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Tittle: Learning and growth measures and organisational performance: a moderating role for managerial support in Ghana's strategic industriesABSTRACT
This study examines how education and development strategies in Ghana contribute to measurable results in the oil, gas, and telecommunications sectors. Although these sectors invest heavily in training and innovation, they often face challenges in translating these efforts into tangible financial benefits. This study analyzed data from 240 practitioners using a blended methodology that combines partial differential equation modelling and thematic interviews to determine the impact of employee-oriented learning initiatives on return on assets and market share. The results show that systematic training and decentralized knowledge exchange significantly improve both indicators, while direct or moderating support from management is ineffective. The findings show how investment in human capital can be translated into quantifiable results in an organization and provide guidance for leaders working in a dynamic and innovative context. This study contributes to the literature by combining the balanced scorecard and dynamic capability theories to evaluate how learning mechanisms result in tangible performance effects. The incorporation of partial differential equation modelling provides a fresh quantitative perspective, and context is provided by thematic interviews. Industry insights show that decentralized learning networks are more effective in both innovation and financial returns than hierarchical counterparts.
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Such insights provide useful guidelines for policymakers and managers to invest in human capital that supports strategic market needs in volatile markets.Keywords:
Learning and Development
Balanced Scorecard
Human Capital
Management Support
Return on Assets
Market Share
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1.0 Introduction
Ghana’s oil, gas, and telecommunications industries are among the country’s most strategic and rapidly transforming sectors, accounting for significant shares of GDP growth, employment, and technological diffusion. However, these sectors’ ability to convert learning and development investments into measurable performance outcomes remains a persistent challenge. Given the uncertainty surrounding the global economy, fluctuating commodity prices, and accelerating digital transformation, organizational learning has become an essential strategic asset, rather than a peripheral human resource function. The competitive survival of firms now depends on how effectively they mobilize and translate intangible resources, such as knowledge, innovation, and employee capabilities, into tangible financial and market outcomes (Barney, 1991; Kaplan & Norton, 1996).
Despite the increasing attention to learning-based strategies, many Ghanaian firms face difficulties in demonstrating the measurable financial benefits of employee training and knowledge management programs, such as Celestin and Gidisu. A. (2025). While firms in the oil and gas industries emphasize technical skills, safety training, and compliance standards, telecom companies invest heavily in digital innovation and customer engagement systems Al-Hajri, A., Hamouda, A. M., & Abdella, G. M. (2025). However, both groups frequently report limited evidence of direct returns on their investments. This gap between learning inputs and quantifiable outputs raises questions regarding the strategic alignment of training initiatives with performance goals (Agyapong et al., 2019; Agyapong et al., 2024).
The broader theoretical and empirical literature suggests that the link between learning and organizational performance is context-dependent and moderated by internal and external factors, including leadership, culture, managerial support, and industry dynamics. K., et al (2025). Studies in developed economies have shown that when management actively supports learning and development systems, employee innovation and organizational performance tend to increase (García-Morales et al., 2018). However, in emerging economies, the evidence is mixed, often reflecting contextual differences in managerial behavior, resource constraints, and institutional maturity (Ojo & Okeke, 2022; Aragón & Morales, 2023).
In Ghana, learning and development programs are often implemented through donor-supported projects or corporate social responsibility frameworks, but their long-term strategic integration remains limited Barima, A. K., & Farhad, A. (2013). Companies continue to struggle with translating learning initiatives into measurable outcomes, such as return on assets (ROA), market share, or productivity growth Peloza, J. (2009). Moreover, the moderating role of managerial support in this process is poorly understood, especially in sectors characterized by hierarchical control and centralized decision-making.
This study addresses these gaps by examining the relationship between learning and growth measures and organizational performance in Ghana’s strategic industries. It investigates the extent to which training, innovation, and knowledge-sharing initiatives enhance firm-level outcomes and tests whether managerial support moderates these relationships. This study integrates the Balanced Scorecard (Kaplan & Norton, 1996) and Dynamic Capability Theory (Teece et al., 1997) frameworks to capture both the structured and adaptive dimensions of performance. To address this gap, this study focuses on two key questions.
(1) To what extent do growth and educational initiatives affect market share and investment returns (2) Does management support reinforce this relationship?
This study was conducted using a sequential mixed-method approach, combining quantitative analysis with qualitative validation to provide practical evidence for business and policymaking. This study is significant for three reasons. First, it provides empirical evidence from an African context, where the intersection of formal and informal learning systems presents unique dynamics. Second, it contributes to the performance management literature by demonstrating how learning-driven strategies operate in volatile and innovation-dependent industries. Finally, it offers practical insights for policymakers and industry leaders seeking to align human capital development with competitive advantages and industrial transformation.
2.0 Research framework and problem definition.
The oil, gas, and telecommunications sectors are central to Ghana’s economic transformation. These industries have been prioritized under the government’s industrialization and digitalization agenda owing to their potential to drive export growth, job creation, and technological diffusion. Despite these contributions, companies in these sectors continue to struggle with a long-standing challenge: translating heavy investments in learning, innovation, and human resource development into tangible financial and market results.
Although employee training and development have become integral components of corporate strategy, a learning–performance gap persists. Many firms view training as an operational necessity rather than a strategic tool for enhancing competitiveness. This misalignment often results in knowledge accumulation that is poorly utilized or disconnected from strategic objectives (Boateng et al., 2019). In practice, the benefits of learning initiatives remain intangible, manifesting in employee satisfaction or capability gains that are rarely linked to measurable outcomes such as return on assets (ROA) or market share.
This disconnection between capacity development and firm-level performance raises several theoretical and managerial concerns. From a Balanced Scorecard (BSC) perspective, learning and growth represent the foundation for financial, customer, and internal process performance (Kaplan & Norton, 1996). However, in many Ghanaian firms, this foundational role has not been systematically measured or linked with organizational outcomes. Dynamic Capability Theory (DCT) further emphasizes that firms must not only acquire resources but also reconfigure them to respond to change (Teece et al., 1997). When applied to Ghana’s strategic sectors, this theory suggests that learning systems must evolve continuously to meet technological and market demands. However, evidence regarding the effectiveness of such learning mechanisms in contributing to adaptability and sustained performance remains scarce.
Empirical studies across emerging economies have demonstrated that well-structured learning systems improve both profitability and innovation (Acquah et al., 2022; Acquah et al., 2023). However, these studies often focus on small or medium enterprises, neglecting capital-intensive industries such as oil, gas and telecommunications. Furthermore, contextual variations, such as limited managerial autonomy, centralization of decision-making, and resource constraints, may alter how learning translates into outcomes in Ghana. Thus, the effectiveness of learning and development measures cannot be fully understood without accounting for managerial support, which may either facilitate or constrain employee-driven innovation (Ojo and Okeke, 2022).
