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TYPE
Original Research
PAGE NO.
08-24
10.37547/tajmei/Volume07Issue07-02
OPEN ACCESS
SUBMITED
21 May 2025
ACCEPTED
24 June 2025
PUBLISHED
03 July 2025
VOLUME
Vol.07 Issue 07 2025
CITATION
Emmanuel Ampong Afoakwah, Kwabena Adjei, & Ernest Kwaku Agyei.
(2025). Procurement Efficiency and Firm Competitive Advantage:
Moderated Mediation Analysis of Unified Theory of Acceptance and Use
of Technology: A Study in Ghana, Ashanti Region. The American Journal
of Management and Economics Innovations, 7(07), 08
–
24.
https://doi.org/10.37547/tajmei/Volume07Issue07-02
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Procurement Efficiency
and Firm Competitive
Advantage: Moderated
Mediation Analysis of
Unified Theory of
Acceptance and Use of
Technology: A Study in
Ghana, Ashanti Region.
Sunyani Technical University, Ghana
Sunyani Technical University, Ghana
PhD Scholar, Sir Padampat Singhania University, India
Abstract:
This study explored how procurement
practices relate to competitive advantage within
organizations, using the Unified Theory of Acceptance
and Use of Technology (UTAUT) to understand the role
of technology in supply chain management. Researchers
employed a quantitative approach, analyzing 245
responses from 100 regional universities using
descriptive statistics and structural equation modeling
(SEM) with SmartPLS software. The findings revealed a
strong
positive
correlation
between
effective
procurement methods and competitive advantage,
leading to improved financial performance, return on
investment, and profit margins. Regression analysis
confirmed that efficient procurement strategically
enhances economic performance. The UTAUT model
highlighted that performance expectancy, effort
expectancy, social influence, and facilitating factors
influence the adoption and use of procurement
technology. The study demonstrates how aligning
procurement digitalization with the UTAUT framework
can optimize sourcing, foster innovation, and boost
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overall profitability in supply chain management.
Ultimately, this research contributes to a deeper
understanding of the link between procurement
practices and achieving a competitive edge in
organizational supply chain management.
Keywords
: Strategic Procurement, Supply Chain
Management,
Competitive
Advantage,
UTAUT,
Procurement Efficiency.
1.
INTRODUCTION
Procurement is essential in supply chain management,
allowing organisations to obtain resources and use
market opportunities. Synchronising procurement
components
with
corporate
strategy
enables
enterprises to enhance market presence, decrease
expenses, and elevate quality. Efficient logistics
management is crucial for sustaining a flexible and
responsive supply chain (Sweeney et al., 2018).
Performance-oriented solutions, such as strategic
procurement management,
emphasise
enduring
supplier relationships to enhance operational efficiency
and save expenses. As supply chains evolve, managing
these complexities becomes a competitive factor
(Foerstl et al., 2021). Advanced procurement capabilities
enhance supply chain performance by improving
responsiveness to market changes, allowing firms to
remain adaptable in dynamic environments (Herold et
al., 2023). Technological advancements such as e-
procurement and AI-driven supplier management
systems have transformed supply chain management,
enhancing efficiency and cost savings through improved
purchasing processes and demand forecasting
(Pattanayak & Punyatoya, 2019). The Unified Theory of
Acceptance and Use of Technology (UTAUT) delineates
critical
determinants
of
technology
adoption,
comprising performance expectancy, effort expectancy,
social impact, and facilitating conditions (Raden Edi,
2022). Performance expectancy denotes the conviction
that technology enhances job performance, whereas
effort expectancy emphasises the simplicity of usage.
Social impact, encompassing industrial standards and
managerial backing, is crucial in adoption (Rana & Arya,
2024).
Enabling
factors,
including
technical
infrastructure and organisational support, guarantee
the effective execution of technology (Khatri et al.,
2023). These technologies allow firms to monitor market
trends, manage logistics, and fortify supplier
relationships, hence improving supply chain efficiency
and competitiveness (Abideen et al., 2023).
Notwithstanding its benefits, the deployment of
procurement
technology
encounters
obstacles,
particularly in regions like Ghana, where conceptual
barriers, cultural disparities, and literacy gaps hinder
implementation (Filipova, 2023Shabalov et al., 2021).
