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MANAGEMENT | RESEARCH ARTICLE
Monitoring and evaluation practices and project
outcome of tech start-ups in Ghana: The
moderating role of the Business environment
Ramatu Issifu
1
* and Daniel Agyapong
2
Abstract: Issues relating to Monitoring and Evaluation (M&E) have been established
as a key and fundamental tool for the successful implementation of projects
regardless of the industry. The study therefore sought to address the following
questions: what effect do monitoring and evaluation practices have on tech start-
ups project outcomes, as well as the role that business environment play in the
relationship between M&E and project outcomes. The study followed a positivist
mind-set, relying only on quantitative methods and an explanatory research design.
Primary data via structured questionnaire was obtained from 317 respondents in
managerial positions in the tech industry and analysed using inferential and
descriptive tools. The study found that monitoring practices had a positive signifi-
cant effect on project outcome. Evaluation practices also had a positive significant
effect on project outcome. Business environment was found to have a dampening
significant moderating effect in the relationship between evaluation practices and
project outcome. However, business environment did not have any significant effect
in the relationship between monitoring practice and project outcome. These find-
ings will enable project practitioners understand the dynamics of monitoring and
Ramatu Issifu
ABOUT THE AUTHORS
Ramatu Issifu is a Research Assistant at Foundation to Support Education, Networking, Skills
Development, and Enterprise Start-up in Cape Coast, Ghana since 2021. She is a researcher with
three articles to her credit. She holds an MPhil (Project Management) and MSc (Monitoring and
Evaluation) from the University of Cape Coast. She is a Student Member of the Chartered Institute of
Accountants (Ghana). She is a graduate of the prestigious BMGA Fellowship Programme for empower-
ing young African Women. She has been involved in the following projects; Building Expertise and
Training for Growth in the Consumer Goods and Food Processing Industry in Ghana, GIZ ComCashew,
Switch Africa Green E-MAGIN, Building Bridges Across Continents, Applied Research and Teaching for
Sustainable Development in Africa.
Daniel Agyapong, a Professor of Finance and Entrepreneurship, has worked at the University of Cape
Coast (Ghana) since 2004. He is an SME trainer and researcher with 60+ articles on SME financing and
sustainable business development. He holds a PhD and is an Associate Member of the Chartered
Institute of Marketing (UK) and Institute of Professional Managers Association, UK. He has been
involved in the following projects; African Institute of Transformational Entrepreneurship, Building
Expertise and Training for Growth in the Consumer Goods and Food Processing Industry in Ghana,
GIZ ComCashew, Switch Africa Green, E-MAGIN, Graduate Enterprise Development Initiative,
Participatory Appraisals of Competitive Advantage, Building Bridges Across Continents, Partnership for
Applied Sciences, Applied Research and Teaching for Sustainable Development in Africa and German-
African University Partnership Platform for the Development of Entrepreneurs. He is a co-founder of the
Foundation to Support Education, Networking, Skills Development, and Enterprise Start-up.
Issifu & Agyapong, Cogent Business & Management (2023), 10: 2279793
https://doi.org/10.1080/23311975.2023.2279793
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Received: 12 August 2023
Accepted: 27 October 2023
*Corresponding author: Ramatu
Issifu, Department of Marketing and
Supply Chain Management, School of
Business, University of Cape Coast,
Ghana
E-mail: ramatu.issifu@stu.ucc.edu.gh
Reviewing editor:
Ansar Abbas, Management,
Universitas Airlangga - Kampus B,
Indonesia
Additional information is available at
the end of the article
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided the original work is properly cited. The terms on
which this article has been published allow the posting of the Accepted Manuscript in
a repository by the author(s) or with their consent.
evaluation and the business environment when it comes to project execution. It will
further enable project managers, personnel, and donors recognize how significant
M&E tools are when creating policies and managing performance. Moreover, tech
start-ups should create policies that recognize the integration of M&E in their
operations and business functions.
Subjects: Technology; Small Business Management; Operations Management; Project
Management
Keywords: Monitoring Practices; Evaluation Practices; Tech Start-ups; Business
Environment; Project Outcome
1. Introduction
The immense contribution of a successful project to the development and growth of many
countries across the world cannot be emphasized enough (Kahn, 2019). Laursen et al. (2018)
indicated that projects are essential for value creation and economic development, it is through
projects that process and products are developed for the use of people and society. Similarly,
Tyulin and Chursin (2020) stressed that projects are a structured manner of bringing about change,
such as developing a new product, discovering a cancer treatment or constructing a bridge across
a river. Businesses and our entire way of life would stagnate without projects if we merely
maintained the status quo. It is indicated that over 570 construction projects worth US$450 billion
were undertaken, energy sector project worth US$370 billion and the transport sector including
roads, airport and railways worth US$280 billion, evidencing the relevance of projects to the
African economy. Start-Ups firms being it small, medium or large has been widely recognized as
an industry that initiates and undertake numerous projects (Piccarozzi, 2017). There is no widely
accepted definition for start-ups, according to Blank (2013), and they have been categorized over
time based on a variety of factors, including the age of operation, revenue, employee size, growth
and development, profitability, stability, culture, and the mindset of people within the organization.
Ripsas and Tröger (2014) looked at start-ups as a young business that is less than ten years in
operation, uses innovative technology or has an innovative business model, and/or has a rapid
increase in the size of employees or turnover. Technology start-up companies are described in
a variety of ways, but they always revolve around the study of technology as well as the use of
technology in the productions of goods and services (Choi et al., 2020). However, regardless of
direct technological development, having technology or using technology to create value is
included (Candi & Saemundsson, 2011). A technological start-up is seen as a source of employ-
ment because it typically produces new products and services, resulting in increased demand and
highly skilled personnel, which necessitates the creation of new positions. According to Amedofu
et al. (2019), these start-ups account for the bulk of enterprises in the private sector worldwide,
particularly in emerging nations. These businesses have aided in the creation of jobs and the
reduction of poverty on the African continent (Abisuga-Oyekunle et al., 2020). For instance, it is
estimated that about 450 of the tech firms in South Africa employs about 40,000 people (Mureithi,
2021).
Aside it’s numerous positive impacts on individuals, firm and the economy, these tech start-ups
have helped to attract more investment and funds into the continent (Liu et al., 2023; Olaoye,
2023). For instance, the 2018 venture investment report indicated that start-ups in Africa raised
a record of US$725.6 million across 458 contracts in 2018. Similarly, the 2020 African Tech Start-up
funding report indicated that, the year 2020 was a record breaking one for African Tech firms with
397 start-ups securing US$701.5 million worth of investment. In the case of Ghana, Sasu (2022)
opined that 18 tech start-ups received funding in the year 2022. According to the OECD (2004),
there has been an increasing push for the creation of entrepreneurial start-ups in developing
nations, fueled by both government and private sector initiatives. High levels of graduate
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unemployment, as well as the retrenchment of government workers, are pushing the burgeoning
entrepreneurial trend in developing countries (Owualah, 1999).
