The Applicability of Rogers’ Innovation Diffusion Model in ... · Email: *[email protected]...

280 International Conference on Education, Proceedings Development & Innovation, 2017

The Applicability of Rogers’ Innovation Diffusion Model in the Ghanaian

Start-Up Ecosystem

KINGSLEY GYAN AGYEKUM & *SENA AGBODJAH AGYEPONG

Ashesi University College

GHANA

Email: *[email protected]

Abstract

As an initial attempt to investigate the applicability of Rogers‟ innovation diffusion model in the

Ghanaian technology start-up ecosystem, this study had two major purposes: to investigate if

technology entrepreneurs and start-ups in Ghana were aware of the best practices to facilitate the

diffusion of their innovations to promote their businesses; and to investigate the applicability of the

Rogers‟ innovation diffusion model in the Ghanaian technology start-up context. Ultimately, it aided

in testing the research design, method and data management techniques to adopt for a larger study. An

exploratory research, which employed the use of an in-depth semi-structured interview, was

conducted using four young companies in Accra. The purpose was to understand diffusion of

innovation from the perspectives of these local entrepreneurs in the bid to answer the two questions

the research sought to answer. An online questionnaire was distributed to randomly selected

customers of one of the companies to understand adopter behaviours. The quantitative data was

analyzed using a time series, linear and logistic regressions and tested for sigmoidal distribution. The

qualitative data was analyzed using thematic analysis. It was concluded from this effort that, the

Roger‟s innovation diffusion model closely applies in the Ghanaian technology start-up ecosystem.

The result also show that Ghanaian technology entrepreneurs do not have adequate and accurate

knowledge of the best practices to facilitate the diffusion of their innovations to promote their

businesses. There is however the need for an investigation that considers more ventures within the

technology start-up ecosystem to allow for a generalization of these conclusions. This larger study is

currently ongoing.

Keywords: Technology Start-up; Innovation Diffusion Models; Innovation.

Introduction

Start-ups

Blank (2010) defined a start-up as a “temporary organization formed to search for a repeatable and

scalable business model”. The Organization for Economic Co-operation and Development (2014) also

defines a start-up as a “newly formed entrepreneurial venture that aims to meet a marketplace need by

delivering an innovative product, service or business process”. Zakaria, (2016) points out that new

companies, also known as start-ups in some contexts, played an outsize role in spearheading

innovation and job creation in the United States of America. It can thus be inferred that start-ups are

crucial to the economy of every country. Hattangadi (2016) observed that entrepreneurs have innate

instinct regarding innovations because they know that innovations in products and services make their

businesses unique and gives them a competitive edge.

Each week, an African startup is showcased on CNN or Forbes for starting innovative products or

service offerings that are addressing crucial social issues and meeting the growing needs of the

continent (Owusu, 2016). The African Network of Entrepreneurs (TANOE) lists Ahonya.com, 233

Apps, and Farmerline as examples of the many competitive and innovative firms that are making

waves in the Ghanaian Start-up ecosystem (Akpah, 2016). These innovations include efforts related to

business models, product/ service lines, customer engagement, market explorations, channels, sales,

among others. These ventures are usually a mix of those searching for business models, as well as


registered business entities who have recently found and are executing a scalable and repeatable

business model. This research effort used the latter description of a start-up within the Ghanaian

technology ecosystem.

Innovation Rogers (2003), defined innovation as “an idea, practice, or project that is perceived as new by an

individual or another unit of adoption”. He underlined the concept of perception as the product does

not have to be objectively new. He added that, if the idea seems new to the market, it is an innovation.

This interpretation of innovation was adopted for this paper.

Start-ups look forward to their products being easily adopted and accepted by the market. If they are

to survive and grow, the innovations they come up with thus need to be diffused into the society.

Perner (2013) stateed that the life cycle of the products and services introduced by these firms are

linked to the phenomenon of diffusion of innovation. According to him, when start-ups come up with

new products or services, they are initially adopted by a small group of people and later spread to

other people.