Addressing these challenges requires investment in
employee training, change management, and digital
infrastructure. Understanding procurement efficiency
through UTAUT is vital for market positioning, yet
research gaps remain, especially regarding its
mod
erated mediation effects in Ghana’s public
procurement (Addy et al., 2024). Overcoming these
barriers requires longitudinal studies to evaluate
technology’s impact on competitive advantage over
time (Oduro et al., 2023). By applying moderated
mediation analysis, procurement managers can
effectively integrate UTAUT constructs, optimizing
procurement processes to align with strategic supply
chain objectives (Asare et al., 2024). Investing
strategically in technology and human capital is essential
for developing resilient and competitive supply
networks. Effective procurement improves agility, cost
control, and delivery efficiency, hence bolstering
organisations' competitiveness in the global market
(Arun & Yildirim Ozmutlu, 2024).
This study examines the effects of procurement
efficiency and social influence on a firm's competitive
edge. This study investigates the mediation of user
acceptance and UTAUT constructs (performance
expectancy, effort expectancy, and facilitating
conditions) in this relationship and analyses the
moderating effect of user acceptance on the strength of
the connection between procurement efficiency and
competitive advantage. This study aims to address the
question arising from the prior debate and the identified
research gaps.
How do procurement efficiency, social influence and
user acceptance collectively impact firm competitive
advantage?
This research question is examined through the lens of
both the Resource-Based View (RBV) and the Unified
Theory of Acceptance and Use of Technology (UTAUT).
Thus, the study presents and empirically tests a model
that examines how procurement efficiency and social
influence impact a firm's competitive advantage, while
investigating the mediating role of UTAUT dimensions
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and the moderating effect of user acceptability. This
study elucidates the impact of procurement efficiency
and user acceptance, as informed by the UTAUT model,
on company performance in Ghana's Ashanti Region. It
emphasises pragmatic measures for improving
competitiveness, directing governments and corporate
leaders in fostering effective procurement and
technology implementation. The research provides
context-specific insights to assist local enterprises in
overcoming procurement issues and facilitates data-
driven decision-making for sustainable competitive
advantage. The subsequent sections of the paper are
structured as follows: The literature evaluation initially
examines the utilised theories and the formulated
hypotheses. The study context and measures are
subsequently delineated, followed by the exposition of
empirical findings. Ultimately, the study culminates in a
discourse on the findings and their ramifications.
2. Literature review, theoretical constructs, and
hypotheses
2.1 The Resource-Based View (RBV)
The Resource-Based View (RBV) hypothesis posits that
strategic procurement diminishes costs and enhances
quality, hence bolstering supply chain competitiveness
(Acquah
et
al.,
2023).
Relationship-Integrated
Procurement (RIP) emphasises the significance of robust
supplier relationships for stability and responsiveness
(Gaudenzi et al., 2023). The amalgamation of
sophisticated procurement methods and sustainability
initiatives improves supply chain efficiency, enabling
organisations to maintain competitiveness in a swiftly
changing global market (Y. K. Dwivedi et al., 2021a;
Khedr & S, 2024). The resource-based view (RBV) posits
that an organisation maintains a competitive edge
through the effective management of its distinctive and
valuable resources (Amaya et al., 2024). According to
Evangelista et al., 2023, efficient procurement
strengthens supply chains, reduces operational costs,
and enhances product quality by responding faster to
market demands, thus maintaining competitiveness.
Effective procurement processes and resource
management bolster a company’s
competitive edge
through efficiencies that support competitiveness
(Susitha et al., 2024).
2.2 The Unified Theory of Acceptance and Use of
Technology (UTAUT)
The constructs of the Unified Theory of Acceptance and
Use of Technology (UTAUT). Performance Expectancy
(PE), Effort Expectancy (EE), Social Influence (SI),
Facilitating Conditions (FC), and User Acceptance (UA)
are pivotal in procurement phases, affecting technology
acceptance and user behaviour (Bajunaied et al. 2023a).
PE is crucial in the Needs Identification phase, improving
operational efficiency (Sivarajah et al., 2017), while EE
improves the Purchase Requisition phase by ensuring
user-friendly systems (Neves et al., 2025). FC supports
the Review of Requisition process by providing
organizational resources (V. Kumar, Sharma, et al.,
2024), and SI impacts Budget Approval through
stakeholder influence (Ding et al., 2024).
UA is essential in the Quotation Request phase,
enhancing supplier communication via digital platforms
(Vincenzo Varriale, 2023), while SI affects the
Negotiation and Contract Award stage by shaping
stakeholder interactions (Marc Hockings, 2021). FC
facilitates
compliance
monitoring
in
Contract
Management (Zhou et al., 2024), and EE improves the
Receiving of Goods/Services phase by streamlining
inspection procedures (Moshtari et al., 2021). The study
highlights
UTAUT’s
relevance
in
procurement,
demonstrating
its
effectiveness
in
analyzing
procurement efficiency and competitive advantage
through non-linear relationships (Rozemeijer, 2000).
