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Absorptive Capacity, Knowledge Acquisition Intensity, and
Productivity: Insights from Ghana’s Service Industry
Roland Yaw Kudozia, Carolyn Elizabeth Kudozia, Nii Ayitey Komey, Daniel Owusu-Donkor
Gdirst Institute, Accra, Greater Accra, Ghana
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000693
Received: 05 November 2025; Accepted: 12 November 2025; Published: 21 November 2025
ABSTRACT
This study examines the association between knowledge acquisition intensity and organizational productivity
among service firms in Accra, Ghana, within the frameworks of the knowledge-based view and absorptive
capacity theory. Using survey data from telecommunications, banking, ICT, and logistics firms, the analysis
explores three hypotheses: that (H1) knowledge acquisition intensity is positively associated with productivity;
(H2) the effects differ across acquisition sources—regulatory directives, customer feedback, competitor
monitoring, and experiential learning; and (H3) firm characteristics such as size and tenure moderate this
relationship. Descriptive and inferential analyses, including Wilcoxon signed-rank tests, Spearman’s correlations,
and multiple-regression and moderation models, reveal statistically significant associations between acquisition
intensity and higher productivity across six performance indicators (p < .001). Regression results further indicate
that regulatory and experiential sources exert the strongest influence, while moderation analysis confirms that
larger and older firms experience amplified productivity benefits. The study acknowledges limitations related to
cross-sectional design, recall bias, and common method variance, mitigated through procedural and statistical
controls. Overall, the findings extend the knowledge-based view by demonstrating that in a regulatory-intensive
African context, compliance-driven knowledge acquisition is strongly associated with organizational
productivity, conditioned by firm capacity and maturity. Implications are drawn for managers and policymakers
on diversifying knowledge sources and transforming regulatory learning into innovation and performance
outcomes..
Keywords: Knowledge acquisition; Organizational productivity; Absorptive capacity; Intellectual capital;
Service sector; Ghana; Emerging economies
LITERATURE REVIEW
The Knowledge-Based View of the Firm
The knowledge-based view (KBV) conceptualizes knowledge as the most strategically significant resource of the
modern organization (Grant, 1996; Teece, 2018). Unlike physical or financial capital, knowledge is dynamic,
renewable, and capable of generating sustained competitive advantage when effectively managed. KBV builds
on the resource-based view (RBV), which emphasizes the value, rarity, inimitability, and non-substitutability
(VRIN) of strategic assets (Barney, 1991). Knowledge, more than any other resource, embodies these VRIN
characteristics and thus constitutes the foundation for long-term competitiveness.
Within this framework, knowledge acquisition is the essential starting point of the knowledge management (KM)
cycle. It provides the informational “inputs” that fuel organizational learning, innovation, and strategic flexibility
(Nonaka & Takeuchi, 1995). Probst (2008) and Hislop, Bosua and Helms (2018) highlights acquisition as one of
the eight building blocks of effective KM, while intellectual capital theory stresses that knowledge must be
embedded across human, structural, and relational capital to create organizational value (Edvinsson & Malone,
1997).
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Knowledge Acquisition as an Organizational Process
Knowledge acquisition refers to the systematic activities through which organizations secure knowledge from
both internal and external sources, including regulatory directives, customer feedback, competitor intelligence,
suppliers, and experiential learning. Acquisition expands the knowledge base of firms, enabling them to recognize
and respond to market opportunities, regulatory changes, and technological shifts.
Theoretically, acquisition is linked to absorptive capacity, defined as the ability of organizations to recognize the
value of external knowledge, assimilate it, and apply it for commercial purposes (Cohen & Levinthal, 1990; Zahra
& George, 2002). Firms with stronger absorptive capacity are better positioned to transform acquisition into
productivity gains.
Recent studies confirm the continuing importance of acquisition. Cabrilo and Dahms (2021) showed that digital
knowledge platforms enhanced acquisition breadth, accelerating innovation in multinational corporations. Donate
and de Pablo (2020) found that knowledge-oriented leadership promoted more effective acquisition and stronger
innovation outcomes. Wu et al. (2024) demonstrated that IT-enabled acquisition supports productivity but only
when combined with systematic knowledge integration mechanisms. These findings confirm that acquisition is a
necessary but not sufficient driver of performance.
Knowledge Acquisition and Organizational Productivity
The link between knowledge acquisition and productivity has been widely discussed but empirically contested.
Productivity—measured as the efficiency and effectiveness with which inputs are transformed into outputs—has
increasingly been recognized as knowledge-driven (OECD, 2023). Firms that engage intensively in acquisition
are generally more innovative, adaptable, and cost-efficient (Kianto et al., 2020).
Nonetheless, some scholars highlight the limitations of acquisition. Andreeva and Kianto (2021) argue that
acquisition without application may result in knowledge redundancy, while Farooq (2022) notes that in
developing contexts, firms often acquire knowledge but fail to institutionalize it due to cultural and infrastructural
barriers. These debates underscore the need to assess acquisition outcomes empirically in different contexts,
particularly in Africa where structural constraints may weaken the acquisition–productivity link.
The African Context
African economies present distinctive conditions for KM. Acquisition is often shaped by regulatory directives,
government policies, and external donor requirements, reflecting the high degree of institutional influence in
emerging economies. Omotayo (2019) and observed that Nigerian firms acquire substantial regulatory and
customer-related knowledge but struggle to translate it into innovation due to weak absorptive capacity. Ndlovu
and Ngwenya (2020) found that South African SMEs engaged in significant acquisition but under-leveraged the
knowledge because of limited knowledge-sharing cultures. Akinwale (2021) reported that Kenyan firms relied
heavily on regulators and business networks, with limited internal capacity to exploit these inputs.
These studies converge on the insight that acquisition in Africa is widespread but uneven in impact. Unlike in
advanced economies where customer analytics and competitor intelligence dominate, African firms tend to
prioritize regulatory directives, reflecting institutional embeddedness and compliance imperatives. This
contextual variation highlights the importance of situating KM theory within local realities.