In this study, managerial support refers to the degree to which organizational leaders endorse, allocate resources, and participate in learning initiatives for employees. While leadership engagement is often assumed to enhance learning outcomes, emerging research suggests that overcentralized management can suppress creativity and delay decision-making, particularly in dynamic environments (Kumar et al., 2024a; Aragón & Morales, 2023). This contradiction presents a critical gap between existing theory and practice. In Ghana’s strategic sectors, where organizational hierarchies tend to be rigid, the balance between autonomy and managerial oversight may significantly influence the effect of learning initiatives on firm performance.
Therefore, the problem definition centers on understanding whether learning and growth measures have a measurable impact on performance outcomes and whether managerial support acts as a reinforcing or limiting factor in this relationship. This study aims to provide empirical evidence by applying a dual theoretical framework (BSC and DCT) and a mixed-method approach to capture both quantitative patterns and contextual insights. By addressing these issues, this study responds to three interconnected challenges in the Ghanaian industrial context: the measurement gap—the lack of empirical tools linking learning and innovation investments to financial and market outcomes. The strategic alignment gap: the disconnect between learning initiatives and business strategy. The managerial effectiveness gap: The uncertain moderating influence of management support in learning-driven performance systems. Bridging these gaps will enhance firm-level decision-making and inform national strategies aimed at strengthening industrial competitiveness and workforce resilience.
3.0 Theoretical Framework
The study is anchored in two complementary theories, the Balanced Scorecard (BSC) and Dynamic Capability Theory (DCT), which together explain how learning and growth strategies translate into measurable organizational performance. The integration of these theories provides a comprehensive framework that connects human capital development, adaptability, and financial and market outcomes.
3.1 The Balanced Scorecard (BSC)
The Balanced Scorecard developed by Kaplan and Norton (1996) revolutionized performance measurement by expanding it beyond traditional financial indicators to include customer satisfaction, internal processes, learning, and growth. The framework proposes that long-term financial success depends on building internal capacity, fostering innovation, and learning. It aligns organizational objectives across four key perspectives—financial, customer, internal process, and learning and growth—to ensure that strategic goals are consistently translated into operational activities (Kaplan, 2009).
In particular, the learning and growth perspective provides the foundation for other performance dimensions. It emphasizes the development of employees, enhancement of information systems, and cultivation of a culture of innovation as prerequisites for achieving a sustainable competitive advantage. This aligns with empirical studies showing that firms that prioritize organizational learning outperform those that treat training as a routine administrative function (García-Morales et al., 2018).
This framework is particularly relevant to Ghana’s strategic industries because firms operate in rapidly changing technological environments that demand agility and innovation. The BSC approach enables firms to measure how internal capabilities, such as skill development and knowledge management, contribute to tangible outcomes such as return on assets and market share (Boateng et al., 2019). However, a major limitation of the BSC in volatile markets is its assumption of environmental stability. Although it provides structured measurement tools, it does not fully account for the dynamic and unpredictable conditions typical of the oil, gas and telecommunications sectors.
3.2 The Dynamic Capabilities Theory (DCT)
Dynamic Capability Theory (Teece, Pisano, & Shuen, 1997) complements the BSC by emphasizing the firm’s ability to integrate, build, and reconfigure internal and external competencies to respond to rapidly changing environments. According to DCT, organizations must continuously adapt their resources and processes to maintain competitiveness. Dynamic capabilities enable firms to sense opportunities, seize them, and transform their resource bases to sustain long-term performance (Teece, 2018).
DCT is particularly applicable in Ghana’s strategic sectors. Oil, gas, and telecommunications firms frequently face market disruptions, regulatory shifts, and technological changes that require constant learning and adaptation. Training, innovation, and knowledge-sharing practices are the mechanisms through which firms develop dynamic capabilities. Empirical work supports this argument: Agyapong et al. (2024) found that organizational learning and innovation capacity significantly enhance performance in African SMEs, while Cabral and Van Winden (2022) demonstrated that firms with adaptive learning systems show stronger market resilience in technology-intensive environments.
Thus, Dynamic Capability Theory provides a flexible lens for understanding how learning-based systems can generate measurable returns, even under volatile conditions. It recognizes that performance outcomes depend not only on the existence of skills and knowledge but also on how effectively these resources are mobilized and transformed into strategic action.
3.3 Integrating BSC and DCT in the Study
The integration of the balanced scorecard and dynamic capability theories provides a robust conceptual foundation for this study. Although the BSC offers a structured approach to performance measurement, the DCT introduces a dynamic and adaptive dimension that accounts for uncertainties and changes. This dual framework allows for a richer analysis of how learning systems influence firm performance in both stable and turbulent environments.
In practical terms, integration recognizes that learning and growth indicators (as captured in the BSC) serve as mechanisms through which dynamic capabilities are developed and exercised (as explained by DCT). For example, a telecommunications firm that invests in employee innovation training (learning and growth) is better positioned to reconfigure its business model or adopt new technologies (dynamic capabilities), which ultimately enhances its market share and profitability.
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Previous studies have rarely combined these frameworks in the context of emerging markets. Most studies have treated these topics separately, resulting in fragmented insights (Sasu, 2023; Aragón & Morales, 2023). By integrating these two, this study addresses a key theoretical gap and provides a comprehensive model linking learning, adaptability, and measurable performance outcomes. This integration also responds to the calls of Teece (2018) and Kaplan (2009) for a more holistic understanding of how internal competencies interact with strategic management systems. Within Ghana’s industrial context, where firms must simultaneously pursue efficiency, innovation, and compliance, this theoretical combination offers a practical roadmap for linking learning strategies with operational and financial success.In summary, the Balanced Scorecard provides the structure and metrics for evaluating performance, whereas Dynamic Capability Theory provides the mechanism and process for achieving it. The combination of these theories offers a solid conceptual base for analyzing the moderating role of managerial support in translating learning and growth measures into organizational outcomes.
4.0 Conceptual Farmwork Design
The conceptual framework in this study illustrates how learning and growth mechanisms influence organizational performance and how managerial support potentially moderates this relationship. The framework integrates the Balanced Scorecard (BSC) and Dynamic Capability Theory (DCT) perspectives, providing both structural and adaptive explanations for firm performance in Ghana’s strategic industries.
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As shown in Fig. 1, the conceptual model aligns key components of learning and development training, innovation, and knowledge exchange with organizational outcomes such as return on assets (ROA) and market share. This framework positions managerial support as a moderating variable that may strengthen or weaken the effects of learning and growth strategies on performance.