Diffusion of innovations, as popularized by Rogers (2003), is a theory that seeks to explain how, why,

and at what rate new ideas and technology spread. Rogers (2003) went ahead to introduce a model,

which became known as the Rogers‟ Innovation Diffusion Model to explain how this theory

practically works. In this model, he extricates five stages in which the product or service may find

itself with five different user groups that accept the product or service offering. Using the model, it

becomes clear how a product or idea disperses among its consumers. The innovation diffusion model

helps a business to understand how a consumer adopts and engages with its new products, services or

technologies over time, making it a crucial tool for visionary entrepreneurs.

However, literature that examines the application of the innovation diffusion models in the startup

ecosystem in Ghana does not exist. Educators in Ghana however use this model in teaching and

training about technology and innovation adoption. This study therefore investigates the applicability

of the Rogers‟ Innovation Diffusion model in the Ghanaian technology start-up ecosystem. The study

further attempts to analyze how start-ups in Ghana can facilitate the adoption of their products or

services in the market

Innovation Diffusion Models

Diffusion of Innovations refers to the spread of abstract ideas and concepts [innovations], technical

information, and actual practices within a social system, where the spread denotes flow or movement

from a source to an adopter, typically via communication and influence (Rogers, 2003). Rogers

(1963) explained that, diffusion of innovation (DOI) is a theory that seeks to explain how, why, and at

what rate new ideas and technology spread through cultures. The origins of the diffusion of

innovation theory are wide-ranging and cover multiple disciplines. It is therefore essential that

innovation diffusion models are derived for various cultures.

The original diffusion research was done as early as 1903 by the French Sociologist Gabriel Tarde,

who plotted the original S-shaped diffusion curve. Tarde‟s 1903 S-shaped curve is of importance

because according to Rogers (1994), “most innovations have an S-shaped rate of adoption”. Tarde

originated key diffusion concepts as the S-curve of diffusion, although he did not refer to these

concepts by the names they are known in literature today (Toews, 2003). These theoretical ideas were

set forth by Tarde in his book, The Laws of Imitation originally published in 1890. Figure 1 below is

an image of the S-curve of diffusion, developed by Tarde and popularized by Rogers in 1963.


Figure 1: S-Shaped Diffusion Curve(Rogers, 2003)

The diagram above illustrates the fact that that few observers adopt a product or service right away

after its launch in the market. With time, the diffusion rate increases and a lot of consumers adopt the

product in a shortest possible time. It takes a lot of time before the innovation is fully adopted in the

related market in the society. The Diffusion of Innovation theory seeks to explain individual adoption

decisions or intentions to adopt innovation in products, services and processes. Mahajan and Peterson

(1979) have stated that the objective of a diffusion model is to present the level of diffusion of an

innovation among a given set of prospective adopters over time. Ryan and Gross (1943) introduced

the adopter categories that were later popularized by Rogers. These models themselves have evolved

over the years from Tarde‟s era to Rogers‟.

Innovation diffusion models are used more often in sales forecasting than any other field. However,

Mahajan and Wind (1986) reiterated the facts that sales forecasting is only one of the objectives of

diffusion models. In addition to forecasting, diffusion models are used for descriptive and normative

purposes. Diffusion models can be used in an instructive model to test specific diffusion-based

hypotheses because it follows a systematic approach in describing the spread of innovation. Because

diffusion models are designed to record the product life cycle of a new product, they can be used for

normative purposes as the basis of how a product should be marketed (Mahajan, 1990).

The process of adopting new innovations has been studied for over sixty years. The two diffusion

theories that underlie most research on the diffusion of innovation were pioneered by Frank Bass

(1969) and Everett Rogers (1983). They are together referred to as the “classical” diffusion models.

