the current state of mobile apps use in the Nigerian agricultural sector by examining the factors that affect the adoption of mobile applications. It also seeks to contribute to mobile apps use literature by exploring and examining the current level of use of mobile apps for agriculture in Abia State and the factors that affect the uptake of this technology. To successfully achieve this fit, this study will seek to provide answers to the following research questions:

I. What are the factors that influence the adoption of mobile apps by farmers?

II. Why are some farmers not adopting mobile apps?

Answers to these questions are required to determine the factors that influence the adoption of mobile apps by farmers in Abia state, Nigeria.

The specific objectives are to:

I. Investigate the types of phones and the operating systems on the phones used by farmers;

II. Identify the current mobile applications being used by farmers and their uses;

III. Identify the factors that distinguish farmers who use mobile apps apart from those who do not use them;

IV. Examine the interest and willingness of farmers to use mobile apps in their daily farming activities; and


25 V. Determine the factors that influence the adoption of mobile apps.



CHAPTER TWO Literature Review

2.1 Theories of Technology Adoption

Mobile application development in the area of mobile communication technology has advanced considerably in the last decade with much improvement in the services and functions obtainable in these mobile applications (Costopoulou et al., 2016; Qiang et al., 2012). The agricultural sector, just as other sectors including finance, education, and entertainment, has witnessed the development of mobile apps to aid farmers in their daily farming activities (Suarez & Suarez, 2013). But the main challenge faced by developers of these apps is to get farmers to adopt and use these applications developed for them (Richard, 2015). To understand and solve this problem of adopting new technology, researchers have developed theories and models that try to explain the rationale behind adopting or rejecting a new technology which has implications for both the developer and the intended users of this technology (Al-Jabri & Sohail, 2012; Lai, 2017; Malik et al., 2017). Some of the empirical theories are: Theory of Diffusion of Innovation (DIT) (Rogers, 2010), Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), Theory of Planned Behaviour (TPB) (Ajzen, 1991), Technology Acceptance Model (TAM) (Davis, 1989; Sharma & Mishra, 2014), Technology Acceptance Model (TAM2) (Venkatesh & Davis, 2000) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003). See Table 2.1 for empirical studies.

2.1.1 Diffusion of Innovation Theory (DIT)

Diffusion of Innovation Theory (DIT) was developed by Rogers in 1960 (Sharma & Mishra, 2014).

According to Lai (2017, p. 22), “Rogers proposed that diffusion of innovation theory was to establish a foundation for researching innovation acceptance and adoption”. Rogers (1995) reviewed over 508 diffusion studies before establishing Diffusion of Innovation Theory for the adoption of innovations among individuals and organisations. Rogers went ahead to explain the importance of the process and channel through which an innovation is communicated over time among the members of a


27 social system. This process and channels were what he referred to as “diffusion”. Lai (2017) described this process of communicating innovation to include understanding, implementation, persuasion, decision and confirmation, which will lead to the development of Rogers (1995) S- shaped adoption curve of innovators, early adopters, early majority and laggards, as can be seen in Figure 2.1. The „S‟ shaped curve represents the cumulative rate of adoption (or diffusion curve). The bell curve depicts the number of new adopters along the same timeline. In an attempt to understand factors that influence adoption of ICT tools, which include mobile phone applications, DIT seem to be the most used theory (see Table 2.1) (Al-Jabri & Sohail, 2012; Genius, Koundouri, Nauges, &

Tzouvelekas, 2013; Martin & Abbott, 2011). According to Al-Jabri and Sohail (2012), DIT is a theory that attempts to analyse how, why and at what rate a new technology and concept spread.

Figure 2.1 Innovation adoption curve Source: (Briscoe, Trewhitt, & Hutto, 2011) 2.1.2 Theory of Reasoned Action (TRA)

TRA was developed by Fishbein and Ajzen (1975), and it is one of the oldest and most popular theories (Lai, 2017). According to Malik et al. (2017, p. 107), in TRA, “intention determines behaviours and attitudes influence this intention and in turn behaviour” (See Fig. 2.2). The theory defines the links between intentions, beliefs, norms, attitude and behaviours of persons. Fishbein and Ajzen (1975) defined attitude as a person’s positive or negative feeling about carrying out a specific action and defined “belief” as a link between an object and some attribute and “behaviour”

as a result of intention. A key factor in TRA is a person’s subjective norms which determine how they


28 perceive their community’s attitude to a certain behaviour or what others will think of a certain behaviour (Lai, 2017) (e.g. “My fellow farmers are using mobile money app and it is prestigious to have one”).