4.1 Theoretical Basis for the Model
The Balanced Scorecard (BSC) provides the foundation for the measurement of learning and growth activities, linking them to financial and non-financial indicators. This framework recognises that performance improvement begins with the development of internal capabilities knowledge, skills, and innovation systems that drive long-term results (Kaplan & Norton, 1996; Kaplan, 2009). Within this structure, learning and growth are treated not as isolated HR activities but as strategic investments that influence profitability and competitiveness. Dynamic Capability Theory (DCT) extends this logic by emphasising the firm’s capacity to adapt, integrate, and reconfigure these internal resources to respond to changing market and technological conditions (Teece et al., 1997; Teece, 2018). This perspective is critical in Ghana’s oil, gas, and telecommunications sectors, where firms must continually evolve to manage global competition, regulatory change, and digital disruption. By combining both theories, the conceptual framework captures how learning initiatives (as structured through the BSC) become the operational foundation for developing dynamic capabilities (as theorised by DCT). This dual alignment enables firms to translate intangible learning investments into a measurable performance.
4.2 Conceptual Relationships
The conceptual framework consisted of three major constructs: Learning and Growth (LGP), Organizational Performance (OP), and Managerial Support (MANST). Each construct plays a distinct role in the hypothesized relationships.
Learning and Growth (LGP):
This construct includes employee training programs, innovation initiatives and knowledge-sharing systems. Learning and growth mechanisms are expected to improve both financial and non-financial outcomes by enhancing employee competence and organizational adaptability (García-Morales et al., 2018; Acquah et al., 2023).
Organizational Performance (OP)
Organizational performance was measured in this study using financial (ROA) and market (market share) indicators. This approach aligns with prior research demonstrating that learning-oriented strategies contribute to superior financial returns and improved competitiveness (Boateng et al., 2022; Boateng et al., 2019).
Managerial Support (MANST)
Managerial support refers to the extent to which leadership is involved in promoting, funding, and evaluating learning initiatives. Management’s moderating role is conceptualized as influencing the strength and direction of the relationship between learning and growth measures and organizational performance. Studies suggest that while supportive leadership can amplify innovation outcomes, over-centralized control can hinder the free flow of ideas and learning (Ojo & Okeke, 2022; Kumar et al., 2024a).
4.3 Hypothesized Relationships
The conceptual framework is guided by the following propositions.
H1: Learning and growth measures have positive and significant effects on the organizational performance.
H2: Managerial support has a direct positive relationship with organizational performance.
H3: Managerial support moderates the relationship between learning and growth measures and the organizational performance.
These hypotheses reflect the expectation that employee development and knowledge management improve performance outcomes, but the degree of management involvement can either strengthen or weaken these effects, depending on leadership style and organizational culture.
4.4 Framework Contribution
This conceptual model makes two main contributions to theory and practice. First, it advances the integration of performance measurement and dynamic capability perspectives within the African context, showing how internal learning systems contribute to external competitiveness. Second, it offers a diagnostic tool for managers and policymakers to determine whether firm-level learning investments are adequately supported by leadership structures that encourage innovation and adaptation. Therefore, the model provides both explanatory and practical value. It clarifies how learning-based strategies can enhance organizational resilience and how the moderating influence of managerial support can determine whether these strategies translate into measurable success.
5.0 Empirical Review
This empirical review synthesises prior studies that have examined how learning and growth initiatives influence organizational performance with a focus on financial returns, market competitiveness, and the role of management support. This section also highlights methodological and contextual gaps that justify the current research in Ghana’s strategic sectors.
5.1 Learning and Financial Performance
A substantial body of evidence supports the argument that investment in employee learning enhances financial outcomes. Studies across different regions demonstrate a consistent link between structured training programs and profitability indicators, such as return on assets (ROA) and return on investment (ROI).
For instance, the OECD (2021) reported that manufacturing firms in developing economies that introduced formal learning frameworks achieved measurable gains in productivity and efficiency. Similarly, Cheng and Edmund (2022) find that leadership development initiatives in Chinese retail companies have a direct positive effect on ROA and market valuation. In the African context, Acquah et al. (2023) established that learning-oriented small and medium enterprises (SMEs) recorded higher profitability and operational efficiency due to continuous staff training and innovation.
In addition to profitability, learning investments contribute to cost control and strategic agility. García-Morales et al. (2018) argued that firms that systematically link learning to performance metrics experience improved decision-making, faster problem-solving, and reduced operational inefficiencies. However, most of these studies focus on small- or medium-sized enterprises and overlook capital-intensive sectors, such as oil, gas, and telecommunications, where large-scale learning systems operate under complex hierarchies.
In Ghana, the evidence remains fragmented. Although large firms have made significant investments in employee development and safety training, few studies have quantified how these efforts translate into measurable returns. This gap highlights the need for sector-specific studies that test the financial outcomes linked to learning initiatives in high-investment environments. This study fills this empirical void by quantifying the relationship between learning and growth measures and ROA in strategic industries.
5.2 Learning and Market Competitiveness
Beyond financial indicators, organizations’ ability to convert learning into market competitiveness is equally critical. Market performance is often reflected in indicators such as market share, customer retention, and brand reputation. Research in this area shows that firms that promote a continuous learning and knowledge-sharing culture are better able to adapt to market shifts and capture emerging opportunities.
Cabral and Van Winden (2022) found that European technology firms prioritising collaborative learning achieved faster market expansion and stronger customer engagement. In a similar study, Boateng et al. (2019) demonstrated that Ghanaian telecommunications firms with a robust internal culture of knowledge sharing enjoyed improved customer retention and loyalty. These findings align with the premise that learning mechanisms can drive innovation and responsiveness to customer needs.
However, other studies reveal that learning alone does not guarantee competitive success unless it is integrated with strategic market orientation. For example, Sasu (2023) argued that while Ghana’s gas companies adopted the Balanced Scorecard to monitor internal processes, the absence of a learning–market linkage limited their ability to translate internal improvements into market dominance. This observation reinforces the argument that learning must be strategically aligned with market positioning to produce sustained advantages.
This study extends this literature by empirically linking learning and growth measures with market share, providing quantitative evidence from Ghana’s strategic sectors, where market volatility and regulatory change make adaptability a key determinant of competitiveness.
5.3 Moderating Role of Managerial Support
Empirical studies on the influence of managerial support on learning outcomes have produced mixed findings. In certain contexts, managerial engagement enhances employee motivation, fosters innovation, and amplifies the benefits of learning systems. In others, excessive control and hierarchical oversight suppress employee autonomy and creativity.
Ojo and Okeke (2022) found that, in Nigerian fintech firms, managerial support was a strong predictor of innovation performance when managers adopted participatory leadership styles. Conversely, Kumar et al. (2024a, 2024b) demonstrated that in Southeast Asian firms, over-centralised managerial structures diluted the positive impact of employee learning programs on innovation outcomes. Aragón and Morales (2023) reported similar results for Latin American utilities, in which employee-driven learning systems produced higher productivity and engagement when managers acted as facilitators rather than controllers.
