INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 615
Adoption of Data Analytics and Artificial Intelligence in Ghanaian
Enterprises: Implications for Organizational Performance
Emmanuel Duncan
Lunara Learning Hub
ORCID ID - https://orcid.org/0009-0009-6038-7749
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000078
Received: 12 September 2025; Accepted: 20 September 2025; Published: 11 November 2025
Abstract: Despite AI and data analytics' growing importance for competitiveness, empirical evidence in Ghana is scarce.
Organizations are urged to adopt digital transformation, but the link to performance is unclear. This study explores AI and
analytics adoption in Ghanaian enterprises, assessing its impact on performance and identifying adoption barriers. A cross-
sectional survey of 1,107 professionals, including HR managers, business leaders, and C-suite executives, was conducted.
Adoption rates were analyzed using descriptive statistics, while correlation and multiple regression were employed to test the
relationship between AI, data analytics, and organizational performance. Results show that 67% of enterprises have adopted AI,
data analytics, or both, while 33% remain non-adopters. Among adopters, 30.3% integrate AI and analytics, 28.9% use analytics
only, and 7.7% use AI only. Sectoral adoption varies, with Financial Services (85%) leading, while Retail and the Public Sector
lag at 50%. Both AI and analytics significantly improve performance, with stronger results when integrated. Organizations should
prioritize analytics as a foundation for AI, invest in workforce capability, and secure leadership commitment to scale adoption
successfully. Wider adoption of AI and data analytics in Ghanaian enterprises has the potential to reshape work and service
delivery across sectors contributing to national digital transformation. The findings advance understanding of how digital
technologies influence performance in emerging market particularly Ghana. Its findings inform the design of strategies and
policies that harness data-driven decision-making to drive organizational performance.
Keywords: Business Intelligence; Data Analytics; Artificial Intelligence; Organizational Performance; ICT; Enterprise systems
I. Introduction
The convergence of Artificial Intelligence (AI) and Data Analytics has emerged as a strategic driver of organizational
performance across industries (Morley et al., 2020; Hossain et al., 2024). According to Gartner (2018) this has given rise to what
is commonly referred to as augmented or intelligent analytics, where AI technologies work alongside human users to process and
interpret data more efficiently. Studies have shown how AI and big data analytics are reshaping performance benchmarks within
organizations (Davenport & Ronanki, 2018;Loso Judijanto et al., 2024). Hossain et al. (2024) noted that this combination enables
decision-makers ensures that analytics outputs inform strategy.
The integration of AI and analytics in Business Intelligence (BI) has yielded a profound impact, notably through the emergence of
self-service analytics. By democratizing access to data, these user-centric tools enable non-technical professionals, such as HR
managers, marketers, and operations teams, to independently explore datasets and generate visualizations without requiring
extensive technical expertise (Kassa & Worku, 2025). This paradigm shift has facilitated a broader range of stakeholders to
engage in evidence-based decision-making, thereby cultivating a culture of data literacy within organizations
Additionally, the incorporation of AI into BI systems has shown tangible results in sectors like healthcare, finance and education.
In education, for example, data analytics platforms are being used to personalize learning journeys and improve student outcomes
through real-time feedback and adaptive content (Li et al., 2022). AI-powered chatbots and virtual assistants help patients with
routine inquiries, freeing up healthcare professionals to focus on complex cases (Adamopoulou, 2020).
However, in African markets, the integration of these technologies remains inconsistent. Some research points to the benefits of
AI and analytics in enhancing productivity, innovation and supporting evidence-based decision-making (Apondi & Chege, 2023;
Kassa & Worku, 2025). Other studies caution that without proper integration, ethical governance, or cultural alignment, these
technologies may deliver suboptimal results (Olayinka, 2022; Aldoseri et al., 2023). Yet, much of this evidence is drawn from
advanced economies, raising a further gap in understanding how such risks and benefits manifest in developing contexts such as
Ghana (Zong & Guan, 2024; Aderibigbe et al., 2023; Mohammadi, 2025). Consequently, the application of such findings could
be limited, given the differences in infrastructure, workforce capacity, and levels of technological and economic development.