These classical works, laid the groundwork for diffusion theory and research, and initiated a decade-

long period of changes. With time, the “classical” frameworks were developed and changed based on

changing socio-economic and academic patterns. The Bass Diffusion Model (BDM) is most of the

times the starting point for scholars who propose changes, though the most popular model is the one

proposed by Rogers.

Wright (2011) indicated that, the Roger‟s model can only be applied after adoption is complete, it has

no predictive capability. He further explained that, the Bass model approximates the probability that

adoption will occur in response to exposure to the innovation and the interaction with other users of

the innovation.

The Classical Diffusion Models: The Bass and Rogers’ Models

The main stimulus underlying diffusion research is the Bass Diffusion Model (BDM). Much of the

literature that exists on the diffusion of innovation follows this model. Scholars have indicated that the

BDM is one of the most successful models in management science in general, and marketing. Rogers

(2003) calls BDM “a lightning rod for marketing scholars”. Including the models proposed by Fourt


and Woodlock (1960) and Mansfield (1961), the BDM assumes that likely adopters of an innovation

are influenced by two means of communication which are the mass media and word of mouth.

Moving on, it further assumed that the adopters of an innovation comprise of two groups. One group

is influenced only by the mass media and communications (external influence) and the other group is

influenced only by the word-of-mouth communication (internal influence). Bass termed the first

group, “Innovators” and the second group “Imitators.” The Bass model conceptually assumes that

“innovators” who adopt exclusively because of the mass-media communication are present at any

stage of the diffusion process. The core assumption in the BDM is that the adoption of a new product

spreads through a population primarily due to contact with prior adopters.

Mahajan and Wind (1986) explained that per the BDM, the probability of adoption of a new product

at time “t” given that it has not yet been adopted, depends linearly on two forces. The first force,

represented by the coefficient „p‟ in the literature, is independent of the number of previous adopters.

„p‟, which reflects adoption due to mass media, was originally termed the coefficient of innovation

and later renamed the coefficient of external influence. The second force, represented by „q‟, is

positively influenced by previous adopters. It was originally termed the coefficient of imitation and

later renamed the coefficient of internal influence. Figure 2 below is a plot of the conceptual structure

underlying the Bass Model.

Figure 2: The Bass Innovation Diffusion Model(Rogers, 2003)

Despite its strengths and strong appeal, the BDM suffers from several limitations, per some critics.

Chandrasekaran and Tellis (2007) suggested that, the model does not include the direct influence of

any marketing variable such as price or advertising. This is a defect in the model because

professionals want to influence sales with the two variables. Chandrasekran and Tellis (2007) also

added that, the product definition in the Bass model is static, that is, it assumes that the product itself

does not change over time. However, per the output of these researchers, there may be several

technological changes within a product itself before a dominant design emerges.

Everett Rogers worked on the criticisms of the BDM and developed an integrated model to explain

the process of diffusion of innovation.Rogers showed that a diffusion process in a social system

follows an S-Curve (refer to Figure 1) and has a sigmoidal distribution in which the adoption of

technology begins with slow change, followed by rapid change and ends in slow change as the

product matures or new technologies emerge. He also held that, people adopt new technological

innovations at different times and at different rates (Rogers, 2003).


Rogers introduced innovativeness – the degree to which an individual is relatively earlier in adopting

new ideas than other members in a social system (Rogers, 2003). He added that, the rate at which

consumers adopt innovations appears to be normally distributed which is like the logistic growth and

frequency-dependent curves. The model emphasizes adopter categorization based on innovativeness,

which is the relative earliness/ lateness with which an innovation is adopted – compared with other

members of a system (Rogers, 2013) as shown below in Figure 3.

Figure 3 : Rogers' Innovation Diffusion Model(Rogers, 2003)

The model shows the percentages of any market that will make up each adopter category. Using a

symmetric bell curve distribution, it says that 2.5% will be innovators, 13.5% will be early adopters,

34% are early majority, another 34% are late majority and the remaining 16% are laggards. The

model basically shows how innovation is adopted by five major adopter categories and how firms can

interact with these types of people.