These mixed results suggest that managerial support functions as a contextual moderator rather than a universal enhancer of learning effectiveness. The moderating effect may vary depending on leadership culture, organizational size, and sectoral complexity. In Ghana’s strategic industries, where formal hierarchies coexist with informal learning networks, managerial roles are particularly complex. Managers are expected to enforce compliance and control costs while simultaneously promoting creativity and innovation. This dual expectation can either enable or inhibit the effectiveness of learning systems depending on how they are managed. Therefore, this study tests the moderating influence of managerial support on the relationship between learning and performance in Ghana’s strategic industries, addressing a key gap in the African management literature.
5.4 Methodological Gaps in Existing Studies
A critical review of prior research reveals methodological limitations that restrict the generalisation of existing findings to the African industrial context. First, many studies rely solely on cross-sectional survey designs, which capture relationships at a single point in time but fail to establish causality (Chowdhury et al., 2023; Kiggundu, 2021). Second, most studies employ quantitative methods without integrating qualitative insights to explain the sector-specific dynamics. This limitation often results in incomplete interpretations of how learning translates into performance under varying managerial and institutional conditions.
Additionally, most research in Africa focuses on SMEs or public-sector organisations, leaving a gap in the literature on large-scale, capital-intensive industries that drive national development. Few studies have examined how human capital strategies function within technologically advanced and regulation-intensive sectors, such as oil, gas, and telecommunications. To address these methodological gaps, this study adopts a sequential mixed-method design that combines quantitative modelling using PLS-SEM with qualitative thematic analysis. This approach ensures both statistical rigor and contextual interpretation, allowing for a deeper understanding of the learning–performance relationship in Ghana’s industrial setting.
5.5 Empirical Contribution of the Current Study
This study contributes to the literature in several ways. First, it provides sector-specific empirical evidence from Ghana’s oil, gas, and telecommunications industries, which have received limited scholarly attention in the field of strategic learning and performance management. Second, it combines two theoretical frameworks the Balanced Scorecard and Dynamic Capability Theory to explain both the structural and adaptive mechanisms linking learning to performance. Third, by incorporating managerial support as a moderating factor, this study explores the contextual nuances that determine whether learning initiatives succeed or fail to improve outcomes.
In addition, the mixed-method design enhances methodological diversity in African management research, demonstrating the value of integrating quantitative and qualitative perspectives to capture measurable trends and underlying processes. The results of this study are expected to provide actionable insights for corporate leaders, policymakers, and researchers seeking to strengthen organizational learning systems and improve competitiveness in dynamic economic environments.
6.0 Methodology
6.1 Research Design and Approach
This study adopted a sequential mixed-method research design, combining quantitative and qualitative approaches, to investigate how learning and growth measures influence organizational performance in Ghana’s oil, gas, and telecommunications sectors. The design was chosen to capture both measurable relationships and contextual insights, thereby improving the validity and interpretive depth of the findings.
The quantitative phase was used to test the hypothesised relationships between the key variables learning and growth, managerial support, and organizational performance—through statistical modelling. The qualitative phase explained the patterns and anomalies in the quantitative results, particularly those related to the moderating role of managerial support. This sequencing ensured methodological complementarity, where numerical data established correlations and qualitative evidence explained the mechanisms behind them.
Mixed-method design is particularly suitable for complex organizational research in emerging economies. According to Creswell and Clark (2018), combining quantitative and qualitative techniques enhances the robustness of the findings and allows for triangulation, which improves both credibility and generalisability. This approach also addresses the limitation of prior research in Africa, which relied predominantly on single-method designs (Chowdhury et al., 2023; Kiggundu, 2021).
6.2 Population, Sampling, and Sample Size
The target population consisted of employees working in Ghana’s oil, gas, and telecommunications industries. They are chosen because they represent key strategic sectors that drive national economic development. These industries were selected because of their reliance on technology, innovation, and skilled human capital, which makes them ideal for testing learning–performance dynamics.
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The sampling process was guided by stratified random sampling to ensure adequate representation of each sector and hierarchical level. Within each stratum, respondents were selected in proportion to their organizational size and function. Participants included engineers, middle managers, HR practitioners, and administrative staff from major companies such as MTN Ghana, Telecel, Ghana Gas, and GNPC.A total of 240 respondents participated in the survey. The sample size was determined using the Cochran (1977) formula for estimating sample adequacy in finite populations, ensuring a 95% confidence level and a 5% margin of error. This size was deemed sufficient for Partial Least Squares Structural Equation Modeling (PLS-SEM), which performs effectively with medium-sized samples (Hair et al., 2016).
To supplement the quantitative data, 12 semi-structured interviews were conducted with managerial and technical professionals who were directly involved in training and innovation programs. The interviews provided interpretive insights into how learning systems are implemented and how managerial support influences their success or failure.
6.3 Data Collection Instruments
The quantitative data were collected using a structured questionnaire developed based on validated scales from previous studies. The questionnaire included five main constructs: learning and growth (LGP), managerial support (MANST), financial performance (FP), market share (MARKS), and return on assets (ROA). Items were measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
The questionnaire was pre-tested with a small group of 20 respondents to ensure clarity and reliability. Minor adjustments were made to refine wording and eliminate ambiguities. The Cronbach’s alpha values for all constructs exceeded 0.80, confirming internal consistency and reliability.
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The qualitative data were collected through semi-structured interviews guided by an interview protocol that focused on three themes: (1) learning and development processes, (2) managerial engagement and decision-making, and (3) organizational performance and innovation outcomes. A
Each interview lasted approximately 45 to 60 minutes and was recorded with participant consent. Transcripts were anonymized before analysis.6.4 Data Analysis Procedures
Quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.0. This technique was chosen because it allows for simultaneous estimation of direct, indirect, and moderating effects among latent constructs. PLS-SEM is particularly suited for complex models involving mediation and moderation, as well as for studies with moderate sample sizes (Hair et al., 2016).
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The analysis followed a two-step approach: Measurement model evaluation, which assessed construct validity and reliability through factor loadings, average variance extracted (AVE), composite reliability (CR), and discriminant validity; and Structural model evaluation, which tested the hypothesized relationships among constructs and the moderating role of managerial support.The qualitative data were analyzed using thematic analysis, following Braun and Clarke’s (2019) six-phase approach: familiarization, coding, theme development, review, definition, and reporting. NVivo software was used to organize and code interview transcripts, ensuring systematic identification of recurring patterns. Integration of the quantitative and qualitative phases occurred at the interpretation stage, where qualitative themes were used to explain and contextualize statistical findings. For example, while PLS-SEM results indicated a weak or negative moderating effect of managerial support, interviews revealed that excessive top-down control often stifled employee initiative and innovation. This integration strengthened both the explanatory and predictive validity of the study.