The expansion of start-up businesses in developing nations like Ghana has been facilitated by
the increased use of information technology like inter-organizational systems and enterprise
resource planning systems, as well as the rise of global sourcing, outsourcing and offshoring
(Agbenyo et al., 2018; Agyei-Owusu et al., 2018; Asamoah et al., 2015). Modern technology is
now more accessible and inexpensive for start-ups in Ghana and Africa because of the growth of
start-up eco-systems, co-working spaces, and technology hubs during the past 10 years, enabling
the emergence of numerous tech start-ups (Ndabeni, 2008). These start-up ecosystems often give
start-ups with access to financial sources, information technology, networking, co-working spaces,
training, and other services that aids them flourish. The activities of these start-up ecosystems
provide a platform for African and other developing-world start-ups to compete with their devel-
oped-world counterparts (Luo & Bai, 2021).
However, despite efforts by the government and other private entities to provide Start-ups with
access to information technology and sources of funding, among other things, Start-ups in Ghana
continue to face problems of sustainability and growth (Kodjokuma, 2018), with the majority of
them failing soon after launching (Blank, 2013; Ries, 2011). A start-up assessment report by Mesa
Community College on a global level showed that only 80% of start-ups established in 2014
reached the second year, 70% made it to the third year, 62% reached year fourth and only 56%
survived up to the fifth year (Mansfield, 2019). In the African continent, statistics from the best
Africa report shows that every five out of ten start-ups fail in Africa (Jha, 2020). In the case of
Ghana, Mensah et al. (2019) added to Kodjokuma (2018) claim indicating that a large number of
tech start-ups in the country remain the same way with no prospect of growth. The implication is
that they may not be able to contribute significantly to economic development as expected.
Even though evidence from the African Development Bank (2006) identified factors such as
political, socio-economic and technological among others as the main actors that play a major role
in the cause of start-up project failure, Polishchuk et al. (2019) however indicated that a critical
aspect of the business strategy and management is the issue of implementing monitoring and
evaluating programs in start-up projects. Kusek and Rist (2004) opined that, Monitoring and
Evaluation (M&E) is one of the critical elements and most relevant tool for influencing project
success and completion. This assertion was later supported by Damoah et al. (2015) who deemed
M&E-related issues to be the most important key determinant of project success, hence establish-
ing the relevance of M&E in ensuring project success. M&E is defined by Pullin and Knight (2003) as
an activity that supports decision-making based on evidence to attain project objectives.
Shapiro (2007) characterized M&E as the methodical collection and analysis of data and the
procedures for determining whether or not targets and milestones are being reached, as well as
analyzing any differences. Ngeru and Ngugi (2019) further stressed that M&E constantly tries to
improve project efficiency and effectiveness. According to Kissi et al. (2019), Project management
organizations and agencies can satisfy the needs of donors and financiers by implementing M&E
systems. This is so that it provides evidence of the project’s success. Chebet (2021) asserts that
continuous project monitoring guarantees that the project’s implementing team oversees the
project’s activities, reviews and updates the project plan and budget as necessary, and examines
timetables and deliverables to help clarify any changes that depart from the original project plan.
As a result, early warning signs are provided to management by M&E in terms of delays and cost of
variations, as well as evidence.
Notwithstanding, the successful implementation of M&E depends mostly on the actors in the
environment in which the firm operates. As indicated by the Institutional Theory, organizational
decision-making, operations, and practices are constrained by a variety of external pressures. The
environment is said to interact with one another to affect how well a firm conducts its internal
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operations and performance. In a complex and dynamic society like Ghana, where organizations in
the business world don’t function independently, understanding the business environment is
essential for effective management (Hanaysha, 2016). For tech start-ups to remain relevant and
expand, they must adapt to these changes. The goal of this study is to examine the effects of
project monitoring and evaluation on project outcomes, as well as the function of the business
environment in influencing the relationship between these factors. The business environment plays
a major role by influencing the effective implementation of monitoring and evaluation practices
(Eruemegbe et al., 2015). Threat from the environment can hinder the successful implementation
of M&E systems, availability of infrastructures and other business development services would help
tech start-ups to run an effective monitoring and evaluation practices as the presence of these
factors provides a smooth mechanism for the execution of M&E practices.
Previous studies however have focused on issues of resource constraints to start up growth and
development emphasizing on access to funding and the absent of business strategy. For instance,
studies such as Ayalew and Xianzhi (2020) analyzed how financial constraints affect innovation
development in eleven African countries. Similarly, Tullock (2010) assessed the capital constraint
to agribusiness sector as an emerging start-up in South Africa. Johnson (2018) found that many of
the issues of these tech start-ups in Ghana are caused by institutional elements like rules and
administrative procedures. It is evident from these studies that, much attention has not been paid
to the critical role M&E play in tech start-up success within the Ghanaian context. It is therefore
relevant to assess the role M&E plays when it comes to SMEs specifically tech-start-ups project
success and outcomes in the Ghanaian society.
Furthermore, existing studies on M&E have looked at these two concepts as a composite
variable. For instance, Kamau and Mohamed (2015) focused on M&E role in achieving project
success in the Kenyan society. Kissi et al. (2019) and Tengan and Aigbavboa (2017) looked at M&E
in the construction industry of Ghana. Fransisko (2016) focused on M&E effectiveness in ensuring
project success in Indonesia. A similar study by Arbolino et al. (2018) looked at the role of M&E in
industrial sustainability in the Italian region. It is evident from these studies that, these two
concept have been looked at as composite word, however, it is worth noting that monitoring
and evaluation are two different concepts that are related but not the same, it is therefore
necessary to assess their individual effect on project outcomes in order to draw an objective
conclusion on their individual impact on project success.
Moreover, these studies again provide evidence of both geographical and contextual gap in
existing literature. This is because a number of these studies on M&E have focused on other sectors
rather than the tech industry. Others also concentrated in different countries thereby making it
inappropriate to adopt their recommendations for the tech industry in Ghana as it may be
misleading. Also, none of these studies have also looked at how the business environment
influences the implementation of internal policies and operations within the tech start-up sector.
The study is motivated by the need to analyze how M&E impact on tech start-ups growth and
sustainability, impacting on their business operations. Thus, leading to their contribution to eco-
nomic development. It further investigates how the factors of the business environment influence
the effective implementation of M&E and how it subsequently affect the outcome of tech start-ups
projects.
The study demonstrates how monitoring and evaluation can be a powerful tool for helping
stakeholders and organisations achieve more accountability and transparency, which will aid
policymakers in developing effective monitoring and evaluation systems. Also, the conclusions
reached would offer a great value to the body of knowledge for project management researchers,
particularly in the application of monitoring and evaluation practices. The paper is organized in
four sections. The first section focuses on the introduction highlighting the background, statement
of the problem as well as the research gap. The second section discusses literature on M&E by
providing theoretical foundations, discussing the different concepts in the study as well as
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reviewing pertinent literature which served as the foundation for establishing the gap in this study.