Therefore, this study contributes to existing literature gap by empirically investigating the adoption impact of artificial
intelligence and data analytics on organizational performance in Ghanaian enterprises. First, assess the impact of data analytics on
organizational performance across organizations; secondly, examine the effect of artificial intelligence adoption on organizational
performance; thirdly, evaluate the impact of artificial intelligence and data analytics on organizational performance; Lastly,
identify organizational challenges facing adoption of data analytics and AI in organizations.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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This study is structured into key sections. Following a comprehensive literature review that establishes the foundation of our
research, we develop hypotheses to guide our investigation. The methodology section outlines the research design, data
collection, and analysis procedures employed to test our hypotheses. The results and discussion section presents our findings and
interprets their implications, while the summary and recommendation section synthesizes the key insights and provides actionable
recommendations for stakeholders.
II. Theoretical and Empirical Review
Resource-Based View (RBV)
The Resource-Based View (RBV), introduced by Wernerfelt (1984) and expanded by Barney (1991), posits that organizations
gain sustained competitive advantage through internal resources that are valuable, rare, inimitable and non-substitutable (VRIN).
Within this perspective, technologies such as AI and data analytics systems can serve as strategic assets when they enable firms to
optimize operations and make data-driven decisions. In the case of Ghana, RBV offers a useful lens to explain how firms
adopting AI and analytics can strengthen competitiveness. Organizations that effectively combine advanced analytics
infrastructure with supportive systems have reported superior performance outcomes (Darke, 2024). This aligns closely with the
present study’s focus on assessing the impact of AI and data analytics adoption on organizational performance. However, while
RBV underscores the strategic value of technology, it places less emphasis on the human capabilities required to maximize these
tools.
Human Capital Theory
Complementing this is Human Capital Theory, which emphasizes the role of employee skills, knowledge, and training in driving
productivity and innovation. Originally developed by Schultz (1961) and expanded by Becker (1993), the theory argues that
investments in human capabilities, such as education and training, enhance productivity and contribute to economic growth. The
theory suggests that workforce competencies are critical enablers of organizational performance and economic development.
For Ghanaian organizations, HCT provides an appropriate framework for understanding barriers to technology adoption.
Challenges such as insufficient data literacy, inadequate training, and employee resistance to change hinder the realization of
potential performance gains from AI and analytics. In the context of data analytics and artificial intelligence, this theory has
gained renewed significance. The successful integration of analytics and AI does not solely depend on technological
infrastructure but also on employees’ capacity to interpret data insights and apply them strategically (Brynjolfsson & McElheran,
2016). This makes HCT especially relevant to the present study’s objective of identifying organizational barriers to adoption.
Technology Acceptance Model (TAM)
According to the Technology Acceptance Model (TAM), developed by Davis (1989), explains a behavioural view for
understanding user acceptance of new technologies based on two core perceptions: usefulness and ease of use. Individuals are
more likely to adopt technology if they believe it will enhance their job performance and find it intuitive and accessible. When
applied to AI-driven performance evaluation systems, TAM suggests that successful implementation in organizations depends not
only on the availability of technology but also on how employees and managers perceive its relevance and usability. In practice,
tools that support performance feedback, analytics dashboards, or AI-assisted reports are more likely to be used when they align
with users’ work expectations and minimize complexity (PwC, 2024). The integration of these three theories provides a holistic
framework to examine data analytics and AI on organizational performance in the Ghanaian business economy.
Figure 1: Theoretical Model framework
Source: Adapted from literature review Barney (1991), Davis (1989), Becker (1993), Schultz (1961)
Development of Hypothesis
Data Analytics and Organizational Performance (DA-OP)
The relationship between data analytics and organizational performance has received growing attention as firms seek to convert
raw data into meaningful insights. The Resource-Based View (RBV) posits that organizations can achieve sustained competitive
AI &
Analytics
Capabilities
Human
Capital
Perceived
Usefulness &
Ease of Use
Adoption &
Use
Organization
al
Performance
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advantage when they possess valuable, rare, inimitable, and non-substitutable (VRIN) data analytics capabilities (Barney, 1991).
Such tools improve decision-making processes, identify patterns, and enhance predictive planning, thereby driving organizational
efficiency and productivity. Complementing this, Human Capital Theory emphasizes that the benefits of data analytics are
maximized when employees possess the skills to interpret insights and apply them strategically (Schultz,1961).