Figure 2.2 Theory of Reasoned Action (TRA) Source: (Fishbein & Ajzen, 1975)

2.1.3 Theory of Planned Behaviour (TPB)

TPB was developed by Ajzen (1991). The theory argues that the performance of a person’s behaviour of interest is influenced by their behavioural, normative and control beliefs. This leads them to carry out a certain behaviour (Malik et al., 2017; Sharma & Mishra, 2014). As can be seen in Figure 3, attitude, just as in TRA is believed to have a positive or negative influence in one’s life. Subjective norms mean that people act in a certain way because of what other people think or say. The last factor is perceived behavioural control which is the control people perceive may limit their behaviour (Lai, 2017) (e.g. “Am I eligible to apply for mobile money apps and what are the requirements?”). The theory predicts that attitude, favourable social norms and high levels of perceived behavioural control are the best predictors for forming a behavioural intention, which in turn leads to certain behaviour or act (see Figure 2.3)


29 Figure 2.3 Theory of Planned Behaviour (TPB)

Source: (Ajzen, 1991)

2.1.4 Technology Acceptance Model (TAM)

TAM was developed by Davis (1986) for his doctoral proposal, and it was developed specifically for the analysis of users’ acceptance of Information Communication Technologies (ICTs) (Lai, 2017).

Davis (1989) used this model to test two specific beliefs which are Perceived Usefulness and Perceived Ease of Use. He was of the opinion that Perceived Usefulness is a potential user’s subjective likelihood that makes them believe that using a particular technology or mobile app will improve their action while Perceived Ease of Use is the degree to which the potential user expects the technology to be easy or effortless to use (see Figure 2.4). Davis (1989) contends that a person’s belief in a technology may be influenced by other external factors or variables. King and He (2006) used the TAM model for analysis and found it very useful and applicable in other areas of study, while Benbasat and Barki (2007) criticised the TAM model, citing that it has a lot of limitations when applied in a rapid-changing IT environment.


30 Figure 2.4 Technology Acceptance Model (TAM)

Source: (Davis, 1989)

2.1.5 Extended Technology Acceptance Model (TAM2)

Technology Acceptance Model (TAM 2) was developed by Venkatesh and Davis (2000). It is a modification of TAM. Their study provided more key determinants that could influence a user’s perceived usefulness and intention to use in their extended TAM model. According to Sharma and Mishra (2014), the key determining factors included are social influence processes (which involve image, voluntariness and subjective norm) and cognitive instrumental processes (which include perceived ease of use, output quality, job relevance and result demonstrability), as can be seen in Figure 2.5.

Figure 2.5 Extended Technology Acceptance Model (TAM2) Source: (Venkatesh & Davis, 2000)


31 2.1.6 Unified Theory of Acceptance and Use of Technology (UTAUT)

UTAUT was developed by Venkatesh et al. (2003) after studying and reviewing the previous technology adoption models and their constructs. They aimed to come up with a comprehensive model that could be applied to a broad area of applications. The model proposed four key constructs which predict users’ behavioural intention. The key constructs are “Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions”, as can be seen in Figure 2.6. These four proposed key constructs were theorised after testing the constructs used in the previous models, and they were found to be the most significant factors that affect the intention to use information technology. The first construct in UTAUT (Performance Expectancy) is derived from five similar constructs from previous models which are Perceived Usefulness, Relative Advantage, Extrinsic Motivation, Job-Fit and Outcome Expectations, while Perceived Ease of Use and Complexity make up Effort Expectancy.Venkatesh et al. (2003) found Social Influence was not significant in voluntary contexts.

According to Sharma and Mishra (2014), previous theories on technology adoption explained just 30- 40 percent variance in adoption behaviour while UTAUT explained 70 percent of the variance, making it the superior model. However, Van Raaij and Schepers (2008) and Casey and Wilson-Evered (2012) criticised UTAUT on the basis of being too complex, not being parsimonious in its approach and unable to justify individual behaviours. Williams, Rana, Dwivedi, and Lal (2011) reviewed 450 articles that cited UTAUT, and they found that only a small number of these articles used UTAUT constructs in their study; instead, they used it in developing their theory.