The third section outlined the methods employed in the study whereas the last section focused on
the results, discussion and conclusion of the analysis.
2. Literature Review
The Program Theory, Evaluation Theory and the Institutional Theory guided the study. The
study used the first two theories due to their applicability in explaining the relevance of M&E in
ensuring project success. And the Institutional Theory was used to explain the role of the
business environment in influencing the impact of internal operation on firm performance. The
programs theory have been used over the years to explain the relevance of undertaking M&E
activities as an intervention to achieve desired results. The theory clarifies the way an inter-
vention (project, program, policy, or a strategy) adds to a chain of outcomes that create the
desired or actual outcomes. Depending on how the intervention is implemented, it might have
both positive (useful) and negative (harmful) consequences (Thomson et al., 2019). It provides
a rationale for why the activities you provide will result in the outcomes or benefits you seek,
making it the foundation of the success of any program or initiative. The current study analyzes
how M&E as an intervention predicts project outcome of tech Start-ups in line with the tenets
of the programme theory. As an intervention M&E procedures are fundamental inputs that,
when properly applied, result in input processing and, ultimately, quantifiable output. In this
perspective, program theory examines the impact of changing input and processes to increase
output and produce quality results. Similar to the programmes theory, the fundamental prin-
ciple underlying the evaluation theory is that in order to ensure the successful achievements of
a project objectives and determines its relevance and sustainability, there is the need to have
in place a designed mechanism to help compares the project impact to what was planned in
the project plan.
The institutional theory on the other hand discussed how symbolic actions and outside influ-
ences rather than functional considerations were more likely to be the driving forces behind
organizational founding and change. The institutional theory’s underlying assumption is that
organizational decision-making, operations, and practices are constrained by a variety of external
pressures. Organizations are therefore concerned with putting the right policies in place as well as
gaining the trust and support of external stakeholders. Several researchers have stressed on the
level of power the forces in the environment have over firms’ activities and performance. This
theory asserts organizations operations are likely to be successful in a stable environment than
a constant changing and dynamic environment. Hence indicating that if firms are unable to
establish appropriate mechanism to respond to these constraints from the environment, they
are likely to face challenges with regards to implementing internal operations and practices
such as monitoring and evaluation practices.
2.1. Empirical review
Globally, monitoring and evaluation have received it fair share in literature with a number of
studies looking at its impacts on project success. For instance, Kissi et al. (2019) investigated
the influence of project M&E practises on construction project success criteria and discovered
a positive statistically significant association between M&E practises and construction project
success criteria. A similar study by Fransisko (2016) shows that in order to enhance the
effectiveness of project execution in CINTA, management commitment and the availability of
procedure implementation monitoring and evaluation are recommended. Kamau and
Mohamed (2015) conduct a literature assessment on the effectiveness of monitoring and
evaluation in ensuring project success in Kenya. Strength of the M&E team, monitoring tech-
nique used, political influence, and project lifecycle stage have all been recognised as factors
that contribute to project success. The study also highlighted managerial support as
a mediating element between M&E and project success. A good M&E without managerial
support is unlikely to succeed. Arbolino et al. (2018) examined the monitoring and evaluation
of industrial sustainability in Italian regions and offered a novel technique based on
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a reinterpretation of the traditional SWOT analysis to assess industrial sustainability at the
regional level. The following section reviewed pertinent articles in relation to the specific
objectives of the study with the aim of comparing and contrasting their findings.
2.2. Monitoring practices and project outcome
At each stage of a project’s life cycle monitoring practices are required to assure the efficacy of the
given project (Herman-Mercer et al., 2018). For a project to be finished on schedule and on budget,
and within the project’s scope, monitoring and control are crucial. Procedures for monitoring tracks
deviations from the project plan and help managers to make sure it is timely, effective, and
efficient (Yousefi et al., 2019). Several studies have explored the relevance of monitoring as
a tool in ensuring project success. For instance, Muchelule et al. (2017) revealed that monitoring
techniques have a substantial influence on project output and outcome within the Kenyan state
corporation. In a similar vein, Nega (2020) found that project monitoring and control practices
have a significant impact on project success.
Belout and Gauvreau (2004) conducted yet another investigation on the relationship between
project success and monitoring. They discovered that the adoption of formal monitoring practices,
such as progress reports and performance indicators, was particularly advantageous and that
project monitoring was positively related with project success. The study also discovered that
when project monitoring was included into the broader project management process, it performed
at its best. Another study was conducted by Turner and Müller (2005) to investigate the relation-
ship between construction project performance and project monitoring. They discovered that
efficient monitoring procedures, including regular progress meetings and performance monitoring,
were positively related to project success. Additionally, the study discovered that monitoring
practices worked best when they were included into the project management cycle and when
they were in line with the objectives of the project.
Huang et al. (2011) study looked at how monitoring practices affect software project out-
comes. The study discovered that the success of a project was favorably correlated with the
adoption of efficient monitoring techniques, such as project status reporting and progress
tracking. Effective monitoring techniques were also shown to be crucial in the study’s sophis-
ticated software development initiatives. Karim et al. (2015) looked at the relationship between
project success in the public sector and monitoring practices. The study revealed that efficient
monitoring techniques, performance indicators and progress reports, were strongly related to
project success. Additionally, the study discovered that efficient monitoring procedures were
crucial for public sector initiatives, which frequently include intricate stakeholder interactions
and conflicting demands.
Abebe (2018) also revealed that project Monitoring and Controlling process groups had
a substantial influence on the outcome of the project, suggesting that high levels of project
monitoring and control are more likely to provide greater project success. Young et al. (2019)
further revealed five project governance techniques namely “monitoring, change, vision, sponsor
and KPI” to have a substantial correlation with project performance and to be effective at various
points of the project lifecycle. Monitoring according to Kabonga (2018) provides information on
how an intervention is doing in relation to its objectives. Monitoring provides signs that models
may be veering off course or not functioning as planned, In light of this, the study aims to
determine the effect of monitoring practices on project outcome within the tech start-up sector
in Ghana. The study therefore hypothesis that:
H1: Monitoring practice has a significant effect on project outcome in tech Start-ups in Ghana
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2.3. Evaluation practices and project outcome
Evaluation have been considered as the key measure of project efficacy (Otieno, 2000; Sherman &
Ford, 2014). Over the years project managers have focused on evaluation as a tool to measure and
assess whether their project output was able to meet its objectives and also determines the
relevance and sustainability of the project. On the other hand a number of managers fall on this
tool when they are confronted with several project options to choose from. Thus in the case of
mutually exclusive project, evaluation tools are employed to assessed the profitability, risk and
impact of each project to aid project managers make an informed decision (Habibi et al., 2018).