6.5 Ethical Considerations
Ethical approval
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for the study was obtained from the Institutional Review Board (IRB) of Accra Technical University (Approval ID: AITIRB 202506). A
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All research activities were conducted in accordance with Ghana’s national research ethics standards and the principles outlined in the Declaration of Helsinki (2013). Participants were informed about the purpose of the research, voluntary participation, and confidentiality measures before data collection. A
Informed consent was obtained in writing, and participants were assured that their identities would remain confidential. Data were stored securely and used solely for academic purposes.Ethical compliance was further maintained through anonymization of responses, exclusion of personal identifiers, and secure digital storage of data. No financial or material incentives were offered to participants, ensuring voluntary participation based on informed understanding.
6.6 Research Validity and Reliability
To ensure methodological rigor, multiple steps were taken to establish validity and reliability. Construct validity was confirmed through factor loadings above 0.70 and AVE values above 0.50, indicating convergence. Discriminant validity was established using the Fornell-Larcker criterion, ensuring that each construct was distinct from others. Reliability was confirmed through Cronbach’s alpha and composite reliability coefficients exceeding 0.80 for all constructs. Additionally, multicollinearity was assessed through Variance Inflation Factor (VIF) values, all of which were below 3.0, confirming the absence of collinearity.
External validity was enhanced by using a diverse sample drawn from multiple industries and by integrating qualitative data that provided context-specific interpretation. Triangulation of data sources also strengthened internal validity, while pilot testing ensured the accuracy and clarity of measurement instruments.
6.7 Integration of Quantitative and Qualitative Phases
The final stage of analysis integrated both data sets to produce a comprehensive understanding of the learning–performance relationship. Quantitative findings identified the strength and direction of relationships among constructs, while qualitative insights explained why certain relationships were stronger or weaker than expected. For instance, while statistical results confirmed that learning and growth significantly influenced ROA and market share, qualitative interviews revealed that the organizational culture of decentralization and peer learning was a key driver behind these outcomes. Conversely, managerial support was found to have limited impact because of bureaucratic structures and rigid reporting lines, which often slowed decision-making and innovation. This integrated interpretation provided a richer and more nuanced understanding of how learning systems operate in Ghana’s strategic industries and how managerial behaviors can either enable or constrain their effectiveness.
7.0 Results and Discussion
This section presents and interprets the results from both the quantitative and qualitative phases of the study. The findings are discussed in relation to the theoretical framework, prior empirical evidence, and the contextual realities of Ghana’s strategic industries. The discussion emphasizes how learning and growth measures influence organizational performance and how managerial support moderates this relationship.
7.1 Descriptive Analysis of Respondents
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Table 1 summarizes the demographic characteristics of the 240 respondents drawn from Ghana’s oil, gas, and telecommunications sectors. The sample consisted of 48.9% male and 51.1% female respondents, indicating a fairly balanced gender representation. Most participants were aged between 31 and 50 years (53.1%), representing a mature yet adaptable workforce.In terms of education, 65.7% of the respondents held at least a bachelor’s degree, while 26.4% possessed professional certifications. This high educational profile underscores the knowledge-intensive nature of Ghana’s strategic industries. Sectoral distribution shows that 60.3% of participants were from telecommunications and 39.7% from oil and gas, reflecting the relative scale and technological advancement of these sectors. This demographic composition supports the representativeness of the sample. The predominance of degree-holding respondents implies that the workforce in these sectors is well-positioned to benefit from structured learning systems. Furthermore, the inclusion of participants across various roles (operational, technical, and managerial) ensures a balanced perspective on how learning initiatives influence performance. These attributes strengthen the reliability and generalizability of the study’s findings.
Variable | Category | Frequency (n) | Percent (%) |
|---|---|---|---|
Gender | Male | 117 | 48.9 |
Female | 122 | 51.1 | |
Age | 20–30 years | 75 | 31.4 |
31–40 years | 61 | 25.5 | |
41–50 years | 66 | 27.6 | |
51–60 years | 35 | 14.6 | |
Education level | Bachelor’s degree | 86 | 36.0 |
Master’s degree | 71 | 29.7 | |
Doctorate (PhD) | 19 | 7.9 | |
Professional certificate | 63 | 26.4 | |
Sector | Oil and Gas | 95 | 39.7 |
Telecommunications | 144 | 60.3 |
A remark. The percentages are based on valid answers (N = 240). Source: Field survey by the author (2025).
7.2 Measurement Model Assessment
Table 2 below presents the factor loadings for all indicators across constructs, showing that each measurement item strongly represents its underlying latent variable. All loadings exceed the recommended threshold of 0.70 (Hair et al., 2016), except for a few items that were retained for theoretical relevance (for example, CUSTP5 = 0.606). The learning and growth construct (LGP) recorded loadings between 0.764 and 0.832, indicating consistent representation across its indicators. Managerial support (MANST) showed loadings ranging from 0.807 to 0.934, confirming that the construct was robustly measured. Similarly, organizational performance indicators, including financial performance (FP) and market share (MARKS), exhibited high internal reliability (0.676–0.853).
The Average Variance Extracted (AVE) values for all constructs exceeded 0.50, and Composite Reliability (CR) values were above 0.80, confirming convergent validity and internal consistency. Moreover, Variance Inflation Factor (VIF) diagnostics ranged from 1.04 to 2.23, indicating that multicollinearity was not a concern. These results confirm that the measurement model meets all psychometric requirements. The constructs are conceptually distinct, however statistically consistent, validating the use of PLS-SEM for further structural analysis. The robustness of these metrics enhances confidence in the causal relationships tested in subsequent models.