Rogers‟ Innovation Diffusion Model underlines the importance of differentiating customer segments.

According to Moore (1991), it depicts the need to convince innovators and early adopters to first

make an innovation successful. Using „innovativeness‟ as a variable, Rogers was the first researcher

to group adopters in a scientific and meaningful way. Also, the model‟s ideal types and percentages

can be used as a first draft to estimate groups for communication purposes, hence ideal for use by

entrepreneurs.

The model shows patterns of consumer adoption at each of the various stages during a product‟s life

cycle by focusing on different characteristics of each adopter category. On the other hand, Tiwari

(2014) stated that the Diffusion of Innovation theory is at its best a descriptive tool. There is doubt

about the extent to which it can give rise to readily refutable hypotheses. Many of its elements may be

specific to the culture in which it was derived namely North America in the 1950s and 1960s, and

hence less relevant in, for example, East Asian and African countries.

Using Rogers‟ Innovation Diffusion model, educators in Ghana teach their entrepreneurship students

to focus on up to 16% of their target market when introducing innovations.This 16% percent includes

the first two adopter categories of the innovation diffusion model which are the innovators (2.5%) and

the early adopters (13. 5%).These two adopter categories can be associated with Blank‟s

(2013)Earlyvangelists; customers who are willing to buy a firm‟s unfinished and untried

products.Therefore,it is crucial to study the process of innovation adoption for startup products and

services in Ghana to determine if their innovation diffusion and adopter categories, follow the Rogers‟

innovation diffusion model taught to them.


Research Method

Over the past sixty years, myriads of diffusion studies have been conducted in numerous disciplines

of study, including sociology, education, communication and marketing, mostly in Northern America.

With few exclusions, these studies have been driven by a methodological approach that has become

established in diffusion research. This approach is characterized by the collection of quantitative data

about an innovation gathered from adopters at a time after widespread diffusion has occurred (Meyer,

2014). A mixed method was thus adopted for this study. Since the interest was also to generate a

theory for how innovation of diffusion occurs in Ghana if the Roger‟s model is found not to be

applicable, an exploratory research strategy was adopted. It thus aided in the generation of an

assumption, as described by Hussey (2008), which was tested.

The two unit of analysis adopted for this research were the technology start-ups in Accra, and

customers of one of these start-ups. The population for this research focused on technology startups

because Greenberg (2014) stated that 70% of startups in Ghana are technology based and these

startups spearhead the development of innovation in the country. Since the Rogers‟ Innovation

Diffusion Model is a descriptive tool and innovation can better be studied when it has already been

diffused, start-ups whose products and services are already on the market were adopted.

Four startups described un Appendix 1, were considered as the sample for the first unit of analysis,

while customers of Venture 3, were chosen as the second unit of analysis because, they were

relatively more accessible as compared to the other three startups sampled. 75 responses were

received. A pilot using one company, not part of the sample, was conducted to test the instruments,

and modifications made, before the study itself was conducted.

The quantitative data collected were processed: edited and coded, and analyzed using a time series,

linear and logistic regressions with SPSS used to test for the sigmoidal distribution. The qualitative

data was analyzed using content analysis to understand the characteristics of the various adopters to

aid in a categorization. The objectives were to determine if the innovation diffusion will be an „S‟-

curve, if the distribution will be sigmoidal, and if the adopter categories will be like that described by

Rogers.

Data Management and Findings

Data was collected from the CEOs of Ventures 1, 2 and 3, and an Executive of Venture 4. The CEO

of Venture 3 sent out the link of the e-survey to his customers. He could not give a definite of the

number of customers he had but sent it out via mass mail. 75 individuals responded to the survey,

hence were used in this research.