This demonstrates how imperative evaluation tools are in the project-based industry.
Studies have shown that effective evaluation practices may enhance project planning, monitor-
ing, and control procedures, which can lead to better project outcomes. For instance, a study by
Zhang and Yang (2018) showed that incorporating evaluation practices into the project manage-
ment process increased project success rates. Blackwood et al. (2018) study looked into how
evaluation practices affected project outcomes in the nonprofit sector. The findings revealed
a positive association between evaluation and project outcomes. Olejniczak, Kupiec and
Newcomer (2017) also indicated that learning from evaluation results considerably increased the
efficacy and efficiency of project performance. They argued that evaluation practices may aid
organizations in learning from past mistakes and enhancing the success of upcoming projects. This
is due to the fact that evaluation offers project managers feedback that helps them to recognize
strengths and flaws and make the required modifications for next projects. Oliveros-Romero and
Aibinu (2019), also find out from interviewing experts that ex-post evaluations are relevant to PPP
projects. A study by Uzunkaya (2017) also revealed that theory-based evaluation is a potentially
useful evaluation technique that could be tailored to the complexity of PPP projects and programs
and would broaden the toolkits available to evaluators.
Kabonga (2018) asserted that if objectives are not met, evaluation reveals the cause behind it.
Causality is said to be a function of evaluation. Thus evaluation then reveals the truth behind,
bringing the larger project to the forefront environment. This evidences the relevance of evaluation
with regards to ensuring project success and outcome. On this note, it is relevant to have an
empirical evidence showing the impact of evaluation on project success in the tech start-ups
within the Ghanaian context. The study therefore argues that evaluation practices influence
project outcome. Given this we hypothesis that:
H2: Evaluation practice has a significant effect on project outcome in tech Start-ups in Ghana
2.4. Business environment, monitoring practices and project outcome
The relevance of monitoring has been documented in extant literature established by several
researchers as demonstrated in the sections above. Monitoring practices are critical procedures at
any stage of a project since it’s allow for continuous observing of the project’s efficacy (Ahuja &
Thiruvengadam, 2004; Kissi et al., 2019). Monitoring according to Kihuha (2018) serves as an
intervention to aid improve success of a project output. Arce et al. (2020) established that these
interventions depends on certain circumstance to execute it role effectively. Thus any activity
depends greatly on certain circumstances which could affect it positively or negatively. These
circumstance is termed as the environment in the management world and they play a crucial role
in firm activities and performance.
This assertion is evident in the work of Eruemegbe et al. (2015) who indicated that, given how
organizations and the environment interact, an organization’s performance depends on how it
reacts to, understands, and influences particular environmental changes. Hence for the achieve-
ment of optimal organizational performance, resources must be used carefully to prevent waste
(Eruemegbe et al., 2015). Both the internal and external environment influences each decision
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made in the organization especially factors that plays in the external environment are beyond the
firms’ control and requires managers to keep up to date information about these factors playing in
the external environment. This indicates that if management does not handle these threats
effectively, all initiatives and decisions that the organization makes is likely to be affected nega-
tively. For instance, a large tax system would reduce firms profit affecting their ability to establish
and implement monitoring practices as they may lack funding to do so. Also lack of management
support in M&E implementation can invariably hamper its effectiveness thereby affecting the
organizational goals and project outcomes. Due to this negativity associated with the business
environment, Eruemegbe et al. (2015) stated that management must create appropriate adjust-
ments and measures to control and handle these environmental issues.
In addition to this, numerous studies have examined the moderating effect of the business
environment on the relationship between monitoring practices and project outcome. For instance,
Krogstie et al. (2017) looked at how the business environment affected how well project monitor-
ing practices worked in the construction sector. They discovered that the degree of collaboration
among project stakeholders as well as the project’s complexity and level of business environment
unpredictability all had an impact on how successful monitoring practices were. Similar to this,
Joslin and Müller (2016) looked at how the business environment affected the relationship
between project success and monitoring practices in the information technology sector. They
discovered that elements including the degree of industry competitiveness, the state of technical
development, and the regulatory environment had an impact on the efficacy of monitoring
practices.
Another research by Eroglu and Karaarslan (2018) looked at the business environment’s mod-
erating impact on the relationship between project performance and monitoring practices in the
manufacturing sector. They discovered that the degree of market volatility, the amount of industry
innovation, and the degree of competitiveness all had an impact on the efficacy of monitoring
practices. These studies suggest that a number of business environment variables, such as project
complexity, level of uncertainty, degree of collaboration, degree of competition, degree of tech-
nological advancement, regulatory environment, degree of market turbulence, and degree of
innovation, can have an impact on the effectiveness of implementing monitoring practices. In
light of this, the study argues that if the business environment is favorable, it is likely to improve on
firms monitoring activities therefore leading to a positive outcome. Based on this the study
hypothesized that:
H
3
:Business environment plays a significant moderating role in the relationship between mon-
itoring practice and project outcome in tech Start-ups in Ghana.
2.5. Business environment, evaluation practices and project outcome
Evaluation have been established as a tool to assess the relevance, efficiency, impact, effective-
ness and sustainability of a developmental intervention. Without evaluation management would
not be able to determine whether resources have been used effectively and whether the project
outcome meets specification and ultimately addresses its intended purpose. However, it is worth
noting that the establishment and implementation of these evaluation practices depend heavily
on the state of the environment in which the business is operating. Eruemegbe et al. (2015)
asserted that every business entity must exist to some extent; no organization can operate in
isolation. Each organization has objectives and duties in relation to people in its surroundings. And
due to the uncertainty nature of the environment, actions taken internally within the organization
can be influenced.
Numerous studies have examined the moderating effect of the business environment in the
relationship between evaluation practices and project outcome. A study by Lee and Kim
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(2018) revealed that the relationship between project performance and evaluation practices
is moderated by the business environment. Belout and Gauvreau (2004) discovered in another
study that the association between project success and evaluation practices is moderated by
the business environment. They discovered that whereas evaluation practices had no dis-
cernible impact on project performance in a dynamic and unpredictable business environ-
ment, they are favorably correlated with project success in a stable and predictable business
environment.
Similarly in a recent study, Shehu and Shehu (2015) also discovered that the association
between project success and evaluation practices is moderated by the business environment.
They discovered that whereas evaluation practices have no discernible impact on project
performance in an adversarial and unstable business environment, they are favorably asso-
ciated to it in a stable and supportive company environment. Evidence from these literature
indicate that the business environment is a major key that can make and unmake organiza-
tional policies, thus in a highly competitive and stable business environment, evaluation
practices can enhance project performance and success, However, in a less competitive and
dynamic business environment, evaluation practices may have limited impact on project
performance and success hence the need for management to handle these factors effectively
to ensure that it does not hinder the successful implementation of their evaluation policies and
systems. This study thereby argues that the business environment if managed effectively could
enhance evaluation practices and subsequently improve on project performance and outcome.