CUSTP | FP | IBP | LGP | MANST | MARKS | OCB | RETA | |
|---|---|---|---|---|---|---|---|---|
CUSTP1 | 0.783 | |||||||
CUSTP2 | 0.873 | |||||||
CUSTP3 | 0.838 | |||||||
CUSTP4 | 0.814 | |||||||
CUSTP5 | 0.606 | |||||||
FP1 | 0.676 | |||||||
FP2 | 0.813 | |||||||
FP3 | 0.689 | |||||||
FP4 | 0.853 | |||||||
FP5 | 0.819 | |||||||
IBP1 | 0.795 | |||||||
IBP2 | 0.859 | |||||||
IBP3 | 0.845 | |||||||
IBP4 | 0.821 | |||||||
IBP5 | 0.804 | |||||||
LGP1 | 0.764 | |||||||
LGP2 | 0.797 | |||||||
LGP3 | 0.830 | |||||||
LGP4 | 0.832 | |||||||
LGP5 | 0.778 | |||||||
MANST1 | 0.815 | |||||||
MANST2 | 0.934 | |||||||
MANST3 | 0.846 | |||||||
MANST4 | 0.807 | |||||||
MARKS1 | 0.802 | |||||||
MARKS2 | 0.827 | |||||||
MARKS3 | 0.800 | |||||||
MARKS4 | 0.792 | |||||||
MARKS5 | 0.784 | |||||||
OCB1 | 0.753 | |||||||
OCB2 | 0.762 | |||||||
OCB3 | 0.790 | |||||||
OCB4 | 0.800 | |||||||
OCB5 | 0.808 | |||||||
RETA1 | 0.753 | |||||||
RETA2 | 0.751 | |||||||
RETA3 | 0.800 | |||||||
RETA4 | 0.738 | |||||||
RETA5 | 0.753 | |||||||
RETA6 | 0.725 |
Source
Author’s field data (2025).
7.3 Structural Model: Financial Performance (FP) Model
Table 3 presents the path coefficients examining how financial performance (FP) and managerial support (MANST) influence organizational outcomes such as market share (MARKS) and return on assets (ROA). The results indicate that financial performance has a strong positive and significant effect on both market share (β = 0.559, p < 0.001) and return on assets (β = 0.591, p < 0.001). This finding supports the argument that firms that maintain consistent financial efficiency are more likely to achieve broader market success and resource optimization.
However, managerial support showed no significant direct effect on either market share (β = 0.012, p = 0.856) or return on assets (β = 0.034, p = 0.625). Interestingly, when tested as an interaction term (MANST × FP), the moderating effect of managerial support became statistically significant but negative for both outcomes (MARKS β = − 0.211, p = 0.021; ROA β = − 0.201, p = 0.015). This result suggests that managerial support, rather than reinforcing financial performance, can sometimes attenuate its impact. In other words, high levels of managerial control may constrain organizational agility, reducing the effectiveness of financial and learning-based performance mechanisms. These findings align with the propositions of Dynamic Capability Theory (Teece et al., 1997), which argues that rigid hierarchies can inhibit organizational adaptability. They also echo prior studies by Aragón and Morales (2023) and Kumar et al. (2024b), which found that excessive top-down management weakens innovation-driven outcomes.
Qualitative insights from interviews further corroborate this pattern. Respondents emphasized that top management often intervened too heavily in innovation-related decisions, slowing down project execution and demotivating employees. Some described learning initiatives as “well-funded but poorly aligned,” reflecting a disconnect between managerial oversight and operational creativity. This highlights the need for balanced leadership that provides guidance without stifling employee autonomy.
Beta Coefficient | Standard deviation | T statistics | P values | |
|---|---|---|---|---|
FP ->MARKS | 0.559 | 0.046 | 12.132 | 0.000 |
FP ->RETA | 0.591 | 0.042 | 14.085 | 0.000 |
MANST ->MARKS | 0.012 | 0.067 | 0.182 | 0.856 |
MANST ->RETA | 0.034 | 0.069 | 0.489 | 0.625 |
MANST x FP ->MARKS | -0.211 | 0.091 | 2.309 | 0.021 |
MANST x FP ->RETA | -0.201 | 0.083 | 2.425 | 0.015 |
Source
Author’s field data (2025).
A remark. β = standard deviation; MANST = management support; ROA = return on assets. Source: Author analysis, SmartPLS 4.0 (2025).
7.4 Structural Model: Learning and Growth (LGP) Pathway
Table 4 presents the results of the learning and growth (LGP) model, showing a clear and statistically significant relationship between learning initiatives and organizational performance. Learning and growth positively influenced both market share (β = 0.596, p < 0.001) and return on assets (β = 0.649, p < 0.001), confirming Hypothesis 1 (H1) that learning and development directly enhance performance outcomes. These results affirm the Balanced Scorecard’s learning and growth perspective, which posits that developing employee competencies and knowledge systems forms the foundation for long-term financial success (Kaplan & Norton, 1996). The findings are also consistent with empirical research in emerging economies showing that investment in learning positively affects firm performance (Acquah et al., 2023; García-Morales et al., 2018).
Conversely, managerial support did not have a significant direct impact on either market share (β = − 0.014, p = 0.846) or return on assets (β = 0.012, p = 0.864). Furthermore, the interaction effects between managerial support and learning and growth (MANST × LGP) were statistically insignificant for both performance indicators (MARKS β = − 0.083, p = 0.277; ROA β = − 0.063, p = 0.395). The absence of a significant moderating effect implies that managerial involvement does not necessarily strengthen the benefits of learning and growth. In some cases, managerial oversight may even neutralize employee-driven innovation processes. This finding supports earlier results by Ojo and Okeke (2022), who observed that managerial involvement enhances learning outcomes only when it fosters autonomy and collaboration rather than control.
Qualitative evidence reinforced this interpretation. Several respondents described their organizations as possessing “informal learning ecosystems,” where peer collaboration and cross-functional problem-solving were more effective than top-down directives. Managers who encouraged flexibility and experimentation were perceived as enablers of learning, while those enforcing rigid procedures were seen as barriers. These findings emphasize the importance of decentralized learning structures, where employees can share knowledge freely and innovate without unnecessary bureaucratic constraints. The effectiveness of learning initiatives thus depends on the organizational culture and leadership approach, not merely the availability of resources.
Table 4 shows the coefficients of the learning and growth path and their impact on performance.
Beta Coefficient | Standard deviation | T Statistics | P values | |
|---|---|---|---|---|
LGP ->MARKS | 0.596 | 0.058 | 10.335 | 0.000 |
LGP ->RETA | 0.649 | 0.047 | 13.823 | 0.000 |
MANST ->MARKS | -0.014 | 0.074 | 0.194 | 0.846 |
MANST ->RETA | 0.012 | 0.071 | 0.171 | 0.864 |
MANST x LGP ->MARKS | -0.083 | 0.076 | 1.087 | 0.277 |
MANST x LGP ->RETA | -0.063 | 0.074 | 0.852 | 0.395 |
Source
Author’s field data (2025).
A remark. β = standardised coefficient; MANST = support to management. Source: Author analysis based on SmartPLS 4.0 outputs (2025).
7.5 Integrative Discussion
The results across all models indicate that learning and growth measures significantly enhance financial and market performance, while managerial support does not consistently moderate these effects. This pattern suggests that employee-driven learning systems, rather than managerial control, are the true drivers of performance improvement in Ghana’s strategic sectors. From a theoretical standpoint, these findings strengthen the argument that Dynamic Capability Theory complements the Balanced Scorecard by explaining how learning-based competencies translate into adaptability and measurable results. The evidence demonstrates that firms that invest in decentralized learning systems are better positioned to reconfigure resources and respond to environmental changes.