Adopter Behaviour

To investigate the applicability of the Roger‟s innovation diffusion model, an online survey was

conducted using Google forms. Respondents were asked many questions, the most crucial among

them being the year of adoption of the innovation from Venture 3. 58 of these respondents were

between the ages of 21 and 30 years representing 85% of the sample. Also, 75% of them were

university undergraduate degree holders. 15% percent had heard and knew of Venture 3 but had not

patronized their services. This group of non-adopters fit Rogers (2003) “laggards” classification.


Table 1: Adopter categories for research sample

Theoretical

Definition

Sample

Definition

Adopter

Category

2.5% 2.7% (2012) Pioneer

(Innovator)

13.5% 13.3% (2013-

2014)

Early Adopter

34.0% 34.7% (2014-

2015)

Early

Majority

34.0% 33.3% (2014-

2017)

Late Majority

16.0% 14.7% (non-

adopters)

Laggards

Influenced by Brancheau and Wetherbe (1990) work investigating the applicability of the Roger‟s

innovation diffusion on the usage of the spreadsheet software, the adopter categories were determined

as shown in Table 1 above. The early adopters of the innovation were all young, between the ages of

21 and 30 years and had a relatively higher level of education (majority having a tertiary education)

just as Rogers (2003) suggested.

When asked, which source were most influential in their first-time hearing of Venture 3, 65% of the

sample chose media advertisements while 28% stated that family and friends were most influential.

However, a higher number, 42.2%, stated that friends and family were most influential in their

deciding to use or patronize the services of Venture 3 as compared to a reduced percentage of 45.3%

who claimed media advertisements were the most influential decision catalyst. This aligns to the

external and internal influences described in the BDM.

Roger‟s innovation diffusion model has a sigmoidal or S- shaped distribution over time. Innovation

diffusion research suggests that within a social system, the number of individuals adopting per period

closely follows a normal bell-shaped curve (Rogers, 2003). Plotted on a cumulative frequency basis,

the same data describe a sigmoid or S-shaped curve as shown in Figure 1. With the Roger‟s

innovation diffusion model, the number of adopters increases slowly at first, due to doubt about the

innovation. This results in a comparatively flat curve. If the innovation is successful, communication

channels fill with positive evaluations. After a period, a contagion effect takes hold and the number of

adopters increases rapidly, resulting in a moderately steep curve. As the market for the innovation

saturates, the “number of new adopters tapers off and the curve flattens again”

(Brancheau&Wetherbe, 1990).

To test this with the data obtained from the online survey, the adoption of Venture 3 was regressed on

time (calendar year of adoption) in two ways using both Microsoft Excel and SPSS. First, the number

of adopters was plotted as against time in Microsoft Excel. Figure 4 below illustrates the result


Figure 4: Number of adopters against time for Ahonya.com Ltd

From the above, it is observed that the number of individuals adopting per period the innovation of

Venture 3 closely follows a normal bell-shaped curve. When plotted on a cumulative frequency basis,

the same data roughly follows an S-shaped curve as shown in Figure 5.

Figure 5: Cumulative number of adopters against time for Ahonya.com

Outputs from innovation diffusion research suggests that cumulative adopter distribution for an

innovation traces a sigmoidal pattern over time such that more of its variance can be explained using a

logistic function that can be explained using a linear function (Brancheau & Wetherbe, 1990). To test

this claim, the data of the research sample was first tested for linearity. If the data follows a linear

model, then it cannot support the sigmoidal distribution claim of the Roger‟s innovation diffusion

model. As shown in Table 2 below, the linear model fit the data well with an R squared value of 0.

974, shown in Figure 6.

R R Square Adjusted R Square

Std. Error of the

Estimate

.987 .974 .968 4.476

Table 2: Model Summary of the linear regression

0

5

10

15

20

25

2010 2012 2014 2016 2018

0

20

40

60

80

2010 2012 2014 2016 2018


Figure 6: Venture 3 adopter distribution

However, a high R squared value does not guarantee that a model fits the data well. It suggested a

graphical residual analysis as a more accurate measure for the fitness of a model to a data. Brancheau

and Wetherbe (1990) used a graphical residual analysis to test for the linearity of its data when it

conducted a similar research on the adoption of the spreadsheet software in firms. Accordingly, a plot

of residuals for the data was created using SPSS. Figure 7. Despite the apparent fit of the linear model

(R squared = 0.974), the graphical residual plots did not show any randomization in the data.