SMART PLS was used to identify complex interactions
affecting procurement and firm competitiveness,
making it a valuable tool in business management
research (Hiran & Dadhich, 2024; Hoang & Le Tan, 2023).
The study is organised as follows: Section 2 offers a
literature review encompassing theoretical constructs
and hypotheses; Section 3 delineates the research
methodology, comprising data collection and analysis;
Section 4 presents empirical findings, including
hypothesis testing and structural modelling utilising
SMART PLS; and the concluding section examines
theoretical implications, limitations, future research
directions, and critical insights regarding procurement
technology adoption for sustaining competitive
advantage.
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Figure I: Procurement Process and UTAUT Adoption.
Source: Authors’ own elaboration
2.3. Technology and Procurement Efficiency
The incorporation of new technologies like Artificial
Intelligence (AI), Blockchain, and the Internet of Things
(IoT) into procurement procedures has revolutionised
supply chain management by improving efficiency,
transparency, and strategic results. Rashid et al. (2024)
assert that these technologies establish resilient,
transparent procurement frameworks that fit with
supply chain goals. AI automates data analysis, decision-
making, and communication with suppliers and
customers, increasing responsiveness and reducing
manual effort (Mohsen, 2023). Blockchain ensures
compliance, reduces fraud, and secures transactions by
tracking goods from origin to delivery, fostering trust
within the supply chain (Agrawal et al., 2021).
Procurement 4.0 focuses on performance-driven
strategies, leveraging these technologies to reduce lead
times, optimize inventory, improve customer service,
and achieve organizational goals (Althabatah et al.,
2023). This shift necessitates a realignment of
procurement processes and increased accountability, as
highlighted by Rejeb et al. (2022). Adopting these
technologies in procurement streamlines operations
and significantly enhances supply chain efficiency,
security, and market responsiveness.
Procurement Efficiency and Firm Competitive
Advantage
Procurement efficiency is paramount for competitive
advantage, driving improvements in cost control,
quality, process agility, and supplier relationships. Cost
control directly boosts profitability (Henri et al., 2016),
while quality enhancement fosters customer loyalty
(Yum & Yoo, 2023). Accelerated processes increase
agility (Alnasser et al., 2024), and strong supplier
management reduces costs and improves quality (Balkhi
et al., 2022). Effective procurement enhances market
efficiency (Kähkönen et al., 2023). Technology is vital in
minimizing errors and optimizing operations (Soori et
al., 2023). The UTAUT framework explains technology
adoption
in
procurement,
concentrating
on
performance, exertion, social impact, and enabling
circumstances (Duarte & Pinho, 2019). Implementing
technology-driven procurement strategies streamlines
operations and secures long-term competitive
advantage. Therefore, it is hypothesized that:
H1: Procurement efficiency positively influences a firm’s
competitive advantage
.
Social influence significantly enhances a firm's
competitive advantage.
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Organisations in competitive markets monitor
competitors and strategic partners, such as key
customers and suppliers, to enhance as crucial for
technology adoption, improving market standing.
Competitive pressure compels managers to adopt
similar technologies, eliminating differentiation to
remain economically viable enhancing departmental
operations, and aligning strategies with market
demands.
Social
influence
is
significant
in
interdependent
environments,
particularly
procurement. (V. Kumar, Ashraf, et al., 2024) argue that
projects attuned to market factors or supplier needs are
more successful in terms of industry integration and
profitability,
enhancing
internal
efficacies
and
competitive advantage. It is therefore hypothesized
that:
H2
:
Social influence affects how competitive pressure
drives technology adoption in procurement, helping
organizations improve efficiency and gain a competitive
advantage.
Performance Expectancy exerts an interaction
influence on the correlation between Procurement
Efficiency and Firm Competitive Advantage
.
Performance
expectancy
drives
procurement
technology adoption, reducing costs and enhancing
efficiency (S. Kumar, Goel, et al., 2024). Technological
advancements
improve
decision-making
and
competitiveness (Radicic & Petković, 2023). Effective
inventory management strengthens resilience (Ikpe &
Shamsuddoha, 2024). Strong supplier relationships (Yeh
et al., 2020) and sustainable procurement (De Oliveira et
al., 2018) enhance stability. It is therefore hypothesized
that:
H3: Higher performance expectancy in adopting
procurement technology boosts cost reduction,
efficiency, decision-making, competitiveness, inventory
resilience, supplier relationships, and sustainable
practices.
Effort Expectancy interacts with the relationship
between Procurement Efficiency and the Firm's
Competitive Advantage.