Practically, the results carry several implications:
Firms should embed learning and growth strategies into their core operations rather than treating them as HR activities. Managerial systems should focus on enabling learning rather than controlling it, encouraging a culture of experimentation and knowledge sharing.
Policymakers should incentivize industry-led training programs that build dynamic capabilities and align human capital investments with national competitiveness goals. Overall, the integration of quantitative and qualitative results confirms that while managerial support remains essential for resource provision, the real catalyst for performance improvement lies in employee-led learning ecosystems and adaptive organizational cultures.
8.0 Discussion and Implications
The findings of this study demonstrate that learning and growth initiatives have a significant and positive influence on organizational performance in Ghana’s oil, gas, and telecommunications sectors. However, the moderating role of managerial support, though theoretically expected to enhance performance, was not empirically supported. These results provide important theoretical, managerial, and policy insights into how learning systems can be structured to yield tangible financial and market outcomes in emerging economies.
8.1 Theoretical Discussion
The study’s results reinforce the Balanced Scorecard (BSC) assertion that learning and growth form the foundation of sustainable performance. Firms that prioritize employee development, innovation, and knowledge-sharing experience higher returns on assets and greater market competitiveness. This validates Kaplan and Norton’s (1996) argument that intangible assets such as human capital and innovation capabilities drive long-term organizational success.
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However, the findings also highlight the limitations of the BSC in explaining performance in volatile environments. The study found that managerial support does not consistently strengthen the learning–performance relationship, and in some cases, managerial intervention dampens it. This underscores the need to complement the BSC with the Dynamic Capability Theory (DCT), which emphasizes organizational agility and the continuous reconfiguration of resources to adapt to change (Teece et al., 1997; Teece, 2018).The integration of both frameworks provides a more complex explanation: while the BSC offers a structured approach to measuring performance, the DCT explains how organizations transform learning into strategic adaptability. The study’s results confirm that in dynamic industries, the ability to learn and adapt rather than managerial oversight alone is what sustains competitive advantage.
The negative moderation effects observed in some models also suggest that the traditional assumption of “more managerial support equals better outcomes” does not always hold in adaptive learning environments. In line with findings by Aragón and Morales (2023) and Ojo and Okeke (2022), the study reveals that overly centralized management can constrain the autonomy required for creative problem-solving and innovation. Thus, the relationship between learning, managerial behavior, and performance is context-dependent and influenced by organizational culture and leadership style.
8.2 Managerial Implications
The results carry several implications for managers operating in Ghana’s strategic sectors and similar emerging markets.
First, organizations must treat learning and development as strategic investments rather than administrative functions. Training programs, innovation teams, and knowledge-sharing systems should be directly linked to strategic objectives and performance metrics. Embedding learning indicators within performance dashboards or balanced scorecards allows managers to monitor how human capital contributes to financial and market outcomes.
Second, leadership styles should shift from supervisory to enabling roles. The data show that excessive managerial control can stifle innovation and diminish the benefits of learning initiatives. Managers should therefore act as facilitators who provide direction, allocate resources, and create spaces for collaboration, while allowing employees autonomy to experiment and generate ideas. This approach aligns with adaptive leadership models that promote distributed decision-making and organizational learning.
Third, firms should strengthen cross-functional and peer-led learning networks. The qualitative findings revealed that informal, team-based learning often produced better results than top-down training programs. Encouraging interdepartmental knowledge exchange and peer mentoring can accelerate innovation and performance outcomes, especially in industries driven by technological change.
Fourth, organizations should adopt continuous feedback systems that link learning activities to performance improvements. Managers need real-time data on how training influences key indicators like productivity, customer satisfaction, and financial performance. This feedback loop ensures that learning investments are continuously evaluated and refined for effectiveness.
Finally, leadership development programs should focus not only on technical management but also on coaching and facilitation skills. Building a culture of learning requires leaders who understand how to nurture creativity and adaptability rather than merely enforcing compliance.
8.3 Policy Implications
The findings also have broader implications for industrial and human capital policy in Ghana.
First, policymakers should strengthen partnerships between industry, academia, and government to enhance workforce capabilities. Collaborative training programs and joint research initiatives can bridge the skills gap between academic knowledge and industrial needs, aligning education systems with sectoral demands.
Second, the government should introduce fiscal incentives for firms that invest in structured learning and innovation systems. Tax deductions or grants for training, digital upskilling, and research and development (R&D) can motivate firms to view human capital investment as a competitive strategy rather than a cost.
Third, national and sectoral regulators should promote agile management frameworks that encourage participatory decision-making and decentralization in public and private enterprises. Policies should reward innovation and adaptability, especially in technology-intensive sectors like telecommunications and energy.
Fourth, expanding digital learning infrastructure is critical. Investment in e-learning platforms, virtual laboratories, and digital knowledge repositories can democratize access to learning and reduce regional inequalities in skills development.
Lastly, policymakers should incorporate learning-based performance indicators into industrial competitiveness assessments. Evaluating firms not only on financial outcomes but also on learning intensity and innovation activity would encourage sustained investment in human capital.
8.4 Implications for Theory and Research
From a scholarly perspective, the study contributes to the growing body of research on learning-based performance systems in emerging economies. It extends the applicability of the Balanced Scorecard and Dynamic Capability Theory to the African context, demonstrating that learning and growth mechanisms can generate measurable outcomes even in environments characterized by volatility and resource constraints. The findings also call for a re-examination of managerial support as a moderating construct in performance models. Rather than treating managerial support as a universally positive factor, future research should distinguish between supportive and restrictive leadership behaviors and their respective effects on innovation and learning outcomes.
Moreover, future studies could employ longitudinal designs to assess how the impact of learning investments evolves over time. Comparative studies across industries or countries could also clarify whether the patterns observed in Ghana’s strategic sectors hold in other emerging markets with different institutional frameworks. Integrating cultural and digital transformation variables could further refine understanding of how organizational learning drives sustainable competitiveness.
8.5 Summary of Key Insights
In summary, this study provides clear evidence that learning and growth mechanisms are vital for improving financial and market performance in Ghana’s strategic sectors. However, the role of management must evolve from directive control to strategic facilitation. Firms that empower employees to learn, innovate, and share knowledge freely outperform those that rely on rigid hierarchies. The theoretical integration of the Balanced Scorecard and Dynamic Capability Theory offers a robust lens for understanding these dynamics. Practically, the results underscore the need for Ghanaian firms to build adaptive learning systems and for policymakers to support human capital development as a driver of national competitiveness. Ultimately, organizational learning is most effective when it is continuous, decentralized, and strategically aligned, supported by leadership that values empowerment over control.