The Ghanaian Context
Ghana’s service sector offers a particularly distinctive case. Telecommunications, banking, ICT, and logistics
firms operate under a strong regulatory framework, making government directives a central source of knowledge.
Firms also acquire knowledge from customer interactions, competitor monitoring, and lessons from products and
services, but the relative dominance of regulatory knowledge sets Ghana apart from global patterns. While
acquisition intensity is high, little research has empirically tested whether this intensity translates into
productivity, nor how firm characteristics condition this relationship.
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Firm Characteristics as Moderators
While much of the KM literature emphasizes acquisition and utilization processes, firm-level characteristics also
play a critical role in determining outcomes. Absorptive capacity theory (Cohen & Levinthal, 1990; Zahra &
George, 2002) suggests that organizational size, tenure, and resource endowments shape the ability to process
and apply acquired knowledge. Larger firms often have more developed infrastructures—such as R&D units, HR
training systems, and IT platforms—that enable them to better leverage acquisition. Smaller firms, by contrast,
may face resource limitations that constrain their capacity to transform acquisition into productivity (Leoni,
2022).
Empirical evidence supports this moderating effect. Kianto et al. (2020) found that organizational learning
capacity significantly strengthened the relationship between acquisition and performance in European firms. In
Africa, Ndlovu and Ngwenya (2020) and Omotayo (2019) both documented that SMEs struggled to reap
productivity benefits from acquisition compared to larger firms, largely due to resource and infrastructural
deficits. The Ghanaian case thus provides an opportunity to test whether firm characteristics similarly condition
the acquisition–productivity link in a regulatory-heavy environment.
Table 1. Comparative Summary of Key Studies on Knowledge Acquisition and Productivity (2019–2025)
Author(s) &
Year
Context
Key Findings
Relevance to Present Study
Donate & de
Pablo (2020)
Spain
(Europe)
Knowledge-oriented leadership enhances
acquisition → innovation outcomes
Confirms acquisition as driver of
performance in advanced economies
Cabrilo &
Dahms (2021)
Multinational
s
Digital platforms broaden acquisition
scope, strengthening innovation and
adaptability
Highlights role of digital enablers in
global KM
Andreeva &
Kianto (2021)
Global
synthesis
Acquisition without application yields
redundancy
Warns against assuming linear
acquisition → productivity link
Farooq (2022)
South Asia
Developing contexts acquire knowledge
but fail to institutionalize it
Resonates with challenges in Africa
Kianto et al.
(2020)
Europe
Learning capacity moderates
acquisition–performance relationship
Supports H3: absorptive capacity as
moderator
Omotayo (2019)
Nigeria
Firms acquire regulatory & customer
knowledge but weak in utilization
Mirrors Ghana’s reliance on
regulatory knowledge
Ndlovu &
Ngwenya (2020)
South Africa
SMEs acquire knowledge but underuse it
due to cultural/structural barriers
African evidence of weak utilization
Akinwale (2021)
Kenya
Reliance on regulators & networks; low
internal absorptive capacity
Confirms regulatory dominance in
African firms
Wu et al. (2024)
China
IT-enabled acquisition boosts
productivity but only with integration
processes
Supports KBV claim of conditional
productivity outcomes
Boateng &
Dzandu (2022)
Multi-country
African cases
(Ghana,
Nigeria,
Kenya, South
Africa)
Comprehensive analysis of how African
organizations conceptualize and
operationalize knowledge management.
Finds that KM practices are
predominantly compliance-driven and
externally motivated by donor or
regulatory frameworks rather than
internally embedded innovation systems.
Highlights infrastructural deficits, weak
knowledge-sharing cultures, and limited
absorptive capacity as barriers to
Reinforces that in African contexts,
especially Ghana, regulatory and
externally mandated knowledge
sources dominate acquisition.
Supports the study’s argument that
institutional embeddedness and
organizational capacity shape how
knowledge acquisition translates into
productivity.
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transforming acquired knowledge into
performance outcomes. Recommends
stronger institutional frameworks and
leadership commitment to move KM
from compliance to value creation.
OECD (2023)
Global
Productivity increasingly recognized as
knowledge-driven
Reinforces global relevance of KM
for performance
Research Gap and Hypotheses
The literature reviewed points to three key gaps. First, although acquisition is widely acknowledged as essential
to firm performance, few studies have directly tested whether acquisition intensity translates into productivity in
African contexts. Second, while the type of acquisition source is theoretically important, its relative impact has
rarely been compared across regulatory, customer, competitor, and experiential domains. Third, the moderating
role of firm-level characteristics such as size and tenure, which absorptive capacity theory predicts, remains
underexplored in Ghanaian firms. To address these gaps, the study develops and tests three hypotheses.
Hypothesis 1 Development: Knowledge Acquisition Intensity and Productivity
The knowledge-based view (Grant, 1996; Teece, 2018) posits that firms derive competitive advantage by
acquiring and mobilizing valuable knowledge resources. Acquisition expands organizational knowledge stocks,
enabling firms to adapt to environmental shifts and improve efficiency. Empirical evidence consistently supports
the performance benefits of acquisition. For instance, Donate and de Pablo (2020) found that knowledge-oriented
leadership promoted acquisition, which in turn enhanced innovation performance in Spanish firms. Cabrilo and
Dahms (2021) reported that digital platforms allowed firms to acquire broader knowledge, resulting in stronger
adaptability and competitiveness. Wu et al. (2024) further demonstrated that IT-enabled acquisition improved
productivity outcomes, though only when knowledge was systematically integrated. In Africa, the productivity
effects of acquisition are less consistently observed. While firms in Nigeria and South Africa actively acquire
knowledge, weak absorptive capacity often undermines performance outcomes (Omotayo, 2019; Ndlovu &
Ngwenya, 2020). Nonetheless, the logic of KBV suggests that higher acquisition intensity should yield
productivity benefits even in such contexts, as it provides organizations with the raw informational inputs needed
for efficiency and innovation.
Thus, the first hypothesis is proposed:
H1: Knowledge acquisition intensity is positively associated with organizational productivity.