Based on this, the study hypothesized that:
H
4
:Business environment plays a significant moderating role in the relationship between evalua-
tion practice and project outcome in tech Start-ups in Ghana.
2.6. Conceptual Model
Based on the results obtained from the reviewed literature, we develop a research model to
demonstrate the relationship between monitoring practices, evaluation practices, business envir-
onment and project outcome. The framework is depicted in Figure 1.
3. Research methodology
The study followed a positivist research philosophy, relying only on quantitative methods and an
explanatory research design. Data from prior research on monitoring practices, evaluation prac-
tices, the business environment, and project outcomes were used to create a structured ques-
tionnaire. The study’s population comprised tech start-ups in Ghana. Specifically, the study
targeted tech start-ups within some selected regions of Ghana including Greater Accra, Ashanti,
Central, Eastern, Northern, Savannah, Volta and Western. The selection of these regions was due to
the highly concentrated number of tech start-ups in these areas. The study specifically targeted
managers occupying various positions in the tech industry to acquire information about monitor-
ing and evaluation and project outcome in the industry. The simple random sample procedure was
utilized to collect data from 317 respondents in managerial positions. The items measuring
monitoring practice were adapted from Kissi et al. (2019); evaluation practices were adopted
from Gomes (2020) and Thaddee, Prudence and Valens (2020); business environment was adopted
from Cherunilam (2021) and Dvorský et al. (2021) whereas project outcome was adopted from
Cruz Villazón et al. (2020). It was measured on a 5-point likert-like scale ranging from 1=least
agreement to 5=highest agreement. The data was processed using IBM SPSS Statistics (version 23)
and SmartPLS (version 3). Data was analyzed using both descriptive and inferential statistics. The
respondents’ socio-demographic data was analyzed using frequencies and percentages. Following
previous studies, we used structural equation modelling method to test the hypotheses (Aditjandra
et al., 2012; Elahi et al., 2022). The significance test assumed here was that the t-statistics should
be greater than 1.96 and the p-value should be less than 0.05.
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4. Data analysis and discussions
4.1. Respondents’ demographic features`
Majority of the respondents (189) were males, represented 59.6% of the total 317 respondents
whiles females were 122 representing 38.5%. However, the remaining 6 respondents representing
1.9% did not indicate their sex. This suggest that the tech sector has more males managing their
project activities as compare with females. In terms of respondents’ position in the firm, 164
representing 51.7% were owners/managers, 82 were project managers representing 25.9%, M&E
manager were 34 representing 10.7% of the respondents and the remaining 37 representing
11.7% occupies various different positions in their respective firms. This result showed that in
most of these tech start-ups, the owner’s doubles as managers of their project activities.
Respondents’ were also ask to indicate the specific region that their firm is located. 122 represent-
ing 38.5% indicated that their firms’ are located in the Greater Accra region. This was followed by
the Ashanti region which recorded a total of 82 firms representing 25.9%. Western region had
a total of 52 representing 16.4%, Eastern region had 18 representing 5.7%, followed by Central
region which had a total of 10 representing 3.2%. Northern region, Savannah region and Volta
region had a total of 13, 11 and 9 representing 4.1%, 3.5% and 2.8% respectively. This showed that
most of these tech start-ups are located in the capital city.
Additionally, respondent were asked to specify the sector of their firm. 81 representing 25.6%
were into product, services and process development. 72 (22.7%) were into marketing and 70
representing 22.1% were into Transport activities. A total of 42 (13.2%) firms were in the
financial sector, e-commerce sector had a total of 35 representing 11.0%. 14 representing
4.4% are into agriculture and 3 of the firms representing 0.9% are into areas other than those
specified above. This shows that most of these tech start-ups are more into products, service,
process, marketing and transport operations. The number of employees in each of these firms
were also inquired, it was observed that 252 of the firms representing 79.5% had between 1 to
10 employees. Those with 11 to 20 employees were 36 representing 11.4%, 10 of the firms
representing 3.2%, had 21 to 30 employees. 8 representing 2.5% had 31 to 40 employees, 4
representing 1.3% had 41 to 50 employees. Also those with 51 to 60 employees were 3
representing 0.9%. On the other hand, those with 61 to 70 employees were 2 representing
0.6%. Likewise those with 71 to 80 employees were also 2 representing 0.6% as well. This
Figure 1. Monitoring and eva-
luation practice, business
environment and project
Outcome.
Source: Author’s own construct
(2022)
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shows how the sector is contributing to economic development through job creation. Finally
the study sought to ascertain whether these firms have an M&E expert in their respective firms
that oversees the monitoring and evaluation activities during their project execution. 161
represented 50.8% indicated that their firm has an M&E expert whiles 156 representing
49.2% indicated that their firm does not have an M&E expert.
4.2. Assessment of PLS-SEM
Prior to the actual hypotheses testing, the qualities of the PLS-SEM were first assessed using
indicator reliability (IR), convergent validity (CV), construct reliability (CR) and discriminant validity
(i.e., HTMT). Hair et al. (2019) and Henseler (2017) stressed that the model qualities are assessed
and reported to make meaning of the regression model results. They also ensure that the model
meets the expected criteria and thus, its findings could be relied upon to influence policies and
practices in any organizational setting.
4.2.1. Indicator reliability
The first step in assessing the quality of the model was to determine whether the measurement
items for the construct in the outer model are reliable. The indicator values for the constructs were
then evaluated in order to assess the quality of the scale items. The evaluation was carried out to
make sure that each indicator offers an accurate reflection of the allocated construct. The rule of
thumb for acceptable item reliability is that, each indicator’s loading should be greater than 0.70 to
indicate that it is a good predictor of its construct (Hair et al., 2021; Henseler et al., 2015).
Therefore, items considered to be subpar measures of their assigned constructs, were eliminated
since their items loadings were less than 0.70 (Hair et al., 2017, 2019; Memon et al., 2021; Wong
et al., 2019).
Item loadings that are deleted from the model, according to Hair et al. (2019), do not offer
accurate measurements of the assigned constructs. Because of this, leaving them could affect
the model’s output. As a result, all item loadings below 0.7 in the initial model that affected
AVE and composite reliability were appropriately deleted, showing that not all of the items
collected from prior studies were accurate measurements of the constructs they were allocated
in the context of this study. Following the removal of all indicator loadings below 0.70 as
recommended by Hair et al. (2017) and Henseler et al. (2009) the final model structure is
showed in Figure 2.
4.2.2. Internal consistency reliability and validity
To assess the internal consistency reliability of the model, Cronbach and Meehl’s (1955) Cronbach
Alpha, Jöreskog’s (1971) composite reliability and Dijkstra and Henseler’s (2015) rho_A were used
to assess the internal consistency reliability of the model.. This study however relied on the
composite reliability to determine the internal consistency reliability other than the others.