32 Figure 2.6 Unified Theory of Acceptance and Use Theory (UTAUT)

Source: (Venkatesh et al., 2003)

Table 2.1 Theories used in ICT adoption studies and their findings

Author Topic Theory/Mode


Dependent variables

Independent variables

Findings Malik et al.


Factors influencing consumers’

attitudes towards adoption and continuous use of mobile applications:

a conceptual model

Adoption and continuous use model

Satisfaction and Habit as mediating variables

Performance Expectancy, Ease of Use, Social Influence, Enjoyment, Incentive, Facilitating Condition, Aesthetics, Trust

Satisfaction is the most important predictor of intention to repurchase an app.

Perception after adoption leads to continued use, and Habit is a crucial determinant that leads to continued usage of an information system (IS).


33 Gichamba


The Extent of ICT Adoption by ACP Farmers:

mAgriculture Adoption in Kenya

Revenue Model

Cost, Network Availability, Language Barrier, Privacy, Risk- Averse

Most farmers were sceptical and wanted to see that applications were working before they would adopt them.

Cost, Network Availability and Language Barrier were the

predominant challenges that hindered adoption.

Lin (2011) An empirical investigation of mobile banking adoption:

The effect of innovation attributes and knowledge- based trust

Innovation Diffusion Theory and Knowledge- Based Trust Model

Attitude and Behavioural Intention of adopting

Perceived Relative Advantage, Ease of Use, Compatibility, Perceived Competence, Benevolence and Integrity

Perceived Relative Advantage, Ease of Use, Compatibility, Competence and Integrity significantly influence Attitude, which in turn lead to Behavioural Intention to adopt (or continue-to- use) mobile banking Al-Jabri and

Sohail (2012)

Mobile banking adoption:

Application of diffusion of innovation theory

Diffusion of Innovation Theory

Mobile banking adoption

Relative Advantage, Complexity, Compatibility, Observability, Trialability, Perceived Risk

Observability, Relative Advantage and

Compatibility affected adoption positively.

Perceived Risk affected adoption negatively, while Complexity and Trialability had no


34 significant effect on adoption.

Fathema (2013)

A Structural Equation Modelling of an Extended Technology Acceptance Model for faculty acceptance of Learning Management Systems (LMSs)

Extended Technology Acceptance Model (TAM2)

Behavioural Intention to Use and Actual Usage

System Quality, Perceived Self- Efficacy, Facilitating Conditions, Perceived Usefulness, Perceived Ease of Use,

Attitudes Towards Using, Behavioural Intention to Use

The study result showed that System Quality, Perceived Self-efficacy and

Facilitating Conditions had a significant effect towards the use of canvas.

The study proposed 13 hypotheses of which 11 were

supported by the results.

Chan et al.


Modelling Citizen Satisfaction with Mandatory Adoption of an E-

Government Technology

UnifiedTheory of Acceptance and Use of Technology (UTAUT)

Satisfaction Awareness, Compatibility, Self-Efficacy, Flexibility, Avoidance of Personal Contact, Trust, Convenience, and Assistance.

Mediating variables (Performance Expectancy, Effort Expectancy, Social

Influence, and Facilitating Conditions)

Performance Expectancy, Effort Expectancy and

Facilitating Conditions were found to have a strong impact on Satisfaction while Social Influence was not significant in

determining Satisfaction.

Zaremohzzabie h et al. (2015)

A test of the technology acceptance model for understandin g the ICT adoption behaviour of rural young entrepreneur

Technology Acceptance Model (TAM)

Entrepreneuri al intention.

Perceived ease of use,

perceived usefulness, and

behavioural intention.

Attitude as a mediator

Perceived Usefulness impacted the adoption of ICT

significantly rather than Perceived Usefulness.

Improving job


35 performance was of more importance to the rural entrepreneurs .

Abdekhoda, Dehnad, Mirsaeed, and Gavgani (2016)

Factors influencing the adoption of E-learning in Tabriz University of Medical Sciences

UnifiedTheory of Acceptance and Use of Technology (UTAUT)

Usage Performance

Expectancy, Effort Expectancy, Social

Influence, and Fascinating Condition.

Behaviour Intention as a mediating variable

Social Influence, Effort Expectancy and

Performance Expectancy affected the faculty members’

behaviour towards adopting e- learning while Facilitating Condition had no effect on it.