Hypothesis 2 Development: Variability of Acquisition Sources
Not all knowledge is equally impactful. The source of acquisition—whether regulatory, customer-driven,
competitor-based, or experiential—shapes its utility and relevance to productivity. Global evidence indicates that
firms in advanced economies prioritize customer feedback and competitor intelligence, often using digital
analytics to capture and process such insights (Alegre & Chiva, 2013; Cabrilo & Dahms, 2021). These sources
are closely tied to market responsiveness and innovation outcomes. In Africa, however, regulatory directives
often dominate acquisition. Omotayo (2019) noted that Nigerian firms relied more on regulatory and compliance-
driven knowledge than on market intelligence. Ndlovu and Ngwenya (2020) found that South African SMEs
frequently adjusted their practices based on institutional directives. Akinwale (2021) observed that Kenyan firms
depended on regulatory and networked knowledge sources, with relatively less emphasis on customer or
competitor inputs. The Ghanaian service sector presents a particularly clear case: telecommunications, ICT, and
banking firms operate under strict regulatory oversight, making government rules the most consistent source of
knowledge acquisition. While firms also acquire customer and experiential knowledge, the dominance of
regulatory inputs distinguishes Ghanaian acquisition patterns from those in advanced economies.
This suggests that productivity outcomes may vary depending on the type of knowledge source.
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Thus, the second hypothesis is proposed:
H2: The impact of knowledge acquisition on productivity varies by source, with regulatory, customer,
competitor, and experiential knowledge having differential effects.
Hypothesis 3 Development: Moderating Role of Firm Characteristics
Although acquisition is important, not all firms are equally capable of converting acquired knowledge into
productivity. Absorptive capacity theory (Cohen & Levinthal, 1990; Zahra & George, 2002) emphasizes that
organizational characteristics such as size, tenure, and resource availability influence the ability to assimilate and
exploit external knowledge. Larger and more established firms often possess the infrastructures—such as R&D
units, training systems, and IT capabilities—that facilitate knowledge utilization. Smaller or younger firms, by
contrast, may lack these resources and thus struggle to derive productivity gains from acquisition. Empirical
research supports this claim. Kianto et al. (2020) showed that organizational learning capacity moderated the
acquisition–performance relationship in European firms. Leoni (2022) found that firm size influenced the strength
of KM–performance links in Italian enterprises. In Africa, Omotayo (2019) and Ndlovu and Ngwenya (2020)
both reported that SMEs were less able than larger firms to transform acquisition into productivity gains,
primarily due to resource and structural limitations. In Ghana, where acquisition is dominated by regulatory
knowledge, the moderating role of firm characteristics may be particularly important. Larger firms may be better
equipped to interpret and integrate regulatory directives into productivity-enhancing strategies, while smaller
firms may treat them primarily as compliance burdens. Testing this moderating effect provides an opportunity to
extend both KBV and absorptive capacity theory within an African regulatory context.
Thus, the third hypothesis is proposed:
H3: The relationship between knowledge acquisition and productivity is moderated by firm characteristics (e.g.,
size, tenure, absorptive capacity), such that larger or more established firms realize stronger productivity gains.
METHODOLOGY
Research Design
This study adopted a quantitative, cross-sectional survey design to examine the relationship between knowledge
acquisition intensity and organizational productivity. The survey approach was selected because it enables the
systematic collection of comparable data across a large number of organizations, thereby allowing for the testing
of hypothesized relationships between constructs (Creswell & Creswell, 2018). Cross-sectional surveys are
widely used in knowledge management (KM) and intellectual capital research (Podsakoff et al., 2012),
particularly when the objective is to establish statistical associations rather than causal claims. Although a cross-
sectional design limits causal inference, it provides a rigorous snapshot of the relationship between acquisition
practices and productivity outcomes in Ghana’s service sector.
Population and Sampling
The population for this study comprised firms within the service sector of Accra, Ghana, including financial
services, telecommunications, ICT, consulting, and logistics. This sector was chosen because it is highly
knowledge-intensive, characterized by continuous interaction with customers, regulators, and competitors, and
thus represents an appropriate setting for studying knowledge acquisition dynamics.
A total of 210 firms participated in the study. The sampling strategy was purposive-random: purposive in the
sense that firms were selected to represent major service subsectors, and random in the sense that respondents
were drawn from managerial, technical, and operational roles within each firm. This ensured coverage of
individuals who were both directly involved in acquiring knowledge and responsible for using it in organizational
processes. The final sample reflected a diverse range of firm sizes and organizational structures, enhancing the
external validity of the results.
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Data Collection
Data were collected using a structured questionnaire administered to firm representatives between March and
June 2024. The questionnaire was pre-tested with 20 respondents drawn from three service subsectors to ensure
clarity and reliability of items. Feedback from the pilot led to minor adjustments in wording and sequencing,
particularly to align with local terminologies used in Ghanaian business environments. Respondents were assured
of confidentiality and anonymity, and participation was voluntary.
The survey instrument comprised three sections. The first section captured demographic and organizational
characteristics, including firm size, subsector, and respondent role. The second section assessed knowledge
acquisition intensity, while the third section measured organizational productivity.
Measurement of Constructs
Knowledge acquisition intensity was measured through items capturing the extent to which firms relied on
various sources of knowledge. These included government rules and regulations, competitor monitoring,
customer feedback, and lessons from successful products and services. Respondents rated each item on a five-
point Likert scale ranging from “very low” (1) to “very high” (5). The descriptive results indicated that
government rules and regulations scored the highest (M = 4.70), followed by lessons from successful products
and services (M = 4.49).
Organizational productivity was measured through pre- and post-acquisition self-assessments across dimensions
including cost efficiency, successful project delivery, achievement of objectives, adaptability and agility,
innovation in products and services, and workforce efficiency. Respondents provided ratings on the same five-
point scale, enabling comparisons of productivity outcomes before and after acquisition.
“Lessons from successful products and services” were conceptualized as a form of externalized experiential
learning, knowledge derived from customer uptake and market feedback rather than purely internal reflection.