Empirical evidence further supports these theoretical claims. Studies in some emerging economies demonstrate that data-driven
decision-making enhances innovation( Brynjolfsson & McElheran, 2016). In Ghana, early research has shown that organizations
that integrate data analytics into operations are more likely to report gains in efficiency and competitiveness (Darke, 2024; Asare
& Boateng, 2021). However, despite the acknowledged potential, adoption remains uneven, which underscores the need for
empirical testing of this relationship in the Ghanaian businesses.
Based on these theoretical and empirical findings, the study proposed the following hypothesis:
H
1
: Data analytics adoption positively and significantly affects organizational performance.
Artificial Intelligence and Organizational Performance (AI-OP)
The adoption of artificial intelligence (AI) has been increasingly associated with improvements in organizational performance
across industries. From the Resource-Based View (RBV), AI constitutes a strategic resource that meets the criteria of being
valuable, rare, inimitable, and non-substitutable (Barney's (1991). When embedded within organizational systems and aligned
with strategic objectives, AI enhances decision-making, streamlines operations, reduces costs, and fosters innovation. This
positions AI as a critical driver of sustained competitive advantage.
Empirical evidence supports these claims. Kassa and Worku (2025) reported that firms adopting AI-driven systems demonstrated
significant productivity gains, particularly when AI was integrated into core business processes. Similarly, Badghish and Soomro
(2024), studying small and medium-sized enterprises (SMEs), found that organizational acceptance of AI technologies predicted
successful adoption, which in turn positively influenced performance outcomes. However, adoption rates remain inconsistent due
to infrastructural and skills-related challenges, underscoring the need for further empirical testing in this context.
Drawing on both theoretical reasoning and prior empirical findings, the study proposed the following hypothesis:
H
2
: Artificial intelligence adoption positively and significantly affects organizational performance.
Artificial Intelligence and Data Analytics on Organizational Performance (AI+DA-OP)
According to the Resource-Based View (RBV) organizational performance is driven by the acquisition and strategic deployment
of unique, valuable, and hard-to-imitate resources. Within this framework, both data analytics and artificial intelligence are seen
as knowledge-based assets that enhance competitive advantage. While data analytics enables firms to extract insights ranging
from descriptive and diagnostic to predictive and prescriptive, AI amplifies this value by providing adaptive, autonomous, and
intelligent decision-making capabilities (Malatji et al., 2020). Through machine learning, automation, and real-time simulation of
human reasoning, AI can elevate the impact of analytics by enabling organizations to act proactively and efficiently in dynamic
environments.
Empirical research supports this synergistic effect. Naz et al. (2024) found that organizations that combined AI and data analytics
capabilities reported superior performance outcomes in terms of innovation and responsiveness. Similarly, Kassa and Worku
(2025) highlighted that AI’s capacity for continuous learning and evolution transforms it from a supportive tool into a
performance catalyst, magnifying the strategic value of analytics systems.
Grounded in this theoretical and empirical evidence, the study proposed the following hypothesis:
H
3
: Artificial Intelligence and data analytics positively and significantly affect organizational performance.
Organizational Challenges in Adoption of AI and Data Analytics
The adoption of technologies such as artificial intelligence (AI) and data analytics is often constrained by organizational readiness
and contextual barriers. The Technology Acceptance Model (TAM) developed by Davis (1989) emphasizes perceived usefulness
and ease of use as key determinants of adoption. TAM has been widely used to predict and explain user acceptance of various
technologies and its principles have been extended and applied in numerous studies. Research shows that despite the recognized
benefits of AI and analytics, challenges related to organizational culture, technical capabilities, and financial investment can
hinder adoption (Innovate Africa Network, 2025; Brookings, 2025; IDCA, 2025). For developing economies such as Ghana, these
barriers are particularly pronounced given resource constraints and skills gaps.
Conceptual Framework
This conceptual framework illustrates the relationship between data analytics and artificial intelligence on organizational
performance, with organizational challenges serving as a moderating variable. The framework provides a structured basis to guide
the study’s objectives.
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Figure 2: Conceptual model
Source: Author's own construct, 2025
Table 1: Definitions of latent variables
Latent Variable
Indicators
Variable
Type
Definition
Author(s)
Data Analytics
Dashboards, Real-
Time Reporting,
Predictive Analytics
Independent
Variable (IV)
Tools and practices used to
extract insights from data
for timely and informed
decision-making.