Effort expectancy, or the perceived usability of a system,
is important in adopting procurement technologies in
supply chain management and their utilization
(Brandon-Jones & Kauppi, 2018). It has been noted that
user-friendly systems experience greater adoption, the
efficiency of use, improved organizational performance,
and resultant competitive edge (Bhatnagr et al., 2024).
High effort expectancy enhances the acceptance of the
technology, reducing the time and costs incurred in
procurement and enabling a higher market share and
profitability (Al Halbusi et al., 2024). Thus, it can be
concluded that effort expectancy is critical to the
understanding of reasons behind procurement
efficiency and competitive advantage in any given
organization. Therefore, it is hypothesized that:
H4: Effort expectancy positively influences the adoption
of procurement technologies, enhancing procurement
efficiency and contributing to a firm's competitive
advantage.
Facilitating Conditions provide an interaction
influence on the correlation between Procurement
Efficiency and a Firm's Competitive Advantage.
Social influence, defined as the belief that important
individuals expect others to utilise a particular system,
profoundly affects employees' choices to embrace
procurement technologies, thus affecting firms'
competitive advantage (Asif Kamran, 2024). Research
has shown that social influence positively affects
procurement system adoption and enhances efficiency
(Hussam Al Halbusi, 2022). When key stakeholders
advocate procurement systems, employees tend to
follow them, resulting in improved processes and
market positioning (Liu et al., 2024). Thus, social
influence interacts with procurement efficiency and
competitive advantage by encouraging technology
adoption, which enhances operational efficiency. It is
consequently posited that:
H5: Social influence positively impacts the adoption of
procurement
technologies,
thereby
enhancing
procurement efficiency and contributing to a firm's
competitive advantage.
3.
RESEARCH METHODOLOGY
A quantitative study methodology was employed to
examine the correlation between procurement
efficiency and competitive advantage, utilising UTAUT
components as moderating variables. The research
utilised stratified random sampling for selecting 100
companies from the manufacturing, service, and
agriculture sectors in the Ashanti Region, Ghana.
Procurement
managers,
IT
managers,
and
procurement personnel were targeted to ensure
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diverse organizational insights. A total of 300 surveys
were distributed, with 245 valid responses analyzed
after excluding incomplete or inconsistent responses.
The final response rate (81%) met the minimum
required sample size of 240 for statistical robustness
(Puyana-Romero et al., 2024). Data collection followed
ethical research principles, ensuring voluntary
participation, confidentiality, and informed consent
(Nwali et al., 2021). Ethical approval was obtained to
uphold research integrity (Mulvihill et al., 2023). To
measure procurement efficiency, validated scales from
Fragkiskaki (2024) assessed process acceleration,
quality improvement, and cost reduction. Porter’s
competitive advantage framework was applied using a
regression model. Data screening procedures removed
responses with low variance (standard deviation <
0.25) to mitigate bias and improve reliability. For data
analysis, SMART-
PLS software was utilized. Pearson’s
correlation coefficient assessed the relationship
between procurement efficiency and competitive
advantage, while Partial Least Squares Structural
Equation Modeling (PLS-SEM) was employed to model
complex
relationships
between
procurement
efficiency, technology adoption, and competitive
advantage. PLS-SEM was chosen for its effectiveness
with small to medium sample sizes and non-normally
distributed data (Sharma & Sharma, 2023).
4. Conceptual framework
This study's conceptual framework seeks to clarify the
interconnections among Procurement Efficiency (PRE),
Competitive Advantage (CA), and User Acceptance (UA).
It asserts that physical education affects cognitive
ability, with user agency serving as a mediator. The
approach utilises the UTAUT model to analyse the
determinants of UA, focussing on Performance
Expectancy (PE), Effort Expectancy (EE), Social Influence
(SI), and Facilitating Conditions (FC). This study aims to
elucidate how strategic procurement and technology
integration, guided by UTAUT, improve business
performance through the analysis of these elements
(Akinnuwesi et al., 2022).
Figure II: Conceptual framework
Source: Authors’ own elaboration
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4.1
Regression model
The regression model used in the analysis was
formulated as follows:
CA=β0+β1(PE)+β2(SI)+β3(UA)+β4(PRE×PE) +β5(PE×EE)
+β6(PE×SI) +β7(PE×FC) +β8(UA×PE) +ϵ.
This study investigates the effects of procurement
efficiency (PE) and social influence (SI) on competitive
advantage (CA), with user acceptance (UA) serving as
both a mediating and moderating variable. The model
examines the connections of performance expectancy
(PE)
and
UTAUT
components,
encompassing
performance expectancy (PE × PE), effort expectancy (PE
× EE), procurement expectancy (PRE × PE), social
influence (PE × SI), and enabling conditions (PE × FC). It
additionally examines the moderating influence of user
approval on procurement efficiency (UA × PE). The
equation comprises an intercept (β₀), coefficients (β₁ to
β₈) that denote the magnitude and direction of effect,
and an error component (ε) that addresses unexplained
deviations.