Although experiential processes often occur internally, in this study they are treated as acquisition because they
involve systematic capture of externally observable outcomes (e.g., user responses, product success metrics).
This aligns with Nonaka and Takeuchi’s (1995) externalization–combination process in the SECI model.
Reliability and Validity
Psychometric testing confirmed the robustness of the measurement scales. Cronbach’s alpha values for the
acquisition and productivity scales ranged from 0.81 to 0.96, exceeding the recommended minimum of 0.70
(Nunnally & Bernstein, 1994). Construct validity was supported by aligning measurement items with established
KM and intellectual capital frameworks (Nonaka & Takeuchi, 1995; Probst, 2008; Edvinsson & Malone, 1997;
Hislop, Bosua &Helms, 2018). Convergent validity was confirmed through average variance extracted (AVE)
values greater than 0.50 for both acquisition and productivity constructs (Fornell & Larcker, 1981). Discriminant
validity was established by testing cross-loadings, which showed that acquisition and productivity constructs
were empirically distinc.
Analytical Strategy
The data were analyzed in several stages. Descriptive statistics were used to summarize the overall intensity of
knowledge acquisition and to identify the most prominent sources. A paired-sample Wilcoxon signed-rank test
was then employed to examine differences between pre- and post-acquisition productivity scores. This non-
parametric test was chosen because the Likert data are ordinal and non-normally distributed. Spearman’s rho
correlation analysis was used to test the relationship between acquisition intensity and productivity. The
correlation coefficient (ρ = 0.387, p < .001) provided evidence of a moderate positive relationship, supporting
the primary hypothesis (H1).
In addition, effect sizes and 95% confidence intervals were calculated to assess the robustness of results.
Analytical procedures were performed using SPSS (version 29) for descriptive and inferential statistics. Where
necessary, robustness checks were conducted using AMOS (version 29) for confirmatory factor analysis to ensure
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construct validity. To align with H3, multi-group comparisons were also considered, disaggregating firms by size
and tenure to explore the moderating role of organizational characteristics.
Ethical Considerations
The study adhered to international standards for research ethics in social science. Ethical clearance was obtained
from the institutional review board of the lead author’s university. Respondents were informed of the purpose of
the study and assured that their responses would remain confidential and used solely for academic purposes.
Participation was voluntary, and respondents had the right to withdraw at any point. Data were anonymized before
analysis to protect organizational identities.
Common Method and Recall Bias
Given that both independent and dependent variables were collected from the same respondents using self-report
instruments, the study recognizes the potential for common method variance (Podsakoff et al., 2012) and recall
bias inherent in retrospective “before–after” evaluations. To mitigate these effects, respondents were assured of
anonymity, reverse-coded items were included to reduce acquiescence bias, and question order was
counterbalanced. Harman’s single-factor test was conducted, showing that no single factor accounted for the
majority of variance (34.6%), suggesting CMB was not a critical threat to validity.
RESULTS
Descriptive Analysis of Knowledge Acquisition Sources
The first step in the analysis examined the relative importance of different knowledge acquisition sources among
firms in Ghana’s service sector. As shown in Table 2, regulatory directives emerged as the most significant and
consistent knowledge source, with a mean of 4.70 (SE = 0.05), rated “very high” on the five-point scale. This
was followed by lessons from successful products and services (M = 4.49, SE = 0.07), customer feedback (M =
4.35, SE = 0.06), and competitor monitoring (M = 4.22, SE = 0.08), all rated as “high.” The overall grand mean
of 4.18 demonstrates that firms in the sector engage in knowledge acquisition intensively across sources.
These descriptive results confirm the dominance of regulatory directives as a distinctive feature of Ghanaian
knowledge acquisition, aligning with African studies that emphasize institutional and compliance-driven
knowledge (Omotayo, 2019; Ndlovu & Ngwenya, 2020). By contrast, in global contexts, customer and
competitor inputs are typically ranked more highly (Cabrilo & Dahms, 2021). These findings provide initial
support for H2, which posited that the impact of acquisition would vary by source.
Table 2 Descriptive Statistics of Knowledge Acquisition Sources
Mean
Std. Error
Interpretation
4.70
0.05
Very High
4.49
0.07
High
4.35
0.06
High
4.22
0.08
High
4.18
0.07
High
Note. Regulatory directives are the most dominant source of knowledge acquisition.
Productivity Outcomes of Knowledge Acquisition
To evaluate whether acquisition intensity translated into improved productivity, respondents rated their firms’
performance before and after acquisition. As presented in Table 3, all six productivity indicators showed
substantial gains. For instance, successful project delivery improved from a mean of 3.65 pre-acquisition to 4.42
post-acquisition, while adaptability and agility rose from 3.73 to 4.41. Workforce efficiency, a critical dimension
of organizational productivity, increased from 3.72 to 4.39.
The Wilcoxon signed-rank test confirmed that all improvements were statistically significant at p < .001, thereby
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supporting H1. These results demonstrate that knowledge acquisition contributes meaningfully to organizational
outcomes across multiple dimensions, reinforcing the theoretical argument that knowledge is a key driver of
productivity.
Table 3 Pre- and Post-Acquisition Productivity Scores
Productivity Indicator
Pre-Acquisition Mean
Post-Acquisition Mean
Z (Wilcoxon)
p-value
Successful project delivery
3.65
4.42
-12.03
< .001
Achievement of objectives
3.78
4.47
-11.56
< .001
Cost efficiency
3.69
4.38
-10.84
< .001
Adaptability and agility
3.73
4.41
-11.01
< .001
Innovation in services
3.70
4.44
-11.23
< .001
Workforce efficiency
3.72
4.39
-10.95
< .001
Overall Productivity Index
3.71
4.42
-11.43
< .001
Note. Productivity indicators all improved significantly following knowledge acquisition.
Correlation Between Knowledge Acquisition and Productivity
The strength of the acquisition–productivity link was further tested using Spearman’s rho correlation analysis.
Results, reported in Table 4, show a moderate but statistically significant positive correlation (ρ = 0.387, p <
.001). This suggests that firms that engage more intensively in acquisition tend to report stronger productivity
outcomes.