Dinh et al. (2020); Asare &
Boateng (2021)
Artificial
Intelligence
Automation Tools, AI-
assisted Systems,
Machine Learning
Independent
Variable (IV)
Technologies that enable
intelligent process
automation and advanced
decision support.
Badghish & Soomro
(2024); Kassa & Worku
(2025)
Organizational
Challenges
Cost Constraints,
Leadership Support,
Infrastructure
Readiness, high cost,
unclear benefits, lack
of technical expertise
Barriers or enablers that
influence the success of AI
and analytics
implementation.
Brookings, 2025;IDCA,
2025
Organizational
Performance
Productivity,
Innovation &
creativity, Decision
making, customer
satisfaction, Employee
retention, Process
efficiency
Dependent
Variable (DV)
The tangible and perceived
improvements in business
outcomes resulting from
technology adoption.
Malatji et al. (2020);Naz et
al. (2024)
Source: Literature review
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III. Methodology
Research Design
A cross-sectional non-probability sampling strategy was employed. The choice of non-probability methods was guided by the
exploratory nature of the research and the difficulty of obtaining a complete sampling frame of all professionals relevant to this
study. Non-probability approaches are frequently used in organizational and management research where the population of
interest is specialized, dispersed, or difficult to fully enumerate (Ahmad, 2025).
The study primarily used purposive sampling, as the survey targeted specific professionals such as HR managers, business
leaders, and C-suite executives whose perspectives were central to the research objectives. However, since the survey was
distributed via online platforms (Facebook, LinkedIn, WhatsApp, and one-on-one chats), convenience sampling also occurred
often recommended for its dependence on respondents’ accessibility and willingness (Makwana et al., 2023). In addition,
snowball sampling technique was employed, as some participants shared the survey link within their professional networks,
thereby broadening the reach. The integration of purposive, convenience, and snowball techniques enabled the researcher to
balance targeted inclusion with wider outreach.
Sample Size
Ghana's business environment is broad and diverse; however, the purpose of this research was to examine specific relationships
between data analytics, artificial intelligence, and organizational performance. Therefore, this study adopted an analytical applied
research approach focusing on targeted domain. A power analysis using G*Power 3.1 was conducted to determine the minimum
required sample size for multiple regression analysis. Using an alpha level of 0.05, a statistical power of 0.80 and a medium effect
size (f² = 0.15) as per Cohen (1988), the recommended minimum sample size was calculated to be 85 participants based on the
four predictors (data analytics, artificial intelligence, organizational challenges and performance).
However, using Cochran’s formula for large populations (95% confidence, with a conservative proportion of p=0.50, and an
achieved sample size of n=1,107) yielded an approximately three (3) percent margin of error for national-level estimates.
Determination of Cochran’s Margin of Error (MOE) for large population
with z=1.96 for 95% confidence level, p=0.50, n=1107
MOE=
=1.96
=1.96 × 0.01503
= 0.0295
=2.95%
Cochran’s required ‘n’ where e=0.03;
n
O
=
=1067
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Data Collection and Procedure
The target respondents included HR professionals, business leaders, data users, and executives across diverse sectors
(Telecommunications, Financial sectors, Healthcare and Pharmaceuticals, Manufacturing and Supply Chain, Transportation and
Logistics, Oil, Gas and Energy, Retail and E-Commerce, Human Resources, and Public Sector) in Ghana.
Data was collected through online survey, distributed through social media channels such as LinkedIn, Facebook, and
professional WhatsApp groups. Some participants were reached through one-on-one online chats on these platforms. These
platforms were chosen because of the direct access to relevant professionals, especially in the absence of centralized online
business directories.
The survey comprised three major sections: background information (job role, industry, experience level and organizational size),
AI and analytics adoption on performance impact. A 4-point Likert scale (1 = Strongly Disagree to 4 = Strongly Agree) was
adopted, with the neutral option intentionally excluded to promote more decisive responses, in line with Versta Research (2016)
recommendations for enhancing the reliability and clarity of business survey data. The survey was in the field between July 4 and
August 5, 2025.
Data Processing and Analysis
The collected survey data was first exported into Excel and subsequently processed in Microsoft Power BI for initial cleaning.