9.0 Conclusion and Recommendations
9.1 Conclusion
This study set out to examine how learning and growth initiatives influence organizational performance in Ghana’s oil, gas, and telecommunications sectors, and whether managerial support moderates this relationship. Using a sequential mixed-method approach, the research integrated the Balanced Scorecard (BSC) and Dynamic Capability Theory (DCT) frameworks to assess both the structural and adaptive dimensions of learning-based performance.
The results provide compelling evidence that learning and growth mechanisms have a significant positive impact on both financial performance (ROA) and market competitiveness (market share). Firms that invest in structured training, innovation teams, and knowledge-sharing systems achieve higher efficiency and stronger market positioning. These findings confirm that human capital development and organizational learning are not peripheral activities but essential drivers of long-term strategic success.
However, the study also found that managerial support did not significantly moderate the relationship between learning and performance. In some cases, excessive managerial control even reduced the effectiveness of learning systems, suggesting that hierarchical oversight can limit creativity and adaptability. This challenges traditional assumptions that stronger managerial involvement always enhances organizational outcomes. Instead, the results highlight the importance of autonomy, participatory decision-making, and decentralized learning networks as key enablers of innovation and performance.
Theoretically, the integration of the BSC and DCT provided a comprehensive framework for linking learning-based initiatives to measurable performance outcomes. The BSC offered the structure for performance measurement, while the DCT explained how learning translates into adaptability and competitive advantage in dynamic environments. Together, these frameworks address the limitations of earlier research by bridging measurement precision with strategic flexibility. In summary, the study concludes that learning and growth initiatives directly drive firm performance, while managerial support plays a more complex and context-dependent role. Organizations that empower employees, encourage collaboration, and align learning strategies with corporate goals are more likely to sustain competitive advantage, particularly in volatile and innovation-driven sectors.
9.2 Managerial Recommendations
Based on the findings, several recommendations are proposed for managers and decision-makers in Ghana’s strategic sectors:
Treat learning as a strategic investment: Training, innovation, and knowledge management should be integrated into the firm’s strategic planning process rather than treated as isolated HR activities. Learning indicators should be included in key performance scorecards to track their direct impact on financial and market outcomes.
Foster a culture of autonomy and innovation: Managers should adopt leadership styles that emphasize empowerment, collaboration, and open communication. Excessive supervision or centralization can suppress innovation and reduce the benefits of employee-driven learning systems.
Encourage cross-functional learning networks: Organizations should create platforms where employees from different departments collaborate on problem-solving and share best practices. Such networks promote creativity and the transfer of tacit knowledge, which are vital for innovation in technology-intensive sectors.
Aligning managerial support with facilitation rather than control: Management should focus on enabling resources, providing mentorship, and removing barriers to learning rather than micromanaging processes. Facilitating leadership encourages experimentation and supports adaptive capacity.
Develop metrics for evaluating learning outcomes: Firms should establish clear frameworks for measuring how training and innovation initiatives affect key performance indicators such as productivity, return on assets, and customer retention. This ensures accountability and continuous improvement in learning investments.
9.3 Policy Recommendations
At the national and institutional levels, the study suggests the following policy measures:
Promote industry–academia collaboration: The Ministry of Education and Ministry of Trade and Industry should facilitate partnerships between universities, research institutions, and industry players to develop tailored training programs that address sector-specific skill needs.
Provide fiscal incentives for corporate learning investments: Government policies should reward firms that invest in employee development, research and development (R&D), and digital learning infrastructure through tax deductions or grants. This can help offset training costs and encourage long-term capacity building.
Encourage digital transformation of learning systems: Investment in e-learning platforms and digital skills programs can expand access to professional training and reduce the geographic concentration of skilled labor in urban centers.
Institutionalize learning-based performance metrics: Regulatory agencies should include learning and innovation indicators as part of organizational performance evaluation criteria, ensuring that firms are assessed on both financial and knowledge-based outcomes.
Support leadership development and governance reforms: National programs should focus on developing managerial competencies that emphasize facilitation, adaptability, and participatory leadership. Encouraging adaptive governance structures will make organizations more resilient to change.
9.4 Future Research Directions
The study opens several avenues for future research. First, longitudinal studies could explore how learning investments influence performance over time, providing stronger causal evidence. Second, cross-sectoral comparative studies could examine whether the relationships observed in Ghana’s strategic sectors are consistent across other industries such as manufacturing, healthcare, and education. Third, researchers could include cultural and technological readiness variables to understand how organizational culture and digital maturity shape learning outcomes. Finally, integrating governance and sustainability perspectives would provide a broader understanding of how learning contributes to long-term national competitiveness and social impact.
9.5 Closing Summary
This study contributes to the broader understanding of how learning-based strategies shape organizational success in emerging economies. It affirms that investing in human capital is not only beneficial but essential for sustaining financial growth, innovation, and competitiveness. For Ghana and similar economies, fostering adaptive learning cultures and leadership styles that balance control with empowerment will be critical for navigating the challenges of technological disruption and global market volatility. In essence, learning is not merely a function—it is a strategy. The findings reaffirm that the organizations best prepared for the future are those that learn continuously, innovate boldly, and lead collaboratively.
Declarations
Ethical Approval and Consent to Participate.
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The study was approved by the Institutional Review Board (IRB) of Accra Technical University (Approval ID: AITIRB 202506) on 15 January 2025. A
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All research was performed in accordance with the relevant guidelines and regulations, including the principles outlined in the Declaration of Helsinki (2013) and Ghana’s national research ethics standards.A
The Institutional Review Board of the Accra Technological University approved the study with the ethical clearance number AITIRB 202506. A
All participants gave informed consent prior to the collection of data, in line with the Helsinki Declaration (2013) and national standards on research ethics.Informed Consent
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Written informed consent was obtained from all participants before data collection began. The consent process took place between February and April 2025, prior to the administration of surveys and interviews. Participants were professionals from Ghana’s oil, gas, and telecommunications sectors. A
They were fully informed about the purpose of the study, voluntary participation, and data confidentiality before giving consent.Consent for Publication
Not applicable.
Not applicable.
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Funding
This research did not receive any specific funding from public, commercial or non-profit-making funding bodies.
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Author Contribution
Suleman Mohammed Yakubu conceived and designed the study, developed the theoretical framework and supervised the entire research process. He collected and analysed quantitative and qualitative data, interpreted his findings, and wrote the manuscript in its entirety.Kingsley Tornyeva contributed to the review of related literature, improved research tools, helped validate data and edited the final version. All authors read and approved the final manuscript and agreed that they were responsible for every aspect of the work.
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Data Availability
Available on requests
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