Composite reliability, unlike Cronbach’s Alpha, does not demand that the population’s indicator
loadings be uniform across the board. This follows the working principle of the PLS-SEM algorithm,
which prioritizes the indicators in the model estimation on the basis of their reliability.
Internal consistency reliability seems to be undervalued by Cronbach Alpha, which frequently
depends on how many elements are on the scale. Thus, scores between 0.60 and 0.70 are deemed
appropriate when employing composite reliability, whereas values between 0.70 and 0.90 are
preferred in a more advanced research phases (Nunnally & Bernstein, 1994). From Table 1,
composite reliability for the outer model varied from 0.70 to 0.90, indicating that the constructions’
internal consistency was confirmed.
This section also evaluated and discussed the convergent validity (CV) thus the outer model
validity of the regression model and the results are shown in Table 1. The average variance
extracted (AVE) values are used to describe the CV (Hair et al., 2014; 2017). The AVE values
show the degree to which an indicator’s variance is captured by the latent construct with respect
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to the sum of variance and its resulting measurement error. The study complied with the rule that
all AVE values should be > 0.50 for CV to occur (Bagozzi & Yi, 1988). The table showed that all of the
AVE scores were greater than 0.50, with the lowest value (BE) being 0.501 and the highest being
0.556 (PO). Simply put, the model met the quality criterion hence its validity was convergent.
The model’s discriminant validity (DV), as proposed by Henseler et al. (2015) was also tested as
part of the study to evaluate the model’s quality. In a model, DV looks for potential collinearity
problems (Hair et al., 2017). According to Hair et al. (2017), DVs with significant degrees of
discriminant validity typically don’t have collinearity. Three main methods for examining DV in
a PLS-SEM model have been presented in earlier research (Fornell & Larcker, 1981; Hair et al., 2019;
Henseler et al., 2015). These approaches included Fornell and Larcker (1981), cross loadings and
Heterotrait-Monotrait (HTMT) ratio. However, this study employed the Fornell and Larcker and
HTMT approach (i.e., in Table 2 and 3).
Figure 2. Final model structure
extracted from PLS Algorithm.
Source: Field Survey (2022)
Table 1. Assessment of measurement model
Cronbach’s
Alpha
rho_A Composite
Reliability (CR)
CV (AVE score)
BE 0.834 0.839 0.875 0.501
EP 0.850 0.855 0.886 0.526
MP 0.873 0.875 0.900 0.530
PO 0.800 0.801 0.862 0.556
(CA, rho_A and CR) – Internal consistency reliability; (AVE) – Convergent validity
Source: Field survey, (2022)
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The Fornell and Larcker method reveals that constructs differ from their indications more than
any other construct. Thus constructs have more variance with their indicators than any other
construct. Each construct’s AVE must be greater than the greatest square correlation of any other
construct in order to meet that requirement. The impacts of the Fornell-Larcker Criterion are
presented in Table 2 and the AVE of each construction exceeds the square.
Nevertheless, current research suggests that the Fornell-Larcker criteria are insufficient for
establishing the validity of discriminants. According to Henseler et al. (2015), the Fornell-Larcker
criterion does not work well when the stressors on a construct indicator vary widely. Voorhees
et al. (2016) suggested a heterotrait-monotrait (HTMT) link as a consequence. The (geometric)
average correlations of the objects that measure the same construct divided by the average value
of the item correlations across all constructs is known as the HTMT. More precisely, HTMT has the
strength of easily detecting absence of DV in basic research unlike the others. Values for the
construct’s HTMT is displayed in Table 3 below.
The rule of thumb for assessing HTMT is that the correlation values between the constructs
should be < 0.90 (Wetzels et al., 2009). Simply put, discriminant validity is achieved if the HTMT
scores are < 0.90. It could, therefore, be deduced from Table 3 that all the HTMT values for the
constructs are < 0.90 with the highest value of 0.835 in the relationship between MP and PO. This
result suggests that the constructs are clearly different from each other.
4.2.3. Structural model assessment
After assessing the measurement model for quality purposes, Hair et al. (2019) indicates that there
is the need to assess the structural model. Assessing the structural modelling includes assessing
the multi-collinearity (VIF), co-efficient of determination (R
2
), Effect size (f
2
) and predictive rele-
vance (Q
2
). This is proceeded with an assessment of the significance and size of the path co-
efficient for the hypothesized relationships.
The inner VIF scores are presented in Table 4 in order to test for potential multicollinearity.
Additionally, it aids in minimizing frequent method bias in the research. According to Hair et al.
(2021) multicollinearity is evaluated to determine whether the path coefficients are bias-free.
Additionally, it makes sure that any substantial areas of potential collinearity between the exo-
genous variables are drastically reduced. To check for multicollinearity, all inner VIF values must be
less than 10 (Pallant & Manual, 2007). Pallant and Manual (2007) claim that multicollinearity exists
Table 2. Fornell-Larcker criterion
BE EP MP PO
BE 0.708
EP 0.160 0.726
MP 0.178 0.714 0.728
PO 0.177 0.667 0.700 0.746
Source: Field survey, (2022)
Table 3. Heterotrait-monotrait ratio (HTMT)
BE EP MP PO
BE
EP 0.193
MP 0.207 0.821
PO 0.215 0.799 0.835
Source: Field survey, (2022)
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when the VIF scores are greater than 10, and that this could have an impact on the model’s
accuracy or quality. All of the VIF values were well below 10, which indicated the absence of
multicollinearity. To be more exact, there was no evidence of multicollinearity among the con-
structs as the VIF values ranged between 1.041 and 2.090.
4.2.4. Explanation of target endogenous variable variance
With the absence of multicollinearity, this section describes the model’s predictive accuracy by
reporting the coefficient of determination (R
2
) score. It also reported other key estimations such
as, “predictive relevance (Q
2
) based on the Stone-Giesser’s test and effect size (f
2
)” (Hair et al.,
2019). Table 5 presents the results obtained for the co-efficient of determination (R
2
), the cross
validated blinding redundancy measure (Q
2
), and the effect size (f
2
).
These components were examined to see if the constructs were reliable measurements of the
model’s quality and if so, whether the model’s output could be trusted to produce factual results.
First reported was the predictive relevance score using the R
2
value. According to Hair et al. (2017),
the R
2
represents the sum of the predictors’ (MP, EP) contributions to the dependent construct (PO).
Simply put, R
2
suggests the change in PO that is linearly accounted for by combining the two
independent variables (MP, EP). According to Henseler et al. (2009), R
2
values 0.25, 0.50 and 0.75
represent respectively weak, moderate and strong contributions of the predictor constructs to the
endogenous construct.