Data cleaning involved sorting, editing, and the removal of incomplete or erroneous entries generated during questionnaire
completion, with blank responses systematically excluded. The cleaned dataset was then coded and imported into IBM SPSS
Statistics (version 27) for further analysis. Both descriptive and inferential statistical techniques were applied to examine the data
and address the research objectives.
Model Specification
Model specification was formulated to define and test the relationships among the study variables. Based on the review literatures
the following regression model was estimated for this study:
OP=β
0
+β
1
(X
1
) + β
2
(X
2
) +ϵ
Where:
OP = Orgnaizational performance
DA = Data analytics
AI = Artificial intelligence
β = coefficients and
ε = Error Term
Evidence and results
Demographic Profile
A total of 1107 professionals from various enterprises in Ghana participated in the study. The demographic characteristics of the
sample are detailed in Table 2.
Table 2: Demographics of respondents
Profile
Frequency (N)
Percentage (%)
Gender
Male
572
51.7
Female
535
48.3
Designation
Business Leader
224
20.2
Executive(C-suite)
280
25.3
HR Managers
603
54.5
Years of work Experience (Current role)
Less than 1 year
83
7.5
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1-3 years
260
23.5
4-6 years
277
25.0
7-10 years
222
20.1
Above 10 years
265
23.9
Organization’s size
Less than 50
224
20.2
50 – 199
280
25.3
200+
603
54.5
Industry
Financial sector
203
18.3
Healthcare & Pharmaceuticals sector
96
8.7
Human Resources sector
168
15.2
Manufacturing & Supply Chain sector
109
9.8
Oil, Gas & Energy sector
86
7.8
Public sector
193
17.4
Retail & E-commerce sector
84
7.6
Telecommunication sector
79
7.1
Transportation & Logistics sector
89
8.0
Total
1107
100.0
Source: Field survey (2025)
The sample was characterized by a near-equal distribution of male (51.7%) and female (48.3%) respondents. The largest group of
participants were HR Managers (54.5%), followed by (C-suite) Executives (25.3%) and Business Leaders (20.2%). The high
participation rate from human resources professionals was noteworthy, as it suggested that the adoption of data-driven
technologies is a matter of significant concern within the human capital management domain. This finding reframes the
discussion of technology adoption beyond purely technical or operational spheres to include the critical human and organizational
change management aspects.
The respondents represent a broad range of experience levels, with a relatively uniform distribution across sectors, with slightly
more individuals in the 4-6 years (25.0%) and above 10 years (23.9%). This diversity in experience provided a rich perspective,
incorporating both the views of seasoned professionals and those new to their roles. A majority of the participants (54.5%) were
from large organizations with more than 200 employees, while the remaining were from small (20.2%) and medium-sized
(25.3%) firms. The dominance of large enterprises in the sample implied that the study's conclusions may be more directly
applicable to well-resourced, large-scale operations in Ghana rather than an extensive small and medium-sized enterprise (SME)
sector, which may face unique constraints not fully captured by this sample.
Adoption of AI and Data Analytics
This study reveals that adoption of AI and data analytics is advancing across Ghanaian enterprises, though unevenly across
sectors. Table 3 presents the overall adoption distribution, while figure 3 shows sector-specific adoption levels.
Table 3. Adoption of AI and Data Analytics
Adoption
Frequency(N)
Percentage (%)
AI and Analytics only
336
30.3
Analytics only
320
28.9
AI only
85
7.7
Total Adopters
741
67.0
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Non-Adopters
366
33.0
Total Respondents
1,107
100.0
Source: Field survey (2025)
Table 3 presents the overall adoption status of AI and data analytics, indicating that 67 percent of Ghanaian enterprises have
adopted either AI, data analytics, or both, while 33 percent remain non-adopters. Among adopters, integrated AI-and-analytics
usage (30.3%) slightly exceeds analytics-only adoption (28.9%), whereas AI-only adoption is comparatively lower (7.7%). This
pattern reflects a stepwise progression in digital maturity, suggesting that AI only has not yet achieved widespread diffusion as a
standalone capability but functions best when layered onto established analytics practices in Ghanaian enterprises.