Figure 1 provides a visual representation of this relationship. The upward slope of the regression line confirms
the positive direction, while the dispersion of data points around the line reflects the moderate effect size. This
pattern supports H1, while also underscoring that acquisition alone explains only part of productivity differences
among firms, hinting at the role of moderating factors such as firm size and absorptive capacity.
Table 4 Correlation Between Knowledge Acquisition and Productivity
Variables
Knowledge Acquisition
Productivity
Knowledge Acquisition
1.000
0.387***
Productivity
0.387***
1.000
***p < .001
Figure 1. Scatterplot of Knowledge Acquisition Intensity and Organizational Productivity.
The scatterplot illustrates the positive correlation (ρ = 0.387, p < .001) between acquisition intensity and
productivity. The regression line indicates a moderate positive association.
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Source Variability and Moderation Effects
The relative weight of acquisition sources, reported in Table 2, provides empirical support for H2, which posited
that acquisition from regulatory, customer, competitor, and experiential sources would have differential impacts.
The data confirm that regulatory directives dominate, a finding consistent with African studies but divergent from
global KM research.
Moreover, the moderate correlation in Table 4 and the dispersion shown in Figure 1 suggest variation in how
effectively firms convert acquired knowledge into productivity. This variation is consistent with H3, which
argued that firm characteristics moderate the acquisition–productivity link. Larger and more established firms
appear better able to leverage regulatory knowledge into performance improvements, while smaller firms face
resource and absorptive capacity constraints.
To summarize these relationships, Figure 2 presents the conceptual model tested in this study, highlighting the
direct pathway (H1), source variability (H2), and the moderating role of firm characteristics (H3).
Figure 2. Conceptual Model of Knowledge Acquisition, Sources, Moderators, and Productivity.
The model illustrates the hypothesized relationships: knowledge acquisition intensity has a positive effect on
productivity (H1), source variability influences the acquisition–productivity link (H2), and firm characteristics
moderate this relationship (H3).
Regression Analysis of Knowledge Sources.
To test H₂ more rigorously, a multiple regression model was estimated with productivity as the dependent variable
and the four knowledge sources—regulatory, customer, competitor, and experiential—as predictors. The model
was significant (F = 18.72, p < .001; R² = 0.42). Regulatory directives (β = 0.46, p < .001) and experiential
learning (β = 0.28, p = .004) emerged as the strongest predictors, while customer feedback (β = 0.19, p = .031)
and competitor monitoring (β = 0.09, ns) had weaker effects. These results confirm that the productivity impact
of knowledge acquisition varies by source, providing statistical support for H₂.
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Robustness Checks and Exploratory Analyses
Sector-Wise Differences
When the sample was disaggregated by sector—telecommunications, banking, ICT, and logistics—the pattern of
results remained largely consistent. Acquisition intensity was highest in telecommunications firms (M = 4.52),
where regulatory directives were especially dominant, followed closely by banking (M = 4.41). ICT firms
reported the most balanced acquisition portfolio, drawing not only on regulations but also on customer and
competitor knowledge, while logistics firms placed greater emphasis on experiential learning. Despite these
sectoral differences, Wilcoxon tests within each sector confirmed significant productivity improvements post-
acquisition (p < .001). These results suggest that while the sources of acquisition vary slightly, the performance-
enhancing effect of acquisition is robust across industries.
Firm Size: SMEs versus Large Firms
A second robustness check examined whether the acquisition–productivity relationship differed between small
and medium enterprises (SMEs) and large firms. SMEs in the sample demonstrated relatively high acquisition
scores (M = 4.12) but reported smaller productivity gains compared to large firms. In contrast, large firms (M =
4.43) exhibited both higher acquisition intensity and more substantial productivity improvements, particularly in
areas such as cost efficiency and innovation. Correlation coefficients were stronger in the large-firm subgroup (ρ
= 0.442, p < .001) than in SMEs (ρ = 0.291, p < .01), suggesting that absorptive capacity, which is more developed
in larger organizations, enhances the acquisition–productivity pathway.
Figure 3. Pre- and Post-Acquisition Productivity Gains by Firm Size.
Mean productivity scores are compared for small and medium enterprises (SMEs) and large firms across six
productivity indicators. Both groups show significant gains after knowledge acquisition, but large firms report
higher post-acquisition outcomes across all dimensions, reflecting their greater absorptive capacity.
Organizational Tenure and Age Effects
An exploratory analysis of organizational tenure revealed that firms operating for more than 10 years
demonstrated greater productivity gains from acquisition compared to younger firms. Older firms, with longer
institutional memory and established routines, scored higher on adaptability (M = 4.53) and innovation (M =
4.55) post-acquisition, whereas younger firms reported higher but more variable gains in project delivery and
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workforce efficiency. This pattern reinforces the idea that institutional maturity strengthens the benefits of
acquisition, though even younger firms realized statistically significant improvements.
Sensitivity of Results to Alternative Measures
As an additional robustness test, productivity improvements were re-examined using standardized scores rather
than raw means. The effect sizes remained significant and positive across all indicators, with Cohen’s d ranging
from 0.61 (moderate) for cost efficiency to 0.89 (large) for successful project delivery. These results confirm that
the findings are not dependent on the measurement scale employed.
Moderation Analysis.
Following the approach of Aiken and West (1991), a hierarchical regression was conducted to test whether firm
size and tenure moderated the acquisition–productivity relationship. In Step 1, knowledge acquisition intensity
significantly predicted productivity (β = 0.33, p < .001). In Step 2, inclusion of size and tenure main effects
improved the model (ΔR² = 0.08, p < .01). Step 3 introduced the interaction terms (acquisition × size; acquisition
× tenure), both of which were significant (β = 0.17, p = .015; β = 0.14, p = .026, respectively). The positive
coefficients indicate that the productivity benefits of knowledge acquisition are stronger in larger and older firms,
supporting H₃.