However, the graphical plots of residuals traced a sinuous pattern indicating that the linearity

assumption was not acceptable. Hence, it is concluded that the data for the research sample data did

not fit a linear model.

Figure 7: Residual Plot of Adopter distribution for Venture 3

Next, parameters for a nonlinear regression model (logistic function) were computed using SPSS. The

model summary is shown in Table 3. It came with an adjusted squared value of 0.854.

R R Square Adjusted R Square Std. Error of the Estimate

.940 .883 .854 .469

Table 3: Model Summary of Logistic Regression

Parameters for an S-curve were also computed for in SPSS. The model summary is shown in Table 4

below. Testing for an s-curve also had a similar r squared value of 0.883. The failure of the linearity


assumption together with a high R squared value for the non-linear models support the sigmoidal

distribution claim of the Roger‟s innovation diffusion model for the research sample, although the

data did not fit the Roger‟s innovation diffusion model perfectly.

R R Square Adjusted R Square Std. Error of the Estimate

.940 .883 .860 1.228

Table 4: Summary of S-Curve fit

The data was discovered to best fit a logistic function. The failure of the linearity assumption

combined with a high R squared vale for the non-linear models support the sigmoidal distribution

claim of the Roger‟s innovation diffusion model although some variations were observed.

It can be concluded that the Rogers‟ innovation model applies closely in the Ghanaian technology

start-up ecosystem. However, because the ventures studied believe that they are still in the start-up

phase, they are continually innovating, accounting for the deviation. This assertion will however be

confirmed in the larger study being conducted.

Entrepreneur’s appreciation of Roger’s Innovation Diffusion Model

Though all 4 companies adopted for the purposes of this research have been in existence for a while

and they are all registered, they described themselves as startup companies. This identification is

crucial since there is an ongoing global debate about the definition and constituents of startups and

what makes a startup. These representatives from the firms acknowledged the debate and agreed to

Steve Blank‟s definition of a start up in principle, as a “a temporary organization formed to search for

a repeatable and scalable business model” (Blank, 2010). They clearly started that they are not

“small” companies as assumed by some stakeholders, but start-ups as they are still exploring scalable

and repeatable business models. For instance, the CEO of Venture 3 mentioned that he does not

consider how long a firm has been in existence for, when defining a startup. Although his firm is over

five years old, he considers it a startup because his firm is still searching for a scalable business

model.

About the Roger‟s innovation diffusion model, although none of the respondents recognized it by

name, they all recognized it when they were shown a visual model. They had seen the model at

conferences and workshops they had attended and from books they have read. One respondent

associated the Roger‟s innovation diffusion model to the product life cycle. The entrepreneurs could

however not relate to the rigorous academic analysis of the diffusion innovation process using

Roger‟s innovation diffusion model, and how it directly applies to them.

The startups interviewed had knowledge about the adopter categories in the Rogers‟ innovation

diffusion model although they did not know them by their mainstream names. They indicated that,

knowledge of the adopter categories, together with their knowledge on new product development

helped them when they launched their products and services.

Rogers (2003), stated that there are five factors that can impact the diffusion process. These factors

were stated as the relative advantage, compatibility, complexity, trialability and observability.

Although the startups interviewed did not again call these factors by their names, they showed a clear

understanding of these factors and had even observed the effect of some of these factors in the

diffusion of their products and services. For instance, since all of them are technology start-ups, they

understood that the level of complexity in a product purchase and usage also affects the diffusion

process. An innovative offering would be easily diffused when there is ease of understanding,

purchase and use. They stated that the easier it is to understand and use a product, the more likely it is

to be accepted quickly, and vice versa.