Figure 3: Sectoral Adoption
Source: Field survey (2025)
Figure 3 presents adoption rates across sectors, revealing a hierarchy of digital readiness. Financial Services (85%),
Manufacturing (79%), and Telecommunications (75%) lead adoption. Mid-tier adoption is seen in healthcare (74%) and
Transportation (69%), while lagging adoption in Retail (50%) and the Public Sector (50%) is still in experimental phases rather
than achieving large-scale deployment. Sectoral variations further underscore uneven readiness, with forthcoming adoption
expected to concentrate in human resources, oil and energy, and public sector organizations.
Reliability and validity
Before proceeding with inferential analysis, the reliability of the measurement instruments for each construct was assessed using
Cronbach's alpha coefficient. Reliability is a measure of internal consistency, indicating whether the items within a scale are
reliably measuring the same underlying construct (Korhonen et al., 2022). As a general rule, a Cronbach's alpha value of 0.70 or
higher is considered acceptable.
The results of the reliability analysis, presented in the table 4, indicate that all four constructs demonstrated higher internal
consistency.
Table 4: Reliability Results
No
Construct
No. of items
Cronbach’s alpha
1
Data analytics
7
.840
2
Artificial Intelligence
6
.925
3
Organizational performance
6
.878
4
Organizational challenges
7
.795
50
50
56
67
69
74
75
79
85
Retail & E-Commerce
Public Sector
Oil, Gas & Energy
Human Resources
Transportation & Logistics
Healthcare & Pharmaceuticals
Telecommunication
Manufacturing & Supply Chain
Financial Services
Adoption Rate (%)
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Source: Field survey (2025)
The data analytics construct with a Cronbach's alpha of 0.840, showed a strong level of internal consistency. The artificial
intelligence construct, with a coefficient of 0.925, demonstrated particularly high reliability, suggesting that the items used to
measure AI adoption were highly correlated and accurately captured the intended concept. Similarly, the organizational
performance and organizational challenges constructs also exhibited strong reliability with alpha values of 0.878 and 0.795,
respectively. The high reliability of all constructs provides a firm foundation for the subsequent inferential statistical analysis,
strengthening the confidence that the relationships found between the variables are a true reflection of the phenomena.
IV. Correlation Results
The correlation coefficients were interpreted according to Cohen's (1988) guidelines, which provided a framework for
understanding the strength of associations. The results are presented in table 5 below:
Table 5: Correlation Analysis
OP
DA
AI
OP
1
DA
.401
**
1
AI
.405
**
.456
**
1
** Correlation is significant at the 0.01 level (2-tailed).
Source: Field survey (2025)
As shown in Table 5, all pairs of variables are positively and significantly correlated at the 0.01 level. Organizational Performance
(OP) has a statistically significant positive correlation with Data Analytics (DA) (r=0.401, p<0.01) and with Artificial Intelligence
(AI) (r=0.405, p<0.01). These findings provided initial support for the study's hypotheses, indicating that higher levels of DA and
AI adoption are associated with organizational performance.
A particularly notable finding is the strong positive correlation between DA and AI (r=0.456, p<0.01). This relationship is
stronger than the correlation of either variable with organizational performance, suggesting a profound symbiotic relationship
between these two technologies. The results indicated within Ghanaian enterprises, the adoption of DA and AI is not occurring in
isolated silos. Instead, enterprises that invest in one are more likely to be investing in the other, viewing them as complementary
components of a unified digital strategy.
Regression Analyses
In order to estimate relationships between DA and AI on performance, the study employed an ordinary least square (OLS)
estimation approach. The general OLS regression model employed was specified as;
OP=β
0
+β
1
(X
1
) + β
2
(X
2
) +ϵ
The final estimable regression model is stated as follows;
OP=β
0
+β
1
(DA)+β
2
(AI)+ϵ
Where OP is organizational performance, DA as data analytics, AI represents artificial intelligence and β and ϵ indicate coefficient
and error term. The results of the regression analysis are presented in Table 6 below;
Table 6: Model summary
Model
R
R Square
Adjusted R
square
Std. Error of the
Estimate
Durbin Watson
1
.472
a
.223
.221
1.51557
2.077
a. Predictors: (Constant), DA, AI
b. Dependent variable: OP
The model summary results in Table 6 revealed that the independent variables (Data Analytics, Artificial Intelligence) collectively
account for 22.3% of the variance in Organizational Performance, as indicated by the R-squared value of 0.223. In social science
and business research, where organizational performance is influenced by a multitude of complex and often unmeasured factors,
explaining over one-fifth of the variance with just two variables is considered a meaningful and substantial finding (Ozili, 2023).