Summary of Findings
The results provide strong evidence that knowledge acquisition intensity significantly enhances productivity
among Ghanaian service firms, though the effect is moderate and conditioned by source type and firm-level
attributes. Regulatory directives dominate acquisition activity, confirming the institutional embeddedness of KM
in Africa, while firm characteristics determine the degree to which productivity gains are realized. Collectively,
the findings strongly support H1, H2, and H3, and set the stage for the Discussion, where theoretical, managerial,
and policy implications are elaborated.
DISCUSSION
The objective of this study was to assess the extent to which knowledge acquisition intensity improves
productivity among service sector firms in Ghana, to determine whether acquisition sources vary in their impact,
and to test whether firm-level characteristics condition this relationship. The findings, supported by descriptive
statistics, correlation, and non-parametric tests, consistently upheld all three hypotheses. This section interprets
the results in relation to theory, compares them with prior research in both African and global contexts, and draws
out the theoretical, managerial, and policy implications.
Hypothesis 1: Knowledge Acquisition Intensity and Productivity
Hypothesis 1 predicted that knowledge acquisition intensity is positively associated with productivity. As shown
in Table 3, productivity scores improved significantly across all indicators when comparing pre- and post-
acquisition ratings. For example, successful project delivery increased from 3.65 to 4.42, and workforce
efficiency rose from 3.72 to 4.39, with all gains significant at p < .001. These results confirm that acquisition
produces tangible performance improvements across multiple productivity domains.
Further support comes from the correlation analysis (Table 4), which revealed a moderate positive association
(ρ = 0.387, p < .001) between acquisition intensity and productivity outcomes. Figure 1 illustrates this
relationship visually, showing an upward-sloping regression line that confirms the positive link, though with
dispersion indicating a moderate effect size. Taken together, these findings demonstrate that while acquisition
clearly enhances productivity, the effect is not overwhelmingly strong, suggesting that acquisition alone cannot
fully explain performance gains.
This pattern aligns with the knowledge-based view (Grant, 1996; Teece, 2018), which asserts that knowledge is
a strategic resource that drives competitiveness. It also mirrors global empirical studies demonstrating positive
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acquisition–performance links (Donate & de Pablo, 2020; Cabrilo & Dahms, 2021). Yet the moderate strength
of the correlation affirms Andreeva and Kianto’s (2021) caution that acquisition without robust utilization can
yield only partial benefits. For Ghanaian firms, the evidence suggests that while acquisition boosts performance,
it must be complemented by stronger application and integration mechanisms to maximize impact.
Hypothesis 2: Variability of Acquisition Sources
Hypothesis 2 proposed that the impact of acquisition would vary depending on the source. The descriptive results
presented in Table 2 confirm this, showing that regulatory directives were the most significant knowledge source
(M = 4.70), followed by experiential learning (M = 4.49), customer feedback (M = 4.35), and competitor
monitoring (M = 4.22). Whereas Cabrilo and Dahms (2021) found digital customer analytics to be the primary
driver of acquisition outcomes in European firms, our results highlight the predominance of regulatory directives
in Ghana’s service sector. This contrast underscores how institutional embeddedness shapes the hierarchy of
knowledge sources in emerging economies, where government rules dominate organizational knowledge flows.
This reliance on regulatory knowledge contrasts with patterns in advanced economies, where customer and
competitor intelligence typically drive acquisition and innovation (Alegre & Chiva, 2013; Cabrilo & Dahms,
2021). In African settings, however, similar trends have been reported: Omotayo (2019) in Nigeria and Akinwale
(2021) in Kenya both observed regulatory dominance in knowledge acquisition, while Ndlovu and Ngwenya
(2020) noted that South African SMEs relied heavily on institutional directives. The Ghanaian findings reinforce
this pattern, suggesting that firms in highly regulated service sectors may prioritize compliance-driven knowledge
over market-driven insights.
The implication is that while regulatory acquisition sustains legitimacy and baseline productivity, it may constrain
opportunities for innovation if overemphasized. Thus, Figure 2 conceptually depicts H2 by illustrating the
multiple sources of acquisition feeding into intensity, acknowledging their differential contributions to
productivity.
Hypothesis 3: Moderating Role of Firm Characteristics
Hypothesis 3 predicted that firm characteristics such as size, tenure, and absorptive capacity would moderate the
acquisition–productivity link. While exploratory, the data suggest that larger and more established firms were
better able to leverage acquisition into productivity gains than smaller or younger firms. This moderating pattern
is consistent with absorptive capacity theory (Cohen & Levinthal, 1990; Zahra & George, 2002), which holds
that organizational resources condition the ability to process and apply external knowledge.
Although the correlation in Table 4 (ρ = 0.387) demonstrates an overall positive relationship, the dispersion
observed in Figure 1 suggests variation in how firms capitalize on acquisition. Larger firms, with more developed
infrastructures such as training systems and IT platforms, likely account for the stronger end of this relationship,
whereas smaller firms may lack the absorptive capacity to fully exploit acquired knowledge. This is consistent
with findings by Kianto et al. (2020) in Europe and by Ndlovu and Ngwenya (2020) in South Africa, both of
whom documented stronger KM–performance effects in firms with greater organizational capacity.
Robustness checks provide further evidence for H3. As shown in Figure 3, large firms reported significantly
greater post-acquisition productivity gains than SMEs across all six productivity dimensions. Similarly, older
firms demonstrated stronger improvements in adaptability and innovation than younger firms. These results
confirm that organizational resources and maturity condition the degree to which acquisition translates into
performance improvements.
Theoretical Implications
The evidence from Tables 2–4 and Figures 1–3 collectively advances theory in several ways. First, the
significant productivity improvements after acquisition empirically validate the KBV proposition that knowledge
acquisition underpins performance. Second, the variability across sources extends intellectual capital theory by
showing that in Ghana, regulatory directives constitute a distinctive form of structural capital, unlike the
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customer-focused emphasis in global KM research. Third, the moderating role of firm characteristics supports
absorptive capacity theory by confirming that organizational attributes condition the acquisition–performance
link. Collectively, these findings demonstrate that KM theory must be contextualized within African institutional
environments, where compliance-driven acquisition plays a central role.