Hsu, Lu, and Hsu (2007)

Adoption of the mobile Internet: An empirical study of multimedia message service (MMS)

Innovation Diffusion Theory (IDT)

Intention to adopt MMS

Relative Advantage, Perceived Ease of Use,

Compatibility, Trialability, Image, Visibility, Result

Demonstrabilit y and


Perceived Ease of Use changed at various stages of innovation diffusion.

They also found a significant difference between potential adopters and users.

Source: Author’s work

2.2 Proposed Extended Technology Adoption Model TAM2 for the Adoption of Mobile Applications by Farmers

TAM, which was first introduced by Davis in 1989 has been used in many studies to successfully analyse and interpret the adoption of various Information Communication Technologies (ICT) in different work environments (Kripanont, 2007; Tarhini, 2013). Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) were the two main factors used in TAM to explain the acceptance or


36 rejection of information technology by a person. PU is said to influence adoption if a user believes that using a technology will enhance or improve their job performance, while PEOU is said to influence adoption if a user believes that a technology would be easy to use. The original TAM model was extended in an effort to apply TAM beyond the workplace environment and into other diverse environments such as entertainment e.g. mobile games (Chen, Rong, Ma, Qu, & Xiong, 2017), consumer services e.g. mobile commerce (Wu & Wang, 2005) and mobile internet (Kim, Chan, &

Gupta, 2007). The first major extension was carried out by Venkatesh and Davis (2000) who tested four different systems in four organisations. They referred to the extended TAM model as TAM2. The major difference between TAM and TAM2 is the inclusion of social influence processes and cognitive instrumental processes which they found to significantly affect user acceptance.

According to Venkatesh (2000), the application of TAM outside workplace environments has always encountered problems because the main TAM constructs do not adequately demonstrate how well a technology meets the needs of the work environment and its tasks. Similarly, Bagozzi (2007) contended that TAM overlooks important aspects of technology adoption such as groups’ social and cultural aspects. In support of the first major extension of TAM made by Venkatesh and Davis (2000), many researchers have emphasised the need to add more variables to TAM for the purpose of establishing a stronger model (Legris, Ingham, & Collerette, 2003; Wu & Wang, 2005). As a result of this argument, many studies have come up with various extended versions of TAM to suit the work environment and the nature of the technology being studied. These studies build upon the original TAM and TAM2 and modify it by adding or removing constructs to better explain the adoption of a technology in a given setting. e.g. (Chen et al., 2017; Hakkak, Vahdati, & Biranvand, 2013; Park & Kim, 2014; Venkatesh & Davis, 2000; Wentzel, Diatha, & Yadavalli, 2013).

2.2.1 Theoretical Model

This study is on mobile applications which are an aspect of Information Communication Technology (ICT), with a focus on what influences their adoption by Nigerian farmers. The workplace environment in an agricultural setting is quite different from the organisational setting in which TAM and its


37 extended version were first applied by Davis (1989) and Venkatesh and Davis (2000) respectively. The reason for adopting the extended TAM is its ability to successfully explain and predict the adoption of information technologies. Rather than sticking to the original TAM or TAM2 constructs, this study will modify TAM by adding additional constructs that best describe farmers and their farming activities and environment. The three main factors considered in formulating these constructs are farmers’

socioeconomic characteristics, their biophysical environment and the nature of their farming operations. These three factors were first examined by Baumüller (2012) in his study on the facilitation of agricultural technology adoption among poor farmers. Although TAM has been modified to suit the study setting, the modification is based on the original extended TAM.

Five main original extended TAM constructs were retained in the study model (Perceived Usefulness, Perceived Ease of Use, Intention to Use, Actual Usage and Social Influence), while six additional constructs were added to modify the original extended TAM to suit the study setting. The six added constructs are Performance Expectancy, Perceived Risk, Perceived Cost, Satisfaction/Experience, Compatibility and Information/Awareness as shown in Figure 2.7. These constructs were carefully selected from reviewed literature on mobile applications and farmer technology adoption studies. Perceived Usefulness (PU)

PU is one of the two main TAM constructs introduced by Davis (1989) to determine a user’s acceptance or rejection of information technology. Davis (p.26) defined it as “the degree to which an individual believes that using a particular system would enhance his or her job performance.” In the context of farmers’ acceptance of mobile applications, PU is defined as the relative advantage a farmer expects to gain from using a mobile app. Apart from Davis (1989) and Venkatesh and Davis (2000), many other studies on ICT use have proved that PU has a significant positive impact on a user’s behavioural intention to use an IT or a system (Kesharwani & Singh, 2012; Park & Kim, 2014; Wentzel et al., 2013). This study hypothesises that PU has a direct positive impact on a farmer’s Intention to use mobile applications.