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The adjusted R-squared of 0.221 further confirms the model's explanatory power. The validity of a regression model relies on the
absence of autocorrelation and serial correlation in the residuals. According to Turner (2019), a Durbin-Watson statistic ranging
from 1.5 to 2.5 indicates satisfactory model performance. The results yielded a Durbin-Watson statistic of 2.077, which
approximates the ideal value of 2.0, indicating that the residuals had no autocorrelation, thereby satisfying a key assumption of
linear regression.
Table 7: ANOVA
a
Model
Sum of
squares
Df
Mean square
F
Sig
1
Regression
458.902
2
242.951
105.755
.001
b
Residual
1695.408
738
2.297
Total
2181.310
740
a. Dependent Variable: OP
b. Predictors:(Constant), DA, AI
The ANOVA results presented in Table 7 show that the regression model is statistically significant (F = 105.755, p < .001),
indicating that the influence of DA and AI has a strong and meaningful explanation of organizational performance. This result
further confirms that the predictors, when considered together, significantly enhance the model’s explanatory power, thereby
validating the general framework of this study.
Table 8: Regression Coefficients
a
Model
Unstandardized
coefficients Beta
Standardize
d coefficients
95.0% confidence
interval for B
Collinearity
Statistics
B
Std.
Error
Beta
T
Sig
Lower
bound
Upper
bound
Toleran
ce
VIF
(Constant)
5.879
.249
23.618
<.001
5.390
6.368
AI
.178
.023
.280
7.685
<.001
.133
.223
.792
1.262
DA
.199
.027
.273
7.487
<.001
.147
.251
.792
1.262
a. Dependent Variable: OP
Table 8 results show that both data analytics and artificial intelligence are statistically significant predictors on organizational
performance.
Hypothesis H
1
: Data analytics adoption positively and significantly affects organizational performance.
The regression analysis showed that data analytics adoption had a significant positive effect on organizational performance (β =
0.199, p < .001). These findings support H
1
, confirming that greater adoption of data analytics is associated with improved
organizational performance.
This result aligns with prior studies emphasizing the strategic value of data analytics capabilities. Consistent with the Resource-
Based View (RBV), data analytics represents a VRIN resource that enhances operational decision-making and supports predictive
planning, thereby improving productivity and performance (Brynjolfsson & McElheran, 2016; Dinh et al., 2020). Similarly,
grounded in Human Capital Theory, the ability of employees to interpret and apply analytics effectively has been shown to
increase organizational value (Becker, 1993; Barney, 1991). This means the final hypothesis is supported by the study so we fail
to reject the hypothesis.
Hypothesis H
2
: Artificial intelligence adoption positively and significantly affects organizational performance.
The regression analysis revealed that AI adoption had a significant positive effect on organizational performance, with an
unstandardized coefficient (B = 0.178, p < .001). The standardized beta coefficient (β = 0.280) indicates a moderate effect size.
This finding is consistent with the Resource-Based View (RBV), which asserts that organizations gain a sustained competitive
advantage when they possess valuable, rare, inimitable, and non-substitutable resources (Barney, 1991). AI capabilities qualify as
such strategic resources when integrated into organizational systems and aligned with business objectives, as they streamline
operations and innovation strategy. Empirical evidence supports this perspective: Kassa and Worku (2025) demonstrated that AI-
driven firms experienced productivity gains when AI was embedded into their core processes, while Badghish and Soomro (2024)
also opined that successful AI adoption significantly enhanced organizational outcomes through technology acceptance and
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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integration. Therefore, findings support H
2
, confirming that the adoption of artificial intelligence is also a significant driver of
organizational performance.
Hypothesis H
3
: Artificial intelligence and data analytics positively affect organizational performance.
Artificial Intelligence and data analytics positively affect organizational performance. As confirmed by the overall model's
statistical significance (F = 105.755, p<.001), the combined effect of both DA and AI is a strong predictor of organizational
performance. This finding supports H
3
and highlights the synergistic value of adopting both technologies.
The results correlate with the Resource-Based View (RBV), which emphasizes that unique hard-to-imitate resources are critical
drivers of sustained competitive advantage (Barney, 1991). Both DA and AI qualify as such resources: DA provides descriptive,
diagnostic, predictive, and prescriptive insights, while AI introduces adaptive, autonomous, and intelligent decision-making
capabilities that amplify the impact of analytics.