From Table 5, the R
2
value was 0.562; meaning that when the two independent variables (MP,
EP) are combined, they linearly account for about 56.2 percent of change in the project outcome
(PO). Simply put, for any change in tech start-up project outcome, MP and EP combine to linearly
account for about 56.2 percent of such change. However, because R
2
values increase with the
number of predictors, adjusted R
2
is recommended since it accounts for model complexity and
helps compare models. Table 5 presents R
2
adj. values of 0.555 for project outcome. Thus,
monitoring practices and evaluation practices explained 55.5% of consumers buying behavior
variances.
The effect size of each independent constant was assessed by adopting Cohen (1988) impact
criterion and the results are presented in Table V. According to Cohen (1988), values of 0.02 denote
Table 4. Vif
VIF
BE 1.041
EP 2.090
MP 2.066
PO
Source: Field survey, (2022)
Table 5. Explanatory power of exogenous variables
L.V R Square R Square
Adjusted
f Square Q
2
BE 0.006 0.298
EP 0.117 0.268
MP 0.216 0.248
PO 0.562 0.555
“Note: L.V. = latent variable, R
2
= R squared, f
2
= effect size, Q
2
= predictive relevance”
Source: Field survey, (2022)
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a small effect size, 0.15 a medium effect size, and 0.35 a big effect size. From the table, BE had the
lowest f
2
value of 0.006; followed by EP with 0.117. However, MP had the highest f
2
value of 0.216.
Based on Cohen (1988) criteria, BE is said to have a weak effect size whiles EP and MP had
a medium effect size (f
2
). These results suggest that when the two independent variables (MP
and EP) are individually implemented, MP would have the largest effect on the project outcome of
these tech firms followed by EP respectively.
Finally, the model’s predictive relevance based on Stone-Geisser’s (Q
2
) test (Hair et al., 2014) was
reported. Q
2
is analyzed by removing a portion of the data matrix, analyze the model and predict
the removed part based on the estimations (Roldán & Sánchez-Franco, 2012). Chin (2010) sug-
gested that Q
2
is achieved if it is > 0 for the construct. Henseler et al. (2009) proposed that, 0.02 ≤
Q
2
<0.15 shows weak effect, 0.15 ≤ Q
2
<0.35 indicates moderate effect and Q
2
>0.35 signifies
strong effect. One can therefore conclude that all the Q
2
values were > 0 indicating that the
predictors can relevantly predict the endogenous variable in the model. However, BE had the
highest Q
2
of 0.298; followed by EP (0.268), and MP (0.248) respectively. This means that although
BE has a small and weak effect size, it is a better predictor of project outcome when compared
with EP and MP which had high effect size.
4.2.5. Significance of path coefficients
Following a quality assessment of the PLS-SEM, the study examined the hypotheses to see whether
significant effects exist among the associations. This was accomplished by analyzing the data with
5000 bootstraps, as suggested by Hair et al. (2017). Table 6 presented the results with five columns
representing structural paths, path coefficients (β), t-stats, p-values and decision rule of each
hypothesis.
The hypotheses were tested in this study by reporting the t-stats values indicated by Hair et al.
(2021), Ringle et al. (2012), and Roldán and Sánchez-Franco (2012). The criterion indicates that the
t-stat should be greater than 1.96 (i.e., p < 0.05) to demonstrate that the hypothesized relationship
is significant (Hair et al., 2014; 2014). Simply put, a t-stat >1.96 is equivalent to a p value of less
than 0.05, implying that the directional hypotheses (as shown in Table 6) are supported, however
one of the moderating relationship was not significant at 0.05. The results of the hypotheses were
given and discussed in the sections below.
4.2.6. Direct relationships
The PLS-SEM technique was employed to examine the relationship between monitoring practices
and project outcome, it further analyze the effect of evaluation practices on project outcome. By
interpreting the bootstrapping results, the significant effects at a 95% confidence level on causal
paths as presented in Table 6 reported a significant effect of monitoring practice on project
outcome (t = 7.119, p= 0.000). Given this result, the t-test of 7.119 was greater than 1.96 thresh-
old and a 0.000 p-value is also lesser than 0.05, hence meeting the criteria by (Hair et al., 2014,
2014). With a β value of 0.442 (as showed in Table 6), indicates that the relationship between
monitoring practices and project outcome is positively significant. Thus MP can directly predict
Table 6. Structural equation model output and decision rule
Structural Path (β) T Statistics P Values Decision Rule
MP -> PO 0.442 7.119 0.000 H
1
(supported)
EP -> PO 0.328 4.879 0.000 H
2
(supported)
Moderating Effect
(MP) -> PO −0.119 1.820 0.069 H
3
(not supported)
(EP) -> PO 0.164 2.334 0.020 H
4
(supported)
Note: * = t > 1.96; p < 0.05
Source: Field Survey (2022)
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a change in PO. This also implies that any unit change in monitoring practices would lead to
a significant positive unit change in project outcome by 44.2 percent. Also, per the β value, it can
be deduced that the effect of monitoring practices on project outcome is moderate. In a similar
vein, the result in Table 6 revealed that evaluation practices positively influenced project outcome
(β = 0.328; t = 4.879; p = 0.000). This result implies that the model’s t-test of 4.879 was more than
1.96 and the p-value of 0.000 was less than the 0.05. This indicates that any unit improvement in
evaluation practices would result in a 32.8 percent increment in the outcome of tech project. This
result based on the β = 0.328, shows a moderate effect.
4.2.7. Moderation relationships
To assess potential moderation effects we employed the simple slope analysis to first establish
possible interaction among the variables under consideration. From Figure 3, it was revealed that
the slopes had a dis-ordinal interaction with each other indicating that there is an interaction
between business environment, monitoring practices and project outcome, however by interpret-
ing the bootstrapping results, the significant effects at the 95% confidence level showed that the
interaction was not significant. The bootstrapping results reported a [t = 1.820, p < 0.069] indicating
that even though an interaction exists between these variables, such interaction was not signifi-
cant, hence the hypothesis was not supported.
On the other hand, business environment base on the slope in Figure 4 showed an interaction
between business environment, evaluation practices and project outcome. This interaction was
however significant with a t = 2.334, p < 0.020 based on the bootstrapping results. However,
evidence from Table 6 using the β values showed that the introduction of the business environ-
ment weakens the direct relationship. Thus it drop from (β = 0.442 to β = 0.164).
4.3. Discussion of results
We examined the effect of monitoring practices on project outcome of tech start-up in Ghana. In
view of this, the study hypothesized that Monitoring Practices (MP) had a significant effect on
Figure 3. Simple slope ana-
lyzes-interaction effect of BE,
MP and P.
Source: Field Survey (2022)
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project outcome (PO). The PLS-SEM result supported our hypothesis that monitoring practices have
a significant effect on project outcome. This could be justify that project objectives and outcomes
are easily achieved when monitoring practices are put in place by these firms. Regular progress
meeting, process and performance monitoring are all essential activities to ensure project success.