38 Perceived Ease of Use (PEOU)

PEOU is the second main TAM construct introduced by Davis (1989) to determine a user’s acceptance or rejection of information technology. Davis (p.26) defined it as “the degree to which an individual believes that using a particular system would be free of physical and mental effort." In the context of farmers’ acceptance of mobile applications, PEOU is defined as a farmer’s assessment of how effortless it is to use a mobile app. Davis noted that PEOU can influence PU because a person who perceives a technology as easy to use would be more likely to perceive it as useful. Most smartphones come with a user-friendly interface. However, Aker et al. (2016) noted that despite the user-friendly interface on most smartphones, farmers with low literacy levels still find it difficult to use a mobile app which can influence their adoption decision. Another factor influencing ease of use is the language barrier. Kaur and Dhindsa (2018) noted that farmers who cannot understand the English language found it difficult to use mobile applications. This study hypothesises that first, PEOU has a direct effect on the PU of mobile apps and secondly PEOU also has a positive significant effect on a farmer’s Intention to use mobile applications. Intention to Use (ITU)

ITU is one of the constructs in Venkatesh’s extended TAM which was originally introduced by Fishbein and Ajzen (1975) in their Theory of Reasoned Action (TRA). Prior to the extension of TAM, Davis (1989) in the original TAM theorised that a for potential user’s behavioural ITU a particular technology is hypothesised to be a major determining factor in whether or not he actually uses it. The theory also has it that a person’s behavioural intention to use a given technology is influenced by two beliefs: PU and PEOU. In the study context, a farmer’s behavioural ITU mobile apps would be a major determinant of whether he eventually uses them. This study hypothesises that ITU has a significant positive effect on the Actual Usage of mobile apps. Social Influence (SI)

SI is a widely recognised factor that influences a person’s technology acceptance behaviour. It was a factor used in Fishbein and Ajzen (1975) Theory of Reasoned Action to explain subjective norms.


39 Fishbein and Ajzen (p.302) defined SI as a “person’s perception that most people who are important to him think he should or should not perform the behaviour in question.” In Venkatesh and Davis (2000) extended TAM, SI was used as a key determinant of TAM’s PU and ITU constructs. Unlike Fishbein and Ajzen, Venkatesh and Davis used Subjective Norm as one of the factors in explaining the SI process.

Subsequent studies on technology adoption (Al-Gahtani, 2016; Hakkak et al., 2013; Taylor & Todd, 1995) have used Subjective Norm and Social Influence interchangeably to explain the impact of other people’s views and opinions on the adoption of information technology. Kesharwani and Singh (2012) argued that interchanging Social Influence and Subjective Norm has led to mixed results and the effect on technology adoption has been inconsistent. In most farming communities, especially in developing countries, social interactions exist within the farmers and would be necessary to see the impact on their PU of mobile applications and their ITU mobile apps. According to Hakkak et al. (2013), such an impact could be favourable or unfavourable. This study, therefore, hypothesises that SI has a significant positive impact on the PU of mobile applications. Performance Expectancy (PE)

The PE construct was introduced by Venkatesh et al. (2003) in their Unified Theory of Acceptance and Use of Technology (UTAUT). They described it as the degree to which any technology can improve the productivity of a user or will assist the user to achieve gains in job performance. Consumers tend to adopt and use applications that they perceive would improve their productivity based on their knowledge of the content of the app. In Malik et al. (2017), PE was found to have a significant effect on adoption, especially on male consumers while Chan et al. (2011) reported that PE leads to continued use when satisfaction is derived from initial use. This study hypothesises that PE has a significant and positive impact on the PU of mobile applications. Perceived Risk (PR)

PR is one of the external variables included in the study’s extended TAM. It has been in use as early as the 1960s to explain consumers’ attitudes towards decision making (Bauer & Cox, 1967). They defined PR with regard to the insecurity and unfavourable outcomes associated with consumers’ expectations.


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