Managerial Implications
For managers, the results in Table 3 confirm that acquisition investments directly improve performance across
all dimensions, from cost efficiency to innovation. However, the correlation strength in Table 4 and variability
by source in Table 2 caution against over-reliance on regulatory knowledge alone. Managers should diversify
acquisition portfolios to include customer and competitor intelligence, while also investing in organizational
systems that enhance knowledge utilization. The productivity improvements across all six indicators (Table 3)
illustrate the benefits of such investments, while the disparities highlighted in Figure 3 emphasize the need for
SMEs to develop absorptive capacity through partnerships, digital tools, and staff training.
Policy Implications
From a policy standpoint, the dominance of regulatory knowledge (Table 2) highlights the role of regulators as
critical knowledge providers. Policymakers should therefore frame regulations as actionable learning inputs, not
merely compliance requirements. Clear communication, capacity-building workshops, and digital dissemination
platforms can help firms transform regulatory directives into productivity gains. At the regional level, African
policymakers can draw lessons from the EU and East Asia, where knowledge-friendly regulations are explicitly
designed to support innovation alongside compliance. By adopting such approaches, Ghanaian regulators can
encourage firms not only to comply but also to innovate.
Synthesis
The data presented across Tables 2–4 and Figures 1–3 provide consistent support for all three hypotheses.
Knowledge acquisition intensity improves productivity (H1), acquisition sources matter (H2), and firm
characteristics moderate the effect (H3). These results reinforce global KM theory while highlighting distinctive
African institutional dynamics. By grounding the theoretical discussion in concrete empirical evidence, the study
offers robust contributions to scholarship, managerial practice, and policy design.
LIMITATIONS AND FUTURE RESEARCH
Although the findings provide strong support for the three hypotheses, several limitations must be acknowledged.
First, the study employed a cross-sectional design, which, while suitable for testing associations, restricts causal
inference. The improvements in productivity associated with acquisition intensity (Tables 2–3) are statistically
robust, but longitudinal studies are needed to establish how sustained acquisition practices influence performance
over time.
Second, the data relied on self-reported measures of both knowledge acquisition and productivity. Although
psychometric tests confirmed reliability and validity, and robustness checks (Figures 2–3) mitigate concerns of
measurement bias, there remains the possibility of common-method variance. Future research should complement
perceptual data with objective performance indicators, such as financial results or innovation outputs, to
triangulate findings.
Third, the study was limited to service firms in Accra, which, while appropriate for investigating knowledge-
intensive and regulation-heavy sectors, constrains generalizability to other regions of Ghana or other African
economies with different institutional settings. Sectoral robustness checks (Section 4.5.1) suggest that
acquisition–productivity links hold across subsectors, but replication in manufacturing or agriculture would
provide broader validation.
Fourth, the study examined firm characteristics such as size and tenure as moderators (H3), confirming their role
in shaping acquisition–productivity effects. However, other dimensions of absorptive capacity—such as
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organizational culture, digital capability, or leadership orientation—were not explicitly tested. Incorporating
these dimensions in future models would deepen understanding of how firms transform acquired knowledge into
productivity.
Future work should employ longitudinal or panel designs to establish temporal causality, triangulate self-reports
with objective indicators such as revenue per employee or project completion rate, and integrate latent-variable
modeling (e.g., SEM or PROCESS) for moderation and mediation effects. Additionally, qualitative follow-ups
could explore how organizational culture and leadership style facilitate the conversion of regulatory knowledge
into innovation.
Finally, while the study focused on Ghana, future comparative research could situate Ghanaian firms alongside
those in other African or global contexts. Cross-national analyses would allow for a richer understanding of how
institutional embeddedness—particularly regulatory dominance in knowledge acquisition—affects performance
relative to more market-driven environments.
By addressing these limitations, future research can build on the evidence presented here, extending both the
theoretical insights and the practical applicability of knowledge acquisition studies in emerging economies.
CONCLUSION
This study set out to examine whether knowledge acquisition intensity improves productivity in Ghana’s service
sector, whether acquisition sources vary in their impact, and whether firm-level characteristics moderate these
effects. By integrating the knowledge-based view (KBV) and absorptive capacity theory, and situating the inquiry
in a regulatory-intensive African context, the research has offered fresh insights into how firms acquire and
leverage knowledge to sustain competitiveness.
The findings consistently supported all three hypotheses. First, as demonstrated in Table 3, productivity scores
improved significantly across all indicators when comparing pre- and post-acquisition outcomes. Gains were
evident in project delivery, cost efficiency, adaptability, and innovation, with Wilcoxon signed-rank tests
confirming statistical significance (p < .001). These results provide strong evidence for Hypothesis 1, affirming
that acquisition intensity drives measurable productivity improvements. The correlation analysis in Table 4
further corroborated this conclusion, showing a moderate but significant positive association (ρ = 0.387, p <
.001). The scatterplot in Figure 1 visualizes this link, highlighting both the positive trajectory and the variability
across firms.
Second, Hypothesis 2 was supported by the descriptive evidence presented in Table 2, which revealed that
regulatory directives dominate knowledge acquisition in Ghanaian firms, followed by experiential learning,
customer feedback, and competitor monitoring. This finding underscores the distinctive institutional context of
Ghana, where regulatory knowledge constitutes a primary driver of organizational learning. It also aligns with
African studies that emphasize regulatory embeddedness (Omotayo, 2019; Akinwale, 2021). By contrast, global
evidence places greater weight on customer and competitor knowledge, as reflected in the literature review. This
variability, depicted conceptually in Figure 2, demonstrates that not all knowledge sources contribute equally to
productivity.
Third, Hypothesis 3 was confirmed by the observed variation in the strength of the acquisition–productivity link.
While the overall correlation was positive (Table 4), the dispersion seen in Figure 1 suggests that firm
characteristics such as size, tenure, and absorptive capacity condition the extent to which firms benefit from
acquisition. Robustness checks further revealed that large and older firms reported stronger productivity gains
than SMEs and younger firms, a pattern clearly shown in Figure 3. These results are consistent with absorptive
capacity theory (Cohen & Levinthal, 1990; Zahra & George, 2002), which emphasizes that organizational
resources and maturity shape the ability to capitalize on acquired knowledge. This moderating pathway is
represented in Figure 2, which explicitly illustrates the role of firm characteristics as amplifiers or constraints.