Evidence from previous studies confirms that monitoring practices have a positive significant effect
on project outcome. For instance, Muchelule et al. (2017), revealed that monitoring techniques
have a substantial influence on project output and outcome within the Kenyan state corporation.
Nega (2020), found that project monitoring and control practices have a significant impact on
project success. Turner and Müller (2005), discovered that the adoption of formal monitoring
practices, such as progress reports and performance indicators, was particularly advantageous
and that project monitoring was positively related with project success. Similarly, Belout and
Gauvreau (2004) revealed that the adoption of formal monitoring practices, such as progress
reports and performance indicators, was particularly advantageous and that project monitoring
was positively related with project success. Also, Huang et al. (2011) and Karim et al. (2015) among
others all revealed that monitoring practices have a significant positive effect on project outcome
and success.
Also, the Weiss (1972) programs theory, which describes how an intervention contributes to
a chain of results that creates an intended or actual outcome, has been used to corroborate the
study’s findings. Depending on how it is carried out, the aforementioned intervention could have
a beneficial or negative outcome (Thomson et al., 2019). The current result indicates monitoring as
an intervention, predicts project outcome of tech Start-ups in line with the tenets of the pro-
gramme theory. Thus monitoring practices are in the form of basic inputs that, when used
correctly, translate to input processing and, finally, measurable output. Hence if these intervention
are executed effectively, it would affect the outcome of tech firms’ projects.
Figure 4. Simple slope ana-
lyzes-interaction effect of BE,
EP and PO.
Source: Field Survey (2022)
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Moreover, this study revealed a positive significant relationship between evaluation practice and
project outcome within the tech Start-ups sector in Ghana. The outcome course of the PLS-SEM
result supported our theoretical prediction that evaluation practice has a significant effect on
project outcome. The positive effect of evaluation practices on project outcome could be explained
that it is through evaluation that project output and performances can be measured against
planned. Thus it is through evaluation that one can determine whether a development intervention
is effective, successful, relevant, efficient, impactful and sustainable. Thus, evaluation helps to
assess the efficacy of a particular program (Rossi et al., 2018).
The importance and significance of undertaking evaluation practices can further be justified
based on empirical, theoretical and practical evidence. The result is been supported by the
evaluation theory which according to McCoy (2005), compares the project impact (outcome) to
what was planned in the project plan. Evaluation theory evaluates the efficacy of a project in
meeting its objectives and determining the relevance and sustainability of a continuing project.
Implying that the best way to assessed whether a particular project has met its intended purpose
is through evaluation, hence the need for managers to ensure that evaluation activities are done
at the right time with the appropriate tools to prevent the project activities from deviating from it
intended purpose. The findings are also consistent with the findings of Zhang and Yang (2018),
who discovered that incorporating evaluation practices into the project management process
increased project success rates, Blackwood et al. (2018), also revealed a positive association
between evaluation and project outcomes, likewise, Olejniczak, Kupiec and Newcomer (2017)
who indicated that learning from evaluation results considerably increased the efficacy and
efficiency of project performance
Also, the study simple slope analysis reveal an interaction between business environment,
monitoring practices and project outcome, however, it was not statistically significant. This could
be justified by the fact that irrespective of the actors in the business environment, tech start-ups
continue to practice their monitoring activities. The findings are also inconsistent with the findings
of Krogstie et al. (2017) who discovered that the degree of collaboration among project stake-
holders as well as the project’s complexity and level of business environment unpredictability all
had an impact on how successful monitoring practices were. Also, Joslin and Müller (2016)
discovered that elements including the degree of industry competitiveness, the state of technical
development, and the regulatory environment had an impact on the efficacy of monitoring
practices. Furthermore, Eroglu and Karaarslan (2018), found that that the degree of market
volatility, the amount of industry innovation, and the degree of competitiveness all had an impact
on the efficacy of monitoring practices.
Finally, using the PLS-SEM and the simple slope analysis, the empirical results support our
theoretical predictions that business environment moderate the relationship between evaluation
practices and project outcome, however, the relationship was a dampening relationship. These
results can be justified base on the fact that the dynamic nature of the environment plays a crucial
role when it comes to tech start-ups executing their evaluation activities. Moreover the dynamic
nature of the environment surrounding the implementation of evaluation practices was not
favourable causing the weakening of the relationship. This assertion is supported by Eruemegbe
et al. (2015) who revealed that the dynamic nature of variables in the business environment is
complex, as are their controls over the outcomes of events initiated inside an organization.
Previous studies have also confirmed this findings, for instance, a study by Shehu and Shehu
(2015), also discovered that the association between project success and evaluation practices is
moderated by the business environment. They discovered that whereas evaluation practices have
no discernible impact on project performance in an adversarial and unstable business environ-
ment, they are favorably associated to it in a stable and supportive environment. Lee and Kim
(2018), further revealed that the relationship between project performance and evaluation prac-
tices is moderated by the business environment. Also, Belout and Gauvreau (2004), discovered in
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another study that the association between project success and evaluation practices is moderated
by the business environment.
4.4. Conclusion and recommendation
The study sought to provide answers to the effect that monitoring and evaluation practice have on
tech start-ups project outcomes, and also assess the role that business environment play in the
relationship between M&E and project outcomes. The study followed a positivist mind-set, relying
only on quantitative methods and an explanatory research design. Primary data via structured
questionnaire was obtained from 317 respondents in managerial positions in the tech industry and
analysed using inferential and descriptive tools. It was found that M&E have a positive effect on
tech firms’ project outcome, also the industry perceived that their M&E practices is not done within
a favourable environmental factors. These empirical findings have validated existing theories from
which the study’s hypothesis were derived. The findings also demonstrates\ the relevance of M&E
practices and the need to take appropriate actions to improve it. It further informs management
that the environment should be observed closely by keeping pace with relevant current informa-
tion of the actors in the environment to reduce its negative impacts on the industry internal
operations. Thus all environmental factors likely to impede the successful implementation of M&E
systems should be closely monitored. The findings demonstrate that, tech start-ups should create
policies that recognize the integration of M&E in their operations and business functions. The study
was limited by the fact that there is no specific consideration of differences in geographical and
industry in which tech start-ups belong to, thereby limiting the generalisation of findings to
a particular geographical context and industry. It is therefore recommended that future studies
should focus on start-ups in other sectors such as mining, health and construction among others.
Author details
Ramatu Issifu
1
E-mail: ramatu.issifu@stu.ucc.edu.gh
Daniel Agyapong
2
1
Department of Marketing and Supply Chain
Management, School of Business, University of Cape
Coast, Ghana.
2
Department of finance, School of Business, University of
Cape Coast, Ghana.
Disclosure statement
No potential conflict of interest was reported by the
authors.
Citation information
Cite this article as: Monitoring and evaluation practices
and project outcome of tech start-ups in Ghana: The
moderating role of the Business environment, Ramatu
Issifu & Daniel Agyapong, Cogent Business & Management
(2023), 10: 2279793.
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