Empirical evidence further supports these findings. Naz et al. (2024) and Kassa and Worku (2025) reported AI’s ability to
continuously learn and evolve transforms it from a supportive tool into a performance catalyst, significantly enhancing the value
of analytics in organizational settings. The synergy between DA and AI, therefore, positions organizations to act proactively,
fostering sustainable competitive edge.
Finally, both variables had a significant impact; a comparison of the standardized beta coefficients indicated further relative
contributions. Data analytics (Beta = 0.199) had a slightly stronger relative effect on organizational performance than artificial
intelligence (Beta = 0.178). This subtle but important difference suggested that, in the Ghanaian business environment, the
foundational practices associated with DA may be a more pervasive and immediate driver of performance than the more advanced
applications of AI.
Organizational Challenges
This study also investigated the primary reasons for non-adoption of DA and AI within enterprises. The findings are challenges
reported by non-adopters, summarized in table 9;
Table 9: Organizational challenges (Non-Adopters)
No
Items
Frequency(N)
Percentage (%)
1
High cost
52
14.2
2
Lack of technical expertise
83
22.7
3
Unclear benefits
25
6.8
4
Low digital infrastructure
46
12.6
5
Leadership not prioritizing it
160
43.7
Total
366
100
Source: field survey (2025)
From table 9, the most significant barrier identified was "Leadership not prioritizing it," cited by 43.7% of respondents. This
shows that the primary bottleneck to technology adoption is not financial or technological, but rather cultural and strategic. While
"High cost" was cited by some, it was far less significant than the lack of a clear strategic mandate from the top.
The second most-cited challenge was "Lack of technical expertise" (22.7%). This is likely a direct consequence of the top-ranking
challenge. When leadership does not prioritize technology adoption, it fails to invest in the training, recruitment and talent
development necessary to build the required in-house capabilities. The finding that "Unclear benefits" is the least significant
barrier (6.8%) suggested that most business leaders are aware of the value proposition of these technologies, yet they fail to
translate this awareness into concrete action and investment. This creates a causal chain where strategic inertia is the root cause
that gives rise to the other challenges, particularly the human capital gap.
V. Conclusions and Recommendations
Based on the findings of this study, three main conclusions can be drawn. First, the adoption of data analytics and artificial
intelligence were proven as significant drivers of improved organizational performance in the Ghanaian business. Enterprises that
proactively invest in and integrate these technologies are more likely to achieve superior performance outcomes.
Second, the relationship between data analytics and artificial intelligence is fundamentally synergistic. Digital transformation
requires a holistic approach where data analytics capabilities are established first to create the necessary infrastructure and data-
driven culture to aid effective deployment and maximization of more advanced AI systems.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
www.ijltemas.in Page 626
Third, the most critical hurdles to technology adoption are not purely technical or financial. According to the study, the root cause
of non-adoption in Ghanaian enterprises is a strategic and human capital issue. A lack of clear, top-down leadership prioritization
hinders investment in the necessary training and talent development, which in turn creates a skills gap that becomes a significant
barrier. Accordingly, we propose the following recommendations. Business leaders should adopt a value-driven strategy, viewing
technology as a strategic investment rather than a cost center. By championing digital transformation and building a robust data
analytics infrastructure. Policymakers should support education and training initiatives to build a pipeline of local talent in data
analytics and AI, which would drive national competitiveness and socioeconomic development.
Ethics Approval
This study did not require formal ethics review board clearance, as it involved a non-clinical, low-risk organizational survey. No
sensitive personal or health-related data were collected. The research was designed to ensure confidentiality and anonymity, with
all responses aggregated for analysis.
Consent to Participate
All respondents, who were most HR managers, business leaders, and C-suite executives, participated voluntarily after being
informed of the study’s purpose. Participation was motivated by professional interest in the findings, and respondents were
assured they would receive a copy of the research report upon completion. Informed consent was obtained, and the data collected
were used strictly for research and professional knowledge-sharing purposes.
Funding
The author did not receive any financial support from funding agencies in the public, commercial, or not-for-profit sectors for this
research.
Competing Interests
The author declares no competing interests.
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