5.
RESULTS AND DISCUSSION
5.1 Measurement model
During the measurement model analysis, items with
factor loadings greater than 0.7 were kept to enhance
construct reliability and validity. If the items were cross-
correlated such that one internal self-correlating item
showed a correlation with another internal self-
correlating item, it was dropped to ensure that the
HTMT ratio was less than 0.9. The elements considered
in the structural route analysis were PE1-PE3, EE1-EE3,
FC1-FC4, FCA2-FCA4, PRE2-PRE4, SI1, SI3-SI4, UA1-UA3,
and UA1 × PRE1. The values of Composite Reliability and
Cronbach’s Alpha above 0.7, indicating internal
consistency. Convergent validity was confirmed with an
Average Variance Extracted (AVE) exceeding 0.5 for each
construct. Discriminant validity was assessed using the
Fornell-Larcker criterion (Fornell & Larcker, 1981), HTMT
ratios not above 0.9 (Henseler et al., 2015), and factor
loadings surpassing those of cross-loading factors. The
research demonstrated strong validity and reliability of
components for subsequent structural route analysis.
5.2 Structural model
The model demonstrated reasonable predictive power
for Firm Competitive Advantage (FCA) with an R² of
0.295 (above the 0.1 thresholds, explaining 29.5% of
variance) and a good fit (SRMR = 0.096, below 0.1)
(Zhang & Takahashi, 2024). Bootstrapping with 5,000
primary splitting samples in SMARTPLS 3.3 further
strengthened model testing and hypothesis approval,
enhancing the accuracy and credibility of the
conclusions due to reliable standard errors and
confidence intervals (Richter & Tudoran, 2024).
Together, the R², SRMR, and bootstrapping results
support the model's reliability and validity in predicting
FCA based on procurement efficiency and technology
adoption, aligning with past research (Wang et al.,
2024).
5.3 Reliability and Validity
The internal consistency assessment among 245
respondents confirmed the reliability of measuring
procurement efficiency and competitive advantage,
with Cronbach’s Alpha ranging from 0.825 to 0.840
(Wang et al., 2022). Determinants like cost containment,
quality, speed, and supplier performance were
significantly interconnected. Competitive advantage
factors,
including
ROI,
cost
advantage,
and
sustainability, recorded Alpha coefficients between
0.845 and 0.875 (Trizano-Hermosilla & Alvarado, 2016).
Technolog
y adoption showed high reliability (α = 0.860–
0.900) (Davit Marikyan, 2023), enhancing procurement
performance (Charpin et al., 2021) and boosting
competitiveness through IT-mediated efficiency (Slam et
al., 2023).
Table I: Measurement model Reliability and Validity Test
Variables
Cronbach's
alpha
Composite
reliability(rho_a)
Composite
reliability(rho_c)
Average
variance
extracted (AVE)
EE1
0.942
0.969
0.958
0.851
FC1
0.935
0.989
0.950
0.825
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FCA1
0.890
0.899
0.932
0.820
PE1
0.965
1.028
0.974
0.903
PRE1
0.913
0.966
0.944
0.850
SI1
0.865
0.981
0.909
0.770
UA1
0.901
1.067
0.935
0.828
Source: Author's own construct
Table II: Correlation between variables
EE1
FC1
FCA1
PE1
PRE1
SI1
UA1
UA1×PRE1
EEI
1.000
-0.126
-0.064
0.039
-0.215
0.152
0.494
-0.003
FC1
-0.126
1.000
-0.095
0.148
0.329
-0.127
-0.288
0.040
FCA1
-0.064
-0095
1.000
-0.279
0.209
-0.344
-0.083
0.283
PE1
0.039
0.148
-0.279
1.000
-0.099
0.111
-0.012
-0.098
PRE1
-0.215
0.329
0.209
-0.099
1.000
-0.107
-0.382
0.609
SI1
0.152
-0.127
-0.344
0.111
-0.107
1.000
0.322
0.153
UA1
0.494
-0.288
-0.083
-0.012
-0.382
0.322
1.000
0.128
UA1×PRE1
-0.003
0.040
0.283
-0.098
0.609
0.153
0.124
1.000
Source:
Author’s own construct
5.4 Heterotrait
–
monotrait ratio (HTMT)
The HTMT analysis in Table 3 assessed discriminant
validity by measuring construct correlations, ensuring all
values remained below the 0.90 threshold. Effort
Expectancy (EE1) and Facilitating Conditions (FC1) had
an HTMT value of 0.164, indicating they are distinct
constructs.