It was observed that these entrepreneurs were equipped with strong knowledge on the diffusion

process although they use different terms than those used in literature. Because they were aware of the

various factors that affect the diffusion of innovation, they were also aware of how to facilitate the

adoption of their products. For instance, since they were aware that complexityis a factor that affects

the diffusion of innovation, the startups interviewed are always working on their products to make

them more user friendly. They all asserted to the fact that, the amount of publicity they get affect the

diffusion of their innovations. They were thus working on increasing the awareness of their products

and services mainly through advertisements on social media.

Conclusions and Recommendations

The innovators, who are also known as risk takers in this sample, all had received a post graduate

education. This supported Rogers (2003) claim that the innovators relatively have a higher level of

education. From the semi-structured interviews conducted, it can be concluded that although the

entrepreneurs were aware of the Roger‟s innovation diffusion model, they do not make a conscious

effort to use the Roger‟s innovation diffusion model during the development and launching of a new

product or service.

Although media advertisements were the most influential in the research sample‟s first time hearing

of the innovation; an important insight was obtained from the customers of Venture 3. It was rather

the opinions and referrals from family and friends that influenced their first-time usage of subscription

and not the media advertisements. This is crucial information because many of the entrepreneurs that

were interviewed put a lot of efforts and resources into media advertisements thinking it was the

factor that will most influence the adoption of their product or services. Though the BDM indicates

that both are essential, this shift to family and friend recommendation supports Rogers (2003) theory.

As evidenced by Rogers (2003), consumers and prospective consumers‟ evaluation of an innovation

depend mostly on a subjective evaluation of an innovation that is conveyed to them from other

individuals who have already adopted the innovation. Later adopters of innovations are thus mostly

influenced by personal feedback from the earlier adopter than they are by the mass media promotion

of innovations. The family and friends here served as the “opinion” leaders talked by Rogers.

Despite the apparent fit of the linear model to the adopter categorization data, a graphical residual plot

does not show any randomization in the data. However, the graphical plots of residuals traced a sinous

pattern indicating that the linearity assumption will not apply for this data. This shows that, innovation

diffusion within the Ghanaian technology start-up ecosystem does follow closely, the Roger‟s

innovation diffusion model, but for the start-ups continuously innovating their products and services.

There is however not enough empirical evidence to generalize the conclusions of this research. The

team is thus replicating this research to a larger sample, to draw conclusions that are generalizable.

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Appendix 1:

The following ventures were adopted for the purposes of this research as they were described as part

of the leading innovative firms in the country per the Ghanaian Startup Ecosystem report published by

Meltwater School of Entrepreneurship (2014) and also appeared on the list of the 100 Globally

Competitive Start-ups in Ghana compiled by The African Network of Entrepreneurs (TANOE), an

award winning Social Enterprise in partnership with Avance Media (Akpah, 2016).


Venture 1: is a mobile and web-based firm that provides farmers and farming investors with the

germane agricultural information to help boost total efficiency. It was founded in 2012. The primary

customers for the firm are individual farmers and SMEs.

Venture 2: This start-up is a company focused on providing application services for e-learning, e-

healthcare, e-business and smart homes for the Ghanaian market. They provide free access to

academic content in podcasts, videos, games, eBooks. It averages 10,000 downloads each day and is a

leader in the EduTech in Ghana.

Venture 3: This start-up is an e-commerce website looking forward to positioning itself as the

Amazon of Ghana. In its first year of operations, it completed over 2,000 orders with over $500,000 in

revenue.

Venture 4: is an Information Technology (IT) firm. They develop IT products, which includes mobile

apps, websites and provide consultation on social media strategy. Their flagship product, Moja app, is

a mobile app that creates the avenue and incentives for volunteering as a blood donor. With a cloud

back-end, the National Blood Service is able to keep its stock replenished, search, match and

conveniently contact inspired donors in case of emergencies.

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