Theoretically, these findings extend the KBV by affirming knowledge as a productivity-enhancing resource even
in regulatory-heavy African economies. They also enrich intellectual capital theory by demonstrating that
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regulatory knowledge forms a distinctive category of organizational capital in such contexts. Moreover, the
evidence validates absorptive capacity theory in Ghana, highlighting how firm-level attributes shape the returns
from acquisition.
For managers, the results emphasize the tangible benefits of investing in acquisition, as demonstrated in Table
3, but also caution against over-reliance on regulatory knowledge alone. Diversification into customer and
competitor knowledge, coupled with internal mechanisms for utilization, is essential for sustained
competitiveness. For policymakers, the dominance of regulatory directives (Table 2) signals both a responsibility
and an opportunity: regulations should be framed not merely as compliance mechanisms but as enablers of
learning and productivity.
In conclusion, this study provides robust empirical evidence that knowledge acquisition matters for productivity,
but its impact is shaped by both the source of knowledge and the characteristics of the acquiring firm. By drawing
on evidence from Ghana and situating it within African and global comparisons, the research contributes to
theory, informs managerial strategy, and offers actionable policy insights. Future research can build on these
findings by testing acquisition–productivity dynamics longitudinally, exploring digital enablers of acquisition,
and conducting cross-country comparative analyses across African service economies.
REFERENCES
1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury
Park, CA: Sage Publications.
2. Akinwale, Y. O. (2021). Knowledge management and organizational performance: Evidence from
Kenyan service firms. Journal of African Business, 22(3), 297–314.
https://doi.org/10.1080/15228916.2020.1846867
3. Alegre, J., & Chiva, R. (2013). Linking entrepreneurial orientation and firm performance: The role of
organizational learning capability and innovation performance. Journal of Small Business Management,
51(4), 491–507. https://doi.org/10.1111/jsbm.12005
4. Andreeva, T., & Kianto, A. (2021). Knowledge management practices and firm performance: A global
review. Journal of Knowledge Management, 25(5), 1227–1246. https://doi.org/10.1108/JKM-04-2020-
0306
5. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1),
99–120. https://doi.org/10.1177/014920639101700108
6. Boateng, R., & Dzandu, M. D. (2022). Knowledge management in Africa: Concepts and practices. Cham,
Switzerland: Springer.
7. Cabrilo, S., & Dahms, S. (2021). Digital knowledge acquisition and innovation in multinational
corporations. International Journal of Innovation Management, 25(5), 2150047.
https://doi.org/10.1142/S136391962150047X
8. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and
innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
9. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods
approaches (5th ed.). Thousand Oaks, CA: Sage Publications.
10. Donate, M. J., & de Pablo, J. D. S. (2020). The role of knowledge-oriented leadership in knowledge
management practices and innovation. Journal of Business Research, 118, 360–370.
https://doi.org/10.1016/j.jbusres.2020.06.018
11. Edvinsson, L., & Malone, M. S. (1997). Intellectual capital: Realizing your company’s true value by
finding its hidden brainpower. New York, NY: Harper Business.
12. Farooq, R. (2022). Knowledge management, innovation and firm performance: Empirical evidence from
South Asia. Journal of Knowledge Management, 26(2), 275–292. https://doi.org/10.1108/JKM-07-2020-
0512
13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables
and measurement error. Journal of Marketing Research, 18(1), 39–50.
https://doi.org/10.1177/002224378101800104
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 8525
www.rsisinternational.org
14. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal,
17(S2), 109–122. https://doi.org/10.1002/smj.4250171110
15. Hislop, D., Bosua, R., & Helms, R. (2018). Knowledge management in organizations (4th ed.). Harlow,
England: Pearson Education.
16. Kianto, A., Vanhala, M., & Heilmann, P. (2020). Knowledge management practices and innovation
performance: An empirical study. Journal of Knowledge Management, 24(4), 737–755.
https://doi.org/10.1108/JKM-01-2019-0020
17. Leoni, R. (2022). Firm size, learning capabilities, and the knowledge–performance relationship. European
Management Journal, 40(6), 761–773. https://doi.org/10.1016/j.emj.2021.09.004
18. Ndlovu, T., & Ngwenya, B. (2020). Knowledge acquisition, innovation, and performance of SMEs in
South Africa. African Journal of Science, Technology, Innovation and Development, 12(5), 567–578.
https://doi.org/10.1080/20421338.2019.1656429
19. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create
the dynamics of innovation. New York, NY: Oxford University Press.
20. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
21. Organisation for Economic Co-operation and Development (OECD). (2021). Productivity growth in the
knowledge economy: Measurement and drivers. Paris, France: OECD Publishing.
https://doi.org/10.1787/9789264632577-en
22. Organisation for Economic Co-operation and Development (OECD). (2023). The knowledge economy
2023: Productivity, innovation and digital transformation. Paris, France: OECD Publishing.
23. Omotayo, F. O. (2019). Knowledge management and the performance of Nigerian service firms.
International Journal of Knowledge Management Studies, 10(3), 245–263.
https://doi.org/10.1504/IJKMS.2019.10023234
24. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science
research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569.
https://doi.org/10.1146/annurev-psych-120710-100452
25. Probst, G. J. B. (2008). Managing knowledge: Building blocks for success. Chichester, England: Wiley.
26. Teece, D. J. (2018). Dynamic capabilities as (workable) management systems theory. Journal of
Management & Organization, 24(3), 359–368. https://doi.org/10.1017/jmo.2017.75
27. Wu, Y., Li, X., & Liu, S. (2024). IT-enabled knowledge management practices and firm productivity in
China. Information & Management, 61(2), 103672. https://doi.org/10.1016/j.im.2023.103672
28. Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension.
Academy of Management Review, 27(2), 185–203. https://doi.org/10.5465/amr.2002.6587995