Facilitating
Conditions
(FC1)
and
Procurement Efficiency (PRE1) had a value of 0.293,
confirming their separation. User Acceptance (UA1) and
Procurement Efficiency (PRE1) had an HTMT value of
0.352, suggesting a reasonable but distinct relationship.
The highest value (0.656) was between Procurement
Efficiency and its interaction with User Acceptance
(UA1xPRE1), showing a connection but maintaining
discriminant validity. Overall, the HTMT results support
the validity of the constructs, ensuring that technology
adoption and procurement efficiency remain distinct.
Table III: Heterotrait
–
Monotrait ratio (HTMT)
EE1
FC1
FCA1
PE1
PRE1
UA1
UA1×PRE1
EE1
FC1
0.164
FCA1
0.082
0.130
PE1
0.064
0.145
0.281
PRE1
0.225
0.293
0.227
0.100
SI1
0.167
0.178
0.340
0.117
0.144
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UA1
0.530
0.269
0.100
0.077
0.352
0.354
UAI×PRE1
0.016
0.303
0.303
0.105
0.656
0.142
0.17
Source:
Author’s own construct
5.5 Cross- Loading
Cross-loading analysis was conducted to evaluate discriminant validity. The results demonstrate strong discriminant
validity, as indicators loaded significantly on their respective constructs and exhibited lower loadings on other
constructs. Specifically: Effort Expectancy indicators (EE1-EE4) showed high loadings (0.910
–
0.936) on the Effort
Expectancy construct, with lower cross-loadings on Facilitating Conditions and Procurement Efficiency, aligning with
established research (Ab Hamid et al., 2017). Facilitating Conditions indicators (FC1-FC4) loaded strongly (up to
0.966) on the Facilitating Conditions construct and had lower correlations with Social Influence and User
Acceptance, supporting model validity (Li et al., 2018). Procurement Efficiency indicators (PRE2-PRE4) displayed
high loadings (0.872
–
0.947) on the Procurement Efficiency construct, with lower cross-loadings on Facilitating
Conditions and User Acceptance (Ayaz & Yanartaş, 2020). User Acceptance indicators (UA1
-UA3) also demonstrated
strong discriminant validity (0.869
–
0.949) and lower correlations with Procurement Efficiency and Social Influence,
reinforcing its distinct role in technology adoption (Beldad & Hegner, 2018).
Overall, the analysis confirms that indicators load more strongly on their intended constructs than on others, thus
supporting the model's reliability and validity, consistent with structural equation modeling literature
Table IV: Cross Loading
EE1
FC1
F CA1
PE1
PRE1
SI1
UA1
UA1×PRE1
EE1
0.936
-0.032
-0.018
-0.013
-0.188
-0.002
0.424
-0.031
EE2
0.910
-0.182
-0.027
0.040
-0.219
0.147
0.360
0.018
EE3
0.918
-0.156
-0.141
0.081
-0.217
0.255
0.594
-0.004
EE4
0.925
-0.055
-0.021
0.017
-0.146
0.116
0.399
0.004
FC1
-0.069
0.958
-0.130
0.164
.0352
-0.126
-0.195
0.095
FC2
-0.219
0.907
-0.030
0.055
0.336
-0.154
00.437
0.0921
FC3
-0.119
0.966
-0.096
0.210
0.268
-0.112
-0.226
-0.000
FC4
0.182
0.192
-0.124
0.096
0.017
0.113
0.025
-0.068
FCA2
-0.016
-0.240
0.915
-0.245
0.189
-0.263
-0.119
0.313
FCA3
-0.073
-0.032
0.961
-0.243
0.153
-0.285
0.015
0.273
FCA4
-0.087
-0.015
0.939
-0.270
0.220
-0.380
-0.110
0.191
PE1
-0.022
0.181
-0.306
0.979
-0.106
0.163
-0.024
-0.077
PE2
0.044
0.011
-0.172
0.913
-0.135
0.084
-0.103
-0.133
PE3
-0.004
0.171
-0.183
0.942
-0.020
-0.027
-0/054
-0.103
PE4
0.111
0.167
-0.330
0.966
-0.098
0.137
0.079
-0.080
PRE2
-0.135
0.340
0.153
-0.049
0.947
-0.038
-0.367
0.580
PRE3
-0.234
0.353
0.228
-0.119
0.944
-0.144
-0.433
0.495
The American Journal of Management and Economics Innovations
17
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PRE4
-0.227
0.178
0.191
-0.104
0.872
-0.108
-0.207
0.660
SI1
0.0.141
-0.205
0.271
0.042
-0.146
0.883
0.196
0.045
SI3
0.128
-0.011
-0.386
0.180
-0.029
0.897
0.332
0.214
SI4
0.140
-0.212
-0.142
-0.015
-0.176
0.852
0.326
0.092
UA1
0.380
-0.291
-0.134
-0.059
-0.443
0.273
0.949
0.033
UA2
0.406
-0.239
0.045
0.006
-0.180
0.231
0.910
0.293
UA3
0.587
-0.236
-0.055
0.054
-0.301
0.364
0.869
0.139
UAI×PRE1
-0.003
0.040
0.283
-0.098
0.609
0.153
0.124
1.000
Source:
Author’s own construct
The bold text in the table indicates the corresponding
factor loadings of the items to their respective latent
constructs. were examined utilising SMART-PLS 4.5
Direct and indirect influences of factors.
The study examines the relationships between various
factors and a firm's competitive advantage using
SMART-PLS4.5. The findings reveal that Effort
Expectancy (EE1) does not significantly contribute to
competitive advantage (p = 0.919), supporting Dwivedi
et al. (2021a), who argued that ease of use is not always
essential for gaining a competitive edge. Likewise,
Facilitating Conditions (FC1) exert no substantial impact
on competitive advantage (p = 0.222) despite their
recognized importance in technology adoption
(Rodríguez-Espíndola et al., 2022). In contrast,
Performance Expectancy (PE1) exerts a favourable and
significant influence on competitive advantage (p =
0.013), reinforcing Camilleri (2024), who found that
when firms expect better performance from technology,
they tend to achieve greater success. Meanwhile,
Procurement Efficiency (PRE1) plays a complex role. It
reduces perceived system complexity (p = 0.002),
meaning that more efficient procurement processes
make technology appear more straightforward.
Additionally, PRE1 enhances Facilitating Conditions
(FC1) (p = 0.001), as better procurement improves
organizational support for technology (Dwivedi et al.,
2022). However, despite these advantages, PRE1 does
not directly influence competitive advantage (p = 0.574),
suggesting that other factors mediate this relationship.
The study also finds that PRE1 does not significantly alter
Performance Expectancy (PE1) (p = 0.216), indicating
that procurement improvements do not necessarily
change how firms perceive the potential benefits of
technology (Mikalef et al., 2020). Moreover, PRE1
negatively affects User Acceptance (UA1) (p < 0.001), as
more efficient procurement processes may lead to
resistance toward new technology adoption (Dwivedi et
al., 2021d). Another significant finding is the role of
Social Influence (SI1), which positively impacts
competitive advantage (p < 0.001), suggesting that
external pressures and industry norms provide a vital
function in shaping firms' success (Kelly et al., 2023).
However, User Acceptance (UA1) alone does not
significantly impact competitive advantage (p = 0.549),
implying that other variables may be more influential
(Aparicio et al., 2021). Nonetheless, UA1 moderates the
relationship between Procurement Efficiency (PRE1)
and competitive advantage (p = 0.004), indicating that
when users are more accepting of technology, the
benefits of efficient procurement are amplified (Uyen
Nguyen et al., 2024). These findings highlight the
intricate interplay between procurement efficiency,
technology adoption, and competitive advantage. They
demonstrate that while some factors directly contribute
to firm performance, others exert their influence
through moderating and mediating effects.
Table V: Direct and Indirect effect of PRE on FCA (mediation, moderation)
Original
sample
(O)
Sample mean
(M)
Standard
deviation
(STDEV)
T statistics
(IO/STDEVI)
P values
Outcome
The American Journal of Management and Economics Innovations
18
https://www.theamericanjournals.com/index.php/tajmei
EE1-˃FCAI
0.009
0.008
0.094
0.101
0.919
Not
supported
FC1-˃FCA1
-0.130
-0.126
0.107
1.218
0.222
Not
Supported
PE1-˃FCA1
-0.188
-0.199
0.076
2.475
0.013
Supported
PRE1-˃EE1
-0.215
-0.223
0.071
3.036
0.002
Supported
PRE1-˃FC1
0.329
0.342
0.097
3.380
0.001
Supported
PREI-˃FCA1
-0.065
-0.056
0.115
0.563
0.574
Not
Supported
PRE1-˃PE1
-0.099
-0.099
0.080
1.238
0.216
Not
Supported
PRE1-˃UA1
-0.382
-0.386
0.056
6.783
0.000
Supported
SI1-˃FCA1
-0.076
-0.388
0.072
5.267
0.000
Supported
UA11-˃FCA1
-0.392
-0.064
0.127
0.599
0.549
Not
Supported
UA1× PRE1-˃FCA1
0.392
0.382
0.136
2.877
0.004
Supported
Source:
Author’s own construct
6.
Theoretical contribution
This research expands literature on procurement
efficiency, technology adoption, and competitive
advantage by integrating the UTAUT model into supply
chain management (Hm et al., 2024).. The findings
emphasize procurement efficiency’s role in cost
optimization, quality improvement, and vendor
management. The study validates the significance of
UTAUT in both emerging and developed markets by
examining the moderating and mediating impacts of
user acceptability, performance expectancy, effort
expectancy, social influence, and facilitating factors
(Kelly et al., 2023). It underscores the relationship
between organisational culture and technology
adoption, illustrating how technology improves
procurement efficiency and fortifies a firm's competitive
advantage (Davit Marikyan, 2023).
7. Practical and Managerial implications
This study emphasises the significance of procurement
efficiency and technological adoption in generating
value and establishing competitive advantage.
Optimising procurement processes results in cost
savings, the quality of products and services, and
fortified supplier relationships, thereby improving
overall
operational
performance.
Advanced
procurement technologies, including e-procurement,
artificial intelligence, and data analytics, facilitate
automation, enhance accuracy, and promote data-
driven decision-making, leading to improved efficiency
and
optimised
resource
allocation.
Effective
technology adoption necessitates cultivating user
acceptance via training, innovation, and a conducive
work atmosphere. Mitigating elements such as effort
expectancy and facilitating environments might
diminish resistance to new procurement processes,
resulting in more seamless transitions and enhanced
performance outcomes. Stakeholders, such as
suppliers, customers, and governments, are essential
in advancing digital transformation and enhancing
supply chain resilience. Investing in procurement
infrastructure and incorporating sustainability and
corporate social responsibility (CSR) concepts boosts
regulatory compliance, mitigates environmental
effect, and elevates corporate reputation. Sustainable
procurement methods enable organisations to fulfil
The American Journal of Management and Economics Innovations
19
https://www.theamericanjournals.com/index.php/tajmei
ethical and legal obligations while attaining a
competitive advantage for enduring success and
growth.
8. Limitation, Future Research Gaps and Conclusion
This study recognises specific limitations that must be
taken into account when evaluating its results. The
emphasis on 245 Ghanaian firms may limit the
applicability of the findings to other geographical areas,
sectors, or economic conditions. Various nations and
industries may present specific procurement issues,
regulatory structures, and market dynamics that could
affect the correlation between procurement efficiency
and competitive advantage in diverse manners.
Subsequent research ought to augment the sample size
to encompass a wider array of organisations across
other industries and geographic regions, hence
improving the study's application and significance. The
study primarily utilises quantitative metrics to evaluate
procurement efficiency and competitive advantage. This
method yields quantifiable and comparable data but
neglects qualitative elements like leadership styles,
organisational culture, workforce engagement, and
supplier relationships, which can profoundly influence
procurement results. Utilising qualitative approaches,
including in-depth interviews, focus groups, and case
studies, would provide deeper insights into the intricate
dynamics that influence procurement success and
competitive positioning. An additional crucial factor is
the changing significance of sustainability in buying
plans.
The study did not thoroughly investigate the impact of
sustainable procurement methods, including ethical
sourcing, environmental considerations, and social
responsibilities, on long-term competitive advantage. As
sustainability increasingly influences business strategy,
future study should investigate its relationship with
procurement efficiency, assessing its capacity to
improve brand reputation, regulatory compliance, and
stakeholder
confidence.
Notwithstanding
these
constraints, the study highlights the essential function of
procurement efficiency in facilitating cost reduction,
enhancing product and service quality, and bolstering
supply chain resilience. These elements are crucial for
businesses aiming to sustain long-term competitiveness,
particularly amid swiftly evolving market demands and
regulatory environments. Addressing the identified
shortcomings in future studies will facilitate a more
thorough knowledge of procurement's strategic
significance, aiding organisations in establishing more
resilient, adaptive, and sustainable procurement
frameworks.
Conflicts of Interest
: The authors declare that we have
no conflicts of interest regarding this article's
publication, research, or authorship.
Funding Statement:
The authors confirm that no
financial support was received.
Declaration of Generative AI and AI-assisted
technologies in the writing process
In the preparation of this research, the writers employed
ChatGPT3.5 to correct grammatical inaccuracies and
improve the flow of the text. Subsequent to utilising
these tools/services, the authors performed an
exhaustive review and executed requisite modifications
to the text. Therefore, we accept whole accountability
for the content contained in this publication.
Acknowledgement:
To all the authors
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